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Item 1. Business

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Our mission is to imagine and pioneer new ways to decode the secrets of the proteome to improve human health.

Our initial product, the Proteograph Product Suite (Proteograph), leverages our proprietary engineered nanoparticle (NP) technology to provide unbiased, deep, rapid and large-scale access to the proteome.

The Proteograph Product Suite is an integrated solution that is comprised of consumables, an automation instrument and software.

We believe that characterizing and understanding the full complexity of the proteome is foundational for accelerating biological insights and will lead to broad potential end-markets for proteomics, encompassing basic research and discovery, translational research, diagnostics and applied applications.

This full understanding of the complexity of the proteome and its dynamic nature requires large-scale, unbiased and deep interrogation of thousands of samples across time, which we believe is unavailable with the proteomic approaches available today.

We believe that the Proteograph Product Suite has the potential to enable researchers to perform these proteomics studies at scale.

Proteins are the functional units of many biological processes and dynamic indicators of physiology that can gauge health over time, inform disease progression and monitor therapeutic response.

Despite the central role proteins play in biology, rich functional content derived from proteomics studies is relatively unexplored compared to the genome, since large-scale proteomic studies have not been possible.

We believe large-scale characterization of the proteome has not been feasible with existing proteomics approaches, which broadly fall into two categories:

(i) unbiased but not scalable, or (ii) scalable but biased.

Current de novo , or unbiased, approaches require complex, lengthy, and labor- and capital-intensive workflows that limit their scalability to small, under-powered studies.

Targeted or biased methods enable interrogation of a limited number of known proteins per sample.

Although targeted approaches are scalable, they lack the breadth and depth necessary to appropriately characterize the proteome and catalog its many protein variants.

Therefore, we believe that proteomics researchers are forced into an unattractive trade-off between the number of samples in a study and the depth and breadth of the analysis.

These trade-offs limit the ability to advance characterization of the proteome to match the characterization of the genome.

We believe deep, unbiased, large-scale proteomic analysis is needed for a more complete understanding of biology.

We are initially focused on driving adoption of the Proteograph with customers in the proteomics and genomics markets, with those researchers who recognize the value of large-scale, unbiased, deep proteomics.

Allied Market Research estimates the proteomics market was $32 billion in 2019.

We believe that the Proteograph’s unique capabilities will enable researchers to undertake unbiased studies not possible today, particularly those of larger scale, and will complement genomics studies by adding critical missing information that can provide functional context to genomic variation.

According to the dbSNP database, over 1 billion individual genetic variants have been identified to date;

however, fewer than 0.2% of those variants have been cataloged in the ClinVar database with a reported relationship between variation and phenotype.

We believe unbiased, deep and large-scale proteomics will help researchers map biological function of genomic variants, identify impactful disease and response-specific risk factors, and accelerate discovery of molecular mechanisms of health and disease.

We believe these capabilities should broadly appeal to researchers and entities undertaking large-scale genomics studies and should attract spending from the genomics market, estimated by Technavio to be $21 billion in 2019.

In addition, we believe the Proteograph is likely to enable novel content discovery that will lead to entirely new applications and market opportunities.

We are initially focused on research applications for the Proteograph Product Suite and are selling and marketing the Proteograph for research use only (RUO).

We commenced the third and final phase of our commercialization plan with broad release in January 2022.

Our first product, the Proteograph TM Product Suite (Proteograph), leverages our proprietary engineered nanoparticle (NP) technology to provide unbiased, deep, rapid and large-scale access to the proteome.

The Proteograph Product Suite is an integrated solution that includes consumables, an automation instrument and software.

The human proteome is incredibly complex, with multiple protein variants derived from each gene.

This complexity arises from multiple biological steps required to create the functional proteome, including transcription, translation, post-translational modifications (PTMs) and protein interactions.

While many protein variants may be benign, others can severely disrupt protein function and contribute to disease.

The complexity of the proteome at a population level is huge.

For example, a study by the UK Biobank published in late 2021 in Nature identified over 900,000 potential protein loss-of-function variants in a cohort of approximately 455,000 individuals, with each individual having an average of more than 200 such variants (Backman et al.

It is essential to catalog the complexity of the proteome and understand the functions of protein variants to decode the links between the proteome, the genome and disease.

This deeper understanding can lead to novel insights into disease mechanisms, the discovery of new biomarkers and the identification of potential therapeutic targets.

We believe that broader access to the proteome is essential, not only to understanding its complexity and accelerating biological insights, but also to expanding end-markets.

These markets may include basic research and discovery, translational research, diagnostics and applied applications.

To comprehend the complexity and dynamic nature of the proteome, researchers must perform population-scale, deep, unbiased interrogation of biological samples over time.

We believe that this level of interrogation was not previously feasible and that the Proteograph can enable researchers to perform these types of proteomics studies.

Before the commercial launch of the Proteograph, we believe that the largest published deep unbiased plasma proteomics study, which measured at least 600 proteins, was conducted on just 48 samples.

However, today, multiple customers have successfully completed, or are planning, deep unbiased plasma proteomics studies with thousands of proteins quantitatively measured across thousands of samples.

This breadth and depth of unbiased plasma proteomics coverage was not previously achievable at scale.

Proteins play a critical role in most biological processes and provide dynamic indicators of physiological changes across health, disease progression, and therapeutic response.

However, compared to the genome, the discovered and cataloged body of proteomic data remains limited.

Current proteomics approaches have not facilitated deep exploration at scale in samples with high dynamic range, because they are either:

(i) unbiased but not scalable;

or (ii) scalable but biased.

Current deep, unbiased approaches require complex, lengthy, labor- and capital-intensive workflows that limit their application to small, under-powered studies.

Targeted or biased methods are scalable but are limited to a specific number of predetermined proteins.

These methods cannot distinguish between variants of the same proteins often present in the same biological samples, because they lack the necessary peptide-level resolution and accuracy needed to characterize the proteome.

These limitations force a trade-off between the number of samples and the depth of protein coverage in a study.

We believe that a more complete understanding of biology requires deep, unbiased, large-scale proteomic analysis with the peptide-level resolution and accuracy needed to distinguish protein variants.

We are focused on driving adoption of the Proteograph by customers in the proteomics and genomics markets who recognize the value of large-scale, unbiased, and deep proteomics.

Allied Market Research estimated the global proteomics market to be approximately $24 billion in 2021.

The Proteograph’s unique capabilities now enable researchers to undertake first-of-their-kind, large-scale unbiased studies, which complement genomics studies by adding critical missing information that can provide functional context to genomic variation.

With the advent of next-generation sequencing and improvement in cost and throughput, researchers have sequenced the equivalent of several million human genomes and human exomes.

Across these studies, according to the dbSNP database, more than one billion individual genetic variants have been identified to date;

however, less than 0.2% of those variants

have been cataloged in the ClinVar database with a reported relationship between variation and phenotype.

This gap in functional annotation is in part due to the gross impedance mismatch between access to the proteome and genome.

We believe that the Proteograph Product Suite will bridge this gap.

Just as large-scale access to genomics has dramatically impacted that field, we believe large-scale access will do the same for proteomics, revealing new content, enabling mapping and cataloging of new protein variants, and driving new disease insights, diagnostics and treatments.

Importantly, by impedance-matching researchers’ access to unbiased genomics content at the nucleotide level with proteomics content at the peptide and amino acid level, researchers can better connect genotype to phenotype.

In this way, we believe customers will be able to develop more accurate biomarkers of disease for diagnostic and therapeutic applications, accelerating multi-omics driven precision medicine.

We believe these capabilities will have broad appeal to researchers and entities undertaking large-scale genomics studies and should attract spending from the genomics market, which was estimated by Technavio to be approximately $26 billion in 2021.

Additionally, we believe that the Proteograph will enable the discovery of novel content that will lead to the creation of value that will promote entirely new applications and market opportunities.

The Importance of Proteomics

Detailed and complex biological information resides at the protein level.

Virtually every function within a living organism occurs by the action of a protein or a group of proteins interacting with each other and in concert.

Thus, proteomics is a key area of focus for researchers.

Proteins are dynamic indicators of health status and can be used to monitor disease progression and therapeutic response.

By contrast, the genome is a static indicator of what a person’s physiology could be, not an indicator of current physiological state.

In short, the genome represents risk, while the proteome reflects status.

Despite the physiological impact, the human proteome is relatively unexplored compared to the human genome.

Through large-scale data collection and widespread adoption of molecular profiling techniques, over one billion genetic variations have been identified across all genomes that have been sequenced.

Although this information has significantly improved the understanding of biology, the functional context at the protein level has not been established for the vast majority of this genomics information.

In other words, researchers have not been able to connect phenotypic information with the relevant genotypic information.

We believe that if we enable researchers to generate large bodies of proteomic data that can be coupled with large bodies of genomic data, they will be better positioned to understand the relationship between variation and function and its impact on biology.

Detailed and complex biological information resides within the proteome.

Nearly all functions of an organism require the interaction of one or more proteins with each other and with other biological molecules.

Proteins serve as dynamic indicators of health status, disease progression and therapeutic response.

As depicted in Figure 1 below, the genome is a static indicator of an individual’s baseline physiology, while the proteome reveals the current physiological state.

Despite its importance, the human proteome is relatively unexplored compared to the human genome.

To link genomic information with phenotypes, understanding the functional context of proteins is critical.

However, this connection is currently limited because the vast majority of genetic variants lack functional context at the protein level.

We believe that enabling researchers to generate large-scale, integrated proteomic and genomic data will equip them to comprehend the relationship between variation, function and biology.

Protein quantitative trait loci (pQTLs) are genomic variants that are associated with the levels or abundance of specific proteins.

Because they influence protein expression or regulation, they are a genetic source of variation in the proteome.

The term “protein quantitative trait loci” is used because protein abundance level is viewed as quantitative traits.

To identify pQTLs, genome-wide association studies (GWAS) compare genetic variants across large populations and their association with differences in protein levels.

These studies can provide valuable insights into the genetic basis of complex diseases, the molecular mechanisms that regulate protein expression, and the identification of potential therapeutic targets for drug development.

However, the study of pQTLs requires large-scale acquisition of proteomic data, which is a challenge for traditional unbiased approaches.

We believe that the Proteograph will bridge this gap.

Moreover, the Proteograph allows for pQTL analysis at the peptide level, thus enabling the association of genomic variants with specific protein variants.

Utility of genomic vs.

proteomic information.

Over one million human genomes have been highly characterized with over one billion variants, but they have low utility and represent a static indicator of risk.

The human proteome is far less characterized, but has a much higher utility as a dynamic indicator of health status.

Challenges of Accessing the Proteome

The human proteome is more dynamic, diverse and complex in structure, composition and number of variants than either the genome or transcriptome.

Starting from the genome, multiple biological steps take place to arrive at the proteome, each step creates increased complexity and diversity.

The human genome has approximately 20,000 genes, which are estimated to give rise to more than 200,000 transcripts, which then give rise to 1,000,000 or more protein variants.

As shown in Figure 2, this is in part because a single gene produces distinct ribonucleic acid (RNA) isoforms through the process of transcription and a myriad of structurally distinct proteins through the process of translation.

Biological processes can further chemically modify these proteins in unique ways, resulting in a large number of protein variants through post-translational modifications.

Overall, these processes result in many levels of protein diversity, from amino acid sequence and structural variations, to post-translational modifications (PTMs), to functional changes due to interactions between the proteins themselves, known as protein-protein interactions

We believe the fundamental challenge with existing proteomics methods is their inability to measure the breadth and depth of the proteome’s complexity, rapidly and at scale.

Limitations of Targeted Approaches to Proteomics

Unlike DNA, the structures, chemistries and concentrations of proteins in any given sample are widely variable.

Proteins also lack a direct amplification mechanism, which creates technological challenges for identifying them at low concentration.

Given the diversity of protein structures, coupled with the lack of a common amplification mechanism, researchers often use analyte-specific reagents (ASRs) to measure proteins.

ASRs are ligands, such as antibodies, that have been designed to bind to specific areas of proteins, and therefore, involve a targeted or biased approach.

This targeted approach is limited in that ASRs do not have the capability to interrogate the entirety of the protein structure of the molecule that they bind to and may not distinguish important protein variants.

The average length of a human protein is approximately 470 amino acids, whereas the average binding site of an ASR is an epitope with a length of five to eight amino acids.

ASRs may not recognize differences between proteins outside of the epitope binding site and, therefore, may not differentiate among protein variants, as demonstrated in recent publications including Pietzner, M.

ASRs and other biased readout technologies are not optimal for discovery given the inherent complexity of proteins.

A large number of ASRs can be designed to detect a large number of different proteins, but this approach is limited in its ability to measure new protein variations.

Targeted approaches, in general, are useful when the scientist or clinician knows what is being analyzed.

This is analogous to the role of microarrays in genomics, where a specific DNA fragment is used in a targeted or biased manner to confirm the presence of a specific mutation, or a single nucleotide polymorphism (SNP), whereas NGS employs an unbiased approach to interrogate the breadth of content in the genome.

With an unbiased approach, as one scales the number of samples in a study, one inherently assays a broader set of novel content.

With targeted approaches, regardless of the number of samples in a study, one will not gain any additional novel content beyond the specific targets of the assay.

Complexity of the Proteome

The human proteome is dynamic, diverse and complex, with approximately 23,000 genes giving rise to over one million protein variants.

As shown in Figure 2 below, these variants arise from various mechanisms, including alternative splicing of RNA transcripts, genetic variations that alter the amino acid sequence of the protein, and post-translational modifications such as phosphorylation and glycosylation.

It is estimated that our approximately 23,000 genes give rise to approximately 69,000 protein isoforms through alternative splicing.

At a population level, a much larger number of protein isoforms exist because of genetic variants and somatic variants that alter RNA processing.

Protein variants can have vastly different biological functions and be expressed in different tissues within the same individual.

For example, two isoforms of the protein encoded by CD99L2 have different interacting proteins and those two proteins’ networks are related to distinct diseases (Yang et al.

An example of a protein that has tissue-specific isoform abundance is FOX1, which has differential isoform presence in muscle and brain tissue (Nakahata and Kawamoto).

Therefore, it is essential to study and understand proteins at the level of protein variants in the appropriate biosample, and this can be achieved only through large-scale analysis of the proteome at the peptide level.

Functional diversity exists through modifications and interactions of different molecules, from static indicators like the genome to increasingly numerous and complex indicators like the proteome and interactome.

Modified from Bludau et al.

Recently, a study by Backman et al.

published in Nature revealed the genomic variation identified in a cohort of approximately 455,000 participants of the UK Biobank exome sequencing study.

The study identified a vast amount of protein variation, including almost nine million protein variants, of which more than six million are potentially deleterious and 915,289 are protein loss-of-function variants.

On the individual level, each participant had on average 9,506 protein variants, of which 2,945 were potentially deleterious and 214 were loss-of-function variants.

However, these variants were only identified at the genomic level and did not account for alternative splicing or post-translational modifications.

Considering these additional sources of protein variants, the actual number of protein variants at both individual and population-wide levels is significantly higher.

These findings emphasize the unmet need to understand protein variants at the peptide level and underscore how little is currently known about the complexity of the proteome.

We believe understanding protein variation at this level could revolutionize how we diagnose, treat, and monitor diseases.

Summary of genetic variation across a population of approximately 455,000 participants of the UK Biobank (Backman et al.)

Limitations of Transcriptomics to Infer Proteomics

RNA sequencing (RNAseq) has been the established method for studying transcriptomics over the last decade.

It is often assumed that transcript and protein abundance levels are highly correlated because of the central dogma of molecular biology describing how transcripts are translated into proteins.

However, several studies have repeatedly demonstrated a poor correlation between transcript and protein levels (Buccitelli and Selbach).

This discrepancy can arise from technical factors, such as noise and bias in methods for assessing both transcripts and proteins, and biological mechanisms such as mRNA translation and degradation.

Although both transcriptomics and proteomics measurements have their uses, proteins are more closely linked to phenotype, making them more useful than

transcripts for understanding function.

Therefore, we believe that direct analysis of the proteome via at-scale proteomics studies will provide unique biological insights for research, discovery and clinical applications.

Limitations of Affinity-Based Approaches to Proteomics

Proteins are highly variable in structure, chemistry and concentration, presenting technological challenges for their identification at low concentration levels.

Due to the lack of a common amplification mechanism, researchers often use ligands to measure proteins.

However, because ligands such as antibodies or aptamers were designed to bind to specific areas of proteins, approaches utilizing them are considered targeted, or biased.

The average length of a human protein is approximately 470 amino acids, whereas the average binding site of a ligand is an epitope five to eight amino acids long.

Panels of ligands used for protein interrogation have several shortcomings, including (i) they do not recognize differences in protein structure outside of the epitope binding site, so that all variants appear the same and cannot be differentiated from one another, (ii) conformational changes of the protein can affect epitope and ligand binding;

for example, those induced by protein-protein interactions or post translational modifications, and (iii) certain protein isoforms may exclude entire protein domains and remove the epitope binding site, yielding false negative results.

Sources of variation.

Left panel is a graphical summary of factors contributing to variation in the affinity-based discovery of the plasma proteome.

Right panel schematically describes reasons for differences in binding profile of aptamer and antibody-based proteomic profiling (PAV protein altering variant;

SNV single-nucleotide variant).

Adapted from Pietzner et al.

In a paper published in Nature Communications, Pietzner et al.

from the University of Cambridge experimentally demonstrated the limitations of two distinct commercially available affinity-based approaches.

Specifically, the authors show that protein altering variants can affect ligand binding, i.e., each affinity-based platform interacts differently with the same protein, depending on the epitopes to which its ligands are binding.

On average, the correlation between the two commercially available affinity-based methods was 0.38.

The distribution of correlations across all proteins is bimodal, with some proteins having a very good correlation, and others have a correlation close to zero (as shown in Figure 5 below).

The authors attribute this poor correlation to differences in epitope binding between platforms and interference from protein-altering variants.

These limitations underscore the importance of studying proteins with peptide-level resolution using a technology that is quantitatively robust in identifying protein variants.

Distribution of correlation coefficients across 937 mapping aptamer–antibody pairs (n = 871 unique protein targets).

Adapted from Pietzner et al.

Affinity-based approaches have limitations when used for pQTL analysis.

These ligands bind to a specific epitope of the protein, as depicted on the left side of Figure 6 below.

However, protein variants can alter the ligands’ binding, as demonstrated in the middle panel of the figure.

Such altered binding can lead to incorrect measurements of protein levels, resulting in false pQTL identifications or misinterpretation of true pQTLs.

In contrast, the Proteograph readout offers multiple peptides per protein, some of which may contain variant peptides, while others may not, as shown in the right panel of the figure.

We believe that the correlation of peptides with genomic variants enables accurate pQTL analysis.

The Proteograph technology provides an advantage over affinity-based approaches in pQTL analysis by offering the necessary peptide-level resolution for detection of protein variants.

Protein variants may cause false associations in affinity-based approaches for proteogenomic studies.

Affinity-based approaches are effective when a known target and a specific epitope measurement is desired, but cannot cover the vast complexity of the proteome.

We believe they are analogous to microarrays in genomics, where a specific DNA fragment is used in a targeted or biased manner to confirm the presence of a specific mutation or a

single nucleotide polymorphism (SNP).

The fundamental limitation of affinity-based approaches is their inability to differentiate between protein variants and accurately survey the complexity of the proteome.

Limitations of Current Unbiased Approaches to Proteomics

Rather than interrogating proteins at the level of predefined epitopes, unbiased approaches can interrogate proteins at the peptide level, providing amino-acid level resolution to protein variants.

However, current unbiased approaches do not scale well due to vastly different protein concentrations in plasma samples.

Plasma protein concentration, for example, can span ten orders of magnitude from abundant proteins like albumin to some of the least abundant proteins, such as cytokines.

The top 22 most abundant proteins account for approximately 99% of the total protein mass in the plasma, yet the many thousands of less abundant proteins comprising the other one percent of the total proteome by mass have significant impact on biology.

Therefore, it is critical to be able to broadly and deeply detect proteins across the proteome, including those proteins that appear in low concentrations in plasma.

Mass spectrometry (MS) is a broadly used technique for detection of proteins and their variants for unbiased discovery, basic research and clinical applications.

Given the wide dynamic range of protein concentrations in

plasma and other biological samples, current MS methods for protein detection often require complex sample preparation workflows that involve depletion of abundant proteins and grouping remaining proteins and peptides into smaller units through fractionation.

We believe current unbiased approaches in complex biosamples with a skewed dynamic range (e.g., blood plasma) are not widely adopted by researchers because the workflows are extremely complex, the process is expensive and the time required to complete such analysis is significant.

As one example of these complex methods, in a paper from Keshishian, H.

, the researchers first depleted the most abundant proteins with immuno-affinity columns and then separated the remaining proteins by many subsequent and complex chromatographic steps and mass spectrometer injections.

The study identified 4,500 different proteins across 16 samples, taking multiple months to complete.

A critical unmet need in proteomic analysis remains how to collect unbiased proteomic data on thousands of proteins in a sample spanning more than ten orders of dynamic range in concentration and to repeat this across thousands of samples in a reasonable amount of time and cost.

Rather than relying on predefined epitopes, unbiased approaches can interrogate proteins at the peptide level, providing amino-acid level resolution to protein variants.

However, traditional, deep, unbiased proteomic approaches rely on complex workflows that do not scale due to the wide range of protein concentrations in biological samples with high dynamic range.

In human plasma, for example, the 22 most abundant plasma proteins account for 99% of the total protein mass, while the many thousands of less-abundant proteins comprise the remaining one percent.

Given the important biological role of both high- and low-abundance proteins, it is critical to detect proteins accurately, precisely and reproducibly across the dynamic range.

Mass spectrometry (MS) is a widely used technique for unbiased discovery, basic research and clinical applications, and is considered the gold standard for identification.

However, the wide dynamic range of protein concentrations in plasma and other biological samples has previously required complex, upfront sample preparation workflows prior to MS analysis, involving depletion of abundant proteins and fractionation of the remaining proteins and peptides.

Deep unbiased proteomics analysis of complex biosamples at scale have not been feasible for wide adoption by researchers due to the high complexity, cost and time requirements.

For example, in a state-of-the-art deep unbiased plasma proteomics study in 2017 prior to commercial availability of the Proteograph Product Suite, Keshishian et al.

depleted the most abundant proteins with immuno-affinity columns and then separated remaining peptides by multiple and complex chromatographic steps and mass spectrometer injections.

The study identified 4,500 different proteins across 16 samples, but took months to complete.

Prior to the commercial availability of the Proteograph Product Suite, we believe the critical unmet need in proteomic analysis was how to collect unbiased proteomic data in a sample on thousands of proteins in a sample spanning more than ten orders of magnitude in concentration (dynamic range), and to repeat this across thousands of samples in a reasonable amount of time and cost.

Genomics faced a similar unmet need before the advent of NGS, which allowed for massively parallel sampling.

The Importance of Unbiased, Peptide-level Resolution Proteomics

Importance of an Unbiased Approach in the Discovery of Novel Content

The ability to perform unbiased sampling of content at scale in biology has been transformational to biological analysis.

Before NGS, genomic approaches were not scalable to either read the entire genome or process very large numbers of samples.

Researchers could only sequence hundreds of fragments of DNA or RNA at a time, and not easily in parallel.

Genetic analysis was limited to biased, shallow genetic studies that were time-consuming and not scalable.

As a result, genomics researchers faced similar challenges that researchers currently face in proteomics.

The introduction of NGS enabled unbiased analysis at scale of small fragments of DNA, allowing researchers to, in parallel, sequence tens of millions (currently tens of billions) of fragments of DNA per sample.

This transformative approach to sampling enabled genomic sequencing at scale and enabled the discovery of novel content.

The discovery of novel content created the path to genomic end-market opportunities across basic research and discovery, translational research and clinical applications, including early cancer detection, recurrence monitoring and non-invasive prenatal testing.

While there are no assurances that the Proteograph Product Suite will have the same effect on the proteomics market as NGS technologies have had on the genomics market, given the utility of proteins for measuring function, health and disease, we believe the same, if not a greater, market opportunity exists for providing unbiased, deep, rapid and scalable access to the proteome.

Figure 3 illustrates the concept of increasing content discovery with an unbiased approach as sample cohorts increase in size.

The ability to perform unbiased sampling at scale has transformed biological analysis.

In genomics, unbiased sequencing of the genome enabled discovery of novel content, creating new end-market opportunities in basic research and discovery, translational research and clinical applications, including early cancer detection, recurrence monitoring and non-invasive prenatal testing.

While there is no guarantee that the Proteograph Product Suite will have the same impact on proteomics that NGS had on genomics, we believe there is a significant market opportunity to provide unbiased, deep, rapid, and scalable access to the proteome.

Figure 7 illustrates how content discovery increases as sample cohorts increase in size with an unbiased approach.

Unbiased approaches increase the identification of protein variants arising from genomic variants, isoforms and post-translational modifications as sample numbers increase, resulting in new biological insights, applications and utility.

Targeted approaches are inherently unable to discover new protein variants.

Importance of Peptide-Level Resolution in the Understanding of Biology

We believe that peptide-level resolution will be critical to the discovery of novel content and new biological insights.

As one example, using data from our Nature Communications paper, we identified several biologically-important novel cancer biomarkers at the peptide level that would have been missed if we had only focused on overall protein expression.

One of those biomarkers, Bone Morphogenic Protein 1 (BMP1) has been reported to

have a dual role in cancer, but the biology is not well understood.

Figure 4 below demonstrates how BMP1 at the protein level ignores the variants of BMP1, showing no apparent difference in the overall expression pattern of BMP1 between cancer and healthy subjects.

If one has resolution at the peptide level, individual variants of BMP1 can be seen.

It is possible that these findings may help to explain the dual role of BMP1 in cancer.

This insight is enabled because an unbiased approach is able to identify peptide sequences at the amino acid-level.

We believe that peptide-level resolution is crucial to the discovery of novel content and new biological insights.

One example is alternative protein isoforms arising from the same gene locus.

At the transcriptome level, these alternative transcripts are known as spliceforms.

The majority of human genes can produce more than one protein spliceform and, according to the Ensembl genome database project, as many as 69,000 protein spliceforms are generated by more than 23,000 human genes through alternative splicing.

If we account for additional spliceforms at the population level, arising from genetic variants and somatic variants including those responsible for cancers (which affect RNA processing), the number is much larger.

Affinity-based approaches generally cannot differentiate between spliceforms, whereas unbiased MS-based approaches survey proteins at the peptide level, enabling differentiation between spliceforms.

Peptide-level resolution is critical for identifying biologically important novel cancer biomarkers, as we demonstrated in our Nature Communications paper (Blume et al.

Using data from that paper, we identified several biomarkers at the peptide level that would have been missed if we had only focused on overall protein expression, including bone morphogenic protein 1 (BMP1).

In a new paper currently in press with a peer-reviewed journal (Donavan et al.

), we find that the known spliceforms of BMP1 exhibit differential abundance in cases and controls;

the single short form is more abundant in cancer cases, while the long forms are more abundant in controls.

In that paper, we propose a mechanism to explain the differential abundance based on lack of domains in the short form.

This highlights the importance of generating data at the peptide level, which is made possible by the Proteograph Product Suite.

Identification of peptide-level variants of BMP1 enabled by an unbiased approach.

Peptide-level identification reveals individual BMP1 variants, showing an opposite pattern of differential expression in the short vs.

long variants of BMP1 in individuals with non-small-cell lung cancer (NSCLC) compared to normal controls.

Our Proprietary Engineered Nanoparticle Technology

Our proprietary engineered nanoparticle technology overcomes the limitations of existing methods and is the foundation for the Proteograph Product Suite’s easy-to-use workflow for unbiased, deep, rapid and scalable proteomic analysis.

Our approach is based on proprietary engineered NPs that enable unbiased sampling of intact proteins across the dynamic range of the proteome, capturing a myriad of molecular information at the level of protein variants as well as PPIs.

Our NPs eliminate the need for complex workflows required by other unbiased approaches, which we believe will make proteomics more accessible to the broader scientific community.

The diameter of a nanoparticle is typically in the tens to hundreds of nanometers.

As a reference, the diameter of a human hair is 80,000 nanometers.

When nanoparticles are placed in contact with a biological sample, a thin layer of intact proteins rapidly, selectively and reproducibly adsorbs onto the surface of a nanoparticle upon contact, forming what is called a protein “corona.” Additional intact proteins can also join the corona layer by binding directly to a protein that has already attached to the nanoparticle through PPIs, and intact protein complexes may also attach to the nanoparticle directly.

Our NPs’ ability to capture whole and intact proteins and their many diverse variants provides access to protein structural information, including information on PPIs.

At binding equilibrium, which occurs within minutes after our NPs encounter the protein, the selective sampling of proteins by our NPs is robust and highly reproducible.

Protein sampling and binding of proteins to the nanoparticle surface are driven by three primary factors:

(i) affinity of a given protein for a given nanoparticle’s physicochemical surface;

(ii) concentration of a given protein in a biological sample;

and (iii) affinity of the proteins for other proteins on the surface of the nanoparticle, forming PPIs.

A variety of different methods and materials are used to design and create different nanoparticles.

Each nanoparticle can have distinct physicochemical properties that generate a unique protein corona pattern and a unique proteomic fingerprint.

We believe the growing body of proteomic data coupled with advanced machine learning will allow us to continue to enhance the unique physicochemical properties of our nanoparticles.

We can combine nanoparticles into panels to provide a representative and thorough sampling across the dynamic range of the proteome, from high to low abundance proteins.

In effect, the properties of protein binding to a panel of nanoparticles are functionally equivalent to, and can replace, complex, biochemical laboratory workflows for the preparation of samples for deep, unbiased MS, and which enable the capture of thousands of proteins from biofluids for large-scale proteomics studies.

Virtually any solubilized biological sample can be interrogated with nanoparticles, including cell or tissue homogenates, blood or blood components (such as plasma or serum, urine), saliva, cerebrospinal fluid and synovial fluid.

The versatility of nanoparticles provides the opportunity to use a vast universe of different nanoparticles with different physicochemical properties, across a broad range of sample types, to selectively, reproducibly and deeply sample the proteome in an unbiased way.

The figure below illustrates the dynamic range of the proteome with high abundance proteins in the upper left of the curve and low abundance proteins in the lower right of the curve.

Each of our unique nanoparticles has different physicochemical properties, which allows it to sample selectively across the breadth of the proteome.

Our NPs enable the unique capabilities of the Proteograph Product Suite, including the ability to:

The Proteograph Product Suite leverages our proprietary engineered NP technology to overcome the limitations of existing methods, and enable an easy-to-use workflow for unbiased, deep, rapid and scalable proteomic analysis.

Our proprietary engineered NPs provide unbiased sampling of intact proteins across the dynamic range of the proteome, capturing molecular information at the peptide-level, including protein variants.

The NPs eliminate the need for complex workflows required by other unbiased approaches, which we believe will make proteomics more accessible to the broader scientific community.

Typically, nanoparticles have a diameter in the tens to hundreds of nanometers, much smaller than a human hair, which has a diameter of 80,000 nanometers.

When nanoparticles come into contact with a biological sample, a thin layer of intact proteins rapidly, selectively and reproducibly adsorbs onto their surface, forming what is called a protein “corona.” Additional intact proteins can also bind directly to proteins already attached to the nanoparticle through protein-protein interactions (PPIs), and intact protein complexes may also attach to the nanoparticle directly.

Our engineered NPs capture intact proteins across the dynamic range without requiring prior knowledge of proteome composition or designing the assay for specific protein targets.

In combination with an unbiased mass spectrometry readout, they reveal molecular information at the peptide level revealing protein variants.

At binding equilibrium, which occurs within minutes after our NPs encounter a biosample, the selective sampling of proteins by our NPs is robust and highly reproducible.

Nanoparticles allow unbiased interrogation of proteoform diversity.

Our nanoparticle technology leverages engineered physicochemical properties to reproducibly bind to proteins without prior knowledge, forming a protein corona.

The binding of proteins to the nanoparticle surface and protein sampling are primarily driven by three factors:

(i) the affinity of a particular protein for the physicochemical surface of a specific nanoparticle;

(ii) the concentration of a specific protein in a biological sample;

and (iii) the affinity of the proteins for other proteins on the surface of the nanoparticle, forming PPIs.

A variety of materials and methods are used to create different nanoparticles with distinct physicochemical properties, which generate a unique protein corona pattern and a unique proteomic fingerprint.

As the amount of proteomic data increases, we will continue to refine the unique physicochemical properties of our NPs with advanced machine learning.

By combining nanoparticles in an assay, we can achieve a representative and thorough sampling across the dynamic range of the proteome, from high- to low-abundance proteins.

In this way, we can replace complex biochemical laboratory workflows for the preparation of samples for deep, unbiased MS, enabling the capture of thousands of proteins from biofluids for large-scale proteomics studies.

Nanoparticles can interrogate almost any solubilized biological sample, including cell or tissue homogenates, blood or blood components (such as plasma or serum), urine, saliva, cerebrospinal fluid and synovial fluid.

This versatility, we believe, strongly suggests that a vast universe of different nanoparticles with different physicochemical properties could be employed across a broad range of sample types, to selectively, reproducibly and deeply sample the proteome in an unbiased way.

The Proteograph Product Suite leverages the power of our proprietary engineered NPs to:

• eliminate complex workflows required by other unbiased proteomic approaches;

• sample in an unbiased manner across the dynamic range of the proteome in a variety of biological samples;

• enable unbiased sampling of a variety of biological samples across the dynamic range of the proteome;

• identify and distinguish protein variants at the peptide level;

• identify and quantify protein variants and PPIs;

• use machine learning to design, synthesize and select different NPs and NP panels to create multiple products and applications;

• be compatible across a wide range of laboratory workflows, automation equipment and sample processing and detection methods, lowering the hurdle for product adoption.

• identify and quantify protein variants;

• create a workflow that is compatible across a wide range of laboratory workflows, automation equipment, and sample processing and detection methods;

• facilitate broad product adoption.

Using machine learning, we are able to design, synthesize and select different NPs and combinations of NPs to create multiple products and applications.

We have validated our NP technology and the principle of protein corona formation as a robust and reproducible method to deeply and broadly profile the proteome in a high-throughput manner.

In our publication in Nature Communications (Blume et al.), we demonstrated a rapid, deep and precise profiling of the plasma proteome with our proprietary engineered NP technology.

We have characterized our technology and its performance in three peer-reviewed publications:

Nature Communications (Blume et al.

), PNAS (Ferdosi et al.

), and Advanced Materials (Ferdosi et al.

The Proteograph Product Suite

Our proprietary engineered NP technology forms the basis for our first product, the Proteograph Product Suite.

The Proteograph is an integrated solution consisting of consumables, an automation instrument and software to perform unbiased, deep proteomic analysis at scale in a matter of hours.

We designed the Proteograph workflow to be efficient and easy-to-use, and to leverage broadly used laboratory instrumentation to enable adoption in both decentralized and centralized settings, making deep, unbiased proteomics accessible to nearly any lab.

The Proteograph consumables consist of our NP panel and all other consumables necessary to assay samples in an automated workflow on our SP100 automation instrument.

Our automated workflow is custom configured for researchers to assay samples in approximately seven hours, which includes thirty minutes of hands-on time and six and a half hours of automated instrument time.

The output from the Proteograph workflow is peptides ready to be processed on an MS instrument.

The Proteograph Product Suite is an integrated solution consisting of consumables, an automation instrument, and software to perform unbiased, deep proteomic analysis at scale in a matter of hours.

We designed the Proteograph workflow to be efficient and easy-to-use, and to leverage common laboratory instrumentation to enable adoption in both centralized and decentralized settings, making deep, unbiased proteomics accessible to nearly any lab.

The Proteograph consumables consist of our NPs and all other consumables necessary to assay samples in an automated workflow on our SP100 automation instrument.

Our automated workflow is custom configured for researchers to assay samples in approximately seven hours, which includes 30 minutes of hands-on time and six and a half hours of automated instrument time.

The output from the Proteograph workflow consists of peptides ready to be processed and evaluated on an MS instrument.

The Proteograph Product Suite is detector agnostic and, we believe, will be adaptable to other protein detection instruments in the future.

The MS component of the Proteograph workflow is either provided by the researcher’s laboratory or can be outsourced to a third-party provider.

We estimate that there are approximately 16,000 MS instruments with configurations typically used to perform proteomic analysis installed worldwide and, therefore, we believe that MS systems are readily accessible by researchers.

The Proteograph Analysis Suite, a data analytics software suite, provides quality control and allows researchers to analyze and interpret the output from the system and gain insights into their data.

For our first Proteograph assay, we employ a panel of five NPs.

The Proteograph consumables also include buffers and reagents for protein lysis and digestion, peptide purification, peptide quantification and the reconstitution of

lyophilized materials.

The Proteograph Analysis Suite, a data analytics software suite, provides quality control and allows researchers to analyze and interpret the output from the system to gain insights from their data.

Proteograph Product Suite comprises consumables, an automation instrument, and software.

For our first Proteograph assay, we employ a panel of five NPs, assay buffers and reagents for protein lysis and digestion, peptide purification, peptide quantification and the reconstitution of lyophilized materials.

We designed the performance specifications of the Proteograph Product Suite to meet the core needs of the market in terms of protein coverage and sample throughput required for proteomic experiments that are unbiased and at-scale.

The current product allows for the interrogation and processing of up to 16 samples in parallel on a single 96-well plate in approximately seven hours.

Each sample incubates separately with each of the five nanoparticles, resulting in 80 wells of peptides in a 96-well plate.The remaining 16 wells are for integrated quality control samples to ensure consistent process performance and to aid in troubleshooting.

The ready availability of the non-particle reagents combined with our ability to efficiently design and fabricate different NPs with different chemical properties, greatly simplifies the development and production of future iterations or additional versions of the Proteograph assays to address potential customer needs, such as expanded coverage or specialized assays.

Additionally, we can introduce new assays that include a different number of NPs and process different sample numbers.

Each sample incubates separately with each of the five nanoparticles, yielding 80 wells of peptides in a 96-well plate.

The remaining 16 wells are reserved for integrated quality control samples to ensure consistent process performance and to aid in troubleshooting.

The ready availability of non-particle reagents, combined with our ability to efficiently design and fabricate different NPs with different chemical properties, greatly facilitates the development and production of future iterations or additional versions of the Proteograph assays to address potential customer needs, such as expanded protein coverage or specialized assays.

Additionally, we can introduce new assays that allow for higher throughput of samples or lower sample volumes.

We expect our second Proteograph assay to be available in 2023.

Automation Instrument

We designed the Proteograph assay to be run in a robust and automated manner on our SP100 automation instrument, which is a custom-configured, industry-standard, liquid handling workstation.

Our SP100 instrument is designed to consistently run experiments and to enable studies of hundreds to thousands of samples.

Our automated workflow allows for rapid highly parallel proteomic sampling with just thirty minutes of set-up time.

The flexibility of our instrument, coupled with the inherent diversity of our NP technology, provides for many potential applications and study workflows that can suit particular experimental needs

Our SP100 instrument is designed to enable studies of hundreds to thousands of samples, with an automated workflow that allows for rapid, highly-parallel sample processing with 30 minutes of set-up time.

We believe the flexibility of our instrument, coupled with the inherent diversity of our NP technology, provides a runway for many future potential applications and workflows.

The Proteograph workflow is driven by the Instrument Control Software (ICS) on the SP100 automation instrument.

The workflow has been configured to process one full 96-well plate at a time, in just seven hours, processing 16 samples in parallel for each 96-well plate run.

Future workflows and NP panels will be able to be run on the SP100 automation instrument, with an accompanying software update.

The output of the Proteograph assay and instrument is peptides that are quantified, dried, and can be reconstituted when ready to inject into a mass spectrometer.

MS provides quantitative detection, either on an instrument provided by the user, or sent out for MS analysis to a third-party provider.

The Proteograph Analysis Suite is designed for ease-of-use and was developed to help users arrive at insights quickly and efficiently following quantitative detection of peptides on an MS instrument.

To accommodate varying customer needs, we have designed the Proteograph Analysis Suite to be deployed as cloud-based and, in the future, more localized solutions to accommodate different customer types and geographies.

Our deployment options will provide a predefined workflow for data management and analysis that leverages publicly available MS data analysis tools, as well as proprietary data analyses developed by us.

Without the Proteograph Analysis Suite, proteomics analysis requires expert knowledge and scalable high-performance computer infrastructure to run efficiently.

We believe that the Proteograph Analysis Suite could accelerate adoption among non-proteomic experts by providing an intuitive user interface that automates and simplifies data handling, processing and analysis, and provides access to a scalable infrastructure that can be used by any lab.

Another potential roadblock for researchers is understanding and evaluating the quality of their results.

The Proteograph assay incorporates a series of controls for monitoring assay performance, and an integrated view of the results of these control runs.

Using the Proteograph Analysis Suite, the customer can evaluate trends over time and implement performance boundaries around the expected values that flag unexpected outcomes in the data.

Providing a simple, consistent interface for customers to evaluate the control data and generate a quality control (QC) report will help them understand our approach to QC in the Proteograph workflow and simplify support.

We expect that the Proteograph Product Suite will enable generation of large volumes of proteomic data, and we have developed the Proteograph Analysis Suite to ensure that handling, management and analysis of data does not create new bottlenecks for researchers.

The Proteograph Analysis Suite offers ease of implementation and addresses key customer needs, which include integrated QC reporting, data management, data visualization, and statistical analysis tools.

The Proteograph Analysis Suite is highly scalable and is designed to accommodate inputs from different MS platforms, rapidly expanding data volumes and emerging data analysis tools.

Finally, as we continue to

improve and extend our product portfolio, we expect to expand the Proteograph Analysis Suite to include advanced data analysis tools, including PPI analysis, mapping of PTMs, genetic polymorphisms, multi-omics integration, and systems biology framework analysis.

From Sample to Data Using the Proteograph Product Suite

Proteomic analysis using the Proteograph Product Suite has five primary steps:

Protein Corona Formation .

NPs can be mixed with a wide variety of soluble biological sample types (matrices), including cell or tissue homogenates, blood or blood components (such as plasma or serum), urine, saliva, cerebrospinal fluid, and synovial fluid.

After combining the biosample and the NP, the mixture is incubated in a solution that mimics physiological conditions, producing protein-corona on the surface of the NPs.

Protein Corona Wash .

NPs are then captured by a magnetic field, after which they undergo repeated cycles of wash with buffer to remove unbound, or loosely bound, proteins.

Peptide Preparation .

Washed NPs are subject to enzymatic digestion to generate peptides, which are collected, quantified, dried and ready for subsequent MS analysis.

Mass Spectrometry .

Digested peptides are prepared for measurement by dissolution in appropriate peptide reconstitution buffer suitable for MS injection, at a volume and concentration that meets MS instrument and liquid chromatography gradient requirements.

Data Analysis.

After data acquisition, typical MS analysis methods are employed within the Proteograph Analysis Suite to identify and quantify the peptides and proteins in the sample.

Quality control metrics are reported for the MS sample data, sample data summaries and output files are created, and initial cross-sample analyses are provided.

Given the seven-hour run time per plate for our initial five-NP panel, one Proteograph Product Suite coupled with two MS instruments can process 48 samples in approximately two and a half days for unbiased and deep proteomic analysis.

By comparison, the workflows developed by leading proteomics labs can take as long as several days to weeks, for sample preparation for MS measurement to reach an equivalent depth of proteomic coverage.

The workflow has been configured to process one full 96-well plate at a time, in just seven hours, processing 16 samples in parallel.

Future workflows and products will be able to be run on the SP100 automation instrument with an accompanying software update.

The output of the Proteograph workflow consists of peptides that are quantified, dried, and can be reconstituted when ready for injection into a mass spectrometer.

MS provides quantitative unbiased detection, either on an instrument provided by the user, or sent out for MS analysis to a third-party provider.

The Proteograph Analysis Suite (PAS) is designed for ease-of-use and efficiency to help users arrive at insights quickly.

To accommodate varying customer needs, we have designed the PAS to be cloud-based and, in the future, to be available via more localized solutions that accommodate different customer types and geographies.

The PAS offers a predefined workflow for data management and analysis, leveraging publicly available MS data analysis tools as well as our own proprietary analysis tools.

Without the PAS, proteomics analysis requires expert knowledge and a scalable high-performance computer infrastructure.

We believe that the PAS can accelerate adoption of the Proteograph among non-experts by providing an intuitive user interface that automates data handling, simplifies processing and analysis and provides access to a scalable infrastructure that can be used by any lab.

Currently, one potential roadblock for researchers is understanding and evaluating the quality of their results.

The Proteograph Assay Kit incorporates a series of controls for monitoring assay performance, and an integrated view of the results of these control runs via the PAS.

Customers can evaluate trends over time and implement performance boundaries around the expected values that flag unexpected outcomes in the data.

We believe providing a simple, consistent interface to evaluate the control data and generate a quality control (QC) report will help customers understand our approach to QC in the Proteograph workflow, simplifying support.

In the third quarter of 2022, we launched the latest version of our Proteograph Analysis Suite, PAS 2.0, which incorporates a proteogenomic workflow that maps peptide-level data to genomic data to identify sample-specific variant peptides not captured in canonical reference databases.

The workflow provides interactive tables and plots, enables visualization of identified peptides’ relationship to gene structure, protein domain information and functional regions;

and creates amino acid-level browsable peptide data maps.

We believe PAS 2.0’s intuitive visualizations make it faster and easier to discover protein targets for a wide range of applications.

As we continue to improve and extend our product portfolio, we expect to continue to expand the capabilities and features in PAS.

Some examples include large dataset management, advanced data analysis tools and applications such as PPI analysis, PTM mapping, transcriptomic, genetic polymorphisms, multi-omics integration and systems biology framework analysis.

Proteograph Product Suite Performance

The Proteograph Product Suite provides four critical attributes:

(i) breadth of protein sampling;

(ii) depth of coverage;

(iii) accuracy and precision of measurement;

and (iv) the ability to scale the number of samples processed in a study.

We believe that the Proteograph Product Suite is the only product to provide these technical and operational capabilities in an integrated solution to enable large-scale proteomic analysis.

The performance of the Proteograph Product Suite as it relates to these technical attributes is described below:

The Proteograph Product Suite provides five essential capabilities:

(i) broad protein sampling with peptide-level resolution;

(ii) deep coverage;

(iii) accurate and precise measurement;

(iv) reproducibility and (v) scalability for high-throughput studies.

We believe that our integrated solution is the only product in the market that combines all

of these technical and operational capabilities.

Furthermore, we rigorously measure and evaluate each of these technical attributes, as we describe below.

• Breadth of protein sampling.

Refers to the ability to conduct unbiased, highly parallel sampling of the proteome.

This capability refers to conducting unbiased, highly-parallel sampling of the proteome.

Each uniquely engineered NP selectively captures hundreds of distinct intact proteins from a biosample based on their abundance and affinity for the NP surface.

Sampling is particularly strong in complex biofluids such as plasma.

Our unique NPs capture significantly more proteins and protein variants than current methods of unbiased proteomic analysis, as shown in Figure 8a below.

In a head-to-head experiment, the Proteograph Product Suite was compared with other unbiased proteomics methods using the same biological sample.

Neat plasma represents the simplest form of unbiased proteomic analysis, requiring minimal processing time, and resulted in a breadth of coverage of 312 proteins.

By adding processing steps such as depletion of high abundance proteins and fractionation (separation of the remaining proteins into multiple fractions), the breadth of protein sampling increased to 670 proteins.

However, with the Proteograph Product Suite, we detected 1,656 proteins in plasma, representing a major expansion in breadth of protein coverage.

This sampling capability is particularly strong in complex biofluids such as plasma.

Our unique NPs capture significantly more proteins than current methods of unbiased proteomic analysis, as shown in Figure 11 below.

In a head-to-head experiment, we directly compared the breadth of the Proteograph Product Suite to other unbiased proteomics methods using the same biological sample.

Neat plasma, which represents the simplest form of unbiased proteomic analysis requiring minimal processing time using another method, resulted in a breadth of coverage of 750 proteins.

By adding processing steps such as depletion of high-abundance peptides and fractionation (separation of the remaining proteins into multiple fractions), the breadth of protein sampling increased to 1,596 proteins.

However, using the Proteograph Product Suite, we detected 2,998 proteins in plasma, representing a major expansion in breadth of protein coverage.

The Proteograph is not limited to a defined set of proteins, and samples across the dynamic range of proteins and protein variants that may be present in biosamples.

We have exemplified the utility of the Proteograph in studying secreted proteins across several different sample types, including cell or tissue homogenates, blood or blood components (such as plasma or serum), urine, saliva, cerebrospinal fluid, synovial fluid and conditioned media.

Across these studies, over 10,000 distinct proteins have been identified.

When factoring in potential variants of these proteins, we believe the number of sampled proteins could be considerably larger.

Importantly, the Proteograph protein data is obtained using an MS detector, which is the gold standard for proteomics, and data is conventionally reported with a one percent False Discovery Rate (FDR).

This means that the reported proteins are identified with 99% confidence.

• Depth of coverage.

Refers to the Proteograph’s ability to evaluate the proteome across the wide dynamic range of protein abundance.

The depth of coverage using our assay is compared to other unbiased proteomic methods shown in Figure 8b below.

The Proteograph assay samples proteins across the entire dynamic range of the plasma proteome, as defined in the Human Plasma Proteome Project database ( Schwenk et al .), with the 75th percentile point of depth of coverage shown with the orange bar.

The depth of coverage for the Proteograph assay reaches further into the low abundant proteins than the fractionation, depletion and neat plasma methods.

The Proteograph is able to evaluate the proteome across a wide dynamic range of protein abundance.

Figure 11 compares our assay’s depth of coverage to that of other scalable unbiased proteomic methods (i.e., 4x versus neat digestion) and not scalable unbiased proteomic methods (i.e., 2x versus fractionation and 3.5x depletion workflows).

The Proteograph assay samples proteins across the entire dynamic range of the plasma proteome, as defined in the Human Plasma Proteome Project database (Schwenk et al .).

The dotted vertical line in the center panel represents the 75th percentile point of depth of coverage for each method.

The Proteograph assay reaches further into low-abundance proteins than fractionation, depletion or neat plasma methods as noted by the dotted line being furthest to the right of all workflows.

Lastly, Figure 11 describes the peptide level resolution of each approach and shows approximately the same relative ratios of peptide counts as the corresponding protein group counts.

The 17,703 peptides quantitatively measured by Proteograph in this single experiment provide additional information and potentially significant biological insight into protein variants.

The Proteograph identifies more proteomic content.

The Proteograph workflow identifies 4x more protein groups than alternative MS-based workflows (left).

Depth of the Proteograph captures protein groups spanning high to low abundance across the dynamic range versus alternative MS-based workflows (center).

Additionally, the

Proteograph better resolves the complexity of the human plasma proteome at the peptide-level versus other MS-based methods (right).

• Accuracy of measurement.

Refers to how close the measured abundance of a protein is to the true abundance in a sample.

Accuracy of protein abundance measurement can be demonstrated by MS signal intensity of the proteins sampled with the Proteograph assay, and comparing these values with measurements obtained directly by immuno-assay (ELISA).

To demonstrate this, purified C-Reactive Protein (CRP) was added or “spiked” in to plasma at levels of 2x, 5x, 10x, and 100x of the baseline measured levels for CRP in the plasma.

Samples with known concentrations of CRP were interrogated with the Proteograph Product Suite and ELISA.

Figure 9 shows the linearity of measurement, as determined by MS signal intensity of four peptides within CRP when compared to the ELISA measurement of CRP.

The Proteograph assay can distinguish changes in protein abundance with significant accuracy, as demonstrated by a slope response approximately equal to one and an r-squared value greater than 0.95.

• Precision of measurement.

Refers to how close several measurements of protein abundance in the same sample are to each other.

Less precision in the measurement adds noise to an experiment, requiring a larger number of samples in the study to observe a true difference.

Precision is typically measured as the coefficient of variation (CV%), or standard deviation divided by the mean times 100.

Therefore, a lower CV% represents a more precise outcome.

The precision of the Proteograph Product Suite was compared with that of depletion, fracti onation, and neat, by evaluating the same sample three times and calculating the CV% for the detected peptides and proteins.

On average across the peptides, the median precision was 16.9 C V%.

At this level of performance, the Proteograph has 80% statistical power to detect a 50% change in a peptide levels with only ten samples per sample group.

Using the Proteograph, lower CV% is achieved than fractionation and depletion methods, while concurrently sampling significantly more proteins, as shown in Figure 8c.

In general, in unbiased assays, CV%s are expected to increase as the number of analytes detected increases.

However, the Proteograph assay can increase the number of analytes that it detects while achieving comparatively better CV%s.

Although neat plasma has a lower CV%, it is limited in the breadth of protein coverage to 312 proteins compared to 1,656 proteins sampled by the Proteograph.

This capability measures how close the measured abundance of a protein is to the true abundance in a sample.

The true abundance of large number of proteins at a protein variant level at scale is not independently possible, so we use the ratio of abundances in two samples to demonstrate the accuracy of protein abundance measurement.

We demonstrate the accuracy of protein abundance measurement by mixing two different plasmas in different ratios and measuring the relative MS signal intensity.

By spiking human plasma with bovine plasma, the Proteograph can detect and quantify peptides that are unique to the bovine proteome.

Peptides differ between the two species because of genetic differences that result in detectable changes at the amino acid level.

By mixing the two plasma samples, the Proteograph can make measurements across thousands of peptides, highlighting the Proteograph Product Suite’s real-world accuracy.

Panel A of Figure 12 shows how the change in MS intensity or intensity fold change varies when mixing the two plasmas at different ratios.

Panel B of Figure 12 shows the results of this experiment, looking at threefold changes:

2X, 5.5X, and 11X.

At each level, the dashed line is the expected fold change.

The gray bars represent the distribution of bovine unique peptides for a neat plasma workflow, and the teal bars represent the unique bovine peptides detected by both the Proteograph and the neat workflow.

In all cases, the median of each distribution is close to the dashed line, indicating the median fold change is close to the expected value.

The Proteograph offers a high-degree of accuracy.

(A) Three representative pairs of spiked-in samples and the expected fold changes of bovine proteins concentration in these pairs.

(B) Distribution of observed fold changes of bovine proteins for three selected comparisons of spiked-in samples.

The color indicates the data source:

(i) neat digestion (gray), or (ii) Proteograph workflow constrained to proteins also identified in neat (teal).

The horizontal dashed lines indicate the expected fold changes.

• Reproducibility of measurement.

Reproducibility, also referred to as precision, is a measure of the consistency of protein abundance measurements (i.e., MS measured intensity) between repeated measurements of the same sample.

A higher reproducibility indicates lower noise, which reduces the number of samples required to observe a true fold change in the study.

Reproducibility is usually measured as the coefficient of variation (CV%), which is the standard deviation divided by the mean multiplied by 100.

A lower CV% represents a more precise measurement.

The CV across individual components of the workflow, including the Proteograph instrument and the mass spectrometry instrument, aggregate to form the overall CV% of the workflow (Figure 13;

The typical CV% of MS instrumentation, derived by running the same peptide mixture in consecutive MS injections, is approximately 10%.

Using the Proteograph assay to make protein measurements within a single plate adds approximately 7%, for a total CV of about 17%.

Running a study across multiple plates and days adds further MS and Proteograph variability for a total system CV% of approximately 20%.

Using data acquired over a long-running study, the Proteograph can derive power curves illustrating the power to detect fold changes of different sizes (Figure 13;

right panel).

For example, 1.5-fold and two-fold changes in protein abundance can be detected with 90% power in sample sizes of 192 and 66, respectively.

Typical biological cohorts are much larger than this to capture biological variability and so we believe that the reproducibility of the Proteograph is well-calibrated for biomarker discovery and clinical proteomics.

(A) contributors to CV;

(B) smaller fold changes can be detected with increased power with larger study sizes.

• Scalability .

The Proteograph Product Suite enables rapid and large-scale proteomic sample processing in a seven-hour workflow, compared to other unbiased solutions that can take days to weeks.

With our current five-NP assay, we can process sixteen samples in a single run of the Proteograph SP100 instrument.Given the seven-hour run time per plate for our initial five-NP panel, one Proteograph Product Suite coupled with two MS instruments can process 48 samples in approximately two and a half days for unbiased and deep proteomic analysis.

We believe the Proteograph will broadly appeal to researchers seeking an easy-to-use, scalable approach with a unique combination of attributes spanning breadth, depth, accuracy and precision of measurement and the speed and throughput necessary for large-scale proteomics studies.

Sampling across the entire dynamic range has been one of the seminal challenges in the field of proteomics, as the range from the most abundant to the least abundant protein in biological samples can vary up to ten orders of magnitude, and the rich diversity of biology likely resides outside the most abundant proteins.

We believe the two primary near-term markets for the Proteograph Product Suite are the proteomics market, which was $32 billion in 2019, according to Allied Market Research, and the genomics market, which was $21 billion in 2019, according to Technavio.

Within these markets, potential applications of the Proteograph solution span basic research and discovery, translational research, diagnostics and applied applications.

Of the $32 billion proteomics market, $25 billion is estimated to be spent on reagents, $5 billion on instruments, and $2 billion on services.

In the near-term, we believe we will compete in both the proteomics reagent and instrument markets.

Furthermore, the $21 billion genomics market consists of approximately $13 billion spent on products and $7 billion spent on services.

In the near-term, we believe we will be able to garner spend from both products and services as genomic customers link genotype to phenotype by supplementing existing genomic data with proteomics data.

While we initially plan to sell and market the Proteograph Product Suite for RUO, we believe that the capabilities of the Proteograph Product Suite may enable other applications.

While we currently do not intend to pursue clinical diagnostics applications, we may in the future seek premarket approval or clearance for the Proteograph Product Suite in order to allow our customers to use the Proteograph in other product offerings.

We believe that the Proteograph Product Suite’s unique value proposition will resonate with proteomics researchers who already value deep and unbiased proteomic information, and who desire to scale experiments to far greater sample sizes at a fraction of the time and cost of current approaches.

We also believe that as more genomics researchers incorporate other omics approaches to elucidate key genomic findings, the Proteograph will uniquely provide large-scale, unbiased and deep proteomic information to complement genomic information, and enable researchers to gain a clearer picture of biology and a deeper understanding of genomic risk factors.

Longer-term, we believe that the

capabilities offered by the Proteograph Product Suite and future products may potentially lead to new end-markets, applications, and business models that complement existing proteomics and genomics markets.

Allied Market Research estimates the global proteomics market was $32 billion in 2019, and is expected to grow to $64 billion in 2024, representing a 15% compound annual growth rate.

According to Allied Market Research, 60% of the proteomics market is focused on life sciences research, 35% for clinical applications and 5% other applications.

Products in the proteomics market include spectrometry, microarray and chromatography instruments as well as reagents, used for both unbiased and biased proteomics.

The majority of proteomic analysis to date either relies on biased or targeted methods or expensive, complex, and laborious unbiased or de novo deep methods that are applied only to tens of samples versus the thousands needed to power large-scale studies.

With our current assay, we can process sixteen samples in a single run of the Proteograph SP100 instrument.

Therefore, a single Proteograph Product Suite coupled with two MS instruments can process 48 samples in approximately two and a half days for unbiased and deep proteomic analysis.

In comparison, the unbiased workflows developed by leading proteomics labs can take weeks for sample preparation and MS measurement to reach an equivalent depth of proteomic coverage.

We believe that the Proteograph will be attractive to researchers who are looking for an easy-to-use, scalable approach with a unique combination of attributes spanning breadth, depth, accuracy, reproducibility and precision of measurement, and the speed and throughput necessary for large-scale proteomics studies.

Furthermore, the peptide-level data that the Proteograph Product Suite provides at scale are crucial for gaining novel biological insights.

We believe that the Proteograph Product Suite has two primary near-term markets:

the approximately $24 billion global proteomics market, and the $26 billion global genomics market, as reported by Allied Market Research and Technavio, respectively.

Potential applications of the Proteograph could span several areas, including basic research and discovery, translational research, diagnostics and applications.

The proteomics market is estimated by Allied Market Research to have spent $18 billion on reagents, $4 billion on instruments, and $1.5 billion on services in 2021.

We believe that we will be competing in both the proteomics reagent and instrument markets in the near term,

while our service provider customers and Centers of Excellence (COEs) will be accessing the services component.

According to Technavio, the genomics market consists of approximately $16 billion spent on products and $9.5 billion spent on services.

We believe that we will similarly be able to attract spending on both products and services as genomic customers link genotype to phenotype by supplementing existing genomic data with proteomics data.

These applications can be used across basic research, translational research, pharmaceutical, commercial and contract research organization (CRO) customer segments.

We currently sell and market the Proteograph Product Suite for research use only (RUO).

However, we believe that the capabilities of the Proteograph Product Suite may enable other applications in the future.

We may in the future seek premarket approval or clearance for the Proteograph Product Suite in order to allow our customers to use the Proteograph in other product offerings.

We expect that the Proteograph Product Suite’s unique value proposition will appeal to proteomics researchers who value deep and unbiased proteomic information and seek to scale experiments to much larger sample sizes with greater speed and efficiency.

Moreover, we believe that as more genomics researchers incorporate other -omics approaches to elucidate key genomic findings, the Proteograph will uniquely provide large-scale, unbiased and deep proteomic information to complement genomic information, and enable researchers to gain a clearer picture of both biology and genomic risk factors.

We anticipate that in the longer term, the capabilities of the Proteograph and future products may yield new end-markets, applications and business models that complement existing proteomics and genomics markets.

According to Allied Market Research, the global proteomics market was valued at approximately $24 billion in 2021, and is expected to grow to $49 billion in 2026, representing a 15% compound annual growth rate.

The market is divided into three categories, 61% focused on drug discovery, 34% on disease diagnosis and 5% on other applications.

Products in the proteomics market include spectrometry, microarray and chromatography instruments as well as reagents, for both unbiased and biased proteomics.

However, most proteomic analyses of high dynamic range samples to date rely on biased or targeted methods or expensive, complex, and laborious unbiased or de novo deep methods, which are limited to analyzing only tens of samples instead of the thousands needed to power large-scale studies.

Few methods are based on capture of intact proteins that enable analysis of proteome complexity at the level of amino acid variants, PTMs and PPIs, all of which have the potential to generate important biological insights.

We believe the unique capabilities of the Proteograph Product Suite will appeal to researchers either as a complement or substitute for current approaches, or in creating an entirely novel path to survey the proteome.

We believe that the unique capabilities of the Proteograph Product Suite will appeal to researchers, either as a complement or alternative to current approaches, or as a wholly-novel way to survey the proteome.

We estimate that there are approximately 16,000 MS instruments with configurations typically used to perform proteomic analysis installed worldwide.

Since the Proteograph can leverage most MS instruments as a detector, we believe that we can take advantage of this installed base to accelerate adoption.

We believe that we have an opportunity to provide a strong alternative to both unbiased and biased proteomics approaches, particularly in the discovery of new biology, and to grow the proteomics market by enabling new applications for unbiased proteomics.

These applications currently span research, translational and clinical settings, and we believe that the Proteograph Product Suite can address all these applications over time.

Technavio estimates the global genomics market was $21 billion in 2019 and is expected to grow to $38 billion by 2024, representing a 13% compound annual growth rate.

We believe that large-scale deep, unbiased proteomics studies, such as those the Proteograph could enable, will provide important missing biological information to improve functional characterization of genomic variants, enabling large-scale proteogenomics.

In genomics markets, complementing large-scale genomics analysis with large-scale proteomic analysis has the potential to enhance and accelerate our understanding of biology, human health and ultimately the treatment of disease.

Therefore, we believe the Proteograph solution can appeal to an increasing number of genomics customers, especially those in translational settings, who are looking to leverage multi-omics approaches to further annotate genomic variants in terms of function and connect genotype to phenotype.

We also believe that the Proteograph Product Suite will enable novel applications and insights leading to new end-markets, like the impact that broad access to genomics products have had in creating new applications, end-markets and business models.

For example, non-invasive prenatal testing and precision oncology currently make up a significant part of the current $21 billion genomics market, yet we believe that it would have been difficult to anticipate these market opportunities a decade ago.

We believe the same dynamic of new market creation will occur in proteomics.

One such application for proteomics is early disease detection.

We spun out a new entity, PrognomIQ, Inc.

(PrognomIQ), which aims to develop and commercialize novel diagnostic tests that leverage the Proteograph Product Suite in combination with genomics and metabolomics information, and will be a participant in the existing ecosystem of early disease detection.

By leveraging the installed base of MS instruments, we believe we can accelerate adoption of the Proteograph’s technology.

The Proteograph could be a robust alternative to both unbiased and biased proteomics approaches, particularly in the discovery of new biology insight.

As a result, the Proteograph has the potential to grow the proteomics market by enabling new applications for unbiased proteomics spanning research, translational and clinical settings.

According to Technavio, the global genomics market was valued at approximately $26 billion in 2021, and is expected to reach $42 billion by 2026, representing a compound annual growth rate of approximately 10%.

We believe that large-scale deep, unbiased proteomics studies enabled by the Proteograph could provide important missing biological information to improve the functional characterization of genomic variants, enabling large-scale proteogenomics.

Complementing large-scale genomics analysis with large-scale proteomic analysis could enhance and accelerate our understanding of biology and human health, and ultimately the treatment of disease.

Therefore, we believe the Proteograph solution can attract an increasing number of genomics customers, especially those in translational settings, who are looking to leverage multi-omics approaches to further annotate genomic variants in terms of function and connect genotype to phenotype.

We also believe that the Proteograph Product Suite, similarly to the commercial impact of a broadened access to genomics products, will enable novel applications and insights, leading to new end-markets.

For example, non-

invasive prenatal testing and precision oncology currently make up a significant part of the current genomics market, which would have been difficult to predict a decade ago.

We anticipate that the same dynamic of new market creation will occur in proteomics, with one such application for proteomics being early disease detection.

In the third quarter of 2020, we spun out a new entity, PrognomiQ, Inc.

(PrognomiQ), which is developing novel early detection diagnostic tests that leverage the Proteograph Product Suite in combination with other -omics, including genomics, metabolomics and lipidomics.

More broadly, we believe the Proteograph solution has the potential to further stimulate growth of new applications and end-markets in additional ecosystems.

The Advantages of the Proteograph Product Suite

We believe the Proteograph Product Suite and its underlying NP technology have the following advantages:

• The first commercially available solution to provide the combination of unbiased, deep, rapid and large-scale access to the proteome.

While other proteomics technologies exist today, we believe that the Proteograph

Product Suite is the first and only product to provide the combination of these four attributes in a single integrated solution with an easy-to-use workflow.

We believe these capabilities fill a gap that to date has been one of the rate-limiting steps in unlocking the complexity of biology.

• A unique solution that provides insight into protein variation and PPIs at a depth and scale that we believe sets a new standard for unbiased and deep proteomics and is unattainable with existing approaches.

The ability to observe the myriad of possible protein variations, which go beyond simple total protein abundance, with the accuracy and precision necessary to extract useful insights across large numbers of subjects, is a key differentiating attribute of our solution.

Furthermore, capturing these variations at scale enables synergistic insights when combined with genomic variations, finally enabling the development of informative, individualized models of biology at population scale.

• A solution that will enable broad adoption by a wide variety of customers in both decentralized and centralized settings.

The Proteograph Product Suite is an integrated solution comprised of consumables, an automation instrument and software, and was designed to deliver ease-of-use, efficiency, robustness and reproducibility of results and to complement existing laboratory infrastructure.

The Proteograph’s simple and integrated workflow enables the customer to use their own MS instrument or leverage a widely available installed base of MS instruments.

We believe these characteristics will facilitate broad adoption of the Proteograph solution across a variety of laboratories and institutions in both decentralized and centralized settings.

• A core technology from which we can develop a range of products, applications and platforms.

From our growing and diverse NP library, we can develop new panels of NP consumables that address a variety of applications and customer needs.

We plan to use machine learning techniques and apply large-scale data analyses of our NP binding properties to understand relationships between NP surfaces and protein binding and interactions in order to rationally design our NP panels.

We believe these characteristics will enable development of additional differentiated products to enable our customers to utilize applications across the life sciences industry, ranging from basic research and discovery, translational research, diagnostics and applied applications.

• A core technology that provides significant operational leverage in research and development, manufacturing and commercialization.

NPs are efficient to design, develop and manufacture.

We believe we will be able to rapidly increase and deploy our understanding of NP design to develop new products with our software and data analytics capabilities.

In the NP manufacturing process, we use well-characterized inputs and methods, which require relatively modest capital equipment and space investments.

This capital-efficient and labor-efficient model has the potential to provide significant operating leverage to our organization.

• A solution with sustainable differentiation.

The Proteograph is uniquely capable of generating robust, reproducible, deep and unbiased proteomic data, and as more of this data gets created over our time and used by more customers to generate insights, we expect to create a cycle that will fuel further adoption of the Proteograph Product Suite throughout the industry.

The Proteograph workflow was designed to fully integrate with customer workflows and provide a unique user experience, supported by our software packages, to create a sustainable solution within our customers’ organizations.

The SP100 automation instrument, software, and NP technology are covered by numerous issued patents and pending patent applications, worldwide, covering improvements in NPs, assay methods and ways to leverage proteomic data and information for life sciences research and clinical diagnostic and drug discovery applications.

We believe the Proteograph Product Suite and its underlying NP technology have unique advantages:

• The first commercially available solution to combine unbiased, deep, rapid and large-scale access to the proteome.

Other proteomics technologies currently exist, but we believe that the Proteograph Product Suite fills a gap by providing all four attributes in a single solution with an easy-to-use workflow.

• Provides unique insight into protein variation at the peptide level, with a depth and scale that sets a new standard for unbiased and deep proteomics.

The Proteograph’s ability to capture protein variations at scale enables synergistic insights when combined with genomic variations, yielding informative individualized models of biology at population scale.

• Allows for wide adoption by customers in both decentralized and centralized settings.

The Proteograph Product Suite is an integrated solution that includes consumables, an automation instrument and software, and was designed to deliver ease-of-use, efficiency, robustness and reproducibility of results and to complement existing laboratory infrastructure.

Its simple and integrated workflow enables the customer to use their own MS instrument or leverage a widely available installed base of MS instruments.

We believe these features will facilitate broad adoption of the Proteograph solution across a variety of laboratories and institutions in both decentralized and centralized settings.

• Offers a core technology with the potential for development of a range of products, applications and platforms.

Our diverse and expanding library of NP surfaces can support the development of new products catering to various applications and customer needs.

We are using machine-learning techniques and conducting large-scale analyses to understand relationships between NP surfaces and protein binding in order to design our future products.

• Provides core technology with significant operational leverage in research and development, manufacturing and commercialization.

NP-based products are efficient to design, develop and manufacture.

We believe that by leveraging our understanding of NP surfaces, software and analytics capabilities, we can rapidly develop new products.

Our NP manufacturing process uses well-characterized inputs and methods, which require relatively modest investments in capital equipment and space.

This capital-efficient and labor-efficient model has high operating leverage potential.

• Presents a solution with sustainable differentiation.

The Proteograph is uniquely capable of generating robust, reproducible, deep and unbiased proteomic data.

As this data is used by more customers to generate insights, we believe this cycle will fuel further adoption of the Proteograph Product Suite throughout the industry.

The Proteograph workflow is fully integrable with customer workflows and provides a unique user experience with the support of our software packages, making it a sustainable solution within customer organizations.

Our NP technology, SP100 automation instrument, and software are protected by numerous issued patents and pending patent applications worldwide, covering improvements in NPs, assay methods and ways to leverage proteomic data and information for life sciences research, clinical diagnostic and drug discovery applications.

Our mission is to imagine and pioneer new ways to decode the secrets of the proteome to improve human health.

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• Drive adoption of the Proteograph Product Suite to enable researchers to create large-scale unbiased proteomic datasets that generate transformative scientific insights.

The Proteograph Product Suite uniquely enables researchers and clinicians to generate unbiased, deep proteomic information at speed and scale that was

not previously possible.

The utility and potential applications of these capabilities are broad, spanning across basic research and discovery, translational research, diagnostics and applied applications.

• Invest in market development activities to establish the importance of large-scale proteomic data and the ability to access it .

To expand and accelerate demand for our products, particularly as new applications are created and adopted by customers, we plan to invest in market development activities to educate prospective customers, funding bodies, commercial entities, government-sponsored omics programs, and other stakeholders of the importance and value of large-scale unbiased and deep proteomic data.

These activities will likely include collaborations with key opinion leaders, generation of peer-reviewed publications, sponsorship of targeted projects, joint publications and seminars, and industry partnerships.

These activities aim to establish the value of large-scale unbiased and deep proteomic data, and demonstrate the unique capabilities offered by our products.

• Continually innovate to develop and commercialize additional transformative products to access the proteome and accelerate our understanding of biology .

We aim to continually innovate and develop new products, applications, workflows and analysis tools that simplify and accelerate researchers and clinicians’ ability to generate proteomic data and to connect proteomic data to genomic and transcriptomic data that drive novel biological insights.

As leaders in NGS have demonstrated, our sustainable advantage will come from continual development and commercialization of new products and applications based on our technology, and we will drive innovation through both internal R&D projects and from collaborations with customers and partners.

• Rapidly build our commercial infrastructure and manufacturing capabilities to enable global customer base expansion.

We are building our commercial infrastructure to sell and support our products directly in the United States, the European Union and United Kingdom.

Our Proteograph Product Suite uniquely enables researchers and clinicians to generate unbiased, deep proteomic information at speed and scale not previously possible.

These capabilities have broad application, spanning basic research and discovery, translational research, diagnostics and applied applications.

• Invest in market development activities to demonstrate the importance of large-scale proteomic data and the ability to access it .

To expand and accelerate demand for our products, particularly as new applications are developed and adopted by customers, we plan to invest in market development activities to educate prospective customers, funding bodies, commercial entities, government-sponsored -omics programs, and other stakeholders of the importance of large-scale unbiased and deep proteomic data.

This effort will likely include collaborations with key opinion leaders, generation of peer-reviewed publications, sponsorship of targeted projects, joint publications and seminars, and industry partnerships.

We plan to demonstrate the value of large-scale unbiased and deep proteomic data, as well as the unique capabilities offered by our products.

• Innovate continuously to develop and commercialize additional transformative products to access the proteome and accelerate our understanding of biology.

We aim to continuously innovate and develop new products, applications, workflows and analysis tools that simplify and accelerate researchers and clinicians’ ability to generate proteomic data and to connect proteomic data to genomic and transcriptomic data that drive novel biological insights.

As leaders in NGS have demonstrated, our sustainable advantage will come from continual development and commercialization of new products and applications based on our technology.

We will drive innovation through both internal R&D projects and from collaborations with customers and partners.

• Build our commercial infrastructure and manufacturing capabilities to enable expansion of our global customer base.

We are establishing our commercial infrastructure to sell and support our products directly in the United States, the European Union,and United Kingdom.

We are expanding access to our products in other geographies, starting with select countries in Asia Pacific through distributors, and eventually to the rest of the world.

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• Foster the creation of an ecosystem of customers, partners and collaborators whose expertise and offerings complement and enhance the power and utility of our products.

We intend to seed and develop a new ecosystem of applications and organizations that can take advantage of large-scale proteomic analysis.

This ecosystem can include areas such as disease detection, large-scale population studies, agriculture, environmental monitoring and food safety.

To help seed the growth of this ecosystem, we spun-out a new company called PrognomIQ, which plans to develop and commercialize diagnostic tests for early disease detection, leveraging the Proteograph Product Suite in combination with other omics technologies.

We intend to seed and develop a new ecosystem of applications and organizations based upon large-scale proteomic analysis.

This ecosystem could include areas such as disease detection, large-scale population studies, agriculture, environmental monitoring and food safety.

To help seed the growth of this ecosystem, we spun-out PrognomiQ, which is developing and will commercialize diagnostic tests for early disease detection, leveraging the Proteograph Product Suite in combination with other -omics technologies.

• Expand our proprietary engineered NP technology to analyze molecules beyond proteins.

Given the inherent flexibility and ability to synthesize myriad NPs, we intend to seek over the long-term to expand the scope of our proprietary engineered NP technology to analyze other biomolecules such nucleic acids, metabolites and small molecules among others.

We intend to expand the scope of our proprietary engineered NP technology to analyze other biomolecules, such nucleic acids and metabolites.

As we continue to work closely with our customers, we will better understand their needs and requirements, which will inform our product development pathway and development of our library of NPs and our software capabilities to address other -omics applications.

We believe our management’s knowledge and experience in both the proteomics and genomics markets will position us to take advantage of such new expansion opportunities as they arise.

The Applications of the Proteograph Product Suite

We believe the ability to generate unbiased, deep proteomic data at scale, with rich content at the protein variant level will have broad applications in proteomics, encompassing basic research and discovery, translational research, diagnostics and applied markets.

We believe this data will be used in many of the same application areas as are used with genomics data, proteomics applications that are uniquely possible with unbiased proteomic data and in new applications that the field will develop in the future.

We believe the ability to generate unbiased, deep, proteomic data at scale, with rich content at the protein variant level, will have a wide range of applications in proteomics, including basic research and discovery, translational research, diagnostics and applied markets.

This data can be used in many of the same application areas as genomics

data, as well as proteomics applications that are uniquely possible with unbiased proteomic data, and in new applications that the field will develop in the future.

In addition, the Proteograph Product Suite’s versatility allows it to analyze not only plasma and serum, but also other biofluids across humans and model organisms.

For example, when we compared the performance of the Proteograph Product Suite workflow with that of neat biological samples across model organism plasma, urine, cerebral spinal fluid, and conditioned media, we noted superior protein group identification by the Proteograph of 4x, 1.5x, 1.5x, and 8.6x, respectively.

Importantly, in each sample, we measured tens of thousands of data points at the peptide level, providing information on thousands of proteins.

We believe this extensibility offers researchers a powerful and flexible tool to utilize across a variety of applications and sample types.

Basic Research and Discovery Applications

We believe that researchers will use the Proteograph for a variety of basic research and discovery applications, including cataloging protein diversity, proteogenomics and exploring the interactome.

While researchers are pursing these applications today, the studies are either limited in scale due to the complex workflows of current unbiased methods or the limited set of ASRs that are available for biased methods.

We believe that the Proteograph will be a valuable tool for researchers across a wide range of basic research and discovery applications, including cataloging protein diversity, proteogenomics and exploring the interactome.

Studies in these areas are currently limited in scale by the complexity of unbiased methods or the limited set of affinity-based reagents available for biased methods.

The Proteograph Product Suite is designed to enable the use of unbiased proteomic data at scale, which we believe will greatly accelerate these areas of basic research and discovery.

Cataloging protein diversity

The Proteograph Product Suite is designed to enable researchers to broadly explore the complexity and diversity of the proteome at the peptide and amino acid levels and discover many distinct protein variants.

We expect that researchers will use the Proteograph solution to catalog these protein variants much like the cataloging of genetic variants that occurred over the past fifteen years, and this will uniquely provide functional context at a scale that is not accessible today with other proteomics methods.

We believe the utility of these protein variants has the potential to impact a broad spectrum of the life sciences field.

In cataloging protein variants, researchers can also gain valuable insights with PTMs, such as phosphorylation.

These PTMs are dynamic and the resulting protein variants can be seen with different states of health and disease.

We believe that as researchers pursue large-scale proteomics studies, the literature that links disease biology to protein variants produced from alternative RNA splicing and from PTMs will exponentially increase.

The Proteograph Product Suite is designed to enable researchers to explore the complexity and diversity of the proteome with peptide level resolution.

We anticipate that researchers will use the Proteograph solution to catalog protein variants in a manner similar to the cataloging of genetic variants over the past 15 years, providing functional context at a scale that is not currently accessible with other proteomics methods.

We believe that the identification of protein variants, including those resulting from PTMs such as glycosylation and phosphorylation, has the potential to significantly transform the life sciences field.

Proteogenomics

Proteogenomics is an emerging area of research, whereby personalized protein sequence databases are generated using genomic and transcriptomic information to help identify novel peptides.

In turn, the proteomic data provides functional context to genomic information and refines gene expression models for transcriptomic information.

The Proteograph generates large-scale unbiased proteomic data, which will facilitate mapping protein variants to genomic variants, and therefore, the advancement of the emerging proteogenomics field.

Given the current level of access to genomic and transcriptomic information, as researchers conduct the large-scale proteomics studies that the Proteograph enables, we expect proteogenomic content to rapidly increase, providing functional information to existing genomics and gene expression information.

The interactome refers to the broad set of interaction networks among molecules, such as those interactions among proteins, also referred to as protein-protein interactions, or PPIs.

Protein interaction networks have been used to infer the function of proteins.

Different types of interaction maps can be composed by the research community for different applications.

These include physical interactions or the functional pathway implications of these interactions.

PPI network maps can be constructed by pegging individual proteins as nodes and linking proteins that interact to them by a drawn line.

These maps naturally cluster into hubs of proteins that fall into related pathways or have related functions.

Access to the deep unbiased proteomic information provided by the Proteograph may enable researchers to better understand biological implications of known PPIs.

Furthermore, given our highly parallel sampling of the proteome across multiple NPs and many samples, we believe researchers using the Proteograph Product Suite may be able to leverage machine learning methods on the resulting large data sets to derive novel PPIs.

Proteogenomics is a rapidly growing field of research that integrates genomic and transcriptomic information with proteomic information, using personalized protein sequence databases to identify novel peptides.

The Proteograph generates large-scale unbiased proteomic data, enabling researchers to map protein variants to genomic variants, advancing the field of proteogenomics.

We anticipate that as researchers conduct large-scale proteomics studies with the Proteograph, proteogenomic content will rapidly increase, providing functional information to existing genomics and gene expression information.

Translational Research Applications

Researchers can use the Proteograph to address translational research applications, which aim to shorten the cycle time from early discovery research to clinical application.

The Proteograph Product Suite allows clinical and translational researchers to conduct unbiased, deep and large-scale proteomics studies in therapeutic and diagnostic research and clinical trials, which can allow for significant advances in biomarker discovery, target identification and exploration, and clinical trial applications.

Researchers can use the Proteograph for translational research applications aimed at shortening the time from early discovery research to clinical application.

The Proteograph Product Suite allows clinical and translational researchers an opportunity to perform unbiased, deep and large-scale proteomics studies in therapeutic and diagnostic research and clinical trials, which can allow for significant advances in biomarker discovery, target identification and exploration and clinical trial applications.

Biomarker Discovery

To date, most de novo biomarker discovery research is limited by the size of studies that can be done in an unbiased way or limited to targeted studies that leverage existing knowledge.

These approaches have yet to uncover the vast number of putative biomarkers that may be available as single markers or as combinations of markers for a range of clinical applications.

We believe the Proteograph can greatly enable the discovery of biomarkers through large-scale, unbiased and deep proteomics studies.

Currently, de novo biomarker discovery research is limited by the size of unbiased studies or is targeted in nature.

These approaches have yet to uncover the large number of potential single biomarkers or combinations of markers for a range of clinical applications.

The Proteograph has the potential to enable the discovery of biomarkers through large-scale, unbiased and deep proteomics studies.

Target Identification and Exploration

We believe that large-scale access to protein variant information that map to different states of health and disease, as enabled by the Proteograph and concurrent advances in proteogenomics, has the potential to lead to the discovery of personalized drug targets that could reach the hundred thousand range.

We believe that the translational application of the Proteograph for potential biomarker development, as exemplified above in our NSCLC study, may also be applied to the identification of novel targets for therapeutic development.

Components of classifiers may directly be targets themselves for drug development, or they may highlight new knowledge with respect to disease mechanism which then could help in the exploration of additional targets and/or help to elucidate the function of potential targets, particularly if these targets are discovered with genomics approaches, and lack protein functional context.

drug targets that could number in the hundreds of thousands.

We believe that the translational application of the Proteograph for potential biomarker development may also be applied to the identification of novel targets for therapeutic development.

Components of classifiers may themselves become targets for drug development, or they may point to new knowledge with respect to disease mechanisms, which could then aid in the exploration of additional targets and/or help to elucidate the function of potential targets, particularly if these targets are discovered with genomics approaches, but lack protein functional context.

Clinical Trial Applications

Clinical researchers can use the Proteograph Product Suite for deep and broad proteomic profiling for subjects in therapeutic clinical trials, including to make observations on efficacy and adverse events.

Applications could include the real-time monitoring of protein-related drug effects, distribution, and metabolism.

Virtually all clinical trials in drug development include monitoring of this type, but currently use biased or targeted panels of proteins.

The Proteograph Product Suite provides clinical researchers with the opportunity to perform deep and broad proteomic profiling of subjects in therapeutic clinical trials, enabling the real-time monitoring of protein-related drug effects, distribution and metabolism.

These attributes are essential in virtually all clinical drug trials.

Current methods use biased or targeted panels of proteins.

It is currently impractical to do this type of monitoring with unbiased proteomic methods, given the inability of these methods to scale to the hundreds or thousands of samples that are evaluated in clinical trials.

The Proteograph Product Suite may also be used to select and group patients in clinical studies based on their proteomics profiles.

As our understanding of the complexity of biology increases with new data accrued from the Proteograph as well as in adjacent omics spaces, our ability to refine patient selection at a higher resolution may improve the ability to confirm efficacy for novel therapies, particularly in complex diseases that involve many inter-related physiological systems.

Genomic approaches are widely used to select patients in cancer and rare genetic disease clinical trials, but the use of genomics-based selection for clinical trials outside of these indications has not been as widely used, given the relative lack of genetic understanding of these diseases.

We believe that the Proteograph has the potential to generate useful proteomic signatures that can complement genomic and other patient selection criteria to improve how clinical researchers select and segment patients for these trials, particularly for indications outside of cancer and rare genetic diseases.

The Proteograph Product Suite may also enable patient selection and grouping based on patients’ proteomics profiles, leading to improved ability to confirm efficacy for novel therapies in complex diseases that involve multiple physiological systems.

While genomic approaches are widely used to select patients in cancer and rare genetic disease clinical trials, their use in other indications has been limited by a lack of genetic understanding of these diseases.

We believe that the Proteograph has the potential to generate useful proteomic signatures that can complement genomic and other patient selection criteria, improving patient selection and segmentation for clinical trials, particularly for indications outside of cancer and rare genetic diseases.

Diagnostic Applications

We see significant opportunities for researchers to use the Proteograph Product Suite for diagnostic development.

Similar to the way in which NGS enabled the development of ecosystems that included genomics-based diagnostics in disease areas such as cancer and rare genetic diseases, we see the unbiased, deep and scalable proteomic information provided by the Proteograph solution potentially creating ecosystems, including proteomics and multi-omics based diagnostics in cancer and other complex disease areas.

We expect that the Proteograph solution will be used by companies in the healthcare testing space, including PrognomIQ, and we will support all of these customers as the ecosystem grows.

We plan to enable our customers by providing the Proteograph Product Suite for their basic research and translational research applications, as they develop their own diagnostic applications.

Applied Applications in Agriculture, Environmental and Food Safety

Outside of the areas related to human health, we believe there are opportunities for the Proteograph solution to be applied in other applied applications, including those applications where broad scale genomics is being widely applied today, and other applications where proteomics can uniquely enable the creation of end-markets.

We believe that unbiased, deep and large-scale proteomic information as enabled by the Proteograph can complement and

extend the value of genomics, transcriptomics, and metabolomics information in fields such as agriculture, environmental monitoring and food safety.

This is exemplified in a recent plant proteomics study that identified PPIs and multi-protein complexes that likely play a role in important agronomic traits.

Pathogen monitoring is a core research area in environmental sciences.

Genomics-based approaches have been applied for environmental monitoring, and we believe that unbiased proteomic data can be used to complement genomic information in monitoring environmental pathogens.

The food industry has complex supply chains where food can be subject to contamination and spoilage in the food product itself as one moves from raw material to processing to distribution, storage and consumption of the food product.

We believe that unbiased proteomic data from the Proteograph Product Suite could complement existing biochemical approaches for tracking signals of contamination and food spoilage.

In August 2020, we transferred certain assets related to disease testing to PrognomIQ, a wholly-owned subsidiary of the Company, in exchange for all of its outstanding equity interests.

Following the transfer, we completed a pro-rata distribution to our stockholders of most of the shares of capital stock of PrognomIQ.

Following the distribution and a subsequent $55.0 million financing of PrognomIQ, we hold approximately 19% of the outstanding equity in PrognomIQ.

We believe that the Proteograph Product Suite also holds significant diagnostic potential.

The unbiased, deep and scalable proteomic data generated by the Proteograph has the potential to create ecosystems, similar to the way in which NGS enabled genomics-based diagnostics for cancer and rare genetic diseases.

We expect that companies in the healthcare testing space, including our spin-out PrognomiQ, will utilize the Proteograph solution, and we are committed to supporting all of our customers as the ecosystem grows, not only in their basic research and translational research applications but also as they develop their own diagnostic applications.

Applied Applications in Agriculture, Animal Health, Environmental Monitoring and Food Safety

We see significant opportunities for the Proteograph solution to be applied in areas beyond human health, including areas where broad-scale genomics is being widely applied today, and applications where proteomics can uniquely enable the creation of end-markets.

We believe that unbiased, deep and large-scale proteomic information, which can be enabled by the Proteograph, can complement and extend the value of genomics, transcriptomics and metabolomics information in fields such as agriculture, animal health, environmental monitoring and food safety.

Given the robustness of the Proteograph Product Suite and the ability of its core NP technology to work across species, we believe there is significant interest and an attractive market opportunity for implementation of the Proteograph Product Suite in model organisms and the animal health markets to pursue opportunities in diagnostic and therapeutic development.

We have already demonstrated the application of the Proteograph Product Suite in projects in mouse, pig, feline, chicken, canine, baboon and bovine plasmas.

In August 2020, we made a strategic decision to transfer certain assets related to disease testing to PrognomiQ, a wholly owned subsidiary of the Company, in exchange for all of its outstanding equity interests.

Following the transfer, we completed a pro-rata distribution to our stockholders of most of the shares of capital stock of

Following the distribution and two subsequent financings of PrognomiQ totaling approximately $102 million, we hold approximately 15% of the outstanding equity in PrognomiQ.

The rationale for this transaction was to enable the growth of ecosystems around new applications that leverage unbiased, deep and large-scale proteomic information.

The transaction allows us to remain focused on our core strategy, which is to be a provider, rather than a consumer, of proteomics solutions to all customers across these ecosystems.

By focusing on our role as a provider of proteomics solutions, we are no longer potentially competing with, or creating the perception that we are competing with, our customers.

The transaction allows us to remain focused on our core strategy of providing proteomics solutions to all customers across these ecosystems, rather than potentially competing with, or creating the perception that we are competing with, our customers.

Our relationship with PrognomiQ does not preclude us from selling the Proteograph Product Suite to any customer in any geography, nor does it preclude our customers from using the Proteograph in any way.

PrognomIQ has indicated that it plans to combine the protein data from the Proteograph with genomics and other omics data, to create a multi-omics approach to health and disease testing, which we believe will help us drive the adoption of the Proteograph Product Suite in these applications.

Omid Farokhzad, Chief Executive Officer and Chair of our board of directors, serves as the Chair of PrognomIQ’s board of directors.

Philip Ma, Ph.D.

our former Chief Business Officer serves as the Chief Executive Officer of PrognomIQ.

Ma has fully transitioned to PrognomIQ, he will remain our consultant until April 2022.

We granted PrognomIQ a non-exclusive license to certain patents and patent applications that we own and a non-exclusive sublicense to certain patent applications we exclusively licensed from The Brigham and Women’s Hospital, Inc.

(BWH), in each case relating to our core technology, to develop, manufacture and commercialize licensed products for the field of human diagnostics on a worldwide basis.

In consideration of the non-exclusive sublicense to certain patent applications licensed from BWH, PrognomIQ paid us a low-five digit figure, and would pay a low single digit royalty, in an amount equivalent to what we would have to pay under our license with BWH, on net sales of sublicensed products beginning with the first commercial sale of a sublicensed product during the term of the agreement.

For further discussion of our license and sublicense arrangement with PrognomIQ, see the section titled “Business — Collaboration and License Agreements — PrognomIQ .” We do not view these amounts to be material to our financial condition and results of operations nor do we expect these amounts to ever be material to us in the future.

PrognomiQ seeks to combine the protein data from the Proteograph with genomics and other -omics data, to develop a multi-omics approach to health and disease testing.

We believe this initiative will increase the adoption of the Proteograph Product Suite in these applications.

Omid Farokhzad, our Chief Executive Officer and President, and Chair of our board of directors, also serves as the Chair of PrognomiQ’s board of directors.

Philip Ma, Ph.D., our former Chief Business Officer, serves as the Chief Executive Officer and President of PrognomiQ.

Ma has fully transitioned to PrognomiQ, he will continue to consult until April 2023 at which time, Dr.

Ma’s consulting agreement will automatically renew for subsequent one year terms unless and until terminated.

Commercial Strategy

The Proteograph Product Suite is an integrated solution comprising consumables, an automation instrument and software.

We have developed the Proteograph solution to simplify and accelerate proteomics workflow, reduce labor and capital requirements, and deliver robust and reproducible performance.

We will focus on growing the installed

base of the Proteograph across a wide variety of customer types and driving applications, scale of experimentation and discoveries that lead to increasing utilization of the Proteograph Product Suite by our customers.

We intend to initially target potential customers who value unbiased and deep proteomic information and are performing proteomic or genomic analysis at academic institutions, translational research groups and biopharmaceutical companies.

Our direct sales and marketing efforts will be focused on the principal investigators, researchers, department heads, research laboratory directors and core facility directors who control the buying decision.

We expect these customers to purchase the Proteograph Product Suite and associated consumables in line with typical purchases of other life science instrumentation and consumables.

We believe that we have priced the Proteograph Product Suite to be affordable to most researchers who can directly make the buying decision, without the need for additional levels of approval, simplifying our sales process.

For example, we price the SP100 automation instrument on a comparable basis to other similar automated fluid handling systems currently available.

We price the Proteograph consumables, on a per sample basis, in a range similar to that of other life sciences consumables that provide deep and unbiased omic information.

The generation of publications and scientific presentations is a core pillar of our market awareness strategy and is important for establishing validity and utility of new products in the life sciences community.

We plan to work closely with our customers, including key opinion leaders, to generate clear use-cases, as well as peer-reviewed publications that illustrate our product performance claims and value proposition.

In addition, we plan to drive awareness by developing and deploying online and in-person training and educational tools that explain our technology and key applications in easy-to-access, easy-to-understand, and scientifically rigorous and credible ways.

We have partnered with select service facilities and core labs globally to be Centers of Excellence for the Proteograph solution.

We are focused on developing the market for deep, unbiased, rapid proteomics at scale by improving accessibility to our technology and growing the installed base of the Proteograph across a wide variety of customer types.

We believe that enabling breakthrough science, demonstrating the power of our technology and catalyzing new applications and markets will lead to increased utilization of the Proteograph Product Suite by our customers.

We are initially focused on research applications for the Proteograph Product Suite and selling and marketing the Proteograph for RUO.

We started broad commercialization of the Proteograph Product Suite in January 2022 and shipped 22 instruments in 2022, bringing our total system shipments to 39 as of December 31, 2022.

Our market development efforts are focused on creating a body of evidence to support unbiased, deep proteomics at-scale by establishing relationships with key thought leaders and driving programs that make it easier for labs of all types to undertake first-of-their-kind studies.

We believe that paving the way with standards, methods and proof in the form of published data empowers the scientific community to move forward more rapidly.

• Centers of Excellence (COE) Program:

We have partnered with select service facilities and core labs globally to be Centers of Excellence for the Proteograph.

These sites have become our customers and provide fee-for-service capabilities that allow interested parties to access and evaluate the Proteograph Product Suite using their own samples.

We expect that these Centers of Excellence will actively promote the Proteograph solution and its capabilities and help us further raise awareness.

To service our potential Proteograph customers, we will provide multiple levels of technical service for the Proteograph Product Suite, depending upon customer need.

We expect that these COEs will actively promote the Proteograph solution and its capabilities, help us further raise awareness, and increase the accessibility of the Proteograph to a wider range of customers.

• Proteogenomics Consortium:

We have formed the Proteogenomics Consortium (PGC) to accelerate access to proteogenomic studies in partnership with Discovery Life Sciences (Discovery) and SCIEX, with the stated eventual goal of developing capacity to analyze 100,000 samples annually.

Discovery announced in December 2022 that it is accepting customer samples for analysis.

• Key Opinion Leader (KOL) Relationships:

The generation of publications and scientific presentations is a core pillar of our market development strategy and is important for establishing validity and utility of new disruptive products in the life sciences community.

We are working closely with our customers, including KOLs, to generate clear use-cases, as well as peer-reviewed publications that illustrate the Proteograph’s performance claims and value proposition.

• Commercial Partnerships:

We have partnered with leading mass spectrometry instrumentation providers, including Thermo Fisher Scientific, Bruker Corporation and SCIEX to establish partnerships that include

lead sharing, co-marketing, and co-development of end-to-end workflows to enable broad education of the market as well as easy-to-implement workflows.

• Geographic Partnerships:

As we expand geographically, we have partnered to enable access in key regions, such as China, where Enlight Medical is our distribution partner and they educate, develop and expand the market for the Proteograph Product Suite.

We are initially targeting potential customers who value unbiased and deep proteomic information and are performing proteomic or genomic analysis at academic institutions, translational research groups and biopharmaceutical companies.

Our direct sales and marketing efforts are focused on the principal investigators, researchers, department heads, research laboratory directors and core facility directors who control the buying decisions.

We expect these customers to purchase the Proteograph Product Suite and associated consumables in line with typical purchases of other life science instrumentation and consumables.

We believe that we have priced the Proteograph Product Suite to be affordable to most researchers who can direct buying decisions, without the need for additional levels of approval, simplifying our sales process.

For example, we price the SP100 automation instrument comparably to other similar automated fluid handling systems currently available.

We price the Proteograph consumables, on a per-sample basis, in a range similar to that of other life sciences consumables that provide deep and unbiased -omics information.

To service our Proteograph customers, we provide multiple levels of technical service for the Proteograph Product Suite, depending upon the customer's needs.

We recognize that excellent customer support can be a critical part of a customer experience, and we will invest accordingly in our technical and application support to achieve the desired levels of service.

Commercial Organization

We are building out our commercial organization across marketing, sales, customer success, and technical support functions to support demand and with the intent to deliver exceptional customer experience.

We believe that coupling exceptional customer experience with a transformative product will allow us to deliver substantial value to our customers, build long-term customer loyalty, enhance our competitive differentiation, and, importantly, use our customer relationships to gain insights that inform our product development to grow our offerings in ways that will benefit our customers.

We expect to expand our customers in North America, the European Union and United Kingdom through direct sales and customer support organizations.

We expect to grow into other geographies over time, initially through distributors, starting with key countries in Asia Pacific.

We expect a highly efficient sales model since the Proteograph Product Suite does not have a large capital expenditure component, can leverage the existing installed base of MS instruments and complements large-scale genomics data ecosystems.

We are currently building out our commercial organization across Marketing, Sales, and Customer Experience functions to support demand, with the goal of delivering an exceptional customer experience.

We believe that coupling an exceptional customer experience with a transformative product will allow us to deliver substantial value to our customers, build long-term customer loyalty, enhance our competitive differentiation and, importantly, use our customer relationships to gain insights that inform our product development to grow our offerings in ways that will benefit our customers.

In North America, the United Kingdom and select countries of the European Union, we have direct sales and customer experience personnel, including Regional Business Managers (RBM), Field Application Scientists and Field Service Engineers.

In addition to these direct personnel, we have significant marketing, customer experience and technical support personnel located in our offices in Redwood City and San Diego, California.

The RBM are focused on identifying potential customers who have a strong interest in deep, unbiased proteomics and access to sufficient sample cohorts and capital to help drive long-term usage.

They work closely with our marketing personnel to identify, qualify and close these customer opportunities.

The Field Application Scientists also help in the sales process, they are primarily responsible for ensuring that customers have an exemplary experience once a purchase has been made.

This ranges from providing customer training to working with each customer to help them optimize their methods and applications.

Our Field Service Engineers perform installation and provide on-site service support for any technical problems or repairs that are needed.

In China, we have entered the market through our distribution partner, Enlight Medical, who provides sales, marketing, distribution and customer support services.

We will continue to evaluate entering other geographies and countries over time and will likely initially enter those markets through distribution partners.

Suppliers and Manufacturing

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We procure certain components of our consumables from third-party manufacturers, which includes the commonly-available raw materials needed for manufacturing our proprietary engineered NPs.

We are currently manufacturing using our pilot line and building out our manufacturing capabilities as we enter broad commercial availability.

We are currently manufacturing using our production-scale and pilot lines and continue to build out our manufacturing capabilities to support broad commercial availability of our products.

We obtain some of the reagents and components used in the Proteograph workflow from third-party suppliers.

While some of these reagents and components are sourced from a single supplier, these products are readily available from numerous suppliers.

While we perform some filling and packaging of the Proteograph assay and the related consumables, in the future, we may have our filling and packaging outsourced to a third-party.

While some of these reagents and components are currently sourced from a single supplier, these products are readily available from numerous suppliers.

While we currently perform some filling and packaging of the Proteograph assay and the related consumables, we may eventually have our filling and packaging outsourced to a third party.

We conduct vendor and component qualification for components provided by third-party suppliers and quality control tests on our NPs.

Automation Instrument

We designed the SP100 automation instrument and have outsourced the manufacturing of the SP100 automation instrument to Hamilton Company, a leading manufacturer of automated liquid handling workstations.

We designed the SP100 automation instrument and have outsourced its manufacturing to Hamilton Company, a leading manufacturer of automated liquid handling workstations.

We have entered into a non-exclusive agreement with Hamilton that covers the manufacturing of the SP100 automation instrument and its continued supply on a purchase order basis.

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Hamilton has represented to us that it maintains ISO 9001 and ISO 13485 certification.

The life sciences technology industry is characterized by rapidly advancing technologies, intense competition and a strong emphasis on intellectual property.

The proteomics market is served by companies that offer a variety of analytical instruments, such as chromatography and MS instruments, and associated reagents.

We believe that competitors in the proteomics market are differentiated by their proprietary technologies, rapid product development capabilities, applications and intellectual property.

We believe that there are currently no commercially available products that offer the capability to conduct unbiased, deep proteomics studies at the same scale and throughput as the Proteograph Product Suite.

Given the potential market opportunity and scientific promise of proteomics, we expect the intensity of the competition to increase and, as a result, one or more competing products emerging in the future.

The life sciences technology industry is highly dynamic, marked by rapidly advancing technologies, intense competition and a strong focus on intellectual property.

In the proteomics market, companies offer a range of analytical instruments, such as chromatography and MS instruments, and associated reagents.

Competition in the proteomics market is based on proprietary technologies, rapid product development capabilities, applications and intellectual property.

We believe that no currently commercially available products offer the capability to conduct unbiased, deep proteomics studies of high dynamic range samples at the same scale and throughput as the Proteograph Product Suite.

However, given the potential market opportunity and scientific promise of proteomics, we expect the competition to increase and, as a result, one or more competing products to emerge in the future.

Competing products may emerge from various sources, including life sciences tools, diagnostics, pharmaceutical and biotechnology companies, third-party service providers, academic research institutions, governmental agencies, and public and private research institutions.

Current companies that provide proteomics products include Agilent Technologies, Bruker, Danaher, DiaSorin and Thermo Fisher Scientific.

Current companies that provide proteomics products include Agilent Technologies, Bruker Corporation, Danaher, DiaSorin and Thermo Fisher Scientific.

There are also a number of companies that provide proteomic analysis services.

In addition, a number of emerging growth companies have developed, or are developing, proteomics products, services and solutions, such as Nautilus Biotechnology, Olink Proteomics, Quanterix, Quantum-Si and SomaLogic.

In addition, multiple emerging growth companies have developed, or are developing, proteomics products, services and solutions, such as Nautilus Biotechnology, Olink Proteomics, Quanterix, Quantum-Si and SomaLogic.

Government Regulation

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Food and Drug Administration (FDA) under the Federal Food, Drug, and Cosmetic Act (FDC Act) and comparable state and international agencies.

FDA defines a medical device as an instrument, apparatus, implement, machine, contrivance, implant, in vitro reagent or other similar or related article, including any component part or accessory, which is (i) intended for use in the diagnosis of disease or other conditions, or in the cure, mitigation, treatment, or prevention of disease, in man or other animals, or (ii) intended to affect the structure or any function of the body of man or other animals and which does not achieve any of its primary intended purposes through chemical action within or on the body of man or other animals and which is not dependent upon being metabolized for the achievement of any of its primary intended purposes.

Medical devices to be commercially distributed in the United States must receive from the FDA either clearance of a premarket

notification, known as 510(k), or premarket approval pursuant to the FDC Act prior to marketing, unless subject to an exemption.

We label and sell our products for research use only (RUO) and expect to sell them to academic institutions, life sciences and research laboratories that conduct research, and biopharmaceutical and biotechnology companies for non-diagnostic and non-clinical purposes.

FDA defines a medical device as an instrument, apparatus, implement, machine, contrivance, implant, in vitro reagent or other similar or related article, including

any component part or accessory, which is (i) intended for use in the diagnosis of disease or other conditions, or in the cure, mitigation, treatment, or prevention of disease, in man or other animals, or (ii) intended to affect the structure or any function of the body of man or other animals and which does not achieve any of its primary intended purposes through chemical action within or on the body of man or other animals and which is not dependent upon being metabolized for the achievement of any of its primary intended purposes.

Medical devices to be commercially distributed in the United States must receive from the FDA either clearance of a premarket notification, known as 510(k), or premarket approval pursuant to the FDC Act prior to marketing, unless subject to an exemption.

We label and sell our products for RUO and expect to sell them to academic institutions, life sciences and research laboratories that conduct research, and biopharmaceutical and biotechnology companies for non-diagnostic and non-clinical purposes.

Our products are not intended or promoted for use in clinical practice in the diagnosis of disease or other conditions, and they are labeled for research use only, not for use in diagnostic procedures.

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However, FDA can reclassify or use “de novo classification” for a device that meets the FDC Act standards for a class II device, permitting the device to be marketed without PMA approval.

To grant such a reclassification, FDA must determine that the FDC Act’s general controls alone, or general controls and special controls together, are sufficient to provide a reasonable assurance of the device’s safety and effectiveness.

To grant such a reclassification, FDA must determine that the FDC Act’s general controls alone, or general controls and

special controls together, are sufficient to provide a reasonable assurance of the device’s safety and effectiveness.

The de novo classification route is generally less burdensome than the PMA approval process.

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To obtain PMA approval, an applicant must demonstrate the reasonable safety and effectiveness of the device based, in part, on data obtained in clinical studies.

studies of investigational medical devices to determine safety and effectiveness must be conducted in accordance with FDA’s investigational device exemption (IDE) regulations, including the requirement for the study sponsor to submit an IDE application to FDA, unless exempt, which must become effective prior to commencing human clinical studies.

All clinical studies of investigational medical devices to determine safety and effectiveness must be conducted in accordance with FDA’s investigational device exemption (IDE) regulations, including the requirement for the study sponsor to submit an IDE application to FDA, unless exempt, which must become effective prior to commencing human clinical studies.

PMA reviews generally last between one and two years, although they can take longer.

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International sales of medical devices are subject to foreign government regulations, which vary substantially from country to country.

In the future, if we decide to distribute or market our diagnostic products as IVDs in Europe, such products will be subject to regulation under the European Union (EU) IVD Directive and/or the IVD Medical Device Regulation (IVDR) European Union (EU) 2017/746.

The IVDR was published in 2017, will replace the IVD Directive, is significantly more extensive than the IVD Directive, including requirements on performance data and quality system, and will become fully enforceable in 2022.

In the future, if we decide to distribute or market our diagnostic products as IVDs in Europe, such products will be subject to regulation under the IVD Medical Device Regulation (IVDR) European Union (EU)

2017/746, which replaces the IVD Directive, is significantly more extensive than the IVD Directive, including requirements on performance data and quality system, and went into application in May 2022.

Recently, the European Parliament voted to extend the transition timelines for IVDR.

Outside of the EU, regulatory approval needs to be sought on a country-by-country basis in order to market medical devices.

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In August 2020, the Department of Health and Human Services (HHS) announced rescission of guidance and other informal issuances of the FDA regarding premarket review of LDT absent notice-and-comment rulemaking, stating that, absent notice-and-comment rulemaking, those seeking approval or clearance of, or an emergency use authorization, for an LDT may nonetheless voluntarily submit a premarket approval application, premarket notification or an Emergency Use Authorization request, respectively, but are not required to do so.

The impact of this HHS rescission policy, including whether or how this policy will be implemented under the current administration, as well as other legislative, executive, and agency actions of the current administration remains unclear.

The Biden administration has also issued a “regulatory freeze” memorandum that directs department and agency heads to review any new or pending rules of the prior administration.

In November 2021, HHS under the Biden administration issued a statement that withdrew the August 2020 policy announcement stating that HHS does not have a policy on LDTs that is separate from FDA’s longstanding approach.

Legislative and administrative proposals to amend the FDA's oversight of LDTs have been introduced in recent years, including the Verifying Accurate Leading-edge IVCT Development Act of 2021 (VALID Act).

In September 2022, Congress passed the FDA user fee reauthorization legislation without substantive FDA policy riders, including the VALID Act, but Congress may revisit the policy riders and enact other FDA programmatic reforms in the future.

It is unclear how future legislation by federal and state governments and FDA regulation will impact the industry, including our business and that of our customers.

Any restrictions or heightened regulatory requirements on LDTs, IVDs, or RUO products by the FDA, HHS, Congress, or state regulatory authorities may decrease the demand for our products, increase our compliance costs, and negatively impact our business and profitability.

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Changes in healthcare regulatory policies could also increase our costs and subject us to additional regulatory requirements that may interrupt commercialization of our products, decrease our revenue and adversely impact sales of, and pricing of and reimbursement for, our products.

For further discussion of the risks we face relating to regulation, see the section titled “Risk factors—Risks related to our business and industry— Our products could become subject to government regulation as medical devices by the FDA and other regulatory agencies even if we do not elect to seek regulatory clearance or approval to market our products for diagnostic purposes, which would adversely impact our ability to market and sell our products and harm our business.

If our products become subject to FDA regulation, the regulatory clearance or approval and the maintenance of continued and post-market regulatory compliance for such products will be expensive, time-consuming, and uncertain both in timing and in outcome .”

For further discussion of the risks we face relating to regulation, see the section titled “Risk factors—Risks related to our business and industry.”

The federal Health Insurance Portability and Accountability Act of 1996 (HIPAA), as amended by the Health Information Technology for Economic and Clinical Health Act of 2009 (HITECH), and their implementing regulations, which impose obligations, including mandatory contractual terms, with respect to safeguarding the transmission, security and privacy of protected health information by covered entities subject to HIPAA, such as health plans, health care clearinghouses and healthcare providers, and their respective business associates that access protected health information.

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For example, in June 2018, the State of California enacted the CCPA, which came into effect on January 1, 2020 and provides new data privacy rights for consumers and new operational requirements for companies.

While we are not currently subject to the CCPA, we may in the future be required to comply with the CCPA, which may increase our compliance costs and potential liability.

While we are not currently subject to the CCPA, we may in the future be required to comply with the CCPA, which may increase our compliance costs and potential

Furthermore, the CCPA could mark the beginning of a trend toward more stringent state privacy legislation in the U.S., which could increase our potential liability and adversely affect our business.

Furthermore, the collection, use, storage, disclosure, transfer, or other processing of personal data regarding individuals in the European Economic Area (EEA), including personal health data, is subject to the GDPR, which became effective on May 25, 2018.

The GDPR is wide-ranging in scope and imposes numerous requirements on

companies that process personal data, including requirements relating to processing health and other sensitive data, obtaining consent of the individuals to whom the personal data relates, providing information to individuals regarding data processing activities, implementing safeguards to protect the security and confidentiality of personal data, providing notification of data breaches, and taking certain measures when engaging third-party processors.

The GDPR is wide-ranging in scope and imposes numerous requirements on companies that process personal data, including requirements relating to processing health and other sensitive data, obtaining consent of the individuals to whom the personal data relates, providing information to individuals regarding data processing activities, implementing safeguards to protect the security and confidentiality of personal data, providing notification of data breaches, and taking certain measures when engaging third-party processors.

The GDPR also imposes strict rules on the transfer of personal data to countries outside the EEA, including the United States, and permits data protection authorities to impose large penalties for violations of the GDPR, including potential fines of up to €20 million or 4% of annual global revenues, whichever is greater.

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Furthermore, following the expiration of the specified period, there will be increasing scope for divergence in application, interpretation and enforcement of the data protection law as between the United Kingdom and EEA.

For further discussion of the risks we face relating to regulation, see the section titled “Risk factors—Risks related to our business and industry— We are currently subject to, and may in the future become subject to additional, U.S., state and foreign laws and regulations imposing obligations on how we collect, store and process personal information.

Our actual or perceived failure to comply with such obligations could harm our business.

Ensuring compliance with such laws could also impair our efforts to maintain and expand our future customer base, and thereby decrease our revenue compliance with such laws could also impair our efforts to maintain and expand our future customer base, and thereby decrease our revenue.

For further discussion of the risks we face relating to regulation, see the section titled “Risk factors—Risks related to our business and industry.”

Intellectual Property

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We use a variety of intellectual property protection strategies, including patents, trademarks, trade secrets and other methods of protecting proprietary information.

As of December 31, 2021, our owned patents and patent applications, if issued, are expected to expire between 2023 and 2043, in each case without taking into account any possible patent term adjustments or extensions and assuming payment of all appropriate maintenance, renewal, annuity, or other governmental fees.

Such patent portfolio owned by us includes:

• pending U.S.

and PCT patent applications that are directed to methods for sampling a proteome at specific levels of protein coverage, methods for sampling a proteome under particular assay conditions, and biosensor compositions for the same;

• a pending PCT patent application that is directed to methods for interrogating protein pathways and PPIs with the biosensors;

• a pending PCT application that is directed to methods for analyzing protein and nucleic acid molecules in a biological sample;

• an issued U.S.

patent and a pending U.S.

patent application directed to the identification and classification of biological states;

• an issued U.S.

patent and a pending PCT patent application directed to methods for biomarker discovery, including an algorithm-based method that uses data sampled by the biosensor platform.

As of December 31, 2022, we owned or exclusively licensed over 125 issued patents and patent applications worldwide.

Our intellectual property portfolio includes patents and patent applications directed to proteomic assays, nanoparticle chemistry, data analysis and automation instruments.

Our owned or exclusively licensed patents and patent applications, if issued, are expected to expire between 2023 and 2043, in each case without taking into account any possible patent term adjustments or extensions and assuming payment of all appropriate maintenance, renewal, annuity or other governmental fees.

We exclusively license U.S.

patents and patent applications, as well as ex-U.S.

patents and pending patent applications from BWH, as of December 31, 2021.

patents and pending patent applications from The Brigham and Women’s Hospital (BWH).

These patents and patent applications are directed to methods for identifying a biological state, including classification and early detection of cancers and other diseases, using nanoparticle and biosensor compositions, as well as other nanoparticle compositions.

Our in-licensed patents and patent applications, if issued, are expected to expire between 2027 and 2037, in each case without taking into account any possible patent term adjustments or extensions and assuming payment of all appropriate maintenance, renewal, annuity, or other governmental fees.

Our in-licensed patents and patent applications, if issued, are expected to expire between 2027 and 2037, in each case without taking into

account any possible patent term adjustments or extensions and assuming payment of all appropriate maintenance, renewal, annuity, or other governmental fees.

In addition to licensing patents and patent applications from BWH, we have also non-exclusively licensed certain of our patents and patent applications to PrognomiQ for use in the field of human diagnostics.

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and foreign patents and patent applications in one patent family related to methods for identifying a biological state using nanoparticle and biosensor compositions and other nanoparticle compositions to develop, manufacture, use and commercialize products and processes in all fields, including but not limited to therapeutic, diagnostic, or other uses, on a worldwide basis.

In addition, we were also granted an exclusive, royalty-bearing, sub-licensable (with approval

from BWH) license to certain U.S.

In addition, we were also granted an exclusive, royalty-bearing, sub-licensable (with approval from BWH) license to certain U.S.

pending patent applications in another patent family to develop, manufacture, use and commercialize products and processes in all fields, including but not limited to therapeutic, diagnostic, or other uses, other than for the treatment of cancer through antigen-specific immune stimulation or the treatment of disease through immune tolerance or immune switching of lymphocyte subclasses.

We may sublicense the patent rights licensed under the agreement subject to certain conditions, including obtaining the review and approval by BWH of such sublicense and any such sublicense must be consistent with and subject to the terms of the agreement.

In consideration for the licenses granted under the agreement, we must pay BWH annual license fees prior to the first commercial sale of a licensed product that range in the low- to mid-five digit figures, and a low single digit royalty on net sales of licensed products beginning with the first commercial sale of a licensed product in any country during the term of the agreement.

In the event we commercialize a product in the therapeutic space, we are also required to make certain drug-approval regulatory and commercialization milestone payments to BWH of up to a mid-seven digit figure in the aggregate for licensed products.

In consideration for the licenses granted under the agreement, we must pay BWH annual license fees and a low single digit royalty on net sales of licensed products in any country during the term of the agreement, which is credited against the annual license fees.

In the event we commercialize a product in the therapeutic space, we are

also required to make certain drug-approval regulatory and commercialization milestone payments to BWH of up to a mid-seven digit figure in the aggregate for licensed products.

In the event we sublicense any of the licensed intellectual property, we must pay BWH a percentage of any sublicense income received by us, which on a going-forward basis will be in the high single digits.

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Furthermore, for a period of two years after the effective date, we are required to negotiate in good faith with PrognomiQ for a license or sublicense, as applicable, to any improvements to the patents and patent applications assigned or licensed or sublicensed, as applicable, under the intellectual property transfer and license agreement and the intellectual property sublicense agreement.

In an amendment to the intellectual property transfer and license agreement on July 28, 2022, we agreed to extend this negotiation period to three years after the effective date.

Neither party may assign the intellectual property transfer and license agreement nor any rights or obligations under the agreement without the other party’s prior written consent, other than to an affiliate or pursuant to an acquisition.

PrognomIQ may not assign the intellectual property sublicense agreement or any rights or obligations under the

agreement without our prior written consent, other than to an affiliate or pursuant to an acquisition, and in any event only with BWH’s prior written consent.

PrognomiQ may not assign the intellectual property sublicense agreement or any rights or obligations under the agreement without our prior written consent, other than to an affiliate or pursuant to an acquisition, and in any event only with BWH’s prior written consent.

Our right to assign the intellectual property sublicense agreement and any rights or obligations under the agreement is subject to the terms and conditions of our license with BWH.

Unless terminated earlier, the terms of the both agreements continue until the expiration of the last to expire intellectual property right granted under such agreement.

Unless terminated earlier, the terms of both agreements continue until the expiration of the last to expire intellectual property right granted under such agreement.

Either party may terminate either agreement for an uncured breach of the other party, upon which all licenses granted under such agreement to the breaching party will terminate.

Collaborators

Oregon Health & Science University (OHSU), an academic health center, and The Broad Institute of MIT and Harvard (Broad Institute), a biomedical and genomic research center, are our collaborators.

Researchers at OHSU are using our product to facilitate various research efforts focused on proteomic profiling of various oncology versus control samples to determine protein signatures common between various cancer samples versus signatures found in control samples.

Researchers at the Broad Institute use our product to analyze protein signatures in diseased vs.

As part of the initial Collaboration phase of our completed three-phase commercial strategy for the Proteograph Product Suite, we entered collaboration agreements with Oregon Health & Science University (OHSU), an academic health center, The Broad Institute of MIT and Harvard (Broad Institute), a biomedical and genomic research center, the Salk Institute for Biological Studies, a multi-disciplinary research institute focused on addressing challenging health issues, including cancer, Alzheimer’s and diabetes, and Discovery Life Sciences, a provider of biomedical and genomic research services.

We have worked closely with our collaborators to help exemplify applications for the Proteograph Product Suite.

For example, researchers at OHSU are using our products to facilitate various research efforts focused on proteomic profiling of various oncology versus control samples to determine protein signatures common between various cancer samples versus signatures found in control samples.

Additionally, researchers at the Broad Institute use our products to analyze protein signatures in diseased vs.

non-diseased samples undergoing drug perturbations in various clinical applications including cardiovascular disease.

In January 2021, Discovery Life Sciences, a biomedical and genomic research center, became one of our collaborators.

In March 2021, the Salk Institute for Biological Studies, a multi-disciplinary research institute, focused on addressing challenging health issues, including cancer, Alzheimer’s, diabetes, became one of our collaborators.

In January 2022, we entered into an agreement to form the Proteogenomics Consortium with Discovery Life Sciences and SCIEX.

Through this multi-year effort, Discovery will expand and offer unbiased proteomics capabilities to their existing genomics customers using the Proteograph Product Suite and the SCIEX ZenoTOF 7600 platform.

Proteogenomics Consortium

In January 2022, we entered into an agreement to form the PGC with Discovery and SCIEX.

Through this multi-year effort, Discovery will expand and offer deep, unbiased proteomics capabilities to their existing genomics customers using the Proteograph Product Suite and the SCIEX ZenoTOF 7600 platform.

The consortium has the objective to build capacity to analyze 100,000 samples per year to enable large-scale, unbiased plasma proteomic studies.

In December 2022, Discovery announced the launch of its Proteomics Services Division, which includes services offered by the PGC, and that it is accepting customer samples from pharmaceutical and biotech companies, government, and non-profit and academic research centers.

The PGC will offer unbiased, high resolution proteogenomic services to help uncover novel biomarkers that can be mapped back to underlying genetic variations.

Scientific Advisory Board

We have assembled a highly-qualified scientific advisory board composed of advisors who have deep expertise in the fields of nanotechnology, proteomics, genomics, medicine, regulatory compliance and data science.

Our scientific advisory board is composed of Robert Langer, Sc.D., Mostafa Ronaghi, Ph.D., Steve Carr, Ph.D., Vivek Farias, Ph.D., Philip Kantoff, M.D., Erwin Böttinger, M.D., Charles Cantor, Ph.D., Bradley Hyman, M.D., Wolfgang Parak, Ph.D., Ralph Weissleder, M.D.

and Luis Diaz, M.D.

Our scientific advisory board is composed of Robert Langer, Sc.D., Mostafa Ronaghi, Ph.D., Erwin Böttinger, M.D., Charles Cantor, Ph.D., Bradley Hyman, M.D., Steve Carr, Ph.D., Joshua Coon, Ph.D., Luis Diaz, M.D., Josh Elias, Ph.D., Vivek Farias, Ph.D., Wolfgang Parak, Ph.D.

and Ralph Weissleder, M.D.

Our employees are guided by our mission to imagine and pioneer news ways to decode the secrets of the proteome to improve human health.

Our core values Better Together, Customer Centric, Difference Makers, People First and Trailblazers guide us on our path toward achieving our mission.

Our core values Better Together, Customer Centric, Difference Makers, People First and Trailblazers guide us toward achieving our mission.

Our core values set the foundation for how we conduct business, interact with each other and our customers and evaluate employee performance.

As of December 31, 2021, we had 118 employees, all based in the United States, many of whom hold masters and doctorate degrees.

Of these employees, 62 were engaged in in research and development activities, 13 were engaged in manufacturing and operations, and 43 were engaged in selling, general and administrative activities.

As of December 31, 2022, we had 164 employees;

160 employees were based in the United States, one employee was based in Canada and three employees were based in the United Kingdom.

Many of our employees are highly educated, holding masters and doctorate degrees.

Of these employees, 80 were engaged in research and development activities, 21 were engaged in manufacturing and operations and 63 were engaged in selling, general, and administrative activities.

We consider our relationship with our employees to be good.

None of our employees are represented by a labor union or covered under a collective bargaining agreement.

Our human capital resources objectives include, as applicable, identifying, recruiting, retaining, incentivizing and integrating our existing and new employees, advisors and consultants.

Diversity, equality and inclusion awareness and training were an important part of our 2022 human capital strategy.

As of December 31, 2022, 66% of our employees were women and people of color.

Our human capital resources objectives include identifying, recruiting, retaining, incentivizing and integrating our existing and new employees, advisors and consultants.

The principal purposes of our equity and cash incentive plans are to attract, retain and reward personnel through the granting of stock-based and cash-based compensation awards, in order to increase stockholder value and the success of our company by motivating such individuals to perform to the best of their abilities and achieve our objectives.

10 unchanged sentences

We do not intend our use or display of other entities’ trade names, trademarks or service marks to imply a relationship with, or endorsement or sponsorship of us by, any other entity.

There are published studies referenced throughout this Annual Report and the citations for those studies are listed below.

The studies listed below are not a part of this prospectus and are not incorporated by reference in this Annual Report.

Published studies referenced throughout this Annual Report are cited below.

These studies are not a part of this prospectus and are not incorporated by reference in this Annual Report.

Backman, J.D.

Exome sequencing and analysis of 454,787 UK Biobank participants.

Nature 599, 628–634 (2021)

Proteomic and interactomic insights into the molecular basis of cell functional diversity.

1 unchanged sentence

Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona.

Buccitelli C.

and Selbach M.

mRNAs, proteins and the emerging principles of gene expression control.

Nat Rev Genet.

21(10):630-644 (2020).

Functionally distinct BMP1 isoforms show an opposite pattern of abundance in plasma from non-small cell lung cancer subjects and controls (in press;

Engineered nanoparticles enable deep proteomic studies at scale by leveraging tunable nano-bio interactions.

119(11) (2022).

Enhanced competition at the nano-bio interface enables comprehensive characterization of protein corona dynamics and deep coverage of proteomes.

Advanced Materials .

34, 2206008 (2022).

Keshishian, H.

4 unchanged sentences

Molec & Cellular Proteomics 14(9) 2375-2393 (2015).

Nakahata and Kawamoto.

Tissue-dependent isoforms of mammalian Fox-1 homologs are associated with tissue-specific splicing activities.

Nucleic Acid Research 33(7) 2078-2089 (2005).

Synergistic insights into human health from aptamer- and antibody-based proteomic profiling.

5 unchanged sentences

16, 4299-4310 (2017).

Szklarczyk, D., et al.

Protein-protein association networks with increased coverage supporting functional discovery in genome-wide experimental datasets.

Nucleic Acids Res .

47, D607-D613 (2019).

Widespread Expansion of Protein Interaction Capabilities by Alternative Splicing.

164(4):805-17 (2016).

Available Information

4 unchanged sentences

Compared sentence by sentence after normalising whitespace, quotation marks, case and digits, so re-formatting and restated figures do not read as changed language. Wording changes appear as one removal and one addition. The current filing and the prior one are authoritative.

What changed 10-K

Item 1. Business