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Advances in ProteomicsMaking Sense of Proteomic Data Pipelines

Proteomics and Psychotic Disorders

Mapping the Human Proteome

Delivering Truly NextGen Proteomics

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S O M AS CA N® D I S C OV E RY

Recent Developments in Proteomics Research 4

Uncovering Key Interactions Between Cancer-Driving Proteins 6

Mass Spectrometry in the Clinic 9

New Platform Connects the Genome and Proteome Worlds 12

Scientists Create Music From the Molecules of Life 14

Making Sense of Proteomic Data Pipelines 16

What Is New in Disease Biomarker Discovery? 17

Proteomics and Psychotic Disorders 21

Mapping the Human Proteome: A Conversation With Professor Chris Overall 22

Contents

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Recent Developments in Proteomics ResearchLaura Elizabeth Lansdowne and Kate Robinson

Proteomics – the term used to describe the large-scale characterization of proteins – was coined in the 1990’s by Mark Wilkins, who was a PhD student at the time. Since then, the field has gained momentum and can now be subdivided into various areas of study – from protein expression profiling and proteome mining to structural and functional proteomics and protein interactions. A cell’s proteome reflects the immediate environment in which it is studied, and various proteomics techniques can be exploited to identify the “set” of proteins within a cell, in turn helping to create an intricate map of the cell, to determine the exact location of individual proteins.

Here, we take a closer look at some recent developments in the world of proteomics research.

Novel Approach Yields Potential Drug Candidate for Bladder Cancer

An international collaboration of scientists has developed a new approach to molecular drug design, allowing for the production of a promising bladder cancer drug. The team applied the “intrinsically disordered proteins” (IDP) concept to their work. Intrinsically disordered proteins make up > 50% of the human proteome – their ability to adapt and change shape allows them to bind different surfaces, and in some cases such as this, result in a gain of function. This concept could explain why drugs based on a “lock and key” approach have limited clinical success. When tested in clinical trials, the novel drug induced

rapid shedding of cancer cells and resulted in a significant reduction in tumor size.

“This research is … extremely exciting as the clinical trials show great impact in reducing tumor size in people with this form of bladder cancer without any side effects,” said Ken H Mok, associate professor in Trinity’s School of Biochemistry and Immunology and the Trinity Biomedical Sciences Institute. “From a scientific perspective – and with a nod to the great potential for other therapeutic discoveries – it is also extremely exciting to have contributed to an entirely new approach to molecular drug design.”

Reference: A. Brisuda, et al. Nat Commun. 12, 3427. (2021)

A Blood Test for the Early Detection of Alzheimer’s Disease

In order to diagnose Alzheimer’s disease (AD), clinicians currently rely on a combination of techniques including cognitive tests, brain imaging and lumbar puncture. These methods are expensive, invasive and frequently unavailable in many countries. To help to address the shortfalls associated with these methods, researchers have designed a high-performance, blood-based test for AD. The test relies on a biomarker panel and a system that distinguishes between patients with early, intermediate and late stages of AD. This test can therefore be used for diagnosis and disease progression monitoring.

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“With the advancement of ultrasensitive blood-based protein detection technology, we have developed a simple, non-invasive, and accurate diagnostic solution for AD, which will greatly facilitate population-scale screening and staging of the disease,” said Prof. Nancy Ip, Morningside professor of life science and the director of the State Key Laboratory of Molecular Neuroscience at HKUST.

Reference: Y. Jiang, et al. Alzheimers Dement. (2021)

Could Cone Snail Venom Be Used To Treat Severe Malaria?

Severe forms of malaria can be deadly even after treatment with currently available parasite-killing drugs. This is due to persistent cyto-adhesion of infected erythrocytes despite the parasites, that reside within the host cells, being dead. Anti-adhesion drugs may therefore hold the key to significantly improving survival rates. New research into the venom produced by Conus nux, a species of sea snail, has revealed its’ ability to disrupt specific interactions (protein–polysaccharide and protein–protein) that contribute to the pathology of malaria. These conotoxinsmay therefore be utilized in the development of novel and cost-effective anti-adhesion drugs or blockade-therapy designed treat severe malaria.

“Among the more than 850 species of cone snails there are hundreds of thousands of diverse venom exopeptides that have been selected throughout several million years of evolution to capture their prey and deter predators,” said Frank Marí, PhD, corresponding author and senior advisor for biochemical sciences at the National Institute of Standards and Technology. “They do so by targeting several surface proteins present in target excitable cells. This immense biomolecular library of conopeptides can be explored for potential use as therapeutic leads against persistent and emerging diseases affecting non-excitable systems.”

Reference: A. Padilla, et al. J. Proteom. 234, 104083. (2021)

Proteomics Confirms Vertical Transmission of SARS-CoV-2 From Mother to Fetus

A research team from the Skoltech Institute of Science and Technology has developed a mass spectrometry-based diagnostic approach to detect the vertical transmission of SARS-CoV-2 from mother to fetus. The study, published in Viruses, assessed the case of a healthy 27-year-old woman that became “moderately sick” with COVID-19 at her 21st week of gestation. The child was delivered prematurely (26th week of gestation) and died shortly after. The study’s senior author, Evgeny Nikolaev, explained that the proteomics-based method was the most reliable method to use to determine vertical transmission, as it can identify

viral proteins with 100% confidence – something that cannot be achieved using other methods, such as PCR.

“We cut the N and S proteins isolated from the virus using a particular enzyme (trypsin) and detect them on a mass spectrometer. We use a standard ionization method – electrospray. Then, we search for the specific sequence of amino acids in mass-spectrometry data, using standard proteomic techniques,” Evgeny Nikolaev, professor at the Center for Computational and Data-Intensive Science and Engineering, Skoltech Institute of Science and Technology.

Reference: G. Sukhikh, et al. Viruses. 13, 447. (2021)

Probing the Proteomic Landscape of Cancer To Discover Drug Targets

Scientists from Baylor College of Medicine have demonstrated that by analyzing protein data from aggressive human cancers it is possible to uncover key drivers of disease that could be exploited as therapeutic targets. The team investigated seven specific cancer types (breast, colon, renal, lung, ovarian, uterine and glioma) and looked to determine “proteomic signatures” associated with clinical measures of aggressive disease. Some signatures were shared between cancer types and included cellular pathways of altered metabolism.

“Our experiments provided proof-of-concept that proteomics analysis is a useful strategy not only to better understand what drives cancer, but to identify new ways to control it or eliminate it,” Diana Monsivais, assistant professor of pathology and immunology, Baylor College of Medicine.

Reference: D. Monsivais, et al. Oncogene. 40, 2081–2095. (2021)

Proteomics

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Uncovering Key Interactions Between Cancer-Driving Proteins Laura Elizabeth Lansdowne

Research conducted as part of the Cancer Cell Map Initiative (CCMI), has revealed interactions between cancer-driving proteins, that were unknown, until now. Findings were published in three separate papers in Science.

The CCMI aims to transform the field of cancer drug discovery by uncovering the molecular networks underlying the disease. It is hoped that insights from the initiative will advance basic research and clinical decision-making through the development of computational cancer cell models. The CCMI comprises investigators from UC San Diego and the University of California, San Francisco (UCSF) with expertise in various areas.

The team was able to consolidate the data generated from each study, allowing them to create a single map of protein pathways underlying cancer pathophysiology. Integrating the information into a single resource provides a clearer picture of how the pathways influence one another, helping researchers to identify interactions that drive cancer growth and metastasis and expose elements that have the potential to be therapeutically modulated.

“Science moves so much more quickly when scientists from different disciplines work together. This is evident in these papers and was seen during the pandemic. In order to accomplish this more effectively across science, the reward system needs to change where groups and collaboration are rewarded more than individuals. The systems need to reward younger scientists more effectively,

especially in a way that encourages them to collaborate,” explains Professor Nevan J. Krogan from the Department of Cellular and Molecular Pharmacology at UCSF, corresponding author of the Science papers.

A better interpretation of the genome

In 2003, The Human Genome Project was declared complete. This international scientific effort marked a revolutionary turning point in the genomics field. Since then, further technological advances in DNA sequencing have enabled researchers to better interrogate the genome, and as a result, it has been possible to identify specific genetic alterations that disrupt the normal functioning of cells, some of which cause cancer.

While understanding the underlying genetic mutations responsible for initiating and driving tumor progression is important, looking beyond the nucleus, is equally valuable. Especially as the majority of cancer drugs act on protein targets.

“We often think of genomes as biological blueprints. But the blueprints of a car do not, themselves, tell you how a car will handle or perform in a crash test. Genomes relate even less straightforwardly to the cells or organisms they describe,” says Marcus R. Kelly, a postdoctoral fellow at the University of California San Diego and joint first author of the Zheng/Kelly study.

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Kelly explains that their paper organizes mutations into protein systems sharing common cellular functions, but he adds that mutations in the same system are likely not even on the same chromosome as each other.

Identifying druggable targets

In recent years, there has been concern that many cancer drugs are directed towards the wrong molecular targets and that this may be to blame for the low clinical success rate in oncology. In 2019, Wong, et al. reported that the failure rate for cancer drug development was almost 97%. Without a comprehensive understanding of the protein–protein interactions involved in cancer, it’s possible to misidentify therapeutic targets, and original targets may be nonessential for cancer cell survival.

“When we look for druggable targets, we are mostly trying to find specific molecules that control functions that cancer cells depend on more than healthy cells do,” says Kelly. To understand these unique functions, a variety of experimental techniques are required.

Kelly elaborates, “Affinity purification coupled with mass spectrometry (AP–MS), for example, tells us which proteins bind one another, which is strong evidence that they perform related functions in the cell.” He adds, “This is why AP–MS experiments are the focus of the Swaney and Kim papers, and why they contribute so significantly to the Zheng/Kelly paper. Rather than looking ‘beyond’ the genome, these other experiments help us interpret the genome more clearly.”

“The Zheng/Kelly study uses the high-quality protein–protein interaction data focused on breast and head/neck cancers along with other datasets to derive a hierarchical model of the cancer cell,” explains Krogan.

Zheng et al. took the data from the Swaney et al. and Kim et al. papers and combined it with existing public data on protein–protein interactions to generate a map of protein pathways that they used to expose hard-to-detect mutations that may play a role in metastasis. The studies provide a resource that will be helpful in interpreting cancer genomic data.

The key findings from the Kim et al. and Swaney et al. papers are described in more detail below.

A protein network map of head and neck cancer

Danielle Swaney, assistant professor of cellular molecular pharmacology at UCSF and colleagues studied protein–protein interactions for genes commonly mutated in head and neck squamous cell carcinoma (HNSCC). Their aim was to determine what impact they have on the molecular

machinery within the cell and the various signaling pathways in which they function. The protein coding gene PIK3CA – the most commonly mutated oncogene in HNSCC (~ 20%) – provides instructions for making the alpha catalytic subunit of phosphoinositide 3-kinase (PI3K). Swaney explains that they observed mutation-enriched interactions between the human epidermal growth factor receptor 3 (HER3) and PIK3CA. This is important because changes in the way they interact could affect response to HER3-targeted therapeutics.

“We find that this interaction depends [specifically] on which mutation PIK3CA has. One mutation can cause low binding to HER3, another mutation can result in high binding. This is important because HER3 is a drug target. We find that when a tumor has a PIK3CA mutation associated with high binding to HER3, you can use a HER3 inhibitor to stop tumor growth,” says Swaney. This knowledge could help to determine the sensitivity of PIK3CA-mutant tumors to HER3 inhibitors.

Probing protein interactions in breast cancer

Kim et al. aimed to assess the specific molecular alterations that occur in breast cancer beyond those commonly associated with the disease, the goal being to improve treatment efficacy and safety through targeted therapy. The researchers found two proteins (UBE2N and spinophilin) that interacted with the tumor suppressor gene BRCA1, as well as two proteins that regulate PIK3CA.

According to the study’s first author Min Kyu Kim, assistant professor of cellular molecular pharmacology, UCSF, while UBE2N is known to be involved in DNA double-strand break repair by homologous recombination, its relevance as a predictive biomarker for response to PARP inhibitors (e.g., carboplatin) and other DNA-damaging drugs hadn’t previously been explored. He elaborates on their findings: “We found that patients with pathologic complete response (basically tumors are eradicated) to PARPi/carboplatin treatment tend to have low mRNA expression of UBE2N in their tumors. So, this result suggests that cancer patients with low expression of UBE2N (but with normal levels of other DNA repair proteins, such as BRCA1) in their tumor could be considered to be treated with PARPi/carboplatin, as BRCA1/2-mutated cancer patients.”

The team was also able to show that spinophilin could regulate the phosphorylation status of many DNA repair proteins (including BRCA1) by removing phospho-marks (dephosphorylation). “This dephosphorylation is an important step to turn on and off the cellular response to DNA damage. Spinophilin is often found amplified in breast cancer patients (8% in TCGA study), so our study also shed light on the pathogenic mechanism of spinophilin-altered cancer patients,” says Kim.

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Talking of the novel PIK3CA-interacting proteins (BPIFA1 and SCGB2A1) Kim says, “Knockdown of these proteins in cells led to up-regulation of PIK3CA-AKT signaling, which is strongly associated with tumorigenesis and cancer cell proliferation. Furthermore, in an in vitro assay, these proteins were shown to preferentially inhibit wild-type PIK3CA kinase activity.” Based on this, Kim says that the team believes BPIFA1 and SCGB2A1 are negative regulators of the PIK3CA-AKT pathway.

“Given their specificity for wild-type PIK3CA, they might have a therapeutic potential towards PIK3CA-amplified tumors,” he adds.

Krogen emphasizes the significance of the protein–protein interactions identified in the Swaney and Kim papers, “[They] were the single most informative data type for the identification of protein systems, and many protein systems simply would not have been identified without that data.”

He notes that while the Swaney and Kim papers were primarily focused on identifying systems mutated in head and neck and breast cancer, they also helped to identify systems mutated in other cancers as well.

Looking beyond cancer

The Zheng study notes that the multiscale map of protein assembly strategies could be generalized to other diseases that are affected by rare genetic alterations. Kelly elaborates, “The CCMI has two sister programs, the Psychiatric Cell Map Initiative, and the Host-Pathogen Map Initiative. Both neurodegenerative diseases and susceptibility to particular pathogens have heritable components, but few truly determinative genes are known in either case. It seems likely that, like cancer, these diseases are best understood in terms of dysregulated protein systems.”

He concludes, “Organizing many rare mutations into a map will help us understand the underlying principles of patient response.”

Marcus R. Kelly, Min Kyu Kim, Nevan Krogan, and Danielle Swaney were speaking to Laura Elizabeth Lansdowne, Managing Editor for Technology Networks.

References1. M. Kim, J. Park, M. Bouhaddou, et al. Science.

374:eabf3066. (2021)2. D.L. Swaney, D.J. Ramms, Z. Wang, et al. Science.

374:eabf2911. (2021)3. F. Zheng, M.R. Kelly, D.J. Ramms, et al. Science.

374:eabf3067. (2021)

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Mass spectrometry (MS) is a robust tool for detecting a variety of analytes within clinical samples. However, until recently, MS has been limited to a select few applications, such as drug testing in toxicology. Today, with technological advances in MS and the emergence of liquid chromatography–tandem MS (LC-MS), laboratories are increasingly looking towards MS as a robust tool for detecting a variety of analytes within clinical samples.

MS technology has evolved so it can be portable, miniaturized, and used in real-time clinical analysis. These features make MS a desirable and robust clinical tool. However, it is important to consider the need for clinical MS before adopting this technology in clinical laboratories. Realizing the full benefits of MS in the clinic will depend on several factors; including considering the patient population to be served by the clinical laboratory and the necessary performance characteristics of the clinical test to be performed.

Sample preparation for MS in the clinic

All MS methodologies require a sample preparation step, which is designed to optimize the sample for MS analysis, ensuring quality and reproducibility of results. In many mass spectrometry ionization methods, sample preparation is made easier by coupling the MS to gas or liquid chromatography. For GC-MS, hydrolysis and derivatization steps are required to make samples more stable for analysis. For LC-MS, which can be used to detect a wider range of analytes, sample preparation is necessary for three reasons:

1. To simplify complex biological samples (matrices), 2. To adjust (concentrate or dilute) the analyte

concentration to meet the detection limit of LC-MS3. To exchange the sample matrix (a complex mix of

molecules and metabolites) to a simpler injection solution compatible with liquid chromatography

Choosing a sample preparation protocol is ultimately a balance of cost and complexity, and the performance of the protocol for concentrating analytes and removing matrix. Table 1 shows a comparison of LC-MS sample preparation protocols.

Clinical toxicology

One of the earliest applications of MS in the clinic was in clinical toxicology – the analysis of drugs and chemicals in body fluids. Clinical toxicology includes screening for common drugs of abuse in urine, tests for common intoxicants such as acetaminophen (paracetamol), aspirin, alcohol and digoxin, targeted panels of drug classes (e.g., stimulants or benzodiazepine) or broad-spectrum drug screens. Immunoassays are routinely used for urine toxicology tests and these need to be followed up by targeted confirmation for some drug classes, such as opioids. This is done using GC-MS or LC-MS.

GC-MS is widely used in the clinic for qualitative and quantitative drug analysis and has been the gold standard for broad-spectrum drug screening for many years. However, its main disadvantage is its inability to directly analyze drugs that are non-volatile, polar or thermally labile. By contrast, LC-MS uses electrospray ionization, which can detect non-volatile, polar and thermally labile

Mass Spectrometry in the ClinicJoanna Owens, PhD

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Sample preparation protocol

Analyte dilution (D) or concentration (C)

Relative cost Relative complexity

Relative matrix removal

Dilution D Low Simple Less

Protein precipitation D Low Simple Least

Liquid-liquid extraction D or C Low Complex More

Phospholipid removal D High Moderate More, selective*

Supported liquid extraction D or C (moderate) High Moderate More

Solid phase extraction D or C High Complex More

Online SPE/Turboflow

D or C High Complex More

* Only phospholipids are removed, other matrix components are not depleted. Table adapted from Mass Spectrometry for the Clinical Laboratory 2017, Chapter 3.

Table 1. Comparison of LC-MS/MS sample preparation protocols

compounds. This makes it particularly advantageous for broad-spectrum drug screening as you can screen for a wide range of drugs on a single instrument. However, it is worth noting a key advantage of GC-MS compared with LC-MS – its reproducibility.

Vitamin D metabolite quantitation

Vitamin D helps maintain correct levels of calcium and phosphate in the body. These nutrients are needed to keep a person’s bones, teeth and muscles healthy. Recognition of vitamin D’s important role in health as well as reports of the implications of widespread deficiency has led to an increased demand for vitamin D testing.

Immunoassays are used in the clinic for measuring vitamin D metabolites. While these automated immunoassays meet the clinical demands for throughput and turnaround time, they suffer from a lack of specificity and variability.

Vitamin D can be converted into more than 40 metabolites – few of which are clinically relevant and many of which circulate at very low concentrations. This makes detection of the target metabolites a challenge. To address this, more laboratories are starting to adopt LC-MS technologies to detect vitamin D metabolites. Sample extraction methods are essential to achieve specificity of metabolite quantitation. Although LC-MS is now the gold standard for vitamin D testing, there have been issues with different laboratories producing comparable results.

Steroid hormones

Measurements of steroid hormones are widely and commonly taken in the clinic to support diagnosis and

treatment of endocrinology disorders. Steroid hormones include androgens like testosterone and estrogens.

One of the key challenges in detecting steroid hormones is the presence of stereo-isomers. There is also a huge variation in steroid hormone concentrations between analytes and within samples – ranging from <10 ng/dL to over 1000 ng/dL. The ability of chromatography and MS to cope with these challenges means they are increasingly used to analyze steroid hormones in the clinic.

Many steroid hormones share the same fragment ions, therefore a chromatography step must be performed to enable them to be analyzed using MS. Traditionally, GC-MS has been the MS of choice for the clinical analysis of steroid hormones, as the non-polar nature of steroid hormones makes them well suited to this approach. However, steroid hormones are often in their glucuronidated form in clinical samples and need to be hydrolyzed before GC-MS analysis. As a result, many labs have shifted to LC-MS analysis to avoid this extensive sample preparation step. LC-MS has the additional capability of measuring compounds with a range of polarities.

Therapeutic drug monitoring

Therapeutic drug monitoring (TDM) involves the measure- ment of medicines in whole blood, serum or plasma. This information, alongside changes in clinical presentation, is used to adjust treatment doses to maximize effectiveness and minimize adverse events.

LC-MS has been used for “rapid” TDM in clinical trials (e.g., for the immunosuppressant drug sirolimus). These advantageous features of LC-MS fuelled its use for monitoring other therapeutics that require same-day turnaround of results.

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LC-MS for TDM has high specificity and sensitivity compared with other options such as high-performance liquid chromatography (HPLC) and immunoassays. It is also amenable to different types of clinical samples and can detect a wide range non-volatile, polar and thermally labile compounds.

Despite these advances, there are still technical consider-ations when adopting LC-MS for such a critical clinical need such as TDM. The first is choosing which compound to analyze and what concentration needs to be measured – as different drugs have different markers of toxicity (i.e., the parent drug or its metabolite). A second relates to the support required to keep an LC-MS TDM platform running.

MS in clinical microbiology

Clinical microbiology labs are tasked with accurately and quickly detecting and identifying pathogens from patient samples. Conventionally this would have been done by culturing the microbes which can take several days. Several MS technologies have been explored for use in clinical microbiology but most are still used in research to determine antimicrobial targets and virulence factors. However, matrix-assisted laser desorption ionization-time of flight MS (MALDI-TOF) is emerging as a faster, cheaper and superior method for identifying microbes to the species-level.

MALDI-TOF works by matching patterns of proteins (a profile) found in a microbe isolate with those in a library of known organism profiles. The sample needs to be purified first so that it is not mixed with other organisms must have sufficient numbers of microbes.The proteins to be analyzed by MALDI-TOF firstly need to be released from the microbe cells – the exact method for doing this varies depending on the type of microbe being analyzed.

MALDI-TOF can be used to identify bacteria, fungi, yeasts and mycobacteria. However, there are some limitations. At the moment, the approach cannot identify microorganisms directly from most clinical specimen types – such as swabs, urine, tissues and sputum. It also struggles to distinguish between different organisms with similar protein profiles. Performance ultimately relies on the extraction method of the isolates (identification is optimal when this matches the extraction method used in the reference library) and the quality and number of protein spectra available in the library for comparison.

Clinical proteomics

Clinics rely heavily on tests that can detect proteins in a patient sample, but they have been limited to one or two proteins at a time. MS has the power to detect all proteins

in an entire sample and distinguish between different post-translational modifications, enabling the translation of proteomics from a research tool in the laboratory into an analytical tool in the clinic.

Targeted proteomics – where specific peptides are detected in a complex mixture of other peptides – is already being used in the clinic. This has the benefit of providing more precise, quantitative, sensitive data. It is achieved by a combination of immunoaffinity capture and triple quadrupole LC-MS or MALDI-TOF MS – and is called a mass spectrometric immunoassay (MSIA).

In contrast to immunoassays, targeted proteomics is not limited by interferences or cross-reactivity. Moreover, it has superior sensitivity – being able to quantitate to picomolar concentrations of peptides. Its main disadvantage is that this approach requires substantial technical time and specialist expertise. However, as more biological therapies make it to the clinic, MS-driven proteomics technologies are likely to play a key role in predicting and monitoring response to treatment.

The future of MS in the clinic

MS is emerging as a valuable and robust tool for clinical analysis. However, there remain a number of challenges standing in the way of its wider adoption. These include the high upfront investment in instrumentation, space, climate control, gas supply required and the subsequent costs of implementation and maintenance. MS platforms are complex and require specialist expertise for routine operation and troubleshooting. The standard training of most clinical lab personnel does not include LC-MS and the hardware and software of most MS applications are designed for specialized research laboratories. Although some reagent kits for MS are now available, sample preparation is still lengthy and labor-intensive making it unsuitable for urgent clinical care. Finally, MS instruments are not standardized across labs and automation is not currently feasible.

Despite these challenges, the opportunities for MS in the clinic are severalfold. The evolving platforms in MS and their combination with other technologies have the potential to provide easy, point-of-care assays with highly sensitive and specific results and fast turnaround times that could transform the clinical tests of tomorrow.

References1. H. Nair, W. Clarke. (eds) Mass Spectrometry for the

Clinical Laboratory. (2017) 2. M. Vogeser, V. Zhang. Clin Mass Spectrom. 9, 1–6. (2018)

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New Platform Connects the Genome and Proteome WorldsMolly Campbell

Technological developments in next-generation sequencing (NGS) approaches and mass spectrometry (MS)-based methods have advanced the landscape of molecular biology research. Scientists are now able to identify and characterize various constituents of a cell, tissue or organism and analyze them in their totality – their “ome” – be it the entire expression of genes (genome), proteins (proteome) or metabolites (metabolome), at any given time.

Piecing together this information helps us to understand the molecular journey from genotype to phenotype and where it can go wrong, in the case of disease phenotypes. As an increasing quantity of omics information becomes accessible to researchers, it has become clear that working with the data sets in segregation can limit their utility. Connectivity between the omics “worlds” is fundamental, but it has been challenging – until now.

In January, the European Molecular Biology Laboratory’s European Bioinformatics Institute (EMBL-EBI) announced that it had launched the Genome Integrations with Function and Sequence, or GIFTS, platform. This novel platform enables scientists that are using Ensembl and UniProt to access all of the up-to-date genomic and protein data for human and mouse genomes. Technology Networks spoke with Beth Flint, Ensembl applications project leader at EMBL-EBI, Maria Martin, team leader in protein function development at EMBL-EBI and Daniel Zerbino, team leader in genome analysis at EMBL-EBI, to learn more about GIFTS and how it will be used to help the research community.

Q: Please can you talk to us about the rationale behind the Genome Integrations with Function and Sequence (GIFTS)?

A: GIFTS aims to provide a clear and unambiguous bridge between two flagship data resources at the EMBL’s European Bioinformatics Institute, namely Ensembl and UniProt. Together, they offer a wealth of information on protein synthesis: Ensembl describes the upstream sequences of nucleotides that encode protein-coding genes, whose transcripts are transcribed into downstream protein isoforms, documented in UniProt. Each resource already points to the other, thus connecting genes to proteins and vice-versa, but because of differences in release cycle calendars these links were not 100% consistent. GIFTS now details our shared understanding of which gene maps to which protein.

Q: Who is behind the development of GIFTS?

A: GIFTS has been developed at EMBL-EBI as a collaboration between Ensembl and UniProt. The project brought together the expertise from these two groups and allowed us to build a tool that will allow people to easily explore the relationships between the data these groups produce. Collaborative projects of this nature take full advantage of the breadth of knowledge and diverse range of skills at EMBL-EBI. The GIFTS project was possible due to the input of curators, annotators, database and API experts, user interface developers and pipeline automation specialists.

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Q: Why is it important to connect the genome and proteome worlds?

A: The genome is the storehouse of the genetic material needed for an organism to function. Proteins are the primary effectors of the instructions encoded in our genomes and they and their products ultimately shape our cells, tissues, organs and bodies in response to our environment. Proteins provide an essential link between genome sequence and the eventual phenotype. The functional analysis of genomic and other large-scale biomedical datasets requires integrated information about many distinct types of biological entity, including individual genes, transcripts and proteins.

Q: Why has this been difficult previously?

A: Ensembl focuses on the annotation of transcripts in reference genomes using available cDNA, EST and RNA-seq data, while UniProt focuses on annotating protein sequences using experimental evidence from the literature, homologs in other species and proteomics experiments. The study of genomes and proteomes requires a very specialized scientific knowledge which needs to be combined to effectively map them.

Q: What do you hope the outcome of launching GIFTS will be?

A: The mappings that GIFTS pipelines produce will help the Ensembl and Uniprot teams update the data

they present via their main sites. Using the GIFTS data will present a uniform view of mappings between the two domains and ensure that consistent information is presented. This provides an enormous benefit to those who use these mappings. Behind the public facing interface of GIFTS are tools used by the annotators and curators in the Ensembl and Uniprot groups. These tools enable them to review and improve mappings. As this process continues, it is hoped that over time canonical UniProt isoforms will be selected for all human genes and that these will match the MANE transcripts from Ensembl.

Q: Are there any intentions to launch a similar platform for other “omics” data?

A: EMBL-EBI resources strive to be interoperable with each other, and this new bridge is merely strengthening a very tight network of data resources. For example, other EMBL-EBI resources, such as the Reactome pathway database or the Gene Expression Atlas already link directly and unambiguously to UniProt proteins or Ensembl genes. A sister project, Structure Integration with Function, Taxonomy and Sequence (SIFTS) now connects UniProt protein sequences to their 3D structures, stored in PDBe. All these interconnections are enabling researchers to make sense of all the resources at EMBL-EBI, as exemplified by our unified search utility.

Daniel Zerbino, Beth Flint and Maria Martin were speaking to Molly Campbell, Science Writer for Technology Networks.

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Scientists Create Music From the Molecules of Life Molly Campbell

Making sense of complex science is not always easy, and the perception that science is a difficult subject can lead individuals to veer away from studying and pursuing a career in the field.

Methods of science communication are becoming increasingly creative to help break down scientific concepts into bitesize, easily digestible pieces of information. Yu Zong Chen, professor in the department of pharmacy at the National University of Singapore and Peng Zhang, currently a post- doc at the Rockefeller University have perhaps taken innovation to the next level, creating melodies based upon the structure of proteins. Their research is published in Heliyon.

“Music together with visual art are the most popular ways for humans to express, propagate and perceive our feelings about, and our understanding of, the world, events and ourselves. Science is generally perceived by the public as something hard to understand. By mapping a basic element of life (As proteins are the work horses of life) to music, the general public can hear the sound of life at a microscopic level, and perceive what science sounds like,” Chen told Technology Networks.

How do we turn proteins into music?

Turning protein structure into music may sound complicated, but if we look closely at how proteins are built, and how music is made, there are some parallels. “Protein structure is like a folded chain, and this chain is composed of small units called amino acids,” Zhang explained.

There are 20 different amino acids that are all labeled with an alphabetic label. “A protein chain can be denoted as a string of these alphabetic letters, very much like a string of music notes in alphabetical notation,” added Zhang. Protein chains fold and wave into patterns that ultimately support their function. These patterns contain ups, downs, turns and loops. “Music string is with sound waves of higher and lower pitches, tempos and repeats. Using algorithms, scientists can map the string of amino acid structural and chemical features into a string of musical features.”

This isn’t a novel concept. However previous attempts haven’t focused on a particular music style, and so the outcome hasn’t been the most enjoyable audio experience. The existing algorithms use a simple approach of mapping the strings of amino acid features onto fundamental music features, such as note length and pitches. However, this mapping does not work well with complex musical features

What are proteins?

Proteins play an essential role in all living organisms. They are complex biological molecules directly involved in many structural, metabolic, transport, immune, signaling and regulatory processes. Proteins are synthesized from a DNA template through a series of steps including transcription and translation.

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like harmony and rhythm.

In this study, Chen and colleagues focused on classical music so that they could guide more complex mappings of different amino acid features to the strong characteristics of this music style.

“Classical music generally presents lighter, homophonic, graceful and emotive melodies. Some of these strong characteristics may be exploited as enforceable guide for protein-to-music mapping. We specifically selected the Romantic period classical music, which typically spans a wide range of the piano keys with features such as chromaticism and chords,” Chen said. He added that music from the mid-1800s Romantic period is typically highly emotive, which enabled the researchers to test a great range of different piano keys in their algorithm.

Which proteins to choose?

TThere are a vast number of different proteins – scientists are still debating just how many exist in the human body. Chen and colleagues focused on 18 proteins belonging to two different groups. The first group of 11 proteins were selected as their functions are involved in emotion, cognition, sensation and performance, consistent with

the lighter, more homophonic and graceful characteristics of classical music. The second group have more diverse functions, such as photosynthesis, fluorescence, food proteins and disease, representing the different “aspects” and “states” of life.

Four pieces of mid-1800s Romantic classical music were analyzed, including Fantasie-Impromptu from Chopin and Wanderer Fantasy from Franz Schubert.

The music produced was complex, with notable variations in pitch, loudness and rhythm, Chen described. No two pieces are alike due to the unique amino acid sequences, so each protein produces a distinct melody. The researchers also discovered that interesting patterns can emerge when making music in this way. “The music generated from the oxytocin receptor has some recurring motifs due to the repetition of certain smaller sequences of amino acids within the protein sequence. Some music also sounds more chromatic than others; the music generated from the cellular tumor antigen p53 is highly chromatic, and there are particularly fascinating phrases where the music sounds almost toccata-like, repeating and ‘developing’ a motif,” Chen said. Music created from the M protein of the coronavirus – which defines the shape of the viral envelope – utilizes a large range of keys, particularly in the bass.

Inspiring a greater understanding of the molecules of life

The scientists hope that their music pieces can draw more attention to the molecules of life, and that it will inspire further research in the future. “Our protein-to-music mapping algorithms were developed with respect to a limited number of classical music pieces, and the algorithms were fined-tuned based on the opinion of a smaller number of people,” said Chen. “A better protein-to-music mapping algorithm may be developed by using a greater number of music pieces and consultation with more diverse groups of people,” he concluded. ReferenceN. WanNi Tay et al. Heliyon. (2021)

A breakdown of protein-to-music algorithm methods:

1) Generate the distribution maps of the musical features

2) Generate the distribution maps of amino acid features

3) Compare the maps of musical features and the maps of amino acid features to find which amino acid features best match each musical feature

4) Use the matched amino acid features for mapping to each musical feature.

Advances in Proteomics

Download the full infographic here

MAKING SENSE OF

The identification and characterization of proteins relies on complex technological equipment and yields enormous amounts of data that are arduoud to process.

Proteomics is the large scale study of proteomes.

1. Peptide mass fingerprinting2. De novo sequencing

There are two main approaches by which MS data can be used to identify proteins:

Quantifying protein expression is integral to proteomics research as it enables scientists to form conclusions regarding the dynamics of the protein across different cells, tissues and organisms.

Once a protein has been identified and quantified, the next objective is to deduce its role. Many bioinformatic pathway analysis tools have been developed to analyze the biological significance of proteomic identification and quantification data.

Understanding a protein’s interaction and physiological function is fundamental to their study. Classifying pathway analysis programs is dificult due to the fact that they are continuously evolving, however they are typically divided into three groups: desktop programs, programming packages, and web-based applications.

Public desposition and storage of proteomics data are unfortunately less advanced compared

to other ‘omics’ disciplines. For coordinated sharing of proteomics research data and

progress for the field as a whole, it is essential that a proteomic pipeline provides feedback to

databases and public repositories.

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What Is New in Disease Biomarker Discovery? Arndt Brachat, PhD

Precise and reliable information about a patient’s disease status is an important prerequisite for an adequate and successful therapy. Disease-related biomarkers provide such information as part of the diagnostic toolkit (diagnostic biomarkers), as markers of disease activity (monitoring biomarkers) or as prognostic markers, predicting a disease course or an event. Importantly, the same biomarker may serve different roles once its value has been clearly demonstrated for each purpose.1

While properly validated and performant biomarkers are undoubtedly of immense value in medical decision making, their discovery faces various hurdles. At a very practical level for example, the tissue containing informative molecules needs to be accessible for sampling. Assays need to reach high levels of sensitivity, specificity, accuracy and reproducibility. Meeting these requirements is even more challenging in the context of diseases with high inter- and intra-patient variability or for samples with dynamic changes of cellular composition, such as blood or inflamed skin. The expansion of our biomarker arsenal has been an area of highly active research in recent years, leading to fascinating progress in several fields, some of which we will highlight here.

Novel modalities

MicroRNAs (miRNAs) are short non-coding RNA molecules that regulate most genes on the post-transcriptional level.2 As they seem to impact virtually all biological processes, it is not surprising that dysregulation of miRNAs was

also found to be linked to diseases ranging from cancer to cardiovascular disease and sepsis.3 The fact that so-called circulating miRNAs also act as messengers between tissues means that they can be isolated from accessible body fluids (“liquid biopsies”) like blood, urine, saliva, breast milk, cerebrospinal- or seminal fluid and even tears. While this is a major advantage, miRNAs also present some tough challenges for biomarker development. In particular, reproducibility of disease association and signal levels has been generally poor. To fully harvest the information content of miRNAs, more robust sampling and sample processing protocols are needed. That extends to data analysis and normalization steps that are suitable for fluids with highly dynamic changes in density and molecular composition.

Circular RNA

Another type of abundant RNA molecule that holds great promise as disease biomarker is circular RNA (circRNA), formed by back-splicing of pre-mRNA transcripts. A known group of nucleic acids for decades, the functional significance of circRNAs has only recently been appreciated.4 circRNA may impact either transcription or translation by binding to and titrating miRNAs (“miRNA sponge”), by interaction with RNA binding proteins, by direct binding to mRNA or even as template for translation. To date, a number of cancer studies have reported on the dysregulation of circRNA species in tumors and have advertised their potential as diagnostic or prognostic markers. However, reports from research in cardiovascular diseases, diabetes, rheumatoid arthritis and others point to the relevance of

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circRNAs in a broad spectrum of indications.5 Chemical stability and high abundance in body fluids contribute to the attractiveness of this class of molecules as biomarkers. The demonstration of robust association of peripheral circRNA levels with diagnostic or prognostic groups is a worthwhile goal for the years to come.

Circulating, cell-free DNA

Similar to miRNA and circRNA, body fluids also transport DNA that is no longer contained in the nucleus but rather travels through the body without the protection of a surrounding cell. This cell-free DNA (cfDNA) has also gained popularity in the biomarker field recently due to improvements in molecular analysis techniques. A common hypothesis about the origin of these short stretches of double-stranded DNA assumes that dying cells, for example blood cells, release these fragments. In healthy subjects, cfDNA levels are relatively low, but can rise significantly in cancer where tumors contain large numbers of apoptotic or necrotic cells.6 Nevertheless, the detection of low abundance DNA fragments is a challenge, but recent improvements in PCR approaches and next generation sequencing have opened up new avenues for sensitive characterization of tumor DNA.7 Recent work on cfDNA in autoimmune rheumatic diseases has identified the origin of cfDNA (nuclear or mitochondrial) and the complexing with carrier molecules as important factors in the biology of cell free DNA. An alternative cell death process, NETosis, was shown to release mitochondrial cfDNA in these diseases.8

Extracellular vesicles

Cell free DNA, miRNAs, circRNAs, lipids, mRNA or signaling proteins and even whole organelles may be packaged in extracellular lipid vesicles (EVs), the multi-purpose delivery vehicles of intercellular communication.9 EVs can be released in response to various stimuli such as cell stress, inflammation or bacterial toxins but may also be produced constitutively. The vesicle’s cargo differs according to cellular context and stimuli and is protected from degradation by the vesicle membrane. EVs were found to have potential as disease biomarkers in oncology and several other indications such as systemic sclerosis, renal, liver or neurodegenerative diseases. What is particularly fascinating about EVs is the fact they can transport biomarkers from inaccessible areas of the body, like the brain, to accessible liquids like the cerebrospinal fluid or the blood.10

Microbiome

The last decade has seen an explosive increase of studies exploring the human microbiome, the complex microbial communities that live in and on our bodies

that play an important role in our well-being and also in disease. Correlations between the presence of microbial communities and diseases such as inflammatory bowel disease, cancer or depression have been described.11,12,13 Challenges for using microbes as biomarkers arise mainly from the sheer infinite combinations of microbial species, microbial metabolites, host genetics and environmental conditions, such as composition of food and physical activity. Changes in microbial communities over time or between individuals may or may not be causally linked to disease. Therefore, the success of efforts to identify clinically useful “microbiome markers” will not only require modern sequencing technology and bioinformatics tools but depend on the presence of rather robust microbial signals that associate with disease sufficiently strongly to be detectable by statistical methods. Combinations of controlled environmental stimuli, such as a a controlled diet, and disease relevant biochemical readouts (like inflammatory cytokine levels) may help to identify disease-relevant microbiome markers.14

Technological advances

Proteomics and lipidomics

Abnormal protein levels or altered regulatory modifications of proteins are often at the core of disease processes and hence reliable detection of such changes is instrumental for diagnosis. Recent improvements of mass spectrometry (MS)-based “bottom-up” proteomics, which analyzes peptide fragments rather than intact proteins, bring these technologies closer to their application in clinical practice.15 To reach sensitivity and precision levels that are required for reliable biomarker assays, selected reaction monitoring (SEM) techniques are used which detect ions in pre-defined mass/charge windows. Higher specificity levels can be reached by parallel reaction monitoring (PRM) using high-resolution mass analyzers. Other approaches that may be well suited for biomarker applications in the clinic are the use of reporter ions or the quantification of pre-defined marker panels that are known to be informative for e.g., the activity of a relevant pathway. If the goal is biomarker discovery, a novel high-level strategy, named “rectangular proteome profiling” seems promising. Here, subject phenotypes of large disease and control cohorts are correlated with analyte-rich proteome profiling.16

In contrast to MS, affinity-based methods rest on the recognition of protein surfaces. Here, a promising technology for proteomics is aptamer-based profiling. While antibody-arrays frequently suffer from cross-reactivity problems, aptamers, chemically modified DNA or RNA molecules can be designed for specific and parallel detection of thousands of proteins. Aptamer technology also holds promise to facilitate sample processing steps and assay throughput.17

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Lipidomics

Lipids are essential components of cells and have both structural as well as signaling functions. Not surprisingly, they are also involved in several illnesses including metabolic and cardiovascular diseases such as atherosclerosis or diabetes but also for example in response to infectious agents. Improvements in ultrahigh performance liquid chromatography-MS (UHPLC-MS) has enabled a series of lipidomics biomarker studies in recent years, often based on a combination of untargeted, exploratory and targeted approaches.18

Single cell analytics

An overriding current trend in biomedical research is to increase resolution at the sample level by moving from tissue sections or complex mixtures of cell types to the level of single cell analysis. This trend is motivated by the realization that tissues do not represent homogeneous assemblies of synchronized cells but are rather a dynamic system of cells which may act heterogeneously, depending

on their history and local stimuli. Hence, biomarker signals may need to be observed at a sub-tissue level to rise above the noise. Remarkably, single cell level techniques now include available methods for genome, epigenome, transcriptome, proteome, and metabolome analysis.19 Recently, an open-source procedure for metabolomics was described that allows for fast metabolite detection in individual cells while maintaining spatial and morphological sample information.20 These techniques may turn out to be particularly helpful for the diagnosis and staging of diseases which originate from individual cells such cancer or developmental disorders.

Advances in imaging

Imaging techniques have a long history of being instrumental in diagnosis and their continuing improvement and brilliant combination lead to ever more impressive results. Continuing technical trends include further increases in resolution and contrast with decreasing radiation exposure as well as AI-assisted image assessment. Equally important is the continuous development of reproducible imaging protocols

Advances in Proteomics

Biomarkers can play a valuable role in understanding the pathology of disease, as well as helping to drive the subsequent development of medical diagnostics and therapeutics. However, their discovery and clinical translation can be a challenging and lengthy process.

What are Biomarkers?

BIOMARKER DISCOVERY

A biomarker, or biological marker, is “a defined characteristic that is measured as an indicator of normal biological processes, pathogenic processes or responses to an exposure or intervention.”1

Biomarkers can be molecular, biochemical, anatomical, or physiological characteristics, ranging from:

Small molecules such as:Nucleic acidsProteinsMetabolites Carbohydrates

To cells and measures such as pulse and blood pressure.

There are several sub-types of biomarkers that can be identified and quantified to aid the diagnosis, monitoring, evaluation, and prediction

of diseases and patient responses to therapies, including:1

Diagnostic biomarkers:Detect the presence of a disease or condition.E.g., Hemoglobin A1c (HbA1c) levels to diagnose Type 2 Diabetes.

Pharmacodynamic biomarkers:Show that a biological response to a medicalproduct or environmental agent has occurred. Can help to guide therapeutic development and patient management.E.g., Sweat chloride to assess response to cystic fibrosis transmembrane conductance regulator (CFTR) modulators.

Predictive biomarkers:Identify individuals more likely to experience favorable or unfavorable effects from a treatment or environmental agent.E.g., BRCA1/2 genes to predict sensitivity to ionizing radiation.

Prognostic biomarkers:Identify likelihood of disease recurrence or progression in an individual who already has the disease.E.g., Level of prostate-specific antigen (PSA) to assess likelihood of cancer progression.

Susceptibility biomarkers:Indicate potential for developing a disease.E.g., Apolipoprotein E (APOE) gene variations to identify predisposition to Alzheimer’s disease.

How arebiomarkers

used?

Genomics and epigenetics A multitude of methods are available for genome analysis, including:

Fluorescence in situ hybridization (FISH)Single nucleotide polymorphism (SNP) arraysNext-generation sequencing (NGS)

TranscriptomicsThe transcriptome adds another layer of information from the genome in the "omics" cascade. It provides a holistic image of the RNA transcripts produced by the genome. It is more complex due to the different types of mRNA molecules that can be produced by a gene via processes such as alternative splicing and RNA editing.

Analysis methods include:MicroarraysRNA sequencing (RNA-seq)

RNA-seq is increasingly popular for biomarker discovery as it offers a wide dynamic range and can detect and quantify the transcription of unknown transcripts.3

MetabolomicsThe metabolome represents the endpoint of the "omics" process. Compared to earlier points in the "omics" cascade, such as the genome and transcriptome, the metabolome more closely reflects the phenotype, and as such is a promising avenue for biomarker discovery. Metabolites exist in a broad range of concentrations and are chemically diverse, so they can be tricky to analyze.

The most commonly adopted methods for metabolomic profiling are:5

Nuclear magnetic resonance (NMR)MS

LipidomicsLipids are critically important in a large number of cellular processes and altered lipid metabolism plays a central role in the development of an array of diseases. As such, lipid biomarkers are an emerging area of research focus.

Lipid analysis methods include:Thin-layer chromatography

Gas chromatography

MS achieves the highest sensitivity and specificity. Particularly, electrospray ionization (EI)-MS has revolutionized lipid analysis for biomarker discovery. 7

ProteomicsProteins are attractive candidates for disease biomarkers as they provide more information about the actual physiological status of a cell or tissue, compared to other biomarkers such as miRNA. The goal here is to understand which of the proteins constituting the human proteome are differentially expressed in diseased and healthy states. Research exploring and validating protein-based biomarkers adopts an array of techniques, including:

Early discovery Untargeted mass spectrometry-based (MS) biomarker discovery via liquid chromatography (LC) coupled with tandem MS (LC-MS/MS).

Two-dimensional gel electrophoresis

Challenges: time-to-result, throughput and sample-size requirements in a clinical environment.

Verification and validation Targeted MS

Immunoassays such as enzyme-linked immunosorbent assay (ELISA)4

Before disease biomarkers can be utilized for these applications, they must first be discovered and validated. The methods and workflows adopted are dependent on the type of biomarker that is being

explored. Here, we'll focus on some of the key "omics" based biomarker research areas.

DISEASE-SPECIFIC BIOMARKERS

The discovery and validation of disease-specific biomarkers will help to realize the potential of personalized medicine and its benefits.

Recent examples include:

Disease biomarker research and clinical implementation faces a number of challenges, including but not limited to:15

Research question – is it clearly defined?Study design – reproducibility Type of specimen

Biomarker discovery

Choice of methodReproducibility and standardization Availability of samples

Analytical validation

Study design CostsAvailability of patients

Clinical utility assessment

Regulations and legislations are continuously changingTechnology continues to advance Large data analysis challenges Effective clinical deployment

Sponsored by

From lab to clinical use

Radiation sickness13

The level of microRNA-150 in the blood has been shown to decrease as a function of radiation dose, whilst levels of microRNA-23a do not change, acting as a normalizer. By comparing the relative levels of the two microRNAs in the blood, radiation sickness can be rapidly identified.

Cancer immunotherapy outcome14

A recent study found that a decrease in circulating tumor DNA fragments in patients treated with pembrolizumab, an immunotherapy drug, was associated with a beneficial response and longer survival. The authors suggest that measuring the levels of circulating DNA could help to predict which patients will respond well to immunotherapy.

COVID-19 severity12

By analyzing the serum of COVID-19 patients, researchers observed that the levels of 27 proteins were associated with COVID-19 severity. The biomarkers could be used to help doctors predict whether a patient is at risk of severe illness, as well as highlight potential new drug targets.

Traumatic brain injury8

NIH researchers have demonstrated that blood levels of neurofilament light chain correlate closely with levels in the cerebrospinal fluid following traumatic brain injury. The findings suggest that neurofilament light chain could be a sensitive blood biomarker to detect brain injury non-invasively and predict recovery.

Amyotrophic lateral sclerosis (ALS)10

A genetic fingerprint that can distinguish blood samples of ALS patients from healthy controls has recently been developed. Based on eight different microRNA sequences obtained from exosomes originating in the brain, the novel biomarker could help to diagnose ALS rapidly via a blood test, as well as assess the effectiveness of treatments.

Triple negative breast cancer11

The presence of circulating tumor DNA and circulating tumor cells in the blood plasma of women who have undergone chemotherapy for triple negative breast cancer was found to be significantly associated with poorer patient outcomes, highlighting their potential as biomarkers for the prediction of disease recurrence and survival.

CHALLENGES IN BIOMARKER RESEARCH

1: FDA-NIH Biomarker Working Group. BEST (Biomarkers, EndpointS, and other Tools) Resource. Silver Spring (MD), Food and Drug Administration (US). 2016. https://www.ncbi.nlm.nih.gov/books/NBK338448/. Accessed September 21, 2020.

2: Califf RM. Biomarker definitions and their applications. Exp Biol Med (Maywood). 2018;243(3):213-221. doi:10.1177/1535370217750088.

3. Akond Z, Alam M, Mollah MNH. Biomarker identification from rna-seq data using a robust statistical approach. Bioinformation. 2018;14(4):153-163. Published 2018 Apr 30. doi:10.6026/97320630014153.

4. Hathout Y. Proteomic methods for biomarker discovery and validation. Are we there yet?. Expert Rev Proteomics. 2015;12(4):329-331. doi:10.1586/14789450.2015.1064771.

5. Shah SH, Kraus WE, Newgard CB. Metabolomic profiling for the identification of novel biomarkers and mechanisms related to common cardiovascular diseases: form and function. Circulation. 2012;126(9):1110-1120. doi:10.1161/CIRCULATIONAHA.111.060368.

6. Li L, Han J, Wang Z, et al. Mass spectrometry methodology in lipid analysis. Int J Mol Sci. 2014;15(6):10492-10507. Published 2014 Jun 11. doi:10.3390/ijms150610492.

7. 1. Zhao Y-Y, Cheng X, Lin R-C. Chapter One - Lipidomics Applications for Discovering Biomarkers of Diseases in Clinical Chemistry. International Review of Cell and Molecular Biology. Vol 313. Academic Press; 2014:1-26. doi:10.1016/B978-0-12-800177-6.00001-3.

8. Shahim P, Politis A, van der Merwe A, et al. Neurofilament light as a biomarker in traumatic brain injury. Neurology. 2020;95(6):e610. doi:10.1212/WNL.0000000000009983.

9. Barthélemy NR, Horie K, Sato C, Bateman RJ. Blood plasma phosphorylated-tau isoforms track CNS change in Alzheimer’s disease. Journal of Experimental Medicine. 2020;217(e20200861). doi:10.1084/-jem.20200861.

10. Banack SA, Dunlop RA, Cox PA. An miRNA fingerprint using neural-enriched extracellular vesicles from blood plasma: towards a biomarker for amyotrophic lateral sclerosis/motor neuron disease. Open Biology. 10(6):200116. doi:10.1098/rsob.200116.

11. Radovich M, Jiang G, Hancock BA, et al. Association of circulating tumor DNA and circulating tumor cells after neoadjuvant chemotherapy with disease recurrence in patients with triple-negative breast cancer: preplanned secondary analysis of the BRE12-158 randomized clinical trial [published online ahead of print, 2020 Jul 9]. JAMA Oncol. 2020;6(9):1-6. doi:10.1001/jamaoncol.2020.2295.

12. Messner CB, Demichev V, Wendisch D, et al. Ultra-high-throughput clinical proteomics reveals classifiers of covid-19 infection. Cell Systems. 2020;11(1):11-24.e4. doi:10.1016/j.cels.2020.05.012.

13. Yadav M, Bhayana S, Liu J, et al. Two-miRNA–based finger-stick assay for estimation of absorbed ionizing radiation dose. Science Translational Medicine. 2020;12(552):eaaw5831. doi:10.1126/s-citranslmed.aaw5831.

14. Bratman SV, Yang SYC, Iafolla MAJ, et al. Personalized circulating tumor DNA analysis as a predictive biomarker in solid tumor patients treated with pembrolizumab. Nature Cancer. 2020;1(9):873-881. doi:10.1038/s43018-020-0096-5.

15. McDermott JE, Wang J, Mitchell H, et al. Challenges in biomarker discovery: combining expert insights with statistical analysis of complex omics data. Expert Opin Med Diagn. 2013;7(1):37-51. doi:10.1517/17530059.2012.718329.

Alzheimer’s disease9

In a recent exploratory study, blood levels of phosphorylated tau 217 were shown to correlate with the presence of amyloid plaques in the brain. Further refinement of the technique used in the study could lead to the development of a tau-based blood test that could identify people who are likely to develop Alzheimer’s disease, before symptoms arise.

This infographic summarizes some of the key technologies adopted in biomarker discovery and explores some of the disease areas in

which biomarker discovery is rapidly advancing.

ELISA

NMR

MS (and MS-based imaging)6

1. Exp Biol Med (Maywood). 243, 213–221. (2018)

20 TECHNOLOGYNETWORKS.COM

that are fine-tuned for each new application andindication. The use of ultrasonography in pediatric rheumatology,21 of shear-wave elastography in musculoskeletal diseases,22 of multiparametric magnetic resonance imaging in prostate cancer,23 or of the EOS imaging system in disorders of the spine24 are just a few examples of this innovative process.

Integrative approaches

Complex diseases are often driven by an interplay of molecules of different classes such as genes (DNA), transcripts (RNA), proteins and metabolites. Therefore, biomarker assessments that are restricted to only one modality will frequently be of restricted diagnostic value. While the combination of these modalities may seem an obvious step, it comes with some hard challenges. Firstly, when many more variables (biomarkers) are available than samples, apparent associations between a phenotype and a biomarker easily occur by random chance and are not reproducible. Secondly, appropriate weighting of markers from different modalities and on different scales needs to be optimized for each diagnostic algorithm. Strategies to address these challenges include efforts to separate informative from non-informative features and to describe samples at an integrated “meta-level”. As examples of multi-omics integration, the DIABLO method25 or matrix factorization approaches26 may be mentioned here.

Outlook

On the technical side, it seems very likely that we will see further efforts to improve signal to noise and robustness of assessments while reducing the cost per analyte, required sample volumes and risk for the patient. At least for biomarker discovery and mechanistic studies of disease, integration across modalities will presumably gain importance. In clinical practice, the need for simplicity and cost savings will push towards reduced panels of assays as long as these prove to be sufficiently informative. Miniaturization may, in some areas, help to reconcile these conflicting tendencies. As a general expectation, progress in assay automation, data analysis workflows and machine learning will need to complement improvements to the analytical methods themselves if we aim for broader application of these fascinating technologies.

References1. R.M. Califf. Exp. Biol. Med. 243, 213–221. (2018)2. R.C. Friedman, K.K. Farh, C.B. Burge, et al. Genome Res. 19,

92–105. (2009)3. C.E. Condrat, D.C. Thompson, M.G. Barbu, et al. Cells. 9, 276.

(2020)4. S.C. Conn, K.A. Pillman, J. Toubia, et al. Cell. 160, 125-34.

(2015)5. Z. Zhang, T. Yang, J. Xiao. EBioMedicine. 34, 267-274. (2018)6. J.L. Park, H.J. Kim, B.Y. Choi, et al. Oncol Lett. 3, 921-926.

(2012)7. L.A. Diaz Jr, A. Bardelli. J Clin Oncol. 32, 579–586. (2014)8. B. Duvvuri, C. Lood. Front. Immunol. 10, 502. (2019)9. P. Simeone, G. Bologna, P. Lanuti, et al. Int J Mol Sci. 21,

2514. (2020)10. A.G. Thompson, E. Gray, S.M. Heman-Ackah, et al. Nat. Rev.

Neurol. 12, 346–357. (2016)11. K.L. Glassner, B.P. Abraham, E.M.M. Quigley. J Allergy Clin

Immunol. 145, 16-27. (2020) 12. E. Elinav, W.S. Garrett, G. Trinchieri, et al. Nat Rev Cancer.

19, 371-376. (2019)13. G. Winter, R.A. Hart, R.P.G. Charlesworth, et al. Rev

Neurosci. 29, 629-643. (2018)14. J.A. Gilbert, M.J. Blaser, J.G. Caporaso et al. Nat Med. 24,

392-400. (2018)15. P. Cifani, A. Kentsis. Proteomics. 17, (1-2). (2017)16. P.E. Geyer, L.M. Holdt, D. Teupser, M. Mann. Mol Syst Biol.

13, 942. (2017)17. J. Jacob, D. Ngo, N. Finkel, et al. Circulation. 137, 1270-1277.

(2018)18. H.F. Avela, H. Sirén. Clin Chim Acta. 510, 123-141. (2020)19. W. Chen, S. Li, A.S. Kulkarni, L. Huang, J. Cao, K. Qian, J.

Wan. Biotechnol J. 15, (1). (2020)20. L. Rappez, M. Stadler, S. Triana, et al. Nat Methods 18,

799-805. (2021)21. L.X. Zou, M.P. Lu, L. Kwok, L. Jung. World J Pediatr. 16, 52-

59. (2020)22. M.S. Taljanovic, L.H. Gimber, G.W. Becker, et al.

Radiographics. 37, 855-870. (2017)23. M.R. Tangel, A.R. Rastinehad. F1000Res. 7, F1000 Faculty

Rev-1337. (2018)24. B. Garg, N. Mehta, T. Bansal, et al. J Clin Orthop Trauma. 11,

786-793. (2020)25. A. Singh, C.P. Shannon, B. Gautier, et al. Bioinformatics. 35,

3055–3062. (2019)26. V. Gligorijević, N. Malod-Dognin, N. Pržulj. Proteomics. 16,

741-58. (2016)

Advances in Proteomics

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Proteomics and Psychotic DisordersMolly Campbell

Can we predict psychotic disorders using a blood test? In this Teach Me in 10, we’re joined by psychiatrist and PhD student Dr. David Mongan, who talks to us about proteomic prediction models for psychotic disorders and psychotic experiences.

How might models that incorporate proteomic data be used to predict the development of psychosis? That’s Mongan’s current research focus, and the subject of his latest paper published in JAMA Psychiatry.

In less than 10 minutes, Mongan explains how psychotic disorders exist on a “continuum”, how his latest research contributes to our understanding of psychotic disorders and takes us a step closer to personalized treatment in psychiatry.

Teach Me in 10Proteomics and Psychotic Disorders With Dr. David Mongan

Watch Now

Advances in Proteomics

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Mapping the Human Proteome: A Conversation With Professor Chris OverallMolly Campbell

In 2020, science celebrated mapping 90% of the human proteome, an endeavor that has been achieved by the Human Proteome Project (HPP). Technology Networks explored the journey to this successful feat by speaking with Chris Overall, professor and Canada research chair in proteinase proteomics and systems biology at the University of British Columbia, and chair of the C-HPP component of the HPP.

Q: In your editorial piece, you talk about how HPP “minds the gap”. Can you talk to us about why there is a 10% missing protein gap, and how the HPP looks to close the gap in the future?

A: Missing proteins may be rarely expressed proteins in rare cells, tissues and fetal/childhood development stage/time, or expressed in too low amounts that challenge sampling and analysis; some proteins are chemically or structurally not amenable to current mass spectrometry or require the most recent infrastructure to be analysed that is not generally available to most proteomics labs due to lack of funding directed to proteomics.

Q: How does it feel as a scientist to be involved in a project that contributes so largely to our understanding of human life?

A: Incredibly exciting! This is all non-funded work in my lab, and in most labs that participated in defining the human proteome, but I feel it is so important a scientific and medical pursuit that we do it unfunded, in our own time. Genomics cannot provide all the answers or diagnostics for diseases lacking a genetic basis. Critically, genomics cannot provide information on disease activity and on-target drug activity. Only proteomics can do so.

For instance, proinflammatory cytokines and chemokines are precisely regulated in expression over time and cell/tissue location. Most chemokines (molecular beacons to control white blood cell migration and activation, are modified by PTMs or precise proteolytic processing that may remove one or a handful of amino acids and this occurs during the disease phase to activate, then inactivate chemokines and even to switch cell surface receptors to lead to a totally different signal in the target cells. Cleavage can also generate antagonists that activity prevent new signals reaching the cell. But all these

Advances in Proteomics

23 TECHNOLOGYNETWORKS.COM

proteoforms are generated from exactly the same gene and mRNA. Hence, knowing that information alone does not provide the temporal information on relative and absolute levels of these chemokine proteoforms.

Deciphering this by proteomics using the features of these proteoforms as biomarkers provides accurate and timely information and the active disease activity status the patient is experiencing. This is vital information to inform diagnosis, treatments and patient management. Thus, proteomics really holds the key to devising new accurate diagnostics for personalised medicine. These analytical approached can then be translated as the simpler ELISA and other tests suitable for deployment in hospitals, diagnostic labs and even bed side. I feel privileged to be involved in such a worthwhile endeavour and this drives me and my lab members. However, our work would progress so much faster and more accurately if funding was more available for essential infrastructure.

Q: How do you expect the data gathered through the HPP to impact the future of modern medicine?

A: Proteins are the fabric of life. Our genomes just provide the instructions on how our proteins are weaved together to form this amazing complex tapestry, that defines our individuality. Our individuality in turn, defines how prone we may be to different diseases,

aging and other stressors. Like clothing, our fabric is ever changing and adapting to the conditions when they change – just like we need different clothes in the different seasons or during the day versus the night. Some genes allow us to make such changes quickly, other genes adapt slower. In this case, “slow” is a method of control as consistency is needed to enable a stable platform for our bodies. It is to understand this pattern, and how it changes, that proteomics is so integral to understanding health and disease and for personalised medicine.

In addition, and separately, machine learning of the normal and pathological cellular expression and distribution of disease relevant protein proteoforms biopsy sample processing by histology will become more accurate and faster, providing more nuanced information that can be key to accurate, early and appropriate medical decision making.

Professor Chris Overall was speaking to Molly Campbell, Science Writer, Technology Networks.

Advances in Proteomics

The SCIEX clinical diagnostic portfolio is For In Vitro Diagnostic Use. Rx Only. Product(s) not available in all countries. For information on availability, please contact your local sales representative or refer to www.sciex.com/diagnostics. All other products are For Research Use Only. Not for use in Diagnostic Procedures. Trademarks and/or registered trademarks mentioned herein, including associated logos, are the property of AB Sciex Pte. Ltd. or their respective owners in the United States and/or certain other countries (see www.sciex.com/trademarks). © 2021 DH Tech. Dev. Pte. Ltd. Related to RUO-MKT-18-13569-A

Access to 14 exclusive technical content pieces (case studies, webinars, technical notes and white papers) to learn how electron activated dissociation (EAD) and the Zeno trap can help you to:

Identify up to 40% more proteins Routinely quantify PTMs Maximize sensitivity, and more...

Gain certainty in your accurate mass workflows for your proteomics research

Now, you can be extraordinary.

Download the content pack at SCIEX.com/ZenoTOF-7600-LSR

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Large-scale, targeted, peptide quantification of 804 peptides with high reproducibility, using Zeno MS/MS Using a 20-minute microflow gradient with a Scheduled MRMHR workflow on the ZenoTOF 7600 system

Christie Hunter SCIEX, USA There are many powerful workflows available for proteomics research on today’s mass spectrometry systems, depending on project goals. They cover a wide range that includes fully untargeted data dependent acquisition approaches for protein identification, comprehensive data independent acquisition strategies for large scale quantification, and also fully targeted quantitative assays for the highest specificity and sensitivity. This last class of assay has been typically performed on triple quadrupole or QTRAP systems because of their very high sensitivity and speed.

The ZenoTOF 7600 system is a QTOF system that can collect high-resolution, high mass accuracy, full-scan MS and MS/MS data. With Zeno trap technology, the system also demonstrates very high sensitivity MS/MS data. The Zeno trap provides

significant increases in MS/MS signal: ~5-fold increase for the higher m/z fragment ions that are typically monitored for peptides (Figure 1). Here, a large-scale targeted assay for peptides in human plasma was developed to explore the quantitative capability of Zeno MS/MS on the ZenoTOF 7600 system. Using the PQ500 kit (Biognosys), an MRMHR assay for 804 peptides was run in human plasma, and the reproducibility and sensitivity of the assay were characterized.

Key features of the ZenoTOF 7600 system for protein ID The ZenoTOF 7600 system delivers a 4 to 25-fold gain in

MS/MS sensitivity across the entire m/z range, using the Zeno trap technology1

Zeno MS/MS acquired at 10 msec accumulation time with ≥30000 resolution and high mass accuracy

For peptide quantification, peak area gains of ~5.6-fold are typical using Zeno MS/MS

Excellent quantitative reproducibility and sensitivity was observed for 804 peptides in human plasma, in a single acquisition method, with a 20 min microflow gradient

Microflow chromatography and the OptiFlow ion source enable fast gradients with excellent retention time reproducibility for large-scale, time-scheduled, targeted assays

Figure 1. Significant gains in peptide area with Zeno trap activated. (Top) Example data of the sensitivity increases observed when the Zeno trap is activated are shown for ANT3.PFLVFIR, with a ~6x gain in peak area. (Bottom) A summary of the observed sensitivity gains for all 804 peptides is shown, plotted according to precursor mass. The average gain is 5.6-fold.

Zeno trap onZeno trap off

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Rapid analysis and interpretation of metabolomics SWATH acquisition data using a cloud-based processing pipeline Using the OneOmics suite with the SCIEX ZenoTOF 7600 system

Alexandra Antonoplis1, Jason Causon2, Christie Hunter1

1SCIEX, USA, 2SCIEX, Canada SWATH acquisition is a data independent acquisition (DIA) workflow that has been demonstrated to improve metabolite coverage over traditional data dependent techniques for untargeted metabolomics.1,2 The workflow enables creation of a digitized record of the metabolome present in a sample, with full (MS1) and MS/MS scans capturing every detectable analyte in a single injection. The richness of SWATH acquisition data offers numerous data analysis opportunities, one of which is to identify differential metabolites across sample groups. While instrumentation and appropriate methods for collecting metabolomics SWATH acquisition data are well-established, the lack of software tools for processing large-scale metabolomics SWATH acquisition studies remains a challenge for wide-spread adoption of the workflow in laboratories.3

In this work, the OneOmics suite, a cloud-based solution for SWATH acquisition data processing, was used to investigate

analytes present in the urine of Zucker Diabetic Fatty (ZDF) vs. control Sprague Dawley (SD) rats. ZDF rats are widely used as an animal model of genetic type 2 diabetes. The OneOmics suite facilitated complete end-to-end processing of the metabolomics data set, starting with ion-library driven extraction of analyte peak groups from the SWATH acquisition data. The platform features built-in false discovery rate (FDR) analysis and normalization algorithms to facilitate accurate identification of differentially expressed analytes. Data sets can be further interrogated using multivariate statistical analysis tools and viewed in a biological context with pathway mapping (Figure 1).

Key features of the OneOmics suite for metabolomics data processing OneOmics suite enables fast and confident identification of

differential metabolites across experiment groups in large-scale metabolomics SWATH acquisition studies

The entire processing workflow, from meta data management to examining biological significance of results, is supported in the platform

OneOmics suite features specific algorithms for metabolomics SWATH acquisition peak extraction and scoring, FDR analysis, and normalization, along with data dashboards for rapid assessment of data quality and results

Enriched analytes can be mapped to biological pathways using the Bioreviews App

Figure 1. Overview of the metabolomics workflow for SWATH acquisition data processing in the OneOmics suite. Following collection of variable window SWATH acquisition data using the SCIEX ZenoTOF 7600 system, data files were uploaded to the OneOmics suite for processing. Experimental meta data was entered, and then the Extractor and Assembler Apps were used to extract and quantify metabolites across the samples. Additional apps were used to visualize the results and perform statistical analysis to determine the metabolite differences between ZDF vs. SD rat urine.

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Complete structural elucidation of lipids in a single experiment using electron activated dissociation (EAD) Analyze on an LC timescale using the ZenoTOF 7600 system

Mackenzie Pearson1, Christie Hunter1, Takashi Baba2

1SCIEX, USA, 2SCIEX, Canada The field of lipid research has grown immensely in recent decades. Lipids were initially thought to simply be structural components of cellular membranes, but the ongoing study of lipids and their functions has shown these diverse molecules are very active participants in many biological processes. Recent studies have shown lipids to play direct or causal roles in many human disease states, such as Alzheimer’s, metabolic syndrome and lysosomal storage disorders.1 They have also been used as potential biomarkers. For instance, a shift in the double bond from a ∆7 to ∆9 in a phospholipid has the potential to be a biomarker for breast cancer,2 and a change in the sn-1 and sn-2 positions of an acyl chain in phosphatidylinositol has the potential to be a marker in urine for prostate cancer.3

Lipids have also garnered a lot of attention in the delivery of vaccines, genetic material and other small molecules. Lipid nanoparticles (LNPs) are a novel drug delivery system consisting of a lipid outer shell with the drug encapsulated in the center. LNPs have now been approved for several therapies as well as for the SARS-CoV-2 vaccines mRNA-1273 (Moderna) and BNT162b2 (BioNTech).4

Although lipid species generally fall into classes that share specific subgroups and configurations, the diversity of lipid

molecules is enormous. Characterization of lipids must not only include the identification of molecular composition but also details about individual components such as class, head groups, lengths of different fatty acids, modifications, attachment points, numbers and positions of double bonds, and even cis/trans configurations. As a result, the complete structural elucidation of lipid molecules has generally been an arduous task composed of a series of characterization steps that use different methodologies.

Here, electron activated dissociation (EAD) on the ZenoTOF 7600 system is used for the complete structural elucidation of glycerophospholipids, sphingolipids, and acylglycerols in a single experiment. In contrast to the more commonly used collision activated dissociation, or CAD, EAD provides an abundance of unique fragment ions critical for complete lipid characterization.

Key features of EAD and the ZenoTOF 7600 system for lipid analysis EAD, an alternative fragmentation mechanism to CAD,

provides richer information for improved characterization of lipids

Tunable kinetic energies for optimization of fragmentation for different applications

EAD combined with the Zeno trap has the sensitivity needed for fast LC-MS analysis and information dependent acquisition (IDA)

Figure 1. Complete characterization of a lipid. One MS/MS spectrum identifies the lipid as PC 16:0/18:1(n-9:cis) by providing class, head group, fatty acid identification, fatty acid position (regioisomerism), double bond location and stereochemistry (cis/trans).

single experiment de novo analysis PC 16:0 / 18:1(n-9:cis)

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Electron activated dissociationA new paradigm for mass spectrometryEAD (electron activated dissociation) provides more

informed decisions, accelerating research and

development as well as improving productivity for

routine analytical applications.SCIEX WHITEPAPER

Multi-omics content pack