Dynamic Proteomics: a database for dynamics and localizations of endogenous fluorescently-tagged proteins in living human cells

Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective

Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.

Allelic correlation is a marker of trade-offs between barriers to transmission of expression variability and signal responsiveness in genetic networks

Genetic networks should respond to signals but prevent the transmission of spontaneous fluctuations. Limited data from mammalian cells suggest that noise transmission is uncommon, but systematic claims about noise transmission have been limited by the inability to directly measure it. Here, we build a mathematical framework modeling allelic correlation and noise transmission, showing that allelic correlation and noise transmission correspond across model parameters and network architectures. Limiting noise transmission comes with the trade-off of being unresponsive to signals, and within responsive regimes, there is a further trade-off between response time and basal noise transmission. Analysis of allele-specific single-cell RNA-sequencing data revealed that genes encoding upstream factors in signaling pathways and cell-type-specific factors have higher allelic correlation than downstream factors, suggesting they are more subject to regulation. Overall, our findings suggest that some noise transmission must result from signal responsiveness, but it can be minimized by trading off for a slower response. A record of this paper's transparent peer review process is included in the supplemental information.

Application of Machine Learning in Spatial Proteomics

Spatial proteomics is an interdisciplinary field that investigates the localization and dynamics of proteins, and it has gained extensive attention in recent years, especially the subcellular proteomics. Numerous evidence indicate that the subcellular localization of proteins is associated with various cellular processes and disease progression. Mass spectrometry (MS)-based and imaging-based experimental approaches have been developed to acquire large-scale spatial proteomic data. To allow the reliable analysis of increasingly complex spatial proteomics data, machine learning (ML) methods have been widely used in both MS-based and imaging-based spatial proteomic data analysis pipelines. Here, we comprehensively survey the applications of ML in spatial proteomics from following aspects: (1) data resources for spatial proteome are comprehensively introduced; (2) the roles of different ML algorithms in data analysis pipelines are elaborated; (3) successful applications of spatial proteomics and several analytical tools integrating ML methods are presented; (4) challenges existing in modern ML-based spatial proteomics studies are discussed. This review provides guidelines for researchers seeking to apply ML methods to analyze spatial proteomic data and can facilitate insightful understanding of cell biology as well as the future research in medical and drug discovery communities.

SUBCELLULAR TRANSCRIPTOMICS & PROTEOMICS: A COMPARATIVE METHODS REVIEW

The internal environment of cells is molecularly crowded, which requires spatial organization via subcellular compartmentalization. These compartments harbor specific conditions for molecules to perform their biological functions, such as coordination of the cell cycle, cell survival, and growth. This compartmentalization is also not static, with molecules trafficking between these subcellular neighborhoods to carry out their functions. For example, some biomolecules are multifunctional, requiring an environment with differing conditions or interacting partners, and others traffic to export such molecules. Aberrant localization of proteins or RNA species has been linked to many pathological conditions, such as neurological, cancer, and pulmonary diseases. Differential expression studies in transcriptomics and proteomics are relatively common, but the majority have overlooked the importance of subcellular information. In addition, subcellular transcriptomics and proteomics data do not always colocate because of the biochemical processes that occur during and after translation, highlighting the complementary nature of these fields. In this review, we discuss and directly compare the current methods in spatial proteomics and transcriptomics, which include sequencing- and imaging-based strategies, to give the reader an overview of the current tools available. We also discuss current limitations of these strategies as well as future developments in the field of spatial -omics.

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Subcellular information of protein and RNA give insights into molecular function.

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This review discusses strategies available to measure subcellular information.

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Hybridization of methods shows promise for exploring the composition of organelles.

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Advances are aiding understanding of the organisation and dynamics of cells.

EvoProDom: Evolutionary modeling of protein families by assessing translocations of protein domains

Here, we introduce a novel ‘evolution of protein domains’ (EvoProDom) model for describing the evolution of proteins based on the ‘mix and merge’ of protein domains. We assembled and integrated genomic and proteomic data comprising protein domain content and orthologous proteins from 109 organisms. In EvoProDom, we characterized evolutionary events, particularly, translocations, as reciprocal exchanges of protein domains between orthologous proteins in different organisms. We showed that protein domains that translocate with highly frequency are generated by transcripts enriched in trans‐splicing events, that is, the generation of novel transcripts from the fusion of two distinct genes. In EvoProDom, we describe a general method to collate orthologous protein annotation from KEGG, and protein domain content from protein sequences using tools such as KoFamKOAL and Pfam. To summarize, EvoProDom presents a novel model for protein evolution based on the ‘mix and merge’ of protein domains rather than DNA‐based evolution models. This confers the advantage of considering chromosomal alterations as drivers of protein evolutionary events.

The neXtProt knowledgebase in 2020: data, tools and usability improvements

The neXtProt knowledgebase (https://www.nextprot.org) is an integrative resource providing both data on human protein and the tools to explore these. In order to provide comprehensive and up-to-date data, we evaluate and add new data sets. We describe the incorporation of three new data sets that provide expression, function, protein-protein binary interaction, post-translational modifications (PTM) and variant information. New SPARQL query examples illustrating uses of the new data were added. neXtProt has continued to develop tools for proteomics. We have improved the peptide uniqueness checker and have implemented a new protein digestion tool. Together, these tools make it possible to determine which proteases can be used to identify trypsin-resistant proteins by mass spectrometry. In terms of usability, we have finished revamping our web interface and completely rewritten our API. Our SPARQL endpoint now supports federated queries. All the neXtProt data are available via our user interface, API, SPARQL endpoint and FTP site, including the new PEFF 1.0 format files. Finally, the data on our FTP site is now CC BY 4.0 to promote its reuse.

Spatial proteomics: a powerful discovery tool for cell biology

Protein subcellular localization is tightly controlled and intimately linked to protein function in health and disease. Capturing the spatial proteome - that is, the localizations of proteins and their dynamics at the subcellular level - is therefore essential for a complete understanding of cell biology. Owing to substantial advances in microscopy, mass spectrometry and machine learning applications for data analysis, the field is now mature for proteome-wide investigations of spatial cellular regulation. Studies of the human proteome have begun to reveal a complex architecture, including single-cell variations, dynamic protein translocations, changing interaction networks and proteins localizing to multiple compartments. Furthermore, several studies have successfully harnessed the power of comparative spatial proteomics as a discovery tool to unravel disease mechanisms. We are at the beginning of an era in which spatial proteomics finally integrates with cell biology and medical research, thereby paving the way for unbiased systems-level insights into cellular processes. Here, we discuss current methods for spatial proteomics using imaging or mass spectrometry and specifically highlight global comparative applications. The aim of this Review is to survey the state of the field and also to encourage more cell biologists to apply spatial proteomics approaches.

The alpha-1 subunit of the Na+,K+-ATPase (ATP1A1) is required for macropinocytic entry of respiratory syncytial virus (RSV) in human respiratory epithelial cells

Human respiratory syncytial virus (RSV) is the leading viral cause of acute pediatric lower respiratory tract infections worldwide, with no available vaccine or effective antiviral drug. To gain insight into virus-host interactions, we performed a genome-wide siRNA screen. The expression of over 20,000 cellular genes was individually knocked down in human airway epithelial A549 cells, followed by infection with RSV expressing green fluorescent protein (GFP). Knockdown of expression of the cellular ATP1A1 protein, which is the major subunit of the Na⁺,K⁺-ATPase of the plasma membrane, had one of the strongest inhibitory effects on GFP expression and viral titer. Inhibition was not observed for vesicular stomatitis virus, indicating that it was RSV-specific rather than a general effect. ATP1A1 formed clusters in the plasma membrane very early following RSV infection, which was independent of replication but dependent on the attachment glycoprotein G. RSV also triggered activation of ATP1A1, resulting in signaling by c-Src-kinase activity that transactivated epidermal growth factor receptor (EGFR) by Tyr845 phosphorylation. ATP1A1 signaling and activation of both c-Src and EGFR were found to be required for efficient RSV uptake. Signaling events downstream of EGFR culminated in the formation of macropinosomes. There was extensive uptake of RSV virions into macropinosomes at the beginning of infection, suggesting that this is a major route of RSV uptake, with fusion presumably occurring in the macropinosomes rather than at the plasma membrane. Important findings were validated in primary human small airway epithelial cells (HSAEC). In A549 cells and HSAEC, RSV uptake could be inhibited by the cardiotonic steroid ouabain and the digitoxigenin derivative PST2238 (rostafuroxin) that bind specifically to the ATP1A1 extracellular domain and block RSV-triggered EGFR Tyr845 phosphorylation. In conclusion, we identified ATP1A1 as a host protein essential for macropinocytic entry of RSV into respiratory epithelial cells, and identified PST2238 as a potential anti-RSV drug.

RSV continues to be the most important viral cause of severe bronchiolitis and pneumonia in infants and young children, and also has a substantial impact in the elderly. It is estimated to claim the lives of ~118,000 children under five years of age annually. No vaccine or antiviral drug suitable for general use is available. The involvement of host factors in RSV infection and replication is not well understood, but this knowledge might lead to intervention strategies to prevent infection. Using a genome-wide siRNA screen to knock down the expression of over 20,000 individual cellular genes, we identified ATP1A1, the major subunit of the Na⁺,K⁺-ATPase, as an important host protein for RSV entry. We showed that ATP1A1 activation by RSV resulted in transactivation of EGFR by Src-kinase activity, resulting in the uptake of RSV particles into the host cell through macropinocytosis. We also showed that the cardiotonic steroid ouabain and the synthetic digitoxigenin derivative PST2238, which bind specifically to the extracellular domain of ATP1A1, significantly reduced RSV entry. Taken together, we describe a novel ATP1A1-enabled mechanism used by RSV to enter the host cell, and describe candidate antiviral drugs that block this entry.

Dynamic proteomics reveals bimodal protein dynamics of cancer cells in response to HSP90 inhibitor

BACKGROUND

Drugs often kill some cancer cells while others survive. This stochastic outcome is seen even in clonal cells grown under the same conditions. Understanding the molecular reasons for this stochastic outcome is a current challenge, which requires studying the proteome at the single cell level over time. In a previous study we used dynamic proteomics to study the response of cancer cells to a DNA damaging drug, camptothecin. Several proteins showed bimodal dynamics: they rose in some cells and decreased in others, in a way that correlated with eventual cell fate: death or survival. Here we ask whether bimodality is a special case for camptothecin, or whether it occurs for other drugs as well. To address this, we tested a second drug with a different mechanism of action, an HSP90 inhibitor. We used dynamic proteomics to follow 100 proteins in space and time, endogenously tagged in their native chromosomal location in individual living human lung-cancer cells, following drug administration.

RESULTS

We find bimodal dynamics for a quarter of the proteins. In some cells these proteins strongly rise in level about 12 h after treatment, but in other cells their level drops or remains constant. The proteins which rise in surviving cells included anti-apoptotic factors such as DDX5, and cell cycle regulators such as RFC1. The proteins that rise in cells that eventually die include pro-apoptotic factors such as APAF1. The two drugs shared some aspects in their single-cell response, including 7 of the bimodal proteins and translocation of oxidative response proteins to the nucleus, but differed in other aspects, with HSP90i showing more bimodal proteins. Moreover, the cell cycle phase at drug administration impacted the probability to die from HSP90i but not camptothecin.

CONCLUSIONS

Single-cell dynamic proteomics reveals sub-populations of cells within a clonal cell line with different protein dynamics in response to a drug. These different dynamics correlate with cell survival or death. Bimodal proteins which correlate with cell fate may be potential drug targets to enhance the effects of therapy.

Assemblages: functional units formed by cellular phase separation

The partitioning of intracellular space beyond membrane-bound organelles can be achieved with collections of proteins that are multivalent or contain low-complexity, intrinsically disordered regions. These proteins can undergo a physical phase change to form functional granules or other entities within the cytoplasm or nucleoplasm that collectively we term “assemblage.” Intrinsically disordered proteins (IDPs) play an important role in forming a subset of cellular assemblages by promoting phase separation. Recent work points to an involvement of assemblages in disease states, indicating that intrinsic disorder and phase transitions should be considered in the development of therapeutics.

A computational algorithm for functional clustering of proteome dynamics during development

Phenotypic traits, such as seed development, are a consequence of complex biochemical interactions among genes, proteins and metabolites, but the underlying mechanisms that operate in a coordinated and sequential manner remain elusive. Here, we address this issue by developing a computational algorithm to monitor proteome changes during the course of trait development. The algorithm is built within the mixture-model framework in which each mixture component is modeled by a specific group of proteins that display a similar temporal pattern of expression in trait development. A nonparametric approach based on Legendre orthogonal polynomials was used to fit dynamic changes of protein expression, increasing the power and flexibility of protein clustering. By analyzing a dataset of proteomic dynamics during early embryogenesis of the Chinese fir, the algorithm has successfully identified several distinct types of proteins that coordinate with each other to determine seed development in this forest tree commercially and environmentally important to China. The algorithm will find its immediate applications for the characterization of mechanistic underpinnings for any other biological processes in which protein abundance plays a key role.

The emergence of proteome-wide technologies: systematic analysis of proteins comes of age

During the lifetime of a cell proteins can change their localization, alter their abundance and undergo modifications, all of which cannot be assayed by tracking mRNAs alone. Methods to study proteomes directly are coming of age, thereby opening new perspectives on the role of post-translational regulation in stabilizing the cellular milieu. Proteomics has undergone a revolution, and novel technologies for the systematic analysis of proteins have emerged. These methods can expand our ability to acquire information from single proteins to proteomes, from static to dynamic measures and from the population level to the level of single cells. Such approaches promise that proteomes will soon be studied at a similar level of dynamic resolution as has been the norm for transcriptomes.

Translational bioinformatics approaches for systems and dynamical medicine

The exponential growth of experimental and clinical data generated from systematic studies, the complexity in health and diseases, and the request for the establishment of systems models are bringing bioinformatics to the center stage of pharmacogenomics and systems biology. Bioinformatics plays an essential role in bridging the gap among different knowledge domains for the translation of the voluminous data into better diagnosis, prognosis, prevention, and treatment. Bioinformatics is essential in finding the spatiotemporal patterns in pharmacogenomics, including the time-series analyses of the associations between genetic structural variations and functional alterations such as drug responses. The elucidation of the cross talks among different systems levels and time scales can contribute to the discovery of accurate and robust biomarkers at various diseases stages for the development of systems and dynamical medicine. Various resources are available for such purposes, including databases and tools supporting "omics" studies such as genomics, proteomics, epigenomics, transcriptomics, metabolomics, lipidomics, pharmacogenomics, and chronomics. The combination of bioinformatics and health informatics methods would provide powerful decision support in both scientific and clinical environments. Data integration, data mining, and knowledge discovery (KD) methods would enable the simulation of complex systems and dynamical networks to establish predictive models for achieving predictive, preventive, and personalized medicine.

LoQAtE--Localization and Quantitation ATlas of the yeast proteomE. A new tool for multiparametric dissection of single-protein behavior in response to biological perturbations in yeast

Living organisms change their proteome dramatically to sustain a stable internal milieu in fluctuating environments. To study the dynamics of proteins during stress, we measured the localization and abundance of the Saccharomyces cerevisiae proteome under various growth conditions and genetic backgrounds using the GFP collection. We created a database (DB) called ‘LoQAtE’ (Localizaiton and Quantitation Atlas of the yeast proteomE), available online at http://www.weizmann.ac.il/molgen/loqate/, to provide easy access to these data. Using LoQAtE DB, users can get a profile of changes for proteins of interest as well as querying advanced intersections by either abundance changes, primary localization or localization shifts over the tested conditions. Currently, the DB hosts information on 5330 yeast proteins under three external perturbations (DTT, H₂O₂ and nitrogen starvation) and two genetic mutations [in the chaperonin containing TCP1 (CCT) complex and in the proteasome]. Additional conditions will be uploaded regularly. The data demonstrate hundreds of localization and abundance changes, many of which were not detected at the level of mRNA. LoQAtE is designed to allow easy navigation for non-experts in high-content microscopy and data are available for download. These data should open up new perspectives on the significant role of proteins while combating external and internal fluctuations.

A novel single-cell screening platform reveals proteome plasticity during yeast stress responses

Unprecedented proteome plasticity in response to stress in yeast is revealed using a novel screening platform that allows tracking of protein localization and abundance at single-cell resolution.

Uncovering the mechanisms underlying robust responses of cells to stress is crucial for our understanding of cellular physiology. Indeed, vast amounts of data have been collected on transcriptional responses in Saccharomyces cerevisiae. However, only a handful of pioneering studies describe the dynamics of proteins in response to external stimuli, despite the fact that regulation of protein levels and localization is an essential part of such responses. Here we characterized unprecedented proteome plasticity by systematically tracking the localization and abundance of 5,330 yeast proteins at single-cell resolution under three different stress conditions (DTT, H₂O₂, and nitrogen starvation) using the GFP-tagged yeast library. We uncovered a unique “fingerprint” of changes for each stress and elucidated a new response arsenal for adapting to radical environments. These include bet-hedging strategies, organelle rearrangement, and redistribution of protein localizations. All data are available for download through our online database, LOQATE (localization and quantitation atlas of yeast proteome).

Identifying biological network structure, predicting network behavior, and classifying network state with High Dimensional Model Representation (HDMR)

This work presents an adapted Random Sampling - High Dimensional Model Representation (RS-HDMR) algorithm for synergistically addressing three key problems in network biology: (1) identifying the structure of biological networks from multivariate data, (2) predicting network response under previously unsampled conditions, and (3) inferring experimental perturbations based on the observed network state. RS-HDMR is a multivariate regression method that decomposes network interactions into a hierarchy of non-linear component functions. Sensitivity analysis based on these functions provides a clear physical and statistical interpretation of the underlying network structure. The advantages of RS-HDMR include efficient extraction of nonlinear and cooperative network relationships without resorting to discretization, prediction of network behavior without mechanistic modeling, robustness to data noise, and favorable scalability of the sampling requirement with respect to network size. As a proof-of-principle study, RS-HDMR was applied to experimental data measuring the single-cell response of a protein-protein signaling network to various experimental perturbations. A comparison to network structure identified in the literature and through other inference methods, including Bayesian and mutual-information based algorithms, suggests that RS-HDMR can successfully reveal a network structure with a low false positive rate while still capturing non-linear and cooperative interactions. RS-HDMR identified several higher-order network interactions that correspond to known feedback regulations among multiple network species and that were unidentified by other network inference methods. Furthermore, RS-HDMR has a better ability to predict network response under unsampled conditions in this application than the best statistical inference algorithm presented in the recent DREAM3 signaling-prediction competition. RS-HDMR can discern and predict differences in network state that arise from sources ranging from intrinsic cell-cell variability to altered experimental conditions, such as when drug perturbations are introduced. This ability ultimately allows RS-HDMR to accurately classify the experimental conditions of a given sample based on its observed network state.

Chimeras taking shape: potential functions of proteins encoded by chimeric RNA transcripts

Chimeric RNAs comprise exons from two or more different genes and have the potential to encode novel proteins that alter cellular phenotypes. To date, numerous putative chimeric transcripts have been identified among the ESTs isolated from several organisms and using high throughput RNA sequencing. The few corresponding protein products that have been characterized mostly result from chromosomal translocations and are associated with cancer. Here, we systematically establish that some of the putative chimeric transcripts are genuinely expressed in human cells. Using high throughput RNA sequencing, mass spectrometry experimental data, and functional annotation, we studied 7424 putative human chimeric RNAs. We confirmed the expression of 175 chimeric RNAs in 16 human tissues, with an abundance varying from 0.06 to 17 RPKM (Reads Per Kilobase per Million mapped reads). We show that these chimeric RNAs are significantly more tissue-specific than non-chimeric transcripts. Moreover, we present evidence that chimeras tend to incorporate highly expressed genes. Despite the low expression level of most chimeric RNAs, we show that 12 novel chimeras are translated into proteins detectable in multiple shotgun mass spectrometry experiments. Furthermore, we confirm the expression of three novel chimeric proteins using targeted mass spectrometry. Finally, based on our functional annotation of exon organization and preserved domains, we discuss the potential features of chimeric proteins with illustrative examples and suggest that chimeras significantly exploit signal peptides and transmembrane domains, which can alter the cellular localization of cognate proteins. Taken together, these findings establish that some chimeric RNAs are translated into potentially functional proteins in humans.

Importin 7 and exportin 1 link c-Myc and p53 to regulation of ribosomal biogenesis

Members of the β-karyopherin family mediate nuclear import of ribosomal proteins and export of ribosomal subunits, both required for ribosome biogenesis. We report that transcription of the β-karyopherin genes importin 7 (IPO7) and exportin 1 (XPO1), and several additional nuclear import receptors, is regulated positively by c-Myc and negatively by p53. Partial IPO7 depletion triggers p53 activation and p53-dependent growth arrest. Activation of p53 by IPO7 knockdown has distinct features of ribosomal biogenesis stress, with increased binding of Mdm2 to ribosomal proteins L5 and L11 (RPL5 and RPL11). Furthermore, p53 activation is dependent on RPL5 and RPL11. Of note, IPO7 and XPO1 are frequently overexpressed in cancer. Altogether, we propose that c-Myc and p53 counter each other in the regulation of elements within the nuclear transport machinery, thereby exerting opposing effects on the rate of ribosome biogenesis. Perturbation of this balance may play a significant role in promoting cancer.

Comparison of parameter-adapted segmentation methods for fluorescence micrographs

Interpreting images from fluorescence microscopy is often a time-consuming task with poor reproducibility. Various image processing routines that can help investigators evaluate the images are therefore useful. The critical aspect for a reliable automatic image analysis system is a robust segmentation algorithm that can perform accurate segmentation for different cell types. In this study, several image segmentation methods were therefore compared and evaluated in order to identify the most appropriate segmentation schemes that are usable with little new parameterization and robustly with different types of fluorescence-stained cells for various biological and biomedical tasks. The study investigated, compared, and enhanced four different methods for segmentation of cultured epithelial cells. The maximum-intensity linking (MIL) method, an improved MIL, a watershed method, and an improved watershed method based on morphological reconstruction were used. Three manually annotated datasets consisting of 261, 817, and 1,333 HeLa or L929 cells were used to compare the different algorithms. The comparisons and evaluations showed that the segmentation performance of methods based on the watershed transform was significantly superior to the performance of the MIL method. The results also indicate that using morphological opening by reconstruction can improve the segmentation of cells stained with a marker that exhibits the dotted surface of cells.

Gene-centric view on the human proteome project: the example of the Russian roadmap for chromosome 18

During the 2010 Human Proteome Organization Congress in Sydney, a gene-centric approach emerged as a feasible and tractable scaffold for assemblage of the Human Proteome Project. Bringing the gene-centric principle into practice, a roadmap for the 18th chromosome was drafted, postulating the limited sensitivity of analytical methods, as a serious bottleneck in proteomics. In the context of the sensitivity problem, we refer to the "copy number of protein molecules" as a measurable assessment of protein abundance. The roadmap is focused on the development of technology to attain the low- and ultralow -"copied" portion of the proteome. Roadmap merges the genomic, transcriptomic and proteomic levels to identify the majority of 285 proteins from 18th chromosome - master proteins. Master protein is the primary translation of the coding sequence and resembling at least one of the known isoforms, coded by the gene. The executive phase of the roadmap includes the expansion of the study of the master proteins with alternate splicing, single amino acid polymorphisms (SAPs) and post-translational modifications. In implementing the roadmap, Russian scientists are expecting to establish proteomic technologies for integrating MS and atomic force microscopy (AFM). These technologies are anticipated to unlock the value of new biomarkers at a detection limit of 10(-18) M, i.e. 1 protein copy per 1 μL of plasma. The roadmap plan is posted at www.proteome.ru/en/roadmap/ and a forum for discussion of the document is supported.