Increasing proteomics throughput

Proteomics-The State of the Field: The Definition and Analysis of Proteomes Should Be Based in Reality, Not Convenience

With growing recognition and acknowledgement of the genuine complexity of proteomes, we are finally entering the post-proteogenomic era. Routine assessment of proteomes as inferred correlates of gene sequences (i.e., canonical 'proteins') cannot provide the necessary critical analysis of systems-level biology that is needed to understand underlying molecular mechanisms and pathways or identify the most selective biomarkers and therapeutic targets. These critical requirements demand the analysis of proteomes at the level of proteoforms/protein species, the actual active molecular players. Currently, only highly refined integrated or integrative top-down proteomics (iTDP) enables the analytical depth necessary to provide routine, comprehensive, and quantitative proteome assessments across the widest range of proteoforms inherent to native systems. Here we provide a broad perspective of the field, taking in historical and current realities, to establish a more balanced understanding of where the field has come from (in particular during the ten years since Proteomes was launched), current issues, and how things likely need to proceed if necessary deep proteome analyses are to succeed. We base this in our firm belief that the best proteomic analyses reflect, as closely as possible, the native sample at the moment of sampling. We also seek to emphasise that this and future analytical approaches are likely best based on the broad recognition and exploitation of the complementarity of currently successful approaches. This also emphasises the need to continuously evaluate and further optimize established approaches, to avoid complacency in thinking and expectations but also to promote the critical and careful development and introduction of new approaches, most notably those that address proteoforms. Above all, we wish to emphasise that a rigorous focus on analytical quality must override current thinking that largely values analytical speed; the latter would certainly be nice, if only proteoforms could thus be effectively, routinely, and quantitatively assessed. Alas, proteomes are composed of proteoforms, not molecular species that can be amplified or that directly mirror genes (i.e., 'canonical'). The problem is hard, and we must accept and address it as such, but the payoff in playing this longer game of rigorous deep proteome analyses is the promise of far more selective biomarkers, drug targets, and truly personalised or even individualised medicine.

Quantitative Proteomics Characterization of the Effect and Mechanism of Trichostatin A on the Hippocampus of Type II Diabetic Mice

Diabetic encephalopathy (DE) is one of the complications of diabetes mellitus with mild-to-moderate cognitive impairment. Trichostatin A (TSA) has been revealed to show protective effect on central nervous systems in Alzheimer's disease (AD) and hypoxic-ischemic brain injury. However, the effect and molecular mechanism of TSA on cognitive function of DE are unknown. Here, we demonstrated that cognitive function was damaged in diabetic mice versus normal mice and treatment with TSA improved cognitive function in diabetic mice. Proteomic analysis of the hippocampus revealed 174 differentially expressed proteins in diabetic mice compared with normal mice. TSA treatment reversed the expression levels of 111 differentially expressed proteins grouped into functional clusters, including the longevity regulating pathway, the insulin signaling pathway, peroxisomes, protein processing in the endoplasmic reticulum, and ribosomes. Furthermore, protein-protein interaction network analysis of TSA-reversed proteins revealed that UBA52, CAT, RPL29, RPL35A, CANX, RPL37, and PRKAA2 were the main hub proteins. Multiple KEGG pathway-enriched CAT and PRKAA2 levels were significantly decreased in the hippocampus of diabetic mice versus normal mice, which was reversed by TSA administration. Finally, screening for potential similar or ancillary drugs for TSA treatment indicated that HDAC inhibitors ISOX, apicidin, and panobinostat were the most promising similar drugs, and the PI3K inhibitor GSK-1059615, the Aurora kinase inhibitor alisertib, and the nucleophosmin inhibitor avrainvillamide-analog-6 were the most promising ancillary drugs. In conclusion, our study revealed that CAT and PRKAA2 were the key proteins involved in the improvement of DE after TSA treatment. ISOX, apicidin, and panobinostat were promising similar drugs and that GSK-1059615, alisertib, and avrainvillamide-analog-6 were promising ancillary drugs to TSA in the treatment of DE.

From volcanoes to the bench: Advantages of novel hyperthermoacidic archaeal proteases for proteomics workflows

Here we introduce hyperthermoacidic archaeal proteases (HTA-Proteases©) isolated from organisms that thrive in nearly boiling acidic volcanic springs and investigate their use for bottom-up proteomic experiments. We find that HTA-Proteases have novel cleavage specificities, show no autolysis, function in dilute formic acid, and store at ambient temperature for years. HTA-Proteases function optimally at 70-90 °C and pH of 2-4 with rapid digestion kinetics. The extreme HTA-Protease reaction conditions actively denature sample proteins, obviate the use of chaotropes, are largely independent of reduction and alkylation, and allow for a one-step/five-minute sample preparation protocol without sample manipulation, dilution, or additional cleanup. We find that brief one-step HTA-Protease protocols significantly increase proteome and protein sequence coverage with datasets orthogonal to trypsin. Importantly, HTA-Protease digests markedly increase coverage and identifications for ribonucleoproteins, histones, and mitochondrial membrane proteins as compared to tryptic digests alone. In addition to increased coverage in these classes, HTA-Proteases and simplified one-step protocols are expected to reduce technical variability and advance the fields of clinical and high-throughput proteomics. This work reveals significant utility of heretofore unavailable HTA-Proteases for proteomic workflows. We discuss some of the potential for these remarkable enzymes to empower new proteomics methods, approaches, and biological insights. SIGNIFICANCE: Here we introduce new capabilities for bottom-up proteomics applications with hyperthermoacidic archaeal proteases (HTA-Proteases©). HTA-Proteases have novel cleavage specificity, require no chaotropes, and allow simple one-step/five-minute sample preparations that promise to reduce variability between samples and laboratories. HTA-Proteases generate unique sets of observable peptides that are non-overlapping with tryptic peptides and significantly increase sequence coverage and available peptide targets relative to trypsin alone. HTA-Proteases show some bias for the detection and coverage of nucleic acid-binding proteins and membrane proteins relative to trypsin. These new ultra-stable enzymes function optimally in nearly boiling acidic conditions, show no autolysis, and do not require aliquoting as they are stable for years at ambient temperatures. Used independently or in conjunction with tryptic digests, HTA-Proteases offer increased proteome coverage, unique peptide targets, and brief one-step protocols amenable to automation, rapid turnaround, and high-throughput approaches.

Data-Driven Optimization of DIA Mass Spectrometry by DO-MS

Mass spectrometry (MS) enables specific and accurate quantification of proteins with ever-increasing throughput and sensitivity. Maximizing this potential of MS requires optimizing data acquisition parameters and performing efficient quality control for large datasets. To facilitate these objectives for data-independent acquisition (DIA), we developed a second version of our framework for data-driven optimization of MS methods (DO-MS). The DO-MS app v2.0 (do-ms.slavovlab.net) allows one to optimize and evaluate results from both label-free and multiplexed DIA (plexDIA) and supports optimizations particularly relevant to single-cell proteomics. We demonstrate multiple use cases, including optimization of duty cycle methods, peptide separation, number of survey scans per duty cycle, and quality control of single-cell plexDIA data. DO-MS allows for interactive data display and generation of extensive reports, including publication of quality figures that can be easily shared. The source code is available at github.com/SlavovLab/DO-MS.

Data-Driven Optimization of DIA Mass Spectrometry by DO-MS

Mass spectrometry (MS) enables specific and accurate quantification of proteins with ever increasing throughput and sensitivity. Maximizing this potential of MS requires optimizing data acquisition parameters and performing efficient quality control for large datasets. To facilitate these objectives for data independent acquisition (DIA), we developed a second version of our framework for data-driven optimization of mass spectrometry methods (DO-MS). The DO-MS app v2.0 ( do-ms.slavovlab.net ) allows to optimize and evaluate results from both label free and multiplexed DIA (plexDIA) and supports optimizations particularly relevant for single-cell proteomics. We demonstrate multiple use cases, including optimization of duty cycle methods, peptide separation, number of survey scans per duty cycle, and quality control of single-cell plexDIA data. DO-MS allows for interactive data display and generation of extensive reports, including publication quality figures, that can be easily shared. The source code is available at: github.com/SlavovLab/DO-MS .

Prioritized mass spectrometry increases the depth, sensitivity and data completeness of single-cell proteomics

Major aims of single-cell proteomics include increasing the consistency, sensitivity and depth of protein quantification, especially for proteins and modifications of biological interest. Here, to simultaneously advance all these aims, we developed prioritized Single-Cell ProtEomics (pSCoPE). pSCoPE consistently analyzes thousands of prioritized peptides across all single cells (thus increasing data completeness) while maximizing instrument time spent analyzing identifiable peptides, thus increasing proteome depth. These strategies increased the sensitivity, data completeness and proteome coverage over twofold. The gains enabled quantifying protein variation in untreated and lipopolysaccharide-treated primary macrophages. Within each condition, proteins covaried within functional sets, including phagosome maturation and proton transport, similarly across both treatment conditions. This covariation is coupled to phenotypic variability in endocytic activity. pSCoPE also enabled quantifying proteolytic products, suggesting a gradient of cathepsin activities within a treatment condition. pSCoPE is freely available and widely applicable, especially for analyzing proteins of interest without sacrificing proteome coverage. Support for pSCoPE is available at http://scp.slavovlab.net/pSCoPE .

Strategies for Increasing the Depth and Throughput of Protein Analysis by plexDIA

Accurate protein quantification is key to identifying protein markers, regulatory relationships between proteins, and pathophysiological mechanisms. Realizing this potential requires sensitive and deep protein analysis of a large number of samples. Toward this goal, proteomics throughput can be increased by parallelizing the analysis of both precursors and samples using multiplexed data independent acquisition (DIA) implemented by the plexDIA framework: https://plexDIA.slavovlab.net. Here we demonstrate the improved precisions of retention time estimates within plexDIA and how this enables more accurate protein quantification. plexDIA has demonstrated multiplicative gains in throughput, and these gains may be substantially amplified by improving the multiplexing reagents, data acquisition, and interpretation. We discuss future directions for advancing plexDIA, which include engineering optimized mass-tags for high-plexDIA, introducing isotopologous carriers, and developing algorithms that utilize the regular structures of plexDIA data to improve sensitivity, proteome coverage, and quantitative accuracy. These advances in plexDIA will increase the throughput of functional proteomic assays, including quantifying protein conformations, turnover dynamics, modifications states and activities. The sensitivity of these assays will extend to single-cell analysis, thus enabling functional single-cell protein analysis.

Sampling the proteome by emerging single-molecule and mass spectrometry methods

Mammalian cells have about 30,000-fold more protein molecules than mRNA molecules, which has major implications in the development of proteomics technologies. We review strategies that have been helpful for counting billions of protein molecules by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and suggest that these strategies can benefit single-molecule methods, especially in mitigating the challenges of the wide dynamic range of the proteome.

Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments

Analyzing proteins from single cells by tandem mass spectrometry (MS) has recently become technically feasible. While such analysis has the potential to accurately quantify thousands of proteins across thousands of single cells, the accuracy and reproducibility of the results may be undermined by numerous factors affecting experimental design, sample preparation, data acquisition and data analysis. We expect that broadly accepted community guidelines and standardized metrics will enhance rigor, data quality and alignment between laboratories. Here we propose best practices, quality controls and data-reporting recommendations to assist in the broad adoption of reliable quantitative workflows for single-cell proteomics. Resources and discussion forums are available at https://single-cell.net/guidelines .

Increasing the throughput of sensitive proteomics by plexDIA

Current mass spectrometry methods enable high-throughput proteomics of large sample amounts, but proteomics of low sample amounts remains limited in depth and throughput. To increase the throughput of sensitive proteomics, we developed an experimental and computational framework, called plexDIA, for simultaneously multiplexing the analysis of peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing proteome coverage or quantitative accuracy. By using three-plex non-isobaric mass tags, plexDIA enables quantification of threefold more protein ratios among nanogram-level samples. Using 1-hour active gradients, plexDIA quantified ~8,000 proteins in each sample of labeled three-plex sets and increased data completeness, reducing missing data more than twofold across samples. Applied to single human cells, plexDIA quantified ~1,000 proteins per cell and achieved 98% data completeness within a plexDIA set while using ~5 minutes of active chromatography per cell. These results establish a general framework for increasing the throughput of sensitive and quantitative protein analysis.

Exploring functional protein covariation across single cells using nPOP

BACKGROUND

Many biological processes, such as cell division cycle and drug resistance, are reflected in protein covariation across single cells. This covariation can be quantified and interpreted by single-cell mass spectrometry with sufficiently high throughput and accuracy.

RESULTS

Here, we describe nPOP, a method that enables simultaneous sample preparation of thousands of single cells, including lysing, digesting, and labeling individual cells in volumes of 8-20 nl. nPOP uses piezo acoustic dispensing to isolate individual cells in 300 pl volumes and performs all subsequent sample preparation steps in small droplets on a fluorocarbon-coated glass slide. Protein covariation analysis identifies cell cycle dynamics that are similar and dynamics that differ between cell types, even within subpopulations of melanoma cells delineated by markers for drug resistance priming. Melanoma cells expressing these markers accumulate in the G1 phase of the cell cycle, display distinct protein covariation across the cell cycle, accumulate glycogen, and have lower abundance of glycolytic enzymes. The non-primed melanoma cells exhibit gradients of protein abundance, suggesting transition states. Within this subpopulation, proteins functioning in oxidative phosphorylation covary with each other and inversely with proteins functioning in glycolysis. This protein covariation suggests divergent reliance on energy sources and its association with other biological functions. These results are validated by different mass spectrometry methods.

CONCLUSIONS

nPOP enables flexible, automated, and highly parallelized sample preparation for single-cell proteomics. This allows for quantifying protein covariation across thousands of single cells and revealing functionally concerted biological differences between closely related cell states. Support for nPOP is available at https://scp.slavovlab.net/nPOP .

Clinical Proteomics for Meningioma: An Integrated Workflow for Quantitative Proteomics and Biomarker Validation in Formalin-Fixed Paraffin-Embedded Tissue Samples

Clinical proteomics is a rapidly emerging frontier in laboratory medicine. High-throughput proteomic investigations of biopsy tissues provide mechanistic insights into complex human diseases. For large-scale proteomics, formalin-fixed and paraffin-embedded (FFPE) tissue samples offer a viable alternative to fresh-frozen (FF) tissues that have restricted availability. In this context, meningioma is one of the most common primary brain tumors where innovation in diagnostics and therapeutic targets can benefit from clinical proteomics. We present here an integrated workflow for quantitative proteomics and biomarker validation of meningioma FFPE tissues. Applying label-free quantitative (LFQ) proteomics, we reproducibly (Pearson's correlation: 0.84-0.91) obtained an in-depth proteome coverage (nearly 4000 proteins per sample) from 120 min gradient of single unfractionated mass spectrometry run. Furthermore, building upon LFQ data and literature curated set of meningioma-associated proteins, we validated VIM, AHNAK, and CLU from FFPE tissues using selected reaction monitoring (SRM) assay and compared its performance with FF tissues. This study illustrates how knowledge from label-free proteomics can be integrated for selecting peptides for targeted validation and suggests that FFPE tissues are comparable to FF tissues for SRM assays. This quantitative clinical proteomics workflow is scalable for large-scale clinical diagnostics studies in the future, for example, utilizing the global repository of FFPE tissues in meningioma and possibly in other cancers.

Human adaptation to high altitude: a review of convergence between genomic and proteomic signatures

Both genomics- and proteomics-based investigations have identified several essential genes, proteins, and pathways that may facilitate human adaptive genotype/phenotype in a population-specific manner. This comprehensive review provides an up-to-date list of genes and proteins identified for human adaptive responses to high altitudes. Genomics studies for indigenous high-altitude populations like Tibetans, Andeans, Ethiopians, and Sherpas have identified 169 genes under positive natural selection. Similarly, global proteomics studies have identified 258 proteins (± 1.2-fold or more) for Tibetan, Sherpa, and Ladakhi highlanders. The primary biological processes identified for genetic signatures include hypoxia-inducible factor (HIF)-mediated oxygen sensing, angiogenesis, and erythropoiesis. In contrast, major biological processes identified for proteomics signatures include 14–3-3 mediated sirtuin signaling, integrin-linked kinase (ILK), phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT), and integrin signaling. Comparing genetic and protein signatures, we identified 7 common genes/proteins (HBB/hemoglobin subunit beta, TF/serotransferrin, ANGPTL4/angiopoietin-related protein 4, CDC42/cell division control protein 42 homolog, GC/vitamin D-binding protein, IGFBP1/insulin-like growth factor-binding protein 1, and IGFBP2/insulin-like growth factor-binding protein 2) involved in crucial molecular functions like IGF-1 signaling, LXR/RXR activation, ferroptosis signaling, iron homeostasis signaling and regulation of cell cycle. Our combined multi-omics analysis identifies common molecular targets and pathways for human adaptation to high altitude. These observations further corroborate convergent positive selection of hypoxia-responsive molecular pathways in humans and advocate using multi-omics techniques for deciphering human adaptive responses to high altitude.

dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts

The dia-PASEF technology uses ion mobility separation to reduce signal interferences and increase sensitivity in proteomic experiments. Here we present a two-dimensional peak-picking algorithm and generation of optimized spectral libraries, as well as take advantage of neural network-based processing of dia-PASEF data. Our computational platform boosts proteomic depth by up to 83% compared to previous work, and is specifically beneficial for fast proteomic experiments and those with low sample amounts. It quantifies over 5300 proteins in single injections recorded at 200 samples per day throughput using Evosep One chromatography system on a timsTOF Pro mass spectrometer and almost 9000 proteins in single injections recorded with a 93-min nanoflow gradient on timsTOF Pro 2, from 200 ng of HeLa peptides. A user-friendly implementation is provided through the incorporation of the algorithms in the DIA-NN software and by the FragPipe workflow for spectral library generation.

Single-Cell Proteomics: The Critical Role of Nanotechnology

In single-cell analysis, biological variability can be attributed to individual cells, their specific state, and the ability to respond to external stimuli, which are determined by protein abundance and their relative alterations. Mass spectrometry (MS)-based proteomics (e.g., SCoPE-MS and SCoPE2) can be used as a non-targeted method to detect molecules across hundreds of individual cells. To achieve high-throughput investigation, novel approaches in Single-Cell Proteomics (SCP) are needed to identify and quantify proteins as accurately as possible. Controlling sample preparation prior to LC-MS analysis is critical, as it influences sensitivity, robustness, and reproducibility. Several nanotechnological approaches have been developed for the removal of cellular debris, salts, and detergents, and to facilitate systematic sample processing at the nano- and microfluidic scale. In addition, nanotechnology has enabled high-throughput proteomics analysis, which have required the improvement of software tools, such as DART-ID or DO-MS, which are also fundamental for addressing key biological questions. Single-cell proteomics has many applications in nanomedicine and biomedical research, including advanced cancer immunotherapies or biomarker characterization, among others; and novel methods allow the quantification of more than a thousand proteins while analyzing hundreds of single cells.

The Essentials of Multiomics

Within the last decade, the science of molecular testing has evolved from single gene and single protein analysis to broad molecular profiling as a standard of care, quickly transitioning from research to practice. Terms such as genomics, transcriptomics, proteomics, circulating omics, and artificial intelligence are now commonplace, and this rapid evolution has left us with a significant knowledge gap within the medical community. In this paper, we attempt to bridge that gap and prepare the physician in oncology for multiomics, a group of technologies that have gone from looming on the horizon to become a clinical reality. The era of multiomics is here, and we must prepare ourselves for this exciting new age of cancer medicine.

Phosphorylation regulation of cardiac proteins in Babesia microti infected mice in an effort to restore heart function

Background

Babesia is a common protozoan parasite that infects red blood cells. In mice infected with Babesia microti, the red blood cells were lysed, resulting in decreased oxygen-carrying capacity. To compensate for low blood oxygen levels, stress on the heart was greatly increased. Babesiosis induces a variety of pathologies; meanwhile, heart tissues initiate self-repair responses to babesiosis-induced tissue damage to restore heart function.

Methods

To discover the molecular mechanisms of the damage and self-repair in the heart after B. microti infection in mice, we investigated the changes in protein expression and phosphorylation modification levels in heart tissues at 0, 5, 8, 11, and 19 days post-infection using data-independent acquisition (DIA) quantitative proteomics.

Results

The numbers of global proteins we identified were 1934, 1966, 1984, 1989, and 1955 and of phosphopeptides were 5118, 5133, 5130, 5133, and 5140 at 0, 5, 8, 11, and 19 days, respectively, in heart cells after infection with B. microti. The results showed that after B. microti infection the differentially expressed proteins in mice mainly include fibrinogen α (Fgα), fibrinogen β (Fgβ), Serpina1b, Serpina1c, cathepsin Z, cytochrome c oxidases (COXs), RPS11, and RPS20. The proteins with phosphorylation changes mainly include 20-kDa light chain of myosin II (MLC20), myosin light chain kinase (MLCK), mitogen-activated protein kinase 14 (MAPK14), and Akt1. These proteins were mainly involved in coagulation processes, cell apoptosis, oxidative phosphorylation, and ribosomes.

Conclusions

The coagulation cascade-related proteins, apoptosis-related proteins, oxidative phosphorylation-related proteins, and other types of proteins are all involved in the damage and self-repair process in the heart after B. microti infection. These results offer a wealth of new targets for further exploration into the causes of heart disease induced by Babesia infection and are of great significance for novel drug development and new opportunities for targeted therapies.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-022-05233-7.

Scaling Up Single-Cell Proteomics

Single-cell tandem MS has enabled analyzing hundreds of single cells per day and quantifying thousands of proteins across the cells. The broad dissemination of these capabilities can empower the dissection of pathophysiological mechanisms in heterogeneous tissues. Key requirements for achieving this goal include robust protocols performed on widely accessible hardware, robust quality controls, community standards, and automated data analysis pipelines that can pinpoint analytical problems and facilitate their timely resolution. Toward meeting these requirements, this perspective outlines both existing resources and outstanding opportunities, such as parallelization, for catalyzing the wide dissemination of quantitative single-cell proteomics analysis that can be scaled up to tens of thousands of single cells. Indeed, simultaneous parallelization of the analysis of peptides and single cells is a promising approach for multiplicative increase in the speed of performing deep and quantitative single-cell proteomics. The community is ready to begin a virtuous cycle of increased adoption fueling the development of more technology and resources for single-cell proteomics that in turn drive broader adoption, scientific discoveries, and clinical applications.