Publications

2023
Wallmann G, Leduc A, Slavov N. Data-Driven Optimization of DIA Mass-Spectrometry by DO-MS. bioRxiv. 2023. Publisher's VersionAbstract
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, we extended the DO-MS app (https://do-ms.slavovlab.net) to optimize and evaluate results from data independent acquisition (DIA) MS. The extension works with 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: https://github.com/SlavovLab/DO-MS.Competing Interest StatementThe authors have declared no competing interest.
Derks J, Slavov N. Strategies for increasing the depth and throughput of protein analysis by plexDIA. Journal of Proteome Research. 2023. Publisher's VersionAbstract
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. Here we demonstrate the improved precisions of RT 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 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.
Slavov N. Framework for multiplicative scaling of single-cell proteomics. Nat. Biotechnol. 2023;41 (1) :23–24. Publisher's VersionAbstract
Many biomedical questions demand scalable, deep, and accurate proteome analysis of small samples, including single cells. A scalable framework of multiplexed data-independent acquisition for mass spectrometry enables time saving by parallel analysis of both peptide ions and protein samples, thereby realizing multiplicative gains in throughput.
Derks J, Leduc A, Wallmann G, Huffman GR, Willetts M, Khan S, Specht H, Ralser M, Demichev V, Slavov N. Increasing the throughput of sensitive proteomics by plexDIA. Nat. Biotechnol. 2023;41 (1) :50–59.Abstract
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.
2022
Leduc A, Huffman GR, Cantlon J, Khan S, Slavov N. Exploring functional protein covariation across single cells using nPOP. Genome Biol. 2022;23 :261.Abstract
Abstract 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.
Leduc A, Huffman R, Cantlon J, Khan S, Slavov N. Highly Parallel Droplet Sample Preparation for Single Cell Proteomics. 2022.Abstract
Protocol for preparing single cells for mass-spec analysis by nPOP as described by Leduc et al., 2021, 2022 DOI: 10.1101/2021.04.24.441211. nPOP uses piezo acoustic dispensing to isolate individual cells in 300 picoliter volumes and performs all subsequent preparation steps in small droplets on a fluorocarbon-coated slide. This design enables simultaneous sample preparation of thousands of single cells in a single batch. This includes lysing, digesting, and labeling individual cells in volumes below 20 nl. nPOP supports different experimental designs including label free analysis, TMT 18plex with carrier, mTRAQ, and TMT-10plex for TOF instruments (see steps).
Petelski AA, Slavov N, Specht H. Single-Cell Proteomics Preparation for Mass Spectrometry Analysis Using Freeze-Heat Lysis and an Isobaric Carrier. J. Vis. Exp. 2022;(190). Publisher's VersionAbstract
Single-cell proteomics analysis requires sensitive, quantitatively accurate, widely accessible, and robust methods. To meet these requirements, the Single-Cell ProtEomics (SCoPE2) protocol was developed as a second-generation method for quantifying hundreds to thousands of proteins from limited samples, down to the level of a single cell. Experiments using this method have achieved quantifying over 3,000 proteins across 1,500 single mammalian cells (500-1,000 proteins per cell) in 10 days of mass spectrometer instrument time. SCoPE2 leverages a freeze-heat cycle for cell lysis, obviating the need for clean-up of single cells and consequently reducing sample losses, while expediting sample preparation and simplifying its automation. Additionally, the method uses an isobaric carrier, which aids protein identification and reduces sample losses. This video protocol provides detailed guidance to enable the adoption of automated single-cell protein analysis using only equipment and reagents that are widely accessible. We demonstrate critical steps in the procedure of preparing single cells for proteomic analysis, from harvesting up to injection to liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Additionally, viewers are guided through the principles of experimental design with the isobaric carrier, quality control for both isobaric carrier and single-cell preparations, and representative results with a discussion of limitations of the approach.
Derks J, Slavov N. Strategies for increasing the depth and throughput of protein analysis by plexDIA. bioRxiv. 2022. Publisher's VersionAbstract
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. Here we demonstrate the improved precisions of RT 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 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.Competing Interest StatementThe authors have declared no competing interest.
MacCoss MJ, Alfaro J, Wanunu M, Faivre DA, Slavov N. Sampling the proteome by emerging single-molecule and mass-spectrometry methods. 2022. Publisher's Version
Gatto L, Aebersold R, Cox J, Demichev V, Derks J, Emmott E, Franks AM, Ivanov AR, Kelly RT, Khoury L, et al. Initial recommendations for performing, benchmarking, and reporting single-cell proteomics experiments. 2022. Publisher's Version
Framework for multiplicative scaling of single-cell proteomics. Nat. Biotechnol. 2022 :1–2. Publisher's VersionAbstract
Many biomedical questions demand scalable, deep, and accurate proteome analysis of small samples, including single cells. A scalable framework of multiplexed data-independent acquisition for mass spectrometry enables time saving by parallel analysis of both peptide ions and protein samples, thereby realizing multiplicative gains in throughput.
Derks J, Leduc A, Wallmann G, Huffman RG, Willetts M, Khan S, Specht H, Ralser M, Demichev V, Slavov N. Increasing the throughput of sensitive proteomics by plexDIA. Nature Biotechnology. 2022. Publisher's VersionAbstract
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.
Burnum-Johnson KE, Conrads TP, Drake RR, Herr AE, Iyengar R, Kelly RT, Lundberg E, MacCoss MJ, Naba A, Nolan GP, et al. New Views of Old Proteins: Clarifying the Enigmatic Proteome. Molecular & Cellular Proteomics. 2022;21 (7). Publisher's VersionAbstract
All human diseases involve proteins, yet our current tools to characterize and quantify them are limited. To better elucidate proteins across space, time, and molecular composition, we provide a >10 years of projection for technologies to meet the challenges that protein biology presents. With a broad perspective, we discuss grand opportunities to transition the science of proteomics into a more propulsive enterprise. Extrapolating recent trends, we describe a next generation of approaches to define, quantify, and visualize the multiple dimensions of the proteome, thereby transforming our understanding and interactions with human disease in the coming decade.
Leduc A, Huffman RG, Cantlon J, Khan S, Slavov N. Exploring functional protein covariation across single cells using nPOP. bioRxiv. 2022. Publisher's VersionAbstract
Many biological processes, such as the cell division cycle, are reflected in protein covariation across single cells. This covariation can be quantified and interpreted by single-cell proteomics with sufficiently high throughput and accuracy. Toward this goal, we developed the nano-ProteOmic sample Preparation (nPOP) method for single-cell proteomics. nPOP uses piezo acoustic dispensing to isolate individual cells in 300 picoliter volumes and performs all subsequent preparation steps in small droplets on a fluorocarbon-coated slide. This design enables simultaneous sample preparation of thousands of single cells, including lysing, digesting, and labeling individual cells in volumes below 20 nl. We used nPOP to prepare 1,888 single cells and 128 negative controls in a single batch. Their analysis enabled quantifying the covariation between thousands of proteins and cell-cycle protein markers. Many protein sets covaried with the cell cycle similarly across all cell types and states, reflecting cell-type independent cell cycle functions. However, the cell cycle covariation of other protein sets differed markedly between cell types, even within subpopulation of melanoma cells expressing markers for drug-resistance priming. The cells expressing these markers accumulated in the G1 phase of the cell cycle and exhibited different covariation of enzymes catabolizing glucose. These results demonstrate that protein covariation across single cells may reveal functionally concerted biological differences between closely related cell states.Competing Interest StatementJoshua Cantlon is an employee of Scienion.
Leduc A, Huffman RG, Cantlon J, Khan S, Slavov N. Exploring functional protein covariation across single cells using nPOP. bioRxiv. 2022. Publisher's VersionAbstract
Many biological processes, such as the cell division cycle, are reflected in protein covariation across single cells. This covariation can be quantified and interpreted by single-cell proteomics with sufficiently high throughput and accuracy. Toward this goal, we developed the nano-ProteOmic sample Preparation (nPOP) method for single-cell proteomics. nPOP uses piezo acoustic dispensing to isolate individual cells in 300 picoliter volumes and performs all subsequent preparation steps in small droplets on a fluorocarbon-coated slide. This design enables simultaneous sample preparation of thousands of single cells, including lysing, digesting, and labeling individual cells in volumes below 20 nl. We used nPOP to prepare 1,888 single cells and 128 negative controls in a single batch. Their analysis enabled quantifying the covariation between thousands of proteins and cell-cycle protein markers. Many protein sets covaried with the cell cycle similarly across all cell types and states, reflecting cell-type independent cell cycle functions. However, the cell cycle covariation of other protein sets differed markedly between cell types, even within subpopulation of melanoma cells expressing markers for drug-resistance priming. The cells expressing these markers accumulated in the G1 phase of the cell cycle and exhibited different covariation of enzymes catabolizing glucose. These results demonstrate that protein covariation across single cells may reveal functionally concerted biological differences between closely related cell states.Competing Interest StatementJoshua Cantlon is an employee of Scienion.
Huffman RG, Leduc A, Wichmann C, di Gioia M, Borriello F, Specht H, Derks J, Khan S, Emmott E, Petelski AA, et al. Prioritized single-cell proteomics reveals molecular and functional polarization across primary macrophages. bioRxiv. 2022. Publisher's VersionAbstract
Major aims of single-cell proteomics include increasing the consistency, sensitivity, and depth of protein quantification, especially for proteins and modifications of biological interest. To simultaneously advance all of these aims, we developed prioritized Single Cell ProtEomics (pSCoPE). pSCoPE ensures duty-cycle time for analyzing prioritized peptides across all single cells (thus increasing data consistency) while analyzing identifiable peptides at full duty-cycle, thus increasing proteome depth. These strategies increased the quantified data points for challenging peptides and the overall proteome coverage about 2-fold. pSCoPE enabled quantifying proteome polarization in primary mouse macrophages and linking it to phenotypic variability in endocytic activity. Proteins annotated to phagosome maturation and proton transport showed concerted variation for both untreated and lipopolysaccharide-treated macrophages, indicating a conserved axis of polarization. pSCoPE further quantified proteolytic products, suggesting a gradient of cathepsin activities within a treatment condition. pSCoPE is easily accessible and likely to benefit many applications, especially mechanistic analysis seeking to focus on proteins of interest without sacrificing proteome coverage.Competing Interest StatementThe authors have declared no competing interest.
Specht H, Slavov N. Beyond Protein Sequence: Protein Isomerization in Alzheimer’s Disease. Journal of Proteome Research. 2022;21 (2) :299-300. Publisher's Version
Slavov N. Counting protein molecules for single-cell proteomics. Cell. 2022;185 (2) :232-234. Publisher's VersionAbstract
Summary Technologies for counting protein molecules are enabling single-cell proteomics at increasing depth and scale. New advances in single-molecule methods by Brinkerhoff and colleagues promise to further increase the sensitivity of protein analysis and motivate questions about scaling up the counting of the human proteome.
Slavov N. Scaling Up Single-Cell Proteomics. Molecular & Cellular Proteomics. 2022;21 (1) :100179. Publisher's VersionAbstract
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.
Slavov N. Learning from natural variation across the proteomes of single cells. PLOS Biology. 2022;20 (1) :1-4. Publisher's VersionAbstract
Biological functions arise from protein interactions, which are reflected in the natural variation of proteome configurations across individual cells. Emerging single-cell proteomics methods may decode this variation and empower inference of biological mechanisms with minimal assumptions.

Pages