Publications

2022
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.
2021
Slavov N. Scaling up single-cell proteomics. Mol. Cell. Proteomics. 2021. Publisher's VersionAbstract
AbstractSingle-cell tandem mass-spectrometry (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. Towards 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.
He L, Jhong J-H, Chen Q, Huang K-Y, Strittmatter K, Kreuzer J, DeRan M, Wu X, Lee T-Y, Slavov N, et al. Global characterization of macrophage polarization mechanisms and identification of M2-type polarization inhibitors. Cell Rep. 2021;37 (5) :109955.Abstract
Macrophages undergoing M1- versus M2-type polarization differ significantly in their cell metabolism and cellular functions. Here, global quantitative time-course proteomics and phosphoproteomics paired with transcriptomics provide a comprehensive characterization of temporal changes in cell metabolism, cellular functions, and signaling pathways that occur during the induction phase of M1- versus M2-type polarization. Significant differences in, especially, metabolic pathways are observed, including changes in glucose metabolism, glycosaminoglycan metabolism, and retinoic acid signaling. Kinase-enrichment analysis shows activation patterns of specific kinases that are distinct in M1- versus M2-type polarization. M2-type polarization inhibitor drug screens identify drugs that selectively block M2- but not M1-type polarization, including mitogen-activated protein kinase kinase (MEK) and histone deacetylase (HDAC) inhibitors. These datasets provide a comprehensive resource to identify specific signaling and metabolic pathways that are critical for macrophage polarization. In a proof-of-principle approach, we use these datasets to show that MEK signaling is required for M2-type polarization by promoting peroxisome proliferator-activated receptor-$\gamma$ (PPAR$\gamma$)-induced retinoic acid signaling.
Khoury L, Slavov N. Comprehensive Identification of Regulatory Protein Networks. Journal of Proteome Research. 2021;20 (11) :4913-4914. Publisher's Version
Slavov N. Driving Single Cell Proteomics Forward with Innovation. Journal of Proteome Research. 2021;20 (11) :4915-4918. Publisher's Version
Derks J, Leduc A, Huffman RG, Specht H, Ralser M, Demichev V, Slavov N. Increasing the throughput of sensitive proteomics by plexDIA. bioRxiv. 2021. 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. We aimed to increase the throughput of high-sensitivity proteomics while achieving high proteome coverage and quantitative accuracy. We developed a general experimental and computational framework, plexDIA, for simultaneously multiplexing the analysis of both peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing proteome coverage or quantitative accuracy. Specifically, plexDIA using 3-plex nonisobaric mass tags enables quantifying 3-fold more protein ratios among nanogram-level samples. Using 1 hour active gradients and first-generation Q Exactive, plexDIA quantified about 8,000 proteins in each sample of labeled 3-plex sets. Furthermore, plexDIA increases the consistency of protein quantification, resulting in over 2-fold reduction of missing data across samples. We applied plexDIA to quantify proteome dynamics during the cell division cycle in cells isolated based on their DNA content. The high sensitivity and accuracy of plexDIA detected many classical cell cycle proteins and discovered new ones. These results establish a general framework for increasing the throughput of highly sensitive and quantitative protein analysis.Competing Interest StatementThe authors have declared no competing interest.
Slavov N. Increasing proteomics throughput. Nature Biotechnology. 2021;39 (7) :809–810. Publisher's VersionAbstract
A new dimension for analyzing mass spectrometry data allows rapid quantification of up to 70 % more peptides.
Slavov N. Measuring Protein Shapes in Living Cells. Journal of Proteome Research. 2021 :null. Publisher's VersionAbstract
Proteins fold into intricate shapes, known as conformations. The activation of many signal transduction proteins, kinases, and transcription factors requires a change in their conformations. Thus the conformation of a protein can indicate its biological activity. This importance of conformational changes has stimulated the development of numerous methods for analyzing protein conformations and interactions, such as native mass spectrometry and cryoelectron microscopy. These methods may achieve detailed characterizations of protein conformations, but they require highly purified proteins; they are challenged by the complexity of in vivo proteomes.
Leduc A, Huffman RG, Slavov N. Droplet sample preparation for single-cell proteomics applied to the cell cycle. bioRxiv. 2021. Publisher's VersionAbstract
Many biological functions, such as the cell division cycle, are intrinsically single-cell processes regulated in part by protein synthesis and degradation. Investigating such processes has motivated the development of single-cell mass spectrometry (MS) proteomics. To further advance single-cell MS proteomics, we developed a method for automated nano-ProteOmic sample Preparation (nPOP). nPOP uses piezo acoustic dispensing to isolate individual cells in 300 picoliter volumes and performs all subsequent preparation steps in small droplets on a hydrophobic slide. This allows massively parallel sample preparation, including lysing, digesting, and labeling individual cells in volumes below 20 nl. Single-cell protein analysis using nPOP classified cells by cell type and by cell cycle phase. Furthermore, the data allowed us to quantify the covariation between cell cycle protein markers and thousands of proteins. Based on this covariation, we identify cell cycle associated proteins and functions that are shared across cell types and those that differ between cell types.Competing Interest StatementThe authors have declared no competing interest.

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