Publications

2022
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.
Petelski AA, Emmott E, Leduc A, Huffman RG, Specht H, Perlman DH, Slavov N. Multiplexed single-cell proteomics using SCoPE2. bioRxiv. 2021. Publisher's VersionAbstract
Many biological systems are composed of diverse single cells. This diversity necessitates functional and molecular single-cell analysis. Single-cell protein analysis has long relied on affinity reagents, but emerging mass-spectrometry methods (either label-free or multiplexed) have enabled quantifying over 1,000 proteins per cell while simultaneously increasing the specificity of protein quantification. Isobaric carrier based multiplexed single-cell proteomics is a scalable, reliable, and cost-effective method that can be fully automated and implemented on widely available equipment. It uses inexpensive reagents and is applicable to any sample that can be processed to a single-cell suspension. Here we describe an automated Single Cell ProtEomics (SCoPE2) workflow that allows analyzing about 200 single cells per 24 hours using only standard commercial equipment. We emphasize experimental steps and benchmarks required for achieving quantitative protein analysis.Competing Interest StatementThe authors have declared no competing interest.
Specht H, Emmott E, Petelski AA, Huffman RG, Perlman DH, Serra M, Kharchenko P, Koller A, Slavov N. Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2. Genome Biology. 2021;22 (1) :50. Publisher's VersionAbstract

Background Macrophages are innate immune cells with diverse functional and molecular phenotypes. This diversity is largely unexplored at the level of single-cell proteomes because of the limitations of quantitative single-cell protein analysis.

Results To overcome this limitation, we develop SCoPE2, which substantially increases quantitative accuracy and throughput while lowering cost and hands-on time by introducing automated and miniaturized sample preparation. These advances enable us to analyze the emergence of cellular heterogeneity as homogeneous monocytes differentiate into macrophage-like cells in the absence of polarizing cytokines. SCoPE2 quantifies over 3042 proteins in 1490 single monocytes and macrophages in 10 days of instrument time, and the quantified proteins allow us to discern single cells by cell type. Furthermore, the data uncover a continuous gradient of proteome states for the macrophages, suggesting that macrophage heterogeneity may emerge in the absence of polarizing cytokines. Parallel measurements of transcripts by 10× Genomics suggest that our measurements sample 20-fold more protein copies than RNA copies per gene, and thus, SCoPE2 supports quantification with improved count statistics. This allowed exploring regulatory interactions, such as interactions between the tumor suppressor p53, its transcript, and the transcripts of genes regulated by p53.

Conclusions Even in a homogeneous environment, macrophage proteomes are heterogeneous. This heterogeneity correlates to the inflammatory axis of classically and alternatively activated macrophages. Our methodology lays the foundation for automated and quantitative single-cell analysis of proteins by mass spectrometry and demonstrates the potential for inferring transcriptional and post-transcriptional regulation from variability across single cells.

Specht H, Slavov N. Optimizing Accuracy and Depth of Protein Quantification in Experiments Using Isobaric Carriers. Journal of Proteome Research. 2021;20 (1) :880-887. Publisher's Version
Slavov N. Single-cell protein analysis by mass spectrometry. Current Opinion in Chemical Biology. 2021;60 :1 - 9. Publisher's VersionAbstract
Human physiology and pathology arise from the coordinated interactions of diverse single cells. However, analyzing single cells has been limited by the low sensitivity and throughput of analytical methods. DNA sequencing has recently made such analysis feasible for nucleic acids but single-cell protein analysis remains limited. Mass spectrometry is the most powerful method for protein analysis, but its application to single cells faces three major challenges: efficiently delivering proteins/peptides to mass spectrometry detectors, identifying their sequences, and scaling the analysis to many thousands of single cells. These challenges have motivated corresponding solutions, including SCoPE design multiplexing and clean, automated, and miniaturized sample preparation. Synergistically applied, these solutions enable quantifying thousands of proteins across many single cells and establish a solid foundation for further advances. Building upon this foundation, the SCoPE concept will enable analyzing subcellular organelles and posttranslational modifications, while increases in multiplexing capabilities will increase the throughput and decrease cost.
2020
Specht H, Emmott E, Petelski A, Huffman RG, Perlman DH, Serra M, Kharchenko P, Koller A, Slavov N. Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity. bioRxiv. 2020. Publisher's VersionAbstract
The fate and physiology of individual cells are controlled by proteins. Yet, our ability to quantitatively analyze proteins in single cells has remained limited. To overcome this barrier, we developed SCoPE2. It substantially increases quantitative accuracy and throughput while lowering cost and hands-on time by introducing automated and miniaturized sample preparation. These advances enabled us to analyze the emergence of cellular heterogeneity as homogeneous monocytes differentiated into macrophage-like cells in the absence of polarizing cytokines. SCoPE2 quantified over 3,042 proteins in 1,490 single monocytes and macrophages in ten days of instrument time, and the quantified proteins allowed us to discern single cells by cell type. Furthermore, the data uncovered a continuous gradient of proteome states for the macrophage-like cells, suggesting that macrophage heterogeneity may emerge even in the absence of polarizing cytokines. Parallel measurements of transcripts by 10x Genomics scRNA-seq suggest that our measurements sampled 20-fold more protein copies than RNA copies per gene, and thus SCoPE2 supports quantification with improved count statistics. Joint analysis of the data illustrates how variability across single cells can reveal transcriptional and post-transcriptional gene regulation. Our methodology lays the foundation for automated and quantitative single-cell analysis of proteins by mass-spectrometry.Competing Interest StatementThe authors have declared no competing interest.

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