Publications

2021
Slavov N. Single-cell protein analysis by mass spectrometry. Current Opinion in Chemical Biology [Internet]. 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 [Internet]. 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.
Petelski AA, Slavov N. Analyzing Ribosome Remodeling in Health and Disease. PROTEOMICS [Internet]. 2020;20 (17-18) :2000039. Publisher's VersionAbstract
Abstract Increasing evidence suggests that ribosomes actively regulate protein synthesis. However, much of this evidence is indirect, leaving this layer of gene regulation largely unexplored, in part due to methodological limitations. Indeed, evidence is reviewed demonstrating that commonly used methods, such as transcriptomics, are inadequate because the variability in mRNAs coding for ribosomal proteins (RP) does not necessarily correspond to RP variability. Thus protein remodeling of ribosomes should be investigated by methods that allow direct quantification of RPs, ideally of isolated ribosomes. Such methods are reviewed, focusing on mass spectrometry and emphasizing method-specific biases and approaches to control these biases. It is argued that using multiple complementary methods can help reduce the danger of interpreting reproducible systematic biases as evidence for ribosome remodeling.
Specht H, Slavov N. Optimizing accuracy and depth of protein quantification in experiments using isobaric carriers. bioRxiv [Internet]. 2020. Publisher's VersionAbstract
The isobaric carrier approach, which combines small isobarically-labeled samples with a larger isobarically-labeled carrier sample, is finding diverse applications in ultrasensitive mass-spectrometry analysis of very small samples, such as single cells. To inform the growing use of isobaric carriers, we characterized the trade-offs of using isobaric carriers in controlled experiments with complex human proteomes. The data indicate that isobaric carriers directly enhances peptide sequence identification without simultaneously increasing the number of protein copies sampled from small samples. The results also indicate strategies for optimizing the amount of isobaric carrier and analytical parameters, such as ion accumulation time, for different priorities such as improved quantification or increased number of identified proteins. Balancing these trade-offs enables adapting isobaric carrier experiments to different applications, such as quantifying proteins from limited biopses or organoids, building single-cell atlases, or modeling protein networks in single cells. In all cases, the reliability of protein quantification should be estimated and incorporated in all subsequent analysis. We expect that these guidelines will aid explicit incorporation of the characterized trade-offs in experimental designs and transparent error propagation in data analysis.Competing Interest StatementThe authors have declared no competing interest.
Petelski AA, Slavov N. Analyzing ribosome remodeling in health and disease. [Internet]. 2020. Publisher's VersionAbstract
Increasing evidence suggests that ribosomes actively regulate protein synthesis. However, much of this evidence is indirect, leaving this layer of gene regulation largely unexplored, in part due to methodological limitations. Indeed, we review evidence demonstrating that commonly used methods, such as transcriptomics, are inadequate because the variability in mRNAs coding for ribosomal proteins (RP) does not necessarily correspond to RP variability. Thus protein remodeling of ribosomes should be investigated by methods that allow direct quantification of RPs, ideally of isolated ribosomes. We review such methods, with emphasis on their biases and approaches to control these biases. We argue that using multiple complementary methods can help reduce the danger of interpreting reproducible systematic biases as evidence for ribosome remodeling.
Slavov N. Single-cell protein analysis by mass-spectrometry. [Internet]. 2020. 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 MS 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 post-translational modifications while increases in multiplexing capabilities will increase the throughput and decrease cost.
Slavov N. Unpicking the proteome in single cells. Science [Internet]. 2020;367 (6477) :512–513. Publisher's Version
2019
Slavov N, Anikeeva P, Boyden E, Brangwynne C, Cissé II, Fiehn O, Fromme P, Gingras AC, Greene CS, Heard E, et al. Voices in methods development. Nature Methods. 2019;16 (10) :945–951.
Specht H, Emmott E, Petelski A, Huffman RG, Perlman DH, Serra M, Kharchenko P, Koller A, Slavov N. Single-cell mass-spectrometry quantifies the emergence of macrophage heterogeneity. bioRxiv [Internet]. 2019. Publisher's VersionAbstract
The fate and physiology of individual cells are controlled by protein interactions. Yet, our ability to quantitatively analyze proteins in single cells has remained limited. To overcome this barrier, we developed SCoPE2. It lowers cost and hands-on time by introducing automated and miniaturized sample preparation while substantially increasing quantitative accuracy. 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 2,700 proteins in 1,018 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 SCoPE2 samples 20-fold more copies per gene, thus supporting quantification with improved count statistics. Joint analysis of the data indicated that most genes had similar responses at the protein and RNA levels, though the responses of hundreds of genes differed. Our methodology lays the foundation for automated and quantitative single-cell analysis of proteins by mass-spectrometry.
Huffman RG, Chen A, Specht H, Slavov N. DO-MS: Data-Driven Optimization of Mass Spectrometry Methods. Journal of Proteome Research [Internet]. 2019;18 (6) :2493-2500. Publisher's VersionAbstract
The performance of ultrasensitive liquid chromatography and tandem mass spectrometry (LC-MS/MS) methods, such as single-cell proteomics by mass spectrometry (SCoPE-MS), depends on multiple interdependent parameters. This interdependence makes it challenging to specifically pinpoint the sources of problems in the LC-MS/MS methods and approaches for resolving them. For example, a low signal at the MS2 level can be due to poor LC separation, ionization, apex targeting, ion transfer, or ion detection. We sought to specifically diagnose such problems by interactively visualizing data from all levels of bottom-up LC-MS/MS analysis. Many software packages, such as MaxQuant, already provide such data, and we developed an open source platform for their interactive visualization and analysis: Data-driven Optimization of MS (DO-MS). We found that in many cases DO-MS not only specifically diagnosed LC-MS/MS problems but also enabled us to rationally optimize them. For example, by using DO-MS to optimize the sampling of the elution peak apexes, we increased ion accumulation times and apex sampling, which resulted in a 370% more efficient delivery of ions for MS2 analysis. DO-MS is easy to install and use, and its GUI allows for interactive data subsetting and high-quality figure generation. The modular design of DO-MS facilitates customization and expansion. DO-MS v1.0.8 is available for download from GitHub: https://github.com/SlavovLab/DO-MS. Additional documentation is available at https://do-ms.slavovlab.net.
Chen AT, Franks A, Slavov N. DART-ID increases single-cell proteome coverage. PLOS Computational Biology [Internet]. 2019;15 (7) :1-30. Publisher's VersionAbstract
Author summary Identifying and quantifying proteins in single cells gives researchers the ability to tackle complex biological problems that involve single cell heterogeneity, such as the treatment of solid tumors. Mass spectrometry analysis of peptides can identify their sequence from their masses and the masses of their fragment ion, but often times these pieces of evidence are insufficient for a confident peptide identification. This problem is exacerbated when analyzing lowly abundant samples such as single cells. To identify even peptides with weak mass spectra, DART-ID incorporates their retention time—the time when they elute from the liquid chromatography used to physically separate them. We present both a novel method of aligning the retention times of peptides across experiments, as well as a rigorous framework for using the estimated retention times to enhance peptide sequence identification. Incorporating the retention time as additional evidence leads to a substantial increase in the number of samples in which proteins are confidently identified and quantified.
Specht H, Emmott E, Koller T, Slavov N. High-throughput single-cell proteomics quantifies the emergence of macrophage heterogeneity. bioRxiv [Internet]. 2019. Publisher's VersionAbstract
The fate and physiology of individual cells are controlled by networks of proteins. Yet, our ability to quantitatively analyze protein networks in single cells has remained limited. To overcome this barrier, we developed SCoPE2. It integrates concepts from Single-Cell ProtEomics by Mass Spectrometry (SCoPE-MS) with automated and miniaturized sample preparation, substantially lowering cost and hands-on time. SCoPE2 uses data-driven analytics to optimize instrument parameters for sampling more ion copies per protein, thus supporting quantification with improved count statistics. 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. We used SCoPE2 to quantify over 2,000 proteins in 356 single monocytes and macrophages in about 85 hours 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. Our methodology lays the foundation for quantitative analysis of protein networks at single-cell resolution.
Emmott E, Jovanovic M, Slavov N. Approaches for Studying Ribosome Specialization. Trends in Biochemical Sciences [Internet]. 2019;44 (5) :478 - 479. Publisher's VersionAbstract
Contrary to the textbook model, recent measurements demonstrated unexpected diversity in ribosomal composition that likely enables specialized translational functions. Methods based on liquid chromatography coupled to tandem mass-spectrometry (LC-MS/MS) enable direct quantification of ribosomal proteins with high specificity, accuracy, and throughput. LC-MS/MS can be ‘top-down’, analyzing intact proteins, or more commonly ‘bottom-up’, where proteins are digested to peptides prior to analysis. Changes to rRNA can be examined using either LC-MS/MS or sequencing-based approaches. The regulation of protein synthesis by specialized ribosomes can be examined by multiple methods. These include the popular ‘Ribo-Seq’ method for analyzing ribosome density on a given mRNA, as well as LC-MS/MS approaches incorporating pulse-labelling with stable isotopes (SILAC) to monitor protein synthesis and degradation.
Emmott E, Jovanovic M, Slavov N. Ribosome stoichiometry: from form to function. Trends in Biochemical Sciences [Internet]. 2019;44 :95–109. Publisher's VersionAbstract
The existence of eukaryotic ribosomes with distinct ribosomal protein (RP) stoichiometry and regulatory roles in protein synthesis has been speculated for over 60 years. Recent advances in mass spectrometry (MS) and high-throughput analysis have begun to identify and characterize distinct ribosome stoichiometry in yeast and mammalian systems. In addition to RP stoichiometry, ribosomes host a vast array of protein modifications, effectively expanding the number of human RPs from 80 to many thousands of distinct proteoforms. Is it possible that these proteoforms combine to function as a ‘ribosome code’ to tune protein synthesis? We outline the specific benefits that translational regulation by specialized ribosomes can offer and discuss the means and methodologies available to correlate and characterize RP stoichiometry with function. We highlight previous research with a focus on formulating hypotheses that can guide future experiments and crack the ribosome code.
Malioutov D, Chen T, Airoldi E, Jaffe J, Budnik B, Slavov N. Quantifying Homologous Proteins and Proteoforms. Molecular & Cellular Proteomics [Internet]. 2019;18 (1) :162–168. Publisher's VersionAbstract
Many proteoforms—arising from alternative splicing, post-translational modifications (PTM), or paralogous genes—have distinct biological functions, such as histone PTM proteoforms. However, their quantification by existing bottom-up mass-spectrometry (MS) methods is undermined by peptide-specific biases. To avoid these biases, we developed and implemented a first-principles model (HIquant) for quantifying proteoform stoichiometries. We characterized when MS data allow inferring proteoform stoichiometries by HIquant and derived an algorithm for optimal inference. We applied this algorithm to infer proteoform stoichiometries in two experimental systems that supported rigorous bench-marking: alkylated proteoforms spiked-in at known ratios and endogenous histone 3 PTM proteoforms quantified relative to internal heavy standards. When compared with the benchmarks, the proteoform stoichiometries interfered by HIquant without using external standards had relative error of 5–15% for simple proteoforms and 20–30% for complex proteoforms. A HIquant server is implemented at: https://web.northeastern.edu/slavov/2014_HIquant/
Huffman G, Specht H, Chen AT, Slavov N. DO-MS: Data-Driven Optimization of Mass Spectrometry Methods. bioRxiv [Internet]. 2019. Publisher's VersionAbstract
The performance of ultrasensitive LC-MS/MS methods, such as Single-Cell Proteomics by Mass Spectrometry (SCoPE-MS), depends on multiple interdependent parameters. This interdependence makes it challenging to specifically pinpoint bottlenecks in the LC-MS/MS methods and approaches for resolving them. For example, low signal at MS2 level can be due to poor LC separation, ionization, apex targeting, ion transfer, or ion detection. We sought to specifically diagnose such bottlenecks by interactively visualizing data from all levels of bottom-up LC-MS/MS analysis. Many search engines, such as MaxQuant, already provide such data, and we developed an open source platform for their interactive visualization and analysis: Data-driven Optimization of MS (DO-MS). We found that in many cases DO-MS not only specifically diagnosed bottlenecks but also enabled us to rationally optimize them. For example, we used DO-MS to diagnose poor sampling of the elution peak apex and to optimize it, which increased the efficiency of delivering ions for MS2 analysis by 370 %. DO-MS is easy to install and use, and its GUI allows for interactive data subsetting and high-quality figure generation. The modular design of DO-MS facilitates customization and expansion. DO-MS is available for download from GitHub: https://github.com/SlavovLab/DO-MS
2018
Budnik B, Levy E, Harmange G, Slavov N. SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. Genome Biology [Internet]. 2018;19 (1) :161. Publisher's VersionAbstract
Some exciting biological questions require quantifying thousands of proteins in single cells. To achieve this goal, we develop Single Cell ProtEomics by Mass Spectrometry (SCoPE-MS) and validate its ability to identify distinct human cancer cell types based on their proteomes. We use SCoPE-MS to quantify over a thousand proteins in differentiating mouse embryonic stem cells. The single-cell proteomes enable us to deconstruct cell populations and infer protein abundance relationships. Comparison between single-cell proteomes and transcriptomes indicates coordinated mRNA and protein covariation, yet many genes exhibit functionally concerted and distinct regulatory patterns at the mRNA and the protein level.
Malioutov D, Chen T, Airoldi E, Jaffe JD, Budnik B, Slavov N. Quantifying homologous proteins and proteoforms. Molecular & Cellular Proteomics [Internet]. 2018. Publisher's VersionAbstract
Many proteoforms – arising from alternative splicing, post-translational modifications (PTM), or paralogous genes – have distinct biological functions, such as histone PTM proteoforms. However, their quantification by existing bottom-up mass–spectrometry (MS) methods is undermined by peptide-specific biases. To avoid these biases, we developed and implemented a first-principles model (HIquant ) for quantifying proteoform stoichiometries. We characterized when MS data allow inferring proteoform stoichiometries by HIquant and derived an algorithm for optimal inference. We applied this algorithm to infer proteoform stoichiometries in two experimental systems that supported rigorous bench-marking: alkylated proteoforms spiked-in at known ratios and endogenous histone 3 PTM proteoforms quantified relative to internal heavy standards. When compared to the benchmarks, the proteoform stoichiometries interfered by HIquant without using external standards had relative error of 5 - 15% for simple proteoforms and 20 - 30% for complex proteoforms. A HIquant server is implemented at: https://web.northeastern.edu/slavov/2014_HIquant/
Specht H, Harmange G, Perlman DH, Emmott E, Niziolek Z, Budnik B, Slavov N. Automated sample preparation for high-throughput single-cell proteomics. bioRxiv [Internet]. 2018. Publisher's VersionAbstract
A major limitation to applying quantitative LC-MS/MS proteomics to small samples, such as single cells, are the losses incured during sample cleanup. To relieve this limitation, we developed a Minimal ProteOmic sample Preparation (mPOP) method for culture-grown mammalian cells. mPOP obviates cleanup and thus eliminates cleanup-related losses while expediting sample preparation and simplifying its automation. Bulk SILAC samples processed by mPOP or by conventional urea-based methods indicated that mPOP results in complete cell lysis and accurate relative quantification. We integrated mPOP lysis with the Single Cell ProtEomics by Mass Spectrometry (SCoPE-MS) sample preparation, and benchmarked the quantification of such samples on a Q-exactive instrument. The results demonstrate low noise and high technical reproducibility. Then, we FACS sorted single U-937, HEK-293, and mouse ES cells into 96-well plates and analyzed them by automated mPOP and SCoPE-MS. The quantified proteins enabled separating the single cells by cell-type and cell-division-cycle phase.
Chen A, Franks A, Slavov N. DART-ID increases single-cell proteome coverage. bioRxiv [Internet]. 2018. Publisher's VersionAbstract
Analysis by liquid chromatography and tandem mass spectrometry (LC-MS/MS) can identify and quantify thousands of proteins in microgram-level samples, such as those comprised of thousands of cells. Identifying proteins by LC-MS/MS proteomics, however, remains challenging for lowly abundant samples, such as the proteomes of single mammalian cells. To increase the identification rate of peptides in such small samples, we developed DART-ID. This method implements a data-driven, global retention time (RT) alignment process to infer peptide RTs across experiments. DART-ID then incorporates the global RT-estimates within a principled Bayesian framework to increase the confidence in correct peptide-spectrum-matches. Applying DART-ID to hundreds of samples prepared by the Single Cell Proteomics by Mass Spectrometry (SCoPE-MS) design increased the peptide and proteome coverage by 30 - 50% at 1% FDR. The newly identified peptides and proteins were further validated by demonstrating that their quantification is consistent with the quantification of peptides identified from high-quality spectra. DART-ID can be applied to various sets of experimental designs with similar sample complexities and chromatography conditions, and is freely available online.

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