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, 367 (6477), 512-513

Unpicking the Proteome in Single Cells

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Unpicking the Proteome in Single Cells

Nikolai Slavov. Science.

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, 16 (10), 945-951

Voices in Methods Development

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Voices in Methods Development

Polina Anikeeva et al. Nat Methods.

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, 15 (7), e1007082
eCollection

DART-ID Increases Single-Cell Proteome Coverage

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DART-ID Increases Single-Cell Proteome Coverage

Albert Tian Chen et al. PLoS Comput Biol.

Abstract

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. This process, however, remains challenging for smaller samples, such as the proteomes of single mammalian cells, because reduced protein levels reduce the number of confidently sequenced peptides. To alleviate this reduction, we developed Data-driven Alignment of Retention Times for IDentification (DART-ID). DART-ID implements principled Bayesian frameworks for global retention time (RT) alignment and for incorporating RT estimates towards improved confidence estimates of peptide-spectrum-matches. When applied to bulk or to single-cell samples, DART-ID increased the number of data points by 30-50% at 1% FDR, and thus decreased missing data. Benchmarks indicate excellent quantification of peptides upgraded by DART-ID and support their utility for quantitative analysis, such as identifying cell types and cell-type specific proteins. The additional datapoints provided by DART-ID boost the statistical power and double the number of proteins identified as differentially abundant in monocytes and T-cells. DART-ID can be applied to diverse experimental designs and is freely available at http://dart-id.slavovlab.net.

Conflict of interest statement

The authors have declared that no competing interests exist.

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, 18 (6), 2493-2500

DO-MS: Data-Driven Optimization of Mass Spectrometry Methods

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DO-MS: Data-Driven Optimization of Mass Spectrometry Methods

R Gray Huffman et al. J Proteome Res.

Abstract

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 .

Keywords: MaxQuant; R; Shiny; method development; optimizing mass spectrometry; quality control; single-cell analysis; single-cell proteomics by mass spectrometry; ultrasensitive proteomics; visualization.

Conflict of interest statement

The authors declare no competing financial interest.

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Review
, 44 (5), 478-479

Approaches for Studying Ribosome Specialization

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Approaches for Studying Ribosome Specialization

Edward Emmott et al. Trends Biochem Sci.

Abstract

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.

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, 44 (2), 95-109

Ribosome Stoichiometry: From Form to Function

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Ribosome Stoichiometry: From Form to Function

Edward Emmott et al. Trends Biochem Sci.

Abstract

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.

Keywords: heterogeneity; mass spectrometry; ribosome; stoichiometry; translation.

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, 19 (1), 161

SCoPE-MS: Mass Spectrometry of Single Mammalian Cells Quantifies Proteome Heterogeneity During Cell Differentiation

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SCoPE-MS: Mass Spectrometry of Single Mammalian Cells Quantifies Proteome Heterogeneity During Cell Differentiation

Bogdan Budnik et al. Genome Biol.

Abstract

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.

Conflict of interest statement

Ethics approval and consent to participate

Ethics approvals were not needed for the study.

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Not applicable

Competing interests

The authors have filed a provisional patent on the entire protocol, application number: 62/618,301. The method is freely available for research and all other non-commercial purposes.

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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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, 18 (1), 162-168

Quantifying Homologous Proteins and Proteoforms

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Quantifying Homologous Proteins and Proteoforms

Dmitry Malioutov et al. Mol Cell Proteomics.

Abstract

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/2014HIquant/.

Keywords: Algorithms; Bioinformatics; Bioinformatics Software; Mass Spectrometry; Mathematical Modeling.

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, 62 (4), 595-605

Single Cell Protein Analysis for Systems Biology

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Single Cell Protein Analysis for Systems Biology

Ezra Levy et al. Essays Biochem.

Abstract

The cellular abundance of proteins can vary even between isogenic single cells. This variability between single-cell protein levels can have regulatory roles, such as controlling cell fate during apoptosis induction or the proliferation/quiescence decision. Here, we review examples connecting protein levels and their dynamics in single cells to cellular functions. Such findings were made possible by the introduction of antibodies, and subsequently fluorescent proteins, for tracking protein levels in single cells. However, in heterogeneous cell populations, such as tumors or differentiating stem cells, cellular decisions are controlled by hundreds, even thousands of proteins acting in concert. Characterizing such complex systems demands measurements of thousands of proteins across thousands of single cells. This demand has inspired the development of new methods for single-cell protein analysis, and we discuss their trade-offs, with an emphasis on their specificity and coverage. We finish by highlighting the potential of emerging mass-spec methods to enable systems-level measurement of single-cell proteomes with unprecedented coverage and specificity. Combining such methods with methods for quantitating the transcriptomes and metabolomes of single cells will provide essential data for advancing quantitative systems biology.

Keywords: antibodies; gene expression and regulation; mass-spectrometry; single cell proteomics.

Conflict of interest statement

Competing interests

The authors declare that there are no competing interests associated with the manuscript.

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, 17 (8), 2565-2571

Transformative Opportunities for Single-Cell Proteomics

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Transformative Opportunities for Single-Cell Proteomics

Harrison Specht et al. J Proteome Res.

Abstract

Many pressing medical challenges, such as diagnosing disease, enhancing directed stem-cell differentiation, and classifying cancers, have long been hindered by limitations in our ability to quantify proteins in single cells. Mass spectrometry (MS) is poised to transcend these limitations by developing powerful methods to routinely quantify thousands of proteins and proteoforms across many thousands of single cells. We outline specific technological developments and ideas that can increase the sensitivity and throughput of single-cell MS by orders of magnitude and usher in this new age. These advances will transform medicine and ultimately contribute to understanding biological systems on an entirely new level.

Keywords: Simpson’s paradox; causal inference; counting noise; disease diagnosis; network inference; sample preparation; single-cell analysis; single-cell mass-spectrometry; systems biology; ultrasensitive proteomics.

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The authors declare no competing financial interest.

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