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Slavov, N.

Publications and source records attributed to Slavov, N..

5 recordsLinked to original sources

Automated sample preparation for high-throughput single-cell proteomics

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.

systems biology

DART-ID increases single-cell proteome coverage

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). This method implements global retention time (RT) alignment to infer peptide RTs across experiments. DART-ID then incor-porates the global RT-estimates within a principled Bayesian framework to increase the confidence in correct peptide-spectrum-matches and decrease confidence in incorrect peptide-spectrum-matches. Applying DART-ID to hundreds of monocyte and T-cell samples pre-pared by the Single Cell Proteomics by Mass Spectrometry (SCoPE-MS) design increased the number of data points by 30 - 50% at 1% FDR, and thus decreased missing data. Quantification benchmarks indicate excellent quantification of peptides upgraded by DART-ID and support their utility for downstream 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://github.com/SlavovLab/DART-ID. Author SummaryIdentifying 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.

genomics

Quantifying homologous proteins and proteoforms

Many proteoforms - arising from alternative splicing, post-translational modifications (PTMs), 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, derived an algorithm for optimal inference, and demonstrated experimentally high accuracy in quantifying fractional PTM occupancy without using external standards, even in the challenging case of the histone modification code. A HIquant server is implemented at: https://web.northeastern.edu/slavov/2014_HIquant/

systems biology

Pervasive Discordance Between mRNA And Protein Expression During Embryonic Stem Cell Differentiation

During in vitro differentiation, pluripotent stem cells undergo extensive remodeling of their gene expression profile. While studied extensively at the transcriptome level, much less is known about protein dynamics. Here, we measured mRNA and protein levels of 7459 genes during differentiation of embryonic stem cells (ESCs). This comprehensive data set revealed pervasive discordance between mRNA and protein. The high temporal resolution of the data made it possible to determine protein turnover rates genome-wide by fitting a kinetic model. This model further enabled us to systematically identify dynamic post-transcriptional regulation. Moreover, we linked different modes of regulation to the function of specific gene sets. Finally, we showed that the kinetic model can be applied to singlecell transcriptomics data to predict protein levels in differentiated cell types. In conclusion, our comprehensive data set, easily accessible through a web application, is a valuable resource for the discovery of post-transcriptional regulation in ESC differentiation.

systems biology

Mass-spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation

Cellular heterogeneity is important to biological processes, including cancer and development. However, proteome heterogeneity is largely unexplored because of the limitations of existing methods for quantifying protein levels in single cells. To alleviate these limitations, we developed Single Cell ProtEomics by Mass Spectrometry (SCoPE-MS), and validated its ability to identify distinct human cancer cell types based on their proteomes. We used SCoPE-MS to quantify over a thousand proteins in differentiating mouse embryonic stem (ES) cells. The single-cell proteomes enabled us to deconstruct cell populations and infer protein abundance relationships. Comparison between single-cell proteomes and transcriptomes indicated coordinated mRNA and protein covariation. Yet many genes exhibited functionally concerted and distinct regulatory patterns at the mRNA and the protein levels, suggesting that post-transcriptional regulatory mechanisms contribute to proteome remodeling during lineage specification, especially for developmental genes. SCoPE-MS is broadly applicable to measuring proteome configurations of single cells and linking them to functional phenotypes, such as cell type and differentiation potentials.

systems biology