RESUMO
Major aims of single-cell proteomics include increasing the consistency, sensitivity and depth of protein quantification, especially for proteins and modifications of biological interest. Here, to simultaneously advance all these aims, we developed prioritized Single-Cell ProtEomics (pSCoPE). pSCoPE consistently analyzes thousands of prioritized peptides across all single cells (thus increasing data completeness) while maximizing instrument time spent analyzing identifiable peptides, thus increasing proteome depth. These strategies increased the sensitivity, data completeness and proteome coverage over twofold. The gains enabled quantifying protein variation in untreated and lipopolysaccharide-treated primary macrophages. Within each condition, proteins covaried within functional sets, including phagosome maturation and proton transport, similarly across both treatment conditions. This covariation is coupled to phenotypic variability in endocytic activity. pSCoPE also enabled quantifying proteolytic products, suggesting a gradient of cathepsin activities within a treatment condition. pSCoPE is freely available and widely applicable, especially for analyzing proteins of interest without sacrificing proteome coverage. Support for pSCoPE is available at http://scp.slavovlab.net/pSCoPE .
Assuntos
Proteoma , Proteômica , Proteoma/análise , Proteômica/métodos , Espectrometria de Massas , Peptídeos/química , MacrófagosRESUMO
Analyzing proteins from single cells by tandem mass spectrometry (MS) has recently become technically feasible. While such analysis has the potential to accurately quantify thousands of proteins across thousands of single cells, the accuracy and reproducibility of the results may be undermined by numerous factors affecting experimental design, sample preparation, data acquisition and data analysis. We expect that broadly accepted community guidelines and standardized metrics will enhance rigor, data quality and alignment between laboratories. Here we propose best practices, quality controls and data-reporting recommendations to assist in the broad adoption of reliable quantitative workflows for single-cell proteomics. Resources and discussion forums are available at https://single-cell.net/guidelines .
Assuntos
Benchmarking , Proteômica , Benchmarking/métodos , Proteômica/métodos , Reprodutibilidade dos Testes , Proteínas/análise , Espectrometria de Massas em Tandem/métodos , Proteoma/análiseRESUMO
Accurate protein quantification is key to identifying protein markers, regulatory relationships between proteins, and pathophysiological mechanisms. Realizing this potential requires sensitive and deep protein analysis of a large number of samples. Toward this goal, proteomics throughput can be increased by parallelizing the analysis of both precursors and samples using multiplexed data independent acquisition (DIA) implemented by the plexDIA framework: https://plexDIA.slavovlab.net. Here we demonstrate the improved precisions of retention time estimates within plexDIA and how this enables more accurate protein quantification. plexDIA has demonstrated multiplicative gains in throughput, and these gains may be substantially amplified by improving the multiplexing reagents, data acquisition, and interpretation. We discuss future directions for advancing plexDIA, which include engineering optimized mass-tags for high-plexDIA, introducing isotopologous carriers, and developing algorithms that utilize the regular structures of plexDIA data to improve sensitivity, proteome coverage, and quantitative accuracy. These advances in plexDIA will increase the throughput of functional proteomic assays, including quantifying protein conformations, turnover dynamics, modifications states and activities. The sensitivity of these assays will extend to single-cell analysis, thus enabling functional single-cell protein analysis.
Assuntos
Algoritmos , Proteômica , Espectrometria de Massas , Proteoma/análiseRESUMO
The Y-linked gene DDX3Y and its X-linked homolog DDX3X survived the evolution of the human sex chromosomes from ordinary autosomes. DDX3X encodes a multi-functional RNA helicase, with mutations causing developmental disorders and cancers. We find that, among X-linked genes with surviving Y homologs, DDX3X is extraordinarily dosage-sensitive. Studying cells of individuals with sex chromosome aneuploidy, we observe that when the number of Y chromosomes increases, DDX3X transcript levels fall; conversely, when the number of X chromosomes increases, DDX3Y transcript levels fall. In 46,XY cells, CRISPRi knockdown of either DDX3X or DDX3Y causes transcript levels of the homologous gene to rise. In 46,XX cells, chemical inhibition of DDX3X protein activity elicits an increase in DDX3X transcript levels. Thus, perturbation of either DDX3X or DDX3Y expression is buffered - by negative cross-regulation of DDX3X and DDX3Y in 46,XY cells, and by negative auto-regulation of DDX3X in 46,XX cells. DDX3X-DDX3Y cross-regulation is mediated through mRNA destabilization - as shown by metabolic labeling of newly transcribed RNA - and buffers total levels of DDX3X and DDX3Y protein in human cells. We infer that post-transcriptional auto-regulation of the ancestral (autosomal) DDX3 gene transmuted into auto- and cross-regulation of DDX3X and DDX3Y as these sex-linked genes evolved from ordinary alleles of their autosomal precursor.
RESUMO
The physiological response of a cell to stimulation depends on its proteome configuration. Therefore, the abundance variation of regulatory proteins across unstimulated single cells can be associatively linked with their response to stimulation. Here we developed an approach that leverages this association across individual cells and nuclei to systematically identify potential regulators of biological processes, followed by targeted validation. Specifically, we applied this approach to identify regulators of nucleocytoplasmic protein transport in macrophages stimulated with lipopolysaccharide (LPS). To this end, we quantified the proteomes of 3,412 individual nuclei, sampling the dynamic response to LPS treatment, and linking functional variability to proteomic variability. Minutes after the stimulation, the protein transport in individual nuclei correlated strongly with the abundance of known protein transport regulators, thus revealing the impact of natural protein variability on functional cellular response. We found that simple biophysical constraints, such as the quantity of nuclear pores, partially explain the variability in LPS-induced nucleocytoplasmic transport. Among the many proteins newly identified to be associated with the response, we selected 16 for targeted validation by knockdown. The knockdown phenotypes confirmed the inferences derived from natural protein and functional variation of single nuclei, thus demonstrating the potential of (sub-)single-cell proteomics to infer functional regulation. We expect this approach to generalize to broad applications and enhance the functional interpretability of single-cell omics data.
RESUMO
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.