Project description: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.
Project description:Single cell proteomic dataset (~420 cells passing filter, 1827 proteins at 1% FDR and 4096 proteins post update via DART-ID) characterizing the Epithelial to Mesenchymal transition induced by TGF-B in MCF10A cells.
Cells were sampled from day 0 (epithelial), day 3 (intermediate) and day 9 (sustained) treatment. The single cell samples were prepped using nPoP and prepared in the SCoPE2 format. Dataset also includes two bulk biological replicates (samples from day 0, 3 and 9 respectively) that were analyzed via label free DIA.
Project description: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.
Project description:Single H358 cells analyzed using SCOPE2 on a TIMSTOF Flex mass spectrometer. Bruker .d folders, MGFs, Proteome Discoverer 2.5 and MaxQuant 1.6.17 results are uploaded.
Project description:We used DART-seq to map m6A methylation of RNA in single HEK293T cells. We also used DART-seq to map m6A from bulk RNA from HEK293T cells. Using the 10X Genomics and SMART-seq2 platforms, we sequenced a total of 19,533 experimental and control cells using the 10X Genomics platform, and 1,471 experimental and control cells using SMART-seq2. We then used a Bullseye, a computational pipeline developed within the lab, to identify m6A sites from the C-to-U mutations in bulk and single-cell datasets. We find that most m6A methylation is highly heterogenous from cell-to-cell. RNAs containing m6A methylation, are infrequently methylated, and that most individual sites are rarely methylated within the population. Additionally, we are able to identify differentially methylated RNAs in different cellular states from within a single population, and use m6A methylation information to perform clustering of single cells to find a source of novel cellular heterogeneity.