Project description:This study aimed to characterize temporal proteome dynamics of hypoxia using single-cell mass spectrometry-based proteomics. HEK293 cells expressing PIP-FUCCI cell cycle markers were grown in suspension culture. Cells were exposed to hypoxia and temporally sampled for single-cell sorting into 384-well plates. Samples were processed and multiplexed using TMT labeling according to the SCoPE2 workflow and analyzed using the RETICLE acquisition method. In parallel, single-cell RNA-seq data were also collected. These datasets were used together to disentangle the effects of hypoxia from cell cycle progression and to construct joint transcriptional and translational trajectories along the hypoxia response.
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:In this work, we compared different protocols to prepare single-cell suspensions used for scRNAseq and suggest an optimized dissociation protocol for mouse retina, which preserves cell morphology to a higher level leading to an overall increase of gene number per cell. We compared scRNAseq libraries generated with our optimized protocol to publicly available scRNAseq data of mouse retina. We further demonstrate a pipeline to reduce noise in scRNAseq caused by multiplets and ambient RNA.
Project description:In this work, we compared different protocols to prepare single-cell suspensions used for scRNAseq and suggest an optimized dissociation protocol for mouse retina, which preserves cell morphology to a higher level leading to an overall increase of gene number per cell. We compared scRNAseq libraries generated with our optimized protocol to publicly available scRNAseq data of mouse retina. We further demonstrate a pipeline to reduce noise in scRNAseq caused by multiplets and ambient RNA.
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.