Radiolaria Single Cells
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REPOSITORIES: ENA
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Project description:Even with recent improvements in sample preparation and instrumentation, single-cell proteomics (SCP) analyses mostly measure protein abundances, making the field unidimensional. In this study, we employ a pulsed stable isotope labeling by amino acids in cell culture (SILAC) approach to simultaneously evaluate protein abundance and turnover in single cells (SC-pSILAC). Using state-of-the-art SCP workflow, we demonstrated that two SILAC labels are detectable from ~4000 proteins in single HeLa cells recapitulating known biology. We investigated drug effects on global and specific protein turnover in single cells and performed a large-scale time-series SC-pSILAC analysis of undirected differentiation of human induced pluripotent stem cells (iPSC) encompassing six sampling times over two months and analyzed >1000 cells. Abundance measurements highlighted cell-specific markers of stem cells and various organ-specific cell types. Protein turnover dynamics highlighted differentiation-specific co-regulation of core members of protein complexes with core histone turnover discriminating dividing and non-dividing cells with potential in stem cell and cancer research. Lastly, correlating the abundance of individual proteins from cells displaying a wide range of diameters show that histones and some proteins involved in the cell cycle do not scale with cell size confirming previous observations in yeast. Our study represents one of the most comprehensive SCP analysis to date, offering new insights into cellular diversity and pioneering functional post-translational measurements beyond protein abundance. This method not only distinguishes SCP from other single-cell omics approaches and enhances its scientific relevance in biological research in a multidimensional manner but also showcase the discovery potential of SCP in fundamental biology.
2025-03-30 | PXD051942 | Pride
Project description:Even with recent improvements in sample preparation and instrumentation, single-cell proteomics (SCP) analyses mostly measure protein abundances, making the field unidimensional. In this study, we employ a pulsed stable isotope labeling by amino acids in cell culture (SILAC) approach to simultaneously evaluate protein abundance and turnover in single cells (SC-pSILAC). Using state-of-the-art SCP workflow, we demonstrated that two SILAC labels are detectable from ~4000 proteins in single HeLa cells recapitulating known biology. We investigated drug effects on global and specific protein turnover in single cells and performed a large-scale time-series SC-pSILAC analysis of undirected differentiation of human induced pluripotent stem cells (iPSC) encompassing six sampling times over two months and analyzed >1000 cells. Abundance measurements highlighted cell-specific markers of stem cells and various organ-specific cell types. Protein turnover dynamics highlighted differentiation-specific co-regulation of core members of protein complexes with core histone turnover discriminating dividing and non-dividing cells with potential in stem cell and cancer research. Lastly, correlating the abundance of individual proteins from cells displaying a wide range of diameters show that histones and some proteins involved in the cell cycle do not scale with cell size confirming previous observations in yeast. Our study represents the most comprehensive SCP analysis to date, offering new insights into cellular diversity and pioneering functional post-translational measurements beyond protein abundance. This method not only distinguishes SCP from other single-cell omics approaches and enhances its scientific relevance in biological research in a multidimensional manner but also showcase the discovery potential of SCP in fundamental biology.
2025-03-30 | PXD054083 | Pride
Project description:Single-cell proteomics (SCP) has advanced significantly, yet it remains largely unidimensional, focusing primarily on protein abundances. This limitation hinders our understanding of the dynamic processes occurring within individual cells, particularly protein turnover, which is crucial for cellular function and regulation. In this study, we employed a pulsed stable isotope labeling by amino acids in cell culture (SILAC) approach to simultaneously evaluate protein abundance and turnover in single cells (SC-pSILAC). Using state-of-the-art SCP workflow, we demonstrated that two SILAC labels are detectable from ~4000 proteins in single HeLa cells recapitulating known biology. We investigated drug effects using protein synthesis and degradation inhibitors on global and specific protein turnover in single cells and performed a large-scale time-series SC-pSILAC analysis of undirected differentiation of human induced pluripotent stem cells (iPSC) encompassing six sampling times over two months and analyzed >1000 cells. Abundance measurements highlighted cell-specific markers of stem cells and various organ-specific cell types. Protein turnover dynamics highlighted differentiation-specific co-regulation of core members of protein complexes with core histone turnover discriminating dividing and non-dividing cells with potential in stem cell and cancer research. Lastly, correlating the abundance of individual proteins from cells displaying a wide range of diameters show that histones and some proteins involved in the cell cycle do not scale with cell size confirming previous observations in yeast. Our study represents the most comprehensive SCP analysis to date, offering new insights into cellular diversity and pioneering functional post-translational measurements beyond protein abundance. This method not only distinguishes SCP from other single-cell omics approaches and enhances its scientific relevance in biological research in a multidimensional manner but also showcase the discovery potential of SCP in fundamental biology.
2025-03-28 | PXD058753 | Pride
Project description:To understand the diversity of expression states within melanoma tumors, we obtained freshly resected samples, dissagregated the samples, sorted into single cells and profiled them by single-cell RNA-seq. Tumors were disaggregated, sorted into single cells, and profiled by Smart-seq2. *Raw data files absent for samples GSM1851356 and GSM1851494.* **Submitter declares reads will be made available through dbGaP.**
2016-04-05 | E-GEOD-72056 | biostudies-arrayexpress
Project description:Cis-regulatory elements (CREs) encode the genomic blueprints for coordinating the spatiotemporal regulation of gene transcription programs necessary for highly specialized cellular functions. To identify cis-regulatory elements underlying cell-type specification and developmental transitions, we implemented single-cell sequencing of Assay for Transposase Accessible Chromatin (scATAC-seq) in an atlas of Zea mays tissues and organs. We describe 92 distinct patterns of chromatin accessibility across more than 165,913 putative CREs, greater than 56,575 cells, and 52 known cell-types using a novel regularized quasibinomial logistic model for estimating single cell accessibility. Cell-type specification could be largely explained by combinatorial accessibility of transcription factors (TFs) and their associated binding. Analysis of cell type-specific co-accessible chromatin recapitulated higher-order chromatin interactions, providing novel insight into cell type-specific regulatory dynamics. Integration of genetic diversity data revealed cell-type specific CREs contributed to specific morphological and molecular phenotypic traits indicative of their cellular functions, expanding our understanding of the molecular influence of complex traits in a eukaryotic species.
2020-09-30 | GSE155178 | GEO
Project description:<p>A growing appreciation of the importance of cellular metabolism and revelations concerning the extent of cell-cell heterogeneity demand metabolic characterization of individual cells. We present SpaceM, an open-source method for in situ single-cell metabolomics that detects >100 metabolites from >1,000 individual cells per hour, together with a fluorescence-based readout and retention of morpho-spatial features. We validated SpaceM by predicting the cell types of cocultured human epithelial cells and mouse fibroblasts. We used SpaceM to show that stimulating human hepatocytes with fatty acids leads to the emergence of two coexisting subpopulations outlined by distinct cellular metabolic states. Inducing inflammation with the cytokine interleukin-17A perturbs the balance of these states in a process dependent on NF-κB signaling. The metabolic state markers were reproduced in a murine model of nonalcoholic steatohepatitis. We anticipate SpaceM to be broadly applicable for investigations of diverse cellular models and to democratize single-cell metabolomics.</p><p><br></p><p>All MALDI-imaging MS data as well as metabolite and lipid annotations and images are publicly available through METASPACE (<a href='https://metaspace2020.eu/project/Rappez_2021_SpaceM' rel='noopener noreferrer' target='_blank'>https://metaspace2020.eu/project/Rappez_2021_SpaceM</a>)</p>
2021-05-11 | MTBLS78 | MetaboLights
Project description:We used single cell RNA sequencing to analyze the diversity of T cells in PBMC
2024-08-08 | GSE255381 | GEO
Project description:Single-cell Circle-seq unveils diversity and cell specificity of circular DNA in single cells
| PRJNA916665 | ENA
Project description:Characterization of cell-type diversity obtained after cardiac differentiation of hiPSC using single-cell RNA-Seq.
2022-12-05 | E-MTAB-11817 | biostudies-arrayexpress
Project description:Gene expression after mammosphere culture, quiescent single cells
| EGAD00010001964 | EGA