Project description:Limiting artifacts during sample preparation can significantly increase data quality in single-cell proteomics experiments. Towards this goal, we characterize the impact of protein leakage by analyzing thousands of primary single cells that were prepared either fresh immediately after dissociation or cryopreserved and prepared at a later date. We directly identify permeabilized cells and use the data to define a signature for protein leakage. We use this signature to build a classifier for identifying damaged cells that performs accurately across cell types and species.
Project description:Head and neck squamous cell carcinoma tissue microarrays were stained via CD20 single plex IHC and then FOVs were selected for B cell neighborhoods (including tertiary lymphoid structures) in confirmed tumor areas via H&E evaluation by a pathologist.
Project description:Circulating Tumor Cells (CTCs) are shed from primary tumors into the bloodstream, mediating the hematogenous spread of cancer to distant organs. Using a pancreatic cancer mouse model, we applied a microfluidic device to isolate CTCs independently of tumor epitopes, subjecting these to single cell RNA-sequencing. This study was conducted to determine the heterogeneity of pancreatic CTCs and to compare these CTCs to matched primary tumors, cell line controls (NB508 cancer cell line and MEF non-cancer cell line), primary tumor single cells, and normal leukocytes/WBCs. We profiled RNA from 75 single cells circulating in mouse blood enriched for circulating tumor cells from 5 mice, 12 single cells from a mouse embryonic fibroblast cell line, 16 single cells from the nb508 mouse pancreatic cancer cell line, 12 single mouse white blood cells, 18 single GFP lineage-traced circulating tumor cells from two mice, 20 single GFP lineage-traced cancer cells from the primary pancreatic tumor of a mouse, and 34 dilutions to 10 or 100 picograms of total RNA from mouse primary pancreatic tumors from 4 mice.
Project description:Despite the availabilty of imaging-based and mass-spectrometry-based methods for spatial proteomics, a key challenge remains connecting images with single-cell-resolution protein abundance measurements. Here we introduce Deep Visual Proteomics (DVP), which combines artificial-intelligence-driven image analysis of cellular phenotypes with automated single-cell or single-nucleus laser microdissection and ultra-high-sensitivity mass spectrometry. DVP links protein abundance to complex cellular or subcellular phenotypes while preserving spatial context. By individually excising nuclei from cell culture, we classified distinct cell states with proteomic profiles defined by known and uncharacterized proteins. In an archived primary melanoma tissue, DVP identified spatially resolved proteome changes as normal melanocytes transition to fully invasive melanoma, revealing pathways that change in a spatial manner as cancer progresses, such as mRNA splicing dysregulation in metastatic vertical growth that coincides with reduced interferon signaling and antigen presentation. The ability of DVP to retain precise spatial proteomic information in the tissue context has implications for the molecular profiling of clinical samples.
Project description:This project used a custom-built capillary electrophoresis (CE) mass spectrometry (MS) platform to demonstrate the scalable analysis of proteins from single cells of varying sizes and protein content in different vertebrate biological models. Neural tissue fated giant cells were identified in cleavage-stage Xenopus laevis (frog) embryos, and 18 ng from its protein content was analyzed. From cultured primary neurons from the mouse, diluted samples containing ~125 pg of protein digest was measured, estimating to a portion of the somal protein content. Compared to traditional nano-flow liquid chromatography (nanoLC) MS, CE-MS provided higher sensitivity and faster analysis. CE-ESI-HRMS offers a viable approach for scalable single-cell proteomics.
Project description:This project used a custom-built capillary electrophoresis (CE) mass spectrometry (MS) platform to demonstrate the scalable analysis of proteins from single cells of varying sizes and protein content in different vertebrate biological models. Neural tissue fated giant cells were identified in cleavage-stage Xenopus laevis (frog) embryos, and 18 ng from its protein content was analyzed. From cultured primary neurons from the mouse, diluted samples containing ~125 pg of protein digest was measured, estimating to a portion of the somal protein content. Compared to traditional nano-flow liquid chromatography (nanoLC) MS, CE-MS provided higher sensitivity and faster analysis. CE-ESI-HRMS offers a viable approach for scalable single-cell proteomics.
Project description:A developmental, single-cell transcriptional timecourse analysis was performed on murine pancreas from embryonic days 12, 14, and 17. Whole embryonic murine pancreas tissue was dissociated to single cells, stained with a dead-cell indicator, and subjected to fluorescence activated cell sorting (FACS) to select all live cells. Single cell RNA-sequencing libraries were then subsequently generated for 4,631 cells at E12, 9,028 cells at E14 (comprised of two independent batches), and 4,635 cells at E17. Cells were sequenced at a depth of ~60,000 reads/cell (E14 Batch 1) or ~30,000 reads/cell (E12, E14, and E17 Batch 2). Identification of a novel epithelial progenitor population and multiple novel mesenchymal populations showcases the power of single cell RNA-sequencing to uncover previously uncharacterized cell types, and highlights the dynamic cellular heterogeneity of the developing pancreas.
Project description:In this study, we aimed to study the gene expression patterns at single cell level across the different cell cycle stages in mESC. We performed single cell RNA-Seq experiment on mESC that were stained with Hoechst 33342 and Flow cytometry sorted for G1, S and G2M stages of cell cycle. Single cell RNA-Seq was performed using Fluidigm C1 system and libraries were generated using Nextera XT (Illumina) kit.