Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1137569 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Single cell transcriptomics have revolutionized fundamental understanding of basic biology and disease. Since transcripts often do not correlate with protein expression, it is paramount to complement transcriptomics approaches with proteome analysis at single cell resolution. Despite continuous technological improvements in sensitivity, mass spectrometry-based single cell proteomics ultimately faces the challenge of reproducibly comparing the protein expression profiles of thousands of individual cells. Here, we combine two hitherto opposing analytical strategies, DIA and Tandem-Mass-Tag (TMT)-multiplexing, to generate highly reproducible, quantitative proteome signatures from ultra-low input samples. While conventional, data-dependent shotgun proteomics (DDA) of ultra-low input samples critically suffers from the accumulation of missing values with increasing sample-cohort size, data-independent acquisition (DIA) strategies do usually not to take full advantage of isotope-encoded sample multiplexing. We also developed a novel, identification-independent proteomics data analysis pipeline to quantitatively compare DIA-TMT proteome signatures across hundreds of samples independent of their biological origin to identify cell types and single protein knockouts. We validate our approach using integrative data analysis of different human cell lines and standard database searches for knockouts of defined proteins. These data establish a novel and reproducible approach to markedly expand the numbers of proteins one detects from ultra-low input samples, such as single cells.
2021-05-17 | PXD023574 | Pride
Project description:Using a mouse model of conditional and inducible in vivo fluorescent myonuclear labeling (HSA-GFP), sorting purification of nuclei, low-input reduced representation bisulfite sequencing (RRBS), and a translatable and reversible model of exercise (progressive weighted wheel running, PoWeR), we provide the first nucleus type-specific epigenetic information on skeletal muscle adaptation and detraining along with whole muscle transcriptomics.
2021-12-08 | GSE180877 | GEO
Project description:This submission is a dataset of two modalities, single-nucleus transcriptomics and single-nuclei spatial transcriptomics in the spinal cords of mice. The single-nuclei transcriptomics data is harvested and profiled using 10x Genomics Chromium Single Cell Kit Version. The single-nuclei spatial transcriptomics data is harvested and profiled using Visium Spatial Gene Expression.
2024-01-30 | GSE234774 | GEO
Project description:Current approaches to profile the single-cell transcriptomics of human pancreatic endocrine cells almost exclusively rely on freshly isolated islets. However, human islets are limited in availability. Furthermore, the extensive processing steps during islet isolation and subsequent single cell dissociation might alter gene expressions. In this work, we cross-compared five nuclei isolation protocols and selected the citric acid method as the best strategy to isolate nuclei with high RNA integrity and low cytoplasmic contamination from human pancreata. We innovated fluorescence-activated nuclei sorting (FANS) based on the positive signal of NKX2-2 antibody to enrich for nuclei of the endocrine population from the entire nuclei pool of the pancreas. Our sample preparation procedure generated high-quality single-nucleus gene-expression libraries while preserving the endocrine population diversity. We observed comparable endocrine cellular composition and cell type signature gene expression between our snRNA-seq libraries and conventional scRNA-seq libraries generated with live cells from freshly isolated human islets. Our work fills a technological gap and helps to unleash archival pancreatic tissue for molecular profiling targeting the endocrine population. We expect that our protocol can be used to enrich nuclei for transcriptomics study from various populations in the pancreas and in different organs/tissues.
2024-04-01 | GSE252614 | GEO
Project description:Single cell studies have transformed our understanding of cellular heterogeneity in disease but the need for fresh starting material can be an obstacle, especially in the context of international multicenter studies and archived tissue. We developed a protocol to obtain high-quality cells and nuclei from dissected human skeletal muscle archived in the preservative Allprotect® Tissue Reagent. After fluorescent imaging microscopy confirmed intact nuclei, we performed four protocol variations that compared sequencing metrics between cells and nuclei enriched by either filtering or flow cytometry sorting. Cells and nuclei (either sorted or filtered) produced statistically identical transcriptional profiles and recapitulated 8 cell types present in skeletal muscle. Flow cytometry sorting successfully enriched for higher-quality cells and nuclei but resulted in an overall decrease in input material. Our protocol provides an important resource for obtaining high-quality single cell genomic material from archived tissue and to streamline global collaborative efforts.
2024-11-21 | GSE282362 | GEO
Project description:To evaluate the impacts of X-chromosome reactivation on repressive H3K27me3 chromatin marks, allele-specific low input CUT&Run experiments were conducted starting from genomic DNA obtained from FACS-sorted primordial germ cells.
2025-01-21 | GSE243464 | GEO
Project description:Single Cell Transcriptome Conservation in Cryopreserved Cells and Tissues
| PRJNA338759 | ENA
Project description:Existing protocols are difficult to obtain high-quality sequencing data for isolating nuclei from long-term cryopreserved frozen tissues, especially such as hearts that contain rich fibrous connective tissue for single-nucleus multiome sequencing (snMultiome-seq, including snRNA-seq and snATAC-seq for the same nucleus). Here, we optimize some current methods for isolating nuclei from frozen tissues and describe an easy-to-operating, prompt, and inexpensive method for obtaining high-quality single-nucleus sequencing data, which is named as Douncer-Filter-Gradient-Centrifugation (DFGC). This protocol takes about 1.5h to complete, including mincing (1min), douncing (3min), filtering (20min) and density gradient centrifugating (40min), depending on how many samples are done at the same time, and does not require special reagents and instruments, so it is versatile and economical for most laboratories. We selected two commonly used isolating nuclei methods, microBeads- and FACS-, as controls and all analysis processes are carried out in accordance with unified standardization, including nucleus morphology, library construction, library quality inspection, data parameters, clustering, cell-type identification, and gene expression distribution. This protocol provides a step-by-step guide to nuclei isolating from frozen heart tissues to generate high-quality single nucleus gene expression and chromatin transposase accessibility sequencing data for the same nucleus.
2025-01-01 | GSE281085 | GEO
Project description:Endosperm is an essential seed tissue with a unique epigenetic landscape. During endosperm development, differential epigenetic regulation of the maternal and paternal genomes plays important roles in regulating gene expression, especially at imprinted genes. Profiling the endosperm epigenetic landscape on a genome-wide scale is challenging due to its small size, mode of development, and close association with maternal tissue. Here, we applied a low input chromatin profiling method, CUT&RUN (cleavage under targets and release using nuclease), to profile parental-specific chromatin modifications using low numbers of Arabidopsis endosperm nuclei. We demonstrate that CUT&RUN generates genome-wide H3K27me3 landscapes with high sensitivity, specificity and reproducibility using around 20,000 endosperm nuclei purified by flow cytometry and fluorescence-activated cell sorting. H3K27me3 peaks identified by CUT&RUN and previous ChIP (chromatin immunoprecipitation) approaches were largely overlapping, with some distinctions in heterochromatin. The versatility and simplicity of CUT&RUN makes it a viable alternative to ChIP, which requires greater amounts of starting material, and will enable the study of tissue or even cell-type specific epigenomes in Arabidopsis and other plant species.
2019-02-22 | GSE123602 | GEO