Cb0eb12f-8a50-45f3-b426-afb7347cd6d2 - samples
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ABSTRACT:
PROVIDER: EGAD00001011171 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) used to dissect myeloid cell identities and states in murine experimental gliomas (GL261).
2022-12-06 | GSE220118 | GEO
Project description:We used cellular indexing of transcriptomes and epitopes-based single cell RNA sequencing (CITE-scRNAseq) coupled with adoptive transfer approaches to determine tissue contribution to neutrophil diversity.
2020-07-15 | GSE154406 | GEO
Project description:Single-cell RNA-sequencing (scRNA-Seq) is widely used to characterize immune cell populations. However, mRNA levels correlate poorly with expression of surface proteins, which are well established to define immune cell types. CITE-Seq (cellular indexing of transcriptomes and epitopes by sequencing) utilizes oligonucleotide-tagged antibodies to simultaneously analyze surface phenotypes and transcriptomes. Considering the high costs of adding surface phenotyping to scRNA-Seq, we aimed to determine which of 188 tested CITE-Seq antibodies can detect their antigens on human peripheral blood mononuclear cells (PBMCs), a commonly interrogated cell population in immunology, and find the optimal concentration for staining. The recommended concentration was optimal for 76 antibodies, whereas staining quality of 7 antibodies improved when the concentration was doubled. 33 and 8 antibodies still worked well when the concentration was reduced to 1/5 or 1/25, respectively. 64 antigens were not detected at any antibody concentration. Optimizing the antibody panel by removing antibodies not able to detect their target antigens and adjusting concentrations of the remaining antibodies could enable a cost reduction of almost 50%. In conclusion, our data are a resource for building an informative and cost-effective panel of CITE-Seq antibodies and use them at their optimal concentrations in future CITE-seq experiments on human PBMCs.
2022-09-18 | GSE213282 | GEO
Project description:Acute myeloid leukemia with complex karyotype (CK-AML) is characterized by three or more chromosomal aberrations, and comprises 10–12% of AML patients. It is associated with complex chromosomal rearrangements, intra-tumor heterogeneity, therapy resistance and poor overall survival. We aimed to transcriptionally characterize CK-AML by performing RNA sequencing on blasts from 4 CK-AML patient samples.
2024-09-20 | E-MTAB-14420 | biostudies-arrayexpress
Project description:Droplet-based single cell transcriptome sequencing (scRNA-seq) technology is able to measure the gene expression from tens of thousands of single cells simultaneously. More recently, coupled with the cutting-edge Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq), the droplet-based system has allowed for immunophenotyping of single cells based on cell surface expression of specific proteins together with simultaneous transcriptome profiling in the same cell. In this study, we developed BREM-SC, a novel Bayesian Random Effects Mixture model that jointly clusters paired single cell transcriptomic and proteomic data, which will greatly facilitate researchers to jointly study transcriptome and surface proteins at the single cell level to make new biological discoveries.
2020-05-18 | GSE148665 | GEO
Project description:We performed cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) of the LSK compartment of TdT-reporter and TdT-fate mapping mice, in order to elucidate the heterogeneity and developmental trajectories within the MPP (lymphoid-primed multipotent progenitor) and HSC (hematopoietic stem cell) compartments.
2022-03-27 | GSE145491 | GEO
Project description:The identity and functions of specialized cell types are dependent on the complex interplay between signaling and transcriptional networks. Recently single-cell technologies such as CITE-seq have been developed that enable simultaneous quantitative analysis of cell-surface receptor expression with transcriptional states. To date, these datasets have not been used to systematically develop cell-context-specific maps of the interface between signaling and transcriptional regulators orchestrating cellular identity and function. We present SPaRTAN (Single-cell Proteomic and RNA based Transcription factor Activity Network), a computational method to link cell-surface receptors to transcription factors (TFs) by exploiting cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) datasets with cis-regulatory information. SPaRTAN is applied to immune cell types in the blood to predict the coupling of signaling receptors with cell context-specific TFs. The predictions are validated by prior knowledge and flow cytometry analyses. SPaRTAN is then used to predict the signaling coupled TF states of tumor infiltrating CD8+ T cells in malignant peritoneal and pleural mesotheliomas. SPaRTAN greatly enhances the utility of CITE-seq datasets to uncover TF and cell-surface receptor relationships in diverse cellular states.
2021-08-16 | GSE172155 | GEO
Project description:The recently developed transcription, epitopes, and chromatin accessibility by sequencing (TEA-seq) and similar DOGMA-seq single-cell trimodal omics assays provide unprecedented opportunities for understanding cell biology, but independent optimization, benchmarking and evaluation are lacking. We explored the utility, pros and cons of DOGMA-seq compared to the bimodal cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) assay in activated and stimulated human peripheral blood T cells. We identified an optimal incubation time and concentration of digitonin (DIG) for cell permeabilization and found that single-cell trimodal omics measurements after DIG permeabilization were generally better than after an alternative “low-loss lysis” (LLL) permeabilization condition. Next, we found that DOGMA-seq with optimized DIG permeabilization and its ATAC library provides more information, even though its mRNA and cell surface protein antibody-derived tag (ADT) libraries have slightly inferior quality, compared to CITE-seq. Finally, we recognized the additional value of DOGMA-seq for studying lineage-specific T helper cells.
2022-06-13 | GSE200417 | GEO
Project description:The advent of tyrosine kinase inhibitors (TKIs) as treatment of Chronic Myeloid Leukemia (CML) is a paradigm in molecularly targeted cancer therapy. Nonetheless, TKI insensitive leukemia stem cells (LSCs) persist in most patients, even after years of treatment. Here, we used cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) to generate high-resolution single cell multiomics maps from CML patients stratified according to molecular response (BCR-ABL IS %) following 12 months of TKI therapy. Simultaneous measurement of global gene expression profiles together with >40 surface markers from the same cells revealed that each patient harbored a unique composition of stem and progenitor cells at diagnosis demonstrating that cellular heterogeneity is a hallmark of CML. The patients with treatment failure at 12 months of therapy had markedly higher abundance of molecularly defined primitive cells at diagnosis compared to the optimal responders. The multi-modal feature landscape enabled visualization of the primitive fraction as a mix of Lin-CD34+38-/low BCR-ABL1+ LSCs and BCR-ABL1- HSCs in variable ratio across patients and guided their prospective isolation by a combination of CD26 and CD35 cell surface markers.
2024-01-17 | GSE236233 | GEO
Project description:We exploited the extensive genomic diversity of the Leucegene cohort of primary human AML specimens to provide an overview of the human AML surfaceome. Due to high cell number requirements, surface proteomics has been underexploited in AML so far, although surface proteome analysis of AML cell lines and small cohorts of primary human AML specimens paved the way for antigen identification23-25. Herein, we compared global and surface proteomic datasets generated from primary human AML specimens and show that surfaceome analysis uniquely identifies a larger subset of cell surface proteins compared to global proteomics. We therefore built a cohort of 100 primary human AML specimens that was subjected to surface proteome analysis and served as a primary dataset for antigen identification. A significant portion of the cohort also underwent single-cell RNA sequencing, which allowed the exploration of antigen expression at the population level and the selection of AML antigens expressed by primitive blasts. These analyses led to the identification of novel AML antigens expressed by the majority of AML specimens of the cohort, of antigens overexpressed by specific AML subgroups, as well as of previously uncovered potential leukemia stem cell (LSC) markers, and represents the first large-scale surface proteomic study in AML.
2024-05-21 | PXD044480 | Pride