Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Characterization of CD4+ and CD8+ T cells responses in the Mixed Lymphocyte Reaction by flow cytometry and single cell RNA sequencing

ABSTRACT: Characterization of CD4+ and CD8+ T cells responses in the Mixed Lymphocyte Reaction by flow cytometry and single cell RNA sequencing

PROVIDER: PRJNA1013407 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

Characterization of CD4+ and CD8+ T cells responses in the Mixed Lymphocyte Reaction by flow cytometry and single cell RNA sequencing

Project description:The Mixed Lymphocyte Reaction (MLR) consists in the allogenic co-culture of monocytes derived dendritic cells (MoDCs) with T cells from another donor. This in vitro assay is largely used for the assessment of immunotherapy compounds. Nevertheless, the phenotypic changes associated with lymphocyte responsiveness under MLR have never been thoroughly evaluated. Here, we used multiplex cytokine and chemokine assays, multiparametric flow cytometry and single cell RNA-sequencing to deeply characterize T cells activation and function in the context of CD4+- and CD8+-specific MLR kinetics. We showed that CD4+ and CD8+ T cells in MLR share common classical markers of response such as polyfunctionality, increased proliferation and CD25 expression but differ in their kinetics and amplitude of activation as well as their patterns of cytokines secretion and immune checkpoints expression. The analysis of immunoreactive KI67+CD25+ T cells identified PBK, LRR1 and MYO1G as new potential biomarkers of MLR response. Using cell-cell communication network inference and pathway analysis on single cell RNA-sequencing data, we also highlighted key components of the immunological synapse occurring between T cells and the stimulatory MoDCs together with downstream signaling pathways involved in CD4+ and CD8+ T cells activation. These results provide a deep understanding of the kinetics of the MLR assay for CD4+ or CD8+ T cells and may allow to identify new strategies for immunotherapy in cancer.

2024-01-05 | GSE242428 | GEO

Human Bone Marrow Assessment by Single Cell RNA Sequencing, Mass Cytometry and Flow Cytometry [scRNA]

Project description:New techniques for single-cell analysis have led to insights into hematopoiesis and the immune system, but the ability of these techniques to cross-validate and reproducibly identify the biological variation in diverse human samples is currently unproven. We therefore performed a comprehensive assessment of human bone marrow cells using both single-cell RNA sequencing and multiparameter flow cytometry from twenty healthy adult human donors across a broad age range. These data characterize variation between healthy donors as well as age-associated changes in cell population frequencies. Direct comparison of techniques revealed discrepancy in the quantification of T lymphocyte and natural killer cell populations. Orthogonal validation of immunophenotyping using mass cytometry demonstrated good correlation with flow cytometry. Technical replicates using single-cell RNA sequencing matched robustly, while biological replicates showed variation. Given the increasing use of single-cell technologies in translational research, this resource serves as an important reference dataset and highlights opportunities for further refinement. [Funding source] Project Number: 1ZIAHL006163-05 Contact PI / Project Leader: HOURIGAN, CHRISTOPHER Title: DETECTION, PREVENTION AND TREATMENT OF ACUTE MYELOID LEUKEMIA (AML) RELAPSE. Awardee Organization: NATIONAL HEART, LUNG, AND BLOOD INSTITUTE

2018-09-21 | GSE120221 | GEO

Intraoperative Flow Cytometry In Colorectal Cancer

Project description:This study was designed to investigate the efficacy of flow cytometry to accurately identify between normal and cancer cells in colon epithelium in humans diagnosed with colorectal cancer.

| 2406350 | ecrin-mdr-crc

Human Bone Marrow Assessment by Single Cell RNA Sequencing, Mass Cytometry and Flow Cytometry

Project description:Human Bone Marrow Assessment by Single Cell RNA Sequencing, Mass Cytometry and Flow Cytometry

| PRJNA493078 | ENA

flow cytometry sorted photosynthetic picoeukaryotes in freshwater lakes

Project description:flow cytometry sorted photosynthetic picoeukaryotes in freshwater lakes Raw sequence reads

| PRJNA419134 | ENA

Human Bone Marrow Assessment by Single Cell RNA Sequencing, Mass Cytometry and Flow Cytometry [scRNA]

Project description:Human Bone Marrow Assessment by Single Cell RNA Sequencing, Mass Cytometry and Flow Cytometry [scRNA]

| PRJNA492227 | ENA

Human Bone Marrow Assessment by Single Cell RNA Sequencing, Mass Cytometry and Flow Cytometry [bulk]

Project description:Human Bone Marrow Assessment by Single Cell RNA Sequencing, Mass Cytometry and Flow Cytometry [bulk]

| PRJNA493081 | ENA

Sorted cells from flow cytometry populations in Great Lake

Project description:Metagenomes from flow cytometry sorted pigmented cells from Laurentian Great Lakes

| PRJNA917635 | ENA

Direct sequencing of human gut virome fractions obtained by flow cytometry

Project description:Direct sequencing of human gut virome fractions obtained by flow cytometry

| PRJEB7515 | ENA

Multicolor spectral flow cytometry evaluation of peripheral blood mononuclear cells from NSCLC patients

Project description:To investigate a non-invasive strategy for immune monitoring the peripheral blood by flow cytometry, to address the critical need to itdentify predictive immunological biomarkers that correlate with treatment response Peripheral blood mononuclear cells (PBMCs) from 19 non–small-cell lung cancer (NSCLC) patients before and after ICI treatment and four healthy human donors were evaluated, utilizing spectral flow to monitor 24 immune cell markers simultaneously over the course of treatment. We performed immune cell profiling analysis using data obtained from RNA-seq of 19 different patients before and after immunotherapy, to validate the multi-color flow based immune profiling

2023-08-03 | GSE235048 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data