Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

High-throughput tissue dissection and cell purification with digital cytometry [scRNA-Seq]

ABSTRACT: Tissue composition is a major determinant of phenotypic variation and a key factor influencing disease outcomes. Although scRNA-Seq has emerged as a powerful technique for characterizing cellular heterogeneity, it is currently impractical for large sample cohorts and cannot be applied to fixed specimens collected as part of routine clinical care. To overcome these challenges, we extended Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) into a new platform for in silico cytometry. Our approach enables the simultaneous inference of cell type abundance and cell type-specific gene expression profiles (GEPs) from bulk tissue transcriptomes. The utility of this integrated framework, called CIBERSORTx, is demonstrated in multiple tumor types, including melanoma, where single cell reference profiles are used to dissect primary clinical specimens, revealing cell type-specific signatures of driver mutations and immunotherapy response. We anticipate that digital cytometry will augment single cell profiling efforts, enabling cost-effective, high throughput tissue characterization without the need for antibodies, disaggregation, or viable cells.

ORGANISM(S): Homo sapiens

PROVIDER: GSE127471 | GEO | 2019/05/06

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

High-throughput tissue dissection and cell purification with digital cytometry [Affymetrix]

Project description:Tissue composition is a major determinant of phenotypic variation and a key factor influencing disease outcomes. Although scRNA-Seq has emerged as a powerful technique for characterizing cellular heterogeneity, it is currently impractical for large sample cohorts and cannot be applied to fixed specimens collected as part of routine clinical care. To overcome these challenges, we extended Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) into a new platform for in silico cytometry. Our approach enables the simultaneous inference of cell type abundance and cell type-specific gene expression profiles (GEPs) from bulk tissue transcriptomes. The utility of this integrated framework, called CIBERSORTx, is demonstrated in multiple tumor types, including melanoma, where single cell reference profiles are used to dissect primary clinical specimens, revealing cell type-specific signatures of driver mutations and immunotherapy response. We anticipate that digital cytometry will augment single cell profiling efforts, enabling cost-effective, high throughput tissue characterization without the need for antibodies, disaggregation, or viable cells.

2019-05-06 | GSE127462 | GEO

High-throughput tissue dissection and cell purification with digital cytometry [healthy adults]

2019-05-06 | GSE127813 | GEO

Robust enumeration of cell subsets from tissue expression profiles (BeadChip)

Project description:We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA specimens for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu). To evaluate the performance of CIBERSORT against flow cytometry, gene expression profiling was performed on a set of 20 PBMC samples comprised of adults of varying ages receiving influenza immunization (NCT01827462). These samples were analyzed by flow cytometry to enumerate several leukocyte subsets. Normalized gene expression data and accompanying flow cytometry data are available at the CIBERSORT website (http://cibersort.stanford.edu/download.php).

2015-03-29 | E-GEOD-65133 | biostudies-arrayexpress

Robust enumeration of cell subsets from tissue expression profiles (HGU133A)

Project description:We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA specimens for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu). To evaluate the performance of CIBERSORT for enumerating Tregs, RNA was extracted from PBMC samples from 6 healthy normal controls and 1 follicular lymphoma (FL) patient. All PBMC samples were also interrogated by flow cytometry for FOXP3+ Tregs.

2015-03-29 | E-GEOD-65134 | biostudies-arrayexpress

Robust enumeration of cell subsets from tissue expression profiles (HGU133Plus2)

Project description:We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA specimens for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu). To evaluate the performance of CIBERSORT, RNA was extracted from the following primary human samples: (i) 14 disaggregated lymph node biopsies from patients with follicular lymphoma (FL), (ii) pre- and/or post-immunotherapy PBMC samples from 3 patients with extranodal marginal zone lymphoma (EMZL) or diffuse large B cell lymphoma (DLBCL), and (iii) B or T cells purified from the tonsils of 5 healthy normal controls.

2015-03-29 | E-GEOD-65135 | biostudies-arrayexpress

Robust enumeration of cell subsets from tissue expression profiles (HGU133Plus2)

2015-03-29 | GSE65135 | GEO

Robust enumeration of cell subsets from tissue expression profiles (HGU133A)

2015-03-29 | GSE65134 | GEO

Robust enumeration of cell subsets from tissue expression profiles (BeadChip)

2015-03-29 | GSE65133 | GEO

Physical activity modulates macrophage phenotype in mammary tissue and inhibits tumor progression

Project description:We generated RNAseq results from the mammary tissues of mice from a 2 x 2 factorial design experiment (running wheels vs. control condition X clodronate liposomes vs. PBS liposomes) where EO771 mammary cancer cells had been engrafted in the mammary tissue for nascent tumor formation. RNAseq results were used to complete digital cytometry with the CIBERSORTx platform to analyze relative proportions of M1-like and M2-like macrophages in the mammary tissue.

2020-05-15 | GSE150620 | GEO