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

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

2019-05-06 | GSE127471 | GEO

High-throughput tissue dissection and cell purification with digital cytometry

Project description:This SuperSeries is composed of the SubSeries listed below.

2019-05-06 | GSE127472 | GEO

Digital cytometry of leukocyte landscapes reveals shared correlates of rejection across organ transplants

Project description:Digital cytometry of leukocyte landscapes reveals shared correlates of rejection across organ transplants

| PRJNA1526486 | ENA

Digital cytometry of leukocyte landscapes reveals shared correlates of rejection across organ transplants

Project description:Background. Improved understanding of the cellular mechanisms of organ transplant rejection is needed for developing targeted therapies to improve outcomes. We used digital cytometry to estimate the digital leukocyte entities (DLE) shared by rejection in biopsies of kidney, heart, lung, and liver organ transplants. Methods. We used CIBERSORTx with the LM22 leukocyte reference to estimate associations between 22 DLEs with rejection in 8422 transplant biopsies - 4898 kidney, 2361 heart, 625 lung, and 538 liver. Results. In all organs, rejection correlated with an increase in DLEs designated as classically activated macrophages and a decrease in DLEs designated regulatory T cells. T cell-mediated rejection (TCMR) was associated with a massive increase in otherwise rare T cell DLEs designated memory-activated CD4 and Tγδ, as well as follicular-helper CD4 and CD8. TCMR also had increased DLE designated plasma cells, compatible with a role for the B cell lineage in antigen presentation in TCMR. Antibody-mediated rejection (ABMR) in kidney and heart transplant biopsies correlated with increased DLEs representing activated mast cells, activated NK cells, and monocytes. Some findings were validated by single-cell/nucleus RNAseq data from kidney and liver biopsies. Conclusion. The shared correlations of DLEs with rejection in all organs - e.g. CD4ma with TCMR and activated mast cells with ABMR - suggest novel aspects of rejection. These findings can guide the search for the in vivo leukocytes that correspond with the digital entities and potential clinical applications.

2026-09-11 | GSE346749 | GEO

High-throughput SuperSAGE for digital gene expression analysis of multiple samples using Next Generation Sequencing

Project description:We established a protocol of the SuperSAGE technology combined with next-generation sequencing, coined “High-Throughput (HT-) SuperSAGE”. SuperSAGE is a method of digital gene expression profiling that allows isolation of 26-bp tag fragments from expressed transcripts. In the present protocol, index (barcode) sequences are employed to discriminate tags from different samples. Such barcodes permit to enable researchers to analyze digital tags from many transcriptomes of many samples in a single sequencing run by simply pooling the libraries. Here, we demonstrated that HT-SuperSAGE provided highly sensitive, reproducible and accurate digital gene expression data. By increasing throughput for analysis in HT-SuperSAGE, various applications were expected and several examples of its applications were introduced in the present study, including analyses of laser-microdissected cells, biological replicates or tag extraction using different anchoring enzymes.

2010-10-01 | GSE20682 | GEO

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