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Updated: Apr 15, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Robust enumeration of cell subsets from tissue expression profiles.

Aaron M Newman¹, Chih Long Liu¹, Michael R Green²

¹1] Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California, USA. [2] Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, California, USA.

|March 31, 2015

Summary

This summary is machine-generated.

We developed CIBERSORT, a new method to identify cell types in tissues using gene expression data. This tool accurately distinguishes various immune cells in complex samples, outperforming existing methods for biomarker discovery.

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Last Updated: Apr 15, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Area of Science:

Bioinformatics
Molecular Biology
Genomics

Background:

Complex tissues contain diverse cell types.
Gene expression profiles offer insights into cellular composition.
Accurate cell type enumeration is crucial for biological research.

Purpose of the Study:

Introduce CIBERSORT, a novel computational method.
Characterize immune cell composition in complex tissues using gene expression data.
Enable large-scale analysis for cellular biomarkers and therapeutic targets.

Main Methods:

Developed CIBERSORT algorithm for cell composition analysis.
Applied CIBERSORT to RNA mixtures from various tissue types (fresh, frozen, fixed).
Evaluated performance against other methods for hematopoietic subset enumeration.

Main Results:

CIBERSORT accurately identified cell compositions in complex tissue RNA mixtures.
Outperformed existing methods in handling noise and unknown mixture components.
Demonstrated superior performance in distinguishing closely related cell types.

Conclusions:

CIBERSORT is a robust method for deconvolution of complex tissue samples.
Facilitates large-scale identification of cellular biomarkers.
Supports the discovery of novel therapeutic targets through precise cell profiling.