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Updated: Jan 21, 2026

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Single-Cell Transcriptome Analysis of T Cells.

Willem Van Der Byl^1,2, Simone Rizzetto^1,2, Jerome Samir^1,2

¹Kirby Institute for Infection and Immunity, The University of New South Wales, Sydney, NSW, Australia.

Methods in Molecular Biology (Clifton, N.J.)

|August 10, 2019

Summary

This summary is machine-generated.

This study presents a reproducible workflow for analyzing single-cell RNA sequencing (scRNA-seq) data from T cells. It details experimental and computational methods for identifying T cell subsets and functions.

Keywords:

Alignment Clustering Differential gene expression Gene expression matrix Single-cell RNA sequencing T cell receptor reconstruction T cells scRNA-seq

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Transcriptome Analysis of Single Cells

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Published on: April 25, 2011

Isolation and Transcriptome Analysis of Plant Cell Types

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Published on: April 7, 2023

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

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Published on: March 29, 2024

Area of Science:

Immunology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) is crucial for understanding T cell heterogeneity.
Identifying novel T cell subsets and functions is essential in immunology.

Purpose of the Study:

To provide a detailed, reproducible experimental and computational workflow for scRNA-seq analysis of T cells.
To facilitate the identification of T cell subsets and their functions using scRNA-seq data.

Main Methods:

Focuses on plate-based sorted T cells using flow cytometry.
Utilizes full-length transcriptome protocols like Smart-Seq2.
The pipeline is adaptable to other high-throughput methods, including UMI-based approaches.

Main Results:

A detailed bioinformatics pipeline for scRNA-seq data analysis is described.
The workflow is designed for ease of reproduction by researchers.
The study addresses current limitations and future directions in T cell scRNA-seq.

Conclusions:

The presented workflow enables robust scRNA-seq analysis of T cells.
This resource aids in the discovery of novel T cell populations and functions.
The methodology supports advancements in T cell biology research.