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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: May 16, 2025

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Transcriptome-wide association studies at cell state level using single-cell eQTL data.

Guanghao Qi, Eardi Lila, Zhicheng Ji

Medrxiv : the Preprint Server for Health Sciences

|April 1, 2025

Summary

This summary is machine-generated.

We developed TWiST, a new method for transcriptome-wide association studies (TWAS) that analyzes gene-disease links at the cell state level. This approach improves power by accounting for cell type heterogeneity, offering new insights into disease genetics.

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Last Updated: May 16, 2025

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Published on: January 7, 2020

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

Area of Science:

Genetics
Computational Biology
Immunology

Background:

Transcriptome-wide association studies (TWAS) are crucial for identifying genes linked to diseases.
Existing TWAS methods often overlook crucial cellular heterogeneity by analyzing bulk or pseudobulk tissues.
This limitation hinders the precise understanding of gene-disease associations within complex biological systems.

Purpose of the Study:

To introduce TWiST, a novel statistical method for TWAS that operates at single-cell resolution.
To leverage single-cell expression quantitative trait loci (eQTL) data for more granular genetic association analyses.
To enable the detection of dynamic and nonlinear gene effects across cell states.

Main Methods:

TWiST utilizes pseudotime to define and analyze distinct cell states.
The method models gene expression effects on traits as continuous pseudotemporal curves.
It allows for flexible hypothesis testing, including global, dynamic, and nonlinear associations.

Main Results:

Simulations and real-world data analyses confirm TWiST's superior statistical power over traditional pseudobulk methods.
TWiST effectively captures cell state heterogeneity, leading to more accurate gene prioritization.
Application to the OneK1K study revealed numerous genes with dynamic effects on autoimmune diseases during immune cell differentiation.

Conclusions:

TWiST significantly enhances the power of TWAS by incorporating cell state information from single-cell eQTL data.
The method provides a powerful framework for dissecting complex genetic architectures of diseases.
TWiST holds substantial promise for advancing our understanding of disease genetics through single-cell analyses.