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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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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Digitaldlsorter: Deep-Learning on scRNA-Seq to Deconvolute Gene Expression Data.

Carlos Torroja¹, Fatima Sanchez-Cabo¹

¹Bioinformatics Unit, Fundación Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain.

Frontiers in Genetics

|November 12, 2019

Summary

This summary is machine-generated.

This study introduces a Deep Learning method to quantify immune cell infiltration in bulk RNA-Seq data from breast and colorectal cancers using single-cell RNA sequencing. The approach accurately identifies various immune cell subtypes and predicts patient survival, advancing personalized cancer therapies.

Keywords:

Cancer Deconvolution algorithm Machine learning immunology single-cell

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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

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Published on: March 12, 2021

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: March 29, 2024

Area of Science:

Genomics and Bioinformatics
Immunology
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-Seq) offers unprecedented resolution into cellular heterogeneity and disease mechanisms.
Bulk RNA sequencing (RNA-Seq) remains prevalent due to cost and complexity of scRNA-Seq, yet it obscures cell-type-specific changes.
Understanding immune cell infiltration is crucial for disease progression and immunotherapy efficacy.

Purpose of the Study:

To develop a Deep Learning (DL) based method for enumerating and quantifying immune cell infiltration in bulk RNA-Seq samples using scRNA-Seq data.
To enable precise quantification of diverse immune cell subpopulations within the tumor microenvironment.
To leverage DL for improved analysis of high-dimensional single-cell data in cancer research.

Main Methods:

A Deep Neural Network (DNN) model was trained using scRNA-Seq data to deconvolve bulk RNA-Seq samples.
The DNN quantifies major immune cell types (lymphocytes, CD8+, CD4 Tmem, CD4Th, CD4Tregs, B-cells) and stromal content.
Signatures were derived from tumor-specific scRNA-Seq data to reflect the tumor microenvironment accurately.

Main Results:

The DL method accurately quantified immune cell infiltration in both synthetic and TCGA bulk RNA-Seq datasets.
The model demonstrated high accuracy in identifying specific immune cell subpopulations.
Quantification of immune infiltration using this method showed strong correlation with survival prediction.

Conclusions:

The developed DL method provides a robust approach to analyze immune infiltration from bulk RNA-Seq data, overcoming limitations of traditional methods.
This technique enhances the utility of scRNA-Seq for understanding disease complexity and developing personalized immunotherapies.
Accurate immune cell quantification has significant implications for predicting patient outcomes and guiding treatment strategies in cancer.