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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 microarray-based...

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An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

Published on: May 23, 2018

Inferring single cell expression profiles from overlapped pooling sequencing data with compressed sensing strategy.

Mengting Huang¹, Yixuan Yang¹, Xingzhao Wen¹

¹State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.

Nucleic Acids Research

|July 10, 2021

Summary

This summary is machine-generated.

High-throughput single-cell RNA sequencing (scRNA-seq) costs can be reduced by compressing expression profiles. This study demonstrates a novel pooling strategy and compressed sensing to infer cell profiles, saving costs while maintaining data quality.

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) generates valuable gene expression data but faces high costs for large-scale studies.
The inherent sparsity of single-cell expression profiles allows for data compression, presenting a potential solution to reduce costs.

Purpose of the Study:

To develop and validate a computational method for compressing and inferring single-cell expression profiles.
To reduce the cost of large-scale scRNA-seq experiments while preserving data accuracy and sensitivity.

Main Methods:

Computational simulation and experimental validation using 54 single cells.
Overlapped pooling strategy to assign cells into multiple groups.
Compressed sensing techniques to infer individual cell expression profiles from pooled data.

Main Results:

Expression profiles can be accurately inferred from pooled data using the proposed overlapped pooling and compressed sensing strategy.
Combining the method with plate-based scRNA-seq maintains gene detection sensitivity and individual cell identity.
The approach achieves approximately 50% reduction in library costs compared to traditional methods.

Conclusions:

This novel pooling and compressed sensing method offers a cost-effective solution for large-scale scRNA-seq.
The technique preserves essential data quality metrics like sensitivity and individual identity.
This approach has implications for improving measurement, storage, and computation of other compressible biological signals.