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Related Experiment Video

Updated: Oct 11, 2025

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Published on: February 2, 2024

Isoform-level quantification for single-cell RNA sequencing.

Lu Pan¹, Huy Q Dinh^2,3, Yudi Pawitan¹

¹Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden.

Bioinformatics (Oxford, England)

|December 5, 2021

Summary

This summary is machine-generated.

Scasa enables isoform-level quantification for single-cell RNA sequencing, revealing cellular subsets missed by gene-level analysis. This method improves biomarker discovery and biological insights from high-throughput sequencing data.

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Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

Area of Science:

Single-cell genomics
Transcriptomics
Bioinformatics

Background:

Gene-level RNA quantification in single-cell RNA sequencing (scRNA-seq) limits biological insights.
Existing scRNA-seq protocols, like 10x Genomics, exhibit 3' bias, hindering comprehensive transcript analysis.
Isoform-level expression provides richer biological information, potentially uncovering novel cellular subsets and biomarkers.

Purpose of the Study:

To develop a novel method for isoform-level quantification in high-throughput scRNA-seq.
To compare the performance of the new method against existing gene-level and isoform-level quantification tools.
To demonstrate the utility of isoform-level analysis in identifying previously undetected cellular populations.

Main Methods:

Developed Scasa, a method leveraging transcription clusters and isoform paralogs for isoform-level quantification.
Performed simulations to compare Scasa with Alevin, Cellranger, Kallisto, Salmon, Terminus, and STARsolo.
Reanalyzed a CITE-Seq dataset using Scasa for isoform-level expression analysis.

Main Results:

Scasa demonstrated comparable or superior performance to existing methods in simulations at both isoform and gene levels.
Isoform-based analysis with Scasa identified a distinct subgroup of CD14 monocytes.
This CD14 monocyte subgroup was not detectable using traditional gene-level quantification methods.

Conclusions:

Scasa provides accurate isoform-level quantification for scRNA-seq data.
Isoform-level analysis is crucial for comprehensive cellular subset identification and biomarker discovery.
Scasa enhances the biological insights obtainable from scRNA-seq, particularly in complex biological samples.