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Related Experiment Video

Updated: Sep 4, 2025

Profiling Individual Human Embryonic Stem Cells by Quantitative RT-PCR

Profiling Individual Human Embryonic Stem Cells by Quantitative RT-PCR

Published on: May 29, 2014

ORIGINS: A protein network-based approach to quantify cell pluripotency from scRNA-seq data.

Daniela Senra¹, Nara Guisoni¹, Luis Diambra¹

¹Centro Regional de Estudios Genómicos, Universidad Nacional de La Plata, Argentina.

|July 20, 2022

Summary

This summary is machine-generated.

We developed ORIGINS, a computational tool to quantify pluripotency in single-cell RNA sequencing (scRNA-seq) data. This method identifies stem cells, crucial for understanding cell differentiation and trajectory inference.

Keywords:

Protein-protein interaction networks Stem cells Trajectory inference scRNA-seq

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Area of Science:

Computational biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) is vital for studying cellular processes.
Determining the origin (stem or progenitor cells) of cell trajectories is essential but challenging.
Existing methods for pluripotency quantification have limitations.

Purpose of the Study:

To introduce ORIGINS, a novel computational tool for quantifying cell pluripotency from scRNA-seq data.
To provide a method for identifying stem and progenitor cells, enabling accurate trajectory inference.
To offer an efficient and memory-saving alternative to existing tools.

Main Methods:

ORIGINS utilizes a protein-protein interaction (PPI) network relevant to differentiation.
It integrates the PPI network with gene expression data to compute a 'differentiation activity' score.
The R package is benchmarked against LandSCENT and CytoTRACE using human datasets (breast, colon, hematopoietic, lung).

Main Results:

ORIGINS effectively quantifies pluripotency across various human tissues.
The tool demonstrates superior efficiency compared to LandSCENT.
ORIGINS requires less RAM memory than comparable methods, making it more accessible.

Conclusions:

ORIGINS provides a robust and efficient method for assessing cell pluripotency from scRNA-seq data.
This tool facilitates accurate identification of stem cells, improving trajectory inference.
The freely available R package offers a valuable resource for the research community.