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RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

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GEMLI: Gene Expression Memory-Based Lineage Inference from Single-Cell RNA-Sequencing Datasets.

A S Eisele¹, D M Suter²

¹Ecole Polytechnique Fédérale de Lausanne, School of Life Sciences, Institute of Bioengineering, Lausanne, Switzerland. almut.eisele@epfl.ch.

Methods in Molecular Biology (Clifton, N.J.)

|January 2, 2025

Summary

Gene expression memory-based lineage inference (GEMLI) predicts cell lineages from single-cell RNA-sequencing data. This computational tool analyzes gene expression patterns to trace cell development without physical separation, easing lineage annotation.

Keywords:

Cell family Clonality Fate decisions Gene expression memory Gene expression stability Lineage inference Lineage tracing Memory genes Monotonically expressed genes Multicellular structure

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Last Updated: May 7, 2025

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Area of Science:

Computational Biology
Genomics
Developmental Biology

Background:

Traditional lineage tracing methods rely on genetic marks or physical cell separation.
Single-cell RNA-sequencing (scRNA-seq) offers a high-throughput approach to study cellular heterogeneity.
Predicting cell lineages from scRNA-seq data is crucial for understanding development and disease.

Purpose of the Study:

To introduce and detail the usage of the Gene expression memory-based lineage inference (GEMLI) R package.
To demonstrate how GEMLI predicts cell lineages using gene expression memory from scRNA-seq data.
To provide a protocol for analyzing lineage information, including gene expression memory and cell fate decisions.

Main Methods:

GEMLI utilizes the principle of gene expression memory, where gene expression levels are maintained through cell divisions.
The study provides a detailed protocol for using the GEMLI R package on scRNA-seq gene expression matrices.
Methods cover parameter adjustment, lineage information extraction, visualization, and fine-tuning.

Main Results:

GEMLI enables lineage prediction solely from scRNA-seq data, shifting away from experimental methods.
The tool facilitates the study of cell lineages in development, homeostasis, regeneration, and disease.
GEMLI can dissect cell type-specific gene expression memory, discriminate cell fate decisions, and reconstruct multicellular structures.

Conclusions:

GEMLI offers a powerful computational approach to infer cell lineages, greatly easing and expanding lineage annotation.
The R package is versatile, applicable to various biological contexts and scRNA-seq data types.
GEMLI is particularly promising for identifying small lineages in human samples where other methods are not applicable.