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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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Integrating representation learning, permutation, and optimization to detect lineage-related gene expression

Hannah M Schlüter^1,2, Caroline Uhler^3,4

¹Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA.

Nature Communications

|January 27, 2025

Summary

This summary is machine-generated.

New computational method PORCELAN identifies key genes and cell lineages driving biological processes like cancer and development. It analyzes single-cell RNA sequencing and lineage data to reveal how gene expression memory is maintained during cell division.

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Area of Science:

Computational Biology
Genomics
Developmental Biology

Background:

Recent advances in barcoding technologies enable lineage tree reconstruction alongside paired single-cell RNA sequencing (scRNA-seq).
These datasets offer a unique opportunity to study gene expression memory maintenance across lineage branching.

Purpose of the Study:

To develop a computational method for identifying lineage-informative genes and subtrees where lineage and gene expression are tightly coupled.
To provide a tool for understanding cell state memory maintenance through cell divisions.

Main Methods:

Developed Permutation, Optimization, and Representation learning based single Cell gene Expression and Lineage ANalysis (PORCELAN).
Validated PORCELAN using synthetic data.
Applied PORCELAN to paired lineage and scRNA-seq data from mouse lung cancer, mouse embryogenesis, and C. elegans embryogenesis.

Main Results:

PORCELAN successfully identified lineage-informative genes and subtrees.
The method pinpointed subtrees associated with metastasis and new cell state formation in lung cancer.
Identified genes overlapped with known lung cancer progression pathways.
Highlighted differences in gene expression memory maintenance between cancer and embryogenesis.

Conclusions:

PORCELAN is an effective tool for identifying lineage-expression coupled genes and subtrees.
The findings provide insights into cell state memory during cell division in various biological systems.
This method can advance the study of cellular dynamics in both development and disease.