Testing for Phylogenetic Signal in Single-Cell RNA-Seq Data

Moravec, Jiří C, Lanfear, Robert, Spector, David L, Diermeier, Sarah D, Gavryushkin, Alex (December 2022) Testing for Phylogenetic Signal in Single-Cell RNA-Seq Data. Journal of Computational Biology. ISSN 1066-5277

URL: https://www.ncbi.nlm.nih.gov/pubmed/36475926
DOI: 10.1089/cmb.2022.0357


Phylogenetic methods are emerging as a useful tool to understand cancer evolutionary dynamics, including tumor structure, heterogeneity, and progression. Most currently used approaches utilize either bulk whole genome sequencing or single-cell DNA sequencing and are based on calling copy number alterations and single nucleotide variants (SNVs). Single-cell RNA sequencing (scRNA-seq) is commonly applied to explore differential gene expression of cancer cells throughout tumor progression. The method exacerbates the single-cell sequencing problem of low yield per cell with uneven expression levels. This accounts for low and uneven sequencing coverage and makes SNV detection and phylogenetic analysis challenging. In this article, we demonstrate for the first time that scRNA-seq data contain sufficient evolutionary signal and can also be utilized in phylogenetic analyses. We explore and compare results of such analyses based on both expression levels and SNVs called from scRNA-seq data. Both techniques are shown to be useful for reconstructing phylogenetic relationships between cells, reflecting the clonal composition of a tumor. Both standardized expression values and SNVs appear to be equally capable of reconstructing a similar pattern of phylogenetic relationship. This pattern is stable even when phylogenetic uncertainty is taken in account. Our results open up a new direction of somatic phylogenetics based on scRNA-seq data. Further research is required to refine and improve these approaches to capture the full picture of somatic evolutionary dynamics in cancer.

Item Type: Paper
Subjects: bioinformatics > genomics and proteomics > annotation
diseases & disorders > cancer
diseases & disorders
bioinformatics > genomics and proteomics > annotation > gene expression profiling annotation
bioinformatics > genomics and proteomics
Investigative techniques and equipment
diseases & disorders > neoplasms
Investigative techniques and equipment > assays
Investigative techniques and equipment > assays > RNA-seq
Investigative techniques and equipment > assays > Single cell sequencing
CSHL Authors:
Communities: CSHL Cancer Center Program
CSHL Cancer Center Shared Resources > Animal Services
CSHL Cancer Center Shared Resources > Flow Cytometry Service
CSHL Cancer Center Shared Resources > Functional Genomics and Genetics Service
CSHL labs > Spector lab
CSHL Cancer Center Program > Gene Regulation and Inheritance Program
SWORD Depositor: CSHL Elements
Depositing User: CSHL Elements
Date: 6 December 2022
Date Deposited: 19 Dec 2022 20:01
Last Modified: 02 May 2024 19:12
PMCID: PMC10125402
URI: https://repository.cshl.edu/id/eprint/40774

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