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

Area of Science:

• Genomics
• Computational Biology
• Cancer Research

Background:

• Phylogenetic methods are crucial for understanding cancer evolution, tumor heterogeneity, and progression.
• Current methods primarily rely on bulk whole genome sequencing or single-cell DNA sequencing, focusing on copy number alterations and single nucleotide variants (SNVs).
• Single-cell RNA sequencing (scRNA-seq) is widely used for gene expression analysis but faces challenges in SNV detection due to low yield and uneven coverage.

Purpose of the Study:

• To demonstrate the utility of scRNA-seq data for phylogenetic analyses in cancer.
• To compare the effectiveness of using gene expression levels versus SNVs derived from scRNA-seq for reconstructing cancer cell phylogenies.
• To assess the stability of phylogenetic patterns derived from scRNA-seq data.

Main Methods:

• Phylogenetic analyses were performed on scRNA-seq data.
• Comparisons were made between phylogenies reconstructed using standardized expression values and SNVs called from the same scRNA-seq data.
• Phylogenetic uncertainty was considered to evaluate the stability of the results.

Main Results:

• scRNA-seq data contain sufficient evolutionary signal for phylogenetic analysis.
• Both standardized expression values and SNVs derived from scRNA-seq are effective in reconstructing phylogenetic relationships between cancer cells.
• The reconstructed phylogenetic patterns accurately reflect tumor clonal composition and remain stable even with phylogenetic uncertainty.

Conclusions:

• scRNA-seq data can be successfully utilized for somatic phylogenetics, opening a new avenue for cancer research.
• This approach provides a complementary method to existing sequencing techniques for studying cancer evolutionary dynamics.
• Further research is needed to optimize these methods for a comprehensive understanding of cancer evolution.