Cell sorting based on single nucleotide variation enables characterization of mutation-dependent transcriptome and...

Area of Science:

• Genomics
• Molecular Biology
• Cancer Research

Background:

• Point mutations in oncogenes and tumor suppressor genes drive tumorigenesis.
• Single nucleotide variants (SNVs) in noncoding regions also impact cancer development.
• Linking SNVs to single-cell transcriptomic and epigenetic changes is challenging.

Purpose of the Study:

• To develop a method for isolating and profiling rare cell populations based on specific genomic alterations.
• To enable the study of transcriptomic and epigenetic heterogeneity associated with noncoding SNVs.
• To provide a tool for connecting genotype to phenotype at the single-cell level in cancer.

Main Methods:

• Developed STAR-FACS (Specific-To-Allele polymerase chain reaction - fluorescence-activated cell sorting) for allele-specific cell labeling via in-cell DNA amplification.
• Sorted labeled cells for downstream analysis using bulk or single-cell transcriptomics.
• Validated compatibility with CUT&Tag for chromatin feature characterization.

Main Results:

• STAR-FACS successfully separated cells based on TERT promoter mutation status.
• The method is applicable to primary cell lines and dissociated solid tumor tissue.
• Distinct transcriptional programs were observed in glioblastoma cells with different TERT promoter SNVs.

Conclusions:

• STAR-FACS is a novel tool for isolating cells based on specific genomic point mutations.
• This method facilitates studies linking subclonal noncoding SNVs to transcriptomic and epigenetic heterogeneity.
• Enables deeper understanding of cancer evolution and therapeutic resistance mechanisms.