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Updated: Jul 16, 2025
Author Spotlight: Shear Assay Protocol for the Determination of Single-Cell Material Properties
Published on: May 19, 2023
Direct measurement of engineered cancer mutations and their transcriptional phenotypes in single cells
Heon Seok Kim1,2,3, Susan M Grimes1, Tianqi Chen1
1Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Abstract:
Genome sequencing studies have identified numerous cancer mutations across a wide spectrum of tumor types, but determining the phenotypic consequence of these mutations remains a challenge. Here, we developed a high-throughput, multiplexed single-cell technology called TISCC-seq to engineer predesignated mutations in cells using CRISPR base editors, directly delineate their genotype among individual cells and determine each mutation's transcriptional phenotype. Long-read sequencing of the target gene's transcript identifies the engineered mutations, and the transcriptome profile from the same set of cells is simultaneously analyzed by short-read sequencing. Through integration, we determine the mutations' genotype and expression phenotype at single-cell resolution. Using cell lines, we engineer and evaluate the impact of >100 TP53 mutations on gene expression. Based on the single-cell gene expression, we classify the mutations as having a functionally significant phenotype.
Insights
This study introduces TISCC-seq, a novel single-cell technology to precisely link cancer mutations to their effects on gene expression. This method helps classify the functional impact of over 100 TP53 mutations in cell lines.
Area of Science:
- Genomics
- Molecular Biology
- Cancer Research
Background:
- Genome sequencing reveals numerous cancer mutations, but their functional consequences are challenging to determine.
- Understanding mutation impact is crucial for targeted cancer therapies.
Purpose of the Study:
- To develop a high-throughput, single-cell technology (TISCC-seq) for linking specific mutations to their transcriptional phenotypes.
- To evaluate the impact of over 100 TP53 mutations on gene expression at single-cell resolution.
Main Methods:
- Engineered predesignated mutations in cells using CRISPR base editors.
- Utilized TISCC-seq combining long-read sequencing for mutation genotyping and short-read sequencing for transcriptome profiling.
- Integrated genotype and expression data for single-cell analysis.
Main Results:
- Successfully engineered and analyzed >100 TP53 mutations in cell lines.
- Determined the genotype and transcriptional phenotype for each engineered mutation at single-cell resolution.
- Classified TP53 mutations based on their functional impact on gene expression.
Conclusions:
- TISCC-seq enables precise determination of mutation genotype-phenotype relationships at single-cell resolution.
- This technology advances the understanding of cancer mutation functional consequences.
- Provides a framework for functional classification of cancer mutations.
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