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Direct measurement of engineered cancer mutations and their transcriptional phenotypes in single cells.
Kim, Heon Seok; Grimes, Susan M; Chen, Tianqi; Sathe, Anuja; Lau, Billy T; Hwang, Gue-Ho; Bae, Sangsu; Ji, Hanlee P.
Afiliación
  • Kim HS; Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
  • Grimes SM; Department of Life Science, College of Natural Sciences, Hanyang University, Seoul, Republic of Korea.
  • Chen T; Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Seoul, Republic of Korea.
  • Sathe A; Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
  • Lau BT; Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
  • Hwang GH; Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
  • Bae S; Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
  • Ji HP; Medical Research Center of Genomic Medicine Institute, Seoul National University College of Medicine, Seoul, Republic of Korea.
Nat Biotechnol ; 2023 Sep 11.
Article en En | MEDLINE | ID: mdl-37697151
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.

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Nat Biotechnol Asunto de la revista: BIOTECNOLOGIA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Nat Biotechnol Asunto de la revista: BIOTECNOLOGIA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos
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