Temporal recording of mammalian development and precancer [WES]
Insights into many biological phenomena requires knowing the temporal order of cellular events, which is traditionally achieved through continuous direct observations [1, 2]. An alternative solution leverages irreversible genetic changes, such as naturally occurring mutations, to create indelible markers that enables retrospective temporal ordering [3-8]. Using Native sgRNA Capture and sequencing (NSC-seq), a newly devised and validated multi-purpose single-cell CRISPR platform, we developed a molecular clock approach to record the timing of cellular events and clonality in vivo while incorporating cell state and lineage information. Using this approach, we uncovered precise timing of tissue-specific cell expansion during murine embryonic development, unconventional developmental relationships between cell types, and new epithelial progenitor states by their unique genetic histories. NSC-seq analysis of murine adenomas coupled to multi-omic and single-cell profiling of human precancers, with clonal analysis of 418 human polyps, demonstrated the occurrence of polyancestral initiation in 15-30% of colonic precancers, revealing their origins from multiple normal founders. Our study presents a multimodal framework that lays the foundation for in vivo recording, integrating synthetic or natural indelible genetic changes with single-cell analyses to explore the origins and timing of development and tumorigenesis in mammalian systems. Overall design: DNA was extracted from resected tumors and normal epithelial tissues using DNA Extraction Kits (Qiagen). Standard whole exome sequencing (WES) library was prepared using mouse exome panel (Twist Bioscience) and sequenced on S4 flow cells on NovaSeq6000 (PE150) to the targeted coverage (50X). WES reads were aligned to the mouse reference genome (mm10) using BWA and indexed by Sambamba. Somatic variants were called using GATK Mutect2. Variants were annotated using ANNOVAR and germline variants were filtered using paired normal samples. Variants were further analyzed and visualized using Maftools.