生物
计算生物学
基因
单细胞测序
单细胞分析
遗传学
基因分型
表型
DNA测序
细胞
基因型
外显子组测序
作者
Livius Penter,Mehdi Borji,Adi Nagler,Haoxiang Lyu,Weidong Lu,Nicoletta Cieri,Katie Maurer,Giacomo Oliveira,Aziz Al’Khafaji,Kiran Garimella,Shuqiang Li,Donna Neuberg,Jérôme Ritz,Robert J. Soiffer,Jacqueline S. Garcia,Kenneth J. Livak,Catherine J. Wu
标识
DOI:10.1038/s41467-023-44137-7
摘要
Abstract Single-cell transcriptomics has become the definitive method for classifying cell types and states, and can be augmented with genotype information to improve cell lineage identification. Due to constraints of short-read sequencing, current methods to detect natural genetic barcodes often require cumbersome primer panels and early commitment to targets. Here we devise a flexible long-read sequencing workflow and analysis pipeline, termed nanoranger , that starts from intermediate single-cell cDNA libraries to detect cell lineage-defining features, including single-nucleotide variants, fusion genes, isoforms, sequences of chimeric antigen and TCRs. Through systematic analysis of these classes of natural ‘barcodes’, we define the optimal targets for nanoranger, namely those loci close to the 5’ end of highly expressed genes with transcript lengths shorter than 4 kB. As proof-of-concept, we apply nanoranger to longitudinal tracking of subclones of acute myeloid leukemia (AML) and describe the heterogeneous isoform landscape of thousands of marrow-infiltrating immune cells. We propose that enhanced cellular genotyping using nanoranger can improve the tracking of single-cell tumor and immune cell co-evolution.
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