European Journal Of HaematologyCancer BiologyPeer-reviewed journal article
Post-hoc long-read sequencing links leukemic mutation status to single-cell transcriptomes
Sofia PapavasileiouiD, Chenyan WuiD, Daryl Zhong Hao BoeyiD, Lucille MargerieiD, 莫介臻, Ulla Olsson‐Strömberg, Stina Söderlund, Gunnar P. NilssoniD, Joakim S. DahliniD
Abstract
Single-cell RNA-sequencing-based characterization of cells that belong to the neoplastic clone is a major challenge in hematologic neoplasms, where malignant and normal cells coexist. Confident molecular profiling requires simultaneous analysis of gene expression and genetic mutations in individual cells, an ability that is not supported by the standard 10X Genomics workflow. Here, we systematically evaluated the potential and limitations of repurposing amplified cDNA generated during the 10X Genomics 3' workflow for post-hoc genotyping of individual cells. We first established a mixed leukemic cell line system comprising one cell line with KIT point mutations and another with the BCR::ABL1 fusion gene. Targeted long-read PacBio sequencing enabled post-hoc assignment of mutation data to transcriptionally profiled cells, but recovery differed between targets. Consistent with ambient RNA in microfluidics-based single-cell workflows, mutation-associated transcripts were detected in cells not expected to carry the corresponding mutations, illustrating how transcript recovery complicates cell-level genotype assignment. Target-specific thresholds mitigated this source of misclassification. In primary chronic myeloid leukemia samples, the post-hoc approach detected BCR::ABL1-positive cells at diagnosis, but not during imatinib treatment. Together, we present a framework for adding mutation status to cells already profiled using the 10X Genomics workflow and highlight broader considerations for transcript-based single-cell genotyping.
- organism: human
- assay: scRNA-seq
- assay: long-read sequencing
- research focus: cell type annotation
- research focus: differential expression
- approach: deep learning
- approach: statistical model
- Citations
- 0
- FWCI
- 0.00
- Field percentile
- top 37%
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