Researchers have developed a single-cell multi-omics method that enables simultaneous analysis of genetic and transcriptional heterogeneity in both fresh and formalin-fixed paraffin-embedded (FFPE) tissue samples. The Genotyping of Transcriptomes for multiple targets and sample types (GoT-Multi) approach provides a new means to examine how somatic mutations interact with downstream gene expression programs in therapy-resistant cancers.
GoT-Multi integrates multiplexed genotyping with single-cell RNA sequencing (scRNA-seq), allowing researchers to capture clonal structure alongside cellular states in complex tumor ecosystems. The technique uses a machine learning-based analytical pipeline to improve genotyping accuracy and optimize mutation calling across thousands of individual cells.
In the study, the method was applied to frozen and FFPE tumor samples from patients with Richter transformation – the progression of chronic lymphocytic leukemia (CLL) into therapy-resistant large B-cell lymphoma. Investigators were able to map 27 somatic mutations across multiple subclones while simultaneously profiling the transcriptome of each cell. This dual-level data enabled reconstruction of the tumor’s clonal architecture and identification of gene-expression programs associated with specific genotypes.
The analysis revealed that genetically distinct subclones can exhibit overlapping transcriptional profiles. For example, several therapy-resistant genotypes converged on an inflammatory transcriptional program, suggesting that different genetic routes can lead to similar phenotypic states that promote resistance. Other subclones displayed increased proliferation signatures or activation of MYC-related transcriptional pathways, further illustrating the heterogeneity of tumor cell behavior.
GoT-Multi’s ability to operate with FFPE samples extends its relevance to clinical diagnostics, where preserved tissue represents the standard format for most pathological specimens. By linking clonal genotypes to gene-expression states at single-cell resolution, the method provides a framework for studying disease evolution directly from archival materials.
According to the study authors, the ensemble machine learning pipeline built into GoT-Multi improved mutation detection compared with conventional genotyping tools, reducing false-negative and ambiguous calls. This analytical precision allowed clearer distinction of subclonal populations within the tumor and enhanced reproducibility across sample types.
The findings suggest that different genetic lesions in lymphoma may lead to shared transcriptional endpoints, complicating efforts to target resistance mechanisms based solely on genotype. For diagnostic laboratories and pathologists, GoT-Multi demonstrates how combined genotyping and transcriptomic data could refine molecular classification and support more nuanced interpretation of tumor heterogeneity.
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