Single-cell lineage tracing is a powerful tool for dissecting the complex dynamics of cell growth and differentiation in health and disease. However, the reliance of most methods on genetic manipulation fundamentally limits their application in situ and prevents the study of tissues and diseases that lack established model systems. To overcome this, natural genetic variation in the mitochondrial genome can be leveraged as an endogenous genetic barcoding system for resolving clonal relationships 1-3. A key debate, though, has been whether patterns in variation between cells truly reflect clonal history or are merely artifacts of sequencing errors and capture sparsity 4-6.
We performed mitochondrial variant enrichment from single-cell RNA sequencing (MAESTER) on cells expressing exogenous SPLINTR barcodes to validate the variant enrichment approach and explore methods of variant selection and cell lineage reconstruction. Across 26,629 cells profiled from 3 cell lines we captured 94% of the expressed mitochondrial genome with a median coverage of 30x per cell and identified 9833 high-confident variants with an average of 651 variants per cell. Whilst most variants had low heteroplasmy (mean single cell VAF = 7.18% ), variant sites were covered by an average of 83 consensus read families and called from an average of 5.6 consensus reads per cell. Crucially, we found high concordance between mitochondrial variant profiles in cells sharing the same SPLINTR barcode and were able to identify closely related populations with different SPLINTR barcodes, highlighting the ability of this method to chart retrospective clonal dynamics.
In summary, we demonstrate that deep sequencing of mitochondrial transcripts enriched from standard scRNA-seq cDNA detects robust variant signatures that are sufficient to accurately resolve clonal populations and reconstruct evolutionary histories. This validation paves the way for retrospective lineage tracing in previously inaccessible human tissues and disease samples.