Single-cell deoxyribonucleic acid typing for forensic mixtures and trace evidence: Opportunities, validation requirements, and reporting limits

Forensic deoxyribonucleic acid (DNA) interpretation is limited less by genotyping technology than by the biochemical and inferential effects of mixed, low-template, and environmentally complex traces. Single-cell and single-molecule strategies, including the United Kingdom Research and Innovation-funded single-cell and single-molecule analysis for DNA identification (SCAnDi) program, aim to preserve cellular resolution. They allow investigators to isolate and type individual cells or defined small-cell pools before heterogeneous evidence is converted into a bulk lysate. In selected validation settings, this approach has yielded near-complete diploid short tandem repeat (STR) profiles from small pools, credible genotype sets tightly concentrated on the true genotype across high-order mixtures, and improved access to donor-specific profiles from sexual assault and other complex samples. However, these studies also show important limits. Many operationally successful "single-cell" workflows are, in practice, single-cell-plus-consensus or few-cell workflows. Stochastic effects remain intrinsic, and cell capture itself becomes a probabilistic sampling step. This opinion review explicitly adopts an evaluative stance: It synthesizes recent validation studies and guidance documents to identify the performance thresholds and reporting boundaries that should be met before targeted forensic deployment of SCAnDi-like workflows. We argue that casework entry should require measured cell-recovery probabilities, phenotype-misclassification rates, locus- and cell-type-specific dropout and stutter models, quantified contamination and drop-in rates, validated minimum cell counts for consensus generation, and explicit database-upload criteria. It should also require strict separation between sub-source reporting and activity-level propositions. If those conditions are met, single-cell typing can complement, rather than replace, bulk STR analysis and probabilistic genotyping in a narrow but important set of high-value forensic scenarios.