SCAnDi: Single-cell and single molecule analysis for DNA identification

DNA profiling is a powerful technique in forensic science; however, the interpretation of samples arising from multiple contributors remains a significant hurdle, requiring computational deconvolution of the mixed DNA profile after it is generated. While it is possible to employ differential extraction techniques to separate lighter epithelial cells from heavier sperm cells when processing sexual assault samples, issues arise in the investigation of mixtures of less distinct cells, or instances where the mixture results from four or more people [1].

DNA is transferred, and persists, as both cellular and acellular material. When DNA is recovered from a sample, any cellular material is lysed, resulting in a mixture of both cellular and acellular DNA molecules, and a complete loss of information of what cell the DNA originated from. Cell of origin information could be critical in deconvoluting mixed samples - especially higher order mixed samples, ascribing a narrative to where a DNA molecule came from, when it was transferred and by whom. Our aim is to use single-cell approaches to study DNA transfer and persistence, linking cell-of-origin information with the DNA profile of the same cell, with the goal of identifying DNA profiles of specific individuals in cases where there are complex, mixed DNA profiles. In a forensic setting, and where cellular material is present, cell phenotype linked with single-cell analysis could be used to link DNA profiles with the tissue of origin (blood, sperm, or different epithelial cell types). This phenotype could be morphological (microscopic) or molecular (e.g. RNA or epigenetic marks).

Single-cell genomic technologies have advanced rapidly in the last decade, with an array of approaches for the analysis of genetic material from individual cells now widely applied in human and model organisms, extending even to the profiling of genome-wide recombination in individual sperm cells [2,3].
These technologies have had limited exploration in a forensic setting, though some applications have been described [4]. A Next Generation Sequencing (NGS) approach is desirable when moving towards single-cell analysis to enable throughput and sensitivity, as hundreds of cells may be profiled from each sample. This increase in throughput will also require the exploration and implementation of microfluidic, advanced imaging and machine learning technologies.