The increasing sensitivity of DNA profiling techniques has enabled successful amplification from the most minimal cellular material. This means profiles containing DNA from more than one contributor are encountered far more regularly in routine casework. These mixed profiles are difficult to interpret, with minor contributors often going undetected.

The UKRI-funded “Single cell Analysis for DNA intelligence” (SCAnDi) project aims to apply single cell genomic techniques to mixed samples by developing approaches to generate fully concordant STR profiles from single cells. This approach could present a methodological shift within the field of forensic human identification (HID), mitigating the need for profile deconvolution, and enabling the identification of minor contributors that may otherwise be obscured by non-target DNA. Whole genome amplification (WGA) methodologies have been used to amplify genomic DNA from individual cells to generate sufficient material for STR profiling through capillary electrophoresis (CE) or next-generation sequencing (NGS). However, WGA methods typically suffer from amplification bias and uneven genome coverage, leading to poor STR recovery.

Using the BD FACSDiscover S8 Imaging Cell Sorter, we have performed parallel imaging and isolation of single cells from a 6-contributor mixture of known donors, using the isolated cells to compare STR recovery from different WGA methods. We have assessed uniformity of genome coverage, allelic recovery, peak height characteristics, and profile concordance from different approaches and identified a novel approach that offers significant improvements over conventional multiple displacement amplification (MDA).

Here we present this new data from our WGA comparisons and discuss the contexts in which these methods, coupled with parallel cell phenotyping using imaging-based cell sorting,  would be advantageous in the deconvolution of DNA mixtures. Additionally, we will consider opportunities for further application of single cell multi-omic methods, such as genome & transcriptome sequencing (G&T-seq) to capture additional cell phenotype information (RNA) and genotype (DNA) from the same cell.

Authors

• Edie E. Holmes (Earlham Institute, United Kingdom)
• Lia Chappell (Earlham Institute, United Kingdom)
• Diya Balloo (Durham University, United Kingdom)
• Andrew Goldson (Earlham Institute, United Kingdom)
• Daniella Shipley (Earlham Institute, United Kingdom)
• Vanda Knitlhoffer (Earlham Institute, United Kingdom)
• David Wright (Earlham Institute, United Kingdom)
• Wilfried Haerty (Earlham Institute, United Kingdom)
• Michael Chen (The University of Edinburgh, United Kingdom)
• Ardhendu Behera (Edge Hill University, United Kingdom)
• Katherine Brown (University of Portsmouth, United Kingdom)
• George Zouganelis (University of Derby, United Kingdom)
• Lorna Dawson (James Hutton Institute, United Kingdom)
• Nick Dawnay (Liverpool John Moores University, United Kingdom)
• Iain C. Macaulay (Earlham Institute, United Kingdom)