Fractionation of DNA and protein from individual latent fingerprints for forensic analysis.

Human touch samples represent a significant portion of forensic DNA casework. Yet, the generally low abundance of genetic material combined with the predominantly extracellular nature of DNA in these samples makes DNA-based forensic analysis exceptionally challenging. Human proteins present in these same touch samples offer an abundant and environmentally-robust alternative. Proteogenomic methods, using protein sequence variants arising from nonsynonymous DNA mutations, have recently been applied to forensic analysis and may represent a viable option looking forward. However, DNA analysis remains the gold standard and any proteomics-based methods would need to consider how DNA could be co-extracted from samples without significant loss. Herein, we describe a simple workflow for the collection, enrichment and fractionation of DNA and protein in latent fingerprint samples. This approach ensures that DNA collected from a latent fingerprint can be analyzed by traditional DNA casework methods, while protein can be proteolytically digested and analyzed via standard liquid chromatography-tandem mass spectrometry-based proteomics methods from the same touch sample. Sample collection from non-porous surfaces (i.e., glass) is performed through the application of an anionic surfactant over the fingermark. The sample is then split into separate DNA and protein fractions following centrifugation to enrich the protein fraction by pelleting skin cells. The results indicate that this workflow permits analysis of DNA within the sample, yet highlights the challenge posed by the trace nature of DNA in touch samples and the potential for DNA to degrade over time. Protein deposited in touch samples does not appear to share this limitation, with robust protein quantities collected across multiple human donors. The quantity and quality of protein remains robust regardless of fingerprint age. The proteomic content of these samples is consistent across individual donors and fingerprint age, supporting the future application of genetically variable peptide (GVP) analysis of touch samples for forensic identification.

An algorithm for random match probability calculation from peptide sequences.

For the past three decades, forensic genetic investigations have focused on elucidating DNA signatures. While DNA has a number of desirable properties (e.g., presence in most biological materials, an amenable chemistry for analysis and well-developed statistics), DNA also has limitations. DNA may be in low quantity in some tissues, such as hair, and in some tissues it may degrade more readily than its protein counterparts. Recent research efforts have shown the feasibility of performing protein-based human identification in cases in which recovery of DNA is challenged; however, the methods involved in assessing the rarity of a given protein profile have not been addressed adequately. In this paper an algorithm is proposed that describes the computation of a random match probability (RMP) resulting from a genetically variable peptide signature. The approach described herein explicitly models proteomic error and genetic linkage, makes no assumptions as to allelic drop-out, and maps the observed proteomic alleles to their expected protein products from DNA which, in turn, permits standard corrections for population structure and finite database sizes. To assess the feasibility of this approach, RMPs were estimated from peptide profiles of skin samples from 25 individuals of European ancestry. 126 common peptide alleles were used in this approach, yielding a mean RMP of approximately 10-2.