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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., (Copyright © 2020 Elsevier B.V. All rights reserved.)

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 ., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: The prevalence of healthcare-acquired infections (HAI) and rising levels of antimicrobial resistance places significant economic and public health burdens on modern healthcare systems. A group of highly drug resistant pathogens known as the ESKAPE pathogens, along with C. difficile, are the leading causes of HAIs. Interactions between patients, healthcare workers, and environmental conditions impact disease transmission. Studying pathogen transfer under varying contact scenarios in a controlled manner is critical for understanding transmission and disinfectant strategies. In lieu of human subject research, this method has the potential to contribute to modeling the routes of pathogen transmission in healthcare settings., Methods: To overcome these challenges, we have developed a method that utilizes a synthetic skin surrogate to model both direct (skin-to-skin) and indirect (skin-to fomite-to skin) pathogen transfer between infected patients and healthy healthcare workers. This surrogate material includes a background microbiome community simulating typical human skin flora to more accurately mimic the effects of natural flora during transmission events., Results: We demonstrate the ability to modulate individual bacterial concentrations within this microbial community to mimic bacterial concentrations previously reported on the hands of human subjects. We also explore the effect of various decontamination approaches on pathogen transfer between human subjects, such as the use of handwashing or surface disinfectants. Using this method, we identify a potential outlier, S. aureus, that may persist and retain viability in specific transfer conditions better than the overall microbial community during decontamination events., Conclusions: Our work describes the development of an in vitro method that uses a synthetic skin surrogate with a defined background microbiota to simulate skin-to-skin and skin-to fomite-to skin contact scenarios. These results illustrate the value of simulating a holistic microbial community for transfer studies by elucidating differences in different pathogen transmission rates and resistance to common decontamination practices. We believe this method will contribute to improvements in pathogen transmission modeling in healthcare settings and increase our ability to assess the risk associated with HAIs, although additional research is required to establish the degree of correlation of pathogen transmission by skin or synthetic alternatives.

Quantitative genomic and proteomic evaluation of human latent fingerprint depositions represents a challenge within the forensic field, due to the high variability in the amount of DNA and protein initially deposited. To better assess recovery techniques for touch depositions, we present a method to produce simple and customizable artificial fingerprints. These artificial fingerprint samples include the primary components of a typical latent fingerprint, specifically sebaceous fluid, eccrine perspiration, extracellular DNA, and proteinaceous epidermal skin material (i.e., shed skin cells). A commercially available emulsion of sebaceous and eccrine perspiration material provides a chemically-relevant suspension solution for fingerprint deposition, simplifying artificial fingerprint production. Extracted human genomic DNA is added to accurately mimic the extracellular DNA content of a typical latent print and comparable DNA yields are recovered from the artificial prints relative to human prints across surface types. Capitalizing on recent advancements in the use of protein sequence identification for human forensic analysis, these samples also contain a representative quantity of protein, originating from epidermal skin cells collected from the fingers and palms of volunteers. Proteomic sequencing by liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis indicates a high level of protein overlap between artificial and latent prints. Data are available via ProteomeXchange with identifier PXD015445. By including known quantities of DNA and protein into each artificial print, this method enables total DNA and protein recovery to be quantitatively assessed across different sample collection and extraction methods to better evaluate extraction efficiency. Collectively, these artificial fingerprint samples are simple to make, highly versatile and customizable, and accurately represent the biochemical composition and biological signatures of human fingerprints., Competing Interests: The authors DSL, KQS, TEM, ARS, MLP, NCA, BCL, KLW, MWG, and FCH are employed by Signature Science, LLC. This does not alter our adherence to PLOS ONE policies on sharing data and materials. The authors declare no other relevant affiliations or financial involvement with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.