Forensic identification using skin bacterial communities

Abstract

Recent work has demonstrated that the diversity of skin-associated bacterial communities is far higher than previously recognized, with a high degree of interindividual variability in the composition of bacterial communities. Given that skin bacterial communities are personalized, we hypothesized that we could use the residual skin bacteria left on objects for forensic identification, matching the bacteria on the object to the skin-associated bacteria of the individual who touched the object. Here we describe a series of studies de-monstrating the validity of this approach. We show that skin-associated bacteria can be readily recovered from surfaces (including single computer keys and computer mice) and that the structure of these communities can be used to differentiate objects handled by different individuals, even if those objects have been left untouched for up to 2 weeks at room temperature. Furthermore, we demonstrate that we can use a high-throughput pyrosequencing-based ap-proach to quantitatively compare the bacterial communities on objects and skin to match the object to the individual with a high degree of certainty. Although additional work is needed to further establish the utility of this approach, this series of studies introduces a forensics approach that could eventually be used to independently evaluate results obtained using more traditional forensic practices.

Fig. 1.

Match between bacterial communities on individual keyboards and the fingers of the owners of the keyboards. Principal coordinates plots showing the degree of similarity between bacterial communities on fingertips of the three individuals sampled as part of this study and their respective keyboards. Plots were generated using the pairwise unweighted (A) and weighted (B) UniFrac distances (22, 23), respectively. The UniFrac algorithm uses the degree of phylogenetic overlap between any pair of communities with points that are close together representing samples with similar bacterial communities.

Fig. 2.

Bacterial community distances between keyboard keys and fingertips. Mean pairwise distances between keys from the same keyboard (black bar), between individual's fingertips and their own keyboard keys (hatched bar), and between individual's fingertips and keys from keyboards not belonging to them (gray bar). Average unweighted and weighted UniFrac distances for each individual are shown (A and B, respectively). Lower UniFrac values indicate that the communities are more similar on average. Bars show 95% confidence intervals for the means. Graph demonstrates that the fingertips of an individual harbor bacterial communities more similar to those found on the keys of that individual's keyboard than to those communities found on keyboard keys not touched by the individual.

Fig. 3.

Effect of storage conditions on skin-associated bacterial communities collected on dry cotton swabs. (A and B) Principal coordinates plots generated using the unweighted and weighted UniFrac distance matrices, respectively. Samples were stored at either −20 °C or +20 °C with DNA extracted from the swabs after 3 days and 14 days, but storage temperature had minimal effects on bacterial community composition. (C) Relative abundances of the most abundant bacterial taxa after 14 days at either −20 °C or at +20 °C. Classifications are to the genus (gen.), family (fam.), or order level. For each taxon, the phylum or subphylum is also indicated: Actino., Actinobacteria; Bacter., Bacteroidetes; Firmic., Firmicutes. Taxa are classified to the highest taxonomic level to which they could be confidently assigned.

Fig. 4.

Accuracy of forensic identification using bacterial communities. Phylogenetic distance between the bacterial communities found on the computer mouse (with the nine mice identified with the x axis labels) and the hand swab from the individual that used the mouse (the unfilled symbols) versus the average phylogenetic distance between the bacterial communities on the computer mouse and the 270 other hand swab samples in the database (filled symbols). Error bars represent 95% confidence intervals. Phylogenetic distance measured using either the unweighted or weighted UniFrac algorithm (red squares and blue circles, respectively); the more similar the communities the lower the distance. Note that in nearly all cases the bacterial community on a given mouse is significantly more similar to those on the owner's hand than to the other hands in the database.