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Evaluating the Skin Microbiome as Trace Evidence on Common Surface Types 2014-R2-CX-K411

Evaluating the Skin Microbiome as Trace Evidence on Common Surface Types 2014-R2-CX-K411. Principal Investigators Rob Knight, PhD (lead PI) David O. Carter, PhD Jessica L. Metcalf, PhD Jack Gilbert, PhD. Key Collaborators Pam Cadiente, MS Chris Happy, MD Nick Harrison, MFS

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Evaluating the Skin Microbiome as Trace Evidence on Common Surface Types 2014-R2-CX-K411

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  1. Evaluating the Skin Microbiome as Trace Evidence on Common Surface Types2014-R2-CX-K411 Principal Investigators • Rob Knight, PhD (lead PI) • David O. Carter, PhD • Jessica L. Metcalf, PhD • Jack Gilbert, PhD Key Collaborators • Pam Cadiente, MS • Chris Happy, MD • Nick Harrison, MFS Key Researchers • Zech Xu, PhD • Se Jin Song, PhD • Whitney Kodama, MS

  2. Forensic Microbiology/Microbial Forensics

  3. Forensic Microbiology • The Human Microbiome Project • https://hmpdacc.org/ • Humans support numerous microorganisms, primarily bacteria. • These microbial communities are unique to individuals and stable over time. • Humans transfer these microbes to objects and surfaces. Costello et al. (2009) Science. 326:1694-1697.

  4. Project Timeline • 10/2014 – award by NIJ • 01/2015 – Rob Knight’s lab moved from CU Boulder to UC San Diego • 10/2015 – Project initiated (10 month delay) • 09/2017 – Project completed • 05/2018 – Project manuscripts describing results will be submitted

  5. Microbiome Sample Workflow Experimental design and collection Interpretation and dissemination

  6. Major Research Questions • Question 1 • How reproducible is a hand microbial signature when it is transferred to different surface types (glass, plastic, metal, ceramic, wood)? How long does the signal last? Is a personalized microbial skin signal detectable in the presence of another skin microbiome? • Question 2 • How rapidly does an individual’s skin microbial signature decay following their death, and can you link that individual’s microbial signature to surfaces they touched using the microbial signature from their dead skin?

  7. Major Research Question 1 times touched signal persistence surface material mixed signals

  8. Skin microbes as trace evidence • desk surfaces soaked with 10% bleach for 15 minutes, wiped off • placed material tiles on cleaned desks • tiles soaked in 10% bleach for 20 minutes, wiped off • tiles rinsed with HPLC grade water, wiped off • included a set of control (cleaned, but not touched) tiles

  9. How well can a person’s skin sample classify across days?

  10. Results • Surface type significantly affects the quality of microbes as trace evidence.

  11. Results • Surface type significantly affects the quality of microbes as trace evidence. • Highest quality surfaces are ceramic and plastic.

  12. Results • Surface type significantly affects the quality of microbes as trace evidence. • Highest quality surfaces are ceramic and plastic. • Lowest quality surface was wood.

  13. Results • The more times a surface is touched, the higher the accuracy of classification 10x:2/3 (67%) 20 individuals Random guessing accuracy: 1/20 20x:9/13 (69%) 30x:10/12 (83%) Correctly Predicted Individual Wrongly Predicted Individual True Individual

  14. Results • Skin and surface microbial communities persist for at least 18 hours

  15. signal persistence

  16. signal persistence

  17. signal persistence

  18. signal persistence

  19. 10 participants signal persistence 5 min sec 18 hrs 1 hr

  20. signal persistence Correctly Predicted Individual Wrongly Predicted Individual True Individual

  21. Results • Skin and surface microbial communities can be associated with an individual, to the exclusion of other individuals.

  22. Death scene skin trace evidence

  23. Results • Skin microbiomes of recently deceased people remain similar after transport to the morgue.

  24. Results • Skin microbiomes of recently deceased people can be linked to personal objects

  25. Acknowledgments Morgue study Chaminade U • Whitney Kodama • David Carter Honolulu ME • Charlotte Carter • Joan Wells Surface experiments UCSD • Rob Knight • Se Jin Song • Zech Xu CSU • Jessica Metcalf Data generation UCSD • Gail Ackermann • Greg Humphrey Funding • NIJ Analysis platforms • QIIME, QIITA

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