eprintid: 10195312
rev_number: 14
eprint_status: archive
userid: 699
dir: disk0/10/19/53/12
datestamp: 2024-09-23 16:38:05
lastmod: 2024-09-23 16:38:05
status_changed: 2024-09-23 16:38:05
type: thesis
metadata_visibility: show
sword_depositor: 699
creators_name: Bennett, Nicola Alicia
title: The Invisible Trail: The Application of The Microbiome in Forensic Investigations
ispublished: unpub
divisions: B02
divisions: B04
divisions: C05
divisions: C08
divisions: D09
divisions: F52
divisions: UCL
note: Copyright © The Author 2024.  Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Licence (https://creativecommons.org/licenses/by-nc-nd/4.0/).  Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms.  Access may initially be restricted at the author’s request.
abstract: Soil, a complex and dynamic substrate, plays a crucial role in forensic investigations due to
its ubiquitous nature and ability to transfer trace evidence. The distinct physical, chemical,
and biological properties of soil vary significantly with location and depth, offering valuable
clues for crime reconstruction. While traditional soil analysis methods have been employed
in forensic science, soil microbiome analysis presents a novel and complementary
approach. This study explores the potential of utilising a 16S rRNA amplicon sequencing to
characterise the soil microbiome, offering a digital signature for distinguishing soil samples
and aiding in criminal investigations.
The findings show that microbial communities differ significantly between surface and subsurface
soils, indicating depth as a strong discriminating factor within a single site.
Moreover, variations in microbial profiles allow for inferences to be made regarding the
origin of soil samples, distinguishing between surficial soils which can be transferred onto
footwear from walking, with soils from depth at a grave site.
Across multiple sites, location emerges as a stronger differentiating factor than depth,
enabling discrimination between samples even within close proximity (less than 1km).
Notably, the microbiome proves valuable in distinguishing soils of the same or differing
classifications.
DNA concentration analysis complements microbiome profiling in determining soil depth.
Informative microbial profiles can be generated from nanogram DNA concentrations,
highlighting the sensitivity of the method. However, these methods are susceptible to
sequencing bias and careful consideration should given to experimental design, sample
collection and analysis pipeline.
From the findings from the studies undertaken in this thesis it was possible to conclude that
soil microbial communities vary with depth and distance; DNA concentration aids in
determining soil depth; microbial profiles can be obtained from minimal DNA amounts;
close-proximity samples can be differentiated; and each soil context warrants independent
assessment.

Overall, this research demonstrates the promising potential of soil microbiome analysis in
forensic investigations, offering a valuable tool for discriminating soil samples based on
depth and location.
date: 2024-07-28
date_type: published
oa_status: green
full_text_type: other
thesis_class: doctoral_open
thesis_award: Ph.D
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2302223
lyricists_name: Bennett, Nicola
lyricists_id: NBENN73
actors_name: Bennett, Nicola
actors_name: Allington-Smith, Dominic
actors_id: NBENN73
actors_id: DAALL44
actors_role: owner
actors_role: impersonator
full_text_status: public
pages: 187
institution: UCL (University College London)
department: Security and Crime Science
thesis_type: Doctoral
citation:        Bennett, Nicola Alicia;      (2024)    The Invisible Trail: The Application of The Microbiome in Forensic Investigations.                   Doctoral thesis  (Ph.D), UCL (University College London).     Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10195312/1/Bennett_10195312_thesis.pdf