Forensic Microbiome Analysis for Detection of Gunshot Residue on the Skin of Serial Offenders: A Novel Approach to Forensic Investigation
Creators: name: P.S, Muhammad Safar
name: Manaf, Jannath
Publisher: name: Zenodo
Year: 2024
Subjects: subject: Forensic sciences
subject: Physical chemistry

description: Research Proposal

Title

Forensic Microbiome Analysis for Detection of Gunshot Residue on the Skin of Serial Offenders: A Novel Approach to Forensic Investigation

Submitted by

Muhammad Safar, P.S.AMITY University DubaiMSc Forensic ScienceJannath A ManafAMITY University DubaiMSc Forensic Science

Abstract

This research proposes an innovative approach to detect gunshot residue (GSR) on the skin of individuals involved in serial killings using forensic microbiome analysis. Traditional GSR detection methods face limitations, particularly when suspects use gloves or attempt to wash off residue. This study hypothesizes that GSR particles can induce detectable alterations in the skin microbiome, potentially providing a more persistent and harder-to-conceal form of evidence. By leveraging advanced sequencing technologies and bioinformatics tools, we aim to establish a novel, reliable method for linking suspects to firearm-related crimes, even in scenarios where conventional techniques may fail.

1. Introduction

1.1 Background

Gunshot residue (GSR) has long been a crucial form of trace evidence in forensic investigations involving firearms. Composed of particles from the primer, propellant, and other components of ammunition, GSR can provide valuable information about a suspect's proximity to a discharged firearm. Traditional methods for GSR detection include chemical tests such as the Griess test and Walker's test, as well as more advanced techniques like scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX).

However, these methods face significant challenges:



GSR can be easily washed off or transferred.

The use of gloves can prevent GSR deposition on hands.

Environmental contamination can lead to false positives.

The composition of GSR can vary depending on ammunition type, making definitive identification challenging.


1.2 Rationale

The human skin microbiome, comprising diverse communities of bacteria, fungi, and other microorganisms, has shown remarkable potential in forensic applications. Recent studies have demonstrated that the skin microbiome can retain traces of various environmental exposures, including chemical compounds. We posit that GSR particles, given their unique chemical composition, may induce specific alterations in the skin microbiome that persist even after attempts to remove physical traces.

This approach offers several potential advantages:



Greater persistence compared to physical GSR traces.

Potential to detect GSR exposure even when gloves are used (e.g., neck or face microbiome).

Unique microbial signatures that could differentiate GSR from environmental contaminants.

Possibility of linking suspects to specific crime scenes or weapons based on unique microbial profiles.


1.3 Objectives



To determine whether GSR exposure induces detectable and consistent changes in the human skin microbiome.

To develop a robust methodological framework for collecting, analyzing, and interpreting skin microbiome data in the context of GSR detection.

To compare the sensitivity and specificity of microbiome-based GSR detection with traditional methods.

To explore the potential of microbiome analysis in differentiating between different types of firearms or ammunition based on their effects on the skin microbiome.


2. Literature Review

2.1 Current Methods for GSR Detection



Griess test: Colorimetric test for nitrites, limited specificity (Dalby et al., 2010).

Walker's test: Detects lead and barium, but can produce false positives (Romolo & Margot, 2001).

SEM-EDX: Gold standard for GSR analysis, but expensive and time-consuming (Brozek-Mucha, 2017).

Electrochemical methods: Emerging techniques with high sensitivity but still in development (Costa et al., 2019).


2.2 Forensic Applications of Microbiome Analysis



Soil microbiome analysis for geographical profiling (Damaso et al., 2018).

Postmortem interval estimation using succession of microbial communities (Metcalf et al., 2016).

Human microbiome as a tool for human identification (Schmedes et al., 2016).


2.3 Skin Microbiome and Environmental Exposures



Effects of cosmetics and hygiene products on skin microbiome (Urban et al., 2016).

Occupational exposures and their impact on skin microbial communities (Prescott et al., 2017).

Stability and individuality of the skin microbiome over time (Oh et al., 2016).


2.4 Research Gaps



Limited exploration of chemical exposures on skin microbiome in forensic contexts.

Lack of studies specifically addressing GSR and microbial community changes.

Need for standardized protocols in forensic microbiome analysis.


3. Methodology

3.1 Study Design

A controlled experimental study with three groups:



GSR-exposed group (n=50): Individuals exposed to GSR under controlled conditions.

Negative control group (n=50): No GSR exposure.

Environmental control group (n=50): Exposed to non-GSR particulates that might be confused with GSR.


3.2 Sample Collection



Collect skin swabs from multiple body sites: hands (palm and back), neck, and face.

Use sterile swabs moistened with sterile saline solution.

Implement strict contamination control protocols.


3.3 GSR Exposure and Traditional Analysis



Conduct controlled firearm discharge events for the GSR-exposed group.

Perform SEM-EDX analysis on a subset of samples to confirm GSR presence.

Apply conventional color tests (Griess and Walker's) for comparison.


3.4 Microbiome Analysis



DNA Extraction: 



Use QIAamp DNA Microbiome Kit or similar for microbial DNA isolation.

Include negative extraction controls to account for reagent contamination.


Library Preparation and Sequencing: 



Target the V3-V4 region of the 16S rRNA gene for bacteria.

Include ITS regions for fungal profiling.

Employ Illumina NovaSeq 6000 platform for high-throughput sequencing.


Bioinformatics Analysis: 



Use QIIME 2 pipeline for initial sequence processing and quality control.

Employ DADA2 for ASV (Amplicon Sequence Variant) calling instead of OTU clustering for higher resolution.

Utilize machine learning approaches (e.g., Random Forests) to identify GSR-specific microbial signatures.



3.5 Data Analysis and Statistics



Analyze alpha diversity (within-sample diversity) and beta diversity (between-sample diversity) using phyloseq R package.

Perform differential abundance analysis using DESeq2 to identify specific taxa associated with GSR exposure.

Use PERMANOVA to assess overall community differences between groups.

Develop and validate a predictive model for GSR exposure based on microbiome profiles.


3.6 Validation and Robustness Testing



Conduct blind tests to assess the accuracy of the developed method.

Evaluate the method's performance under various conditions (e.g., different time points post-exposure, after washing).


4. Expected Results



Identification of specific microbial taxa or community structures associated with GSR exposure.

Development of a statistical model for predicting GSR exposure based on skin microbiome profiles.

Comparison of sensitivity and specificity between microbiome-based and traditional GSR detection methods.


5. Timeline



Months 1-3: Study setup, participant recruitment, and preliminary testing.

Months 4-9: Sample collection and processing.

Months 10-15: Sequencing and bioinformatics analysis.

Months 16-18: Statistical analysis, model development, and validation.

Months 19-24: Data interpretation, manuscript preparation, and submission.


6. Ethical Considerations



Obtain approval from the institutional ethics committee.

Ensure informed consent from all participants.

Implement strict data anonymization and protection protocols.


7. Budget

[Detailed budget to be prepared based on institutional guidelines and available funding]

8. Potential Impact and Future Directions

This research has the potential to revolutionize forensic investigations involving firearms. If successful, it could:



Provide a more persistent form of evidence in GSR-related cases.

Enhance the ability to link suspects to crime scenes even after attempted evidence removal.

Potentially differentiate between different types of firearms or ammunition based on their microbiome impact.


Future research could explore:



The application of this technique to other types of trace evidence.

Integration with metagenomic approaches for even more detailed profiles.

Development of rapid, field-deployable microbiome analysis tools for forensic use.