This is the Final Summary Overview of a project that continued a previously funded project that used high-throughput sequencing (HTS) of eight forensically relevant biological fluids to identify miRNAs with tissue-specific expression.
The current work developed the initial miRNA panel further by working to 1) identify markers for vaginal secretions and perspiration; 2) expand the population set; 3) develop a regression tree for body fluid identification using the miRNA markers; and 4) evaluate the final panel for identification success in mixed samples. This project also performed a comparative analysis between analysis methodologies, assessed the limit of detection, performance in DNA extracts, species and tissue specificity, and stability in compromised samples. Samples were collected from 325 donors, and 505 samples were analyzed using each miRNA marker in the panel under approved Humana Subjects Research Protocol. The demographics of the population samples reflected the donor demographics in a diverse, urban university. Descriptions of the project design and method address sample collection and preparation, RNA isolation and analysis, DNA isolation, and data analysis. The development of candidate miRNAs and initial validation completed in the previous project was a first step toward an eventual commercial assay for body fluid identification that is robust and reliable when used by practitioners. The miRNA panel validated in the current project provides quantifiable confidence in the body fluids present in the sample. If future work on the miRNA panel using DNA extracts is successful, a significant barrier to implementation is removed, i.e., additional analyst time, reagent costs, and sample consumption required for a separate RNA isolation method. 2 tables and 2 references
Downloads
Similar Publications
- Assessing the Impact of Plea Bargaining on Subsequent Violence for Firearm Offenders
- Recovery and Detection of Ignitable Liquid Residues from the Substrates by Solid Phase Microextraction – Direct Analysis in Real Time Mass Spectrometry
- OpenSense: An Open-World Sensing Framework for Incremental Learning and Dynamic Sensor Scheduling on Embedded Edge Devices