This article reports on a project that developed a new prototype massively parallel sequencing (MPS) mRNA profiling assay for organ tissue identification that is designed to definitively identify 13 organ/tissue types using a targeted panel of 48 mRNA biomarkers.
Molecular analysis of the RNA transcriptome from a putative tissue fragment should permit assignment to a specific organ since each tissue will exhibit a unique pattern of gene expression. Determination of the organ source of tissues from crime scenes may aid in shooting and stabbing investigations. In the current project, the identifiable organs and tissues included brain, spinal cord, lung, trachea, liver, skeletal muscle, heart, kidney, adipose, intestine, stomach, skin, and spleen. The biomarkers were chosen after iterative specificity testing of numerous candidate genes in various tissue types. The assay is very specific with little cross reactivity with non-targeted tissue, and can detect RNA mixtures from different tissues, including two- to five-tissue admixtures. The sensitivity of the assay was evaluated, as well as assay reproducibility between library preparations and sequencing runs. Researchers also demonstrate the ability of the assay to successfully identify the tissue source of origin in cadaver samples, tissue samples with varying postmortem intervals (PMI), and mock and bona fide casework samples. The data are being used to train a multivariate statistical model that predicts the tissue type based on the mRNA profile. By considering co-expression of markers, the model can recognize distinct expression patterns in each tissue. (publisher abstract modified)
Downloads
Similar Publications
- Microscopical Discrimination of Human Head Hairs Sharing a Mitochondrial Haplogroup
- Transient Hypoxia Drives Soil Microbial Community Dynamics and Biogeochemistry During Human Decomposition
- Enhancing Fault Ride-Through Capacity of DFIG-Based WPs by Adaptive Backstepping Command Using Parametric Estimation in Non-Linear Forward Power Controller Design