De-convolution of Body Fluid Mixtures: Cell Type Identification and Genetic Profiling of Micro-Dissected Cells

• NCJ Number: 248485
• Author(s): Jack Ballantyne Ph.D.
• Date Published: July 2013
• Length: 106 pages
• Grant Number(s): 2008-DN-BX-K007
• Agencies: NIJ-Sponsored
• Publication Type: Report (Grant Sponsored)
• Award: De-convolution of Body Fluid Mixtures: Cell Type Identification and Single Source Genetic Profiling of Micro-Dissected Cells

Annotation

This research addresses the need to limit sample mixture commonly associated with forensic DNA evidence, and introduces laser capture micro-dissection (LCM) and a novel micro-manipulation approach to isolate single epithelial cells.

Abstract

Forensic DNA mixtures are frequently encountered in analysis due to the commission of multiple person crimes, 'touch DNA', or other unavoidable contamination with pre-crime DNA. Analysis and interpretation of these mixed DNA samples is often complex and challenging. A limited number of tools are available in order to aid in the interpretation or handling of mixtures. This research sought to provide a novel strategy for the de-convolution of body fluid mixtures containing non-distinguishable cell types (e.g. epithelial cells and leukocytes) through an isolation of individual cells using laser capture micro-dissection and recovery of genetic material (RNA and DNA) for cell type identification (RNA profiling methods) and donor identification (autosomal STR analysis). Thus the cells comprising mixtures would be separated out according to body fluid source and single source profiles obtained from each of the individual mixture contributors. Such an approach would permit the attribution of each DNA profile in a mixture to a particular contributing body fluid. During the research, the ability to isolate single epithelial cells using laser capture micro-dissection (LCM) and a novel micro-manipulation approach was demonstrated. The methodologies developed in the current work may help to resolve forensic mixtures and permit facile genotyping of individual contributors.