Developmental validation of an mRNA kit: A 5-dye multiplex assay designed for body-fluid identification

Yuanyuan Xiao1, Mengyu Tan1, Jinlong Song1

  • 1Department of Forensic Genetics, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan 610041, PR China.

Insights

Forensic investigations benefit from a new mRNA kit for body fluid identification (BFI). This tool accurately identifies blood, semen, saliva, and vaginal fluids, enhancing crime scene analysis.

Area of Science:

  • Forensic science
  • Molecular biology
  • Genetics

Background:

  • Accurate identification of biological samples at crime scenes is vital for forensic investigations.
  • Messenger RNA (mRNA) offers high specificity and stability as a marker for body fluid identification (BFI).
  • A commercial mRNA kit specifically for BFI is currently unavailable.

Purpose of the Study:

  • To develop and validate a novel mRNA kit for the identification of four forensic-relevant body fluids: blood, semen, saliva, and vaginal fluids.
  • To assess the sensitivity, specificity, stability, precision, and repeatability of the developed mRNA kit.
  • To create classifiers for identifying single body fluids and mixtures using the mRNA kit.

Main Methods:

  • Development of an mRNA kit comprising 21 specific mRNA markers and 3 housekeeping genes for BFI.
  • Testing of 451 single-body-fluid samples and validation studies in triplicates.
  • Construction of five classifiers, including Random Forest, to analyze body fluid samples and mixtures.

Main Results:

  • The developed mRNA kit demonstrated high reliability and suitability for BFI.
  • The kit achieved a sensitivity of 0.1 ng and was validated using various mixtures and casework samples.
  • The Random Forest classifier exhibited the highest precision at 94% for identifying body fluids.

Conclusions:

  • A novel mRNA kit for body fluid identification has been successfully developed.
  • The kit is a promising tool for forensic practice, offering reliable identification of key body fluids.
  • The developed classifiers effectively identify single fluids and mixtures, with Random Forest showing superior performance.

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