Validation of the InnoXtract™ DNA extraction method for bone, teeth, and rootless hair.

Autor: Sinha, Sudhir K., Brown, Hiromi, Holt, Hailey, Khan, Mah‐ro, Sgueglia, Joanne B., Murphy, Gina
Předmět:
Zdroj: Journal of Forensic Sciences; May2023, Vol. 68 Issue 3, p1020-1035, 16p
Abstrakt: Forensic casework samples often include human hairs, teeth, and bones. Hairs with roots are routinely processed for DNA analysis, while rootless hairs are either not tested or processed using mitochondrial DNA. Bones and teeth are submitted for human remains identifications for missing persons and mass disaster cases. DNA extraction from these low templates and degraded samples is challenging. The new InnoXtract DNA extraction method utilizes magnetic beads that are optimized to bind small DNA fragments, as small as 100 base pairs, to purify high‐yield DNA from compromised samples. This validation study evaluates InnoXtract's ability to obtain amplifiable DNA from samples such as rootless hairs and skeletal remains. Studies performed include sensitivity, stability, repeatability, reproducibility, non‐probative samples, and comparison to standard organic extractions. Sensitivity studies demonstrate average yield recoveries ranging from 53% to 100% and 73% to 85% for the InnoXtract hair and bone methods, respectively. Studies demonstrate consistent results across a range of sample types, such as insulted and un‐insulted bone and teeth, as well as hair shafts from donors of various ages, gender, race, and hair characteristics. The InnoXtract bone method outperformed organic extraction. The method was successfully automated on a MagMAX™ Express‐96, with recoveries over 70% relative to the manual version. InnoXtract has the potential as an automated high‐throughput, high‐yield bone extraction method with 6 h of total extraction time for up to 96 samples. The validation study results demonstrate that the InnoXtract kits produce high‐yield and high‐quality DNA from compromised bone, teeth, and hair shaft samples. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index