Saliva identification in forensic samples by automated microextraction and intact mass analysis of statherin

通过自动微萃取和完整质量分析法医样本中的唾液进行鉴定

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作者:Catherine O Brown, Christian G Westring, Phillip B Danielson, Kevin M Legg

Abstract

The enzyme α-amylase has long been a commonly targeted protein in serological tests for saliva. While being especially abundant in saliva, α-amylase is detectable in vaginal secretions, sweat, fecal matter, breast milk and other matrices. As a result, assays for α-amylase only provide a presumptive indication of saliva. The availability of mass spectrometry-based tools for the detection of less abundant, but more specific, protein targets (e.g., human statherin) has enabled the development of high confidence assays for human saliva. Sample throughput, however, has traditionally been low due to multi-step workflows for protein extraction, quantitation, enzymatic digestion, solid phase cleanup, and nano-/capillary-based chromatography. Here, we present two novel "direct" single-stage extraction strategies for sample preparation. These feature immunoaffinity purification and reversed-phase solid-phase microextraction in conjunction with intact mass analysis of human statherin for saliva identification. Mass analysis was performed on the Thermo Scientific Q-Exactive™ Orbitrap mass spectrometer with a 10-min analytical run time. Data analysis was performed using Byos® from Protein Metrics. Two sample sets were analyzed with a population of 20 individuals to evaluate detection reliability. A series of casework-type samples were then assayed to evaluate performance in an authentic forensic context. Statherin was confidently identified in 92% and 71% of samples extracted using the immunoaffinity purification and solid phase microextraction approaches, respectively. Overall, immunoaffinity purification outperformed the solid phase microextraction, especially with complex mixtures. In toto, robotic extraction and intact mass spectrometry enable the reliable identification of trace human saliva in a variety of sample types.

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