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    Obtaining forensically relevant information beyond who deposited a biological stain on how and under which circumstances it was deposited is a question of increasing importance in forensic molecular biology. In the past few years, several studies have been produced on the potential of gene expression analysis to deliver relevant contextualizing information, e.g. on nature and condition of a stain as well as aspects of stain deposition timing. However, previous attempts to predict the time-of-day of sample deposition were all based on and thus limited by previously described diurnal oscillators. Herein, we newly approached this goal by applying current sequencing technologies and statistical methods to identify novel candidate markers for forensic time-of-day predictions from whole transcriptome analyses. To this purpose, we collected whole blood samples from ten individuals at eight different time points throughout the day, performed whole transcriptome sequencing and applied biostatistical algorithms to identify 81 mRNA markers with significantly differential expression as candidates to predict the time of day. In addition, we performed qPCR analysis to assess the characteristics of a subset of 13 candidate predictors in dried and aged blood stains. While we demonstrated the general possibility of using the selected candidate markers to predict time-of-day of sample deposition, we also observed notable variation between different donors and storage conditions, highlighting the relevance of employing accurate quantification methods in combination with robust normalization procedures.This study's results are foundational and may be built upon when developing a targeted assay for time-of-day predictions from forensic blood samples in the future. Copyright © 2023 Elsevier B.V. All rights reserved.

    Citation

    A Gosch, A Bhardwaj, C Courts. TrACES of time: Transcriptomic analyses for the contextualization of evidential stains - Identification of RNA markers for estimating time-of-day of bloodstain deposition. Forensic science international. Genetics. 2023 Nov;67:102915

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    PMID: 37598452

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