New markers for old stains: stable mRNA markers for blood and saliva identification from up to 16-year-old stains

In forensic science, the unequivocal identification of the cellular origin of crime scene samples used for DNA profiling can provide crucial information for crime scene reconstruction. We have previously shown that various mRNA markers from genes with expression patterns specific for blood and saliva can be established from whole-genome expression analysis of time-wise degraded samples and were stable enough to specifically identify blood and saliva stains up to 180 days of age. Here, we showed that nine blood-specific and five saliva-specific mRNA markers can be amplified successfully and reliably in much older blood (13–16 years) and saliva (2–6 years) stains, respectively, suggesting their suitability for tissue identification in forensic case work. Moreover, our findings imply that forensic RNA testing can be reliable and robust if degraded samples are considered in the marker ascertainment procedure, with promising expectations beyond tissue identification purposes. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users. Miscellaneous  The gene name SPRR3 needs to be replaced by SPRR2A in our previous publication Zubakov et al. Int J Legal Med 122(2):135–142, 2008.     

Estimation of bloodstain deposition time within a 24-h day-night cycle with rhythmic mRNA based on a machine learning algorithm

Estimating the time that bloodstains are left at a crime scene can provide invaluable evidence for law enforcement investigations, including determining the time of the crime, linking the perpetrator to the crime scene, narrowing the pool of possible suspects, and verifying witness statements. There have been some attempts to estimate the time since deposition of bloodstains, i.e., how much time has passed since the bloodstain was left at a crime scene. However, most studies focus on the time interval of days. As far as we know, previous study have been conducted to estimate the deposition time of blood within a 24-h day-night cycle. To date, there is a lack of studies on whether rhythmic mRNA of blood is suitable for bloodstain samples. In this study, we estimated the bloodstain deposition time within a 24-h day-night cycle based on the expression of messenger RNAs (mRNAs) by real-time quantitative polymerase chain reaction. Bloodstain samples were prepared from eight individuals at eight time points under real and uncontrolled conditions. Four mRNAs expressed rhythmically and were used to construct a regression model using the k-nearest neighbor (KNN) algorithm, resulting in a mean absolute error of 3.92 h. Overall, using the rhythmic mRNAs, a machine learning model was developed which has allowed us to predict the deposition time of bloodstains within the 24-h day-night cycle in East Asian populations. This study demonstrates that mRNA biomarkers can be used to estimate the bloodstain deposition time within a 24-h period. Furthermore, rhythmic mRNA biomarkers provide a potential method and perspective for estimating the deposition time of forensic traces in forensic investigation. Case samples in forensic analysis are usually limited or degraded, so the stability and sensitivity of rhythmic biomarkers need to be further investigated.     

Human age estimation from blood using mRNA,DNA methylation,DNA rearrangement,and telomere length

Establishing the age of unknown persons, or persons with unknown age, can provide important leads in police investigations, disaster victim identification, fraud cases, and in other legal affairs. Previous methods mostly relied on morphological features available from teeth or skeletal parts. The development of molecular methods for age estimation allowing to use human specimens that possess no morphological age information, such as bloodstains, is extremely valuable as this type of samples is commonly found at crime scenes. Recently, we introduced a DNA-based approach for human age estimation from blood based on the quantification of T-cell specific DNA rearrangements (sjTRECs), which achieves accurate assignment of blood DNA samples to one of four 20-year-interval age categories. Aiming at improving the accuracy of molecular age estimation from blood, we investigated different types of biomarkers. We started out by systematic genome-wide surveys for new age-informative mRNA and DNA methylation markers in blood from the same young and old individuals using microarray technologies. The obtained candidate markers were validated in independent samples covering a wide age range using alternative technologies together with previously proposed DNA methylation, sjTREC, and telomere length markers. Cross-validated multiple regression analysis was applied for estimating and validating the age predictive power of various sets of biomarkers within and across different marker types. We found that DNA methylation markers outperformed mRNA, sjTREC, and telomere length in age predictive power. The best performing model included 8 DNA methylation markers derived from 3 CpG islands reaching a high level of accuracy (cross-validated R² = 0.88, SE ± 6.97 years, mean absolute deviation 5.07 years). However, our data also suggest that mRNA markers can provide independent age information: a model using a combined set of 5 DNA methylation markers and one mRNA marker could provide similarly high accuracy (cross-validated R² = 0.86, SE ± 7.62 years, mean absolute deviation 4.60 years). Overall, our study provides new and confirms previously suggested molecular biomarkers for age estimation from blood. Moreover, our comparative study design revealed that DNA methylation markers are superior for this purpose over other types of molecular biomarkers tested. While the new and some previous findings are highly promising, before molecular age estimation can eventually meet forensic practice, the proposed biomarkers should be tested further in larger sets of blood samples from both healthy and unhealthy individuals, and markers and genotyping methods shall be validated to meet forensic standards.     

Stable RNA markers for identification of blood and saliva stains revealed from whole genome expression analysis of time-wise degraded samples

Human body fluids such as blood and saliva represent the most common source of biological material found at a crime scene. Reliable tissue identification in forensic science can reveal significant insights into crime scene reconstruction and can thus contribute toward solving crimes. Limitations of existing presumptive tests for body fluid identification in forensics, which are usually based on chemoluminescence or protein analysis, are expected to be overcome by RNA-based methods, provided that stable RNA markers with tissue-specific expression patterns are available. To generate sets of stable RNA markers for reliable identification of blood and saliva stains we (1) performed whole-genome gene expression analyses on a series of time-wise degraded blood and saliva stain samples using the Affymetrix U133 plus2 GeneChip, (2) consulted expression databases to obtain additional information on tissue specificity, and (3) confirmed expression patterns of the most promising candidate genes by quantitative real-time polymerase chain reaction including additional forensically relevant tissues such as semen and vaginal secretion. Overall, we identified nine stable mRNA markers for blood and five stable mRNA markers for saliva detection showing tissue-specific expression signals in stains aged up to 180 days of age, expectedly older. Although, all of the markers were able to differentiate blood/saliva from semen samples, none of them could differentiate vaginal secretion because of the complex nature of vaginal secretion and the biological similarity of buccal and vaginal mucosa. We propose the use of these 14 stable mRNA markers for identification of blood and saliva stains in future forensic practice. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.