The combined use of multiple omics allows to study complex interrelated biological processes in their entirety. We applied a combination of metabolomics, lipidomics and proteomics to human bones to investigate their combined potential to estimate time elapsed since death (i.e., the postmortem interval [PMI]). This ‘ForensOMICS’ approach has the potential to improve accuracy and precision of PMI estimation of skeletonized human remains, thereby helping forensic investigators to establish the timeline of events surrounding death. Anterior midshaft tibial bone was collected from four female body donors before their placement at the Forensic Anthropology Research Facility owned by the Forensic Anthropological Center at Texas State (FACTS). Bone samples were again collected at selected PMIs (219-790-834-872days). Liquid chromatography mass spectrometry (LC-MS) was used to obtain untargeted metabolomic, lipidomic, and proteomic profiles from the pre- and post-placement bone samples. The three omics blocks were investigated independently by univariate and multivariate analyses, followed by Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies (DIABLO), to identify the reduced number of markers describing postmortem changes and discriminating the individuals based on their PMI. The resulting model showed that pre-placement metabolome, lipidome and proteome profiles were clearly distinguishable from post-placement ones. Metabolites in the pre-placement samples suggested an extinction of the energetic metabolism and a switch towards another source of fuelling (e.g., structural proteins). We were able to identify certain biomolecules with an excellent potential for PMI estimation, predominantly the biomolecules from the metabolomics block. Our findings suggest that, by targeting a combination of compounds with different postmortem stability, in the future we could be able to estimate both short PMIs, by using metabolites and lipids, and longer PMIs, by using proteins.

Bone-mineral density (BMD) is a measure of the inorganic mineral content in bone, and is one of the more informative assessments of bone quality in both clinical studies and forensic investigations. Several factors, such as age, sex, disease, genetics, and lifestyle, affect BMD measurements, and normative standards must be applied for specific groups and individuals. One of the most common disorders associated with low BMD is osteoporosis and increased fracture risk, due to a decrease in bone strength and an increase in bone fragility. Medical conditions like diabetes or hyperthyroidism and other parameters like peak bone mass and postmenopausal estrogen deficiency also impact BMD. Single- and dual-energy photon absorptiometry, quantitative computet tomography, and magnetic resonance imaging are some of the technological modalities for BMD quantification, and each presents distinct advantages and limitations, depending on the purpose of the analysis, the specific characteristics of the individual, the bone site under examination, and the equipment and trained personnel available. Recently, BMD values were applied to forensic medicine in a variety of scenarios ranging from age and sex estimation to the assessment of malnutrition and the use of finite-element modelling. Despite technical and methodological inconsistencies reported in the literature on BMD readings, there is scope for expanding the use of this variable in forensic settings.

Establishing a biological profile of skeletal remains is a key task of forensic anthropologists. Sex estimation is essential in forensic examination, as other elements of the biological profile, such as age at death or stature, are sex dependent. Visual assessment is considered low-cost and quick, therefore it is a commonly applied method of sex estimation. The most reliable results can be obtained with the analysis of the anterior part of the pelvis, however, these skeletal elements are fragile and prone to destruction. In contrary, the more robust posterior portion of the pelvis is often recovered. Several features of the posterior pelvis have been explored in the context of sexual dimorphism. The aim of the present study was to test three previously published methods of sex assessment based on the analysis of the inferior shape of the auricular surface (Novotný, 1975), the greater sciatic notch shape (Walker, 2005 (revised)) and overall morphology, apex morphology and inflection of the auricular surface (Luna et al., 2017). The sample consisted of 194 individuals of Greek origin from a documented modern collection. Four features of the auricular surface and shape of the greater sciatic notch were examined. Logistic regression analysis was applied to produce a sex discriminatory formula. The method proposed by Luna et al. (2017) failed to produce satisfactory results with overall accuracies of 36%, 50% and 53% for overall morphology, apex morphology and inflection respectively. Slightly better results (64%) were obtained with the inferior shape morphology (Novotný, 1975). However, the highest accuracy rate of 81% was noted for the greater sciatic notch shape (Walker, 2005 (revised)). The formula produced in this study allowed correct classification of 83.2% of the sample. This study illustrates that in spite of the presence of sexual dimorphism in the posterior portion of ilium, features of the auricular surface proposed as sex indicators by Novotný (1975) and Luna et al. (2017) should not be used for sex estimation purposes in the Greek population. The formula produced in this study and the greater sciatic notch shape should only be used as additional methods in cases where neither the cranium nor the anterior portion of the pelvis is present.

Considering the growing importance of microbiome analyses in forensics for identifying individuals, this study explores the transfer of the skin microbiome onto clothing, its persistence on fabrics over time, and its transferability from the environment and between different garments. Furthermore, this project compares three specific QIAGEN microbiome extraction kits to test their extraction efficiency on fabric samples. Additionally, this study aims to check if these extracts contain human DNA, providing a chance to obtain more information from the same evidence for personal identification. The results obtained show: (1) variations in the skin microbiome between the volunteers, potentially due to their different sex; (2) differences in microbial composition between worn and unworn clothing; (3) the influence of the environment on the microbial signature of unworn clothing; (4) the potential use of certain phyla as biomarkers to differentiate between worn and unworn garments, even over extended periods; (5) a tendency towards extraction biases in the QIAampMP^® DNA microbiome kit among the three tested ones; and (6) none of the extraction kits allow for the typing of human genetic profiles suitable for comparison. In conclusion, our study offers supplementary insights into the potential utility of time-transferred microbiome analysis on garments for forensic applications.

Age estimation remains one of the most challenging tasks in forensic practice when establishing a biological profile of unknown skeletonised remains. Morphological methods based on developmental markers of bones can provide accurate age estimates at a young age, but become highly unreliable for ages over 35 when all developmental markers disappear. This study explores the changes in the biomechanical properties of bone tissue and matrix, which continue to change with age even after skeletal maturity, and their potential value for age estimation. As a proof of concept we investigated the relationship of 28 variables at the macroscopic and microscopic level in rib autopsy samples from 24 individuals. Stepwise regression analysis produced a number of equations one of which with seven variables showed an R² = 0.949; a mean residual error of 2.13 yrs ±0.4 (SD) and a maximum residual error value of 2.88 yrs. For forensic purposes, by using only bench top machines in tests which can be carried out within 36 hrs, a set of just 3 variables produced an equation with an R² = 0.902 a mean residual error of 3.38 yrs ±2.6 (SD) and a maximum observed residual error 9.26yrs. This method outstrips all existing age-at-death methods based on ribs, thus providing a novel lab based accurate tool in the forensic investigation of human remains. The present application is optimised for fresh (uncompromised by taphonomic conditions) remains, but the potential of the principle and method is vast once the trends of the biomechanical variables are established for other environmental conditions and circumstances.