Estimation of early postmortem interval in rats by GC-MS-based metabolomics

From flesh to bones: Multi-omics approaches in forensic science

Recent advancements in omics techniques have revolutionised the study of biological systems, enabling the generation of high-throughput biomolecular data. These innovations have found diverse applications, ranging from personalised medicine to forensic sciences. While the investigation of multiple aspects of cells, tissues or entire organisms through the integration of various omics approaches (such as genomics, epigenomics, metagenomics, transcriptomics, proteomics and metabolomics) has already been established in fields like biomedicine and cancer biology, its full potential in forensic sciences remains only partially explored. In this review, we have presented a comprehensive overview of state-of-the-art analytical platforms employed in omics research, with specific emphasis on their application in the forensic field for the identification of the cadaver and the cause of death. Moreover, we have conducted a critical analysis of the computational integration of omics approaches, and highlighted the latest advancements in employing multi-omics techniques for forensic investigations.

A pilot study investigating early postmortem interval of rats based on ambient temperature and postmortem interval-related metabolites in blood

Estimation of the postmortem interval (PMI), especially the early PMI, plays a key role in forensic practice. Although several studies based on metabolomics approaches have presented significant findings for PMI estimation, most did not examine the effects of ambient temperature. In this study, gas chromatography-mass spectrometry (GC‒MS)‒based metabolomics was adopted to explore the changes in metabolites in the cardiac blood of suffocated rats at various ambient temperatures (5 °C, 15 °C, 25 °C, and 35 °C) from 0 to 24 h after death. Isoleucine, alanine, proline, valine, glycerol, glycerol phosphate, xanthine, and hypoxanthine were found to contribute to PMI in all temperature groups. Hypoxanthine and isoleucine were chosen to establish estimation models (equations) with an interpolation function using PMI as the dependent variable (f(x, y)), relative intensity as the independent variable x, and temperature as the independent variable y. Thereafter, these two models were validated with predictive samples and shown to have potential predictive ability. The findings indicate that isoleucine, alanine, proline, valine, glycerol, glycerol phosphate, xanthine, and hypoxanthine may be significant for PMI estimation at various ambient temperatures. Furthermore, a method to determine PMI based on ambient temperature and PMI-related metabolites was explored, which may provide a basis for future studies and practical applications.

Lipidomes in Cadaveric Decomposition and Determination of the Postmortem Interval: A Systematic Review

Lipids are a large group of natural compounds, together with proteins and carbohydrates, and are essential for various processes in the body. After death, the organism's tissues undergo a series of reactions that generate changes in some molecules, including lipids. This means that determining the lipid change profile can be beneficial in estimating the postmortem interval (PMI). These changes can also help determine burial sites and advance the localization of graves. The aim was to explore and analyze the decomposition process of corpses, focusing on the transformation of lipids, especially triglycerides (TGs) and fatty acids (FAs), and the possible application of these compounds as markers to estimate PMI and detect burial sites. A systematic review of 24 scientific articles from the last 23 years (2000-2023) was conducted. The results show that membrane glycerophospholipids (such as phosphatidylcholine and phosphatidylglycerol, among others) are the most studied, and the most promising results are obtained, with decreasing patterns as PMI varies. Fatty acids (FAs) are also identified as potential biomarkers owing to the variations in their postmortem concentration. An increase in saturated fatty acids (SFAs), such as stearic acid and palmitic acid, and a decrease in unsaturated fatty acids (UFAs), such as oleic acid and linoleic acid, were observed. The importance of intrinsic and extrinsic factors in decomposition is also observed. Finally, as for the burial sites, the presence of fatty acids and some sterols in burial areas of animal and human remains can be verified. In conclusion, glycerophospholipids and fatty acids are good markers for estimating PMI. It has been observed that there are still no equations for estimating the PMI that can be applied to forensic practice, as intrinsic and extrinsic factors are seen to play a vital role in the decomposition process. As for determining burial sites, the importance of soil and textile samples has been demonstrated, showing a direct relationship between saturated fatty acids, hydroxy fatty acids, and some sterols with decomposing remains.

A novel method for determining postmortem interval based on the metabolomics of multiple organs combined with ensemble learning techniques

Determining postmortem interval (PMI) is one of the most challenging and essential endeavors in forensic science. Developments in PMI estimation can take advantage of machine learning techniques. Currently, applying an algorithm to obtain information on multiple organs and conducting joint analysis to accurately estimate PMI are still in the early stages. This study aimed to establish a multi-organ stacking model that estimates PMI by analyzing differential compounds of four organs in rats. In a total of 140 rats, skeletal muscle, liver, lung, and kidney tissue samples were collected at each time point after death. Ultra-performance liquid chromatography coupled with high-resolution mass spectrometry was used to determine the compound profiles of the samples. The original data were preprocessed using multivariate statistical analysis to determine discriminant compounds. In addition, three interrelated and increasingly complex patterns (single organ optimal model, single organ stacking model, multi-organ stacking model) were established to estimate PMI. The accuracy and generalized area under the receiver operating characteristic curve of the multi-organ stacking model were the highest at 93% and 0.96, respectively. Only 1 of the 14 external validation samples was misclassified by the multi-organ stacking model. The results demonstrate that the application of the multi-organ combination to the stacking algorithm is a potential forensic tool for the accurate estimation of PMI.

The 'ForensOMICS' approach for postmortem interval estimation from human bone by integrating metabolomics, lipidomics, and proteomics

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.

New contributions to the relationship between sequential changes of ATP-related metabolites and post-mortem interval in rats

In recent years, it has become a new research direction to post-mortem interval (PMI) estimation from the changes in metabolite content generated during the process of cadaver corruption, and lead to more and more attention has been paid to the biochemical changes of muscles after death. The ATP degradation process has long been accepted as a precise manner to estimate freshness of meat products in food science. ATP-related metabolites may also serve as an indicator for PMI estimation of corpses. The purpose of this study was to test this possibility. Firstly, this study optimized the detection method of ATP-related metabolites in skeletal muscles of rat. Moreover, in animal experiment, ATP-related metabolites (ATP, ADP, AMP, IMP, Ino, Hx) in lower hind limb skeletal muscles of sixty rats were measured by RP-HPLC at different PMI for 168 h at 20 °C ± 1 °C, the change of freshness indicators (K value, K1 value, K0 value, H value, P value, G value, IMP ratio) were carefully analyzed, and investigate the relationship between them and PMI. The overall results showed that IMP, AMP, Hx, Total metabolites, K value, K1 value, K0 value, H value, P value, G value, and IMP ratio in skeletal muscles changed significantly with PMI (R2 = 0.864-0.971, all P < 0.01) while ATP, ADP, Ino, IMP precursors and IMP degradants had minor changes during the same period (R2 = 0.171-0.706). Specifically, significant linear positive correlations between H value of skeletal muscles and PMI were found, and the coefficients of their regression functions were R2 = 0.971. The correlation between AMP and PMI was slightly lower (R2 = 0.864), but the scope of PMI estimation was the widest (168 h). It can be concluded that the determination of ATP-related metabolites in skeletal muscle may be a potential tool in the PMI estimation. However, more researches on its influencing factors are needed to facilitate its final use in practice.

Identifying the scene of a crime through pollen analysis

The forensic analysis of pollen involves the comparison of crime scene and reference pollen samples. Successful matches are frequently used to solve time- or location-related crimes. Despite its prospects in criminal investigation, forensic palynology is still underused in casework due to inherent shortcomings such as its limited evidential weighting, scarcity of skilled palynologists dedicated to forensic casework and the laborious nature of analytical procedures. To address these challenges, the current state-of-the-art in forensic palynology is transiting from the traditional light microscopic methods that dominated the early days of palynology to more contemporary approaches like Raman spectroscopy, stable isotope analysis and DNA metabarcoding. The major challenges of these methods, however, include a lack of optimisation to forensic expectations and the unavailability of robust databases to permit accurate data interpretation, and quests to resolve these problems constitute the theme of current research. While reiterating the usefulness of pollen analysis in criminal investigation, this report recommends orthogonal testing as a way of improving the evidential weighting of forensic palynology.

Omics era in forensic medicine: towards a new age

Background/aim

Forensic medicine and sciences is a multidisciplinary branch of science, which frequently benefit from novel technologies. State of the art omics technologies have begun to be performed in forensic medicine and sciences, particularly in postmortem interval, intoxication, drugs of abuse, diagnosis of diseases and cause of death. This review aims to discuss the role and use of great omics (metabolomics, proteomics, genomics and transcriptomics) in forensic sciences, in detail.

Materials and methods

A detailed review of related literature was performed, and studies were subdivided as per the type of omics.

Results and conclusion

Omics seems as a revolutionary step in forensic science and sure carries it towards a new age. The number of forensic studies utilizing omics steadily increases in last years. Omics strategies should be used together in order to gather more accurate and certain data. Additional studies need to be performed to incorporate omics into routine forensic methodology.

Postmortem metabolomics: Correlating time-dependent concentration changes of xenobiotic and endogenous compounds

Postmortem redistribution (PMR) describes the artificial postmortem concentration changes of xenobiotics that may pose major challenges in forensic toxicology. Only a few studies have systematically investigated time-dependent postmortem drug concentration changes so far and the a posteriori estimation of the occurrence of PMR is not yet possible. In this context, the general concept that postmortem biochemical changes in blood might parallel drug redistribution mechanisms seems promising. Thus, the current study investigated the possible correlations between time-dependent postmortem concentration changes of xenobiotic and endogenous compounds; exemplified for authentic morphine (n = 19) and methadone (n = 11) cases. Peripheral blood samples at two time-points postmortem were analyzed for morphine and methadone concentrations and an (un)targeted postmortem metabolomics approach was utilized to combine targeted quantitative analysis of 56 endogenous analytes and untargeted screening for endogenous compounds (characterizing 1174 features); liquid and gas chromatography-mass spectrometry was used respectively. Individual statistically significant correlations between morphine/methadone and endogenous compounds/features could be determined. Hence, the general applicability of the proposed concept could successfully be confirmed. To verify the reproducibility and robustness of the correlating behavior, a larger dataset must be analyzed next. Once a marker/set of markers is found (e.g. robust correlation with specific xenobiotic or xenobiotic class), these could be used as surrogates to further study the time-dependent PMR in a broader variety of cases (e.g. independent of a xenobiotic drug present). A crucial next step will also be the attempt to create a statistical model that allows a posteriori estimation of PMR occurrence of xenobiotics to assist forensic toxicologists in postmortem case interpretation.

Analytical considerations for postmortem metabolomics using GC-high-resolution MS

Metabolomics studies that aim to qualitatively and quantitatively characterize the entirety of small endogenous biomolecules in an organism are widely conducted in the clinical setting. They also become more and more popular in the field of forensics (toxicology), e.g., to assist in postmortem investigations by objective postmortem interval estimation. However, other issues in postmortem toxicology, such as the phenomenon of (time-dependent) postmortem redistribution, have not yet been tackled by metabolomics studies. Hence, the aim of the current study was to develop an (un)targeted gas chromatography-high-resolution mass spectrometry-based method for endogenous metabolites as a tool for large-scale (un)targeted human postmortem metabolomics investigations (e.g., to objectively assess PMR) with thorough analytical evaluation of this method to ensure fitness-to-purpose in terms of reliability and robustness. This was achieved by using a targeted metabolite subset (n = 56) and a targeted processing workflow. Evaluation experiments have shown that using an artificial matrix (revised simulated body fluid (rSBF) + 5% bovine serum albumin (BSA)) for calibration purposes, all parameters lay within the scope of the method (sensitivity, selectivity, calibration model, accuracy, precision, processed sample stability, and extraction efficiency). When applying this method to large-scale studies, samples should be run in randomized order if analysis time is expected to exceed 18-24 h and potential biomarkers that are found with this method should be verified by a specialized, targeted method (e.g., by using standard addition in authentic matrix for quantification purposes). Overall, the current method can be successfully used for conduction of time-dependent postmortem metabolomics investigations. Graphical abstract.

Metabolomics of Eothenomys miletus from five Hengduan Mountains locations in summer

Climatic characteristics of Hengduan Mountains region were diverse, and Eothenomys miletus was a native species throughout this region. To investigate adaptive strategies of E. miletus to environmental factors in different locations in this region, five locations were selected, including Deqin (DQ), Xianggelila (XGLL), Lijiang (LJ), Jianchuan (JC) and Ailaoshan (ALS). Then, body mass, visceral organ masses, and serum and liver metabolomes of E. miletus from each location were examined. The results showed that body mass was significantly different among these five sites. Liver mass was lower in ALS than in other locations. PLS-DA analysis, metabolite tree maps and heat maps of serum and liver metabolites showed that samples from DQ and XGLL clustered together, as did the samples from LJ, JC and ALS. Serum concentrations of lipid and amino acid metabolites, concentrations of TCA cycle intermediates, lipid metabolites and amino acid metabolites in livers from DQ and XGLL were higher than those from other three regions. However, the concentrations of glycolytic metabolites were lower in DQ and XGLL. All these results indicated that E. miletus adapts to changes in environmental temperature and altitude of this region by adjusting body mass and serum and liver metabolite concentrations.

A 1H NMR metabolomic approach for the estimation of the time since death using aqueous humour: an animal model

INTRODUCTION

The estimation of the time since death, or post-mortem interval (PMI), still remains a main conundrum in forensic science. Several approaches have been so far proposed from either a qualitative or a quantitative point of view, but they still lack reliability and robustness. Recently, metabolomics has shown to be a potential tool to investigate the time-related post-mortem metabolite modifications in animal models.

OBJECTIVES

Here we propose, for the first time, the use of a ¹H NMR metabolomic approach for the estimation of PMI from aqueous humour (AH) in an ovine model.

METHODS

AH samples were collected at different times after death (from 118 to 1429 min). ¹H NMR experiments were performed and spectral data analysed by multivariate statistical tools.

RESULTS

A multivariate calibration model was built to estimate PMI on the basis of the metabolite content of the samples. The model was validated with an independent test set, obtaining a prediction error of 59 min for PMI < 500 min, 104 min for PMI from 500 to 1000 min, and 118 min for PMI > 1000 min. Moreover, the metabolomic approach suggested a picture of the mechanisms underlying the post-mortem biological modifications, highlighting the role played by taurine, choline, and succinate.

CONCLUSION

The time-related modifications of the ¹H NMR AH metabolomic profile seem to be encouraging in addressing the issue of a reproducible and robust model to be employed for the estimation of the time since death.

An experimental study on investigating the postmortem interval in dichlorvos poisoned rats by GC/MS-based metabolomics

The estimation of the postmortem interval (PMI) is always a key issue in forensic science. Although many attempts based on metabolomics approaches have been proven to be feasible and accurate for PMI estimation, there have been no reports regarding the determination of the PMI in acute dichlorvos (DDVP) poisoning. In this study, all rats were killed by acute DDVP poisoning at a dose three fold the oral LD₅₀ (240 mg/kg). Gas chromatography-mass spectrometry (GC/MS) was applied to investigate the metabolic profiling of blood samples at various times after death up to 72 h. A total of 39 metabolites were found to be associated with PMI, and the combinations of various numbers of metabolites were used to establish support vector regression (SVR) models to investigate the PMI. The SVR model constructed by 23 metabolites had a minimum mean squared error (MSE) of 5.49 h for the training set. Then, the SVR model was validated by prediction set with an MSE of 10.33 h, suggesting good predictive ability of the model for investigating the PMI. The findings demonstrated the great potential of GC/MS-based metabolomics combined with the SVR model in determining the PMI of DDVP poisoned rats and provided an experimental basis for the application of this approach in investigating the PMI of other toxicants.