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

Abstract

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.

Keywords: biochemistry; chemical biology; decomposition; human; human bone; lipidomics; metabolomics; multi-omics; postmortem interval; proteomics.

Figure 1.. Results for the tuned model.

(A) Arrow plot showing multiblock contexts for the overall model. (B) Optimal number of components to explain model variable calculated via cross-validation (error bars provide standard deviation). (C) Loading plot showing how each variable contributes to the covariance of each group. (D) The clustered image map (CIM) shows the selected compounds in the final model. It is possible to see that most compounds decrease in intensity after decomposition except for few metabolites and two lipids that specifically increase in certain postmortem interval (PMI) intervals.

Figure 1—figure supplement 1.. Results for the metabolomics data.

(A) Clustered image map (CIM), (B) sample plot, (C) and boxplot for the metabolomics data.

Figure 1—figure supplement 2.. Results for the lipidomics data.

(A) Clustered image map (CIM) and (B) sample plot for the lipidomics data.

Figure 1—figure supplement 3.. Results for the proteomics data.

(A) Clustered image map (CIM), (B) sample plot, (C) and boxplot for the proteomics data.

Figure 1—figure supplement 4.. Balanced error variations across variable selection steps.

Figure 1—figure supplement 5.. Score plots for partial least square discriminant analysis (PLS-DA) results of all the omics blocks considered.

Figure 2.. DIABLO selected variables correlated with PMI.

(A) Boxplots of the selected variables after tuning that shows variation with postmortem interval (PMI). Variables are expressed in standardized values. (B) Correlation between different omics blocks highlighting the correlations between different compounds obtained with the three omics selected in the final discriminant analysis model.

Figure 3.. Metabolite set enrichment analysis based on differentially expressed metabolites identified in bone.

Figure 4.. Positioning of the bodies in the single graves (left) pre-decomposition and (right) after complete skeletonization.

Figure 4—figure supplement 1.. Flowchart of the experimental design of the study.