The ‘ForensOMICS’ approach for postmortem interval estimation from human bone by integrating metabolomics, lipidomics, and proteomics
Curation statements for this article:-
Curated by eLife
eLife assessment
This well-presented and sophisticated study provides significant proof-of-concept for the application of the ForensOMICS approach as a new pathway for forensic taphonomy with great promise to advance future research. The solid foundation of the research combining metabolomics, proteomics, and lipidomics is considered very exciting, strong, and expands the boundaries of forensics research.
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (eLife)
- Biochemistry and Chemical Biology (eLife)
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.
Article activity feed
-
-
Author Response
Reviewer #3 (Public Review):
In this paper, for the first time, metabolomics, proteomics, and lipidomics are combined to multi-dimensionally obtain more objective and scientific clues about early and advanced PMI, compared to the traditional methods of PMI estimation that relies on the subjective judgment of morphology. The "ForensOMICS" pipeline establishes a multi-omics analysis pipeline based on the LC-MS platform, which will bring influence and inspiration to the related research of PMI estimation based on molecular biological markers in the foreseeable future. However, due to the limitation of the availability of bone samples and metadata (which might contain covariates with latent influences on the PMI estimation), the current research is still a proof-of-concept study which is incomplete for the "ForensOMICS" …
Author Response
Reviewer #3 (Public Review):
In this paper, for the first time, metabolomics, proteomics, and lipidomics are combined to multi-dimensionally obtain more objective and scientific clues about early and advanced PMI, compared to the traditional methods of PMI estimation that relies on the subjective judgment of morphology. The "ForensOMICS" pipeline establishes a multi-omics analysis pipeline based on the LC-MS platform, which will bring influence and inspiration to the related research of PMI estimation based on molecular biological markers in the foreseeable future. However, due to the limitation of the availability of bone samples and metadata (which might contain covariates with latent influences on the PMI estimation), the current research is still a proof-of-concept study which is incomplete for the "ForensOMICS" approach to be applied in court.
Strengths:
Combing multiple omics and bioinformatics, as claimed by the authors, the "ForensOMICS" approach is more accurate and precise than the conventional morphological methods and molecular biological methods using single omics. Moreover, the research does not stop at developing time-dependent models using several omics biomarkers but carries on the enrichment analysis of relevant markers to further explore the pathophysiology mechanism behind the great changes in the internal environment after death, so as to provide meaningful reference data for the basic forensic research of death.
Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies (DIABLO) method and multiple features selecting tools are used in the bioinformatic process to analyze multiple omics data, and PMI classification model constructed based on PLS-DA, with parameters optimized by 3-fold/100 repeats cross-validation. The overall analysis process is relatively complete, and the data and classification model provided have scientific values for reference.
The "ForensOMICS" workflow in principle is compatible across metabolomics, proteomics, and lipidomics data obtained in different domains of proof-of-concept studies focusing on forensic-related time estimation (e.g. post-mortem submersion interval and time since deposit), for offering relatively complete analysis process.
Weaknesses:
Although the paper does have strengths in principle, the limitation of the availability of bone samples and metadata leads to the major weaknesses of the paper. Therein, age bias samples with single bone type and lack of analysis for environmental factors are the major weaknesses that argue against the key claims in the manuscript by the data presented.
The mean age of body donors is 74 years with {plus minus}11.6 years of standard deviation, while there was only one type of bone tissue (left anterior midshaft tibia). Different structures and locations of the sampled bone tissue as well as metabolic changes and bone degeneration caused by aging may lead to significant discrepancies in different multi-omics data. Moreover, most of the dead found at crime scenes are in the prime of life, and in addition to the tibia, other skeletal remains found at the scenes are commonly skull, ribs, upper limb bones, and teeth. Therefore, the relevant conclusions obtained from the research based on the limited bone samples cannot meet the actual needs for estimating the PMI of skeletal remains. As mentioned by the authors in the discussion, due to the difficulty in acquiring human remain samples with definite post-mortem intervals, this study is still proof-of-concept. If possible, the authors can focus on a larger sample set of different bone remains in younger age groups in future studies.
The reviewer is describing exactly the purpose of this manuscript. As highlighted by them, this paper is not intended to be an applicable method for PMI estimation at this stage, as we are aware of the differences that may exist between multiple skeletal elements and the omics results (at least, for proteomics data, as we published several papers on this topic). However, this is the proof of concept to demonstrate the potential that multiple omics combined together may have in addressing the PMI. We are committed to increase our sample size in order to develop a forensic technique for PMI estimation, that should anyway be then validated on multiple skeletal elements.
Tibia is frequently recovered from scenes also involving the presence of incomplete human remains subjected to long PMIs; our previous studies have also demonstrated that midshaft tibia may be an ideal candidate for proteomics analyses, due to its small intra-individual variability in comparison with other bones. Therefore, the selected sample for this pilot has been the anterior midshaft tibia. We do agree with the reviewer that such samples may not be representative of the whole bone proteome, metabolome, and lipidome composition (with particular regards to cortical and trabecular parts); however, this could be addressed as part of future studies on the topic.
We do agree with the reviewer about the possible confounding factor related to the relatively high variability in terms of age at death differences, that was indeed due to the difficult in acquiring human bodies with a known PMI.
Although in-life physiological and/or pathological conditions (i.e., osteoporosis) might be responsible for variability among baseline samples and between baseline and different long PMIs’ samples seen in several metabolites and proteins, we believe the biological phenomena underlying PMI are strong enough to overcome such limitations in the design of the experiment. This is also supported by the small inter-individual variability observed amongst the fresh/baseline samples.
It is suggested that metadata which may be influence factors of PMI such as temperature, humidity, UV-exposure, and deposition context (which is already recorded) should be recorded and statistically analyzed, so as to further optimize the "ForensOMICS" classification model by considering these possible environmental covariates. In addition, according to the No Free Lunch theorem, PLS-DA is very likely not to be the optimal solution for all the above-mentioned PMI classification tasks based on multi-omics data under different environmental conditions. It is recommended to develop and compare more different classification models for improving the generalization performance of the "ForensOMICS" approach.
We agree with the reviewer that these factors are crucial in the decomposition process. In our opinion, however, at this stage it is not appropriate to include these metadata in the statistical analysis as covariates by applying additional classification models, due to the small sample size available. Additionally, the main focus of the paper is exclusively on PMI-driven modifications. Environmental data have been added for reference in Supplementary File 2 and will be taken into account in future works when a bigger sample size will be evaluated.
Due to the limitation of sample size and the discrete-time gradients, the omics data obtained in the paper could only be applied to build a classification model rather than the regression model. Since such a model does not give a specific predicted PMI with MSE and RMSE indicating its performance, and the current "ForensOMICS" approach failed to distinguish different samples of late PMI (219-834 days), there is still a distance for "ForensOMICS" approach to apply in the actual forensic practice.
Thank you for your comments. We agree, and stressed across the whole manuscript, that this is far from being appliable to forensic practice. The proof-of-concept nature of the study represents a mandatory step for the building of a regression model than can be challenged in the future with the highly rigorous standard required in the forensic setting (i.e., Daubert criteria). We appreciate the understanding of the reviewer for the choice of modelling the data using classification rather than regression.
-
eLife assessment
This well-presented and sophisticated study provides significant proof-of-concept for the application of the ForensOMICS approach as a new pathway for forensic taphonomy with great promise to advance future research. The solid foundation of the research combining metabolomics, proteomics, and lipidomics is considered very exciting, strong, and expands the boundaries of forensics research.
-
Reviewer #1 (Public Review):
The 'ForensOMICS' approach is an exciting new area that clearly needs further attention. Despite the current paper being a proof-of-concept, the authors have taken due care and diligence to present the findings of the work in a transparent manner, being careful not to draw hard conclusions based on preliminary experimentation.
Despite being one of the most critical aspects of forensic investigations involving human remains, the estimation of PMI still presents significant challenges. This issue forms the premise of the current work, and this is clearly addressed in both the results and the thorough discussion. The selection of bone tissue as the target matrix is also quite unique and valuable, particularly in scenarios where other more common matrices (like soft tissues) are depleted, as is explained in the …
Reviewer #1 (Public Review):
The 'ForensOMICS' approach is an exciting new area that clearly needs further attention. Despite the current paper being a proof-of-concept, the authors have taken due care and diligence to present the findings of the work in a transparent manner, being careful not to draw hard conclusions based on preliminary experimentation.
Despite being one of the most critical aspects of forensic investigations involving human remains, the estimation of PMI still presents significant challenges. This issue forms the premise of the current work, and this is clearly addressed in both the results and the thorough discussion. The selection of bone tissue as the target matrix is also quite unique and valuable, particularly in scenarios where other more common matrices (like soft tissues) are depleted, as is explained in the work. It is clear that, given further studies and validation, this approach could have a profound impact on the operational world of forensics.
-
Reviewer #2 (Public Review):
The authors applied an innovative and very interesting approach based on different -omics platforms to study the biological post-mortem transformations of human bone. Despite the study being a proof-of-concept, because of the small number of collected samples and the lack of external validation, the methodology is promising. The study will have a strong impact on the field and present the state-of-art of -omics sciences.
-
Reviewer #3 (Public Review):
In this paper, for the first time, metabolomics, proteomics, and lipidomics are combined to multi-dimensionally obtain more objective and scientific clues about early and advanced PMI, compared to the traditional methods of PMI estimation that relies on the subjective judgment of morphology. The "ForensOMICS" pipeline establishes a multi-omics analysis pipeline based on the LC-MS platform, which will bring influence and inspiration to the related research of PMI estimation based on molecular biological markers in the foreseeable future. However, due to the limitation of the availability of bone samples and metadata (which might contain covariates with latent influences on the PMI estimation), the current research is still a proof-of-concept study which is incomplete for the "ForensOMICS" approach to be …
Reviewer #3 (Public Review):
In this paper, for the first time, metabolomics, proteomics, and lipidomics are combined to multi-dimensionally obtain more objective and scientific clues about early and advanced PMI, compared to the traditional methods of PMI estimation that relies on the subjective judgment of morphology. The "ForensOMICS" pipeline establishes a multi-omics analysis pipeline based on the LC-MS platform, which will bring influence and inspiration to the related research of PMI estimation based on molecular biological markers in the foreseeable future. However, due to the limitation of the availability of bone samples and metadata (which might contain covariates with latent influences on the PMI estimation), the current research is still a proof-of-concept study which is incomplete for the "ForensOMICS" approach to be applied in court.
Strengths:
Combing multiple omics and bioinformatics, as claimed by the authors, the "ForensOMICS" approach is more accurate and precise than the conventional morphological methods and molecular biological methods using single omics. Moreover, the research does not stop at developing time-dependent models using several omics biomarkers but carries on the enrichment analysis of relevant markers to further explore the pathophysiology mechanism behind the great changes in the internal environment after death, so as to provide meaningful reference data for the basic forensic research of death.
Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies (DIABLO) method and multiple features selecting tools are used in the bioinformatic process to analyze multiple omics data, and PMI classification model constructed based on PLS-DA, with parameters optimized by 3-fold/100 repeats cross-validation. The overall analysis process is relatively complete, and the data and classification model provided have scientific values for reference.
The "ForensOMICS" workflow in principle is compatible across metabolomics, proteomics, and lipidomics data obtained in different domains of proof-of-concept studies focusing on forensic-related time estimation (e.g. post-mortem submersion interval and time since deposit), for offering relatively complete analysis process.
Weaknesses:
Although the paper does have strengths in principle, the limitation of the availability of bone samples and metadata leads to the major weaknesses of the paper. Therein, age bias samples with single bone type and lack of analysis for environmental factors are the major weaknesses that argue against the key claims in the manuscript by the data presented.
The mean age of body donors is 74 years with {plus minus}11.6 years of standard deviation, while there was only one type of bone tissue (left anterior midshaft tibia). Different structures and locations of the sampled bone tissue as well as metabolic changes and bone degeneration caused by aging may lead to significant discrepancies in different multi-omics data. Moreover, most of the dead found at crime scenes are in the prime of life, and in addition to the tibia, other skeletal remains found at the scenes are commonly skull, ribs, upper limb bones, and teeth. Therefore, the relevant conclusions obtained from the research based on the limited bone samples cannot meet the actual needs for estimating the PMI of skeletal remains. As mentioned by the authors in the discussion, due to the difficulty in acquiring human remain samples with definite post-mortem intervals, this study is still proof-of-concept. If possible, the authors can focus on a larger sample set of different bone remains in younger age groups in future studies.
It is suggested that metadata which may be influence factors of PMI such as temperature, humidity, UV-exposure, and deposition context (which is already recorded) should be recorded and statistically analyzed, so as to further optimize the "ForensOMICS" classification model by considering these possible environmental covariates. In addition, according to the No Free Lunch theorem, PLS-DA is very likely not to be the optimal solution for all the above-mentioned PMI classification tasks based on multi-omics data under different environmental conditions. It is recommended to develop and compare more different classification models for improving the generalization performance of the "ForensOMICS" approach.
Due to the limitation of sample size and the discrete-time gradients, the omics data obtained in the paper could only be applied to build a classification model rather than the regression model. Since such a model does not give a specific predicted PMI with MSE and RMSE indicating its performance, and the current "ForensOMICS" approach failed to distinguish different samples of late PMI (219-834 days), there is still a distance for "ForensOMICS" approach to apply in the actual forensic practice.
-
