Accurate predictions of postmortem interval using linear regression analyses of gene meter expression data

Spatial transcriptomics at the brain-electrode interface in rat motor cortex and the relationship to recording quality

Study of the foreign body reaction to implanted electrodes in the brain is an important area of research for the future development of neuroprostheses and experimental electrophysiology. After electrode implantation in the brain, microglial activation, reactive astrogliosis, and neuronal cell death create an environment immediately surrounding the electrode that is significantly altered from its homeostatic state.Objective.To uncover physiological changes potentially affecting device function and longevity, spatial transcriptomics (ST) was implemented to identify changes in gene expression driven by electrode implantation and compare this differential gene expression to traditional metrics of glial reactivity, neuronal loss, and electrophysiological recording quality.Approach.For these experiments, rats were chronically implanted with functional Michigan-style microelectrode arrays, from which electrophysiological recordings (multi-unit activity, local field potential) were taken over a six-week time course. Brain tissue cryosections surrounding each electrode were then mounted for ST processing. The tissue was immunolabeled for neurons and astrocytes, which provided both a spatial reference for ST and a quantitative measure of glial fibrillary acidic protein and neuronal nuclei immunolabeling surrounding each implant.Main results. Results from rat motor cortex within 300µm of the implanted electrodes at 24 h, 1 week, and 6 weeks post-implantation showed up to 553 significantly differentially expressed (DE) genes between implanted and non-implanted tissue sections. Regression on the significant DE genes identified the 6-7 genes that had the strongest relationship to histological and electrophysiological metrics, revealing potential candidate biomarkers of recording quality and the tissue response to implanted electrodes.Significance. Our analysis has shed new light onto the potential mechanisms involved in the tissue response to implanted electrodes while generating hypotheses regarding potential biomarkers related to recorded signal quality. A new approach has been developed to understand the tissue response to electrodes implanted in the brain using genes identified through transcriptomics, and to screen those results for potential relationships with functional outcomes.

RNA quality score evaluation: A preliminary study of RNA integrity number (RIN) and RNA integrity and quality number (RNA IQ)

In the past several years, with the in-depth development of RNA-related research, exploring the application of transcriptome and corresponding RNA biomarkers has become one of the research hotspots in the field of forensic science. High-quality RNA is essential for successful downstream workflows, especially in the steps of screening biomarkers by microarray or RNA sequencing (RNA-seq). Thus, accurately evaluating the quality of RNA samples is a critical step in obtaining meaningful expression data. The RNA integrity number (RIN) generated from the Agilent Bioanalyzer system has been widely used for RNA quality control in the past two decades. Recently, Thermo Fisher Scientific launched a ratiometric fluorescence-based method to quickly check whether an RNA sample has degraded, and the results are presented as RNA integrity and quality number (RNA IQ). Both quality score systems determine RNA quality using a numerical system based on a scale of 1-10, with 1 denoting significantly degraded specimens and 10 representing high-quality, intact RNA samples. In this preliminary study, we evaluated the consistency, reproducibility and linearity of two quality scores in RNA quality determination by analyzing heat- and RNase- artificially degraded samples. Meanwhile, the expression levels of three microRNAs (hsa-let-7 g-5p, hsa-miR-93-5p and hsa-miR-191-5p) in intact and severely degraded RNA samples were estimated by TaqMan-qPCR and droplet digital PCR. Overall, both quality scores showed good repeatability and reproducibility in their respective tests. In the samples subjected to thermal degradation, RIN showed a trend corresponding to heating time, while RNA IQ value showed almost no change on the time gradient. However, in RNase A mediated degradation, RNA IQ value observed better linearity. Furthermore, the expression levels of three microRNAs in the severely degraded samples did not show significant changes compared to the intact RNA samples. RNA degradation is a very complex and highly variable process, which is difficult to comprehensively evaluate through any one index and cannot directly compare these two parameters. Nevertheless, combined with previous research results and the expression levels of three microRNAs in this study, analyzing RNA biomarkers with stable regions or small sizes in challenged samples may be a conservative and reliable approach.

Zapico S. Partners in Postmortem Interval Estimation: X-ray Diffraction and Fourier Transform Spectroscopy

The postmortem interval (PMI) is difficult to estimate in later stages of decomposition. There is therefore a need to develop reliable methodologies to estimate late PMI. This study aims to assess whether there is a correlation between changes in the mineral composition of human teeth and the estimation of PMI. X-ray diffraction (XRD) and attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy techniques were performed to address this challenge. Forty healthy human teeth obtained from odontological clinics were stored at different times (0, 10, 25, 50 years; N = 10/group). XRD and ATR-FTIR parameters related to the structure and composition of teeth were studied. Our results showed that the crystallinity index, crystal size index, mineral-to-organic matrix ratio (M/M) and carbonate/phosphate ratio (C/P) had the strongest association with PMI. For larger PMIs, there was a significant increase in crystallinity, crystal size and M/M ratio, while the C/P ratio showed a specific decrease with increasing PMI. According to our results, the parameters of crystallinity, crystal size, M/M ratio and C/P ratio can be considered highly accurate in determining a PMI of 10 years of data; crystallinity and mineral maturity can be considered useful in determining a PMI of 25 years; and crystallinity and mineral maturity can be considered highly accurate in determining a PMI of 50 years. A particular XRD index was identified as the most suitable parameter to estimate PMI: crystallinity. The joint use of XRD and ATR-FTIR analyses could be a promising alternative for dating human teeth.

Combining with lab-on-chip technology and multi-organ fusion strategy to estimate post-mortem interval of rat

Background

The estimation of post-mortem interval (PMI) is one of the most important problems in forensic pathology all the time. Although many classical methods can be used to estimate time since death, accurate and rapid estimation of PMI is still a difficult task in forensic practice, so the estimation of PMI requires a faster, more accurate, and more convenient method.

Materials and methods

In this study, an experimental method, lab-on-chip, is used to analyze the characterizations of polypeptide fragments of the lung, liver, kidney, and skeletal muscle of rats at defined time points after death (0, 1, 2, 3, 5, 7, 9, 12, 15, 18, 21, 24, 27, and 30 days). Then, machine learning algorithms (base model: LR, SVM, RF, GBDT, and MLPC; ensemble model: stacking, soft voting, and soft-weighted voting) are applied to predict PMI with single organ. Multi-organ fusion strategy is designed to predict PMI based on multiple organs. Then, the ensemble pruning algorithm determines the best combination of multi-organ.

Results

The kidney is the best single organ for predicting the time of death, and its internal and external accuracy is 0.808 and 0.714, respectively. Multi-organ fusion strategy dramatically improves the performance of PMI estimation, and its internal and external accuracy is 0.962 and 0.893, respectively. Finally, the best organ combination determined by the ensemble pruning algorithm is all organs, such as lung, liver, kidney, and skeletal muscle.

Conclusion

Lab-on-chip is feasible to detect polypeptide fragments and multi-organ fusion is more accurate than single organ for PMI estimation.

Insights into how environment shapes post-mortem RNA transcription in mouse brain

Most biological features that occur on the body after death were already deciphered by traditional medicine. However, the molecular mechanisms triggered in the cellular microenvironment are not fully comprehended yet. Previous studies reported gene expression alterations in the post-mortem condition, but little is known about how the environment could influence RNA degradation and transcriptional regulation. In this work, we analysed the transcriptome of mouse brain after death under three concealment simulations (air exposed, buried, and submerged). Our analyses identified 2,103 genes differentially expressed in all tested groups 48 h after death. Moreover, we identified 111 commonly upregulated and 497 commonly downregulated genes in mice from the concealment simulations. The gene functions shared by the individuals from the tested environments were associated with RNA homeostasis, inflammation, developmental processes, cell communication, cell proliferation, and lipid metabolism. Regarding the altered biological processes, we identified that the macroautophagy process was enriched in the upregulated genes and lipid metabolism was enriched in the downregulated genes. On the other hand, we also described a list of biomarkers associated with the submerged and buried groups, indicating that these environments can influence the post-mortem RNA abundance in its particular way.

Selective time-dependent changes in activity and cell-specific gene expression in human postmortem brain

As a means to understand human neuropsychiatric disorders from human brain samples, we compared the transcription patterns and histological features of postmortem brain to fresh human neocortex isolated immediately following surgical removal. Compared to a number of neuropsychiatric disease-associated postmortem transcriptomes, the fresh human brain transcriptome had an entirely unique transcriptional pattern. To understand this difference, we measured genome-wide transcription as a function of time after fresh tissue removal to mimic the postmortem interval. Within a few hours, a selective reduction in the number of neuronal activity-dependent transcripts occurred with relative preservation of housekeeping genes commonly used as a reference for RNA normalization. Gene clustering indicated a rapid reduction in neuronal gene expression with a reciprocal time-dependent increase in astroglial and microglial gene expression that continued to increase for at least 24 h after tissue resection. Predicted transcriptional changes were confirmed histologically on the same tissue demonstrating that while neurons were degenerating, glial cells underwent an outgrowth of their processes. The rapid loss of neuronal genes and reciprocal expression of glial genes highlights highly dynamic transcriptional and cellular changes that occur during the postmortem interval. Understanding these time-dependent changes in gene expression in post mortem brain samples is critical for the interpretation of research studies on human brain disorders.

Forensic transcriptome analysis using massively parallel sequencing

The application of transcriptome analyses in forensic genetics has experienced tremendous growth and development in the past decade. The earliest studies and main applications were body fluid and tissue identification, using targeted RNA transcripts and a reverse transcription endpoint PCR method. A number of markers have been identified for the forensically most relevant body fluids and tissues and the method has been successfully used in casework. The introduction of Massively Parallel Sequencing (MPS) opened up new perspectives and opportunities to advance the field. Contrary to genomic DNA where two copies of an autosomal DNA segment are present in a cell, abundant RNA species are expressed in high copy numbers. Even whole transcriptome sequencing (RNA-Seq) of forensically relevant body fluids and of postmortem material was shown to be possible. This review gives an overview on forensic transcriptome analyses and applications. The methods cover whole transcriptome as well as targeted MPS approaches. High resolution forensic transcriptome analyses using MPS are being applied to body fluid/ tissue identification, determination of the age of stains and the age of the donor, the estimation of the post-mortem interval and to post mortem death investigations.

At the dawn of the transcriptomic medicine

Progress in genomic analytical technologies has improved our possibilities to obtain information regarding DNA, RNA, and their dynamic changes that occur over time or in response to specific challenges. This information describes the blueprint for cells, tissues, and organisms and has fundamental importance for all living organisms. This review focuses on the technological challenges to analyze the transcriptome and what is the impact of transcriptomics on precision medicine. The transcriptome is a term that covers all RNA present in cells and a substantial part of it will never be translated into protein but is nevertheless functional in determining cell phenotype. Recent developments in transcriptomics have challenged the fundamentals of the central dogma of biology by providing evidence of pervasive transcription of the genome. Such massive transcriptional activity is challenging the definition of a gene and especially the term "pseudogene" that has now been demonstrated in many examples to be both transcribed and translated. We also review the common sources of biomaterials for transcriptomics and justify the suitability of whole blood RNA as the current optimal analyte for clinical transcriptomics. At the end of the review, a brief overview of the clinical implications of transcriptomics in clinical trial design and clinical diagnosis is given. Finally, we introduce the transcriptome as a target for modern drug development as a tool for extending our capacity for precision medicine in multiple diseases.

The Molecular Floodgates of Stress-Induced Senescence Reveal Translation, Signalling and Protein Activity Central to the Post-Mortem Proteome

The transitioning of cells during the systemic demise of an organism is poorly understood. Here, we present evidence that organismal death is accompanied by a common and sequential molecular flood of stress-induced events that propagate the senescence phenotype, and this phenotype is preserved in the proteome after death. We demonstrate activation of “death” pathways involvement in diseases of ageing, with biochemical mechanisms mapping onto neurological damage, embryonic development, the inflammatory response, cardiac disease and ultimately cancer with increased significance. There is sufficient bioavailability of the building blocks required to support the continued translation, energy, and functional catalytic activity of proteins. Significant abundance changes occur in 1258 proteins across 1 to 720 h post-mortem of the 12-week-old mouse mandible. Protein abundance increases concord with enzyme activity, while mitochondrial dysfunction is evident with metabolic reprogramming. This study reveals differences in protein abundances which are akin to states of stress-induced premature senescence (SIPS). The control of these pathways is significant for a large number of biological scenarios. Understanding how these pathways function during the process of cellular death holds promise in generating novel solutions capable of overcoming disease complications, maintaining organ transplant viability and could influence the findings of proteomics through “deep-time” of individuals with no historically recorded cause of death.

Genes of the undead: hibernation and death display different gene profiles

A degree of regulation continues into death according to post-mortem transcriptome studies, which have identified 'zombie genes' that come alive hours and days after organismal death. We hypothesized that hibernation, representing the closest natural mammalian phenomenon to death, would display similar gene expression profiles. Exploring zombie genes using qPCR and available transcriptomic resources from multiple torpid tissues in 13-lined ground squirrels showed little in common with gene profiles observed following death. Hibernators repress transcription, surviving only on the transcripts required during profound slowdowns of metabolic rate and of most physiological functions, therefore not requiring zombie gene expression that could be the cell's last resort during stress. This is the first study to explore zombie gene responses to a near-death situation in a living system.

Microbiome Data Accurately Predicts the Postmortem Interval Using Random Forest Regression Models

Death investigations often include an effort to establish the postmortem interval (PMI) in cases in which the time of death is uncertain. The postmortem interval can lead to the identification of the deceased and the validation of witness statements and suspect alibis. Recent research has demonstrated that microbes provide an accurate clock that starts at death and relies on ecological change in the microbial communities that normally inhabit a body and its surrounding environment. Here, we explore how to build the most robust Random Forest regression models for prediction of PMI by testing models built on different sample types (gravesoil, skin of the torso, skin of the head), gene markers (16S ribosomal RNA (rRNA), 18S rRNA, internal transcribed spacer regions (ITS)), and taxonomic levels (sequence variants, species, genus, etc.). We also tested whether particular suites of indicator microbes were informative across different datasets. Generally, results indicate that the most accurate models for predicting PMI were built using gravesoil and skin data using the 16S rRNA genetic marker at the taxonomic level of phyla. Additionally, several phyla consistently contributed highly to model accuracy and may be candidate indicators of PMI.

The suitability of visual taphonomic methods for digital photographs: An experimental approach with pig carcasses in a tropical climate

In the context of increased scrutiny of the methods in forensic sciences, it is essential to ensure that the approaches used in forensic taphonomy to measure decomposition and estimate the postmortem interval are underpinned by robust evidence-based data. Digital photographs are an important source of documentation in forensic taphonomic investigations but the suitability of the current approaches for photographs, rather than real-time remains, is poorly studied which can undermine accurate forensic conclusions. The present study aimed to investigate the suitability of 2D colour digital photographs for evaluating decomposition of exposed human analogues (Sus scrofa domesticus) in a tropical savanna environment (Hawaii), using two published scoring methods; Megyesi et al., 2005 and Keough et al., 2017. It was found that there were significant differences between the real-time and photograph decomposition scores when the Megyesi et al. method was used. However, the Keough et al. method applied to photographs reflected real-time decomposition more closely and thus appears more suitable to evaluate pig decomposition from 2D photographs. The findings indicate that the type of scoring method used has a significant impact on the ability to accurately evaluate the decomposition of exposed pig carcasses from photographs. It was further identified that photographic taphonomic analysis can reach high inter-observer reproducibility. These novel findings are of significant importance for the forensic sciences as they highlight the potential for high quality photograph coverage to provide useful complementary information for the forensic taphonomic investigation. New recommendations to develop robust transparent approaches adapted to photographs in forensic taphonomy are suggested based on these findings.

Gene Meter: Accurate abundance calculations of gene expression

We previously reported that thousands of transcripts in the mouse and zebrafish significantly increased in abundance in a time series spanning from life to several days after death. Transcript abundances were determined by: calibrating each microarray probe using a dilution series of pooled RNAs, fitting the probe-responses to adsorption models, and back-calculating abundances using the probe signal intensity of a sample and the best fitting model. The accuracy of the abundance measurements was not assessed in our previous study because individual transcript concentrations in the calibration pool were not known. Accurate transcript abundances are highly desired for modeling the dynamics of biological systems and investigating how systems respond to perturbations. In this study, we show that accurate transcript abundances can be determined by calibrating the probes using a calibration pool of transcripts with known concentrations. Instructions for determining accurate transcript abundances using the Gene Meter approach are provided.

Gene expression in the twilight of death: The increase of thousands of transcripts has implications to transplantation, cancer, and forensic research

After a vertebrate dies, many of its organ systems, tissues, and cells remain functional while its body no longer works as a whole. We define this state as the "twilight of death" - the transition from a living body to a decomposed corpse. We claim that the study of the twilight of death is important to ethical, legal and medical science. We examined gene expression at the twilight of death in the zebrafish and mouse reaching the conclusion that apparently thousands of transcripts significantly increase in abundance from life to several hours/days postmortem relative to live controls. Transcript dynamics of different genes provided "proof-of-principle" that models accurately predict an individual's elapsed-time-of-death (i.e. postmortem interval). While many transcripts were associated with survival and stress compensation, others were associated with epigenetic factors, developmental control, and cancer. Future studies are needed to determine whether the high incidence of cancer in transplant recipients is due to the postmortem processes in donor organs.

Tracing the dynamics of gene transcripts after organismal death

In life, genetic and epigenetic networks precisely coordinate the expression of genes-but in death, it is not known if gene expression diminishes gradually or abruptly stops or if specific genes and pathways are involved. We studied this by identifying mRNA transcripts that apparently increase in relative abundance after death, assessing their functions, and comparing their abundance profiles through postmortem time in two species, mouse and zebrafish. We found mRNA transcript profiles of 1063 genes became significantly more abundant after death of healthy adult animals in a time series spanning up to 96 h postmortem. Ordination plots revealed non-random patterns in the profiles by time. While most of these transcript levels increased within 0.5 h postmortem, some increased only at 24 and 48 h postmortem. Functional characterization of the most abundant transcripts revealed the following categories: stress, immunity, inflammation, apoptosis, transport, development, epigenetic regulation and cancer. The data suggest a step-wise shutdown occurs in organismal death that is manifested by the apparent increase of certain transcripts with various abundance maxima and durations.

Datasets used to discover the microbial signatures of oral dysbiosis, periodontitis and edentulism in humans

This article provides supporting data for the research article ‘Microbial Signatures of Oral Dysbiosis, Periodontitis and Edentulism Revealed by Gene Meter Methodology’ (M.C. Hunter, A.E. Pozhitkov, P.A. Noble, 2016) [1]. In that article, we determined the microbial abundance signatures for patient with periodontics, edentulism, or health using Gene Meter Technology. Here we provide the data used to make the DNA microarray and the resulting microbial abundance data that was determined using the calibrated probes and the 16S rRNA genes harvested from patients. The first data matrix contains two columns: one is the GenInfo Identifier (GI) numbers of the 16S rRNA gene sequences and the other is the corresponding oral bacterial taxonomy. The probes were then screened for redundancy and if they were found to be unique, they were synthesized onto the surface of the DNA microarrays. The second data matrix consists of the abundances of the 576 16S rRNA genes that was determined using the median value of all individual calibrated probes targeting each gene. The data matrix consists of 16 columns and 576 rows, with the columns representing the 16 patients and the rows representing 576 different oral microorganisms. The third data matrix consists of the abundances of 567 16S rRNA genes determined using the calibrated abundance of all aggregated probes targeting the same 16S rRNA gene. The data matrix of the aggregated probes consists of 16 samples and 567 rows.