Assessing time dependent changes in microbial composition of biological crime scene traces using microbial RNA markers

Response of salivary microbiome to temporal, environmental, and surface characteristics under in vitro exposure

The microbiome of saliva stains deposited at crime scenes and in everyday settings is valuable for forensic investigations and environmental ecology. However, the dynamics and applications of microbial communities in these saliva stains have not been fully explored. In this study, we analyzed saliva samples that were exposed to indoor conditions for up to 1 year and to different carriers (cotton, sterile absorbent cotton swab, woolen, dacron) in both indoor and outdoor environments for 1 month using high-throughput sequencing. The analysis of microbial composition and Mfuzz clustering showed that the salivary flora, specifically Streptococcus (cluster7), which was associated with microbial contamination, remained stable over short periods of time. However, prolonged exposure led to significant differences due to the invasion of environmental bacteria such as Pseudomonas and Achromobacter. The growth and colonization of environmental flora were promoted by humidity. The neutral model predictions indicated that the assembly of salivary microbial communities in outdoor environments was significantly influenced by stochastic processes, with environmental characteristics having a greater impact on community change compared to surface characteristics. By incorporating data from previous studies on fecal and vaginal secretion microbiology, we developed RF and XGBoost classification models that achieved high accuracy (>98 %) and AUC (>0.8). Additionally, a RF regression model was created to determine the time since deposition (TsD) of the stains. Time inference models yielded a mean absolute error (MAE) of 7.1 days for stains exposed for 1 year and 14.2 h for stains exposed for 14 days. These findings enhance our understanding of the changes in the microbiome of saliva stains over time, in different environments, and on different surfaces. They also have potential applications in assessing potential microbial contamination, identifying body fluids, and inferring the time of deposition.

Body fluids should be identified before estimating the time since deposition (TsD) in microbiome-based stain analyses for forensics

Identification and the time since deposition (TsD) estimation of body fluid stains from a crime scene could provide valuable information for solving the cases and are always difficult for forensics. Microbial characteristics were considered as a promising biomarker to address the issues. However, changes in the microbiota may damage the specific characteristics of body fluids. Correspondingly, incorrect body fluid identification may result in inaccurate TsD estimation. The mutual influence is not well understood and limited the codetection. In the current study, saliva, semen, vaginal secretion, and menstrual blood samples were exposed to indoor conditions and collected at eight time points (from fresh to 30 days). High-throughput sequencing based on the 16S rRNA gene was performed to characterize the microbial communities. The results showed that a longer TsD could decrease the discrimination of different body fluid stains. However, the accuracies of identification still reached a quite high value even without knowing the TsD. Correspondingly, the mean absolute error (MAE) of TsD estimation significantly increased without distinguishing the types of body fluids. The predictive TsD of menstrual blood reached a quite low MAE (1.54 ± 0.39 d). In comparison, those of saliva (6.57 ± 1.17 d), semen (6.48 ± 1.33 d), and vaginal secretion (5.35 ± 1.11 d) needed to be further improved. The great effect of individual differences on these stains limited the TsD estimation accuracy. Overall, microbial characteristics allow for codetection of body fluid identification and TsD estimation, and body fluids should be identified before estimating TsD in microbiome-based stain analyses.IMPORTANCEEmerged evidences suggest microbial characteristics could be considered a promising tool for identification and time since deposition (TsD) estimation of body fluid stains. However, the two issues should be studied together due to a potential mutual influence. The current study provides the first evidence to understand the mutual influence and determines an optimal process for codetection of identification and TsD estimation for unknown stains for forensics. In addition, we involved aged stains into our study for identification of body fluid stains, rather than only using fresh stains like previous studies. This increased the predictive accuracy. We have preliminary verified that individual differences in microbiotas limited the predictive accuracy of TsD estimation for saliva, semen, and vaginal secretion. Microbial characteristics could provide an accurate TsD estimation for menstrual blood. Our study benefits the comprehensive understanding of microbiome-based stain analyses as an essential addition to previous studies.

Time-dependent change in the microbiota structure of seminal stains exposed to indoor environmental

Seminal stains acquired from fabric surfaces stand as pivotal biological evidence of utmost significance for elucidating sexual assault cases. The ability to determine the temporal aspect of a forensic incident via the analysis of a biological specimen found at the crime scene is crucial in resolving most cases. This study aimed to investigate the time-dependent change in the microbiota structure of human seminal stains exposed to indoor environmental conditions. Stains on polyester fabric generated using semen samples from five male volunteers were kept indoors for varying durations of up to 20 days, followed by sequencing of the V1-V9 regions of the 16S rRNA gene of the microbial DNA extracted from the stains. The acquired data provided the taxonomic composition, and microbial alterations across different days were examined. The most abundantly detected phyla in all samples were Firmicutes, Proteobacteria, and Bacteroidetes, and the relative abundances of bacteria were observed to change over time. Statistically significant changes at the species level were found for Treponema medium, Corynebacterium tuberculostearicum, Faecalibacterium prausnitzii, and Anaerostipes hadrus. Alterations observed in the samples between the analyzed time periods were investigated. The changes during the specified time periods were examined, identifying rare bacterial species that were initially present on certain days but later ceased to exist in the environment. Conversely, bacterial species that were absent before exposure but emerged at a later stage were also identified. The findings of this study demonstrate that species-level evaluations, in particular, can provide crucial insights into semen stain age.

Focus on studying the effects of different exposure durations on the microbial structures and characteristics of three types of body fluids

BACKGROUND

Body fluid traceability inferences can provide important clues to the investigation of forensic cases. Microbiome has been proven to be well applied in forensic body fluid traceability studies. Most of the specimens at crime scenes are often exposed to the external environment when collected, so it is extremely important to exploring the structure characteristics of microbial communities of body fluid samples under different exposure durations for tracing the origin of body fluids based on microorganisms.

METHODS

Full-length 16S rRNA sequencing technology and multiple data analysis methods were used to explore the microbial changes in three types of body fluid samples at five different exposure time points.

RESULTS

With increasing exposure time, the Proteobacteria abundance gradually increased in the negative control and body fluid samples, and the Bacteroidetes and Firmicutes abundance decreased gradually, but the relative abundance of dominant genera in each body fluid remained dynamically stable. The microbial community structures of those samples from the same individual at different exposure durations were similar, and there were no significant differences in the microbial community structures among the different exposure time points. LEfSe and random forest analyses were applied to screen stable and differential microbial markers among body fluids, such as Streptococcus thermophilus, Streptococcus pneumoniae and Haemophilus parainfluenzae in saliva; Lactobacillus iners and Streptococcus agalactiae in vaginal fluid.

CONCLUSIONS

There were no significant differences in microbial community structures of the three types of body fluid samples exposed to the environment for various time periods, although the relative abundance of some microbes in these samples would change. The exposed samples could still be traced back to their source of the body fluid samples using the microbial community structures.

Assessment of body fluid identification and DNA profiling after exposure to tropical weather conditions

Despite current advances in body fluid identification, there are few studies evaluating the effect of environmental conditions. The present work assessed the detection of body fluids, blood, semen, and saliva, through lateral flow immunochromatographic (LFI) tests, exposed to tropical weather conditions over time, also evaluating the possibility of obtaining STR (short tandem repeat) profiles and identifying mitochondrial DNA (mtDNA) polymorphisms. Blood, semen, saliva samples, and mixtures of these fluids were deposited on polyester clothes and exposed to open-air tropical weather conditions for 1 month. The test versions from LFI (SERATEC®, Germany) Lab and crime scene (CS) used for the detection - one per each body fluid type - demonstrated that it is possible to identify body fluids and their mixtures up to 14 days after deposition. At 30 days, blood and semen were detected but not saliva. Full STR profiles were obtained from 14-day-old blood samples, and partial profiles were obtained from the remaining samples. It was possible to sequence mtDNA in the samples previously analyzed for STR profiling, and haplogroups could be assigned. In conclusion, this study demonstrated for the first time the possibility of body fluid identification and DNA profiling after exposure to tropical weather conditions for 1 month and also demonstrated the value of mtDNA analysis for compromised biological evidence.

Spitting in the wind?-The challenges of RNA sequencing for biomarker discovery from saliva

Forensic trace contextualization, i.e., assessing information beyond who deposited a biological stain, has become an issue of great and steadily growing importance in forensic genetic casework and research. The human transcriptome encodes a wide variety of information and thus has received increasing interest for the identification of biomarkers for different aspects of forensic trace contextualization over the past years. Massively parallel sequencing of reverse-transcribed RNA ("RNA sequencing") has emerged as the gold standard technology to characterize the transcriptome in its entirety and identify RNA markers showing significant expression differences not only between different forensically relevant body fluids but also within a single body fluid between forensically relevant conditions of interest. Here, we analyze the quality and composition of four RNA sequencing datasets (whole transcriptome as well as miRNA sequencing) from two different research projects (the RNAgE project and the TrACES project), aiming at identifying contextualizing forensic biomarker from the forensically relevant body fluid saliva. We describe and characterize challenges of RNA sequencing of saliva samples arising from the presence of oral bacteria, the heterogeneity of sample composition, and the confounding factor of degradation. Based on these observations, we formulate recommendations that might help to improve RNA biomarker discovery from the challenging but forensically relevant body fluid saliva.

Preliminary exploratory research on the application value of oral and intestinal meta-genomics in predicting subjects' occupations-A case study of the distinction between students and migrant workers

Background

In the field of forensic science, accurately determining occupation of an individual can greatly assist in resolving cases such as criminal investigations or disaster victim identifications. However, estimating occupation can be challenging due to the intricate relationship between occupation and various factors, including gender, age, living environment, health status, medication use, and lifestyle habits such as alcohol consumption and smoking. All of these factors can impact the composition of oral or gut microbial community of an individual.

Methods and results

In this study, we collected saliva and feces samples from individuals representing different occupational sectors, specifically students and manual laborers. We then performed metagenomic sequencing on the DNA extracted from these samples to obtain data that could be analyzed for taxonomic and functional annotations in five different databases. The correlation between occupation with microbial information was assisted from the perspective of α and β diversity, showing that individuals belonging to the two occupations hold significantly different oral and gut microbial communities, and that this correlation is basically not affected by gender, drinking, and smoking in our datasets. Finally, random forest (RF) models were built with recursive feature elimination (RFE) processes. Models with 100% accuracy in both training and testing sets were constructed based on three species in saliva samples or on a single pathway annotated by the KEGG database in fecal samples, namely, "ko04145" or Phagosome.

Conclusion

Although this study may have limited representativeness due to its small sample size, it provides preliminary evidence of the potential of using microbiome information for occupational inference.

TrACES of time: Transcriptomic analyses for the contextualization of evidential stains - Identification of RNA markers for estimating time-of-day of bloodstain deposition

Obtaining forensically relevant information beyond who deposited a biological stain on how and under which circumstances it was deposited is a question of increasing importance in forensic molecular biology. In the past few years, several studies have been produced on the potential of gene expression analysis to deliver relevant contextualizing information, e.g. on nature and condition of a stain as well as aspects of stain deposition timing. However, previous attempts to predict the time-of-day of sample deposition were all based on and thus limited by previously described diurnal oscillators. Herein, we newly approached this goal by applying current sequencing technologies and statistical methods to identify novel candidate markers for forensic time-of-day predictions from whole transcriptome analyses. To this purpose, we collected whole blood samples from ten individuals at eight different time points throughout the day, performed whole transcriptome sequencing and applied biostatistical algorithms to identify 81 mRNA markers with significantly differential expression as candidates to predict the time of day. In addition, we performed qPCR analysis to assess the characteristics of a subset of 13 candidate predictors in dried and aged blood stains. While we demonstrated the general possibility of using the selected candidate markers to predict time-of-day of sample deposition, we also observed notable variation between different donors and storage conditions, highlighting the relevance of employing accurate quantification methods in combination with robust normalization procedures.This study's results are foundational and may be built upon when developing a targeted assay for time-of-day predictions from forensic blood samples in the future.

Quantitative PCR analysis of bloodstains of different ages

An accurate method to estimate the age of a stain or the time since deposition (TsD) would represent an important tool in police investigations for evaluating the true relevance of a stain. In this study, two laboratories reproduced an mRNA-based method for TsD estimation published by another group. The qPCR-based assay includes four transcripts (B2M, LGALS2, CLC, and S100A12) and showed preferential degradation of the 5' end over the 3' end. In this study, the blood-specific marker ALAS2 was added to examine whether it would show the same degradation pattern. Based on our qPCR data several elastic net models with different penalty combinations were created, using training data from the two laboratories separately and combined. Each model was then used to estimate the age of bloodstains from two independent test sets each laboratory had prepared. The elastic net model built on both datasets with training samples up to 320 days old displayed the best prediction performance across all test samples (MAD=18.9 days). There was a substantial difference in the prediction performance for the two laboratories: Restricting TsD to up to 100 days for test data, one laboratory obtained an MAD of 2.0 days when trained on its own data, whereas the other laboratory obtained an MAD of 15 days.

A bibliometric analysis of microbial forensics from 1984 to 2022: progress and research trends

Microbial forensics is a rapidly evolving discipline that has gained significant momentum in recent years. The study evaluated relevant results over the last four decades from 1984 to 2022 all over the world, aiming to analyze the growing trends and research orientations of microbial forensics. Using "microbial forensics" as the search topic in the Web of Science Core Collection, the systematic retrieval identified 579 documents relevant to the field and draw many statistical tables and maps to make the retrieval results visible. According to further bibliometric analysis, there are an increasing number of publications related to microbial forensics from the overall trend, with the highest number of publications recorded in 2021. In terms of the total number of articles, the USA and China were both the leading contributors to the field among 40 countries. The field has developed rapidly in recent years based on the development of next-generation sequencing. Over the course of its development, there are rich keywords in the research of scholars, which focus on diversity and identification. Moreover, despite the early hot topic being PCR (the use of PCR to probe microorganisms), in recent years, the topics, markers, and the potential application of microorganisms in forensic practice have become hot, which also indicates the future research directions of microbial forensic.

Microbiome-Based Stain Analyses in Crime Scenes

Recent advances in next-generation sequencing technologies (NGS) coupled with machine learning have demonstrated the potential of microbiome-based analyses in applied areas such as clinical diagnostics and forensic sciences. Particularly in forensics, microbial markers in biological stains left at a crime scene can provide valuable information for the reconstruction of crime scene cases, as they contain information on bodily origin, the time since deposition, and donor(s) of the stain. Importantly, microbiome-based analyses provide a complementary or an alternative approach to current methods when these are limited or not feasible. Despite the promising results from recent research, microbiome-based stain analyses are not yet employed in routine casework. In this review, we highlight the two main gaps that need to be addressed before we can successfully integrate microbiome-based analyses in applied areas with a special focus on forensic casework: one is a comprehensive assessment of the method's strengths and limitations, and the other is the establishment of a standard operating procedure. For the latter, we provide a roadmap highlighting key decision steps and offering laboratory and bioinformatic workflow recommendations, while also delineating those aspects that require further testing. Our goal is to ultimately facilitate the streamlining of microbiome-based analyses within the existing forensic framework to provide alternate lines of evidence, thereby improving the quality of investigations.

Trends in forensic microbiology: From classical methods to deep learning

Forensic microbiology has been widely used in the diagnosis of causes and manner of death, identification of individuals, detection of crime locations, and estimation of postmortem interval. However, the traditional method, microbial culture, has low efficiency, high consumption, and a low degree of quantitative analysis. With the development of high-throughput sequencing technology, advanced bioinformatics, and fast-evolving artificial intelligence, numerous machine learning models, such as RF, SVM, ANN, DNN, regression, PLS, ANOSIM, and ANOVA, have been established with the advancement of the microbiome and metagenomic studies. Recently, deep learning models, including the convolutional neural network (CNN) model and CNN-derived models, improve the accuracy of forensic prognosis using object detection techniques in microorganism image analysis. This review summarizes the application and development of forensic microbiology, as well as the research progress of machine learning (ML) and deep learning (DL) based on microbial genome sequencing and microbial images, and provided a future outlook on forensic microbiology.

Microbiome analysis: An emerging forensic investigative tool

Microbial diversity's potential has been investigated in medical and therapeutic studies throughout the last few decades. However, its usage in forensics is increasing due to its effectiveness in circumstances when traditional approaches fail to provide a decisive opinion or are insufficient in forming a concrete opinion. The application of human microbiome may serve in detecting the type of stains of saliva and vaginal fluid, as well as in attributing the stains to the individual. Similarly, the microbiome makeup of a soil sample may be utilised to establish geographic origin or to associate humans, animals, or things with a specific area, additionally microorganisms influence the decay process which may be used in depicting the Time Since death. Further in detecting the traces of the amount and concentration of alcohol, narcotics, and other forensically relevant compounds in human body or visceral tissues as they also affect the microbial community within human body. Beside these, there is much more scope of microbiomes to be explored in terms of forensic investigation, this review focuses on multidimensional approaches to human microbiomes from a forensic standpoint, implying the potential of microbiomes as an emerging tool for forensic investigations such as individual variability via skin microbiomes, reconstructing crime scene, and linking evidence to individual.

Estimating the time since deposition (TsD) in saliva stains using temporal changes in microbial markers

Determining the time since deposition (TsD) of traces could be helpful in the investigation of criminal offenses. However, there are no reliable markers and models available for the inference of short-term TsD. The goal of this study was to investigate the potential of the succession pattern of human salivary microbial communities to serve as an efficiency TsD prediction tool in the resolution of the forensic cases. Saliva stains exposed to indoor conditions up to 20 days were collected and analyzed by 16S rRNA profiling using high-throughput sequencing technique. Noticeable differences in microbial composition were observed between different time points, and the indoor exposure time of saliva stains were inversely correlated with alpha diversity estimates across the measured time period. The sequencing results were used to identify TsD-dependent bacterial indicators to regress a generalized random forest model, resulting in a mean absolute deviation (MAD) of 1.41 days. Furthermore, a simplified TsD predictive model was also developed utilizing Enhydrobacter, Paenisporosarcina, and Janthinobacterium by quantitative PCR (qPCR) with a MAD of 1.32 days, and then forensic practice assessment were also performed by using mock samples with a MAD of 3.53 days. In conclusion, this study revealed significant changes in salivary microbial abundance as the prolongation of TsD. It demonstrated that the microbial biomarkers could be invoked as a "clock" for TsD estimation in human dried saliva stains.

Integrating the human microbiome in the forensic toolkit: Current bottlenecks and future solutions

Over the last few years, advances in massively parallel sequencing technologies (also referred to next generation sequencing) and bioinformatics analysis tools have boosted our knowledge on the human microbiome. Such insights have brought new perspectives and possibilities to apply human microbiome analysis in many areas, particularly in medicine. In the forensic field, the use of microbial DNA obtained from human materials is still in its infancy but has been suggested as a potential alternative in situations when other human (non-microbial) approaches present limitations. More specifically, DNA analysis of a wide variety of microorganisms that live in and on the human body offers promises to answer various forensically relevant questions, such as post-mortem interval estimation, individual identification, and tissue/body fluid identification, among others. However, human microbiome analysis currently faces significant challenges that need to be considered and overcome via future forensically oriented human microbiome research to provide the necessary solutions. In this perspective article, we discuss the most relevant biological, technical and data-related issues and propose future solutions that will pave the way towards the integration of human microbiome analysis in the forensic toolkit.