A probabilistic approach for the interpretation of RNA profiles as cell type evidence

Developing an interpretation model for body fluid identification

Criminal investigations, particularly sexual assaults, frequently require the identification of body fluid type in addition to body fluid donor to provide context. In most cases this can be achieved by conventional methods, however, in certain scenarios, alternative molecular methods are required. An example of this is the detection of menstrual fluid and vaginal material, which are not able to be identified using conventional techniques. Endpoint reverse-transcription PCR (RT-PCR) is currently used for this purpose to amplify body fluid specific messenger RNA (mRNA) transcripts in forensic casework. Real-time quantitative reverse-transcription PCR (RT-qPCR) is a similar method but utilises fluorescent markers to generate quantitative results in the form of threshold cycle (Cq) values. Despite the uncertainty surrounding body fluid identification, most interpretation guidelines utilise categorical statements. Probabilistic modelling is more realistic as it reflects biological variation as well as the known performance of the method. This research describes the application of various machine learning models to single-source mRNA profiles obtained by RT-qPCR and assesses their performance. Multinomial logistic regression (MLR), Naïve Bayes (NB), and linear discriminant analysis (LDA) were used to discriminate between the following body fluid categories: saliva, circulatory blood, menstrual fluid, vaginal material, and semen. We identified that the performance of MLR was somewhat improved when the quantitative dataset of the original Cq values was used (overall accuracy of approximately 0.95) rather than presence/absence coded data (overall accuracy of approximately 0.94). This indicates that the quantitative information obtained by RT-qPCR amplification is useful in assigning body fluid class. Of the three classification methods, MLR performed the best. When we utilised receiver operating characteristic curves to observe performance by body fluid class, it was clear that all methods found difficulty in classifying menstrual blood samples. Future work will involve the modelling of body fluid mixtures, which are common in samples analysed as part of sexual assault investigations.

RNA Analysis in Forensic Molecular Biology

BACKGROUND

Different types of RNA take on multiple crucial functions in living cells and tissues. Messenger RNA (mRNA) is a temporary molecular carrier of genetic information. Analysis of the composition of all mRNA contained in a cell at a given moment, the so-called transcriptome, enables the determination of the type of cell and its condition, e.g., in pathologically altered states.

METHODS

This review is based on pertinent publications retrieved by a selective literature search.

RESULTS

The analysis of differential gene expression has already been used in forensic molecular biology to determine the type of tissue contained in biological specimens. It is also being used in criminal investigations to determine the composition of mixed traces of various bodily fluids and/or organ tissues. The method is limited by degradation of the mRNA molecules through environmental influences. The use of newly developed molecular biological methods such as massive parallel sequencing can expand the information obtainable by this investigative method. Current research also addresses the forensic potential of deriving relevant information about the crime-e.g., its timing, or the condition of the involved persons-from the totality of mRNA species present in the specimens.

CONCLUSION

Forensic RNA analysis can yield a great deal of relevant information. It is likely to be applicable in a much wider variety of forensic situations in the near future.

Partial validation of multiplexed real-time quantitative PCR assays for forensic body fluid identification

Confirmatory body fluid identification using messenger RNA (mRNA) is a well-established technique to address issues encountered with conventional testing - such as poor sensitivity, specificity, and a lack of available tests for all body fluids of interest. For over a decade, endpoint reverse-transcription polymerase chain reaction (RT-PCR) assays have been used in forensic casework for such purposes. However, in comparison with real-time quantitative RT-PCR (RT-qPCR), endpoint RT-PCR has lower sensitivity, precision, and linear dynamic range. This research details the multiplexing and partial validation of confirmatory RT-qPCR assays. We have previously described novel assays for a range of body fluid targets and identified an optimal commercial kit for their amplification. Here, multiplexing was undertaken to form three assays: circulatory blood (SLC4A1) and menstrual fluid (STC1), saliva (HTN3) and vaginal material (CYP2B7P), and spermatozoa (PRM1) and seminal fluid (KLK2), all including a synthetic internal control RNA. Partial validation of the multiplexed assays incorporated the MIQE guidelines, ISO requirements, and SWGDAM guidelines. Using receiver operating characteristic (ROC) curves, each marker was significantly different from an uninformative assay and optimal cut-offs were all above 35 cycles. All assays showed a wide LDR (ranging from 3 to 5 logs with most R2 > 0.99), and high precision (most mean CV < 1 %). STC1 showed some instances of sporadic expression in blood, semen, and vaginal material at high CT values. CYP2B7P showed off-target expression in semen and blood. The sensitivities were approximated as; saliva: 1 in 1,000 dilution of a whole buccal swab, circulatory blood: 0.01-0.1 µL blood, menstrual fluid: 1 in 10,000 dilution of a whole menstrual swab, spermatozoa: 0.001 µL semen, seminal fluid: 0.01 µL semen, and vaginal material: 1 in 1,000 dilution of a whole vaginal swab. A total of 16 mock body fluid extract mixtures and 18 swab mixtures were tested and had 100% and 99% detection of target markers below each specific cut-off, respectively. Some mixtures containing high volumes of blood and semen showed off-target CYP2B7P expression. The successful application of a probabilistic model to the RT-qPCR data was also demonstrated. Further work will involve full developmental validation.

Identification of Mixtures of Two Types of Body Fluids Using the Multiplex Methylation System and Random Forest Models

OBJECTIVE

Body fluid mixtures are complex biological samples that frequently occur in crime scenes, and can provide important clues for criminal case analysis. DNA methylation assay has been applied in the identification of human body fluids, and has exhibited excellent performance in predicting single-source body fluids. The present study aims to develop a methylation SNaPshot multiplex system for body fluid identification, and accurately predict the mixture samples. In addition, the value of DNA methylation in the prediction of body fluid mixtures was further explored.

METHODS

In the present study, 420 samples of body fluid mixtures and 250 samples of single body fluids were tested using an optimized multiplex methylation system. Each kind of body fluid sample presented the specific methylation profiles of the 10 markers.

RESULTS

Significant differences in methylation levels were observed between the mixtures and single body fluids. For all kinds of mixtures, the Spearman's correlation analysis revealed a significantly strong correlation between the methylation levels and component proportions (1:20, 1:10, 1:5, 1:1, 5:1, 10:1 and 20:1). Two random forest classification models were trained for the prediction of mixture types and the prediction of the mixture proportion of 2 components, based on the methylation levels of 10 markers. For the mixture prediction, Model-1 presented outstanding prediction accuracy, which reached up to 99.3% in 427 training samples, and had a remarkable accuracy of 100% in 243 independent test samples. For the mixture proportion prediction, Model-2 demonstrated an excellent accuracy of 98.8% in 252 training samples, and 98.2% in 168 independent test samples. The total prediction accuracy reached 99.3% for body fluid mixtures and 98.6% for the mixture proportions.

CONCLUSION

These results indicate the excellent capability and powerful value of the multiplex methylation system in the identification of forensic body fluid mixtures.

On the Identification of Body Fluids and Tissues: A Crucial Link in the Investigation and Solution of Crime

Body fluid and body tissue identification are important in forensic science as they can provide key evidence in a criminal investigation and may assist the court in reaching conclusions. Establishing a link between identifying the fluid or tissue and the DNA profile adds further weight to this evidence. Many forensic laboratories retain techniques for the identification of biological fluids that have been widely used for some time. More recently, many different biomarkers and technologies have been proposed for identification of body fluids and tissues of forensic relevance some of which are now used in forensic casework. Here, we summarize the role of body fluid/ tissue identification in the evaluation of forensic evidence, describe how such evidence is detected at the crime scene and in the laboratory, elaborate different technologies available to do this, and reflect real life experiences. We explain how, by including this information, crucial links can be made to aid in the investigation and solution of crime.

Feasibility of using probabilistic methods to analyse microRNA quantitative data in forensically relevant body fluids: a proof-of-principle study

Several studies have confirmed that microRNAs (miRNAs) are promising markers for body fluid identification since they were introduced to this field. However, there is no consensus on the choice of reference genes and identification strategies. In this study, 13 potential candidate miRNAs were screened from three forensically relevant body fluid datasets, and the expression of 12 markers in five body fluids was determined using a real-time quantitative method. Two probabilistic approaches, Naive Bayes (NB) and partial least squares discriminant analysis (PLS-DA), were then applied to predict the origin of the samples to determine whether probabilistic methods are helpful in body fluid identification using miRNA quantitative data. Furthermore, 14 reference combinations were used to validate the influence of different reference choices on the predicted results simultaneously. Our results showed that in the NB model, leave-one-out cross-validation (LOOCV) achieved 100% accuracy and the prediction accuracy of the test set was 100% in most reference combinations. In the PLS-DA model, the first two components could interpret about 80% expression variance and LOOCV achieved 100% accuracy when miR-92a-3p was used as the reference. This study preliminarily proved that probabilistic approaches hold huge potential in miRNA-based body fluid identification, and the choice of references influences the prediction results to a certain extent.

A novel multiplex assay system based on 10 methylation markers for forensic identification of body fluids

Identifying the source of body fluids found at a crime scene is an essential forensic step. Some methods based on DNA methylation played significant role in body fluids identification. Since DNA methylation is related to multiple factors, such as race, age, and diseases, it is necessary to know the methylation profile of a given population. In this study, we tested 19 body fluid-specific methylation markers in a Chinese Han population. A novel multiplex assay system based on the selected markers with smaller variation in methylation and stronger tissue-specific methylation were developed for the identification of body fluids. The multiplex assay were tested in 265 body fluid samples. A random forest model was established to predict the tissue source based on the methylation data of the 10 markers. The multiplex assay was evaluated by testing the sensitivity, the mixtures, and old samples. For the result, the novel multiplex assay based on 10 selected methylation markers presented good methylation profiles in all tested samples. The random forest model worked extremely well in predicting the source of body fluids, with an accuracy of 100% and 97.5% in training data and test data, respectively. The multiplex assay could accurately predict the tissue source from 0.5 ng genomic DNA, six-months-old samples and distinguish the minor component from a mixture of two components. Our results indicated that the methylation multiplex assay and the random forest model could provide a convenient tool for forensic practitioners in body fluid identification.

Calculating LRs for presence of body fluids from mRNA assay data in mixtures

Messenger RNA (mRNA) profiling can identify body fluids present in a stain, yielding information on what activities could have taken place at a crime scene. To account for uncertainty in such identifications, recent work has focused on devising statistical models to allow for probabilistic statements on the presence of body fluids. A major hurdle for practical adoption is that evidentiary stains are likely to contain more than one body fluid and current models are ill-suited to analyse such mixtures. Here, we construct a likelihood ratio (LR) system that can handle mixtures, considering the hypotheses H₁: the sample contains at least one of the body fluids of interest (and possibly other body fluids); H₂: the sample contains none of the body fluids of interest (but possibly other body fluids). Thus, the LR-system outputs an LR-value for any combination of mRNA profile and set of body fluids of interest that are given as input. The calculation is based on an augmented dataset obtained by in silico mixing of real single body fluid mRNA profiles. These digital mixtures are used to construct a probabilistic classification method (a 'multi-label classifier'). The probabilities produced are subsequently used to calculate an LR, via calibration. We test a range of different classification methods from the field of machine learning, ways to preprocess the data and multi-label strategies for their performance on in silico mixed test data. Furthermore, we study their robustness to different assumptions on background levels of the body fluids. We find logistic regression works as well as more flexible classifiers, but shows higher robustness and better explainability. We test the system's performance on lab-generated mixture samples, and discuss practical usage in case work.

Distinct spectrum of microRNA expression in forensically relevant body fluids and probabilistic discriminant approach

MicroRNA is attracting worldwide attention as a new marker for the identification of forensically relevant body fluids. A probabilistic discriminant model was constructed to identify venous blood, saliva, semen, and vaginal secretion, based on microRNA expression assessed via RT-qPCR. We quantified 15 candidate microRNAs in four types of body fluids by RT-qPCR and found that miR-144-3p, miR-451a-5p, miR-888-5p, miR-891a-5p, miR-203a-3p, miR-223-3p and miR-1260b were helpful to discriminate body fluids. Using the relative expression of seven candidate microRNAs in each body fluid, we implemented a partial least squares-discriminant analysis (PLS-DA) as a probabilistic discriminant model and distinguished four types of body fluids. Of 14 testing samples, 13 samples were correctly identified with >90% posterior probability. We also investigated the effects of microRNA expression in skin, semen infertility, and vaginal secretion during different menstrual phases. Semen infertility and menstrual phases did not affect our body fluid identification system. Therefore, the selected microRNAs were effective in identifying the four types of body fluids, indicating that probabilistic evaluation may be practical in forensic casework.

Optimal small-molecular reference RNA for RT-qPCR-based body fluid identification

MicroRNA (miRNA) -based body fluid identification (BFID) plays a prominent role in a forensic practice, and the selected reference RNA is indispensable for a robust normalization in BFID performed using reverse transcription-quantitative PCR. In this study, we first examined sample quality using RNA integrity number, then evaluated the consistency of expression of candidate reference RNAs in 4 forensically relevant body fluids using NormFinder and BestKeeper, and lastly used each rank and index output from these tools for selecting the optimal reference RNA and the combination of the multiple RNAs using the RankAggreg package of R. We found that RNA integrity number was small in our samples, despite the use of pristine body fluids; 5S-rRNA was the optimal reference RNA for the identification of forensically relevant body fluids; and the combination of 5S-rRNA and miR-92a-3p and/or miR-484 enhanced the normalization quality. Our findings enable us to perform stringent normalization of the expression of body fluid-specific RNAs, and thus, can contribute to the development of small RNA-based BFID systems.

Human-associated microbial populations as evidence in forensic casework

In forensic investigations involving human biological traces, cell type identification is often required. Identifying the cell type from which a human STR profile has originated can assist in verifying scenarios. Several techniques have been developed for this purpose, most of which focus on molecular characteristics of human cells. Here we present a microarray method focusing on the microbial populations that are associated with human cell material. A microarray with 863 probes targeting (sets of) species, specific genera, groups of genera or families was designed for this study and evaluated with samples from different body sites: hand, foot, groin, penis, vagina, mouth and faeces. In total 175 samples from healthy individuals were analysed. Next to human faeces, 15 feline and 15 canine faeces samples were also included. Both clustering and classification analysis were used for data analysis. Faecal and oral samples could clearly be distinguished from vaginal and skin samples, and also canine and feline faeces could be differentiated from human faeces. Some penis samples showed high similarity to vaginal samples, others to skin samples. Discriminating between skin samples from different skin sites proved to be challenging. As a proof of principle, twenty-one mock case samples were analysed with the microarray method. All mock case samples were clustered or classified within the correct main cluster/group. Only two of the mock case samples were assigned to the wrong sub-cluster/class; with classification one additional sample was classified within the wrong sub-class. Overall, the microarray method is a valuable addition to already existing cell typing techniques. Combining the results of microbial population analysis with for instance mRNA typing can increase the evidential value of a trace, since both techniques focus on independent targets within a sample.