Identification of Body Fluids Using RNA Profiling: Recent Developments and Challenges

Identifying which body fluid a stain might be is often the first step in a forensic biology workflow. It provides clues to investigators about what might have been happening and determines which sample to submit for DNA typing. The main fluids found in a crime scene – blood, semen and saliva are identified at the scene using routine methods such as alternative light sources, chemical reactions, lateral-flow immunochromatographic tests and microscopy. These tests are not complete. Saliva dries very quickly and is difficult to see. RNA profiling provides a molecular alternative, as each fluid and tissue has a distinct array of transcripts that are expressed. This review covers five recent open-access papers focused on identifying body fluids using RNA, and synthesises their choices of markers, analysis platforms, and the growing trend of identifying body fluids and linking them to a person.

Choosing the Marker: mRNA and miRNA

Messenger RNA has been studied the longest for this purpose and has been used to identify blood, semen, saliva, vaginal secretions, menstrual blood, and rectal mucosa. A newer method, using microRNA, has other advantages. miRNAs are very short and therefore less prone to degradation than mRNA, and seem to be sensitive enough for forensic purposes. Blood was one of the first fluids that was targeted, and a target was chosen because of the expression of miR-451a in erythrocytes. The reviewers note that fluid-specific markers, especially epigenetic markers, are not yet standardised, and that reference genes for miRNA normalisation would benefit from further development.

A study from the Virginia Commonwealth University demonstrates the capabilities of a miRNA assay. The group had previously published a panel of 8 miRNAs which has been shown to classify RNA extracts from venous and menstrual blood, faeces, urine, saliva, semen and vaginal secretions with 93% accuracy by quadratic discriminant analysis. They then performed tests to see how it reacted to the DNA from 50 donors in each fluid. This is important as it eliminates the need for an RNA extraction, thus conserving the sample. Initial classification accuracy was 87%, and further miRNAs increased this to 92% and 72% to 98% accuracy when classifying unknown samples from donors of mixed age, ethnicity and gender. However, compromised samples and biological cycles were not as consistent. The weakest classes were semen and saliva, and mixed samples were never tested. While this is encouraging, it does indicate that the accuracy can be high at the top but lower at the bottom for specific fluids.

The selection of the Platform: Capillary Electrophoresis and Digital PCR

The choice of the marker is correlated with the detector. To date, capillary electrophoresis (CE) of RT-PCR products has been the preferred method for forensic laboratories, with proven workflows and good multiplexing capabilities. A brief comparison between dPCR and digital PCR (dPCR) shows that dPCR is a highly sensitive alternative that enables absolute quantification of target transcripts without standard curves and is particularly suited to low-template and degraded samples. At the same time, droplet digital PCR experiments have found markers for today’s fluids and provided a more precise measure of RNA degradation.

There are costs associated with dPCR. Multiplexing capability is currently limited, and costs are higher. The review thus does not present CE and dPCR as alternatives to each other, but as complementary methods and as a combination (or as a particular context) may yield the most powerful results. The authors point out that, in practice, both methods have two drawbacks, namely, they are not very sensitive to low levels of RNA, and they cannot be used to analyse high complexity RNA mixtures. They also note that massively parallel sequencing has greatly expanded RNA typing and that this naturally leads to the next theme.

What Fluid? to Whose Fluid?

The latest change is that the use of RNA has been introduced to determine which donor the fluid is from. Sequencing can provide genotype information directly related to the fluid, if a coding region SNP (cSNP) is present on the transcript. This is important in mixtures because it cannot determine which one of the DNA contributors was missing which component of the mixture.

A New Zealand study validated this using samples of saliva, blood, vaginal and skin tissues from 11 individuals with an Illumina MiSeq assay for 23 cSNPs. The expression pattern was as predicted,d and a Bayesian network was constructed to explain the results. Variant calling was partially successful, but donor differentiation and sequencing quality for some transcripts were compromised. The authors also refer to a design issue: for some genes in the flu,id the suitable cSNPs are simply not available. The skin markers showed the best performance, with LCE1C being the most sensitive and previously reported as sensitive and recoverable from touched objects. The sample is limited, and the results are encouraging, but not conclusive.

A Chinese group went the computer route. They constructed reference matrices and created in silico mixtures of two to four individuals from bulk transcriptome data of five forensic body fluids. The unnormalized expression profiles yielded high-accuracy deconvolution estimates of the fluid proportions using robust regression-based deconvolution. After the composition is determined, SNPs within the genes of just one component can be separated off and used to determine the donor of that component. High likelihood ratios were obtained with SNPs from in silico and real mixtures. The authors state that it is a proof of concept and that the quality of the RNA in the samples varies, thus affecting the expression profile and consequently the deconvolution. This is a key limitation in real casework RNA, where it is often much harder to obtain good data than in clean laboratory data.

Synthesis

These five papers have three themes that will be highlighted. First is the expansion of the field beyond mRNA.The first is that the field is expanding its toolbox beyond mRNA. miRNA is stable and compatible with DNA extracts and performs differently across fluids. Secondly, platforms are becoming increasingly diverse. In addition to being the standard method, CE is also the most practical, while dPCR is the most sensitive and quantitative, and sequencing opens the door to donor attribution, although it is more expensive and less multiplexed. Third, the desire has expanded beyond just naming the fluid to associating it with someone, albeit with limited numbers of samples or in silico evidence.

Common weaknesses recur. Saliva and semen are two of the most difficult fluids to get repeatedly. The mixtures and degraded RNA are unresolved. Not all markers and reference genes are standardised yet.

Conclusion

RNA profiling is no longer a specialised research tool, but an ensemble of tools capable of identifying fluids, preserving samples using DNA extracts and, in early studies, determining the donors of fluids. The recent evidence is one of optimism, with realism. Now the goal is not just finding new markers, but validating existing markers in realistic, degraded, and mixed samples; normalising and interpreting the markers; and demonstrating the utility of donor attribution for sequencing in larger proof-of-concept studies.

References:

1. Macfarlane, D., Roca, G., Stadler, C., & Zapico, S. C. (2026). The Mystery of the Hidden Trace: Emerging Genetic Approaches to Improve Body Fluid Identification. Genes, 17(2), 146. https://doi.org/10.3390/genes17020146
2. Rhodes, C., Lewis, C., Price, K., Valentine, A., Creighton, M.-R. A., Boone, E., & Seashols-Williams, S. (2023). Evaluation and Verification of a microRNA Panel Using Quadratic Discriminant Analysis for the Classification of Human Body Fluids in DNA Extracts. Genes, 14(5), 968. https://doi.org/10.3390/genes14050968
3. Rocha, A. M., Lareu, M. V., Xavier, C., & Gomes, I. (2026). Forensic Body Fluid RNA Typing: Evaluating the Roles of CE and dPCR Technologies. Genes, 17(7), 812. https://doi.org/10.3390/genes17070812
4. Huan Yu, Jiayan Li, Jiaxin Ji, Jiaqi Wang, Zidong Liu, Hailing Yang, Xiaoxiao Wang, Jinding Liu, Zeqin Li, Gengqian Zhang, Identification of the body fluid donors from mixture stains using bulk transcriptomes data, Briefings in Bioinformatics, Volume 26, Issue 6, November 2025, bbaf668, https://doi.org/10.1093/bib/bbaf668
5. Correll Trnka, A. N., Richards, R., Opperman, S., & Harbison, S. (2026). An evaluation of molecular and statistical methods for the attribution of body fluid sources to donors. Forensic Science International: Genetics, 84, 103455. https://doi.org/10.1016/j.fsigen.2026.103455