RNA-Based Workflow Identifies Active Skin Microbes for Dermatology Research

DNA-based surveys catalog who is present without clarifying what they are doing. Low microbial biomass, abundant human cells, and RNA fragility have further limited transcript-level analyses directly on skin. Researchers now demonstrate an RNA-focused workflow that profiles microbial activity in situ.

At the A*STAR Genome Institute of Singapore (A*STAR GIS), in collaboration with A*STAR Skin Research Labs (A*STAR SRL), investigators developed a skin metatranscriptomics workflow that analyzes microbial RNA directly from naturally colonized skin. The approach is designed to reveal which microbes are active and which genes they express on the body surface. By addressing low microbial yield, human genetic material that masks microbial signals, and the inherent instability of RNA, the workflow provides a functional view of the cutaneous microbiome beyond DNA-based presence alone.

The team paired metatranscriptomics with metagenomics to separate microbial activity from abundance. In a study of five skin sites sampled from 27 healthy adults, the investigators observed that activity does not necessarily mirror presence. Malassezia fungi and Staphylococcus bacteria were frequently among the most active taxa, even when not dominant by DNA-based measures.

Microbes also exhibited site-specific transcriptional programs, with scalp communities showing distinct lipid-associated gene expression compared with those on the cheek. The analysis detected antimicrobial genes expressed directly on human skin, including previously uncharacterized candidates, offering insight into how microbial communities maintain balance. Collectively, these findings outline a practical foundation for interrogating pathway-level activity relevant to skin health and disease.

The study, “Skin metatranscriptomics reveals a landscape of variation in microbial activity and gene expression across the human body,” was published in Nature Biotechnology. According to the team, combining this workflow with genomics, metabolic modeling, and culture-based experiments could help clarify pathways linked to skin disease and reveal molecules with potential therapeutic value. They plan to refine the method and apply it in clinical studies, noting its potential to support future research into acne, eczema, and psoriasis.

“With this workflow, we can now see what skin microbes are actually doing on the skin. That gives us a much richer picture of how microbial communities function, adapt to different skin sites, and potentially influence health and disease,” said Chia Minghao, Senior Scientist, A*STAR GIS.

“This approach gives researchers and clinicians a new way to profile microbial activity directly on the skin. By revealing biological pathways linked to microbial activity and skin health, it can help identify markers and mechanisms that may be relevant for prediction, diagnosis and treatment,” said Niranjan Nagarajan, Associate Director, AI & Compute, A*STAR GIS.

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