Minimally Invasive Skin Transcriptome Extraction Using a Dermal Biomarker Patch

Advances in the scientific understanding of the skin and characteristic genomic dermal signatures continue to develop rapidly. Nonetheless, skin diagnosis remains predicated on a subjective visual examination, frequently followed by biopsy and histology. These procedures often are not sufficiently sensitive, and in the case of many inflammatory diseases, biopsies are not justified, creating a situation where high-quality samples can be difficult to obtain. The wealth of molecular information available and the pace at which new data are acquired suggest that methods for minimally invasive biomarker collection could dramatically alter our understanding of skin disease and positively impact treatment paradigms.

A chemical method was optimized to covalently modify custom dermal patches with single-stranded DNA that could bind to messenger RNA. These patches were applied to ex vivo skin samples and penetration evaluated by histological methods. Patches were then applied to both the skin of normal human subjects (lower arm) as well as lesional skin of psoriasis patients, and the transcriptome captured (N = 7; 33 unique samples). Standard RNA-Seq processing was performed to assess the gene detection rate and assessments made of the reproducibility of the extraction procedure as well as the overlap with matched punch biopsy samples from the same patient.

We have developed a dermal biomarker patch (DBP) designed to be minimally invasive and extract the dermal transcriptome. Using this platform, we have demonstrated successful molecular analysis from healthy human skin and psoriatic lesions, replicating the molecular information captured with punch biopsy.

This DBP enables an unprecedented ability to monitor the molecular "fingerprint" of the skin over time or with various interventions, and generate previously inaccessible rich datasets. Furthermore, use of the DBP could be favored by patients relative to biopsy by limiting pain resulting from biopsy procedures. Given the large dynamic range observed in psoriatic skin, analysis of complex phenotypes is now possible, and the power of machine-learning methods can be brought to bear on dermatologic disease.