Method for high-plex analysis of immune cells in human skin using the GeoMx system.

Specific T cell populations in the skin have been demonstrated as important disease drivers in several dermatoses. Due to the unique skin architecture, these cells are not grouped together in structures but dispersedly spread out throughout the epidermis. Following tissue disruption and isolation, only about 10% of skin T cells are recovered and any in vitro expansion may alter their bona fide phenotype. The Nanostring GeoMx system was developed to address cellular phenotype and protein expression in a tissue spatial context. To do so, regions of interest (ROI) must exceed a certain area threshold (usually 100 µm in diameter) to generate a sufficient signal-to-noise ratio. Here, we present an approach that allows for the pooling of numerous smaller ROIs within the skin, enabling T cell and melanocyte phenotyping. Skin samples from healthy individuals and vitiligo patients were analysed using the GeoMx system and several immune profiling panels. A sufficient signal-to-noise ratio was achieved by pooling smaller ROIs and analysing them as a single group. While this prevents spatial analysis, this method allows for detailed analysis of cells as a population in the context of their physiological environment, making it possible to investigate in situ phenotype of rare cells in different tissue compartments.

Initial steps towards developing a predictive algorithm of disease progression for hidradenitis suppurativa (HS): results from a Cox proportional hazard regression analysis on disease progression among a cohort of 335 Danish patients with HS.

BACKGROUND: Hidradenitis suppurativa (HS) is a chronic inflammatory and scarring disease with a wide spectrum of disease severity. The amount of scarring is proportional to the preceding tissue damage and poses a challenge to patients. Severe HS is most often treatment recalcitrant, but hypothetically avoidable through early biologic treatment. Early prediction of individual risk of disease progression is therefore essential for patient management. OBJECTIVES: To investigate risk factors associated with disease progression and to design an algorithm capable of predicting disease -progression. METHODS: A prospective cohort study of 335 Hurley III-naïve patients with HS, not treated with biologics, was followed for a median of 2 years. Potential risk factors covered basic demographics, HS anamnestic factors and clinical HS factors collected during physical examination. Two separate Cox proportional hazard regression (CPHR) analyses were conducted. A summated 'progression score' was calculated and used in the predictive algorithm of severe disease. Subsequent bootstrap sampling was used to validate the predictability of the predictive algorithm. RESULTS: The CPHR analysis of Transition to severe disease found that active smoking [hazard ratio (HR) 4.01, 95% confidence interval (CI) 1.71-9.40, P = 0.001]; body mass index (BMI) points > 25 at baseline (each point: HR 1.06, 95% CI 1.02-1.09, P < 0.001); active disease in 2 (HR 4.26, 95% CI 1.23-14.84, P = 0.02) and ≥ 3 areas (HR 6.54, 95% CI 1.89-22.72, P = 0.003) all constituted substantial risk factors. Conversely, the CPHR analysis of Disease progression did not yield results of clinical relevance. A 'progression score' of 3.04 was used as a threshold in the predictive algorithm of Transition to severe disease and achieved the following test specifics: sensitivity = 0.51, specificity = 0.86, positive predictive value = 0.50, negative predictive value = 0.86. CONCLUSIONS: We found a disparity between factors increasing the risk of simple Disease progression and those increasing the risk of Transition to severe disease. For the latter, active smoking, BMI points > 25, active disease in 2 or ≥ 3 areas were all shown to be the clinically relevant factors that could be used to construct an algorithm that correctly predicted progression to severe HS in more than half of all instances.

Secukinumab and Dead Sea Climatotherapy Impact Resolved Psoriasis Skin Differently Potentially Affecting Disease Memory.

Secukinumab and Dead Sea treatment result in clear skin for many psoriasis patients, through distinct mechanisms. However, recurrence in the same areas after treatments suggests the existence of a molecular scar. We aimed to compare the molecular and genetic differences in psoriasis patients who achieved complete response from secukinumab and Dead Sea climatotherapy treatments. We performed quantitative immunohistochemical and transcriptomic analysis, in addition to digital spatial profiling of skin punch biopsies. Histologically, both treatments resulted in a normalization of the lesional skin to a level resembling nonlesional skin. Interestingly, the transcriptome was not normalized by either treatments. We revealed 479 differentially expressed genes between secukinumab and Dead Sea climatotherapy at the end of treatment, with a psoriasis panel identifying SERPINB4, SERPINB13, IL36G, IL36RN, and AKR1B10 as upregulated in Dead Sea climatotherapy compared with secukinumab. Using digital spatial profiling, pan-RAS was observed to be differentially expressed in the microenvironment surrounding CD103+ cells, and IDO1 was differentially expressed in the dermis when comparing the two treatments. The differences observed between secukinumab and Dead Sea climatotherapy suggest the presence of a molecular scar, which may stem from mechanistically different pathways and potentially contribute to disease recurrence. This may be important for determining treatment response duration and disease memory.