Spatial proteomics identifies JAKi as treatment for a lethal skin disease

1The skin is the organ that is most frequently affected by adverse drug reactions, and nearly 2% of such reactions are severe and life-threatening. The spectrum of cutaneous adverse drug reactions (CADRs) ranges from self-resolving maculopapular rash (MPR) to rare and life-threatening conditions, such as drug reaction with eosinophilia and systemic symptoms (DRESS), Stevens–Johnson syndrome (SJS) and TEN. TEN is characterized by fulminant epidermal detachment exceeding 30% of the body surface area, whereas its milder form SJS–TEN overlap disease affects 10−30% of skin surface area. TEN is fatal in one-third of all cases. Despite diverse proposed pathogenic mechanisms–, the main drivers of cytotoxicity remain unknown and consensus therapy of TEN remains primarily supportive care.

2Spatial omics has recently emerged as a powerful strategy to obtain a global, molecular view of intact tissue samples,. When applied at single-cell resolution, these methods can profile different cell types within their native environment. Although a variety of such technologies exist, translation into direct clinical benefit remains largely aspirational. Deep visual proteomics (DVP) provides spatial proteomics data of individual cell types from formalin-fixed paraffin-embedded (FFPE) archived biopsies. DVP combines high content imaging, artificial intelligence-guided cell segmentation and classification and laser microdissection of individual target cells coupled with ultra-sensitive mass spectrometry-based proteomics. Here, we utilized DVP to study mechanisms of cutaneous drug reactions, investigate molecular features in the different CADRs, and functionally validate targeted small-molecule inhibitors for rapid therapeutic intervention in TEN, the most severe CADR.

3We performed DVP on a retrospective cohort of lesional FFPE skin biopsies from patients with mild (MPR) and severe (TEN or DRESS) CADRs, as well as healthy individuals (Fig. 1a). We stained FFPE tissue sections for CD45+ immune cells and pan-cytokeratin+ keratinocytes, followed by machine learning-based cell segmentation (Extended Data Fig. 1a,b). For each individual, contours per cell type were laser microdissected and combined separately to maintain patient and cell-type specificity, prior to peptide extraction and acquisition of mass spectrometry data (Fig. 1a). This identified around 5,000 unique proteins in each cell type across all participants, with distinct profiles for keratinocytes and immune cells (Extended Data Fig. 1c–f).

4To identify disease- and cell-type-specific molecular mechanisms, we first analysed the protein expression profiles of lesional keratinocytes from the CADRs (Fig. 1b and Extended Data Fig. 2a–c). Keratinocyte proteomes clustered according to the type of drug reaction, with principal component 1 (PC1) separating TEN from other CADRs and healthy individuals (Fig. 1c). PC1 was strongly associated with proteins involved in calcium homeostasis and cellular stress response (HSPA5, TMCO1 and ATP2A2; Fig. 1d). Of note, STAT3 was a main driver of separation, along with a dominant interferon signature (Fig. 1d,e). The number of differentially expressed proteins (DEPs) compared with cells from healthy individuals increased with the severity of the CADRs (Extended Data Fig. 2d). DRESS keratinocytes expressed more than double the amount of the major histocompatibility complex class I proteins TAP1, TAP2, TAPBP and HLA-A, suggesting that keratinocytes participate actively in culprit-drug-associated antigen presentation (Extended Data Fig. 2e). Among significantly regulated proteins in keratinocytes from patients with TEN, many were involved in antimicrobial response, such as lysozyme and clusterin (Fig. 1f). WARS1, one of the most potently upregulated proteins, is strongly induced by inflammatory signals and especially IFNγ, and has not been associated with TEN. Superoxide dismutase 1 (SOD1) levels were halved in the TEN keratinocytes, potentially exacerbating the oxidative stress cascade that is known to upregulate keratinocyte expression of Fas ligand, a key cytolytic molecule that is involved in keratinocyte apoptosis and epidermal detachment in TEN,.

5To identify protein signatures of keratinocytes that are uniquely associated with specific CADRs, we examined hierarchical clusters of DEPs, which showed a clear segregation for TEN among three distinct protein clusters (Fig. 1g). The TEN cluster 1 was enriched for terms involved in the regulation of complement activation and blood clotting (Fig. 1h and Extended Data Fig. 2f,g). In accordance, nearly all complement factors were uniquely upregulated in keratinocytes of TEN and their presence may reflect the degree of dying keratinocytes in TEN (Fig. 1i and Extended Data Fig. 2h). Thus, proteomes of keratinocytes derived from DRESS and TEN reveal distinct disease-associated protein expression signatures.

6Cutaneous immune cell infiltration patterns differ markedly among CADRs. To understand underlying molecular differences in immune cells, we assessed their proteomes (Extended Data Fig. 2i–l). They clustered by disease phenotype, but in contrast to keratinocytes, PC1 separated DRESS from other CADRs (Fig. 2a and Extended Data Fig. 2m). It was prominently associated with targets of E2F and MYC, indicating a highly proliferative state (Extended Data Fig. 2o). PC1 and PC3 together revealed unique features of DRESS and TEN. In DRESS, multiple viral pathway terms were enriched, consistent with viral reactivation in its pathogenesis (Fig. 2b). By contrast, TEN had a dominant interferon signature with STAT1 as a main driver (Fig. 2b and Extended Data Fig. 2n). DRESS resulted in the largest number of DEPs compared with healthy immune cells, followed by TEN and MPR (Fig. 2c–e and Extended Data Fig. 2p). Few proteins were unique to specific CADR subsets (Extended Data Fig. 2q). One such protein was histone methylase EZH2, which was detectable only in immune cells from patients with DRESS (Fig. 2f). This proteomic observation was confirmed by immunofluorescence and by a report of almost exclusive transcription in a patient with therapy-resistant DRESS (Extended Data Fig. 2r,s). Among proteins previously associated with specific CADRs, granzyme B has been described as a key mediator of cytotoxicity in TEN, however, it was present in similar amounts in non-cytotoxic DRESS (Fig. 2f).

7Unsupervised hierarchical clustering analysis on DEPs in immune cells across all cohorts revealed three visually distinct protein clusters with perfect disease alignment (Fig. 2g). Cluster 2, which predominantly represents DRESS, was enriched for DNA replication processes, highlighting the proliferative nature of infiltrating immune cells in this disease (Extended Data Fig. 2t). Its most prominent term (activation of ATR in response to replication stress) was dominated by the DNA replication factors RFC and MCM, establishing a potential link to the viral pathogenesis of DRESS (Extended Data Fig. 2u). Notably, cluster 1 contained proteins that are predominantly associated with type I and II interferon signalling (Fig. 2h). These proteins were markedly overexpressed in TEN, but showed only slightly increased expression in MPR and DRESS (Fig. 2g).

8Although they indicated a role for the interferon pathway in TEN, our experiments did not identify the specific immune cell subtypes that contributed to this response. To this end, we harnessed a multiplexed data-independent acquisition (mDIA) workflow with a reference channel and the novel Astral mass analyser for the DVP workflow,. This enabled proteomic investigation of CD163+ macrophages, CD4+ T helper cells and CD8+ cytotoxic T cells infiltrating the skin of patients with TEN and SJS–TEN overlap, despite their extreme sparsity (Fig. 3a). From only 20 cell equivalents per immune cell type and participant, we covered a median of 2,104 and a total of 5,302 proteins (Fig. 3b,c). Macrophages had the highest expression of proteins related to the interferon pathway, potentially reflecting the higher expression of IFNγ receptor (Fig. 3d). In particular, the amount of the mediator of interferon signalling STAT1 was consistently higher in macrophages than in CD4+ and CD8+ T cells from the same individuals (Fig. 3e). Compared with cytotoxic T cells, macrophages showed significant enrichment for myeloid-specific lineage markers (MNDA and MRC1) and interferon-driven proteins (STAT1 and GBP5; Fig. 3f).

9We next investigated spatial differences between detached (blister roof) and attached (blister side) keratinocytes from within the same biopsy from only 20 cells per type and patient (Fig. 3g,h). Among three distinct clusters, the largest comprised the complement system and inflammation (Fig. 3i). These proteins were upregulated in both attached and detached keratinocytes of TEN, indicating that the inflammatory pathways driving TEN are already active in keratinocytes adjacent to blistered skin. Several proteins followed a linear relationship from healthy to attached to detached keratinocytes (Extended Data Fig. 3a). Proteomic differences between attached and detached keratinocytes were subtle, but sufficient to separate them according to spatial location and away from healthy keratinocytes (Fig. 3j). This separation was again driven by proteins of the complement and inflammation cascade (including MX1, C3, KRT6 and KRT16, among others; Fig. 3k–m). Furthermore, five proteins were associated with attached versus detached cells and may represent early proteomic changes before detachment (Fig. 3k–m).

10Severe inflammatory disorders are characterized by altered cellular cross-talk and feedback loops that exacerbate the immune response; we investigated this using the cell-type-resolved proteome (Extended Data Fig. 3b). To further unravel such disease-specific feedback loops, we hypothesized that proteins that were strongly regulated in both keratinocytes and immune cells would identify therapeutically relevant pathways. Six such proteins (WARS1, STAT1, S100A9, LYZ, GBP1 and APOL2) were upregulated at least fourfold in keratinocytes and immune cells of TEN compared with healthy controls (Fig. 4a). We confirmed the proteomic findings of WARS1 using immunohistochemistry (Extended Data Fig. 4a–c). By contrast, DRESS had only one (STAT1) and MPR had no such overlapping proteins (Extended Data Fig. 3c). Notably, all six of these proteins are in the interferon pathway, responsible for signal transduction (via STAT1) or triggered by it–. Interferons signal through the JAK/STAT pathway; we therefore examined molecules in this pathway (Fig. 4b and Extended Data Fig. 4d,e). STAT1 and STAT3 were upregulated in both immune cells and keratinocytes, whereas STAT2 was upregulated only in keratinocytes, and STAT5 was upregulated in immune cells. Most interferon-stimulated gene (ISG) products were either potently upregulated, illustrated by a ninefold increase of GBP1, or only detectable in TEN—for example, GBP5.

11Cytokines activate the JAK/STAT pathway; to study the activation of this pathway, we utilized targeted transcriptomics in an expanded cohort of patients with CADR (Extended Data Fig. 5a–c). Among the genes encoding cytokines that are known to signal through the JAK/STAT pathway, IFNG(encoding IFNγ) expression was the most potently upregulated in TEN, exceeding the upregulation of genes such as TNF, which is already known to be involved in TEN (Fig. 4c). Whereas targeted transcriptomics was not able to separate between the different CADRs, specific interrogation of JAKs and STATs confirmed broad upregulation of these molecules in TEN and SJS–TEN overlap (Fig. 4d and Extended Data Fig. 5d–i). Antibody-based visualization of STAT1 in FFPE tissue sections further confirmed that it was significantly upregulated and localized to the nucleus in keratinocytes and other cell types in TEN (Fig. 4e and Extended Data Fig. 3d). Notably, the degree of single-cell nuclear STAT1 upregulation in immune cells of TEN was similar to that in DRESS, in which therapeutic intervention with JAKi has proved successful, despite their differing histopathology, clinical presentation and disease course. This upregulation of STAT1 suggests that severe CADRs share a common inflammatory pathway, potentially driven by T cell activation. To further validate STAT1 activation in TEN at the signalling level, we assessed the phosphorylation status of STAT1. Histological analysis revealed a profound degree of STAT1 phosphorylation in skin-infiltrating immune cells and keratinocytes of patients with TEN (Fig. 4f). We extended this observation using global phosphoproteome analysis, measuring more than 16,000 class I phosphosites in an independent cohort of TEN, which further confirmed our findings (Extended Data Fig. 6). Collectively, these results demonstrate that activation of the JAK/STAT pathway is a key driver of TEN.

12To investigate the clinical significance of our experimental findings, we developed a novel in vitro model to authentically replicate key aspects of cutaneous drug reactions (Fig. 5a). In this autologous co-culture model, activated peripheral blood mononuclear cells (PBMCs) efficiently killed keratinocytes within 72 h, and this effect was inhibited in a dose-dependent manner by the pan-JAK inhibitor tofacitinib (Fig. 5b).

13Encouraged by these results, we next evaluated the efficacy of oral JAKi in established mouse models of TEN,–. Subcutaneous injection of a small-molecule antagonist of inhibitor of apoptosis (IAP) proteins (second mitochondria-derived activator of caspases (smac) mimetic) induced TEN-like cutaneous inflammation, epidermal detachment and histological features of TEN (Fig. 5c–f). Oral treatment with the pan-JAK inhibitor tofacitinib or the JAK1 and JAK2 inhibitor baricitinib successfully reduced macroscopic disease severity on days 1 and 3 of treatment (Fig. 5d and Extended Data Fig. 7a,b). Mirroring human TEN, a strong increase in cutaneous phosphorylated STAT1 signal was detectable, which was significantly reduced upon JAKi treatment (Fig. 5e and Extended Data Fig. 7c). Similarly, average dermal thickness was restored to normal levels (Fig. 5f and Extended Data Fig. 7d). Further, JAKi decreased keratinocyte cell death (TUNEL+cleaved caspase-3+ cells; Fig. 5e and Extended Data Fig. 7e) and reduced immune cell infiltration (CD45+ cells; Extended Data Fig. 7c). Of note, epidermal recovery was accelerated in JAKi-treated mice, as demonstrated by the rapid clinical recovery and upregulated expression of the proliferation marker Ki67 in keratinocytes of the basal layer (Extended Data Fig. 7c,f). This argues against detrimental effects of JAKi treatment on wound healing in the context of TEN.

14As JAK1 is the primary mediator of interferon signalling, we tested whether inhibition of JAK1 alone would be sufficient to treat the disease. Indeed, treatment with the JAK1 inhibitors abrocitinib or upadacitinib significantly reduced disease severity (Fig. 5g and Extended Data Fig. 7g,h). We also explored a therapeutic model in which JAKi was administered after disease induction (Fig. 5h). This similarly significantly reduced clinical severity, lesion size and time of recovery (Fig. 5h–j).

15We further tested the efficacy of baricitinib in an established humanized mouse model of TEN, in which we injected PBMCs from a TEN survivor intravenously into immunodeficient mice (Fig. 5k). Severe ocular conjunctivitis and cell death of conjunctival epithelium reminiscent of TEN occurred at day 14 following daily oral administration of the causative drug (Fig. 5l–n and Extended Data Fig. 8a–c). Baricitinib treatment significantly reduced ocular conjunctivitis and cell death of conjunctival epithelium compared with vehicle treatment (Fig. 5l–n and Extended Data Fig. 8a–d). Together, the in vitro and in vivo data clearly demonstrate the efficacy of JAKi in preclinical models of TEN.

16On the basis of our compelling preclinical data and the urgent clinical need in this devastating disease, we treated seven patients with TEN or SJS–TEN overlap with JAKi off-label. One such individual was a 59-year-old man who was receiving carboplatin, etoposide and the PD1 antagonist serplulimab for small cell lung cancer (Fig. 6). After the third cycle of cancer treatment, he developed TEN with epidermal detachment affecting 35% of his body surface and a severity-of-illness score (SCORTEN) of 4, indicating a predicted mortality rate of 58.3%. Epidermal detachment had progressed despite high-dose intravenous methylprednisolone, prompting us to initiate JAK1 inhibitor rescue therapy with abrocitinib, which was recently approved for treatment of atopic dermatitis. The disease stopped progressing within 2 days and re-epithelialization was visible within four days, reaching near-completeness after 16 days (95%), after which the patient was discharged following full recovery from TEN. All seven patients treated with JAKi survived at day 30 with no side effects (Extended Data Figs. 9a–d and 10a–c). Notably, cutaneous levels of phosphorylated STAT1 were markedly reduced in all seven patients following JAKi treatment (Extended Data Figs. 9e and 10d). These clinical data show the potential of JAK1 or pan-JAK inhibition for the treatment of TEN.

17Here we used DVP to characterize the spatial proteome of keratinocytes and immune cells in FFPE tissue sections from patients with drug-induced skin reactions, quantifying up to around 5,000 proteins per cell type. These proteomic analyses across all major types of cutaneous drug reactions with cell-type resolution provided insights into the molecular mechanism of each CADR that we leveraged for therapeutic interventions.

18Proteomics analysis revealed an in-depth view of the biological molecules involved in CADRs. For instance, the histone methyltransferase EZH2 was detected exclusively in skin-infiltrating immune cells in DRESS. EZH2 has a key role in epigenetic gene regulation by modifying histone H3 at lysine 27 (H3K27me3). In addition, E2F pathway enrichment detected in our proteomic dataset of immune cells in DRESS correlates with overexpression of EZH2, which functions as a downstream effector in this signalling cascade. These findings suggest a role of EZH2, and by extension epigenetic modifications, in the pathogenesis of DRESS that would be of interest for future studies.

19The key finding of our study is the marked upregulation of the JAK/STAT pathway in TEN, which is not observed in less severe CADRs. Our results pinpoint this signalling pathway as the main driver of cutaneous inflammation, keratinocyte cytotoxicity and epidermal detachment, with macrophages showing the greatest inflammatory response among tissue immune cells. Whereas activation of STAT1 and STAT2 specifically indicates interferon signalling, STAT3 can be activated by cytokines other than interferons. Our spatial analysis revealed pronounced inflammation in keratinocytes, both at the site of epidermal detachment and immediately adjacent to it. This observation aligns closely with clinical findings showing that light pressure near an existing blister induces further blistering (‘indirect Nikolsky sign’). It also suggests an active role for keratinocytes in contributing to the skin-directed cytotoxic immune response. Upregulation of JAK/STAT signalling in both immune cells and keratinocytes may explain the extensive tissue damage in TEN through a potentially detrimental self-amplifying inflammatory cascade. Further study of macrophages in this context could clarify the role of the innate immune system in TEN. Finally, we comprehensively investigated the effect of JAKi in vitro in a novel cell culture model and in vivo in two independent mouse models, all of which demonstrated consistent therapeutic benefits.

20Having identified the JAK/STAT pathway as an actionable therapeutic target in TEN using spatial proteomics, and given our extensive preclinical evidence, we treated seven patients with TEN or SJS–TEN overlap syndrome, including three who were previously unresponsive to high-dose systemic corticosteroids. All seven responded well to JAKi and were discharged in good health after treatment. These data pave the way for a clinical trial of early JAKi therapy in TEN with the potential to change the outcome of this lethal adverse reaction to common drugs.

21Owing to the immediate risk of mortality associated with TEN and the short duration of specific therapy required, the rapid onset of action and benefits of short-term JAKi therapy would be substantial, if they are confirmed in larger clinical trials. Given the strong association of TEN with immune checkpoint inhibitor therapy (odds ratio = 4.33), JAKi may also prove useful in such cases by improving outcome and enabling rapid reintroduction of oncological treatment,.

22Our study is a successful example of directly linking emerging cell-type-resolved spatial omics technologies, especially spatial proteomics, with a new treatment modality that is of benefit to patients. We envision that similar approaches could be transformative in a broad set of inflammatory or oncological conditions by helping to identify key druggable targets and select treatments more precisely.

23Skin tissue biopsies were obtained during routine histopathological diagnostic procedures. In the standard DVP proteomic cohort, patients with TEN had a minimal affected skin area of 30%. In the other cohorts, patients with SJS–TEN overlap were included (minimal affected skin >10%). All patients with DRESS had a RegiSCORE of 6–7 (definite diagnosis). In addition to the typical clinical features, the lymphocyte transformation test or skin tests were positive in all patients with MPR. The number of samples per condition is indicated in the relevant figures and corresponding legends. Each sample represents one individual. Exceptions are the phosphoproteomic cohort where two distinct biopsies from each of the four healthy individuals yielded eight samples, and the mDIA–DVP cohort, where two distinct biopsies from three out of seven patients resulted in a total of ten samples. Histopathological diagnosis was re-validated in all cases by a board-certified dermatopathologist using a fresh H&E-stained tissue section. Baseline characteristics of all cohorts are described in Supplementary Tables 1–5 and were statistically evaluated using ANOVA for numeric variables (age) and a Chi-squared test of independence for categorical variables (sex). Retrospective analysis was performed with informed consent and ethical approval in place (Munich: 22-0342, 22-0343; Zurich: BASEC: Req-2021-00226 and 2017--00494; Fujian: MRCTA, ECFAH of FMU[2023]400). All experiments were performed in accordance with the Declaration of Helsinki.

24Regular assessment and vigilant surveillance of the vital signs, haematological parameters and coagulation markers was performed throughout treatment. Patients with active infections were excluded. All patients were additionally treated according to current best supportive care guidelines. Oral abrocitinib was administered at a regimen of 200 mg daily for 5–7 days, and tapered to 100 mg daily for 5–7 days. Similarly, oral tofacitinib was administered at a dosage of 10 mg daily for 5–7 days, and then tapered to 5 mg daily for 5–7 days. In cancer patients with high SCORTEN, a reduction in JAKi dosage was initiated after observing a certain degree of re-epithelialization. Treatment was approved by the local ethics committee and the institutional review board of the First Affiliated Hospital of Fujian Medical University (Fujian: MRCTA, ECFAH of FMU[2023]400), and the patient provided written informed consent.

25As previously published, C57BL/6 mice were injected subcutaneously with 100 μl of 1 mg ml−1 smac mimetic (CompA) (TetraLogic Pharmaceuticals) or vehicle (12% Captisol). Weight dependent doses of JAKi (10 mg kg−1 baricitinib, 30 mg kg−1 tofacitinib, 20 mg kg−1 abrocitinib or 10 mg kg−1 upadacitinib; Selleckchem) or vehicle (20% Captisol; 50 μl; Selleckchem) was administered by oral gavage twice daily starting one day before (pre-treat model) or 3 h after (treatment model) subcutaneous injection of smac mimetic. Mice were euthanized on day one or day three after smac-mimetic injection, the site was photographed, scored, and samples collected for ex-vivo analysis. We used a four-point ordinal scale (0–4) to assess the three clinical associated macroscopic criteria of TEN (Oedema, rubor and epidermal disruption (Nikolksy sign/lesion severity)) at day one or three after smac-mimetic injection. They were assessed and scored as 0 (not present), 1 (mild), 2 (moderate) or 3 (severe) and the scores were combined for an overall clinical score. P values were calculated using the unpaired t-test with Welch’s correction. The corresponding data can be found in Supplementary Table 13. All pre-treatment experiments were performed using male mice only, and the treatment model was performed using female mice. Mice were maintained at the appropriate biosafety level under constant temperature and humidity conditions with a 12 h light cycle. Animals were allowed food and water ad libitum. Experiments were approved by the local ethics committee (WEHI AEC 2022.009).

26Immunocompromised NOD/Shi-scid, Il2rgnull(NOG) mice at 6 weeks of age were included. PBMCs (2 × 106) from an individual who had recovered from SJS–TEN 1 year earlier were injected intravenously into the NOG mice, followed by oral administration of the causative drugs (acetaminophen, 1.5 mg per 100 μl) at day one and once daily thereafter. The dosage used in this model was based on mg per kg body weight, converted from the normal adult human dose. Baricitinib (10 mg kg−1) or vehicle (20% Captisol; 50 μl) was administered by oral gavage twice daily from day one. Body weight, ocular reactions or cutaneous changes were assessed daily. On day 14, the degree of conjunctivitis was evaluated after ocular dislocation under general anaesthesia and scored (0, no conjunctivitis; 1, mild conjunctivitis; 2, severe conjunctivitis). The ratio of the number of TUNEL-positive cells to the total conjunctival cell count in a 400× magnified field was calculated for all mice. The corresponding data can be found in Supplementary Table 14. The patients received no systemic glucocorticoids at the time of PBMCs collection. Mice were maintained at the appropriate biosafety level under constant temperature and humidity conditions with a 12 h light cycle. Animals were allowed food and water ad libitum. Experiments were approved by the local ethics committee and the institutional review board of Niigata University, and the patient provided written informed consent.

27Two-micrometre PEN membrane slides (MicroDissect GmbH) were exposed to UV light (254 nm) for one hour and then coated with Vectabond (Vector laboratories; SP-1800-7) according to the manufacturers protocol. Three-micrometre-thick tissue sections of each FFPE block were mounted on the pretreated membrane slides and dried overnight at 37 °C. Immunofluorescence was performed according to our previously optimized protocol for membrane slides. In brief, slides were heated to 56 °C for 20 min and immediately deparaffinized and rehydrated (Xylol 2× 2 min, 100% ethanol 2× 1 min, 90% ethanol 2× 1 min, 75% ethanol 2× 1 min, 30% ethanol 2× 1 min, ddH2O 2× 1 min). Slides were then transferred to prewarmed glycerol-supplemented Antigen Retrieval buffer (DAKO pH9 S2367 + 10% Glycerol) at 88 °C for 20 min, followed by a 20 min cooldown at room temperature. Slides were then washed in water and blocked with 5% BSA/PBS for 30 min, followed by overnight primary antibody incubation at 4 °C (CD45 or cytokeratin) or 90 min at room temperature (CD4, CD8 and CD163; cytokeratin and CD163) in a humid staining chamber. After washing in PBS, secondary antibodies were incubated for 1 h at room temperature. All primary and secondary antibodies used reported in the Supplementary Table 6. After washing in PBS, sections were counterstained with SYTOX Green (1:400, Invitrogen S7020) for 10 min, washed again and coverslipped using Slowfade Diamond Antifade Mountant (Invitrogen, S36963). Sections were imaged on a Zeiss Axioscan at 20× magnification with a tile overlap of 10%. At the corresponding excitation wavelength at 100% laser intensity, except for 50% for the 493 nm channel. Illumination time was adapted to the optimal spectral properties. Up to 5 z-stacks at an interval of 1.25 μm were acquired. Multi-scene images were then split into single scenes, z-stacks were combined to a single plane by extended depth of focus where applicable (variance method, standard settings) and stitched, using the proprietary Zeiss Zen Imaging software. Images were imported as.czi files into the Biological Image Analysis Software (BIAS) with the packaged import tool. Single-cell segmentation for Figs. 1 and 2 was performed using deep neural network on the basis of pan-cytokeratin for keratinocytes and CD45 for immune cells at 1.0 input spatial scaling, 50% detection confidence and 30% contour confidence. Segmentation of images for Fig. 3 was performed using a custom-trained Cellpose model. Only contours between 30 μm2 and 200 μm2 were taken into consideration. After removal of duplicates at tile-overlapping regions, a supervised machine learning approach was used to remove false identifications and overlapping cell types. Contours were exported together with three calibration points that were chosen at characteristic tissue positions. Contour outlines were simplified by removing 99% of data points. For keratinocytes, only maximally every second shape was (randomly) chosen in order to prevent membrane instability while cutting multiple adjacent cells. Contour outlines were imported after reference point alignment, and shapes were cut by laser microdissection with the LMD7 (Leica) with a 63× objective in a semi-automated manner at the following settings: power 57, aperture 1–2, speed 23, middle pulse count 1, final pulse −3, head current 46–53%, pulse frequency 2,600, offset 210.

28For proteomic analysis of CD45+ immune cells and pan-CK+ keratinocytes, we collected 700 contours for each keratinocyte sample and 1,000 for each immune cell sample. For each participant, biological duplicates were collected when possible, from the same slide at different regions. Dissected contours for each participant and cell type were collected directly into the same underlying low-binding 384-well plate (Eppendorf 0030129547), with immune cells in the top half of the plate and keratinocytes in the bottom half, leaving an empty well between each sample.

29Semi-automated sample preparation and digestion was performed in the collection plate using a Bravo pipetting robot (Agilent) as previously described. For this, wells were washed with 28 μl of 100% acetonitrile and dried in a SpeedVac for 20 min at 45 °C. The contours were then resuspended in 4 μl of 60 mM triethylammonium bicarbonate (TEAB, Sigma) in mass spectrometry-grade H2O, sealed with two adhesive foils and heated at 95 °C for 60 min in a 384-well thermal cycler (Eppendorf). 1 μl of 60% acetonitrile in 60 mm TEAB (12.5% final acetonitrile concentration) was added, followed by heating at 75 °C for 60 min in a 384-well thermal cycler. Samples were then predigested with 4 ng of trypsin for 4 h followed by overnight digestion with 6 ng LysC in a 384-well thermal cycler at 37 °C. After 18 h the reaction was quenched with 1.5 µl 6% trifluoroacetic acid (1% final concentration). Samples were then manually transferred to single PCR tubes, dried in a SpeedVac for approx. 60 min at 45 °C and stored at −20 °C.

30Peptides were resuspended in 4.1 μl MS loading buffer (2% acetonitrile (v/v) + 0.1% trifluoroacetic acid (v/v) in mass spectrometry-grade H20) immediately prior to measurement. All samples were stratified into cell type and replicates, and further randomized using the rand function in Excel. An EASY nanoLC 1200 (Thermo Fisher Scientific) was coupled to a timsTOF SCP mass spectrometer (Bruker) via a nanoelectrospray ion source (Captive spray source, Bruker). Peptides were separated on a 50 cm in-house packed HPLC column (75 μm inner diameter, 1.9 μm ReproSil-Pur C18-AQ silica beads (Dr. Maisch)), which was heated to 60 °C by an in-house manufactured oven. A linear gradient of 120 min was ramped at a constant flow rate of 300 nl min−1 from 3 to 30% buffer B in 95 min, followed by an increase to 60% for 5 min, washed at 95% buffer B for 10 min and re-equilibration for 10 min at 5% buffer B (buffer A: 0.1% formic acid and 99.9% ddH2O; buffer B: 0.1% formic acid, 80% acetonitrile and 19.9% ddH2O). The timsTOF SCP mass spectrometer was operated in dia-PASEF mode using the standard 16 dia-PASEF scan acquisition scheme with 4 IM steps per dia-PASEF scan, covering a m/z range from 400 to 1,200 and ion mobility of 0.6 to 1.6 V cm−2. All other settings were standard as described in ref..

31A library-free search was performed, using a DL predicted spectral library in DIA-NN (v1.8.0). Uniprot human databases UP000005640_9606 and UP000005640_9606_additional were used. Mass spectrometry raw files from immune cells and keratinocytes were searched separately in DIA-NN, apart from data shown in Extended Data Fig. 1d–f and Supplementary Fig. 1 (ligand–receptor interaction). Methionine oxidation was defined as variable modification and missed cleavages were limited to one. The precursor charge ranged from 2 to 4, precursor mass range was set to 300 to 1,800, and peptide length from 7 to 35. Mass and MS1 accuracy were set to 15, based on prior estimation. Isotopologues, and MBR were enabled and neural network classifier was set to single-pass mod. The ‘--relaxed-prot-inf’ function was activated using the command line for more conservative protein grouping. Proteins were inferred from FASTA. Library generation was set as ‘Smart profiling’, ‘RT-dependent’ as cross-run normalization an ‘Robust LC (high precision)’ as quantification strategy.

32For proteomic analysis of different immune cell subsets (CD163+ macrophages, CD8+ and CD4+ T cells) and spatially resolved keratinocytes, we collected ~35 contours for each keratinocyte sample and ~100 for each immune cell sample (equivalent to 5,000 µm2) into an underlying 384-well plate and further processed.

33Dimethyl labelling of reference channel (∆0) and samples (laser microdissected macrophages, CD8+ T cells, CD4+ T cells or keratinocytes; ∆8) was performed as previously described. For the reference channel, an equal amount (measured prior using the tryptophan assay) of digested bulk TEN tissue (5 µm FFPE section), tonsil tissue (5 µm FFPE section) and primary keratinocytes (cell culture) was combined and diluted to 1 ng µl−1. Each sample was loaded together with 10 ng of the reference channel on an Evotip Pure (Evosep).

34Peptides were separated by the Evosep One liquid chromatography system and the standardized whisper 40SPD connected to the Orbitrap Astral (Thermo) using the EASY-Spray source (Thermo). Spray voltage was set to 1,900 V. The 15 cm column with an inner diameter of 75 µm containing 1.7 µm C18 beads (Aurora Elite TS, IonOpticks) was heated to 50 °C. All samples on the Orbitrap Astral were acquired in data-independent acquisition (DIA) mode at an MS1 resolution of 240,000 and scan range of 380−980 m/z. Normalized AGC target was 500%. Isolation windows of 8 Th scanning with a maximum injection time of 16 ms were recorded. Isolated ions were fragmented at an HCD collision energy of 25%. All samples were acquired with field asymmetric ion mobility spectrometry (FAIMS) enabled at a compensation voltage of −40 V. Gas flow was reduced to 2.5 l min−1.

35The DIA-NN search was performed using a custom spectral library and postprocessed with Refquant as described previously in detail. The ‘Channel.Q.Value’ cut-off was likewise set to 0.15. The spectral library was created by gas-fractionation of the bulk samples used for the reference channel (10 fractions, 50 ng each, same mass spectrometry method as above). The output of Refquant was then used for further bioinformatic analysis.

36Frozen tissue samples stored in RNA Later at −80 °C were transferred with as little RNA Later as possible to single tissueTUBE TT1 (Covaris) and immediately pulverized using a chilled hammer. Five-hundred microlitres of PreOmics Lysis Buffer was then added, resuspended and transferred to a 1.5 ml Eppendorf tube, followed by boiling at 95 °C for 60 min and protein concentration measurement (Tryptophan assay). To remove any potential remnants of RNA Later that could inhibit tryptic digestion, proteins were transferred to a new 1.5 ml Eppendorf tube using Resyn magnetic beads (Hydroxyl modified, 20 µg µl−1 stock) at a protein to bead ratio of 1:5 and according to the manufacturers protocol. Samples and beads were resuspended in 200 µl 60 mM TEAB followed by overnight tryptic digestion(LysC and Trypsin at a protein to enzyme ratio of 1:100) at 37 °C and 1,200 rpm. Next day, enzyme activity was quenched using trifluoroacetic acid (1% final concentration) and peptide concentration was again measured. Twenty micrograms from each sample was transferred to a low-bind 96-well Eppendorf plate followed by fully automated phospho enrichment on the Agilent AssayMAP Bravo using High-Capacity Fe(iii)-NTA cartridges and elution in 500 mM ammonium hydrogen phosphate (pH 4). Enriched peptides were then loaded on Evotip Pure (Evosep). Peptides were eluted into the mass spectrometer using the Evosep One liquid chromatography system and the standardized whisper 40SPD. A 15 cm column with an inner diameter of 75 µm containing 1.7 µm C18 beads (IonOpticks), heated at 50 °C and connected to the Orbitrap Astral (Thermo) using the EASY-Spray source (Thermo). Spray voltage was set to 1,900 V. All samples on the Orbitrap Astral were acquired in DIA mode at an MS1 Orbitrap resolution of 120,000 and scan range of 380−1,180 m/z. Normalized AGC target was 500%. Isolation windows of 3 Th scanning with a maximum injection time of 5 ms were recorded. Isolated ions were fragmented at an HCD collision energy of 25%. All samples were acquired with field asymmetric ion mobility spectrometry (FAIMS) enabled at a compensation voltage of −40 V. Gas flow was reduced to 2.5 l min−1. MS raw files were processed by the Spectronaut software version 17 in directDIA mode (Biognosys) against the human FASTA database (21,039 entries, 2019). Peptides between 7–52 with a FDR less than 1% at the peptide and protein levels were taken into account. Variable modification were defined as serine/threonine/tyrosine phosphorylation, N-terminal acetylation and methionine oxidations. Cysteine carbamidomethylation was defined as a fixed modification. Enzyme specificity was set as Trypsin/P with a maximum of two missed cleavages. The resulting report was then exported and analysed in R. Class I phosphosites were defined as phosphosites with a localization probability > 75% or otherwise defined as non-class I. Functional analysis was performed on the imputed dataset. Redundancies in data, caused by multiplicity, were removed by retaining the ones that have higher fold change and higher –log (p.value), as previously described. t-test was performed using the base R function, and the resulting P values were adjusted for multiple testing (Benjamini–Hochberg, FDR < 0.05). Gene-centric enrichment analysis was performed using the clusterProfiler package, accessing the MsigDB C5 database. Specifically, overrepresentation analysis was performed on proteins of the corresponding phosphosites proteins upregulated by fold change >0.5, while gene set enrichment analysis was performed on all proteins, including the corresponding fold change. The GOplot package was implemented to generate the circosplot. Phosphosite-specific analysis was performed using the ssGSEA2 package accessing the PTMSigDB and with the default settings.

37Ten-micrometre FFPE human skin sections were cut from each sample and deparaffinized using ROTICLEAR (Carl Roth). RNA was isolated using RecoverAll Total NucleicAcid Isolation Kit (AM1975, Thermo Fisher Scientific) and concentrated using the Savant SpeedVac DNA 130 Integrated Vacuum Concentrator (Thermo Fisher Scientific). Concentration was measured using Nanodrop One/Onec (Thermo Fisher Scientific). One-hundred and fifty micrograms of RNA from each sample was hybridized overnight with unique probe pairs for 624 genes, including 594 genes from the nCounter Immunology Panel, 15 internal reference genes, and 30 user defined genes using the Panel Plus option (NanoString Technologies). Data were collected using the nCounter SPRINT Profiler (NanoString Technologies). Normalization was performed using nanostringr v0.4.2.

38Bioinformatics data analysis was carried out using the R statistical computing environment version 4.0.2. For subsequent statistical analysis, protein or gene intensities were log2-transformed and average intensities of the biological replicates were computed for each participant and cell type where applicable. Box plots show the median (centre line) with interquartile range of 25% to 75%, whiskers extend to furthest data points within 1.5× the interquartile range from the box boundaries. For the ANOVA, the R stats package was used; variables were zero-centred and scaled prior to analysis. Subsequent multiple testing correction (adj. P) was conducted using the Benjamini–Hochberg method with a FDR cut-off of 5%; post hoc Tukey’s HSDs were calculated at a confidence level of 0.95. For differential expression, the non-paired t-test was performed using the rstatix package with standard settings, followed by multiple comparison correction (Benjamini–Hochberg method, FDR 5%) from the stats package. Overrepresentation analyses (ORA) was performed with WebGestalt 2019 (ref. ) and GSEA with the clusterProfiler package (MsigDB H, C2 or C5 database) within the R environment. The heat maps were generated using the pheatmap package, with the data being zero-centred and scaled before display. Only proteins that exhibited an identification rate of 70% or higher in at least one cohort for each cell type independently were included for functional analysis of the proteomic data. Imputation was performed per individual based on a normal distribution (width of 0.3, downshift of 1.8) for principal component analyses, functional analysis of the phosphoproteomic dataset and the mDIA–DVP data. All other statistical analyses were performed on non-imputed data. For the comparison between TEN/DRESS/MPR and healthy for selected proteins in both cell types (shown in Fig. 4b and Extended Data Fig. 3c), a non-paired t-test was performed and the log2 fold change value of TEN over healthy was visualized in the figure using a consistent range and colour-coding for both cell types. The fill colour of the (semi-circles) was manually adjusted using the ‘eyedropper’ tool in Adobe illustrator.

39Cell–cell interaction analysis was performed in Python. CK and CD45 files were processed together and loaded via Pandas. Data was filtered to have fewer than 30% missing values on protein level. Missing data points where imputed using scikit-learn’s IterativeImputer with a RandomForestRegressor. Significant genes were identified by performing one-way-anove using Scipy’s stats package and a P value cut-off of 0.01. Significant genes were identified by performing a one-way ANOVA using the stats package from SciPy, applying a P value cut-off of 0.01. Results were further adjusted by applying a pairwise Tukey test from the Pingouin package, requiring a minimum of five significant comparisons at a Tukey cut-off of 0.05. Protein expression data were averaged by condition. Potential interactions were retrieved from OmniPath (version 0.16.4) and matched by gene name. Interaction plots were generated using CircosPlots. For each potential interaction, the intensity difference was calculated in each direction by subtracting the target intensity from the source intensity. To identify disease-specific interactions, the difference in healthy conditions was subtracted from the difference in disease conditions. The plots were created using the D3Blocks package, and the resulting HTML files were automatically converted to figures using Selenium, CairoSVG, and pdf2image. Only interactions with a delta greater than zero were displayed in the plots. Bubble plots were created using Matplotlib. For each interaction, a matrix was constructed to compare all differences across disease conditions and cell types and plotted as scatterplot. Colour indicates the difference according to a colormap and bubble size reflects the magnitude. Bubble plots for the selected protein interactions across different cell types and conditions are provided in the Supplementary Fig. 1.

40Hair follicles were placed on thin-Matrigel (Corning, 354234) coated T25 flasks and covered with 10 μl of 1.8 mg ml−1 Matrigel to prevent hair follicle detachment. Following 1 h incubation at 37 °C/5% CO2, 1 ml of conditioned mouse embryonic fibroblast medium, from irradiated J2-3T3 cells, supplemented with 10 μM Y-27632 (CST 13624) and 10 ng ml−1 FGF2 (Cell Guidance GFH146-50), was carefully added. Medium was changed every 48 h. When outgrowth of keratinocytes became visible, medium was aspirated and replaced with 2 ml Keratinocyte Expansion Medium DermaCult (Stemcell 100-0500, with supplement), and changed every 48 h. When confluent, keratinocytes were detached using TrypLE Express Enzyme (Gibco 12604039) and defined trypsin inhibitor (Gibco R007100) and replated on collagen I (Corning, 354236) coated plates for further expansion.

41PBMCs were isolated using Ficol according to standard protocol. Cells were frozen in liquid nitrogen until further usage. Keratinocyte expansion and PBMC isolation were approved by the local ethics committee with written informed consent (Munich: 23-0544).

42Human PBMCs (2 × 105 per well per 200 μl) were stimulated in U-shape 96-well plates in the presence or absence of CD3/28 (Gibco Dynabeads, 11161D) at a ratio of 1:1, in X-VIVO 15 (Lonza, 881026) for 5 days at 37 °C/5% CO2. On day 4 of PBMC stimulation, hair follicle-derived keratinocytes from the same person were plated on collagen I-coated 96-well plates at a density of 8 × 103 per well. On day 5 of PBMC stimulation, keratinocytes were fluorescently labelled (Celltracker Red; Invitrogen C34552) according to the manufacturer’s recommendation. Activated or non-activated (‘resting’) PBMCs were then added to the keratinocytes at a ratio of 1:1, after magnetic removal of CD3/28 beads where necessary. Tofacitinib (CST, 14703) or control (DMSO) was added in the indicated concentration as well as Celltox Green (Promega, G8731, 1:8,000 final dilution) to reach a final volume of 200 μl per well. Every 2 h images were acquired using a live-cell analysis system (Incucyte Sx3, Sartorius) for 72 h at 37 °C/5% CO2. Quantification of dead keratinocytes (Celltox+Celltracker+/total Celltracker+) was performed using the proprietary analysis software (Incucyte, Sartorius).

43Three-micrometre FFPE tissue sections were mounted on regular Superfrost-Plus glass slides and exposed to high temperatures for improved tissue adherence. Subsequently, sections were deparaffinized and subjected to heat-induced antigen retrieval and blocking solution. The primary antibody was applied to the tissue section for one hour at room temperature or overnight at 4 °C (pSTAT1 and cleaved caspase-3). For immunofluorescence staining, a species-specific secondary antibody coupled to a fluorophore was added for an additional hour, followed by a nuclear counterstain. Alternatively, immunohistochemical staining was conducted using horseradish peroxidase (HRP) coupled secondary antibodies, or signal amplified using biotinylated secondary antibodies followed by labelling with and Avidin–Biotin Conjugate (ABC) HRP Kit. HRP-labelled samples were then colour-developed with 3,3′-diaminobenzidine (DAB) and haematoxylin counterstain. TUNEL staining was performed according to the manufacturer’s instruction prior to application of the anti-cleaved caspase-3 primary antibody for co-staining. H&E staining was performed according to standard protocol. Images were acquired on a Zeiss Axioscan 7 or a DP72 microscope and cellSens imaging software (Olympus), or entire slides were scanned with a 3D Histech Virtual Slide Microscope (Olympus), viewed using CaseCenter (3D Histech) and image captured on a MacBook Pro 13-inch retina display (Ki67, CD45 and H&E). Reagents used are reported in Supplementary Table 6.

44Three-micrometre-thick FFPE tissue sections of all participants from the proteomic cohort were subjected to multiplex immunofluorescence staining for STAT1, pan-cytokeratin and Hoechst. All stainings were then uniformly acquired as.czi files using the Zeiss Axioscan 7. Single-cell segmentation was performed within QuPath (v0.4.1) using the standard nuclear segmentation algorithm. After segmentation, all available features were extracted, including staining intensities for all channels and nuclei. Segmentation and feature extraction was automated with QuPath using the script editor, to ensure equality. In R (v4.0.2) 5000 nuclei and their corresponding measurements were then randomly chosen per individual and merged into a single file, excluding samples with fewer than 5,000 cells (n = 1). To account for staining variation between slides, mean STAT1 fluorescence intensity was divided by its own mean Hoechst intensity on a per cell basis. Statistical significance between cohorts was determined by a unpaired two-sided t-test on mean normalized STAT1 values. Data are visualized using ggplot2.

45Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

46Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-024-08061-0.