Three‑Dimensional Immune Cartography Uncovers Subclinical Remodeling in Psoriasis

Three‑Dimensional Immune Cartography Uncovers Subclinical Remodeling in Psoriasis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Three‑Dimensional Immune Cartography Uncovers Subclinical Remodeling in Psoriasis Longjie LI, Lily VU, Paul DRURY, Kok Haur ONG, Weimiao YU, Philip TONG This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7581947/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Psoriasis is characterized by a complex immune micro-environment, yet most spatial studies rely on two-dimensional (2D) immunohistochemistry (IHC) histology. We investigated whether routine immunohistochemistry coupled with three-dimensional (3D) digital reconstruction can provide quantitative insight into immune-epithelial architecture across the psoriatic spectrum. Skin biopsies from 15 patients with plaque psoriasis and 52 healthy donors were serially sectioned to generate 174 tissue stacks (10,700 whole-slide images). CD3 + , CD68 + and mast cell were labelled and their Euclidean distance to dermal–epidermal junction (DEJ) were calculated in 3D. Compared with controls, lesional skin showed pronounced super-ficialisation of immune clusters: median CD3 + and CD68 + cluster distance to DEJ significantly decreased. Mast cell distribution revealed a biphasic pattern: peri-lesional area displayed decreased cell density at deeper dermal levels, whereas established plaques demonstrated higher cell density at superficial dermis. Averaging multiple 2D sections obscured these distributional features, underlining the necessity of volumetric analysis. High-resolution 3D reconstruction reliably maps the spatial dynamics of T cells, macrophages and mast cells in psoriasis and detects subclinical immunological priming in peri-lesional skin. The proposed pipeline bridges routine pathology and advanced spatial omics, offering a scalable tool for early disease detection, patient stratification and therapeutic monitoring. Health sciences/Biomarkers Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Biological sciences/Immunology Health sciences/Medical research Digital Pathology Psoriasis Serial Sectioning Tissue 3D Reconstruction Immune Cell 3D Distribution Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Psoriasis is a chronic systemic condition driven by immune system dysregulation and abnormal keratinocyte differentiation. This process results in sharply demarcated, erythematous, and scaly plaques [ 1 ] but can have extra-cutaneous manifestations including psoriatic arthritis and associated with cardiovascular disease. The visible psoriatic plaques represent only part of a complex underlying immune dysregulation that involves both the innate and adaptive arms of the immune system. The disease pathogenesis often involves activating multiple Antigen-Presenting Cells (APCs), T-cell subsets and cytokine pathways such as tumour-necrosis factor alpha (IFN-α), interleukins (IL-6, IL-20, IL-23), and nitric oxide [ 2 – 7 ] . Several T-cell subsets—most notably Th1, Th17, Tc1, and Tc17—play key roles in driving the inflammatory cascade through the production of cytokines such as interferon gamma (IFN-γ), tumour necrosis factor alpha (TNF-α), IL-17, and IL-22 [ 1 – 7 ] . These cytokines promote keratinocyte hyperproliferation, altered differentiation, and sustained inflammation in the epidermis. Tissue-resident memory T cells (Trm) are increasingly implicated in disease persistence and recurrence. CD8 + Trm cells in the epidermis and CD4 + Trm cells in the dermis, activated by IL-23, are thought to remain in the skin long after visible plaques resolve, contributing to relapse at previously affected sites [ 8 ] . Transcriptomic analyses have further revealed clear differences between lesional, non-lesional, and normal skin [ 9 – 11 ] . Distinct gene expression profiles allow for differentiation between these tissue types. For example, Chiricozzi et al showed upregulation of IL17-signaling in non-lesional skin of moderate-to-severe psoriasis vulgaris compared with normal skin [ 9 ] . The higher expression of IL-17-signature genes (e.g. DEFB4 and S100A7) also leads to increase activity of CD3 + , CD8 + and DC-LAMP + cells in non-lesional skin compared to normal skin [ 9 ] . Additionally, distinct gene transcriptional differences between psoriatic lesional and non-lesional skin have been demonstrated [ 10 , 12 ] . Furthermore, in particular, genes such as RGS1, SOCS3, and NAMPT have been identified as markers that distinguish both lesional and non-lesional skin from normal skin, while others—including PTRRC, IL8, and ZC3H12A—appear to differentiate non-lesional from both lesional and healthy tissue [ 12 ] . These findings support the notion that psoriasis exists on a spectrum, and that areas of skin which appear clinically unaffected may still reflect an altered, disease-prone state. Recent advancements in techniques such as Cytometry by Time-of-Flight (CyTOF), NanoString, and Spatial Transcriptomics (ST) have significantly enhanced the ability to visualize the distribution of immune cells in psoriasis [ 13 – 18 ] . CyTOF employs metal-tagged antibodies and direct isotope analysis to concurrently measure multiple markers, thereby eliminating the need for spectral overlap compensation and reducing laboratory errors [ 13 , 14 ] . The NanoString nCounter system enables reliable expression analysis of up to 800 genes or 228 gene fusions [ 16 ] . Spatial Transcriptomics (ST) leverages spatially barcoded arrays to map tissue sections with a 50-micron resolution, facilitating precise determination of gene expression by cell type and histological location [ 17 , 18 ] . Despite these sophisticated techniques, there remains a need for accessible, reproducible, and scalable tools in routine pathology practice. Importantly, previous studies on immune cell presence and distribution in psoriasis have been largely confined to two-dimensional analyses, which do not fully capture the complex, three-dimensional nature of disease progression over time. As a result, the spatial organization and dynamics of key immune cell populations in the 3D tissue context of psoriasis remain poorly understood. In our study, we aim at using 3D computational analysis as an alternative method distinct from other approaches discussed above, to re-evaluate and validate previous findings regarding the spatial distribution and interactions of immune cells in psoriasis. Among the available approaches for 3D reconstruction of skin tissue, two major methodologies are widely used: direct 3D imaging techniques and serial sectioning followed by image registration and volumetric stacking. While direct 3D imaging techniques—such as light-sheet fluorescence microscopy (LSFM) [ 19 ] and full-field optical coherence tomography (FFOCT) [ 20 ] —offer the advantage of in situ volumetric visualization without the need for physical sectioning or alignment, they are generally limited in their ability to incorporate specific immunohistochemical (IHC) markers due to restrictions in staining protocols and tissue compatibility. Consequently, these methods are typically suited for visualizing gross morphological features or structural compartments, such as the stratum corneum, dermis, epidermal appendages, and collagen bundles, rather than enabling precise identification and localization of individual cell populations. In contrast, the serial sectioning approach is particularly well-suited for studies requiring cell-type-specific labelling. Since each section can be independently stained, it supports a wide range of histological and immunohistochemical protocols. This enables high-resolution, cell-level analysis of tissue architecture and cellular distribution. For example, Liu et al employed serial sections with H&E staining to reconstruct a 3D dermal model and quantify features such as porosity and pore diameter, demonstrating the method’s effectiveness for detailed structural analysis [ 21 ] . Given our aim to map specific immune cell populations in skin, the compatibility of this approach with IHC staining and its ability to capture fine spatial detail make it the most appropriate choice for our study. Our pipeline is shown in Fig. 1 , we used immunohistochemical staining for CD3 (T cells), CD68 (macrophages), and MCT (mast cell tryptase, as a marker for mast cells) to highlight immune cell dynamics and their interactions with the epidermis in lesional, peri-lesional, and normal skin samples for T cells, macrophages, and mast cells. The image registration strategy shown in Fig. 2 enables precise analysis of immune cell spatial relationship to the dermal-epidermal junction (DEJ) in 3D. These quantitative metrics offer insights into disease activity and progression beyond traditional histological scoring. Our approach provides a novel validation of prior work. Consistent with previous findings, we observed a significant reduction in the distance of immune cell clusters—especially CD3 + T cells—from the DEJ in psoriatic lesions compared to normal skin. The average median DEJ distance for CD3 + T cells in lesional skin was approximately 70 µm, compared to over 450 µm in normal samples. This superficial positioning likely reflects the migration of effector immune cells into proximity with proliferating keratinocytes—a hallmark of psoriasis pathophysiology. In tandem, we observed a decrease in the distance of CD68 + macrophages to the DEJ in lesional skin (p = 5.78 × 10 − 4 ), suggesting enhanced macrophage activity near the epidermis during active disease. Interestingly, the data also highlighted dynamic changes in mast cell distribution: peri-lesional skin exhibited greater DEJ distance, whereas fully developed plaques showed mast cells clustered closer to the epidermis. This biphasic pattern may reflect the shifting functional roles of mast cells—from initiating inflammation to sustaining chronic lesions [ 3 ] . These findings further support the emerging paradigm that clinically uninvolved skin in psoriasis is not immunologically inert. Spatial profiling revealed that “normal-appearing” skin from psoriasis patients often displayed altered spatial distributions akin to lesional tissue. This implies a preclinical inflammatory milieu in peri-lesional skin, which could precede and potentially predict future plaque development. By integrating computational spatial metrics with traditional histopathological and immunohistochemical techniques, our study provides an objective framework for assessing immune architecture in psoriasis. Such approaches may refine disease classification, inform personalised treatment strategies, and identify early changes in at-risk populations. Results T‑Cell 3D Architectural Remodelling Revealed by CD3 + Cluster Mapping Several key differences emerge in both T cell density and its proximity to the dermal–epidermal junction (DEJ) when comparing truly normal skin (from individuals without psoriasis) to clinically uninvolved normal-appearing skin in psoriasis patients (peri‐lesional) and frank psoriatic lesions. For example, in Fig. 3 a, compared with normal skin, both peri-lesional and lesional tissues exhibited significantly shorter median distances between CD3⁺ cells and the DEJ, with lesional skin showing the most pronounced superficial clustering. In parallel, CD3⁺ cell densities were markedly reduced in peri-lesional skin compared to controls, but increased again in lesional plaques (Fig. 3 b), suggesting a dynamic redistribution of T cells during disease evolution. Probability density function (PDF) curves of CD3⁺ cell distance to the DEJ further illustrated this shift, as shown in Fig. 3 c, with a progressive leftward skew from normal to lesional samples, indicating a higher likelihood of T cells residing near the DEJ in disease states. These observations were corroborated by 3D visualisations (Fig. 3 d–f), where red-stained CD3⁺ clusters increasingly occupied the superficial dermis as disease advanced. These data highlight the closer localization of T cell infiltrates to the DEJ in psoriasis and underscore the heightened immunologic activity characteristic of psoriatic plaques. Moreover, they strongly suggest that normal-appearing skin in psoriasis patients already demonstrates a distinct immunological architecture—in terms of T cell spatial relationship to the DEJ—compared with true normal skin from non‐psoriatic individuals. Clinically, this finding raises the possibility that a targeted biopsy of uninvolved skin in at‐risk individuals (such as those with a family history of psoriasis) could identify preclinical immunological changes predictive of future disease development, thereby offering an avenue for earlier intervention and closer monitoring. Subclinical Myeloid Accumulation and Spatial Expansion of CD68⁺ Aggregates Preceding Psoriatic Lesions Figure 4 illustrates the spatial remodeling of CD68 + myeloid cells in psoriasis and supports the concept of subclinical immunological priming. Figure 4 a demonstrates a significant reduction in median CD68 + cell distance to the dermal–epidermal junction (DEJ) in lesional psoriasis compared to both normal and peri-lesional skin (p = 5.78×10 − 4 and 8.3×10 − 3 , respectively). Notably, although peri-lesional skin appears clinically normal, its CD68 + cells are already positioned significantly closer to the DEJ than in healthy controls, indicating early immune recruitment. Figure 4 b shows a marked increase in CD68 + cell density in lesional skin, significantly higher than both peri-lesional (p = 2.84×10 − 3 ) and normal tissue (p = 2.11×10 − 2 ), consistent with macrophage accumulation during active disease. Figure 4 c further substantiates this with PDF curves revealing a leftward shift—indicating higher density of CD68 + cells near the DEJ—in lesional and peri-lesional samples, compared to the broader and deeper distribution in normal skin. The 3D renderings in Fig. 4 d-f offer visual confirmation of these findings, showing a progressive upward migration and clustering of CD68 + cells (red) toward the epidermis (green) from normal (Fig. 4 d) to peri-lesional (Fig. 4 e) to lesional (Fig. 4 f) states. Together, these spatial and volumetric data suggest that peri-lesional skin is not immunologically quiescent, but harbours latent macrophage remodeling that may presage overt plaque formation. Recent insights into the immunopathogenesis of psoriasis have shed new light on the century-old phenomenon known as the Woronoff ring, a transient hypopigmented halo around regressing plaques [ 22 ] . Mechanistically, this ring has been linked to an HLA class I‐restricted autoimmune response involving CD8 + T cells, which target melanocytes in psoriatic lesions. Although these T cells are not primarily cytotoxic, they characteristically release interleukin (IL)‐17, IL‐22 and tumour necrosis factor (TNF)‐α, each of which synergistically enhances melanocyte proliferation while paradoxically inhibiting melanin synthesis [ 22 ] . As a result, the melanocyte population in psoriatic plaques increases in number but displays reduced melanin content. When plaques regress, the remaining influence of IL‐17 and TNF‐α in peri‐lesional areas perpetuates a hypopigmented zone, which becomes visible as the Woronoff ring. This phenomenon, therefore, can be understood through the lens of persistent cytokine‐driven melanocyte modulation that persists even after clinically apparent lesion resolution. In parallel, analyses of T cell and CD68 + cell distributions in normal, peri-lesional and lesional psoriatic skin indicate that seemingly “normal” skin in psoriasis patients differs significantly from truly normal skin in individuals without psoriasis. Closer proximity to the dermal–epidermal junction (DEJ) in peri‐lesional sites support the hypothesis that an immunologically primed microenvironment is present before overt lesion formation. This subclinical inflammatory milieu provides a plausible backdrop for understanding the lasting cytokine effects on melanocytes implicated in the Woronoff ring [ 22 ] . From a clinical standpoint, these findings underline the importance of considering biopsies from uninvolved psoriatic skin—particularly in at‐risk individuals—for the potential early detection of immunological and melanocytic changes preceding visible lesion development. By connecting these immunopathological hallmarks to a well‐documented yet enigmatic clinical sign, investigators and clinicians alike may gain deeper insights into psoriasis pathogenesis, enabling both earlier intervention and a more nuanced appreciation of psoriasis‐associated pigmentary dysregulation. Biphasic Mast Cell Redistribution from Deep Dermis to DEJ During Psoriatic Progression Mast cells (MCs) have long been recognized as critical players in the inflammatory cascade of psoriasis, with evidence linking their activation and degranulation to both the development of new lesions and the evolution of established plaques. Recent findings, highlighting variations in the spatial distribution of MC clusters relative to the dermal–epidermal junction (DEJ), lend further support to their dynamic, stage-dependent function. In peri‐lesional regions, elevated MC distance from the DEJ may signal an early, amplified inflammatory drive, aligning with previous reports of heightened MC recruitment and activation at the inception of lesion formation. Subsequently, in fully established psoriatic plaques, MCs appear to re‐localize closer to the superficial dermis. This shift could represent a functional or phenotypic transition, as MCs modulate their pro‐inflammatory mediator output to sustain chronic inflammation rather than initiate it [ 23 ] . These positional changes corroborate earlier clinical and histological observations that MCs surge in number and activity during the initial phases of plaque development, only to decline or assume altered functional profiles in mature lesions. In Fig. 5 a, median distance of MCs to the dermal–epidermal junction (DEJ) is broadly similar across groups, suggesting that mast cells initially accumulate deeper in the dermis during early inflammation. Figure 5 b shows that mast cell density is modestly reduced in peri-lesional samples, with partial restoration in lesional skin, hinting at dynamic turnover or redistribution during disease progression. The PDF curves in Fig. 5 c illustrate these trends more clearly: lesional tissue shows a left-shifted peak (towards superficial clustering) relative to normal, consistent with MC migration toward the DEJ in mature plaques. These spatial findings are visualized in Fig. 5 d–f, where representative 3D renderings show deep dermal distribution in normal skin (Fig. 5 d), increased but scattered clustering in peri-lesional skin (Fig. 5 e), and more dense, superficial aggregation in lesional samples (Fig. 5 f). Together, these data suggest a biphasic pattern wherein MCs initially localize farther from the DEJ in peri-lesional skin—potentially reflecting active recruitment or redistribution—then migrate toward the superficial dermis as fully developed plaques become established. MCs facilitate innate and adaptive immune responses via cytokine release (e.g. IL-1β, IL‐6, TNF‐α) and contribute to tissue remodelling through proteases such as tryptase and chymase. Their capacity to interact with dendritic cells and T‐cell subsets underscores their pivotal regulatory role in shaping local immune responses throughout the psoriatic disease course. Thus, the spatial metrics indicating a biphasic MC pattern—greater distances from the DEJ early on, followed by closer clustering in well‐formed plaques—supports the view that MCs serve as key orchestrators of psoriatic inflammation, with therapeutic implications for targeting MC‐associated pathways to modulate disease progression. These positional shifts underscore the dynamic contributions of MCs throughout psoriasis pathogenesis. Early in the disease process, the increased peri-lesional MC cluster distances from the DEJ may mirror enhanced mast‐cell‐mediated signaling and inflammatory recruitment. In contrast, the subsequent clustering nearer the DEJ in mature plaques could reflect an evolved, possibly more chronic, MC phenotype that supports sustained inflammation rather than initiating new lesions. Such observations reinforce the concept of MCs as pivotal mediators in both the onset and progression of psoriatic pathology. From a therapeutic standpoint, targeting MC‐associated pathways may offer opportunities for interrupting the early stages of plaque formation and modulating established inflammation, thereby potentially improving clinical outcomes in patients with psoriasis. Conclusion and Discussion We proposed a comprehensive solution of skin tissue 3D profiling, the workflow incorporates tissue serial sectioning, image registration, image segmentation, and 3D reconstruction. Compared to direct 3D imaging solutions, our approach is cost-effective and supports incorporation of immunohistochemical staining and high-resolution analysis, which enables precise cellular-level specific immune profiling. Consistent with earlier two-dimensional studies, we observed a marked superficialisation of CD3 + T cell and CD68 + macrophage clusters in psoriatic tissue. Most CD3 + cluster median distances to DEJ fell from > 200 µm in normal skin to around 100 µm or less in plaques (p = 4.8 × 10 − 5 ), while CD68 + clusters shifted by a similar magnitude (p = 5.78 × 10 − 4 ). Notably, normal-appearing peri-lesional skin already displayed a pathological architecture: more than half CD3 + clusters median DEJ distances in peri-lesional skin fell below 200 µm (p = 3.81 × 10 − 3 ), demonstrating clear evidence of subclinical immune activation. The quantitative spatial profiling of mast cell indicated a biphasic pattern, with mast cells initially positioned deeper in peri-lesional dermal layers, progressively migrating closer to the DEJ in mature plaques. Probability density functions further highlighted this transitional pattern, with peri-lesional tissue showing intermediate superficial clustering compared to lesional skin. These findings underscore the evolving functional roles of mast cells—from driving initial inflammatory processes to sustaining chronic plaque activity. Collectively, the 174 reconstructed 3D tissue stacks (95 control and 79 psoriatic) provides quantitative evidence that immune–epithelial topography in healthy skin differs fundamentally from that of psoriasis skin and, critically, from clinically uninvolved peri-lesional skin within the same patient cohort. The data support the concept that clinically inactive skin in psoriasis patients is immunologically primed and may serve as a reservoir for future flare sites. The advantages of 3D reconstruction over traditional 2D analysis were validated through quantitative comparison of probability density curves generated from full 3D stacks versus averaged 2D slices, the results confirmed that volumetric analyses capture biologically meaningful cellular distribution patterns that are lost or obscured in 2D. This underscores the critical need for employing full volumetric methods in studies of complex immune environments like psoriasis. Our 3D profiling pipeline confirms and extends the paradigm that psoriatic inflammation is defined by superficial clusters of T cells and macrophages at the DEJ, preceded by subclinical changes in peri-lesional skin. Stage-specific repositioning of mast cells further underscores the dynamic orchestration of innate and adaptive immunity. By providing quantitatively robust, scalable metrics from routine histology, this method offers a practical bridge between conventional pathology and high-dimension spatial omics, with potential applications in early diagnosis, patient stratification and therapeutic monitoring of psoriasis. Materials and Methods Patient Cohort In this study, 98 subjects were recruited, including 16 patients with psoriasis, 3 patients with eczema, 2 patients with drug-induced skin conditions, and 77 healthy controls. This study was approved by the Human Research Ethics Committee (HREC) of the Royal Prince Alfred Hospital (RPAH) Zone. Informed consent was obtained from all participants, with adherence to the National Statement on Ethical Conduct in Human Research (2007). All procedures complied with the ethical standards of the institutional and national research committees. Participant confidentiality was ensured by anonymizing all data. Total 118 whole-mount skin tissue samples were cut from 98 subjects, where 79, 29, 6, and 4 samples were cut from healthy controls, psoriasis patients, eczema patients, and patients with drug-induced skin conditions, respectively. Especially, for all eczema patients and patients with drug-induced skin conditions, 2 samples were cut from each patients, where one sample was cut from lesional tissue area and another sample was cut from peri-lesional tissue area. And for psoriasis patients, 16 patients were conducted lesional/peri-lesional area paired cutting and 3 patients were conducted lesional area cutting only, resulting 29 samples in total. All 10 samples from eczema patients and patients with drug-induced skin conditions were excluded from analysis since the sample amount is not big enough. In psoriasis patients, 2 samples from 1 patient were excluded due to low image quality. In healthy controls, 4 samples from 3 subjects were excluded due to data corruption and 22 samples from 22 subjects were excluded due to low image quality. Eventually, 53 samples from 52 healthy controls and 27 samples from 15 psoriasis patients were involved in this study. The profile of all subjects are shown in Supplementary Table S1 . Sample Preparation and Image Acquisition All the skin samples were fixed in 10% neutral-buffered formalin (equivalent to 4% formaldehyde) for a minimum of 48 hours. The fixed blocks were then processed using a 6-hour automated cycle on a Leica Peloris II processor, passing through graded alcohols, xylene and then paraffin wax. Embedded blocks were then trimmed and sectioned at 4 µm using a Leica RM2235 microtome. Sections were floated on a Medite TFB 35 waterbath at 45°C using distilled water. For blocks from healthy controls, a total of 80 or 160 or 240 serial sections were obtained for each block and mounted onto Trajan adhesive slides with 10 serial sections per glass slide. For blocks from psoriasis patients, serial cutting for 80 or 120 consecutive sections was performed. All glass slides were then heated to 60°C for 30 minutes to ensure tissue adherence. For tissue blocks from healthy controls, we defined a group of 80 consecutive sections (i.e. 8 glass slides) as a tissue stack, while a tissue stack in psoriasis patients tissue block was defined as a group of 40 consecutive sections (4 glass slides). Eventually, we obtained 95 tissue stacks (80-slice-stack) from 52 healthy controls and 79 tissue stacks (40-slice-stack) from 15 psoriasis patients. The 174 stacks were then subject to immunohistochemistry (IHC) staining to visualize the immune cell including T cell, macrophage, and mast cell. The three type of cells are labelled by the CD3 marker (Roche, clone 2GV6, 790–4341, RTU), CD68 marker (Roche, clone KP-1, 790–2931, RTU), and mast cell tryptase (MCT) (Cell Marque, clone G3, 342M-14, RTU), respectively. The staining was performed by an automated Roche Ventana Ultra machine and following are the staining protocols for each antibody. CD3: Heat-induced epitope retrieval (HIER) with CC1 for 24 minutes at 100°C, antibody incubation for 28 minutes at 37°C. CD68: HIER with CC1 for 32 minutes at 100°C, antibody incubation for 12 minutes at 37°C. MCT: HIER with CC1 for 8 minutes at 100°C, antibody incubation for 8 minutes at 37°C. All antibody protocols used the Optiview DAB detection system (Ventana, 760 − 700), followed by counterstaining with Hematoxylin II (Ventana, 790–2208, RTU) and Bluing reagent (Ventana, 760–2037, RTU). All glass slides were then dehydrated in graded alcohols, cleared in xylene, and coverslipped. The detailed stack staining profiles are shown in Supplementary Table S2. The stained stacks were then digitized at the resolution of 0.35 × 0.35 µm per pixel. It is worth noting that among 95 80-image-stacks, there were 6 stacks only had 70 images each, since 10 images were excluded due to low image quality, hence total 10,700 whole slide images (WSIs) are obtained in this study. The tissue serial cutting, staining, and scanning pipeline are illustrated in Fig. 1 a, 1 b. Image Registration and Segmentation As the WSI was scanned using relatively high resolution, most of the original WSIs were in large scale. Specifically, most WSIs had both width and height exceeding 10,000 pixels. Due to computational and memory resources limitation, all WSIs were first down sampled by 8 times before any subsequent processing, the image resolution was hence reduced to 2.8 × 2.8 µm per pixel. No down sampling (extraction) or up sampling (interpolation) was performed in Z direction so the Z resolution was still 4µm per layer, i.e., the slice cutting interval. After the down sampling process, the resolution of 3D image stack became 2.8(X) × 2.8(Y) × 4(Z) µm per voxel, which is still enough for cellular level analysis. An image registration strategy was designed to make sure all images in same stack were well aligned. As shown in Fig. 2 a, for each stack, the first image was regarded as fixed image and the next image was registered to the fixed image by a scale-invariant feature transform (SIFT) [ 24 ] based algorithm. Then the second image became fixed image and the next image was registered to it. This process was performed iteratively until the last image of the stack was registered to its fixed image, so that all the images were registered to the first image. Manual review was performed in the end as a quality control mechanism to ensure the alignment quality. The quantitative image alignment quality assessment result is shown in Fig. 2 b, we calculated the mean distance between matched SIFT keypoints in original stacks (orange line) and aligned stacks (blue bar), the mean distance dropped after alignment for every stack, indicating that the image registration strategy we deployed effectively enhanced the image alignment quality and enabled the accurate spatial analysis. After image registration, an image segmentation model was trained for pixel level image segmentation. The pathologists performed annotation on 130 WSIs in ilastik software. The annotation categories included dermis, epidermis, and immune cell. The annotated WSIs were used to train a random forest algorithm [ 25 ] based image segmentation model using ilastik software. The pathologists reviewed the prediction results to make sure the model performance was good, and then the model was applied to every stack to generate segmentation results. The image registration and segmentation pipeline is shown in Fig. 1 c and Fig. 1 d, the 3D rendering of a representative stack is shown in Fig. 1 e. Metric Calculation and Necessity of 3D Reconstruction For segmented tissue stack, we proposed a parameter named cell distance to dermal-epidermal junction (DEJ). For a cell voxel, the distance to DEJ was defined as the euclidean distance between itself and the nearest DEJ to it. To be specifically, for cell voxel located in dermis region, its distance to DEJ is defined as the distance between itself and the nearest epidermis voxel; for cell voxel located in epidermis region, its distance to DEJ is defined as the distance between itself and the nearest dermis voxel. A probability density function (PDF) curve of distance to DEJ was then fitted to illustrate the immune cell distribution profile along the depth into skin. We designed an experiment to demonstrate the advantages of serial sectioning and 3D reconstruction. We selected a healthy control tissue stack with 80 slides and calculated the cell distance to DEJ in two ways: across the 3D stack, and within each slide independently. The PDF curves were fitted for both situations, and we calculated the average value of 80 PDF curves derived from individual 2D slides to compare with the PDF curve derived from 3D stack. As shown in Fig. 2 c, the individual 2D slide PDF curves are demonstrated in the middle panel and right panel. The average curve of 2D slide PDF curves (blue dashed line) and the 3D stack PDF curve (red solid line) are shown in the left panel. There is a distinct difference between the PDF curve from 2D average and from 3D, which means that simply averaging multiple 2D slides does not yield the same information as analyzing 3D stack. The root cause of the difference is that the peaks and valleys (corresponding to cell distribution patterns) in individual 2D slide PDF curves will become unrecognizable after averaging process, eventually resulting a smoothed curve. The actual 3D cell distribution pattern will only be preserved when the calculation is performed in 3D manner. From another perspective, none of the individual 2D slide PDF curves shows a pattern similar to that of the 3D stack PDF curve, indicating that accurate estimation of 3D stack from single 2D slide is not feasible due to sampling bias. In short, by including more thickness of tissue, we can reveal the immune cell spatial distribution in a more precise way, and minimize the bias introduced by random sampling. Abbreviations DP Digital Pathology DEJ Dermal-Epidermal Junction MC Mast Cell MCT Mast Cell Tryptase Declarations Ethical Approval and Consent of Participate This study was approved by the Human Research Ethics Committee (HREC) of the Royal Prince Alfred Hospital (RPAH) Zone. Informed consent was obtained from all participants, with adherence to the National Statement on Ethical Conduct in Human Research (2007). All procedures complied with the ethical standards of the institutional and national research committees. Consent for Publication All authors of this work agreed to publish with Scientific Reports once accepted. Availability of the Data and Material The data and code of this work will be available upon reasonable request. Competing Interest: The authors declare no potential competing interests. Funding This work was supported by the LEO Foundation Grant and the Australasian College of Dermatologist’s Scientific Research Fund. This work is also jointly supported by research funding from BII and IMCB under BMRC, A*STAR. Additionally, it is supported by the Industrial Alignment Funding - Pre-Positioning Programme – (IAF-PP Grant ID: H24J4a0044) awarded to Prof. Weimiao Yu and his iDMP lab. Authors Contribution P. T. and W. Y. conceived the project and designed the experiments; P. D. contributed to sample preparation; L. L. performed model development and validation, and data analysis; L. L. designed and prepared the figures with the assistance of K. H. O.; L. L. and L. 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Supplementary Files Suppfile.zip Cite Share Download PDF Status: Published Journal Publication published 23 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Nov, 2025 Reviews received at journal 09 Nov, 2025 Reviews received at journal 09 Nov, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers agreed at journal 20 Oct, 2025 Reviews received at journal 17 Oct, 2025 Reviewers agreed at journal 12 Oct, 2025 Reviewers invited by journal 09 Oct, 2025 Editor invited by journal 15 Sep, 2025 Editor assigned by journal 12 Sep, 2025 Submission checks completed at journal 11 Sep, 2025 First submitted to journal 10 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":646435,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e-\u003cstrong\u003eb\u003c/strong\u003e). Formalin-Fixed Paraffin-Embedded (FFPE) skin samples were trimmed and sectioned at 4μm interval. The tissue sections were then subject to immunohistochemistry (IHC) staining to visualize the immune cell including T cell, macrophage, and mast cell. The stained sections were then digitized at the resolution of 0.35×0.35μm per pixel, the images were downsized by 8 times for calculation consideration. \u003cstrong\u003ec\u003c/strong\u003e-\u003cstrong\u003ed\u003c/strong\u003e). Images were aligned by a Scale-Invariant Feature Transform (SIFT)-based algorithm in rigid manner (translation + rotation) and segmented by a random forest-based model, where red, green, blue represents immune cell, epidermis, and dermis respectively. \u003cstrong\u003ee\u003c/strong\u003e). Visualization of 3D rendering of a representative tissue stack, where red, green, blue represents immune cell, epidermis, and dermis respectively.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7581947/v1/8752d9fffe8151b05bc89df6.png"},{"id":94223582,"identity":"2331721d-33cf-49d8-b3d6-593843510c07","added_by":"auto","created_at":"2025-10-23 19:08:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1080721,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e). Image stack alignment strategy, left column shows original images (before alignment), right column shows aligned images. The first image remains untouched, then the second image is aligned to the first image, and then the third image is aligned to the second image,… Repeat this process until all images are aligned to the first image. \u003cstrong\u003eb\u003c/strong\u003e). Quantitative alignment quality assessment. The mean SIFT-matched point distances between slides are substantially reduced across the image stack after registration (blue bars) compared to the original stack (orange line). This alignment step is crucial for accurate spatial quantification. \u003cstrong\u003ec\u003c/strong\u003e). Probability density function (PDF) of immune cell distances to the dermal–epidermal junction (DEJ) are compared. The left plot highlights how the 3D-derived PDF (red line) differs markedly from the 2D-averaged estimate (blue dashed line), with the latter failing to capture the peaks and valleys of cellular distribution. The centre and right plots demonstrate that individual 2D sections show high variability and fail to recapitulate the coherent pattern revealed by 3D stacking. These results underscore that averaging or selecting single 2D slices introduces sampling bias and smooths biologically meaningful spatial features, which are preserved only through full volumetric analysis.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7581947/v1/41ca63add5311f25ab09c870.png"},{"id":94224227,"identity":"6960cc39-8e3d-4ba9-9653-05775d7acdcb","added_by":"auto","created_at":"2025-10-23 19:16:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":673234,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e). The CD3 cell median distance to DEJ in psoriasis patients lesional skin is significantly lower than that in psoriasis patients peri-lesional skin and that in healthy controls skin. The CD3 cell median distance to DEJ in psoriasis patients peri-lesional skin is significantly lower than that in healthy controls skin as well. \u003cstrong\u003eb\u003c/strong\u003e). The CD3 cell densityin psoriasis patients peri-lesional skin is significantly lower than that in psoriasis patients lesional skin and that in healthy controls skin. There is no significance between the CD3 cell density in psoriasis patients lesional skin and that in healthy controls skin. The statistical test performed in panel \u003cstrong\u003ea\u003c/strong\u003e). and \u003cstrong\u003eb\u003c/strong\u003e).: independent two-sample t-test, two-sided. \u003cstrong\u003ec\u003c/strong\u003e). The mean probability density function curves of the CD3 cell distance to DEJ in healthy controls skin, psoriasis patients peri-lesional and lesional skin, respectively. \u003cstrong\u003ed\u003c/strong\u003e).-\u003cstrong\u003ef\u003c/strong\u003e). Visualization of reconstructed 3D tissue stacks, where red, green, blue represents immune cell, epidermis, and dermis, respectively. The marked cell is CD3 cell. The stack in \u003cstrong\u003ed\u003c/strong\u003e). is from healthy control skin, the stack in \u003cstrong\u003ee\u003c/strong\u003e). is from psoriasis patients peri-lesional skin, the stack in \u003cstrong\u003ef\u003c/strong\u003e). is from psoriasis patients lesional skin.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7581947/v1/ef7dabd474426b1340c9a6d0.png"},{"id":94224230,"identity":"a68980c7-8659-4198-b696-d7f3dacca413","added_by":"auto","created_at":"2025-10-23 19:16:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":654748,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e). The CD68 cell median distance to DEJ in psoriasis patients lesional skin is significantly lower than that in psoriasis patients peri-lesional skin and that in healthy controls skin. There is no significance between the CD68 cell median distance to DEJ in psoriasis patients peri-lesional skin and that in healthy controls skin. \u003cstrong\u003eb\u003c/strong\u003e). The CD68 cell densityin psoriasis patients lesional skin is significantly higher than that in psoriasis patients peri-lesional skin and that in healthy controls skin. There is no significance between the CD68 cell density in psoriasis patients peri-lesional skin and that in healthy controls skin. The statistical test performed in panel \u003cstrong\u003ea\u003c/strong\u003e). and \u003cstrong\u003eb\u003c/strong\u003e).: independent two-sample t-test, two-sided. \u003cstrong\u003ec\u003c/strong\u003e). The mean probability density function curves of the CD68 cell distance to DEJ in healthy controls skin, psoriasis patients peri-lesional and lesional skin, respectively. \u003cstrong\u003ed\u003c/strong\u003e).-\u003cstrong\u003ef\u003c/strong\u003e).Visualization of reconstructed 3D tissue stacks, where red, green, blue represents immune cell, epidermis, and dermis, respectively. The marked cell is CD68 cell. The stack in \u003cstrong\u003ed\u003c/strong\u003e). is from healthy control skin, the stack in \u003cstrong\u003ee\u003c/strong\u003e). is from psoriasis patients peri-lesional skin, the stack in \u003cstrong\u003ef\u003c/strong\u003e). is from psoriasis patients lesional skin.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7581947/v1/307fc8108750feba6fd0906b.png"},{"id":94223583,"identity":"6d7e6c71-83fc-4fa6-a12a-ea909f088318","added_by":"auto","created_at":"2025-10-23 19:08:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":859171,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e). There is no significance between the mast cell median distance to DEJ in healthy controls skin and that in psoriasis patients lesional/peri-lesional skin. \u003cstrong\u003eb\u003c/strong\u003e). There is no significance between the mast cell density in healthy controls skin and that in psoriasis patients lesional/peri-lesional skin. The statistical test performed in panel \u003cstrong\u003ea\u003c/strong\u003e). and \u003cstrong\u003eb\u003c/strong\u003e).: independent two-sample t-test, two-sided. \u003cstrong\u003ec\u003c/strong\u003e). The mean probability density function curves of the mast cell distance to DEJ in healthy controls skin, psoriasis patients peri-lesional and lesional skin, respectively. \u003cstrong\u003ed\u003c/strong\u003e).-\u003cstrong\u003ef\u003c/strong\u003e).Visualization of reconstructed 3D tissue stacks, where red, green, blue represents immune cell, epidermis, and dermis, respectively. The marked cell is mast cell. The stack in \u003cstrong\u003ed\u003c/strong\u003e). is from healthy control skin, the stack in \u003cstrong\u003ee\u003c/strong\u003e). is from psoriasis patients peri-lesional skin, the stack in \u003cstrong\u003ef\u003c/strong\u003e). is from psoriasis patients lesional skin.\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7581947/v1/8c271b2f7994575accb263eb.png"},{"id":103765625,"identity":"398deace-58e3-4d29-bbf1-6e11a9fe8aa3","added_by":"auto","created_at":"2026-03-02 16:06:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6188867,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7581947/v1/89ebd27a-1aa6-401a-bc4e-edba3c126e31.pdf"},{"id":94223601,"identity":"1594df85-13cc-433d-ba44-1c4578c38f41","added_by":"auto","created_at":"2025-10-23 19:08:46","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":23543002,"visible":true,"origin":"","legend":"","description":"","filename":"Suppfile.zip","url":"https://assets-eu.researchsquare.com/files/rs-7581947/v1/5786ff5b1fdf279f2bee5e5a.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Three‑Dimensional Immune Cartography Uncovers Subclinical Remodeling in Psoriasis","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePsoriasis is a chronic systemic condition driven by immune system dysregulation and abnormal keratinocyte differentiation. This process results in sharply demarcated, erythematous, and scaly plaques \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e but can have extra-cutaneous manifestations including psoriatic arthritis and associated with cardiovascular disease. The visible psoriatic plaques represent only part of a complex underlying immune dysregulation that involves both the innate and adaptive arms of the immune system.\u003c/p\u003e\u003cp\u003eThe disease pathogenesis often involves activating multiple Antigen-Presenting Cells (APCs), T-cell subsets and cytokine pathways such as tumour-necrosis factor alpha (IFN-α), interleukins (IL-6, IL-20, IL-23), and nitric oxide \u003csup\u003e[\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Several T-cell subsets\u0026mdash;most notably Th1, Th17, Tc1, and Tc17\u0026mdash;play key roles in driving the inflammatory cascade through the production of cytokines such as interferon gamma (IFN-γ), tumour necrosis factor alpha (TNF-α), IL-17, and IL-22 \u003csup\u003e[\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. These cytokines promote keratinocyte hyperproliferation, altered differentiation, and sustained inflammation in the epidermis. Tissue-resident memory T cells (Trm) are increasingly implicated in disease persistence and recurrence. CD8\u003csup\u003e+\u003c/sup\u003e Trm cells in the epidermis and CD4\u003csup\u003e+\u003c/sup\u003e Trm cells in the dermis, activated by IL-23, are thought to remain in the skin long after visible plaques resolve, contributing to relapse at previously affected sites \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTranscriptomic analyses have further revealed clear differences between lesional, non-lesional, and normal skin \u003csup\u003e[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Distinct gene expression profiles allow for differentiation between these tissue types. For example, Chiricozzi et al showed upregulation of IL17-signaling in non-lesional skin of moderate-to-severe psoriasis vulgaris compared with normal skin \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. The higher expression of IL-17-signature genes (e.g. DEFB4 and S100A7) also leads to increase activity of CD3\u003csup\u003e+\u003c/sup\u003e, CD8\u003csup\u003e+\u003c/sup\u003e and DC-LAMP\u003csup\u003e+\u003c/sup\u003e cells in non-lesional skin compared to normal skin \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Additionally, distinct gene transcriptional differences between psoriatic lesional and non-lesional skin have been demonstrated \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Furthermore, in particular, genes such as RGS1, SOCS3, and NAMPT have been identified as markers that distinguish both lesional and non-lesional skin from normal skin, while others\u0026mdash;including PTRRC, IL8, and ZC3H12A\u0026mdash;appear to differentiate non-lesional from both lesional and healthy tissue \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. These findings support the notion that psoriasis exists on a spectrum, and that areas of skin which appear clinically unaffected may still reflect an altered, disease-prone state.\u003c/p\u003e\u003cp\u003eRecent advancements in techniques such as Cytometry by Time-of-Flight (CyTOF), NanoString, and Spatial Transcriptomics (ST) have significantly enhanced the ability to visualize the distribution of immune cells in psoriasis \u003csup\u003e[\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. CyTOF employs metal-tagged antibodies and direct isotope analysis to concurrently measure multiple markers, thereby eliminating the need for spectral overlap compensation and reducing laboratory errors \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The NanoString nCounter system enables reliable expression analysis of up to 800 genes or 228 gene fusions \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Spatial Transcriptomics (ST) leverages spatially barcoded arrays to map tissue sections with a 50-micron resolution, facilitating precise determination of gene expression by cell type and histological location \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDespite these sophisticated techniques, there remains a need for accessible, reproducible, and scalable tools in routine pathology practice. Importantly, previous studies on immune cell presence and distribution in psoriasis have been largely confined to two-dimensional analyses, which do not fully capture the complex, three-dimensional nature of disease progression over time. As a result, the spatial organization and dynamics of key immune cell populations in the 3D tissue context of psoriasis remain poorly understood. In our study, we aim at using 3D computational analysis as an alternative method distinct from other approaches discussed above, to re-evaluate and validate previous findings regarding the spatial distribution and interactions of immune cells in psoriasis.\u003c/p\u003e\u003cp\u003eAmong the available approaches for 3D reconstruction of skin tissue, two major methodologies are widely used: direct 3D imaging techniques and serial sectioning followed by image registration and volumetric stacking. While direct 3D imaging techniques\u0026mdash;such as light-sheet fluorescence microscopy (LSFM) \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e and full-field optical coherence tomography (FFOCT) \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e\u0026mdash;offer the advantage of in situ volumetric visualization without the need for physical sectioning or alignment, they are generally limited in their ability to incorporate specific immunohistochemical (IHC) markers due to restrictions in staining protocols and tissue compatibility. Consequently, these methods are typically suited for visualizing gross morphological features or structural compartments, such as the stratum corneum, dermis, epidermal appendages, and collagen bundles, rather than enabling precise identification and localization of individual cell populations.\u003c/p\u003e\u003cp\u003eIn contrast, the serial sectioning approach is particularly well-suited for studies requiring cell-type-specific labelling. Since each section can be independently stained, it supports a wide range of histological and immunohistochemical protocols. This enables high-resolution, cell-level analysis of tissue architecture and cellular distribution. For example, Liu et al employed serial sections with H\u0026amp;E staining to reconstruct a 3D dermal model and quantify features such as porosity and pore diameter, demonstrating the method\u0026rsquo;s effectiveness for detailed structural analysis \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Given our aim to map specific immune cell populations in skin, the compatibility of this approach with IHC staining and its ability to capture fine spatial detail make it the most appropriate choice for our study.\u003c/p\u003e\u003cp\u003eOur pipeline is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, we used immunohistochemical staining for CD3 (T cells), CD68 (macrophages), and MCT (mast cell tryptase, as a marker for mast cells) to highlight immune cell dynamics and their interactions with the epidermis in lesional, peri-lesional, and normal skin samples for T cells, macrophages, and mast cells. The image registration strategy shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e enables precise analysis of immune cell spatial relationship to the dermal-epidermal junction (DEJ) in 3D. These quantitative metrics offer insights into disease activity and progression beyond traditional histological scoring.\u003c/p\u003e\u003cp\u003eOur approach provides a novel validation of prior work. Consistent with previous findings, we observed a significant reduction in the distance of immune cell clusters\u0026mdash;especially CD3\u003csup\u003e+\u003c/sup\u003e T cells\u0026mdash;from the DEJ in psoriatic lesions compared to normal skin. The average median DEJ distance for CD3\u003csup\u003e+\u003c/sup\u003e T cells in lesional skin was approximately 70 \u0026micro;m, compared to over 450 \u0026micro;m in normal samples. This superficial positioning likely reflects the migration of effector immune cells into proximity with proliferating keratinocytes\u0026mdash;a hallmark of psoriasis pathophysiology.\u003c/p\u003e\u003cp\u003eIn tandem, we observed a decrease in the distance of CD68\u003csup\u003e+\u003c/sup\u003e macrophages to the DEJ in lesional skin (p\u0026thinsp;=\u0026thinsp;5.78 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), suggesting enhanced macrophage activity near the epidermis during active disease. Interestingly, the data also highlighted dynamic changes in mast cell distribution: peri-lesional skin exhibited greater DEJ distance, whereas fully developed plaques showed mast cells clustered closer to the epidermis. This biphasic pattern may reflect the shifting functional roles of mast cells\u0026mdash;from initiating inflammation to sustaining chronic lesions \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThese findings further support the emerging paradigm that clinically uninvolved skin in psoriasis is not immunologically inert. Spatial profiling revealed that \u0026ldquo;normal-appearing\u0026rdquo; skin from psoriasis patients often displayed altered spatial distributions akin to lesional tissue. This implies a preclinical inflammatory milieu in peri-lesional skin, which could precede and potentially predict future plaque development.\u003c/p\u003e\u003cp\u003eBy integrating computational spatial metrics with traditional histopathological and immunohistochemical techniques, our study provides an objective framework for assessing immune architecture in psoriasis. Such approaches may refine disease classification, inform personalised treatment strategies, and identify early changes in at-risk populations.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eT‑Cell 3D Architectural Remodelling Revealed by CD3\u003csup\u003e+\u003c/sup\u003e Cluster Mapping\u003c/h2\u003e\u003cp\u003eSeveral key differences emerge in both T cell density and its proximity to the dermal–epidermal junction (DEJ) when comparing truly normal skin (from individuals without psoriasis) to clinically uninvolved normal-appearing skin in psoriasis patients (peri‐lesional) and frank psoriatic lesions. For example, in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, compared with normal skin, both peri-lesional and lesional tissues exhibited significantly shorter median distances between CD3⁺ cells and the DEJ, with lesional skin showing the most pronounced superficial clustering. In parallel, CD3⁺ cell densities were markedly reduced in peri-lesional skin compared to controls, but increased again in lesional plaques (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), suggesting a dynamic redistribution of T cells during disease evolution. Probability density function (PDF) curves of CD3⁺ cell distance to the DEJ further illustrated this shift, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, with a progressive leftward skew from normal to lesional samples, indicating a higher likelihood of T cells residing near the DEJ in disease states. These observations were corroborated by 3D visualisations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed–f), where red-stained CD3⁺ clusters increasingly occupied the superficial dermis as disease advanced.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese data highlight the closer localization of T cell infiltrates to the DEJ in psoriasis and underscore the heightened immunologic activity characteristic of psoriatic plaques. Moreover, they strongly suggest that normal-appearing skin in psoriasis patients already demonstrates a distinct immunological architecture—in terms of T cell spatial relationship to the DEJ—compared with true normal skin from non‐psoriatic individuals. Clinically, this finding raises the possibility that a targeted biopsy of uninvolved skin in at‐risk individuals (such as those with a family history of psoriasis) could identify preclinical immunological changes predictive of future disease development, thereby offering an avenue for earlier intervention and closer monitoring.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSubclinical Myeloid Accumulation and Spatial Expansion of CD68⁺ Aggregates Preceding Psoriatic Lesions\u003c/h3\u003e\n\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the spatial remodeling of CD68\u003csup\u003e+\u003c/sup\u003e myeloid cells in psoriasis and supports the concept of subclinical immunological priming. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea demonstrates a significant reduction in median CD68\u003csup\u003e+\u003c/sup\u003e cell distance to the dermal–epidermal junction (DEJ) in lesional psoriasis compared to both normal and peri-lesional skin (p = 5.78×10\u003csup\u003e− 4\u003c/sup\u003e and 8.3×10\u003csup\u003e− 3\u003c/sup\u003e, respectively). Notably, although peri-lesional skin appears clinically normal, its CD68\u003csup\u003e+\u003c/sup\u003e cells are already positioned significantly closer to the DEJ than in healthy controls, indicating early immune recruitment. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb shows a marked increase in CD68\u003csup\u003e+\u003c/sup\u003e cell density in lesional skin, significantly higher than both peri-lesional (p = 2.84×10\u003csup\u003e− 3\u003c/sup\u003e) and normal tissue (p = 2.11×10\u003csup\u003e− 2\u003c/sup\u003e), consistent with macrophage accumulation during active disease. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec further substantiates this with PDF curves revealing a leftward shift—indicating higher density of CD68\u003csup\u003e+\u003c/sup\u003e cells near the DEJ—in lesional and peri-lesional samples, compared to the broader and deeper distribution in normal skin. The 3D renderings in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed-f offer visual confirmation of these findings, showing a progressive upward migration and clustering of CD68\u003csup\u003e+\u003c/sup\u003e cells (red) toward the epidermis (green) from normal (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed) to peri-lesional (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee) to lesional (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef) states. Together, these spatial and volumetric data suggest that peri-lesional skin is not immunologically quiescent, but harbours latent macrophage remodeling that may presage overt plaque formation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRecent insights into the immunopathogenesis of psoriasis have shed new light on the century-old phenomenon known as the Woronoff ring, a transient hypopigmented halo around regressing plaques \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Mechanistically, this ring has been linked to an HLA class I‐restricted autoimmune response involving CD8\u003csup\u003e+\u003c/sup\u003e T cells, which target melanocytes in psoriatic lesions. Although these T cells are not primarily cytotoxic, they characteristically release interleukin (IL)‐17, IL‐22 and tumour necrosis factor (TNF)‐α, each of which synergistically enhances melanocyte proliferation while paradoxically inhibiting melanin synthesis \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. As a result, the melanocyte population in psoriatic plaques increases in number but displays reduced melanin content. When plaques regress, the remaining influence of IL‐17 and TNF‐α in peri‐lesional areas perpetuates a hypopigmented zone, which becomes visible as the Woronoff ring. This phenomenon, therefore, can be understood through the lens of persistent cytokine‐driven melanocyte modulation that persists even after clinically apparent lesion resolution.\u003c/p\u003e\u003cp\u003eIn parallel, analyses of T cell and CD68\u003csup\u003e+\u003c/sup\u003e cell distributions in normal, peri-lesional and lesional psoriatic skin indicate that seemingly “normal” skin in psoriasis patients differs significantly from truly normal skin in individuals without psoriasis. Closer proximity to the dermal–epidermal junction (DEJ) in peri‐lesional sites support the hypothesis that an immunologically primed microenvironment is present before overt lesion formation. This subclinical inflammatory milieu provides a plausible backdrop for understanding the lasting cytokine effects on melanocytes implicated in the Woronoff ring \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. From a clinical standpoint, these findings underline the importance of considering biopsies from uninvolved psoriatic skin—particularly in at‐risk individuals—for the potential early detection of immunological and melanocytic changes preceding visible lesion development. By connecting these immunopathological hallmarks to a well‐documented yet enigmatic clinical sign, investigators and clinicians alike may gain deeper insights into psoriasis pathogenesis, enabling both earlier intervention and a more nuanced appreciation of psoriasis‐associated pigmentary dysregulation.\u003c/p\u003e\n\u003ch3\u003eBiphasic Mast Cell Redistribution from Deep Dermis to DEJ During Psoriatic Progression\u003c/h3\u003e\n\u003cp\u003eMast cells (MCs) have long been recognized as critical players in the inflammatory cascade of psoriasis, with evidence linking their activation and degranulation to both the development of new lesions and the evolution of established plaques. Recent findings, highlighting variations in the spatial distribution of MC clusters relative to the dermal–epidermal junction (DEJ), lend further support to their dynamic, stage-dependent function. In peri‐lesional regions, elevated MC distance from the DEJ may signal an early, amplified inflammatory drive, aligning with previous reports of heightened MC recruitment and activation at the inception of lesion formation. Subsequently, in fully established psoriatic plaques, MCs appear to re‐localize closer to the superficial dermis. This shift could represent a functional or phenotypic transition, as MCs modulate their pro‐inflammatory mediator output to sustain chronic inflammation rather than initiate it \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThese positional changes corroborate earlier clinical and histological observations that MCs surge in number and activity during the initial phases of plaque development, only to decline or assume altered functional profiles in mature lesions. In Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, median distance of MCs to the dermal–epidermal junction (DEJ) is broadly similar across groups, suggesting that mast cells initially accumulate deeper in the dermis during early inflammation. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb shows that mast cell density is modestly reduced in peri-lesional samples, with partial restoration in lesional skin, hinting at dynamic turnover or redistribution during disease progression. The PDF curves in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec illustrate these trends more clearly: lesional tissue shows a left-shifted peak (towards superficial clustering) relative to normal, consistent with MC migration toward the DEJ in mature plaques. These spatial findings are visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed–f, where representative 3D renderings show deep dermal distribution in normal skin (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed), increased but scattered clustering in peri-lesional skin (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee), and more dense, superficial aggregation in lesional samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef). Together, these data suggest a biphasic pattern wherein MCs initially localize farther from the DEJ in peri-lesional skin—potentially reflecting active recruitment or redistribution—then migrate toward the superficial dermis as fully developed plaques become established.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eMCs facilitate innate and adaptive immune responses via cytokine release (e.g. IL-1β, IL‐6, TNF‐α) and contribute to tissue remodelling through proteases such as tryptase and chymase. Their capacity to interact with dendritic cells and T‐cell subsets underscores their pivotal regulatory role in shaping local immune responses throughout the psoriatic disease course. Thus, the spatial metrics indicating a biphasic MC pattern—greater distances from the DEJ early on, followed by closer clustering in well‐formed plaques—supports the view that MCs serve as key orchestrators of psoriatic inflammation, with therapeutic implications for targeting MC‐associated pathways to modulate disease progression.\u003c/p\u003e\u003cp\u003eThese positional shifts underscore the dynamic contributions of MCs throughout psoriasis pathogenesis. Early in the disease process, the increased peri-lesional MC cluster distances from the DEJ may mirror enhanced mast‐cell‐mediated signaling and inflammatory recruitment. In contrast, the subsequent clustering nearer the DEJ in mature plaques could reflect an evolved, possibly more chronic, MC phenotype that supports sustained inflammation rather than initiating new lesions. Such observations reinforce the concept of MCs as pivotal mediators in both the onset and progression of psoriatic pathology. From a therapeutic standpoint, targeting MC‐associated pathways may offer opportunities for interrupting the early stages of plaque formation and modulating established inflammation, thereby potentially improving clinical outcomes in patients with psoriasis.\u003c/p\u003e"},{"header":"Conclusion and Discussion","content":"\u003cp\u003eWe proposed a comprehensive solution of skin tissue 3D profiling, the workflow incorporates tissue serial sectioning, image registration, image segmentation, and 3D reconstruction. Compared to direct 3D imaging solutions, our approach is cost-effective and supports incorporation of immunohistochemical staining and high-resolution analysis, which enables precise cellular-level specific immune profiling.\u003c/p\u003e\u003cp\u003eConsistent with earlier two-dimensional studies, we observed a marked superficialisation of CD3\u003csup\u003e+\u003c/sup\u003e T cell and CD68\u003csup\u003e+\u003c/sup\u003e macrophage clusters in psoriatic tissue. Most CD3\u003csup\u003e+\u003c/sup\u003e cluster median distances to DEJ fell from \u0026gt; 200 µm in normal skin to around 100 µm or less in plaques (p = 4.8 × 10\u003csup\u003e− 5\u003c/sup\u003e), while CD68\u003csup\u003e+\u003c/sup\u003e clusters shifted by a similar magnitude (p = 5.78 × 10\u003csup\u003e− 4\u003c/sup\u003e). Notably, normal-appearing peri-lesional skin already displayed a pathological architecture: more than half CD3\u003csup\u003e+\u003c/sup\u003e clusters median DEJ distances in peri-lesional skin fell below 200 µm (p = 3.81 × 10\u003csup\u003e− 3\u003c/sup\u003e), demonstrating clear evidence of subclinical immune activation.\u003c/p\u003e\u003cp\u003eThe quantitative spatial profiling of mast cell indicated a biphasic pattern, with mast cells initially positioned deeper in peri-lesional dermal layers, progressively migrating closer to the DEJ in mature plaques. Probability density functions further highlighted this transitional pattern, with peri-lesional tissue showing intermediate superficial clustering compared to lesional skin. These findings underscore the evolving functional roles of mast cells—from driving initial inflammatory processes to sustaining chronic plaque activity.\u003c/p\u003e\u003cp\u003eCollectively, the 174 reconstructed 3D tissue stacks (95 control and 79 psoriatic) provides quantitative evidence that immune–epithelial topography in healthy skin differs fundamentally from that of psoriasis skin and, critically, from clinically uninvolved peri-lesional skin within the same patient cohort. The data support the concept that clinically inactive skin in psoriasis patients is immunologically primed and may serve as a reservoir for future flare sites.\u003c/p\u003e\u003cp\u003eThe advantages of 3D reconstruction over traditional 2D analysis were validated through quantitative comparison of probability density curves generated from full 3D stacks versus averaged 2D slices, the results confirmed that volumetric analyses capture biologically meaningful cellular distribution patterns that are lost or obscured in 2D. This underscores the critical need for employing full volumetric methods in studies of complex immune environments like psoriasis.\u003c/p\u003e\u003cp\u003eOur 3D profiling pipeline confirms and extends the paradigm that psoriatic inflammation is defined by superficial clusters of T cells and macrophages at the DEJ, preceded by subclinical changes in peri-lesional skin. Stage-specific repositioning of mast cells further underscores the dynamic orchestration of innate and adaptive immunity. By providing quantitatively robust, scalable metrics from routine histology, this method offers a practical bridge between conventional pathology and high-dimension spatial omics, with potential applications in early diagnosis, patient stratification and therapeutic monitoring of psoriasis.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePatient Cohort\u003c/h2\u003e\u003cp\u003eIn this study, 98 subjects were recruited, including 16 patients with psoriasis, 3 patients with eczema, 2 patients with drug-induced skin conditions, and 77 healthy controls. This study was approved by the Human Research Ethics Committee (HREC) of the Royal Prince Alfred Hospital (RPAH) Zone. Informed consent was obtained from all participants, with adherence to the National Statement on Ethical Conduct in Human Research (2007). All procedures complied with the ethical standards of the institutional and national research committees. Participant confidentiality was ensured by anonymizing all data. Total 118 whole-mount skin tissue samples were cut from 98 subjects, where 79, 29, 6, and 4 samples were cut from healthy controls, psoriasis patients, eczema patients, and patients with drug-induced skin conditions, respectively. Especially, for all eczema patients and patients with drug-induced skin conditions, 2 samples were cut from each patients, where one sample was cut from lesional tissue area and another sample was cut from peri-lesional tissue area. And for psoriasis patients, 16 patients were conducted lesional/peri-lesional area paired cutting and 3 patients were conducted lesional area cutting only, resulting 29 samples in total. All 10 samples from eczema patients and patients with drug-induced skin conditions were excluded from analysis since the sample amount is not big enough. In psoriasis patients, 2 samples from 1 patient were excluded due to low image quality. In healthy controls, 4 samples from 3 subjects were excluded due to data corruption and 22 samples from 22 subjects were excluded due to low image quality. Eventually, 53 samples from 52 healthy controls and 27 samples from 15 psoriasis patients were involved in this study. The profile of all subjects are shown in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSample Preparation and Image Acquisition\u003c/h3\u003e\n\u003cp\u003eAll the skin samples were fixed in 10% neutral-buffered formalin (equivalent to 4% formaldehyde) for a minimum of 48 hours. The fixed blocks were then processed using a 6-hour automated cycle on a Leica Peloris II processor, passing through graded alcohols, xylene and then paraffin wax. Embedded blocks were then trimmed and sectioned at 4 \u0026micro;m using a Leica RM2235 microtome. Sections were floated on a Medite TFB 35 waterbath at 45\u0026deg;C using distilled water. For blocks from healthy controls, a total of 80 or 160 or 240 serial sections were obtained for each block and mounted onto Trajan adhesive slides with 10 serial sections per glass slide. For blocks from psoriasis patients, serial cutting for 80 or 120 consecutive sections was performed. All glass slides were then heated to 60\u0026deg;C for 30 minutes to ensure tissue adherence. For tissue blocks from healthy controls, we defined a group of 80 consecutive sections (i.e. 8 glass slides) as a tissue stack, while a tissue stack in psoriasis patients tissue block was defined as a group of 40 consecutive sections (4 glass slides). Eventually, we obtained 95 tissue stacks (80-slice-stack) from 52 healthy controls and 79 tissue stacks (40-slice-stack) from 15 psoriasis patients. The 174 stacks were then subject to immunohistochemistry (IHC) staining to visualize the immune cell including T cell, macrophage, and mast cell. The three type of cells are labelled by the CD3 marker (Roche, clone 2GV6, 790\u0026ndash;4341, RTU), CD68 marker (Roche, clone KP-1, 790\u0026ndash;2931, RTU), and mast cell tryptase (MCT) (Cell Marque, clone G3, 342M-14, RTU), respectively. The staining was performed by an automated Roche Ventana Ultra machine and following are the staining protocols for each antibody. CD3: Heat-induced epitope retrieval (HIER) with CC1 for 24 minutes at 100\u0026deg;C, antibody incubation for 28 minutes at 37\u0026deg;C. CD68: HIER with CC1 for 32 minutes at 100\u0026deg;C, antibody incubation for 12 minutes at 37\u0026deg;C. MCT: HIER with CC1 for 8 minutes at 100\u0026deg;C, antibody incubation for 8 minutes at 37\u0026deg;C. All antibody protocols used the Optiview DAB detection system (Ventana, 760\u0026thinsp;\u0026minus;\u0026thinsp;700), followed by counterstaining with Hematoxylin II (Ventana, 790\u0026ndash;2208, RTU) and Bluing reagent (Ventana, 760\u0026ndash;2037, RTU). All glass slides were then dehydrated in graded alcohols, cleared in xylene, and coverslipped. The detailed stack staining profiles are shown in Supplementary Table S2. The stained stacks were then digitized at the resolution of 0.35 \u0026times; 0.35 \u0026micro;m per pixel. It is worth noting that among 95 80-image-stacks, there were 6 stacks only had 70 images each, since 10 images were excluded due to low image quality, hence total 10,700 whole slide images (WSIs) are obtained in this study. The tissue serial cutting, staining, and scanning pipeline are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb.\u003c/p\u003e\n\u003ch3\u003eImage Registration and Segmentation\u003c/h3\u003e\n\u003cp\u003eAs the WSI was scanned using relatively high resolution, most of the original WSIs were in large scale. Specifically, most WSIs had both width and height exceeding 10,000 pixels. Due to computational and memory resources limitation, all WSIs were first down sampled by 8 times before any subsequent processing, the image resolution was hence reduced to 2.8 \u0026times; 2.8 \u0026micro;m per pixel. No down sampling (extraction) or up sampling (interpolation) was performed in Z direction so the Z resolution was still 4\u0026micro;m per layer, i.e., the slice cutting interval. After the down sampling process, the resolution of 3D image stack became 2.8(X) \u0026times; 2.8(Y) \u0026times; 4(Z) \u0026micro;m per voxel, which is still enough for cellular level analysis.\u003c/p\u003e\u003cp\u003eAn image registration strategy was designed to make sure all images in same stack were well aligned. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, for each stack, the first image was regarded as fixed image and the next image was registered to the fixed image by a scale-invariant feature transform (SIFT) \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e based algorithm. Then the second image became fixed image and the next image was registered to it. This process was performed iteratively until the last image of the stack was registered to its fixed image, so that all the images were registered to the first image. Manual review was performed in the end as a quality control mechanism to ensure the alignment quality. The quantitative image alignment quality assessment result is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, we calculated the mean distance between matched SIFT keypoints in original stacks (orange line) and aligned stacks (blue bar), the mean distance dropped after alignment for every stack, indicating that the image registration strategy we deployed effectively enhanced the image alignment quality and enabled the accurate spatial analysis.\u003c/p\u003e\u003cp\u003eAfter image registration, an image segmentation model was trained for pixel level image segmentation. The pathologists performed annotation on 130 WSIs in ilastik software. The annotation categories included dermis, epidermis, and immune cell. The annotated WSIs were used to train a random forest algorithm \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e based image segmentation model using ilastik software. The pathologists reviewed the prediction results to make sure the model performance was good, and then the model was applied to every stack to generate segmentation results. The image registration and segmentation pipeline is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed, the 3D rendering of a representative stack is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eMetric Calculation and Necessity of 3D Reconstruction\u003c/h2\u003e\u003cp\u003eFor segmented tissue stack, we proposed a parameter named cell distance to dermal-epidermal junction (DEJ). For a cell voxel, the distance to DEJ was defined as the euclidean distance between itself and the nearest DEJ to it. To be specifically, for cell voxel located in dermis region, its distance to DEJ is defined as the distance between itself and the nearest epidermis voxel; for cell voxel located in epidermis region, its distance to DEJ is defined as the distance between itself and the nearest dermis voxel. A probability density function (PDF) curve of distance to DEJ was then fitted to illustrate the immune cell distribution profile along the depth into skin.\u003c/p\u003e\u003cp\u003eWe designed an experiment to demonstrate the advantages of serial sectioning and 3D reconstruction. We selected a healthy control tissue stack with 80 slides and calculated the cell distance to DEJ in two ways: across the 3D stack, and within each slide independently. The PDF curves were fitted for both situations, and we calculated the average value of 80 PDF curves derived from individual 2D slides to compare with the PDF curve derived from 3D stack. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, the individual 2D slide PDF curves are demonstrated in the middle panel and right panel. The average curve of 2D slide PDF curves (blue dashed line) and the 3D stack PDF curve (red solid line) are shown in the left panel. There is a distinct difference between the PDF curve from 2D average and from 3D, which means that simply averaging multiple 2D slides does not yield the same information as analyzing 3D stack. The root cause of the difference is that the peaks and valleys (corresponding to cell distribution patterns) in individual 2D slide PDF curves will become unrecognizable after averaging process, eventually resulting a smoothed curve. The actual 3D cell distribution pattern will only be preserved when the calculation is performed in 3D manner. From another perspective, none of the individual 2D slide PDF curves shows a pattern similar to that of the 3D stack PDF curve, indicating that accurate estimation of 3D stack from single 2D slide is not feasible due to sampling bias. In short, by including more thickness of tissue, we can reveal the immune cell spatial distribution in a more precise way, and minimize the bias introduced by random sampling.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDigital Pathology\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDEJ\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDermal-Epidermal Junction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMast Cell\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMCT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMast Cell Tryptase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent of Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Human Research Ethics Committee (HREC) of the Royal Prince Alfred Hospital (RPAH) Zone. Informed consent was obtained from all participants, with adherence to the National Statement on Ethical Conduct in Human Research (2007). All procedures complied with the ethical standards of the institutional and national research committees.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors of this work agreed to publish with Scientific Reports once accepted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of the Data and Material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data and code of this work will be available upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no potential competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the LEO Foundation Grant and the Australasian College of Dermatologist\u0026rsquo;s Scientific Research Fund. This work is also jointly supported by research funding from BII and IMCB under BMRC, A*STAR. Additionally, it is supported by the Industrial Alignment Funding - Pre-Positioning Programme \u0026ndash; (IAF-PP Grant ID: H24J4a0044) awarded to Prof. Weimiao Yu and his iDMP lab.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eP. T. and W. Y. conceived the project and designed the experiments; P. D. contributed to sample preparation; L. L. performed model development and validation, and data analysis; L. L. designed and prepared the figures with the assistance of K. H. O.; L. L. and L. V. wrote the manuscript with the assistance and feedback of all the other co-authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLowes, M. A., Bowcock, A. M. \u0026amp; Krueger, J. G. 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Med.\u003c/em\u003e \u003cb\u003e47\u003c/b\u003e, 31\u0026ndash;39 (2017).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Digital Pathology, Psoriasis, Serial Sectioning, Tissue 3D Reconstruction, Immune Cell 3D Distribution","lastPublishedDoi":"10.21203/rs.3.rs-7581947/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7581947/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePsoriasis is characterized by a complex immune micro-environment, yet most spatial studies rely on two-dimensional (2D) immunohistochemistry (IHC) histology. We investigated whether routine immunohistochemistry coupled with three-dimensional (3D) digital reconstruction can provide quantitative insight into immune-epithelial architecture across the psoriatic spectrum. Skin biopsies from 15 patients with plaque psoriasis and 52 healthy donors were serially sectioned to generate 174 tissue stacks (10,700 whole-slide images). CD3\u003csup\u003e+\u003c/sup\u003e, CD68\u003csup\u003e+\u003c/sup\u003e and mast cell were labelled and their Euclidean distance to dermal–epidermal junction (DEJ) were calculated in 3D. Compared with controls, lesional skin showed pronounced super-ficialisation of immune clusters: median CD3\u003csup\u003e+\u003c/sup\u003e and CD68\u003csup\u003e+\u003c/sup\u003e cluster distance to DEJ significantly decreased. Mast cell distribution revealed a biphasic pattern: peri-lesional area displayed decreased cell density at deeper dermal levels, whereas established plaques demonstrated higher cell density at superficial dermis. Averaging multiple 2D sections obscured these distributional features, underlining the necessity of volumetric analysis. High-resolution 3D reconstruction reliably maps the spatial dynamics of T cells, macrophages and mast cells in psoriasis and detects subclinical immunological priming in peri-lesional skin. The proposed pipeline bridges routine pathology and advanced spatial omics, offering a scalable tool for early disease detection, patient stratification and therapeutic monitoring.\u003c/p\u003e","manuscriptTitle":"Three‑Dimensional Immune Cartography Uncovers Subclinical Remodeling in Psoriasis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 19:08:40","doi":"10.21203/rs.3.rs-7581947/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-10T15:52:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-09T13:48:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-09T13:38:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"243463739734643549424126933876893421036","date":"2025-10-21T07:44:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175206262405263752004527111665817572302","date":"2025-10-20T11:06:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-17T07:08:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160949832227652294611994499809960006447","date":"2025-10-12T06:59:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-10T00:41:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-15T16:53:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-12T08:35:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-11T14:36:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-10T10:33:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1fecc830-87db-4b56-afd1-31379ef7ed12","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":56622434,"name":"Health sciences/Biomarkers"},{"id":56622435,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":56622436,"name":"Health sciences/Diseases"},{"id":56622437,"name":"Biological sciences/Immunology"},{"id":56622438,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-03-02T16:02:49+00:00","versionOfRecord":{"articleIdentity":"rs-7581947","link":"https://doi.org/10.1038/s41598-026-39838-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-02-23 15:59:23","publishedOnDateReadable":"February 23rd, 2026"},"versionCreatedAt":"2025-10-23 19:08:40","video":"","vorDoi":"10.1038/s41598-026-39838-0","vorDoiUrl":"https://doi.org/10.1038/s41598-026-39838-0","workflowStages":[]},"version":"v1","identity":"rs-7581947","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7581947","identity":"rs-7581947","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}