Autoimmunity Against Surfactant Protein B Is Associated with Pneumonitis During Checkpoint Blockade.

RATIONALE: Immune checkpoint inhibitor-related pneumonitis is a serious autoimmune event affecting up to 20% of patients with non-small cell lung cancer, yet the factors underpinning its development in some patients and not others are poorly understood. OBJECTIVES: To investigate the role of autoantibodies and autoreactive T cells against surfactant-related proteins in the development of pneumonitis. METHODS: The study cohort consisted of non-small cell lung cancer patients who gave blood samples before and during immune checkpoint inhibitor treatment. Serum was used for proteomics analyses and to detect autoantibodies present during pneumonitis. T cell stimulation assays and single-cell RNA sequencing were performed to investigate the specificity and functionality of peripheral autoreactive T cells. The findings were confirmed in a validation cohort comprising patients with non-small cell lung cancer and patients with melanoma. MEASUREMENTS AND MAIN RESULTS: Across both cohorts, patients who developed pneumonitis had higher pre-treatment levels of immunoglobulin G autoantibodies targeting surfactant protein-B. At the onset of pneumonitis, these patients also exhibited higher frequencies of CD4+ interferon-gamma-positive surfactant protein B-specific T cells, and expanding T cell clonotypes recognizing this protein, accompanied by a pro-inflammatory serum proteomic profile. CONCLUSIONS: Our data suggest that the co-occurrence of surfactant protein-B-specific immunoglobulin G autoantibodies and CD4+ T cells is associated with the development of pneumonitis during ICI therapy. Pre-treatment levels of these antibodies may represent a potential biomarker for elevated risk of developing pneumonitis and on-treatment levels may provide a diagnostic aid. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Epstein-Barr Virus Orchestrates Spatial Reorganization and Immunomodulation within the Classic Hodgkin Lymphoma Tumor Microenvironment.

Classic Hodgkin Lymphoma (cHL) is a tumor composed of rare malignant Hodgkin and Reed-Sternberg (HRS) cells nested within a T-cell rich inflammatory immune infiltrate. cHL is associated with Epstein-Barr Virus (EBV) in 25% of cases. The specific contributions of EBV to the pathogenesis of cHL remain largely unknown, in part due to technical barriers in dissecting the tumor microenvironment (TME) in high detail. Herein, we applied multiplexed ion beam imaging (MIBI) spatial pro-teomics on 6 EBV-positive and 14 EBV-negative cHL samples. We identify key TME features that distinguish between EBV-positive and EBV-negative cHL, including the relative predominance of memory CD8 T cells and increased T-cell dysfunction as a function of spatial proximity to HRS cells. Building upon a larger multi-institutional cohort of 22 EBV-positive and 24 EBV-negative cHL samples, we orthogonally validated our findings through a spatial multi-omics approach, coupling whole transcriptome capture with antibody-defined cell types for tu-mor and T-cell populations within the cHL TME. We delineate contrasting transcriptomic immunological signatures between EBV-positive and EBV-negative cases that differently impact HRS cell proliferation, tumor-immune interactions, and mecha-nisms of T-cell dysregulation and dysfunction. Our multi-modal framework enabled a comprehensive dissection of EBV-linked reorganization and immune evasion within the cHL TME, and highlighted the need to elucidate the cellular and molecular fac-tors of virus-associated tumors, with potential for targeted therapeutic strategies.

Multimodal and spatially resolved profiling identifies distinct patterns of T cell infiltration in nodal B cell lymphoma entities.

The redirection of T cells has emerged as an attractive therapeutic principle in B cell non-Hodgkin lymphoma (B-NHL). However, a detailed characterization of lymphoma-infiltrating T cells across B-NHL entities is missing. Here we present an in-depth T cell reference map of nodal B-NHL, based on cellular indexing of transcriptomes and epitopes, T cell receptor sequencing, flow cytometry and multiplexed immunofluorescence applied to 101 lymph nodes from patients with diffuse large B cell, mantle cell, follicular or marginal zone lymphoma, and from healthy controls. This multimodal resource revealed quantitative and spatial aberrations of the T cell microenvironment across and within B-NHL entities. Quantitative differences in PD1+ TCF7- cytotoxic T cells, T follicular helper cells or IKZF3+ regulatory T cells were linked to their clonal expansion. The abundance of PD1+ TCF7- cytotoxic T cells was associated with poor survival. Our study portrays lymphoma-infiltrating T cells with unprecedented comprehensiveness and provides a unique resource for the investigation of lymphoma biology and prognosis.

[Effects of upstream laboratory processes on the digitization of histological slides]. / Auswirkungen vorgeschalteter Laborprozesse auf die Digitalisierung histologischer Schnittpräparate.

BACKGROUND: Several factors in glass slide (GS) preparation affect the quality and data volume of a digitized histological slide. In particular, reducing contamination and selecting the appropriate coverslip have the potential to significantly reduce scan time and data volume. GOALS: To objectify observations from our institute's digitization process to determine the impact of laboratory processes on the quality of digital histology slides. MATERIALS AND METHODS: Experiment 1: Scanning the GS before and after installation of a central console in the microtomy area to reduce dirt and statistical analysis of the determined parameters. Experiment 2: Re-coverslipping the GS (post diagnostics) with glass and film. Scanning the GS and statistical analysis of the collected parameters. CONCLUSION: The targeted restructuring in the laboratory process leads to a reduction of GS contamination. This causes a significant reduction in the amount of data generated and scanning time required for the digitized sections. Film as a coverslip material minimizes processing errors in contrast to glass. According to our estimation, all the above-mentioned points lead to considerable cost savings.

Histological and functional characterization of 3D human skin models mimicking the inflammatory skin diseases psoriasis and atopic dermatitis.

Three-dimensional (3D) human skin equivalents have emerged as valuable tools in skin research, replacing animal experimentation and precluding the need for patient biopsies. In this study, we advanced 3D skin equivalents to model the inflammatory skin diseases atopic dermatitis and psoriasis by cytokine stimulation, and were successful in integrating TH1 T cells into skin models to develop an immunocompetent 3D psoriasis model. We performed in-depth histological and functional characterization of 3D skin equivalents and validated them in terms of tissue architecture, pathological changes, expression of antimicrobial peptides and Staphylococcus aureus colonization using 3D reconstruction by multiphoton microscopy and phenotyping by highly multiplexed 'co-detection by indexing' (CODEX) microscopy. We show that our skin equivalents have a structural architecture with a well-developed dermis and epidermis, thus resembling human skin. In addition, the skin models of atopic dermatitis and psoriasis show several phenotypic features of inflammatory skin disease, including disturbed epidermal differentiation and alterations in the expression of epidermal barrier genes and antimicrobial peptides, and can be reliably used to test novel treatment strategies. Therefore, these 3D equivalents will be a valuable tool in experimental dermatological research.

Spatial subsetting enables integrative modeling of oral squamous cell carcinoma multiplex imaging data.

Oral squamous cell carcinoma (OSCC), a prevalent and aggressive neoplasm, poses a significant challenge due to poor prognosis and limited prognostic biomarkers. Leveraging highly multiplexed imaging mass cytometry, we investigated the tumor immune microenvironment (TIME) in OSCC biopsies, characterizing immune cell distribution and signaling activity at the tumor-invasive front. Our spatial subsetting approach standardized cellular populations by tissue zone, improving feature reproducibility and revealing TIME patterns accompanying loss-of-differentiation. Employing a machine-learning pipeline combining reliable feature selection with multivariable modeling, we achieved accurate histological grade classification (AUC = 0.88). Three model features correlated with clinical outcomes in an independent cohort: granulocyte MAPKAPK2 signaling at the tumor front, stromal CD4+ memory T cell size, and the distance of fibroblasts from the tumor border. This study establishes a robust modeling framework for distilling complex imaging data, uncovering sentinel characteristics of the OSCC TIME to facilitate prognostic biomarkers discovery for recurrence risk stratification and immunomodulatory therapy development.

Corrigendum: A review on deep learning applications in highly multiplexed tissue imaging data analysis.

[This corrects the article DOI: 10.3389/fbinf.2023.1159381.].

Single-cell mapping identifies MSI+ cells as a common origin for diverse subtypes of pancreatic cancer.

Identifying the cells from which cancers arise is critical for understanding the molecular underpinnings of tumor evolution. To determine whether stem/progenitor cells can serve as cells of origin, we created a Msi2-CreERT2 knock-in mouse. When crossed to CAG-LSL-MycT58A mice, Msi2-CreERT2 mice developed multiple pancreatic cancer subtypes: ductal, acinar, adenosquamous, and rare anaplastic tumors. Combining single-cell genomics with computational analysis of developmental states and lineage trajectories, we demonstrate that MYC preferentially triggers transformation of the most immature MSI2+ pancreas cells into multi-lineage pre-cancer cells. These pre-cancer cells subsequently diverge to establish pancreatic cancer subtypes by activating distinct transcriptional programs and large-scale genomic changes, and enforced expression of specific signals like Ras can redirect subtype specification. This study shows that multiple pancreatic cancer subtypes can arise from a common pool of MSI2+ cells and provides a powerful model to understand and control the programs that shape divergent fates in pancreatic cancer.

Neutrophil extracellular traps enhance S. aureus skin colonization by oxidative stress induction and downregulation of epidermal barrier genes.

Staphylococcus aureus is the most common cause of bacterial skin infections in humans, including patients with atopic dermatitis (AD). Polymorphonuclear neutrophils (PMNs) are the first cells to infiltrate an infection site, where they usually provide an effective first line of defense, including neutrophil extracellular trap (NET) formation. Here, we show that infiltrating PMNs in inflamed human and mouse skin enhance S. aureus skin colonization and persistence. Mechanistically, we demonstrate that a crosstalk between keratinocytes and PMNs results in enhanced NET formation upon S. aureus infection, which in turn induces oxidative stress and expression of danger-associated molecular patterns such as high-mobility-group-protein B1 (HMGB1) in keratinocytes. In turn, HMGB1 enhances S. aureus skin colonization and persistence by promoting skin barrier dysfunctions by the downregulation of epidermal barrier genes. Using patient material, we show that patients with AD exhibit enhanced presence of PMNs, NETs, and HMGB1 in the skin, demonstrating the clinical relevance of our finding.

Case Report: Paraneoplastic psoriasis in thymic carcinoma.

Thymic carcinomas are exceedingly rare and very aggressive malignancies of the anterior mediastinum. While thymomas exhibit a high association with paraneoplastic syndromes, these phenomena are a rarity in thymic carcinomas. In general, acanthotic syndromes such as acroceratosis neoplastica and acanthosis nigricans maligna are commonly observed as paraneoplastic phenomena in patients with carcinomas. In contrast, psoriasis vulgaris, another acanthotic disease, rarely occurs as a paraneoplasia. We report the case of a 36-year-old patient with progressive thymic carcinoma (undifferentiated carcinoma, T3N2M1a) and paraneoplastic psoriasis occurring ten months before the initial diagnosis of the carcinoma. Over the course of the disease, new psoriatic flares heralded relapse or progression of the carcinoma. To our knowledge, this is the first reported case of paraneoplastic psoriasis in thymic carcinoma.

A review on deep learning applications in highly multiplexed tissue imaging data analysis.

Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of DL-based pipelines used in preprocessing highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients. Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of the DL-based pipelines used in preprocessing the highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients.

Organization of the human intestine at single-cell resolution.

The intestine is a complex organ that promotes digestion, extracts nutrients, participates in immune surveillance, maintains critical symbiotic relationships with microbiota and affects overall health1. The intesting has a length of over nine metres, along which there are differences in structure and function2. The localization of individual cell types, cell type development trajectories and detailed cell transcriptional programs probably drive these differences in function. Here, to better understand these differences, we evaluated the organization of single cells using multiplexed imaging and single-nucleus RNA and open chromatin assays across eight different intestinal sites from nine donors. Through systematic analyses, we find cell compositions that differ substantially across regions of the intestine and demonstrate the complexity of epithelial subtypes, and find that the same cell types are organized into distinct neighbourhoods and communities, highlighting distinct immunological niches that are present in the intestine. We also map gene regulatory differences in these cells that are suggestive of a regulatory differentiation cascade, and associate intestinal disease heritability with specific cell types. These results describe the complexity of the cell composition, regulation and organization for this organ, and serve as an important reference map for understanding human biology and disease.

Expanded vacuum-stable gels for multiplexed high-resolution spatial histopathology.

Cellular organization and functions encompass multiple scales in vivo. Emerging high-plex imaging technologies are limited in resolving subcellular biomolecular features. Expansion Microscopy (ExM) and related techniques physically expand samples for enhanced spatial resolution, but are challenging to be combined with high-plex imaging technologies to enable integrative multiscaled tissue biology insights. Here, we introduce Expand and comPRESS hydrOgels (ExPRESSO), an ExM framework that allows high-plex protein staining, physical expansion, and removal of water, while retaining the lateral tissue expansion. We demonstrate ExPRESSO imaging of archival clinical tissue samples on Multiplexed Ion Beam Imaging and Imaging Mass Cytometry platforms, with detection capabilities of > 40 markers. Application of ExPRESSO on archival human lymphoid and brain tissues resolved tissue architecture at the subcellular level, particularly that of the blood-brain barrier. ExPRESSO hence provides a platform for extending the analysis compatibility of hydrogel-expanded biospecimens to mass spectrometry, with minimal modifications to protocols and instrumentation.

Concerted epithelial and stromal changes during progression of Barrett's Esophagus to invasive adenocarcinoma exposed by multi-scale, multi-omics analysis.

Esophageal adenocarcinoma arises from Barrett's esophagus, a precancerous metaplastic replacement of squamous by columnar epithelium in response to chronic inflammation. Multi-omics profiling, integrating single-cell transcriptomics, extracellular matrix proteomics, tissue-mechanics and spatial proteomics of 64 samples from 12 patients' paths of progression from squamous epithelium through metaplasia, dysplasia to adenocarcinoma, revealed shared and patient-specific progression characteristics. The classic metaplastic replacement of epithelial cells was paralleled by metaplastic changes in stromal cells, ECM and tissue stiffness. Strikingly, this change in tissue state at metaplasia was already accompanied by appearance of fibroblasts with characteristics of carcinoma-associated fibroblasts and of an NK cell-associated immunosuppressive microenvironment. Thus, Barrett's esophagus progresses as a coordinated multi-component system, supporting treatment paradigms that go beyond targeting cancerous cells to incorporating stromal reprogramming.

Impact of P-selectin-PSGL-1 Axis on Platelet-Endothelium-Leukocyte Interactions in Fatal COVID-19.

In critically ill patients infected with SARS-CoV-2, early leukocyte recruitment to the respiratory system was found to be orchestrated by leukocyte trafficking molecules accompanied by massive secretion of proinflammatory cytokines and hypercoagulability. Our study aimed to explore the interplay between leukocyte activation and pulmonary endothelium in different disease stages of fatal COVID-19. Our study comprised 10 COVID-19 postmortem lung specimens and 20 control lung samples (5 acute respiratory distress syndrome, 2 viral pneumonia, 3 bacterial pneumonia, and 10 normal), which were stained for antigens representing the different steps of leukocyte migration: E-selectin, P-selectin, PSGL-1, ICAM1, VCAM1, and CD11b. Image analysis software QuPath was used for quantification of positive leukocytes (PSGL-1 and CD11b) and endothelium (E-selectin, P-selectin, ICAM1, VCAM1). Expression of IL-6 and IL-1ß was quantified by RT-qPCR. Expression of P-selectin and PSGL-1 was strongly increased in the COVID-19 cohort compared with all control groups (COVID-19:Controls, 17:23, P < .0001; COVID-19:Controls, 2:75, P < .0001, respectively). Importantly, P-selectin was found in endothelial cells and associated with aggregates of activated platelets adherent to the endothelial surface in COVID-19 cases. In addition, PSGL-1 staining disclosed positive perivascular leukocyte cuffs, reflecting capillaritis. Moreover, CD11b showed a strongly increased positivity in COVID-19 compared with all controls (COVID-19:Controls, 2:89; P = .0002), indicating a proinflammatory immune microenvironment. Of note, CD11b exhibited distinct staining patterns at different stages of COVID-19 disease. Only in cases with very short disease course, high levels of IL-1ß and IL-6 mRNA were observed in lung tissue. The striking upregulation of PSGL-1 and P-selectin reflects the activation of this receptor-ligand pair in COVID-19, increasing the efficiency of initial leukocyte recruitment, thus promoting tissue damage and immunothrombosis. Our results show that endothelial activation and unbalanced leukocyte migration play a central role in COVID-19 involving the P-selectin-PSGL-1 axis.

Dimethyl fumarate treatment in relapsed and refractory cutaneous T-cell lymphoma: a multicenter phase 2 study.

Targeted therapies for cutaneous T-cell lymphoma (CTCL) are limited and curative approaches are lacking. Furthermore, relapses and drug induced side effects are major challenges in the therapeutic management of patients with CTCL, creating an urgent need for new and effective therapies. Pathologic constitutive NF-κB activity leads to apoptosis resistance in CTCL cells and, thus, represents a promising therapeutic target in CTCL. In a preclinical study we showed the potential of dimethyl fumarate (DMF) to block NF-κB and, specifically, kill CTCL cells. To translate these findings to applications in a clinical setting, we performed a multicentric phase 2 study evaluating oral DMF therapy in 25 patients with CTCL stages Ib to IV over 24 weeks (EudraCT number 2014-000924-11/NCT number NCT02546440). End points were safety and efficacy. We evaluated skin involvement (using a modified severity weighted assessment tool [mSWAT]), pruritus, quality of life, and blood involvement, if applicable, as well as translational data. Upon skin analysis, 7 of 23 (30.4%) patients showed a response with >50% reduction in the mSWAT score. Patients with high tumor burden in the skin and blood responded best to DMF therapy. Although not generally significant, DMF also improved pruritus in several patients. Response in the blood was mixed, but we confirmed the NF-κB-inhibiting mechanism of DMF in the blood. The overall tolerability of the DMF therapy was very favorable, with mostly mild side effects. In conclusion, our study presents DMF as an effective and excellently tolerable therapeutic option in CTCL to be further evaluated in a phase 3 study or real-life patient care as well as in combination therapies. This trial was registered at www.clinicaltrials.gov as #NCT02546440.

Towards multiomic analysis of oral mucosal pathologies.

Oral mucosal pathologies comprise an array of diseases with worldwide prevalence and medical relevance. Affecting a confined space with crucial physiological and social functions, oral pathologies can be mutilating and drastically reduce quality of life. Despite their relevance, treatment for these diseases is often far from curative and remains vastly understudied. While multiple factors are involved in the pathogenesis of oral mucosal pathologies, the host's immune system plays a major role in the development, maintenance, and resolution of these diseases. Consequently, a precise understanding of immunological mechanisms implicated in oral mucosal pathologies is critical (1) to identify accurate, mechanistic biomarkers of clinical outcomes; (2) to develop targeted immunotherapeutic strategies; and (3) to individualize prevention and treatment approaches. Here, we review key elements of the immune system's role in oral mucosal pathologies that hold promise to overcome limitations in current diagnostic and therapeutic approaches. We emphasize recent and ongoing multiomic and single-cell approaches that enable an integrative view of these pathophysiological processes and thereby provide unifying and clinically relevant biological signatures.

High-multiplex tissue imaging in routine pathology-are we there yet?

High-multiplex tissue imaging (HMTI) approaches comprise several novel immunohistological methods that enable in-depth, spatial single-cell analysis. Over recent years, studies in tumor biology, infectious diseases, and autoimmune conditions have demonstrated the information gain accessible when mapping complex tissues with HMTI. Tumor biology has been a focus of innovative multiparametric approaches, as the tumor microenvironment (TME) contains great informative value for accurate diagnosis and targeted therapeutic approaches: unraveling the cellular composition and structural organization of the TME using sophisticated computational tools for spatial analysis has produced histopathologic biomarkers for outcomes in breast cancer, predictors of positive immunotherapy response in melanoma, and histological subgroups of colorectal carcinoma. Integration of HMTI technologies into existing clinical workflows such as molecular tumor boards will contribute to improve patient outcomes through personalized treatments tailored to the specific heterogeneous pathological fingerprint of cancer, autoimmunity, or infection. Here, we review the advantages and limitations of existing HMTI technologies and outline how spatial single-cell data can improve our understanding of pathological disease mechanisms and determinants of treatment success. We provide an overview of the analytic processing and interpretation and discuss how HMTI can improve future routine clinical diagnostic and therapeutic processes.

A tissue atlas of ulcerative colitis revealing evidence of sex-dependent differences in disease-driving inflammatory cell types and resistance to TNF inhibitor therapy.

Although literature suggests that resistance to TNF inhibitor (TNFi) therapy in patients with ulcerative colitis (UC) is partially linked to immune cell populations in the inflamed region, there is still substantial uncertainty underlying the relevant spatial context. Here, we used the highly multiplexed immunofluorescence imaging technology CODEX to create a publicly browsable tissue atlas of inflammation in 42 tissue regions from 29 patients with UC and 5 healthy individuals. We analyzed 52 biomarkers on 1,710,973 spatially resolved single cells to determine cell types, cell-cell contacts, and cellular neighborhoods. We observed that cellular functional states are associated with cellular neighborhoods. We further observed that a subset of inflammatory cell types and cellular neighborhoods are present in patients with UC with TNFi treatment, potentially indicating resistant niches. Last, we explored applying convolutional neural networks (CNNs) to our dataset with respect to patient clinical variables. We note concerns and offer guidelines for reporting CNN-based predictions in similar datasets.