The immune system operates within organized tissue landscapes, where spatial relationships between cells shape the nature and outcome of immune responses. Over the past decade, protein imaging-based spatial technologies have transformed how immune responses are studied within their native tissue context, enabling simultaneous high-plex detection of proteins and transcripts at single-cell resolution. Driven by advances in computational methods such as tissue image cytometry, these approaches now allow quantitative extraction of spatial metrics such as cellular neighborhoods, cell-cell distances, coexpression patterns, and tissue architecture that are emerging as next-generation spatial biomarkers of immune function and disease diagnosis and/or prognosis. This review provides a consolidated overview of immunology through the lens of imaging-based spatial proteomics and tissue image cytometry, discussing the multiplex imaging platforms, staining strategies, and computational tools that enabled this shift. We survey applications spanning tumor immunology, autoimmune and infectious diseases, immunometabolism, neuroimmunology, and transplant immunology, revealing that immune organization is not random but spatially determined and directly linked to disease outcome. We further evaluate the strengths and limitations of current approaches and explore future directions including 3D imaging, artificial intelligence-driven spatial analysis, and the clinical translation of spatial biomarkers.

Oral squamous cell carcinoma (OSCC) accounts for a major part of cancer mortality, with survival outcomes highly dependent on early diagnosis. While many approaches have been proposed for OSCC survival prediction, they often rely on unimodal data, which may be suboptimal. In this study, we introduced a unified cross-attention-based deep learning framework that integrates whole-slide histopathology images (WSIs) and transcriptomic data from OSCC patients for survival prediction. The framework employed an autoencoder for transcriptomic feature extraction and a state-of-the-art pathology foundation model-evaluated across five alternatives-to derive WSI embeddings. These embeddings were subsequently integrated using cross-attention and concatenation within a Cox proportional hazards model. The multimodal approach outperformed nearly all unimodal counterparts, achieving a maximum concordance index of $0.780 \pm 0.059$ with cross-attention and $0.766 \pm 0.050$ with concatenation. The results indicate that pathotranscriptomic integration could improve survival prediction for OSCC patients. The implementation is available on GitHub at: https://github.com/kountaydwivedi/multimodal_fusion.git.

Motivation This study introduces a fusion-based multiomics approach to identifying non-small cell lung cancer (NSCLC)-relevant genes. We evaluated the NSCLC-subtype classification performance of various state-of-the-art machine learning models using single omics and fused multiomics approaches. The models were trained separately on individual omics datasets. Subsequently, a weighted-average-based decision-level fusion mechanism was employed to integrate the individual predictions of the trained models. Finally, the prediction performance across all the approaches was compared.Results The decision-level fusion-based approach yielded a superior classification performance as compared to the performance achieved by models trained on individual omics datasets. Finally, a set of 47 NSCLC-relevant genes were identified. For the first time, ABCF3, ACAP2, LSG1, TBCCD1, UCN2, WDR53, ZNF639 and FYTTD1 appeared in the context of NSCLC. In conclusion, the integration of multiple omics types showed potential to deliver a more concise selection of NSCLC-relevant genes that could be clinically targeted in future.Availability and implementation Data and source code are available on: https://github.com/kountaydwivedi/multiomics_fusion.git.

Accurate classification of skin lesions from dermatoscopic images is essential for diagnosis and treatment of skin cancer. In this study, we investigate the utility of a dermatology-specific foundation model, PanDerm, in comparison with two Vision Transformer (ViT) architectures (ViT base and Swin Transformer V2 base) for the task of skin lesion classification. Using frozen features extracted from PanDerm, we apply non-linear probing with three different classifiers, namely, multi-layer perceptron (MLP), XGBoost, and TabNet. For the ViT-based models, we perform full fine-tuning to optimize classification performance. Our experiments on the HAM10000 and MSKCC datasets demonstrate that the PanDerm-based MLP model performs comparably to the fine-tuned Swin transformer model, while fusion of PanDerm and Swin Transformer predictions leads to further performance improvements. Future work will explore additional foundation models, fine-tuning strategies, and advanced fusion techniques.

Idiopathic pulmonary fibrosis (IPF) is a disease with a poor prognosis and no curative therapies. Fibroblast activation by transforming growth factor β1 (TGFβ1) and disrupted metabolic pathways, including the arginine–polyamine pathway, play crucial roles in IPF development. Polyamines are agonists of the calcium/cation-sensing receptor (CaSR), activation of which is detrimental for asthma and pulmonary hypertension, but its role in IPF is unknown. To address this question, we evaluated polyamine abundance using metabolomic analysis of IPF patient saliva. Furthermore, we examined CaSR functional expression in human lung fibroblasts (HLFs), assessed the anti-fibrotic effects of a CaSR antagonist, NPS2143, in TGFβ1-activated normal and IPF HLFs by RNA sequencing and immunofluorescence imaging, respectively; and NPS2143 effects on polyamine synthesis in HLFs by immunoassays. Our results demonstrate that polyamine metabolites are increased in IPF patient saliva. Polyamines activate fibroblast CaSR in vitro, elevating intracellular calcium concentration. CaSR inhibition reduced TGFβ1-induced polyamine and pro-fibrotic factor expression in normal and IPF HLFs. TGFβ1 directly stimulated polyamine release by HLFs, an effect that was blocked by NPS2143. This suggests that TGFβ1 promotes CaSR activation through increased polyamine expression, driving a pro-fibrotic response. By halting some polyamine-induced pro-fibrotic changes, CaSR antagonists exhibit disease-modifying potential in IPF onset and development.

Images document scientific discoveries and are prevalent in modern biomedical research. Microscopy imaging in particular is currently undergoing rapid technological advancements. However, for scientists wishing to publish obtained images and image-analysis results, there are currently no unified guidelines for best practices. Consequently, microscopy images and image data in publications may be unclear or difficult to interpret. Here, we present community-developed checklists for preparing light microscopy images and describing image analyses for publications. These checklists offer authors, readers and publishers key recommendations for image formatting and annotation, color selection, data availability and reporting image-analysis workflows. The goal of our guidelines is to increase the clarity and reproducibility of image figures and thereby to heighten the quality and explanatory power of microscopy data. Community-developed checklists offer best-practice guidance for biologists preparing light microscopy images and describing image analyses for publications.

Genetic variation at the 19q13.3 KLK locus is linked with prostate cancer susceptibility in men. The non-synonymous KLK3 single nucleotide polymorphism (SNP), rs17632542 (c.536T>C; Ile163Thr-substitution in PSA) is associated with reduced prostate cancer risk, however, the functional relevance is unknown. Here, we identify that the SNP variant-induced change in PSA biochemical activity mediates prostate cancer pathogenesis. The 'Thr' PSA variant leads to small subcutaneous tumours, supporting reduced prostate cancer risk. However, 'Thr' PSA also displays higher metastatic potential with pronounced osteolytic activity in an experimental metastasis in-vivo model. Biochemical characterisation of this PSA variant demonstrates markedly reduced proteolytic activity that correlates with differences in in-vivo tumour burden. The SNP is associated with increased risk for aggressive disease and prostate cancer-specific mortality in three independent cohorts, highlighting its critical function in mediating metastasis. Carriers of this SNP allele have reduced serum total PSA and a higher free/total PSA ratio that could contribute to late biopsy decisions and delay in diagnosis. Our results provide a molecular explanation for the prominent 19q13.3 KLK locus, rs17632542 SNP, association with a spectrum of prostate cancer clinical outcomes. The PSA (KLK3) genetic variant rs17632542 is associated with reduced prostate cancer risk and lower serum PSA levels, although the underlying reasons are unclear. Here, the authors show that this PSA variant reduced proteolytic activity and leads to smaller tumours, but also increases invasion and bone metastasis, indicating its dual risk association depending on tumour context; the variant is associated with both lower risk and poor clinical outcomes.

Opinion statementProstate cancer (PCa) is the second most diagnosed malignant neoplasm and is one of the leading causes of cancer-related death in men worldwide. Despite significant advances in screening and treatment of PCa, given the heterogeneity of this disease, optimal personalized therapeutic strategies remain limited. However, emerging predictive and prognostic biomarkers based on individual patient profiles in combination with computer-assisted diagnostics have the potential to guide precision medicine, where patients may benefit from therapeutic approaches optimally suited to their disease. Also, the integration of genotypic and phenotypic diagnostic methods is supporting better informed treatment decisions. Focusing on advanced PCa, this review discusses polygenic risk scores for screening of PCa and common genomic aberrations in androgen receptor (AR), PTEN-PI3K-AKT, and DNA damage response (DDR) pathways, considering clinical implications for diagnosis, prognosis, and treatment prediction. Furthermore, we evaluate liquid biopsy, protein biomarkers such as serum testosterone levels, SLFN11 expression, total alkaline phosphatase (tALP), neutrophil-to-lymphocyte ratio (NLR), tissue biopsy, and advanced imaging tools, summarizing current phenotypic biomarkers and envisaging more effective utilization of diagnostic and prognostic biomarkers in advanced PCa. We conclude that prognostic and treatment predictive biomarker discovery can improve the management of patients, especially in metastatic stages of advanced PCa. This will result in decreased mortality and enhanced quality of life and help design a personalized treatment regimen.

Genetic variation at the 19q13.3 KLK locus is linked with prostate cancer susceptibility. The non-synonymous KLK3 SNP, rs17632542 (c.536T>C; Ile163Thr-substitution in PSA) is associated with reduced prostate cancer risk, however, the functional relevance is unknown. Here, we identify that the SNP variant-induced change in PSA biochemical activity as a previously undescribed function mediating prostate cancer pathogenesis. The ‘Thr’ PSA variant led to small subcutaneous tumours, supporting reduced prostate cancer risk. However, ‘Thr’ PSA also displayed higher metastatic potential with pronounced osteolytic activity in an experimental metastasis in-vivo model. Biochemical characterization of this PSA variant demonstrated markedly reduced proteolytic activity that correlated with differences in in-vivo tumour burden. The SNP is associated with increased risk for aggressive disease and prostate cancer-specific mortality in three independent cohorts, highlighting its critical function in mediating metastasis. Carriers of this SNP allele had reduced serum total PSA and a higher free/total PSA ratio that could contribute to late biopsy decisions and delay in diagnosis. Our results provide a molecular explanation for the prominent 19q13.3 KLK locus, rs17632542 SNP, association with a spectrum of prostate cancer clinical outcomes.

Supplementary Figures S1-S1-; Supplementary Tables S1-S2.

In chronic lymphocytic leukemia (CLL), epigenetic alterations are considered to centrally shape the transcriptional signatures that drive disease evolution and that underlie its biological and clinical subsets. Characterizations of epigenetic regulators, particularly histone-modifying enzymes, are very rudimentary in CLL. In efforts to establish effectors of the CLL-associated oncogene T-cell leukemia 1A (TCL1A), we identified here the lysine-specific histone demethylase KDM1A to interact with the TCL1A protein in B-cells in conjunction with an increased catalytic activity of KDM1A. We demonstrate that KDM1A is upregulated in malignant B-cells. Elevated KDM1A and associated gene expression signatures correlated with aggressive disease features and adverse clinical outcomes in a large prospective CLL trial cohort. Genetic Kdm1a knockdown (Kdm1a-KD) in Eμ-TCL1A mice reduced leukemic burden and prolonged animal survival, accompanied by upregulated p53 and pro-apoptotic pathways. Genetic KDM1A depletion also affected milieu components (T-, stromal, monocytic cells), resulting in significant reductions of their capacity to support CLL cell survival and proliferation. Integrated analyses of differential global transcriptomes (RNA-seq) and H3K4me3 marks (ChIP-seq) in Eµ-TCL1A vs. iKdm1aKD;Eµ-TCL1A mice (confirmed in human CLL) implicate KDM1A as an oncogenic transcriptional repressor in CLL by altering histone methylation patterns with pronounced effects on defined cell death and motility pathways. Finally, pharmacologic KDM1A inhibition altered H3K4/9 target methylation and revealed marked anti-B-cell-leukemic synergisms. Overall, we established the pathogenic role and effector networks of KDM1A in CLL, namely via tumor-cell intrinsic mechanisms and impacts in cells of the microenvironment. Our data also provide rationales to further investigate therapeutic KDM1A targeting in CLL.

Prostate cancer (PCa) is one of the burgeoning cancers worldwide. Of late, the number of cases in the Indian subcontinent has been doubling and the incidence as per the population-based cancer registries (PBCR) has increased at an alarming rate. In assessing the trends of PCa, there needs a statistical framework on incidence, comparing PBCRs vis-a-vis with hospital-based cancer registries not only to that of cohorts from India but also to that of West and other consortia. Our erstwhile pilot study on inferring whole-exome sequencing variants has yielded distinct polymorphisms in the Indian phenotype of PCa. There are impending challenges and gray areas that we discuss in this review in lieu of PCa pathogenesis and therapeutics.

Even in the era of precision medicine, with various molecular tests based on omics technologies available to improve the diagnosis process, microscopic analysis of images derived from stained tissue sections remains crucial for diagnostic and treatment decisions. Among other cellular features, both nuclei number and shape provide essential diagnostic information. With the advent of digital pathology and emerging computerized methods to analyze the digitized images, nuclei detection, their instance segmentation and classification can be performed automatically. These computerized methods support human experts and allow for faster and more objective image analysis. While methods ranging from conventional image processing techniques to machine learning-based algorithms have been proposed, supervised convolutional neural network (CNN)-based techniques have delivered the best results. In this paper, we propose a CNN-based dual decoder U-Net-based model to perform nuclei instance segmentation in hematoxylin and eosin (H&E)-stained histological images. While the encoder path of the model is developed to perform standard feature extraction, the two decoder heads are designed to predict the foreground and distance maps of all nuclei. The outputs of the two decoder branches are then merged through a watershed algorithm, followed by post-processing refinements to generate the final instance segmentation results. Moreover, to additionally perform nuclei classification, we develop an independent U-Net-based model to classify the nuclei predicted by the dual decoder model. When applied to three publicly available datasets, our method achieves excellent segmentation performance, leading to average panoptic quality values of 50.8%, 51.3%, and 62.1% for the CryoNuSeg, NuInsSeg, and MoNuSAC datasets, respectively. Moreover, our model is the top-ranked method in the MoNuSAC post-challenge leaderboard.

Foot ulcer is a common complication of diabetes mellitus and, associated with substantial morbidity and mortality, remains a major risk factor for lower leg amputations. Extracting accurate morphological features from foot wounds is crucial for appropriate treatment. Although visual inspection by a medical professional is the common approach for diagnosis, this is subjective and error-prone, and computer-aided approaches thus provide an interesting alternative. Deep learning-based methods, and in particular convolutional neural networks (CNNs), have shown excellent performance for various tasks in medical image analysis including medical image segmentation. In this paper, we propose an ensemble approach based on two encoder-decoder-based CNN models, namely LinkNet and U-Net, to perform foot ulcer segmentation. To deal with a limited number of available training samples, we use pre-trained weights (EfficientNetB1 for the LinkNet model and EfficientNetB2 for the U-Net model) and perform further pre-training using the Medetec dataset while also applying a number of morphological-based and colour-based augmentation techniques. To boost the segmentation performance, we incorporate five-fold cross-validation, test time augmentation and result fusion. Applied on the publicly available chronic wound dataset and the MICCAI 2021 Foot Ulcer Segmentation (FUSeg) Challenge, our method achieves state-of-the-art performance with data-based Dice scores of 92.07% and 88.80%, respectively, and is the top ranked method in the FUSeg challenge leaderboard. The Dockerised guidelines, inference codes and saved trained models are publicly available at https://github.com/masih4/Foot_Ulcer_Segmentation.

The composition of the gut microbiome influences the clinical course after allogeneic hematopoietic stem cell transplantation (HSCT), but little is known about the relevance of skin microorganisms. In a single-center, observational study, we recruited a cohort of 50 patients before undergoing conditioning treatment and took both stool and skin samples up to one year after HSCT. We could confirm intestinal dysbiosis following HSCT and report that the skin microbiome is likewise perturbed in HSCT-recipients. Overall bacterial colonization of the skin was decreased after conditioning. Particularly patients that developed acute skin graft-versus-host disease (aGVHD) presented with an overabundance of Staphylococcus spp. In addition, a loss in alpha diversity was indicative of aGVHD development already before disease onset and correlated with disease severity. Further, co-localization of CD45+ leukocytes and staphylococci was observed in the skin of aGVHD patients even before disease development and paralleled with upregulated genes required for antigen-presentation in mononuclear phagocytes. Overall, our data reveal disturbances of the skin microbiome as well as cutaneous immune response in HSCT recipients with changes associated with cutaneous aGVHD.

Neuroblastoma is the most common extracranial solid tumor of childhood, with heterogeneous clinical manifestations ranging from spontaneous regression to aggressive metastatic disease. The calcium-sensing receptor (CaSR) is a G protein-coupled receptor (GPCR) that senses plasmatic fluctuation in the extracellular concentration of calcium and plays a key role in maintaining calcium homeostasis. We have previously reported that this receptor exhibits tumor suppressor properties in neuroblastoma. The activation of CaSR with cinacalcet, a positive allosteric modulator of CaSR, reduces neuroblastoma tumor growth by promoting differentiation, endoplasmic reticulum (ER) stress and apoptosis. However, cinacalcet treatment results in unmanageable hypocalcemia in patients. Based on the bias signaling shown by calcimimetics, we aimed to identify a new drug that might exert tumor-growth inhibition similar to cinacalcet, without affecting plasma calcium levels. We identified a structurally different calcimimetic, AC-265347, as a promising therapeutic agent for neuroblastoma, since it reduced tumor growth by induction of differentiation, without affecting plasma calcium levels. Microarray analysis suggested biased allosteric modulation of the CaSR signaling by AC-265347 and cinacalcet towards distinct intracellular pathways. No upregulation of genes involved in calcium signaling and ER stress were observed in patient-derived xenografts (PDX) models exposed to AC-265347. Moreover, the most significant upregulated biological pathways promoted by AC-265347 were linked to RHO GTPases signaling. AC-265347 upregulated cancer testis antigens (CTAs), providing new opportunities for CTA-based immunotherapies. Taken together, this study highlights the importance of the biased allosteric modulation when targeting GPCRs in cancer. More importantly, the capacity of AC-265347 to promote differentiation of malignant neuroblastoma cells provides new opportunities, alone or in combination with other drugs, to treat high-risk neuroblastoma patients.

Progress in cancer research is substantially dependent on innovative technologies that permit a concerted analysis of the tumor microenvironment and the cellular phenotypes resulting from somatic mutations and post-translational modifications. In view of a large number of genes, multiplied by differential splicing as well as post-translational protein modifications, the ability to identify and quantify the actual phenotypes of individual cell populations in situ, i.e., in their tissue environment, has become a prerequisite for understanding tumorigenesis and cancer progression. The need for quantitative analyses has led to a renaissance of optical instruments and imaging techniques. With the emergence of precision medicine, automated analysis of a constantly increasing number of cellular markers and their measurement in spatial context have become increasingly necessary to understand the molecular mechanisms that lead to different pathways of disease progression in individual patients. In this review, we summarize the joint effort that academia and industry have undertaken to establish methods and protocols for molecular profiling and immunophenotyping of cancer tissues for next-generation digital histopathology—which is characterized by the use of whole-slide imaging (brightfield, widefield fluorescence, confocal, multispectral, and/or multiplexing technologies) combined with state-of-the-art image cytometry and advanced methods for machine and deep learning.