The pathogenesis of type 1 diabetes, particularly at autoantibody-positive preclinical stages, remains poorly understood, in part due to limited sample availability. Here we show imaging mass cytometry data of pancreas samples from 88 organ donors, including 28 single and 10 multiple autoantibody-positive donors. We imaged 16 million single-cells using 79 antibodies to characterize β-cell states and the islet-immune interface, correcting for relevant covariates. We identified interactions between pro-inflammatory macrophages and exhausted-like (PD1+TIM3+) T cells. These interactions were characteristic of early disease and of insulitic islets, indicating a key role of macrophages in disease development. β-cells showed loss of IAPP in preclinical disease, insulitic interferon signatures and no increase in three measured endoplasmic reticulum stress markers in disease samples relative to control. Multiple immune cell subtypes were associated with young age and insulitis, potentially contributing to greater disease severity in younger patients. Our data present a resource describing early type 1 diabetes progression and reveal potentially clinically actionable features before extensive β-cells loss.

Figure S1. Study flow diagram and FFPE vs frozen tumor biopsies comparisons

Figure S5. Patients with high-B2M tumors have a better survival than patients with B2M-low tumors when treated with PD(L)1 inhibitors, related to figure 5

Figure S7. Genomic alterations associated with substance abuse and primary disease location.

5126 Background: Epithelial cell features, such as morphology and cellular organization within the tumor architecture (e.g. Gleason Score), have long been known to be prognostic in prostate cancer. However, the specific epithelial cell subtypes driving clinical outcomes in patients remains unclear. Traditional tumor profiling techniques lack the resolution needed to interrogate complex single-cell biomarkers. Imaging Mass Cytometry (IMC) now enables high-dimensional identification and detailing of epithelial, stromal, and immune cell subtypes within patient tumors. Using a custom IMC panel, we profiled tumor biopsies from a large prospective biopsy cohort with long-term (median >12-year) clinical follow-up to identify potentially targetable epithelial cell populations associated with adverse outcomes in localized prostate cancer. Methods: Spatially resolved protein expression profiling was performed on primary prostate cancer tumor biopsies using a custom IMC assay enriched for targetable tumor markers. Cell segmentation and single-cell expression measurement were performed using the published ‘steinbock’ toolkit. Biochemical progression-free survival (bPFS) and cancer-specific survival (CSS) were pre-specified clinical endpoints. Univariate and multivariable survival analyses stratified by cell abundance tertiles were performed using a Cox proportional hazards model. The Mann-Whitney U test was used to assess for pairwise differences in cell abundance between patient groups. All significance testing was performed using a two-tailed significance level of 0.05. Results: Protein co-expression patterns in >3.4 million cells comprising 573 biopsy samples obtained from 385 patients were measured. Single-cell analysis revealed 15 cell clusters representing luminal prostate cancer cells (including PSMA-high, PSMA-intermediate, and PSMA-low), basal epithelial cells, and lymphocytes. Patients with tumors enriched for PSMA-high epithelial cells demonstrated impaired bPFS ( P <0.001) and CSS ( P =0.016). PSMA-high epithelial cells were enriched in high-grade (Gleason Score 8+) tumors (P<0.05). Multivariable analysis revealed PSMA-high epithelial cell enrichment to be prognostic of bPFS independently of Gleason Score and serum PSA at time of diagnosis ( P <0.05). Co-expression analysis demonstrated that these prognostic PSMA-high cells also expressed high levels of the cell-surface targets KLK2, B7-H3, and protein. Conclusions: We identified an epithelial single-cell biomarker associated with adverse clinical outcomes in localized prostate cancer. If validated through ongoing experiments in an independent cohort, our findings support new potential strategies for treatment intensification using targeted therapies in select high-risk patients.

Table S1. List of copy number alterations analyzed. Table S2. List of oncogenic pathways analyzed. Table S3. List of genes present in each module. Table S4. mIF panels. Table S5. mIF phenotypes. Table S6. Frequencies of genes alterations in HPV-positive R/M SCCHN and in HPV-negative R/M SCCHN, related to Figure 1A-E. Table S7. Frequencies of genes alterations in smoker and/or drinker HPV-negative patients and in non-smoker and non-drinker HPV-negative patients, related to Figure S5A. Table S8. Frequencies of genes alterations in HPV-negative R/M SCCHN according to primary disease location related to Figure S5B. Table S9. Frequencies of genes alterations in locoregional recurrence only and in distant metastatic disease SCCHN, related to Figure 2A-B. Table S10. Frequencies of genes alterations according to number of R/M treatment lines prior biopsy, related to Figure 3B and Figure S7A-B.

Figure S3. Three multiplex immunofluorescent staining of SCCHN samples with 3 different 6-plex panels and their deconvolution

Patient cohort profiling increasingly includes structured views for multiple modalities, such as single-cell RNA sequencing, spatial transcriptomics or proteomics, and histology, each providing multiple subobservations per patient, including single cells, spatial spots or patches. To model such data along with simple patient-level views, current multimodal integration methods typically rely on separately precomputed summaries and fail to fully leverage information in structured views. Here we present FACTMx, a variational framework that jointly models structured and simple views to learn interpretable patient-level representations. FACTMx couples latent patient factors with subobservation clustering and per-patient component proportions, enabling direct interpretation and downstream association analyses. The framework supports different structured-view mixture assumptions, including topic- and Gaussian-structured data, while retaining modular encoder-decoder parameterisations. In simulations spanning sparse and dense dependencies and multiple noise regimes, FACTMx improved reconstruction, integration and recovery of structured components relative to previous methods. Applied to non-small cell lung cancer cohorts, FACTMx captured survival-associated latent signals linked to immune microenvironments, gene expression pathways and spatially coherent histological patterns. In a longitudinal coronary syndrome cohort, FACTMx highlighted an outcome-associated axis connected to ejection-fraction change, immune cell states, soluble mediators and cardiac injury markers. These results support joint structured-simple modelling for interpretable multimodal patient stratification.

Figure S6. B2M expression is complementary to other biomarkers of survival under PD(L)1 inhibitors, related to figure 5

Figure S9. The treatment history has an impact on the molecular and immune landscape of R/M SCCHN, related to Figure 3.

Figure S8. No differences in immune proportions according to disease extent

Anti-PD-1 therapies improve survival in recurrent/metastatic (R/M) squamous cell carcinoma of the head and neck (SCCHN), but only a minority of patients achieve durable responses. The mechanisms driving resistance to anti-PD-1 in SCCHN remain poorly understood. Using the IMMUcan multiomics workflow, we characterized the molecular and immune profiles of R/M SCCHN progressing on anti-PD-1 treatment and compared them with an anti-PD-1-naïve cohort. Tumor biopsies from patients with anti-PD-1-resistant SCCHN exhibited significantly more EGFR and MYCL amplifications, along with increased MYC pathway alterations. Transcriptomic and proteomic analyses revealed that anti-PD-1-secondary resistant SCCHN had increased CD8+ T-cell infiltration with higher levels of immune exhaustion markers than primary resistant and naïve SCCHN. Additionally, high beta-2-microglobulin (B2M) expression correlated with greater T-cell infiltration and improved survival following anti-PD-1 therapy. Tumor cell B2M expression was independent of TMB and PD-1L expression, suggesting that B2M expression could serve as an additional biomarker for anti-PD-1 response.

Figure S4. FFPE vs frozen tumor biopsies comparisons

Imaging mass cytometry (IMC) is a highly multiplexed tissue-imaging technology that enables the simultaneous detection of up to 45 markers in situ at subcellular resolution. By combining probes such as antibodies conjugated to metal isotopes with laser ablation and time-of-flight mass spectrometry, IMC generates spatially resolved, high-dimensional single-cell data. Since its introduction in 2014, IMC has become a widely adopted platform in spatial biology. Researchers have used IMC to advance our molecular-level understanding of cancer as well as autoimmune and infectious diseases, and the platform is also increasingly used in the translational and clinical setting. In this Primer, we provide a comprehensive guide to the experimental IMC workflow, covering antibody conjugation and panel design, sample preparation, data acquisition and quality control. We detail the computational analysis pipeline, including image preprocessing, segmentation, feature extraction, and both single-cell and spatial analyses. Key applications to biological and clinical research are highlighted. We discuss current limitations and optimization strategies. Finally, future directions are outlined, including IMC foundation models, multi-modal integration and the path to clinical implementation. This Primer seeks to provide both new users and IMC experts with detailed insights and practical guidance to unlock the full potential of IMC. Imaging mass cytometry is a highly multiplexed tissue-imaging technique that allows the simultaneous in situ detection of up to 45 markers with subcellular-level resolution. In this Primer, Meyer et al. provide a comprehensive guide to the experimental imaging mass cytometry workflow.

Abstract In women with high-grade serous ovarian cancer, chemotherapy remains the primary standard treatment, despite growing recognition of the disease as highly heterogeneous. Here, we examine the feasibility and clinical utility of comprehensive multimodal molecular profiling to inform treatment decisions. We analyze blood, single-cell and bulk tumor tissue, and malignant ascites using up to eleven technologies (DNA, RNA, protein, and functional assays) within a four-week turnaround time. Hypothetical treatment recommendations are altered for 76% of patients, and multi-omics-guided maintenance therapy is associated with prolonged overall survival in a subset of patients. Subsequent cohort analysis reveals distinct cellular and molecular profiles in ascites-derived single-cells compared to solid tumor tissue, unique per-patient ex vivo drug responses, and a marked increase in cancer cell heterogeneity following chemotherapy exposure. This coincides with genomic signature alterations in whole-genome-amplified patients. Our data suggest that molecularly guided treatments should be tested as adjuvant therapies prior to chemotherapy in the future.

Figure S5. HPV status affects the genomic and transcriptomic profiles but not the immune infiltration

Figure S3. Some genomic alterations are associated with tumor B2M expression level and high tumor B2M expression is associated with increased T cell infiltration, related to figure 3