Laboratory Investigation

Different omics types such as genomics and proteomics all contribute to deciphering biology. Applying these omics approaches in a spatial context helps reveal biology in situ at a single cell level. Here we present a protocol for the combined multiplexed detection of targeted genes using DNA FISH, and proteins using multiplexed immunofluorescence. The protocol is integrated on the commercial PhenoCycler platform and generates one single dataset with gene and protein readout at a single cell level in large tissue sections, allowing for a throughput of thousands to millions of cells. The workflow can be used for characterising malignant cells in large tumor areas based on genetic aberrations, while deciphering the cellular landscape and microenvironment from multiplexed protein detection using immunofluorescence.

Osteocytes play a central role in bone remodeling, mineral metabolism, and skeletal homeostasis, but direct molecular analysis of human osteocytes remains technically challenging because they are embedded within the mineralized bone matrix. Surgically obtained human bone specimens provide valuable material for studying human bone biology; however, surface-associated cells, marrow-derived cells, and adherent soft tissues can confound downstream transcript analysis. Here, we describe a bone fragment-based protocol for preparing surgically obtained human bone specimens for molecular analysis of osteocyte-associated transcripts. The protocol consists of mechanical trimming, mincing into small bone fragments, repeated washing, and five sequential rounds of collagenase digestion to reduce non-osteocytic cellular components associated with the bone surface and marrow spaces. The remaining mineralized bone fragments are then frozen in liquid nitrogen, cryogenically pulverized, and lysed in TRIzol reagent for total RNA extraction. Histological validation using residual maxillary bone specimens showed that sequential collagenase digestion markedly reduced adherent soft tissue and extra-matrix nuclei while preserving osteocyte lacunar occupancy. This protocol provides a practical workflow for bone fragment-based RNA analysis focused on osteocyte-associated transcripts in human bone specimens. Specifications table O_TBL View this table: org.highwire.dtl.DTLVardef@1cec618org.highwire.dtl.DTLVardef@2f746forg.highwire.dtl.DTLVardef@1854247org.highwire.dtl.DTLVardef@1c26c1aorg.highwire.dtl.DTLVardef@1473a88_HPS_FORMAT_FIGEXP M_TBL C_TBL

The tissue-resident immune system involves complex 3D assemblies that interact with extended structures such as blood vessels and nerves. These interactions are difficult to study using conventional 2D profiling because they span many tissue sections. In animal tissues, volumetric imaging approaches such as light-sheet fluorescence microscopy (LSFM) are widely used to study 3D tissue organization, with labelling often aided by genetically encoded reporters and vascular dyes. In contrast, LSFM of human specimens remains underdeveloped because most clinical samples are available only as formalin-fixed paraffin-embedded (FFPE) tissue, limiting labeling strategies primarily to dyes and antibodies. Here, we present a volumetric cyclic immunofluorescence (v-CyCIF) and virtual H&E toolbox that overcomes key barriers to multiplexed imaging of immune cells and nerves in human specimens up to 1 mm thick. We use v-CyCIF to study neuroimmune interactions in normal and cancer tissues and to immunoprofile intact secondary and tertiary lymphoid structures. Re-embedding and sectioning of specimens following volumetric imaging enables high-plex high-resolution analysis of subcellular structures and cell-cell interactions associated with immune cell activity. v-CyCIF therefore provides a flexible framework for multi-scale 3D profiling of clinical specimens across imaging formats and resolutions.

Single-cell RNA-sequencing-based characterization of cells that belong to the neoplastic clone is a major challenge in hematologic neoplasms, where malignant and normal cells coexist. Confident molecular profiling requires simultaneous analysis of gene expression and genetic mutations in individual cells, an ability that is not supported by the standard 10X Genomics workflow. Here, we developed a post-hoc targeted genotyping method for samples processed with the 10X Genomics 3 workflow. To establish the approach, we mixed two types of leukemic cells harboring distinct mutations and subjected them to single-cell RNA-sequencing. Repurposing an intermediate product of the experimental process allowed us to enrich for transcripts containing mutation sites. Long-read PacBio sequencing genotyped the transcripts and captured the associated cellular and molecular barcodes, allowing us to bioinformatically integrate the mutation and transcriptomic data at single-cell resolution. Our method demonstrates the detection of mast cell leukemia-associated point mutations in the KIT gene and chronic myeloid leukemia-associated BCR::ABL1 fusion transcripts. Single-cell analysis of primary leukocytes from chronic myeloid leukemia detected mutated cells at diagnosis, but not during imatinib treatment. Taken together, the method constitutes a broadly applicable framework for post-hoc genotyping of cells analyzed with single-cell RNA-sequencing.

PurposeTriple-negative breast cancer (TNBC) exhibits substantial clinical heterogeneity, with some patients experiencing early recurrence and poor survival despite similar clinicopathologic features. We sought to determine whether quantitative measures of intratumoral collagen architecture and composition derived from standard histopathologic specimens can identify patients at risk of recurrence and adverse survival outcomes. Experimental DesignWe analyzed a retrospective cohort of 79 TNBC tumors assembled into a tissue microarray using a multimodal computational pathology framework integrating Massons Trichrome staining with COL1 and COL3 immunohistochemistry. Collagen architecture was quantified using fiber-based image analysis and unsupervised clustering, while collagen composition was assessed using a normalized COL3:COL1 ratio. Associations with recurrence-free interval (RFI) and overall survival (OS) were evaluated using Kaplan-Meier analysis, restricted mean survival time (RMST), and Cox proportional hazards modeling. ResultsUnsupervised analysis identified four distinct collagen architectural states, which were consolidated into low-risk and high-risk groups based on recurrence patterns. High-risk collagen architecture was associated with significantly worse long-term RFI (log-rank p=0.025; RMST difference 10.1 months). Independently, a higher COL3:COL1 ratio was associated with improved OS (log-rank p=0.042; RMST difference 9.4 months). Integration of architectural and compositional biomarkers further refined risk stratification, identifying a subgroup with high-risk architecture and low COL3:COL1 ratio that exhibited the poorest survival outcomes. Notably, collagen-based stratification identified patients with divergent outcomes not readily predicted from tumor stage alone. ConclusionsQuantitative assessment of intratumoral collagen architecture and composition provides clinically meaningful prognostic information in TNBC and enables stratification of recurrence and survival risk. These findings support extracellular matrix phenotyping as a practical and scalable computational pathology approach for refining risk assessment in TNBC. Translational RelevanceTriple-negative breast cancer (TNBC) remains clinically challenging due to heterogeneous outcomes that are not fully captured by standard clinicopathologic variables. In this study, we demonstrate that quantitative features of intratumoral collagen architecture and composition, derived from routine pathology specimens, provide clinically meaningful prognostic information. Collagen-based biomarkers, including distinct collagen architectural phenotypes and the COL3:COL1 ratio, identify patient subgroups with distinct recurrence and survival outcomes, particularly among individuals whose risk is not adequately predicted by conventional staging. Importantly, these features can be extracted from widely available histological stains and immunohistochemistry, supporting the potential integration into existing pathology workflows. These findings support the tumor microenvironment as an underutilized source of biomarkers and suggest that extracellular matrix-based phenotyping may improve risk stratification and inform clinical decision-making in TNBC.

BackgroundFor pancreatic cancer, practical blood-based tests for early detection and postoperative surveillance remain elusive. We sought to develop a qPCR-measurable serum microRNA (miRNA) panel that robustly discriminates pancreatic cancer from non-cancer controls and other malignancies. MethodsWe profiled 255 serum miRNAs in batch 1 (n=72) and selected 27 candidates. Candidates were refined in batch 2 (n=552) and cross-batch evaluation was performed with batch 3 (n=391) to derive a miRNA model. Independent validation used batch 4 (n=616). Clinical relevance was assessed in an independent clinical cohort of resection patients with samples obtained preoperatively and at 1 and 12 months postoperatively. ResultsThe miRNA model trained on batches 2 and 3 achieved an area under the curve (AUC) of 0.91 and 0.83 for pancreatic cancer versus non-cancer controls and non-cancer plus other cancers, respectively, when independently validated in batch 4. Stage-wise AUCs in batch 4 were 0.91 (I), 0.94 (II), 0.86 (III) and 0.90 (IV). In the clinical batch, the score decreased postoperatively (preoperative vs month 1; p<0.01) and was higher in recurrence than non-recurrence (p<0.001). ConclusionsThe developed compact miRNA qPCR assay discriminated pancreatic cancer across independent assay batches and showed clinical relevance for postoperative surveillance. Clinical Trial RegistrationNot applicable.

Metastasis remains the major cause of cancer-related mortality, and circulating tumor cells (CTCs) are both candidate liquid-biopsy biomarkers and plausible intermediates of metastatic dissemination. Because CTCs are extremely rare in peripheral blood, platform comparisons have often focused solely on recovery. That focus is insufficient for applications that depend on the quality of the recovered material, including single-cell profiling, short-term culture, and functional testing. Here, we compared four CTC isolation approaches: TellDx CTC System, Genesis System, RosetteSep, and flow cytometry, using spike-in experiments in human blood. Capture efficiency was evaluated across all four platforms; purity was assessed for TellDx, Genesis, and RosetteSep; and post-isolation GFP signal persistence in culture was assessed for TellDx and Genesis as an exploratory proxy for short-term post-isolation preservation. Under the conditions tested, TellDx showed the highest recovery (88.1% {+/-} 3.7%), followed by Genesis (40.6% {+/-} 12.1%), RosetteSep (36.5% {+/-} 9.0%), and flow cytometry (7.6% {+/-} 4.5%). TellDx also showed the highest purity score (3.76), whereas Genesis (2.25) and RosetteSep (2.09) did not differ substantially. In the short-term culture assay, TellDx-derived samples retained a higher normalized GFP signal than Genesis-derived samples at 48 h and 72 h. To synthesize these readouts, we propose the Recovery Performance Index (RPI), a composite score integrating recovery, purity, and post-isolation signal persistence. Within this experimental framework, TellDx achieved the highest RPI. These data support two conclusions. First, platform benchmarking for CTC workflows benefits from multidimensional evaluation rather than recovery alone. Second, under this spike-in model and within the specific workflows used here, TellDx performed best among the platforms tested. The principal contribution of this study is therefore the establishment of a practical benchmarking framework that can be expanded in future work using clinical samples, multiple CTC phenotypes, and orthogonal viability assays.

BackgroundPostoperative pancreatic fistula (POPF) is a major cause of morbidity after pancreatoduodenectomy, particularly in patients with high-risk pancreatic remnants. Preventive strategies based solely on surgical technique have yielded inconsistent results, and thus there has been growing interest in strategies aiming to modify the biological behavior of the pancreatic remnant. This preclinical study evaluated the biological and histopathological effects of preoperative endoluminal radiofrequency ablation (ERFA) of the main pancreatic duct (MPD) performed 4 weeks before pancreatic transection in a porcine model. MethodsAnimals underwent laparoscopic MPD occlusion followed by pancreatic transection at 4 weeks and necropsy 15 days thereafter. Feasibility, safety, histological atrophy, and macroscopic findings associated with POPF risk were assessed. As a secondary objective, outcomes were compared with a that underwent MPD occlusion using cyanoacrylate glue. ResultsPreoperative ERFA was technically feasible and safe. At 4 weeks, ERFA induced marked and homogeneous acinar atrophy that was significantly greater than that observed after glue occlusion (p = 0.018), indicating effective biological conditioning of the pancreatic remnant. At necropsy, pseudocyst formation and intra-abdominal adhesions, known surrogate markers of pancreatic fistula in pigs, were significantly more frequent in the glue group and absent in ERFA-treated animals. Serum amylase levels, postoperative weight gain, complication rates, and preservation of endocrine architecture were comparable between groups. ConclusionsDuctal ablation of the MPD via ERFA induced stable, progressive exocrine pancreatic atrophy, effectively preconditioning the gland prior to pancreatic transection. Experimental evidence suggests that its biological effects stabilize approximately 4 weeks after treatment. Compared to cyanoacrylate occlusion, ERFA achieved more homogeneous early biological effects and fewer fistula-related macroscopic complications. These findings support the further investigation of preoperative pancreatic conditioning as a potential adjunct strategy for POPF risk reduction, although clinical studies are needed to clarify its role alongside established reconstructive approaches.

Cutaneous squamous cell carcinoma (cSCC) is a common skin cancer associated with substantial morbidity and mortality in advanced stages. Despite its well-described stepwise progression from actinic keratosis to invasive disease, robust molecular markers for stage discrimination and clinical decision-making remain limited. We sought to define the transcriptional continuum underlying cSCC progression, identify stage-associated biomarkers, and assess the broader relevance of these programs across human malignancies. Bulk RNA sequencing (HTG EdgeSeq) and spatial transcriptomics (GeoMx) were performed on biopsies from eight patients, each presenting multiple disease stages (healthy skin, premalignant lesion, tumor core, and invasive front) within the same lesion field, enabling within-patient analysis of progression. Spatial transcriptomic analyses identified more than 2,000 differentially expressed genes whose expression varied across disease stages. These genes were organized into 18 coordinated expression programs reflecting progressive biological rewiring during tumor evolution. Proliferation, extracellular matrix remodeling, inflammation, and stress-response pathways were progressively upregulated, whereas epithelial differentiation and metabolic processes, including lipid and amino acid metabolism, were downregulated. Macrophages exhibited distinct metabolic reprogramming, with increased purine metabolism, glycolysis, and pyruvate metabolism across progression. To evaluate the broader clinical relevance of these progression-associated programs, we developed a reproducible Snakemake pipeline to systematically screen 32 solid and hematologic malignancies from The Cancer Genome Atlas (TCGA). A combined cSCC-progression signature was significantly associated with poor overall survival (P < 0.05) in 10 additional cancer types. Finally, we identified 12 stage-informative biomarkers, whose spatially restricted expression patterns were validated using Visium HD. This study provides a spatially resolved and stage-aware transcriptomic map of cSCC progression, identifies coordinated gene programs underlying disease evolution, and defines progression-associated signatures with prognostic relevance across multiple cancers, highlighting their potential translational value.

Treatment of advanced head and neck squamous cell carcinoma (HNSCC) often involves radiotherapy combined with chemotherapy, targeted therapy, or immunotherapy. However, due to its anatomical and molecular heterogeneity, identifying the most effective treatment for each patient remains a major clinical challenge. To address this need, we developed a high-throughput organoid-based drug screening platform that uses patient-derived organoids to assess candidate treatment regimens. We validated the platform by establishing bioprinted 3D organoids of human HNSCC cell lines and exposing them to X-ray radiation in combination with various small-molecule drugs and biologics. We quantified viability using ATP release assays and assessed extracellular matrix (ECM) invasion with a machine learning-based brightfield image analysis pipeline. Proof-of-concept experiments with HPV-negative HNSCC lines (HN30 and HN31, established from primary and metastatic disease from the same patient) and HPV-positive HNSCC cells (SCC154) revealed different therapy agents that can radiosensitize each cell line. Image analysis showed that copanlisib, afatinib, and ibrutinib could limit ECM invasion of HN31, while the AKT inhibitor ipatasertib promotes invasion of HN30 cells, consistent with previous studies. Application of the platform to patient-derived HPV+ oropharyngeal tumor organoids showed that they shared sensitivity to several agents while also exhibiting differences against certain therapies. Cetuximab, sorafenib, and nedisertib significantly radiosensitized organoids from two clinical samples. This work demonstrates the feasibility of performing sensitivity screening by integrating bioprinting, conventional viability assays, and advanced image analysis techniques. This platform has the potential to enable a personalized therapeutic pipeline for patients with advanced HNSCC, optimizing responses to radiotherapy and targeted agents to improve clinical outcomes while avoiding modulators that may promote tumor invasion.

Soft tissue sarcomas are a rare, heterogeneous group of tumors whose diagnosis remains challenging because of overlapping morphology and limited access to sarcoma-specialized pathologists. Although pathology foundation models have shown promise in computational pathology, their clinical translation remains limited by insufficient interpretability, particularly in diagnostically complex settings such as sarcoma diagnosis. Here, we developed and evaluated an H&E-based AI framework for sarcoma subtype classification that focused on explanability. Using the CONCH v1.5 foundation model, we computed embeddings from a tissue microarray cohort of 2,545 cases spanning 19 sarcoma subtypes and trained an attention-based multiple-instance learning model that achieved a balanced accuracy of 77.38% (SD 1.88). To move explainability beyond attention-based localization, we trained a sparse autoencoder on patch-level embeddings to learn 768 recurring visual concepts. 90 high-activation concepts were reviewed by three senior pathologists and curated into morphologically meaningful and non-meaningful categories, yielding a semantic dictionary of 41 diagnostically relevant tissue concepts. We then trained a linear attention-based model on the 768-concept vectors, which retained much of the performance of the raw embedding-based ABMIL model, achieving a balanced accuracy of 73.74% (SD 1.30). When restricting the linear model to pathologist-curated morphologic concepts only, balanced accuracy further decreased to 67.04% (SD 1.27), suggesting that the residual performance gain in the full concept model was driven by inconsistent, technical, or diagnostically irrelevant concepts. Concept-level explanations of the curated linear attention-based model aligned with known sarcoma morphology, including lipogenic, myxoid, spindle-cell, pleomorphic, vascular, small round blue cell, and matrix-forming patterns, and reproduced patterns of diagnostic overlap observed in human sarcoma pathology. Together, these results show that H&E-based foundation-model representations capture meaningful diagnostic structure within the known limitations of H&E in sarcoma diagnostics, but that their clinical value depends on whether this structure can be made interpretable to pathologists. Sparse autoencoder-derived concepts can address this critical gap by converting embedding-level signal into recurring morphologic patterns that pathologists can review and name, providing the foundation to link these patterns to subtype predictions. In doing so, this approach turns concept discovery into a practical form of diagnostic explanation, while also revealing where model performance is supported by recognizable histopathology and where it relies on diagnostically irrelevant or inconsistent visual patterns.

Light microscopy imaging with histological stains is central to disease diagnosis and research. It is enhanced with immunostaining to reveal cellular composition and complexity linked to clinical utility and biological mechanisms. Emerging multiplex imaging technologies like Phenocycler markedly increase the coverage to capture the cellular diversity but are costly, technically demanding, and inaccessible to most clinical laboratories. We developed DigitAb, a deep learning framework that classifies cell types directly from hematoxylin and eosin (H&E) stained slides, eliminating the need for specialized assays. Using Phenocycler imaging, we generated highlZlresolution ground truths for [~]3.5 million cells from 29 human kidney samples across four multi-institutional datasets to train a semantic segmentation model for 10 cell types, achieving a balanced accuracy of 0.78. By employing an integrated adversarial domain adaptation module, we tested DigitAb on unlabeled and untested biopsy samples from kidney transplant and diabetic samples. We were able to predict several cell types just from histology images, without using any special technology or immunostains, and demonstrate high concordance with clinical gold-standard Banff schema in kidney transplant rejection, and clinical characteristics of diabetic nephropathy. Our cloudlZlbased tool, DigitAb, provides scalable, accessible, labellZlfree cellular segmentation for research and clinical pathology.

This work aimed to establish a translationally viable, xeno-free, serum-free platform and protocol for the isolation and expansion of human salivary stem/progenitor cells (hS/PCs) suitable for regulatory qualification and future FDA-approved first-in-human autologous regenerative therapy trials for the treatment of hyposalivation disorders. Parotid gland specimens from non-cancerous regions/tissues were collected from consented surgical patients. Primary hS/PCs were isolated from tissue specimens, cultured in animal-component-free conditions, expanded to produce millions of cells, then enriched for CD44+ stem/progenitor cells by magnetic cell sorting. Normal epithelial purity was assessed using cytokeratins 5/14. Anti-CD133/PROM1 (cancer marker) and anti- fibroblast (clone TE-7) antibodies were used to demonstrate a lack of contaminating cells. Phenotype validation was performed by flow cytometry and immunocytochemistry on both CD44+ sorted and unsorted populations. Senescence-associated beta-galactosidase (SA-{beta}-gal) assays were performed across serial passages (P1-P6). Pluripotency was demonstrated by culture under conditions supporting lineage-specific differentiation. Primary hS/PCs demonstrated consistent expansion and epithelial morphology under serum-free conditions. CD44 expression remained high (>95%) throughout expansion, with negligible detection of CD133 or fibroblast markers, confirming epithelial purity and absence of tumorigenic or stromal contamination. Immunocytochemistry corroborated these expression profiles. SA-{beta}-gal staining revealed only a minor, passage-dependent increase (5-16%) in senescent cells from multiple donors, indicating retention of proliferative potential. Our defined, animal-free culture system supports stable expansion of pure low passage hS/PCs under conditions compatible with good manufacturing practice (GMP).

BackgroundBreast cancer exhibits extensive molecular heterogeneity across intrinsic subtypes, yet convergent metabolic reprogramming may represent an obligate feature of tumour initiation. We hypothesised that suppression of nuclear-encoded mitochondrial fatty acid oxidation (FAO) constitutes such a convergence point, defining a shared metabolic phenotype independent of subtype. MethodsRNA-seq data from 1,106 primary breast tumours and 113 normal-adjacent tissues (TCGA-BRCA) were intersected with 1,079 nuclear-encoded mitochondrial genes from MitoCarta 3.0. Differential expression was assessed using Welch t-test with Benjamini-Hochberg correction at all tumour stages, at Stage I specifically, and stratified across PAM50 subtypes. A 55-gene core FAO signature was derived by three-way intersection. Ten candidate genes were selected by pre-specified objective scoring, locked before any clinical testing. Gene set enrichment analysis (GSEA) was performed using MitoCarta 3.0 pathway annotations. Diagnostic performance, clinical associations, survival, and mutation independence were characterised. External validation used two independent GEO cohorts (GSE42568, n = 121; GSE109169, n = 50); prognostic validation used METABRIC (Molecular Taxonomy of Breast Cancer International Consortium; n = 1,980). DESeq2 was applied as methodological cross-validation. ResultsAmong 126 differentially expressed mitochondrial genes, fatty acid oxidation was the most significantly depleted pathway (normalised enrichment score -2.130; false discovery rate 0.001). The 55-gene core signature replicated in both external cohorts with 100% directional concordance (hypergeometric p < 10-{superscript 1}). All 10 candidate genes discriminated tumour from normal tissue (area under the curve 0.915-0.979) and demonstrated broad clinical associations. The composite FAO suppression score predicted overall survival in METABRIC (log-rank p = 7.82 x 10-) and MAOA achieved independent prognostic significance in multivariable Cox regression (hazard ratio 0.890; adjusted p = 0.009). DESeq2 cross-validation confirmed Spearman {rho} = 0.980 concordance. ConclusionsNuclear-encoded FAO suppression is a robust, pan-subtype feature of breast cancer detectable at Stage I and validated across independent platforms and cohorts. These 10 candidate genes constitute a consistent initiation-phase mitochondrial signature, implicating FAO suppression as a potential convergence point in breast cancer oncogenesis and motivating targeted functional investigation.

Cryo-electron tomography (cryo-ET) is a powerful approach for visualizing macromolecular structures directly within cells, but its broader application is limited by the difficulty of reliably targeting specific structures for imaging. In particular, capturing small or rare objects within FIB-milled lamellae remains a major bottleneck. Here, we establish fluorescence-guided cryo-FIB milling workflows that overcome key sources of targeting error and enable routine capture of structures across a wide size range. For larger objects (>500 nm), we develop a single step registration-based targeting strategy that combines FIB-milled fiducials with physically grounded depth correction to account for focal shifts arising from refractive index mismatch. For smaller targets (150-500 nm), we implement real-time fluorescence-guided milling on a commercially available FIB SEM instrument with an integrated cryo fluorescence microscope allowing dynamic monitoring and precise termination of milling at the onset of target ablation. Using this strategy, we achieve consistent recovery of lamellae containing the targeted structure, including small single-copy organelles such as centrioles and cilia. Together, these workflows expand the accessible target space for cryo-ET and provide practical solutions for studying cellular structures that have previously been difficult to capture.

BackgroundTubulitis is a defining histologic feature of T cell-mediated rejection (TCMR), while glomerulitis is often characteristic of antibody mediated rejection (AMR). Histologic quantification of tubulitis and glomerulitis using Banff criteria is subject to interobserver variability. Bulk transcriptomic assays (e.g., MMDx) have introduced molecular correlations of tubulitis with TCMR and glomerulitis with AMR, but lack spatial resolution. MethodsWe applied a web-based platform, FUSION (Functional Unit State Identification in Whole Slide Images), to a cohort of 8 cases (n=2 per condition) with kidney allograft biopsy samples acute TCMR, active AMR, chronic active AMR, and no rejection (control). The machine-learning (ML) platform enabled integrated visualization and analysis of spatial transcriptomics (10x Genomics Visium v2) together with high-resolution whole-slide histology. ResultsTranscriptomics-derived immune cell proportions within AI-segmented tubular and glomerular regions were used to generate spatial Banff t- and g-scores. Derived t-scores showed full concordance with pathologist scores in both acute TCMR cases; g-scores showed concordance in 2 of 4 AMR cases, with discordant cases characterized by low absolute immune signal near the classification boundary. ConclusionsWe demonstrate the feasibility of using AI-based FTU segmentation integrated with spatial transcriptomics-derived immune cell proportions to generate spatially informed t- and g-scores aligned with Banff criteria, with full concordance in severe rejection and partial concordance in mild rejection. This approach lays the foundation for validated, spatial transcriptomics-augmented t-scores and g-scores that enhance diagnostic precision, reduces inter-observer variability among renal pathologists, and support potential clinical adoption.

Introduction: With recent approvals of multiple targeted therapies for triple-negative breast cancer (TNBC), including antibody-drug conjugates and immunotherapy in biomarker-selected populations, it is critical to define the temporal evolution of cell-surface target expression from early-stage to metastatic disease, the co-expression patterns across these markers, and optimal quantification methodologies. Here we report biomarker expression profiles measured by multi-omics and pathology-based platforms in patients with TNBC using a large cohort of matched longitudinal tumor samples. Methods: Patients who underwent neoadjuvant chemotherapy (NAC) for stage I-III TNBC or were diagnosed with any stage TNBC and developed metastatic recurrence were retrospectively identified from an institutional database and prospective research metastatic biopsy protocol. Tumor samples from diagnosis (DX), residual disease (RD) post-NAC (if applicable), and metastasis/recurrence (MR) were collected. Quantification of HER2, TROP2, and PD-L1 expression was performed by immunohistochemistry (IHC), whole-exome sequencing, transcriptome sequencing, and targeted mass spectrometry (MS). For HER2, TROP2, and stromal tumor-infiltrating lymphocytes (sTILs), both manual pathologist assessment and computational pathology quantification were obtained. HER2 status was categorized as HER2-0 or HER2-low by local (L-IHC) and central (C-IHC) review, TROP2 status was defined as low (H-score <100), medium (H-score 100-200) or high (H-score >200), and PD-L1 as low (tumor area positivity, TAP <5%) or high (TAP [&ge;]5%). Pathologist-assessed sTILs were classified as low (<10%), medium ([&ge;]10% and <40%) or high ([&ge;]40%). Biomarkers were compared between primary (DX/RD) and MR, and between pre- vs post-NAC (DX-RD) samples. Correlations between markers, quantification methods, inferred PAM50 subtype, and clinical variables of interest were evaluated. Results: A total of 359 samples from 110 patients with TNBC with data available from at least one platform were included in the analysis. HER2-low prevalence at DX, RD, and MR was: 51% (50/98), 40% (21/53), and 27% (16/60); TROP2 high/medium was 90% (47/52), 91% (42/46), and 88% (28/32); PD-L1-high was 51%, 50%, and 38% (9/24); and sTILs-high/medium was 88% (59/67), 80% (40/50), and 49% (17/35), respectively. While TROP2-high/medium vs low remained stable over time, HER2 IHC and sTILs significantly decreased from DX/RD to MR samples, both at the cohort-level (HER2, p=0.0081; sTILs, p=4.6x10e-5) and longitudinal patient-level (HER2, p=0.030; sTILs, p=0.0077), with a similar decreasing trend for PD-L1 that did not reach statistical significance. HER2 concordance (0 vs low) between L-IHC and C-IHC was 78% (91/116). ERBB2, TACSTD2 and CD274 mRNA expression were significantly correlated with IHC protein levels, though only TACSTD2 had limited overlap in distribution of gene expression between high/medium vs low groups. Strong correlation between protein membrane staining intensity from computational pathology, protein expression measured by MS, and pathologist-assed IHC was observed across all biomarkers tested by each method. In comparisons between biomarkers, pathologist-assessed PD-L1 IHC and sTILs were significantly correlated (p=0.0001); 94% (51/54) of PD-L1-high tumors were classified as sTILs high/medium. PAM50 subtype was not significantly correlated with time point or biomarker status, although there was a trend toward more HER2-enriched tumors in HER2-low (20%, 5/25) vs HER2-0 (6%, 3/52) (p=0.086). Across biomarkers and clinical variables, an association between age and sTILs was observed (p=0.038, FDR=0.42) due to a decrease in sTILs high/medium tumors with age, primarily driven by post-treatment (RD/MR) but not DX samples. Conclusions: Multi-platform and multi-omics profiling in this large unique cohort of longitudinal TNBC samples revealed distinct patterns of expression and dynamic changes of key biomarkers of interest for targeted therapies. Given variability with manual IHC scoring, improved methods for quantification of expression may help optimize treatment selection in an individualized manner.

Fibrogenesis is essential to wound healing, but aberrant fibrogenesis is a driver of many chronic diseases and cancers. Lysyl oxidases (LOX) play a pivotal role in fibrogenesis by catalyzing the oxidation of lysine residues to reactive aldehydes (allysine) in collagens and elastin, resulting in the crosslinking and excessive deposition of these extracellular matrix components. Currently, rapid and robust histological assays to visualize the spatial distribution of LOX activity are lacking, hindering the precise validation of anti-fibrotic therapies. Here, we present a histological fluorescent staining method to visualize fibrogenesis (active fibrosis) and LOX activity in tissue sections utilizing a bioorthogonal tag and a click reaction with a turn-on fluorophore. Notably, requiring only two commercial reagents, this protocol can be completed in under two hours and is compatible with other imaging modalities, including second-harmonic generation and immunofluorescence staining. We validated this method across various healthy and fibrotic mouse and human tissue specimens.

Current treatment of IDH-wildtype glioblastoma (GBM) relies on the first-line chemotherapy-temozolomide. Although MGMT methylation is routinely conducted to predict chemosensitivity, its efficacy is often compromised. Thus, there is an urgent need to discover more accurate prognostic biomarkers. Cholesteryl ester (CE) has been recently recognized as a key feature of GBM, however, its role in GBM prognosis remains poorly understood. We first employed label-free stimulated Raman scattering (SRS) imaging to quantitatively analyze CE level in intact tumor tissues obtained from IDH-wildtype GBM patients. Our result revealed significantly prolonged 2-year overall survival (OS) in patients with CE level [&ge;] 40% compared to those with CE level < 40%. CE outperformed MGMT methylation for 2-year OS prognosis (AUC: 0.836 vs. 0.763). Importantly, CE also achieved superior prognostic performance over MGMT methylation on an independent cohort, with higher sensitivity (0.856 vs. 0.667), specificity (0.833 vs. 0.583), NPV (1.00 vs. 0.667), PPV (0.833 vs. 0.583). Given synergistic effects between CE and MGMT methylation, we developed a prognostic model combining these two biomarkers. Specially, machine learning (XGBoost) model exhibited optimal performance in the training cohort (AUC: 0.920), and maintained its superior performance on the independent cohort (sensitivity: 0.946, specificity: 0.873, NPV: 1.00; PPV: 0.917). Mechanistically, integrative analysis of TCGA database linked poor prognosis to the coordinated upregulation of genes involved in cholesterol efflux, hydrolysis, transport, and inhibition of de novo synthesis, unraveling a possible underlying mechanism between poor prognosis and cholesterol metabolism. This work identified CE as a prognostic biomarker for IDH-wildtype GBM.

Lung adenocarcinomas frequently harbour actionable oncogenic mutations that are vulnerable to treatment with targeted therapies. While responses to targeted therapies are often initially dramatic, relapse is almost inevitable and prevents durable responses in advanced-stage patients. Relapse is, in part, caused by drug tolerant persister cells (DTPs) which are able to survive treatment by entering a reversible, dormant state. Although long non-coding RNAs (lncRNAs) regulate processes thought to allow DTPs to survive and become stably resistant, the potential roles of lncRNAs in DTPs are largely unknown. In this study, we sought to investigate the expression of lncRNAs in in vitro DTP models of lung adenocarcinoma. We found that the lncRNAs Metastasis-Associated Lung Adenocarcinoma Transcript 1 (MALAT1) and Nuclear Paraspeckle Assembly Transcript 1 (NEAT1) were enriched in DTPs and that knocking down MALAT1 enhanced the effect of targeted therapies in both EGFR- and KRAS-mutant DTP models. To better understand pathways that MALAT1 might regulate in DTPs, bulk RNA-sequencing was performed and several pathways that may contribute to the actions of MALAT1 in DTPs were identified. Overall, our work describes a role for the lncRNA MALAT1 in DTPs in NSCLC and suggests that MALAT1 may be a novel target for the prevention of drug tolerance and subsequent resistance to targeted therapy in NSCLC.