Laboratory Investigation

Selection of regions of interest (ROIs) is often a crucial step in spatial molecular profiling and many pathology tasks, with substantial implications for research reproducibility and biological interpretability. To provide a reproducible and adaptive framework for AI-guided ROI selection, we developed a modular generalist-specialist solution across spatial profiling platforms. In a cohort comprising 55 tumor types from 160 tissue donors profiled using NanoString Digital Spatial Profiling and multiplex immunofluorescence, we first established a protein-profiling reference atlas capturing compartment-specific immune, checkpoint, stromal, and proliferation patterns. We then developed an AI Specialist Task-Oriented Model for ROI Selection (ASTROS) and tested comprehensive benchmarks considering specialist-only (ASTROS), generalist-only (PLIP/GFM), and hybrid generalist-specialist strategies, showing that the latter provides a balanced tradeoff across slide-level signal preservation, pathologist-reference concordance, within-slide placement consistency, and large-slide computational efficiency. We further demonstrated the feasibility of virtual staining for ROI preview and modular ROI placement for other spatial omics technologies, Visium and Visium HD workflows. Together, these results support our proposed framework to enable ROI selection responding to unmet needs for reducing inter-rater variability, reproducibility, and versatility in spatial profiling experiments.

Objectives. To determine whether medical machine olfaction via tracking the activation of mammalian G-Protein Coupled odorant Receptors (GPCR) stabilized by proprietary co-polymers on a photonic MZI chip can be used to diagnose prostate cancer (PCa) via urine scent. Specifically, scent character is compared against the current diagnostic PCa screening gold-standard in the US: the serum level of prostate specific antigen (PSA). The device is an artificial nose sensor built on a commercial photonic platform that reads interchangeable Mach-Zehnder interferometer (MZI) chips. These chips were functionalized with a stabilised panel of mammalian olfactory G-protein-coupled receptors (GPCRs). These samples had been characterized into POSITIVE or CONTROL for PCa six to eight years prior by standard hospital diagnostic procedures and by trained medical detection dogs, then stored at -80 Celcius. A subset of 80 patients urine samples was subsequently thawed and used for training and testing the medical machine olfaction system of RealNose as an initial validation of the novel technology and methodological approach. We posed two primary research questions: (a) whether the cancer-associated odor profile would remain detectable by machine-based systems following long-term storage and with what accuracy could it be used to cluster (YES and AUC 0.79 from scent character alone), and (b) what technical and procedural requirements would be necessary to translate such a signal into a clinically useful diagnostic assay (more training samples (500 predicted to yield 0.93) and increased breadth of receptors per chip and/or more chips per device in next iteration seen as helpful). Design, setting, participants. Retrospective diagnostic-accuracy feasibility study on 80 biobanked urine samples (40 PCa, 40 non-cancer; 368 sensor runs; a subset of unknown Gleason grade) from a single UK NHS urology service, the same collection used to train canine detectors. Main outcome measures: Patient-level Receiver Operating Characteristic (ROC) area under the curve (AUC) under patient-grouped cross-validation with a fold-honest pooled-control reference (reconstructed from training-partition controls only); sensitivity, specificity and predictive values at pre-specified operating points; 1000-fold whole-procedure label-permutation significance; patient bootstrap 95% CIs; and leave-one-day-out / leave-one-chip-out generalisation. Results. An L2-regularised linear classifier when allowed to see between three and six chips outcome on a patient sample extracted within-instrument AUC 0.79 (95% CI 0.69 to 0.88; 1000-permutation p = 0.001) from urine scent alone, exceeding this cohort own serum prostate-specific antigen (PSA) discrimination (AUC 0.645; itself within the population range for PSA 0.67) and obtained without a blood draw (at the Youden point, sensitivity 0.75, specificity 0.78, PPV 0.77, NPV 0.76). Upon allowing PSA the total AUC rose to 0.82. This was not a plateau: AUC rose from chance at 30 training samples, passed the serum-PSA range at 40, and reached 0.79 at 80 patients (0.82 if PSA was included), with an inverse-power fit projecting 0.93 by n = 500 and 0.96 by n = 1000. The discriminant was a genuine multivariate receptor pattern, independent of patient age (Spearman 0.09; the cohort is not age-matched). So at least for these data, neither age, nor collection day, ambient humidity/temperature, or overall signal amplitude (sometimes thought of as intensity of smell) were predictive of prostate cancer status, yet the scent character was. Transfer to a new sensor chip fell to AUC 0.57 without calibration, meaning the remaining obstacles are hardware portability rather than signal existence: much as a detection dog acclimatizes to a new setting, the system improves with on-site calibration prior to use. Conclusions: A genuine, confound-controlled olfactory PCa signature is recoverable from 80 samples, surpasses this cohort serum PSA (0.645) and exceeds the population PSA range, and improves monotonically with training-set size. We present this as a small-sample feasibility benchmark, not yet a validated diagnostic; the dominant remaining factor is training-set size, and the path to clinical-utility and improved AUC is clearly found to be a larger, multi-site, age-matched, and ideally prospective training cohort. A transferable small-sample lesson is also reported: adaptive feature searches (evolutionary and self-calibrating-protocol handle search) artificially inflate cross-validation and collapse under whole-procedure permutation, whereas non-adaptive averaging survives, giving a robust scent signal obtainable from the headspace of urine samples and recordable by the RealNose device that keeps improving with expanding sample training set.

Background: The Prosigna Breast Risk of Recurrence test is based on the PAM50 classifier and was originally validated as an in vitro diagnostic (IVD) test on the Dx enabled nCounter(R) Analysis System. The Prosigna test is intended for early-stage, hormone receptor+ (HR+) breast cancer and provides the risk of recurrence (ROR) score (0-100), intrinsic subtype (Luminal A, Luminal B, HER2-enriched, and Basal-like), and the 10-year probability of distant recurrence. We describe the performance of the Prosigna test as a whole transcriptome RNA sequencing laboratory developed test (LDT) for measuring the Prosigna ROR score and intrinsic subtypes on tissue from surgical resection and core needle biopsy as compared to the Prosigna test on the nCounter system. Methods: We evaluated three separate breast cancer cohorts to 1) bridge the IVD test on the nCounter system and NGS LDT test (n = 245), 2) validate the bridged algorithm on an independent biobank sample set (n = 187), and 3) retrospectively test performance on long-term archival samples from a previous study (n = 109). Results: Bridging analysis showed minimal score variability and robust correlation of Prosigna ROR scoring in surgical resections (SR) (2.459, SD; 0.981, R2) and core needle biopsy (CNB) (2.338, SD; 0.970, R2) samples. In the validation set, the Prosigna NGS LDT ROR scores maintained high correlation to the scores of the nCounter system (SR = 0.968, CNB = 0.966, R2), exhibited minimal score variability (SR = 2.488, CNB = 2.558, SD), and demonstrated high concordance in subtype classifications (SR = 92.3% CNB = 92.8%). Further testing demonstrated comparable performance across tumor fractions, a lower limit of detection (LLOD) of 5 ng, and robustness to exogenous ethanol or genomic DNA contamination. When testing previously extracted RNA from the clinical cohort, we observed high correlation (0.974, R2) and low variance (3.078, SD) of ROR scores with original values on the nCounter system, along with strong risk group (95.4%) and subtype (94.5%) concordance. Conclusions: This study describes the analytical validation of the Prosigna NGS-based LDT measuring the Prosigna ROR score and intrinsic subtypes with robust analytical performance on SR and CNB specimens, providing confidence for clinicians utilizing the NGS-based version of this well-established test.

Nephrons are the functional units of the kidney; within each nephron, the glomerulus is the initial site of selective filtration that allows removal of waste products while preserving proteins in the bloodstream. Each glomerulus consists of a network of capillaries surrounded by specialized epithelial cells, podocytes, which mediate selective filtration. Abnormalities in glomerular structure impair renal function, resulting in proteinuria and kidney disease. Although several microscopy-based approaches exist to characterize glomerular architecture and structural abnormalities, quantitative analysis is often limited by labor-intensive image segmentation. In this study we present a semi-automated approach for segmentation and analysis of glomerular architecture from three-dimensional confocal microscopy data. Using mTmG transgenic mice that express membrane-associated EGFP in podocytes and membrane-associated tdTomato across all other cell types, we reconstruct podocyte processes and glomerular capillaries from volumetric renal images. This semi-automated approach reduces manual segmentation effort and supports more efficient, standardized analysis of glomerular architecture in three-dimensional confocal microscopy datasets.

The spatial arrangement of immune cells in the tumor microenvironment (TME) varies widely, from dispersed to clustered and tumor excluded to infiltrating. Multiplexed spatial profiling is an effective means of characterizing tumor-infiltrating lymphocytes (TILs) and immune complexes such as tertiary lymphoid structures (TLS) in the TME. However, few approaches have been described for objectively parametrizing patterns of immune organization and assessing their association with biological or clinical variables. This makes it difficult to evaluate whether a set of tumors is relatively immunologically cold or hot. Here we describe an intuitive set of statistical tools (available in the R package, tlsR) for characterizing lymphocyte patterns in the TME of solid cancers. We apply tlsR to primary prostate cancer (PCa), which is often described as immunologically cold. Using a cohort of 29 radical prostatectomy specimens stratified into low Gleason-grade (LGG; n=15) and high Gleason-grades (HGG; n =14) we show that HGG PCa is significantly more infiltrated than LGG PCa with lymphocytes organized into B cell or T cell enriched immune clusters (BICs and TICs). A subset of these ICs have the B and T cell zonation and follicular dendritic cells characteristic of a bona fide TLS. HGGs are also enriched with ICs containing precursor exhausted T cells (Tpex) and proliferating B cells and their tumor compartments harbor granzyme-B+ cytotoxic T cells in contact with cancer cells. Thus, far from being cold, a subset of HGG PCa has features associated with active immune surveillance, a finding with implications for emerging PCa immunotherapies.

An autocrine pro-adhesive/pro-contractile signaling loop, through the mechanosensitive transcriptional cofactor YAP, promotes fibrosis. The CCN family of matricellular proteins modify adhesive signaling. Of these, CCN3 is antifibrotic. We show that BLR-200, a CCN3-derived peptide, has anti-fibrotic properties in the bleomycin-induced model of scleroderma skin fibrosis. In vitro, BLR-200 delayed, but did not abolish, fibroblast adhesion to collagen and nuclear YAP localization. In vivo, BLR-200 prevented/treated bleomycin-induced skin fibrosis, and reduced bleomycin-induced expression of profibrotic genes including alpha-smooth muscle actin, CCN1 and CCN2. Lineage tracing and scRNA-seq analyses revealed that the myofibroblasts in this model were quantitatively derived from collagen-lineage Pi16+/Col15+ve fibroblasts. BLR-200 prevented myofibroblast differentiation in this model and trajectory of fibroblasts toward a Sfrp2-positive subset, a cell type associated with poor clinical outcome. BLR-200 impairs YAP activation in vitro and appearance of translationally-relevant fibroblast subtypes in vivo and is a novel anti-fibrotic agent for SSc skin fibrosis.

Plasma-derived cell-free small non-coding RNAs are promising non-invasive biomarkers for cancer detection and monitoring. However, variability in sequencing output limits standardization, and cross-platform performance for plasma small RNA profiling has not been systematically evaluated. Illumina short-read sequencing is the current standard, whereas the newcomer, Ultima-Genomics platform, has been less extensively studied for circulating small RNA in plasma. To directly compare platform performance, we sequenced plasma cell-free RNA from 39 patients with pancreatic cancer and 39 matched controls on both platforms. After filtering, Ultima-Genomics retained more mature microRNA reads, whereas Illumina achieved slightly higher enrichment efficiency and mapping rates. Despite these technical differences, both platforms produced concordant expression profiles, with strong cross-platform correlations for shared microRNAs and clear separation of cases and controls within each dataset. Differential expression analysis identified 14 significant microRNAs on both platforms with concordant directions of change, most of which are supported by pancreatic cancer databases. Pathway enrichment analysis highlighted signaling pathways implicated in pancreatic cancer, supporting the biological relevance of both shared and platform-specific signatures. These findings indicate that both Illumina and Ultima Genomics platforms are suitable for plasma small RNA profiling and capture biologically relevant signals in pancreatic cancer.

Background The relationship between total cell-free DNA (cfDNA) concentration and multi-cancer early detection (MCED) sensitivity is non-obvious on account of competing considerations. On the one hand, this concentration is elevated in cancer and increases in advanced disease, suggesting higher concentrations may be associated with more biologically active tumors that are easier to detect. On the other hand, this elevation is known to be largely leukocyte-derived, which may dilute tumor-derived DNA (ctDNA) and make detection harder. The net direction of these competing effects on detection sensitivity has not been systematically examined. Methods EMERGE is an observational case-control study conducted at 43 Indian sites from June 2022-February 2025. It prospectively enrolled and analyzed 1,030 treatment-naive participants with malignant or benign conditions, most presenting symptomatically, along with 450 controls aged [≥]50 years without prior malignancy. Plasma cfDNA underwent targeted hybrid-capture enzymatic methylation sequencing. Classifiers were trained for cancer detection and tissue-of-origin prediction, and tested on the independent validation set. Primary outcomes were the associations between total cfDNA concentration and (i) detection sensitivity and (ii) tissue-of-origin accuracy, evaluated in an independent validation cohort. Results After adjustment for cancer type, stage, demographic and technical covariates, cfDNA concentration was significantly associated with detection sensitivity (p=6x10-4) but not with tissue-of-origin accuracy (p=0.67). At 0.986 specificity (95% CI: 0.968-1.000), stage I sensitivity rose monotonically from 0.52 (95% CI: 0.34-0.69) in the lowest cfDNA concentration tertile to 0.85 (95% CI: 0.73-0.97) in the highest. This association was mechanistically supported by a region-specific increase in hypermethylation scores within regions identified as differentially hypermethylated in TCGA tumor tissue, while panel-wide scores declined. The dissociation between the concentration-sensitivity and concentration-tissue-of-origin associations, together with inverse or insignificant correlations between ctDNA fraction and cfDNA concentration at early stages in published datasets, suggests that the concentration-sensitivity association is partly independent of ctDNA fraction. Conclusions Total cfDNA concentration is a routinely measured determinant of MCED assay sensitivity, reflecting enrichment of tumor-associated aberrant methylation partly independent of ctDNA fraction, an association likely most pronounced in symptomatic cohorts. Standardized reporting of cfDNA concentration could improve cross-study benchmarking. Study Registration Clinical Trials Registry, India: CTRI2022/05/042936 Keywords Cell-free DNA (cfDNA), Tumor Fraction, Circulating Tumor DNA (ctDNA), Circulating Mutant Allele Frequency (cMAF), Multi-cancer early detection (MCED), cfDNA Concentration, Tissue-of-Origin (TOO), Methylation, Epigenomics

In vivo infrared thermography is limited by the inherently poor spatial resolution at long wavelengths, low contrast, and the lack of biocompatible contrast agents. Here, we present 3-5 m mid-wave infrared (MWIR) thermography enhanced by an artificial intelligence (AI) network and cold phosphate-buffered saline (PBS) as a thermal contrast agent for noninvasive in vivo imaging with high contrast and resolution. MWIR imaging enabled high thermal sensitivity with microscale spatial resolution, strong relative thermal contrast, and facilitated visualization of the subcutaneous vasculature in the human arm, hand, ankle, the femoral artery and vein in rats, and the femoral vessels in mice, with image contrast further enhanced by AI networks. In a 4T1 tumor-bearing mouse model, AI-enhanced MWIR resolved early-stage tumors of ~2.3 mm and metastases as small as ~1.7 mm. Using cold PBS as a MWIR thermal contrast agent, we achieved precise tumor boundary visualization and real-time imaging-guided tumor resection. AI-enhanced MWIR offers a promising solution for early diagnosis and improved surgical precision.

Background: Single cell RNA sequencing provides a wealth of information to explore the complexities of the tumour microenvironment, but crucially the spatial topology of the tumour is lost and studying cellular interactions is limited. Spatial transcriptomics aims to address this however the technique remains cost prohibitive for the generation of data from meaningfully-sized clinical cohorts. In contrast, spatial proteomic profiling with multiplex immunofluorescence, preserves spatial interactions, is relatively cost accessible, and is scalable for large clinical cohorts to address powerful translational questions. Whilst multiplex approaches have advanced in recent years, we note that cancer-associated fibroblasts (CAFs) have been explored in less detail, potentially due to difficulties associated with CAF heterogeneity and the diversity of markers used to define them. Methods: We designed, optimised, and validated a multiplex immunofluorescence panel that combines four frequently used CAF markers; alpha smooth muscle actin (aSMA), fibroblast activation protein (FAP), podoplanin (PDPN) and platelet-derived growth factor receptor alpha (PDGFRa) with CD8 and pan-cytokeratin. Here we share our methodology and the practical considerations taken to inform the final panel design. We also highlight the benefits of robust optimisation experiments.

Colorectal cancer (CRC) screening remains limited by patient adherence and sub-optimal sensitivity for early-stage disease. While liquid biopsy has revolutionized cancer diagnostics, cfDNA-based methods often struggle with early-stage detection due to low analyte levels. Here, we present a robust cell-free RNA (cfRNA) platform for the early detection of CRC. Using a retrospective cohort of 255 healthy controls and 250 CRC patients, we implemented an optimized workflow featuring a RUVg-based normalization strategy to remove platelet-driven transcriptomic noise. We identified differentially expressed genes enriched in key CRC-associated biological pathways, including inflammation, EMT, and metabolic dysregulation. An XGBoost classifier trained on these features achieved a mean AUC of 0.92 in cross-validation and 0.89 in a validation cohort, demonstrating 67% sensitivity at 90% specificity. Notably, our platform showed particular efficacy in identifying early stage cancer (stage I and II), achieving 73.7% sensitivity at 90% specificity. These findings suggest that cfRNA profiling offers a powerful, non-invasive orthogonal approach to CRC screening, capable of overcoming the sensitivity limitations of DNA-based assays in early-stage disease.

Feature selection is a commonly used method in biomedical artificial intelligence and machine learning to identify a subset of high-quality variables that can be used to train downstream predictive models. It has been suggested that quantum feature selection (QFS), which takes advantage of the properties of quantum computers, may better identify variables that are correlated with the outcome while simultaneously reducing redundancy between selected variables. However, there are a limited number of studies evaluating their performance, particularly in real-world data sets. Here, we assess the performance of two QFS methods compared to random forest (RF) feature selection based on feature stability and the performance of a downstream classification algorithm when used to predict urinary tract infections in the emergency department from 211 original features extracted from the electronic health record. We found that a quantum binary quadratic model (BQM) and constrained quadratic model (CQM) had similar performance to RF feature selection (median F1 score of 0.60, 0.61, and 0.61 respectively) when 10 features were selected for an XGBoost classification model. The BQM and RF also had similar feature stability (0.91 and 0.94, respectively) while the CQM had lower stability (0.72). These findings show that QFS can be used with large, clinical data sets to identify features with high stability and predictive performance. As the capacity and quality of quantum computers continue to increase, these methods may offer additional benefits to classical feature selection methods.

Whole exome sequencing (WES) of circulating tumour DNA (ctDNA) enables longitudinal monitoring of tumour dynamics, evolution and treatment response but remains technically challenging in low-input, low-shedding settings such as pancreatic ductal adenocarcinoma (PDAC). Here, we systematically compared three commercially available low-input WES workflows incorporating Agilent (V6, V8) and Qiagen exome capture designs using ultra-low input cfDNAs extracted from multiple matched longitudinal plasma samples from PDAC patients. Using predefined performance metrics including coverage, duplication rate and variant detection and additional metrics relevant for clinical genomic profiling in patient care, we show that all three workflows produced high-quality sequencing data, even from very low input cfDNA. Within the conditions tested here, the Agilent V8 workflow provided the most favourable balance of coverage uniformity, sequencing efficiency and hotspot coverage for low input, low tumour fraction cfDNA WES. These findings demonstrate that workflow design, including capture footprint, substantially influences ctDNA WES performance in low-input clinical contexts. These findings are particularly relevant in early stage and/or minimal residual disease settings, where tumour fractions are low and recovery of genomic information from limited-input samples is critical.

Fibroproliferative diseases such as systemic sclerosis are complex conditions characterized by chronic skin inflammation and progressive fibrosis, with fibroblast activation as a central feature. While Transforming Growth Factor Beta (TGF-{beta}) signaling is a well-established driver of fibrosis in SSc, inflammatory pathways such as Nuclear Factor Kappa B (NF-{kappa}B) also contribute substantially to disease morbidity. We previously identified CD109 as a TGF-{beta} co-receptor and negative regulator of fibrotic signaling; however, its role in inflammatory signaling remains unknown. Here, we investigate the function of CD109 in regulating inflammatory signaling in skin fibroblasts. We show that, CD109 co-localizes and associates with Toll-like receptors (TLR2, TLR4) and tumor necrosis factor receptors (TNFRI, TNFRII), and that loss of CD109 enhances TNF--induced NF-{kappa}B activation and reprograms cytokine production in human dermal fibroblasts. Furthermore, both global and fibroblast-specific CD109 knockout mice exhibit increased immune cell infiltration and skin inflammation. In parallel, single-cell transcriptomic analyses across a pan-disease fibroblast atlas show that CD109 expression is preferentially maintained in structural and homeostatic fibroblast subtypes, whereas immune-interacting fibroblast subsets consistently display decreased CD109 levels. Pathway-level analyses of fibroblast pseudobulk samples reveal altered activity of canonical inflammatory pathways in SSc compared to healthy skin. Together, these findings identify CD109 as a fibroblast-intrinsic negative regulator of inflammatory signaling and suggest a broader role for CD109 in modulating inflammatory responses in systemic sclerosis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/736423v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@be9e08org.highwire.dtl.DTLVardef@794173org.highwire.dtl.DTLVardef@b81eb5org.highwire.dtl.DTLVardef@1e811f5_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract: CD109 Restrains Fibroblast-Driven Inflammation by Modulating NF-{kappa}B Signaling. Generated using FigureLabs.ai and edited using Adobe Photoshop. C_FIG

Longitudinal monitoring of circulating tumor DNA (ctDNA) has emerged as a promising framework for characterizing treatment response dynamics in cancer. Scalable tumor-naive approaches for quantifying ctDNA often involve whole-genome sequencing (WGS) or DNA methylation profiling, but their comparative performance and capacity for complementary integration remain poorly understood. Here we systematically benchmarked tumor-naive WGS- and methylation-based ctDNA quantification methods using plasma from 150 patients with colorectal, lung and breast cancer. Using paired high-depth WGS and EM-seq data, we generated 40,000 in silico samples and evaluated detection accuracy, limits of detection (LoD) and quantification (LoQ) across cancer types and sequencing depths (0.1x-30x). We further assessed single- and multimodal method combinations, identifying conditions under which integrated approaches enhance analytical performance for detection and quantification relative to single modalities. This benchmark delineates key performance trade-offs and provides a practical framework to support method development and guide future research applications in ctDNA-based biomarker studies.

The intensification of neoadjuvant therapy for early triple-negative breast cancer (eTNBC) - through the addition of carboplatin to standard chemotherapy and the incorporation of pembrolizumab - has markedly improved prognosis in recent years. However, this escalation carries a substantial risk of toxicity, and not all patients require the full regimen to achieve benefit. Realizing individualized treatment strategies will therefore depend on prognostic and predictive biomarkers that can forecast treatment response and long-term outcome. In the present study, we interrogated public gene expression datasets to develop transcriptomic signatures predicting response to neoadjuvant treatment and risk of recurrence. To validate these signatures, we used the Multiplex8+ platform for spatially informed comprehensive transcriptomic profiling in a real-world, multicenter, retrospective cohort of 590 patients diagnosed with eTNBC and treated with neoadjuvant chemotherapy with or without immunotherapy. The diagnostic Multiplex8+ test uses H&E and multiplexed RNA-FISH to guide the selection of specific tumor areas for the whole transcriptome sequencing and signature analysis. In the real-world cohort, the Multiplex8+ signatures were associated with both response and prognosis, remaining highly significant in multivariable models that included clinical parameters. The signatures were complementary to established biomarkers such as stromal tumor-infiltrating lymphocytes. These findings warrant prospective integration of the signatures into risk-stratified clinical trials to support future de-escalation and escalation strategies, enabling a better balance of efficacy, toxicity, cost, and drug availability.

Targeted long read sequencing (LRS) of native genomic DNA (gDNA) using Oxford Nanopore Technologies (ONT) is an economically and computationally accessible method for sequencing selected genomic regions without the limitations associated with amplification-based approaches. At present, efficiency, multiplexing, and scalability remain key challenges for existing targeted LRS. We have developed Cas12a-Targeted Multiplexed Nanopore Sequencing (CTM-nSeq), which combines Cas12a-targeting, DNA fragment enrichment, and optimized adapter ligation using T7 DNA ligase. Unlike previously established protocols, CTM-nSeq is compatible with the latest ONT flow cell chemistry. Performing CTM-nSeq on a single sample with an R10.4 MinION flow cell routinely yields hundreds of on-target reads. Furthermore, CTM-nSeq enables targeting of multiple loci and is the first targeted ONT sequencing method, allowing reliable, barcode-assisted multiplexing. CTM-nSeq is an efficient and accessible method for sequencing native gDNA and analysing DNA methylation, repeat expansions, and sequence integrity. As such, CTM-nSeq has a wide range of analytical and diagnostic applications.

Introduction Conjunctival melanoma (CM) is a rare cancer with a potentially high recurrence rate. The mechanics of its progression, its relationship with neighboring tissues, and its molecular characteristics are largely unknown. Diagnosis currently requires a biopsy and the time and expertise of a pathologist. Methods Archived human biopsies containing CM were submitted to Xenium spatial transcriptomic analysis. Regions were graded by disease progression through histopathology. Differential expression (DE) and composition analysis were performed across disease states. Results From three patients, 12 formalin-fixed paraffin-embedded (FFPE) tissue specimens were recovered. Composition analysis showed that melanoma depletes fibroblast and epithelial cells while melanocytes proliferate. DE signatures specific to each state show a clear pattern of progression from inflammation, to cellular restructuring, and then to tumor progression and malignancy. Conclusion Spatial transcriptomics allows single-cell transcriptomics techniques to compare spatially relevant annotations that are difficult to separate by library. This study proposes disease progression biomarker candidates that may elucidate the mechanics of CM progression and function as objective diagnostic and prognostic tools in the future.

Spatial transcriptomics can reveal localized tumor-immune relationships, but thousands of spots from one tissue section do not provide thousands of biological replicates. We evaluated the distinction between within-section association and patient-level reproducibility using public breast cancer datasets. In a 10x Genomics Visium discovery section containing 3,798 spots, hypoxia-related transcription was inversely associated with cytotoxic gene activity in neighboring spots (Spearman{rho} = -0.202). High-hypoxia spots also had lower neighborhood cytotoxic scores than low-hypoxia spots (rank-biserial effect = -0.286). We then tested the directional association in an independent HER2-positive cohort comprising 36 sections, 13,619 spots, and eight patients. Only 19 of 36 sections and five of eight patients showed negative associations. The median patient-level correlation was -0.043 and did not differ from zero in a one-sided exact Wilcoxon test (P = 0.473). Sensitivity analyses using alternative cytotoxic and hypoxia signatures, neighborhood sizes, and Kendall correlation did not support a consistent inverse patient-level effect. Thus, a strong single-section association did not consistently reproduce across patients. These results caution against interpreting spot-level spatial associations from one section as patient-level biological effects.

High-resolution 3D imaging is an important strategy for visualizing and analysing complex skeletal tissue architecture and the bone marrow microenvironment. However, multicolor immunolabeling and imaging of intact skeletal tissues are technologically challenging. The current immunolabeling and clearing methods for intact skeletal elements are very limited, time-consuming and generate low-resolution data or depend on the use of reporter mice. Here, we describe a protocol for efficient clearing and immunolabeling of intact calcified tissues that enables superfast, single-cell resolution, and quantitative 3D light-sheet imaging of intact skeletal elements and teeth. A key aspect of our protocol is the addition of a collagenase digestion step after fixation and decalcification. This step enhances antibody penetration, resulting in deep, comprehensive staining throughout immunostained bones and other calcified tissues. The protocol includes soft tissue removal, fixation, decalcification, bone dehydration, and bleaching, followed by antigen retrieval and permeabilization before the collagenase digestion step. This procedure is performed to prepare the samples for the tissue clearing process that improves bone tissue transparency prior to light-sheet imaging. The entire protocol, from bone collection to image analysis and quantification, takes about 4 days to complete, thus offering significant improvements over previous methods. This protocol is broadly applicable to the visualization of bone microstructure, bone marrow analysis, vascular and neural network mapping, and the study of signaling molecules in bone development and growth. The protocol requires experience with standard tissue processing and immunostaining techniques, and prior experience in tissue clearing and light-sheet imaging is beneficial but not essential. Key pointsO_LIA protocol for efficient clearing and immunolabeling of intact calcified tissues that enables superfast, high-resolution, and quantitative 3D imaging of various intact bones and teeth. C_LIO_LIThe entire protocol takes only 4 days to complete the comprehensive staining and perfect transparency throughout the intact bones, offering significant improvements over previous methods. C_LI Key referencesBiswas, L. et al. Cell 186, 382-397.e24 (2023): https://doi.org/10.1016/j.cell.2022.12.031