Single-cell transcriptomic analyses reveal distinct immune cell contributions to epithelial barrier dysfunction in checkpoint inhibitor colitis.

Immune checkpoint inhibitor (ICI) therapy has revolutionized oncology, but treatments are limited by immune-related adverse events, including checkpoint inhibitor colitis (irColitis). Little is understood about the pathogenic mechanisms driving irColitis, which does not readily occur in model organisms, such as mice. To define molecular drivers of irColitis, we used single-cell multi-omics to profile approximately 300,000 cells from the colon mucosa and blood of 13 patients with cancer who developed irColitis (nine on anti-PD-1 or anti-CTLA-4 monotherapy and four on dual ICI therapy; most patients had skin or lung cancer), eight controls on ICI therapy and eight healthy controls. Patients with irColitis showed expanded mucosal Tregs, ITGAEHi CD8 tissue-resident memory T cells expressing CXCL13 and Th17 gene programs and recirculating ITGB2Hi CD8 T cells. Cytotoxic GNLYHi CD4 T cells, recirculating ITGB2Hi CD8 T cells and endothelial cells expressing hypoxia gene programs were further expanded in colitis associated with anti-PD-1/CTLA-4 therapy compared to anti-PD-1 therapy. Luminal epithelial cells in patients with irColitis expressed PCSK9, PD-L1 and interferon-induced signatures associated with apoptosis, increased cell turnover and malabsorption. Together, these data suggest roles for circulating T cells and epithelial-immune crosstalk critical to PD-1/CTLA-4-dependent tolerance and barrier function and identify potential therapeutic targets for irColitis.

Mesothelin CAR T Cells Secreting Anti-FAP/Anti-CD3 Molecules Efficiently Target Pancreatic Adenocarcinoma and its Stroma.

PURPOSE: Targeting solid tumors with chimeric antigen receptor (CAR) T cells remains challenging due to heterogenous target antigen expression, antigen escape, and the immunosuppressive tumor microenvironment (TME). Pancreatic cancer is characterized by a thick stroma generated by cancer-associated fibroblasts (CAF), which may contribute to the limited efficacy of mesothelin-directed CAR T cells in early-phase clinical trials. To provide a more favorable TME for CAR T cells to target pancreatic ductal adenocarcinoma (PDAC), we generated T cells with an antimesothelin CAR and a secreted T-cell-engaging molecule (TEAM) that targets CAF through fibroblast activation protein (FAP) and engages T cells through CD3 (termed mesoFAP CAR-TEAM cells). EXPERIMENTAL DESIGN: Using a suite of in vitro, in vivo, and ex vivo patient-derived models containing cancer cells and CAF, we examined the ability of mesoFAP CAR-TEAM cells to target PDAC cells and CAF within the TME. We developed and used patient-derived ex vivo models, including patient-derived organoids with patient-matched CAF and patient-derived organotypic tumor spheroids. RESULTS: We demonstrated specific and significant binding of the TEAM to its respective antigens (CD3 and FAP) when released from mesothelin-targeting CAR T cells, leading to T-cell activation and cytotoxicity of the target cell. MesoFAP CAR-TEAM cells were superior in eliminating PDAC and CAF compared with T cells engineered to target either antigen alone in our ex vivo patient-derived models and in mouse models of PDAC with primary or metastatic liver tumors. CONCLUSIONS: CAR-TEAM cells enable modification of tumor stroma, leading to increased elimination of PDAC tumors. This approach represents a promising treatment option for pancreatic cancer.

Dysregulated Repeat Element Viral-like Immune Response in Hepatocellular Carcinoma.

Purpose: Dysregulation of viral-like repeat RNAs are a common feature across many malignancies that are linked with immunological response, but the characterization of these in hepatocellular carcinoma (HCC) is understudied. In this study, we performed RNA in situ hybridization (RNA-ISH) of different repeat RNAs, immunohistochemistry (IHC) for immune cell subpopulations, and spatial transcriptomics to understand the relationship of HCC repeat expression, immune response, and clinical outcomes. Experimental Design: RNA-ISH for LINE1, HERV-K, HERV-H, and HSATII repeats and IHC for T-cell, Treg, B-cell, macrophage, and immune checkpoint markers were performed on 43 resected HCC specimens. Spatial transcriptomics on tumor and vessel regions of interest was performed on 28 specimens from the same cohort. Results: High HERV-K and high LINE1 expression were both associated with worse overall survival. There was a positive correlation between LINE1 expression and FOXP3 T-regulatory cells (r = 0.51 p < 0.001) as well as expression of the TIM3 immune checkpoint (r = 0.34, p = 0.03). Spatial transcriptomic profiling of HERV-K high and LINE-1 high tumors identified elevated expression of multiple genes previously associated with epithelial mesenchymal transition, cellular proliferation, and worse overall prognosis in HCC including SSX1, MAGEC2, and SPINK1. Conclusion: Repeat RNAs may serve as useful prognostic biomarkers in HCC and may also serve as novel therapeutic targets. Additional study is needed to understand the mechanisms by which repeat RNAs impact HCC tumorigenesis.

Immune Responses in Checkpoint Myocarditis Across Heart, Blood, and Tumor.

Immune checkpoint inhibitors (ICIs) are widely used anti-cancer therapies that can cause morbid and potentially fatal immune-related adverse events (irAEs). ICI-related myocarditis (irMyocarditis) is uncommon but has the highest mortality of any irAE. The pathogenesis of irMyocarditis and its relationship to anti-tumor immunity remain poorly understood. We sought to define immune responses in heart, tumor, and blood during irMyocarditis and identify biomarkers of clinical severity by leveraging single-cell (sc)RNA-seq coupled with T cell receptor (TCR) sequencing, microscopy, and proteomics analysis of 28 irMyocarditis patients and 23 controls. Our analysis of 284,360 cells from heart and blood specimens identified cytotoxic T cells, inflammatory macrophages, conventional dendritic cells (cDCs), and fibroblasts enriched in irMyocarditis heart tissue. Additionally, potentially targetable, pro-inflammatory transcriptional programs were upregulated across multiple cell types. TCR clones enriched in heart and paired tumor tissue were largely non-overlapping, suggesting distinct T cell responses within these tissues. We also identify the presence of cardiac-expanded TCRs in a circulating, cycling CD8 T cell population as a novel peripheral biomarker of fatality. Collectively, these findings highlight critical biology driving irMyocarditis and putative biomarkers for therapeutic intervention.

Single-Cell Transcriptional Landscape Reveals the Regulatory Network and Its Heterogeneity of Renal Mitochondrial Damages in Diabetic Kidney Disease.

Diabetic kidney disease (DKD) is one of the common chronic microvascular complications of diabetes in which mitochondrial disorder plays an important role in its pathogenesis. The current study delved into the single-cell level transcriptome heterogeneity of mitochondrial homeostasis in db/db mice, an animal model for study of type 2 diabetes and DKD, with single-cell RNA sequencing (scRNA-Seq) and bulk RNA-seq analyses. From the comprehensive dataset comprising 13 meticulously captured and authenticated renal cell types, an unsupervised cluster analysis of mitochondria-related genes within the descending loop of Henle, collecting duct principal cell, endothelial, B cells and macrophage, showed that they had two types of cell subsets, i.e., health-dominant and DKD-dominant clusters. Pseudotime analysis, cell communication and transcription factors forecast resulted in identification of the hub differentially expressed genes between these two clusters and unveiled that the hierarchical regulatory network of receptor-TF-target genes was triggered by mitochondrial degeneration. Furthermore, the collecting duct principal cells were found to be regulated by the decline of Fzd7, which contributed to the impaired cellular proliferation and development, apoptosis and inactive cell cycle, as well as diminished capacity for material transport. Thereby, both scRNA-Seq and bulk RNA-Seq data from the current study elucidate the heterogeneity of mitochondrial disorders among distinct cell types, particularly in the collecting duct principal cells and B cells during the DKD progression and drug administration, which provide novel insights for better understanding the pathogenesis of DKD.

Ultrasensitive Detection of Circulating LINE-1 ORF1p as a Specific Multicancer Biomarker.

Improved biomarkers are needed for early cancer detection, risk stratification, treatment selection, and monitoring treatment response. Although proteins can be useful blood-based biomarkers, many have limited sensitivity or specificity for these applications. Long INterspersed Element-1 (LINE-1) open reading frame 1 protein (ORF1p) is a transposable element protein overexpressed in carcinomas and high-risk precursors during carcinogenesis with negligible expression in normal tissues, suggesting ORF1p could be a highly specific cancer biomarker. To explore ORF1p as a blood-based biomarker, we engineered ultrasensitive digital immunoassays that detect mid-attomolar (10-17 mol/L) ORF1p concentrations in plasma across multiple cancers with high specificity. Plasma ORF1p shows promise for early detection of ovarian cancer, improves diagnostic performance in a multianalyte panel, provides early therapeutic response monitoring in gastroesophageal cancers, and is prognostic for overall survival in gastroesophageal and colorectal cancers. Together, these observations nominate ORF1p as a multicancer biomarker with potential utility for disease detection and monitoring. SIGNIFICANCE: The LINE-1 ORF1p transposon protein is pervasively expressed in many cancers and is a highly specific biomarker of multiple common, lethal carcinomas and their high-risk precursors in tissue and blood. Ultrasensitive ORF1p assays from as little as 25 µL plasma are novel, rapid, cost-effective tools in cancer detection and monitoring. See related commentary by Doucet and Cristofari, p. 2502. This article is featured in Selected Articles from This Issue, p. 2489.

Spatial transcriptomics reveals distinct tissue niches linked with steroid responsiveness in acute gastrointestinal GVHD.

Severe acute graft-versus-host disease (aGVHD) is associated with significant mortality and morbidity, especially in steroid-resistant (SR) cases. Spatial transcriptomic technology can elucidate tissue-based interactions in vivo and possibly identify predictors of treatment response. Tissue sections from 32 treatment-naïve patients with biopsy-confirmed lower gastrointestinal (GI) aGVHD were obtained. The GeoMx digital spatial profiler was used to capture transcriptome profiles of >18 000 genes from different foci of immune infiltrates, colonic epithelium, and vascular endothelium. Each tissue compartment sampled showed 2 distinct clusters that were analyzed for differential expression and spatially resolved correlation of gene signatures. Classic cell-mediated immunity signatures, normal differentiated epithelial cells, and inflamed vasculature dominated foci sampled from steroid-sensitive cases. In contrast, a neutrophil predominant noncanonical inflammation with regenerative epithelial cells and some indication of angiogenic endothelial response was overrepresented in areas from SR cases. Evaluation of potential prognostic biomarkers identified ubiquitin specific peptidase 17-like (USP17L) family of genes as being differentially expressed in immune cells from patients with worsened survival. In summary, we demonstrate distinct tissue niches with unique gene expression signatures within lower GI tissue from patients with aGVHD and provide evidence of a potential prognostic biomarker.

Size-tunable energy gaps of hydrogen-terminated biphenylene segments.

The electronic properties of hydrogen-terminated biphenylene (BP) segments of different sizes on the sub-nanoscale are explored using density functional theory, and the size dependence of the energy gap is evaluated using a structural parameter as a function of the bond lengths and the electronic density contributions. More importantly, the energy gap is observed to decrease linearly with the reduced hydrogen-to-carbon ratio of the corresponding structures, while the decrease-rate undergoes a diminution of four times at a gap of 0.5 eV due to the transformed distribution of the lowest unoccupied molecular orbital. The results give a deep insight into the size-tunable energy gaps of BPs and provide a possibility for the preparation of hydrogen-terminated carbon materials with a desirable energy gap.

Plasmon-Mediated Hydrogen Dissociation with Symmetry Tunability.

The atomic-scale mechanism of plasmon-mediated H2 dissociation on gold nanoclusters is investigated using time-dependent density functional theory. The position relationship between the nanocluster and H2 has a strong influence on the reaction rate. When the hydrogen molecule is located in the interstitial center of the plasmonic dimer, the hot spot here has a great field enhancement, which can promote dissociation effectively. The change in the molecular position results in symmetry breaking, and the molecular dissociation is inhibited. For the asymmetric structure, direct charge transfer from the gold cluster to the antibonding state of the hydrogen molecule by plasmon decay makes a prominent contribution to the reaction. The results provide deep insights into the influence of structural symmetry on plasmon-assisted photocatalysis in the quantum regime.

TSRL-Net: Target-aware supervision residual learning for stroke segmentation.

Accurate stroke segmentation is a crucial task in establishing a computer-aided diagnostic system for brain diseases. However, reducing false negatives and accurately segmenting strokes in MRI images is often challenging because of the class imbalance and intraclass ambiguities problems. To address these issues, we propose a novel target-aware supervision residual learning framework for stroke segmentation. Considering the problem of imbalance of positive and negative samples, a creatively target-aware loss function is designed to dilate strong attention regions, pay high attention to the positive sample losses, and compensate for the loss of negative samples around the target. Then, a coarse-grained residual learning module is developed to gradually fix the lost residual features during the decoding phase to alleviate the problem of high number of false negatives caused by intraclass ambiguities. Here, our reverse/positive attention unit suppresses redundant target/background noise and allows relatively more focused highlighting of important features in the target residual region. Extensive experiments were performed on the Anatomical Tracings of Lesions After Stroke and Ischemic Stroke Lesion Segmentation public datasets, with results suggesting the effectiveness of our proposed method compared to several state-of-the-art methods.

Ultrasensitive detection of circulating LINE-1 ORF1p as a specific multi-cancer biomarker.

Improved biomarkers are needed for early cancer detection, risk stratification, treatment selection, and monitoring treatment response. While proteins can be useful blood-based biomarkers, many have limited sensitivity or specificity for these applications. Long INterspersed Element-1 (LINE-1, L1) open reading frame 1 protein (ORF1p) is a transposable element protein overexpressed in carcinomas and high-risk precursors during carcinogenesis with negligible detectable expression in corresponding normal tissues, suggesting ORF1p could be a highly specific cancer biomarker. To explore the potential of ORF1p as a blood-based biomarker, we engineered ultrasensitive digital immunoassays that detect mid-attomolar (10-17 M) ORF1p concentrations in patient plasma samples across multiple cancers with high specificity. Plasma ORF1p shows promise for early detection of ovarian cancer, improves diagnostic performance in a multi-analyte panel, and provides early therapeutic response monitoring in gastric and esophageal cancers. Together, these observations nominate ORF1p as a multi-cancer biomarker with potential utility for disease detection and monitoring.

AGMR-Net: Attention-guided multiscale recovery framework for stroke segmentation.

Automatic and accurate lesion segmentation is critical to the clinical estimation of the lesion status of stroke diseases and appropriate diagnostic systems. Although existing methods have achieved remarkable results, their further adoption is hindered by: (1) intraclass inconsistency, i.e., large variability between different areas of the lesion; and (2) interclass indistinction, in which normal brain tissue resembles the lesion in appearance. To meet these challenges in stroke segmentation, we propose a novel method, namely attention-guided multiscale recovery framework (AGMR-Net) in this paper. Firstly, a coarse-grained patch attention (CPA) module in the encoding is adopted to obtain a patch-based coarse-grained attention map in a multistage, explicitly supervised way, enabling target spatial context saliency representation with a patch-based weighting technique that eliminates the effect of intraclass inconsistency. Secondly, to obtain more detailed boundary partitioning to meet the challenge of interclass indistinction, a newly designed cross-dimensional feature fusion (CFF) module is used to capture global contextual information to further guide the selective aggregation of 2D and 3D features, which can compensate for the lack of boundary learning capability of 2D convolution. Lastly, in the decoding stage, an innovative designed multiscale deconvolution upsampling (MDU) is used for enhanced recovery of target spatial and boundary information. AGMR-Net is evaluated on the open-source dataset Anatomical Tracings of Lesions After Stroke, achieving the highest Dice similarity coefficient of 0.594, Hausdorff distance of 27.005 mm, and average symmetry surface distance of 7.137 mm, which demonstrates that our proposed method outperforms state-of-the-art methods and has great potential for stroke diagnosis.

Two-stage segmentation network with feature aggregation and multi-level attention mechanism for multi-modality heart images.

Accurate segmentation of cardiac substructures in multi-modality heart images is an important prerequisite for the diagnosis and treatment of cardiovascular diseases. However, the segmentation of cardiac images remains a challenging task due to (1) the interference of multiple targets, (2) the imbalance of sample size. Therefore, in this paper, we propose a novel two-stage segmentation network with feature aggregation and multi-level attention mechanism (TSFM-Net) to comprehensively solve these challenges. Firstly, in order to improve the effectiveness of multi-target features, we adopt the encoder-decoder structure as the backbone segmentation framework and design a feature aggregation module (FAM) to realize the multi-level feature representation (Stage1). Secondly, because the segmentation results obtained from Stage1 are limited to the decoding of single scale feature maps, we design a multi-level attention mechanism (MLAM) to assign more attention to the multiple targets, so as to get multi-level attention maps. We fuse these attention maps and concatenate the output of Stage1 to carry out the second segmentation to get the final segmentation result (Stage2). The proposed method has better segmentation performance and balance on 2017 MM-WHS multi-modality whole heart images than the state-of-the-art methods, which demonstrates the feasibility of TSFM-Net for accurate segmentation of heart images.

An integrated deep learning framework for joint segmentation of blood pool and myocardium.

Simultaneous and automatic segmentation of the blood pool and myocardium is an important precondition for early diagnosis and pre-operative planning in patients with complex congenital heart disease. However, due to the high diversity of cardiovascular structures and changes in mechanical properties caused by cardiac defects, the segmentation task still faces great challenges. To overcome these challenges, in this study we propose an integrated multi-task deep learning framework based on the dilated residual and hybrid pyramid pooling network (DRHPPN) for joint segmentation of the blood pool and myocardium. The framework consists of three closely connected progressive sub-networks. An inception module is used to realize the initial multi-level feature representation of cardiovascular images. A dilated residual network (DRN), as the main body of feature extraction and pixel classification, preliminary predicts segmentation regions. A hybrid pyramid pooling network (HPPN) is designed for facilitating the aggregation of local information to global information, which complements DRN. Extensive experiments on three-dimensional cardiovascular magnetic resonance (CMR) images (the available dataset of the MICCAI 2016 HVSMR challenge) demonstrate that our approach can accurately segment the blood pool and myocardium and achieve competitive performance compared with state-of-the-art segmentation methods.

Segmentation and visualization of left atrium through a unified deep learning framework.

PURPOSE: Left atrium segmentation and visualization serve as a fundamental and crucial role in clinical analysis and understanding of atrial fibrillation. However, most of the existing methods are directly transmitting information, which may cause redundant information to be passed to affect segmentation performance. Moreover, they did not further consider atrial visualization after segmentation, which leads to a lack of understanding of the essential atrial anatomy. METHODS: We propose a novel unified deep learning framework for left atrium segmentation and visualization simultaneously. At first, a novel dual-path module is used to enhance the expressiveness of cardiac image representation. Then a multi-scale context-aware module is designed to effectively handle complex appearance and shape variations of the left atrium and associated pulmonary veins. The generated multi-scale features are feed to gated bidirectional message passing module to remove irrelevant information and extract discriminative features. Finally, the features after message passing are efficiently combined via a deep supervision mechanism to produce the final segmentation result and reconstruct 3D volumes. RESULTS: Our approach primarily against the 2018 left atrium segmentation challenge dataset, which consists of 100 3D gadolinium-enhanced magnetic resonance images. Our method achieves an average dice of 0.936 in segmenting the left atrium via fivefold cross-validation, which outperforms state-of-the-art methods. CONCLUSIONS: The performance demonstrates the effectiveness and advantages of our network for the left atrium segmentation and visualization. Therefore, our proposed network could potentially improve the clinical diagnosis and treatment of atrial fibrillation.

[Selection of advantage prediction model for forest fire occurrence in Tahe, Daxing'an Mountain].

This study chose zero-inflated model and Hurdle model that have been widely used in economic and social fields to model the fire occurrence in Tahe, Daxing'an Mountain. The AIC, LR and SSR were used to compare the models including zero-inflated Poisson model (ZIP), zero-inflated negative binomial model (ZINB), Poisson-Hurdle model (PH) and negative Binomial Hurdle (NBH) (two types, four models in total) so as to determine a better-fit model to predict the local fire occurrence. The results illustrated that ZINB model was superior over the other three models (ZIP, PH and NBH) based on the result of AIC and SSR tests. LR test revealed that the negative binomial distribution was suitable to both the "count" portion of zero-inflated model and hurdle model. Furthermore, this paper concluded that the zero-inflated model could better fit the fire feature of the study area according to the hypotheses of the two types of models.