Hepatocytes coordinate immune evasion in cancer via release of serum amyloid A proteins.

T cell infiltration into tumors is a favorable prognostic feature, but most solid tumors lack productive T cell responses. Mechanisms that coordinate T cell exclusion are incompletely understood. Here we identify hepatocyte activation via interleukin-6/STAT3 and secretion of serum amyloid A (SAA) proteins 1 and 2 as important regulators of T cell surveillance of extrahepatic tumors. Loss of STAT3 in hepatocytes or SAA remodeled the tumor microenvironment with infiltration by CD8+ T cells, while interleukin-6 overexpression in hepatocytes and SAA signaling via Toll-like receptor 2 reduced the number of intratumoral dendritic cells and, in doing so, inhibited T cell tumor infiltration. Genetic ablation of SAA enhanced survival after tumor resection in a T cell-dependent manner. Likewise, in individuals with pancreatic ductal adenocarcinoma, long-term survivors after surgery demonstrated lower serum SAA levels than short-term survivors. Taken together, these data define a fundamental link between liver and tumor immunobiology wherein hepatocytes govern productive T cell surveillance in cancer.

Feasibility of using a depth camera or pressure mat for visual feedback balance training with functional electrical stimulation.

Individuals with incomplete spinal-cord injury/disease are at an increased risk of falling due to their impaired ability to maintain balance. Our research group has developed a closed-loop visual-feedback balance training (VFBT) system coupled with functional electrical stimulation (FES) for rehabilitation of standing balance (FES + VFBT system); however, clinical usage of this system is limited by the use of force plates, which are expensive and not easily accessible. This study aimed to investigate the feasibility of a more affordable and accessible sensor such as a depth camera or pressure mat in place of the force plate. Ten able-bodied participants (7 males, 3 females) performed three sets of four different standing balance exercises using the FES + VFBT system with the force plate. A depth camera and pressure mat collected centre of mass and centre of pressure data passively, respectively. The depth camera showed higher Pearson's correlation (r > 98) and lower root mean squared error (RMSE < 10 mm) than the pressure mat (r > 0.82; RMSE < 4.5 mm) when compared with the force plate overall. Stimulation based on the depth camera showed lower RMSE than that based on the pressure mat relative to the FES + VFBT system. The depth camera shows potential as a replacement sensor to the force plate for providing feedback to the FES + VFBT system.

The postnatal injection of AAV9-FOXG1 rescues corpus callosum agenesis and other brain deficits in the mouse model of FOXG1 syndrome.

Heterozygous mutations in the FOXG1 gene manifest as FOXG1 syndrome, a severe neurodevelopmental disorder characterized by structural brain anomalies, including agenesis of the corpus callosum, hippocampal reduction, and myelination delays. Despite the well-defined genetic basis of FOXG1 syndrome, therapeutic interventions targeting the underlying cause of the disorder are nonexistent. In this study, we explore the therapeutic potential of adeno-associated virus 9 (AAV9)-mediated delivery of the FOXG1 gene. Remarkably, intracerebroventricular injection of AAV9-FOXG1 to Foxg1 heterozygous mouse model at the postnatal stage rescues a wide range of brain pathologies. This includes the amelioration of corpus callosum deficiencies, the restoration of dentate gyrus morphology in the hippocampus, the normalization of oligodendrocyte lineage cell numbers, and the rectification of myelination anomalies. Our findings highlight the efficacy of AAV9-based gene therapy as a viable treatment strategy for FOXG1 syndrome and potentially other neurodevelopmental disorders with similar brain malformations, asserting its therapeutic relevance in postnatal stages.

Mapping the breast tumor microenvironment: proximity analysis reveals spatial relationships between macrophage subtypes and metastasis-initiating cancer cells.

Metastasis is responsible for the majority of cancer-related fatalities. We previously identified specific cancer cell populations responsible for metastatic events which are cytokeratin-14 (CK14) and E-cadherin positive in luminal tumors, and E-cadherin and vimentin positive in triple-negative tumors. Since cancer cells evolve within a complex ecosystem comprised of immune cells and stromal cells, we sought to decipher the spatial interactions of these aggressive cancer cell populations within the tumor microenvironment (TME). We used imaging mass cytometry to detect 36 proteins in tumor microarrays containing paired primary and metastatic lesions from luminal or triple-negative breast cancers (TNBC), resulting in a dataset of 1,477,337 annotated cells. Focusing on metastasis-initiating cell populations, we observed close proximity to specific fibroblast and macrophage subtypes, a relationship maintained between primary and metastatic tumors. Notably, high CK14 in luminal cancer cells and high vimentin in TNBC cells correlated with close proximity to specific macrophage subtypes (CD163intCD206intPDL1intHLA-DR+ or PDL1highARG1high). Our in-depth spatial analysis demonstrates that metastasis-initiating cancer cells consistently colocalizes with distinct cell populations within the TME, suggesting a role for these cell-cell interactions in promoting metastasis.

Multimodal immune phenotyping reveals microbial-T cell interactions that shape pancreatic cancer.

Microbes are an integral component of the tumor microenvironment. However, determinants of microbial presence remain ill-defined. Here, using spatial-profiling technologies, we show that bacterial and immune cell heterogeneity are spatially coupled. Mouse models of pancreatic cancer recapitulate the immune-microbial spatial coupling seen in humans. Distinct intra-tumoral niches are defined by T cells, with T cell-enriched and T cell-poor regions displaying unique bacterial communities that are associated with immunologically active and quiescent phenotypes, respectively, but are independent of the gut microbiome. Depletion of intra-tumoral bacteria slows tumor growth in T cell-poor tumors and alters the phenotype and presence of myeloid and B cells in T cell-enriched tumors but does not affect T cell infiltration. In contrast, T cell depletion disrupts the immunological state of tumors and reduces intra-tumoral bacteria. Our results establish a coupling between microbes and T cells in cancer wherein spatially defined immune-microbial communities differentially influence tumor biology.

Multi-omic profiling of intraductal papillary neoplasms of the pancreas reveals distinct expression patterns and potential markers of progression.

In order to advance our understanding of precancers in the pancreas, 69 pancreatic intraductal papillary neoplasms (IPNs), including 64 intraductal papillary mucinous neoplasms (IPMNs) and 5 intraductal oncocytic papillary neoplasms (IOPNs), 32 pancreatic cyst fluid samples, 104 invasive pancreatic ductal adenocarcinomas (PDACs), 43 normal adjacent tissues (NATs), and 76 macro-dissected normal pancreatic ducts (NDs) were analyzed by mass spectrometry. A total of 10,246 proteins and 22,284 glycopeptides were identified in all tissue samples, and 756 proteins with more than 1.5-fold increase in abundance in IPMNs relative to NDs were identified, 45% of which were also identified in cyst fluids. The over-expression of selected proteins was validated by immunolabeling. Proteins and glycoproteins overexpressed in IPMNs included those involved in glycan biosynthesis and the immune system. In addition, multiomics clustering identified two subtypes of IPMNs. This study provides a foundation for understanding tumor progression and targets for earlier detection and therapies. Significance: This multilevel characterization of intraductal papillary neoplasms of the pancreas provides a foundation for understanding the changes in protein and glycoprotein expression during the progression from normal duct to intraductal papillary neoplasm, and to invasive pancreatic carcinoma, providing a foundation for informed approaches to earlier detection and treatment.

PanIN and CAF transitions in pancreatic carcinogenesis revealed with spatial data integration.

This study introduces a new imaging, spatial transcriptomics (ST), and single-cell RNA-sequencing integration pipeline to characterize neoplastic cell state transitions during tumorigenesis. We applied a semi-supervised analysis pipeline to examine premalignant pancreatic intraepithelial neoplasias (PanINs) that can develop into pancreatic ductal adenocarcinoma (PDAC). Their strict diagnosis on formalin-fixed and paraffin-embedded (FFPE) samples limited the single-cell characterization of human PanINs within their microenvironment. We leverage whole transcriptome FFPE ST to enable the study of a rare cohort of matched low-grade (LG) and high-grade (HG) PanIN lesions to track progression and map cellular phenotypes relative to single-cell PDAC datasets. We demonstrate that cancer-associated fibroblasts (CAFs), including antigen-presenting CAFs, are located close to PanINs. We further observed a transition from CAF-related inflammatory signaling to cellular proliferation during PanIN progression. We validate these findings with single-cell high-dimensional imaging proteomics and transcriptomics technologies. Altogether, our semi-supervised learning framework for spatial multi-omics has broad applicability across cancer types to decipher the spatiotemporal dynamics of carcinogenesis.

Transfer Learning Reveals Cancer-Associated Fibroblasts Are Associated with Epithelial-Mesenchymal Transition and Inflammation in Cancer Cells in Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by an immunosuppressive tumor microenvironment enriched with cancer-associated fibroblasts (CAF). This study used a convergence approach to identify tumor cell and CAF interactions through the integration of single-cell data from human tumors with human organoid coculture experiments. Analysis of a comprehensive atlas of PDAC single-cell RNA sequencing data indicated that CAF density is associated with increased inflammation and epithelial-mesenchymal transition (EMT) in epithelial cells. Transfer learning using transcriptional data from patient-derived organoid and CAF cocultures provided in silico validation of CAF induction of inflammatory and EMT epithelial cell states. Further experimental validation in cocultures demonstrated integrin beta 1 (ITGB1) and vascular endothelial factor A (VEGFA) interactions with neuropilin-1 mediating CAF-epithelial cell cross-talk. Together, this study introduces transfer learning from human single-cell data to organoid coculture analyses for experimental validation of discoveries of cell-cell cross-talk and identifies fibroblast-mediated regulation of EMT and inflammation. SIGNIFICANCE: Adaptation of transfer learning to relate human single-cell RNA sequencing data to organoid-CAF cocultures facilitates discovery of human pancreatic cancer intercellular interactions and uncovers cross-talk between CAFs and tumor cells through VEGFA and ITGB1.

AI-Based Geometry Recognition for Evaluating the Feasibility of Intensified Reaction and Separation Systems.

Reactive distillation (RD) provides notable advantages over conventional processes, regarding reduced energy requirements and CO2 emissions. However, as the benefits of RD may not be universally applicable, a comprehensive feasibility assessment is necessary. This study introduced an automated feasibility evaluation procedure for an RD column using an AI-based region recognition approach, reducing the reliance on expert knowledge and heuristics in graphical methods. Through k-means clustering-based image segmentation, topological information on the reaction and separation reachable region was extracted from ternary diagram landscapes. Subsequently, the extracted information was integrated into tray-by-tray calculations to automate the evaluation. This geometric calculation procedure was applied to assess the feasibility of RD columns with different types of reactions. The feasibility results were obtained within seconds, demonstrating the efficiency of the proposed approach. Furthermore, case studies validated the feasibility of the evaluation results for three practical examples using rigorous simulations, confirming its reliability and applicability.