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Tuberculosis (TB) remains a global public health threat. Understanding the dynamics of host-pathogen interactions within TB granulomas will assist in identifying what leads to the successful elimination of infection. In vitro TB models provide a controllable environment to study these granuloma dynamics. Previously we developed a biomimetic 3D spheroid granuloma model that controls bacteria better than a traditional monolayer culture counterpart. We used agent-based simulations to predict the mechanistic reason for this difference. Our calibrated simulations were able to predict heterogeneous bacterial dynamics that are consistent with experimental data. In one group of simulations, spheroids are found to have higher macrophage activation than their traditional counterparts, leading to better bacterial control. This higher macrophage activation in the spheroids was not due to higher counts of activated T cells, instead fewer activated T cells were able to activate more macrophages due to the proximity of these cells to each other within the spheroid. In a second group of simulations, spheroids again have more macrophage activation but also more T cell activation, specifically CD8+ T cells. This higher level of CD8+ T cell activation is predicted to be due to the proximity of these cells to the cells that activate them. Multiple mechanisms of control were predicted. Simulations removing individual mechanisms show that one group of simulations has a CD4+ T cell dominant response, while the other has a mixed/CD8+ T cell dominant response. Lastly, we demonstrated that in spheroids the initial structure and movement rules work synergistically to reduce bacterial load. These findings provide valuable insights into how the structural complexity of in vitro models impacts immune responses. Moreover, our study has implications for engineering more physiologically relevant in vitro models and advancing our understanding of TB pathogenesis and potential therapeutic interventions. Author summary: Tuberculosis (TB) is a respiratory infection that is associated with granulomas, organized clusters of immune cells that form around the bacteria. Recent work has focused on making in vitro models that better approximate these granulomas, such as our spheroid granuloma model. We found that this spheroid was better able to control bacteria than its traditional culture counterpart, so, in this work, we use agent-based modeling to explore the causes of this differential control. The simulated spheroids have increased macrophage activation. Surprisingly, this is not due to larger numbers of activated T cells, which activate these macrophages. Rather, the increase in activated macrophages is due to their proximity to fewer activated T cells. We also see a subset of simulations where the spheroids have increased cytotoxic CD8+ T cell activation. The simulation is further used to simulate changes in the structure of the in vitro models to explore its impact on immune response and bacterial control. These findings reveal the importance of structural complexity in in vitro models and offer insights into TB pathogenesis. Ultimately, our work paves the way for more realistic in vitro models and a deeper understanding of how our bodies combat TB. [ABSTRACT FROM AUTHOR]

The human immune system consists of a highly intelligent network of billions of independent, self-organized cells that interact with each other. Machine learning (ML) is an artificial intelligence (AI) tool that automatically processes huge amounts of image data. Immunotherapies have revolutionized the treatment of blood cancer. Specifically, one such therapy involves engineering immune cells to express chimeric antigen receptors (CAR), which combine tumor antigen specificity with immune cell activation in a single receptor. To improve their efficacy and expand their applicability to solid tumors, scientists optimize different CARs with different modifications. However, predicting and ranking the efficacy of different "off-the-shelf" immune products (e.g., CAR or Bispecific T-cell Engager [BiTE]) and selection of clinical responders are challenging in clinical practice. Meanwhile, identifying the optimal CAR construct for a researcher to further develop a potential clinical application is limited by the current, time-consuming, costly, and labor-intensive conventional tools used to evaluate efficacy. Particularly, more than 30 years of immunological synapse (IS) research data demonstrate that T cell efficacy is not only controlled by the specificity and avidity of the tumor antigen and T cell interaction, but also it depends on a collective process, involving multiple adhesion and regulatory molecules, as well as tumor microenvironment, spatially and temporally organized at the IS formed by cytotoxic T lymphocytes (CTL) and natural killer (NK) cells. The optimal function of cytotoxic lymphocytes (including CTL and NK) depends on IS quality. Recognizing the inadequacy of conventional tools and the importance of IS in immune cell functions, we investigate a new strategy for assessing CAR-T efficacy by quantifying CAR IS quality using the glass-support planar lipid bilayer system combined with ML-based data analysis. Previous studies in our group show that CAR-T IS quality correlates with antitumor activities in vitro and in vivo. However, current manually quantified IS quality data analysis is time-consuming and labor-intensive with low accuracy, reproducibility, and repeatability. In this study, we develop a novel ML-based method to quantify thousands of CAR cell IS images with enhanced accuracy and speed. Specifically, we used artificial neural networks (ANN) to incorporate object detection into segmentation. The proposed ANN model extracts the most useful information to differentiate different IS datasets. The network output is flexible and produces bounding boxes, instance segmentation, contour outlines (borders), intensities of the borders, and segmentations without borders. Based on requirements, one or a combination of this information is used in statistical analysis. The ML-based automated algorithm quantified CAR-T IS data correlates with the clinical responder and non-responder treated with Kappa-CAR-T cells directly from patients. The results suggest that CAR cell IS quality can be used as a potential composite biomarker and correlates with antitumor activities in patients, which is sufficiently discriminative to further test the CAR IS quality as a clinical biomarker to predict response to CAR immunotherapy in cancer. For translational research, the method developed here can also provide guidelines for designing and optimizing numerous CAR constructs for potential clinical development. Trial Registration: ClinicalTrials.gov NCT00881920. Author summary: Adoptive transfer of chimeric antigen receptor (CAR)-modified immune cells (including CAR-T and CAR-NK cells) have revolutionized the treatment of cancer with success in clinical trials treating multiple myeloma, leukemia, sarcoma, and neuroblastoma. However, CAR-modified immune cells (particularly CAR-T cells) must form a functional immunological synapse (IS) with their susceptible tumor cells to be effective in clinics. Currently, there are no effective biomarkers to predict CAR efficacy in vivo. In this study, we develop a state-of-the-art machine learning (ML) detection, and segmentation method to measure the quality of the CAR-T cell IS using CAR-T samples from patients. We automate the IS quality analysis to develop effective prognostic applications of CAR-T therapies for cancer patients. The fast, easy-to-implement Synapse Predicts Efficacy (SPE) assay we propose will streamline CAR development and selection, ultimately optimizing clinical outcome(s) for patients undergoing these rapidly evolving immunotherapies. This technology can lead to development of fast and easy tools to predict CAR-T cell effectiveness in cancer patients. [ABSTRACT FROM AUTHOR]

New technologies, such as multiplex immunofluorescence microscopy (mIF), are being developed and used for the assessment and visualization of the tumor immune microenvironment (TIME). These assays produce not only an estimate of the abundance of immune cells in the TIME, but also their spatial locations. However, there are currently few approaches to analyze the spatial context of the TIME. Therefore, we have developed a framework for the spatial analysis of the TIME using Ripley's K, coupled with a permutation-based framework to estimate and measure the departure from complete spatial randomness (CSR) as a measure of the interactions between immune cells. This approach was then applied to epithelial ovarian cancer (EOC) using mIF collected on intra-tumoral regions of interest (ROIs) and tissue microarrays (TMAs) from 160 high-grade serous ovarian carcinoma patients in the African American Cancer Epidemiology Study (AACES) (94 subjects on TMAs resulting in 263 tissue cores; 93 subjects with 260 ROIs; 27 subjects with both TMA and ROI data). Cox proportional hazard models were constructed to determine the association of abundance and spatial clustering of tumor-infiltrating lymphocytes (CD3+), cytotoxic T-cells (CD8+CD3+), and regulatory T-cells (CD3+FoxP3+) with overall survival. Analysis was done on TMA and ROIs, treating the TMA data as validation of the finding from the ROIs. We found that EOC patients with high abundance and low spatial clustering of tumor-infiltrating lymphocytes and T-cell subsets in their tumors had the best overall survival. Additionally, patients with EOC tumors displaying high co-occurrence of cytotoxic T-cells and regulatory T-cells had the best overall survival. Grouping women with ovarian cancer based on both cell abundance and spatial contexture showed better discrimination for survival than grouping ovarian cancer cases only by cell abundance. These findings underscore the prognostic importance of evaluating not only immune cell abundance but also the spatial contexture of the immune cells in the TIME. In conclusion, the application of this spatial analysis framework to the study of the TIME could lead to the identification of immune content and spatial architecture that could aid in the determination of patients that are likely to respond to immunotherapies. Author summary: New technologies, such as multiplex immunofluorescence microscopy, are being developed and used for the assessment and visualization of the tumor immune microenvironment (TIME). These assays produce not only an estimate of the abundance of immune cells in the TIME, but also their spatial locations; however, there are currently few approaches to analyze the spatial context of the TIME. Thus, we have developed a framework for the spatial analysis of the TIME and applied this method to the analysis of T-cells collected from patients with high-grade serous ovarian carcinoma in the African American Cancer Epidemiology Study. We found that patients with high abundance and low spatial clustering of tumor-infiltrating lymphocytes and T-cell subsets in their tumors had the best overall survival. Additionally, best survival was observed for patients with tumors displaying high co-occurrence of cytotoxic T-cells and regulatory T-cells. These findings underscore the prognostic importance of evaluating not only immune cell abundance but also the spatial contexture of the immune cells in the ovarian TIME. The use of our framework for spatial analysis of the TIME and immune cell clustering may be applicable in other cancers and provide a novel approach to identification of biomarkers for predicting patient outcomes. [ABSTRACT FROM AUTHOR]

Persistent destruction of pancreatic β-cells in type 1 diabetes (T1D) results from multifaceted pancreatic cellular interactions in various phase progressions. Owing to the inherent heterogeneity of coupled nonlinear systems, computational modeling based on T1D etiology help achieve a systematic understanding of biological processes and T1D health outcomes. The main challenge is to design such a reliable framework to analyze the highly orchestrated biology of T1D based on the knowledge of cellular networks and biological parameters. We constructed a novel hybrid in-silico computational model to unravel T1D onset, progression, and prevention in a non-obese-diabetic mouse model. The computational approach that integrates mathematical modeling, agent-based modeling, and advanced statistical methods allows for modeling key biological parameters and time-dependent spatial networks of cell behaviors. By integrating interactions between multiple cell types, model results captured the individual-specific dynamics of T1D progression and were validated against experimental data for the number of infiltrating CD8+T-cells. Our simulation results uncovered the correlation between five auto-destructive mechanisms identifying a combination of potential therapeutic strategies: the average lifespan of cytotoxic CD8+T-cells in islets; the initial number of apoptotic β-cells; recruitment rate of dendritic-cells (DCs); binding sites on DCs for naïve CD8+T-cells; and time required for DCs movement. Results from therapy-directed simulations further suggest the efficacy of proposed therapeutic strategies depends upon the type and time of administering therapy interventions and the administered amount of therapeutic dose. Our findings show modeling immunogenicity that underlies autoimmune T1D and identifying autoantigens that serve as potential biomarkers are two pressing parameters to predict disease onset and progression. Author summary: Pancreatic β-cells secreting insulin upon metabolic demand are the core to regulate glucose concentration for healthy states. Type 1 diabetes (T1D) is a group of metabolic disorders in which β-cells are targeted by biased decisions of the immune system. The barrier in sampling experimental data from pancreatic tissues in high-risk T1D subjects and the complexity of the mechanisms regulating T1D makes the use of quantitative modeling approaches an intriguing opportunity to analyze this disease. To decipher complex system behaviors resulting from inter- and intra-cellular and signaling networks linking the immune system and metabolism during T1D progression, we developed a hybrid computational framework to unravel high computational complexity levels for individual-specific T1D development in non-obese diabetic mice. We also discovered non-intuitive biological parameters, the potential for therapeutic strategies. These effective strategies associated with hybrid agent-based modeling can serve as a prototype for in- silico experiments of therapy-directed trials potentially regulating T1D progression. We expect that the proposed methods will play an important role in the quest for standardized applications of agent-based modeling to simulating complex disease progression. [ABSTRACT FROM AUTHOR]