An immunogenic cell injury module for the single-cell multiplexed activity metabolomics platform to identify promising anti-cancer natural products.

Natural products constitute and significantly impact many current anti-cancer medical interventions. A subset of natural products induces injury processes in malignant cells that recruit and activate host immune cells to produce an adaptive anti-cancer immune response, a process known as immunogenic cell death. However, a challenge in the field is to delineate forms of cell death and injury that best promote durable antitumor immunity. Addressing this with a single-cell chemical biology natural product discovery platform, like multiplex activity metabolomics, would be especially valuable in human leukemia, where cancer cells are heterogeneous and may react differently to the same compounds. Herein, a new ten-color, fluorescent cell barcoding-compatible module measuring six immunogenic cell injury signaling readouts are as follows: DNA damage response (γH2AX), apoptosis (cCAS3), necroptosis (p-MLKL), mitosis (p-Histone H3), autophagy (LC3), and the unfolded protein response (p-EIF2α). A proof-of-concept screen was performed to validate functional changes in single cells induced by secondary metabolites with known mechanisms within bacterial extracts. This assay was then applied in multiplexed activity metabolomics to reveal an unexpected mammalian cell injury profile induced by the natural product narbomycin. Finally, the functional consequences of injury pathways on immunogenicity were compared with three canonical assays for immunogenic hallmarks, ATP, HMGB1, and calreticulin, to correlate secondary metabolite-induced cell injury profiles with canonical markers of immunogenic cell death. In total, this work demonstrated a new phenotypic screen for discovery of natural products that modulate injury response pathways that can contribute to cancer immunogenicity.

An Expandable Informatics Framework for Enhancing Central Cancer Registries with Digital Pathology Specimens, Computational Imaging Tools, and Advanced Mining Capabilities.

BACKGROUND: Population-based state cancer registries are an authoritative source for cancer statistics in the United States. They routinely collect a variety of data, including patient demographics, primary tumor site, stage at diagnosis, first course of treatment, and survival, on every cancer case that is reported across all U.S. states and territories. The goal of our project is to enrich NCI's Surveillance, Epidemiology, and End Results (SEER) registry data with high-quality population-based biospecimen data in the form of digital pathology, machine-learning-based classifications, and quantitative histopathology imaging feature sets (referred to here as Pathomics features). MATERIALS AND METHODS: As part of the project, the underlying informatics infrastructure was designed, tested, and implemented through close collaboration with several participating SEER registries to ensure consistency with registry processes, computational scalability, and ability to support creation of population cohorts that span multiple sites. Utilizing computational imaging algorithms and methods to both generate indices and search for matches makes it possible to reduce inter- and intra-observer inconsistencies and to improve the objectivity with which large image repositories are interrogated. RESULTS: Our team has created and continues to expand a well-curated repository of high-quality digitized pathology images corresponding to subjects whose data are routinely collected by the collaborating registries. Our team has systematically deployed and tested key, visual analytic methods to facilitate automated creation of population cohorts for epidemiological studies and tools to support visualization of feature clusters and evaluation of whole-slide images. As part of these efforts, we are developing and optimizing advanced search and matching algorithms to facilitate automated, content-based retrieval of digitized specimens based on their underlying image features and staining characteristics. CONCLUSION: To meet the challenges of this project, we established the analytic pipelines, methods, and workflows to support the expansion and management of a growing repository of high-quality digitized pathology and information-rich, population cohorts containing objective imaging and clinical attributes to facilitate studies that seek to discriminate among different subtypes of disease, stratify patient populations, and perform comparisons of tumor characteristics within and across patient cohorts. We have also successfully developed a suite of tools based on a deep-learning method to perform quantitative characterizations of tumor regions, assess infiltrating lymphocyte distributions, and generate objective nuclear feature measurements. As part of these efforts, our team has implemented reliable methods that enable investigators to systematically search through large repositories to automatically retrieve digitized pathology specimens and correlated clinical data based on their computational signatures.

PEGylated PLGA Nanoparticle Delivery of Eggmanone for T Cell Modulation: Applications in Rheumatic Autoimmunity.

BACKGROUND: Helper T cell activity is dysregulated in a number of diseases including those associated with rheumatic autoimmunity. Treatment options are limited and usually consist of systemic immune suppression, resulting in undesirable consequences from compromised immunity. Hedgehog (Hh) signaling has been implicated in the activation of T cells and the formation of the immune synapse, but remains understudied in the context of autoimmunity. Modulation of Hh signaling has the potential to enable controlled immunosuppression but a potential therapy has not yet been developed to leverage this opportunity. METHODS: In this work, we developed biodegradable nanoparticles to enable targeted delivery of eggmanone (Egm), a specific Hh inhibitor, to CD4+ T cell subsets. We utilized two FDA-approved polymers, poly(lactic-co-glycolic acid) and polyethylene glycol, to generate hydrolytically degradable nanoparticles. Furthermore, we employed maleimide-thiol mediated conjugation chemistry to decorate nanoparticles with anti-CD4 F(ab') antibody fragments to enable targeted delivery of Egm. RESULTS: Our novel delivery system achieved a highly specific association with the majority of CD4+ T cells present among a complex cell population. Additionally, we have demonstrated antigen-specific inhibition of CD4+ T cell responses mediated by nanoparticle-formulated Egm. CONCLUSION: This work is the first characterization of Egm's immunomodulatory potential. Importantly, this study also suggests the potential benefit of a biodegradable delivery vehicle that is rationally designed for preferential interaction with a specific immune cell subtype for targeted modulation of Hh signaling.

Real-time visualization of titin dynamics reveals extensive reversible photobleaching in human induced pluripotent stem cell-derived cardiomyocytes.

Fluorescence recovery after photobleaching (FRAP) has been useful in delineating cardiac myofilament biology, and innovations in fluorophore chemistry have expanded the array of microscopic assays used. However, one assumption in FRAP is the irreversible photobleaching of fluorescent proteins after laser excitation. Here we demonstrate reversible photobleaching regarding the photoconvertible fluorescent protein mEos3.2. We used CRISPR/Cas9 genome editing in human induced pluripotent stem cells (hiPSCs) to knock-in mEos3.2 into the COOH terminus of titin to visualize sarcomeric titin incorporation and turnover. Upon cardiac induction, the titin-mEos3.2 fusion protein is expressed and integrated in the sarcomeres of hiPSC-derived cardiomyocytes (CMs). STORM imaging shows M-band clustered regions of bound titin-mEos3.2 with few soluble titin-mEos3.2 molecules. FRAP revealed a baseline titin-mEos3.2 fluorescence recovery of 68% and half-life of ~1.2 h, suggesting a rapid exchange of sarcomeric titin with soluble titin. However, paraformaldehyde-fixed and permeabilized titin-mEos3.2 hiPSC-CMs surprisingly revealed a 55% fluorescence recovery. Whole cell FRAP analysis in paraformaldehyde-fixed, cycloheximide-treated, and untreated titin-mEos3.2 hiPSC-CMs displayed no significant differences in fluorescence recovery. FRAP in fixed HEK 293T expressing cytosolic mEos3.2 demonstrates a 58% fluorescence recovery. These data suggest that titin-mEos3.2 is subject to reversible photobleaching following FRAP. Using a mouse titin-eGFP model, we demonstrate that no reversible photobleaching occurs. Our results reveal that reversible photobleaching accounts for the majority of titin recovery in the titin-mEos3.2 hiPSC-CM model and should warrant as a caution in the extrapolation of reliable FRAP data from specific fluorescent proteins in long-term cell imaging.