Sickle Cell Anemia Mediates Carotid Artery Expansive Remodeling That Can Be Prevented by Inhibition of JNK (c-Jun N-Terminal Kinase).

OBJECTIVE: Sickle cell anemia (SCA) causes chronic inflammation and multiorgan damage. Less understood are the arterial complications, most evident by increased strokes among children. Proteolytic mechanisms, biomechanical consequences, and pharmaceutical inhibitory strategies were studied in a mouse model to provide a platform for mechanistic and intervention studies of large artery damage due to sickle cell disease. Approach and Results: Townes humanized transgenic mouse model of SCA was used to test the hypothesis that elastic lamina and structural damage in carotid arteries increased with age and was accelerated in mice homozygous for SCA (sickle cell anemia homozygous genotype [SS]) due to inflammatory signaling pathways activating proteolytic enzymes. Elastic lamina fragmentation observed by 1 month in SS mice compared with heterozygous littermate controls (sickle cell trait heterozygous genotype [AS]). Positive immunostaining for cathepsin K, a powerful collagenase and elastase, confirmed accelerated proteolytic activity in SS carotids. Larger cross-sectional areas were quantified by magnetic resonance angiography and increased arterial compliance in SS carotids were also measured. Inhibiting JNK (c-jun N-terminal kinase) signaling with SP600125 significantly reduced cathepsin K expression, elastin fragmentation, and carotid artery perimeters in SS mice. By 5 months of age, continued medial thinning and collagen degradation was mitigated by treatment of SS mice with JNK inhibitor. CONCLUSIONS: Arterial remodeling due to SCA is mediated by JNK signaling, cathepsin proteolytic upregulation, and degradation of elastin and collagen. Demonstration in Townes mice establishes their utility for mechanistic studies of arterial vasculopathy, related complications, and therapeutic interventions for large artery damage due to SCA.

Age-dependent characterization of carotid and cerebral artery geometries in a transgenic mouse model of sickle cell anemia using ultrasound and microcomputed tomography.

To define morphological changes in carotid and cerebral arteries in sickle cell transgenic mice (SS) as they age, a combination of ultrasound and microcomputed tomography of plastinated arteries was used to quantify arterial dimensions and changes in mice 4, 12, and 24 weeks of age. 12-week SS mice had significantly larger common carotid artery diameters than AS mice, which continued through to the extracranial and intracranial portions of the internal carotid artery (ICA). There were also side specific differences in diameters between the left and right vessels. Significant ICA tapering along its length occurred by 12- and 24-weeks in SS mice, decreasing by as much as 70%. Significant narrowing along the length was also measured in SS anterior cerebral arteries at 12- and 24-weeks, but not AS. Collectively, these findings indicate that sickle cell anemia induces arterial remodeling in 12- and 24-weeks old mice. Catalog of measurements are also provided for the common carotid, internal carotid, anterior cerebral, and middle cerebral arteries for AS and SS genotypes, as a reference for other investigators using mathematical and computational models of age-dependent arterial complications caused by sickle cell anemia.

Single-Cell and Spatial Analysis of Emergent Organoid Platforms.

Organoids have emerged as a promising advancement of the two-dimensional (2D) culture systems to improve studies in organogenesis, drug discovery, precision medicine, and regenerative medicine applications. Organoids can self-organize as three-dimensional (3D) tissues derived from stem cells and patient tissues to resemble organs. This chapter presents growth strategies, molecular screening methods, and emerging issues of the organoid platforms. Single-cell and spatial analysis resolve organoid heterogeneity to obtain information about the structural and molecular cellular states. Culture media diversity and varying lab-to-lab practices have resulted in organoid-to-organoid variability in morphology and cell compositions. An essential resource is an organoid atlas that can catalog protocols and standardize data analysis for different organoid types. Molecular profiling of individual cells in organoids and data organization of the organoid landscape will impact biomedical applications from basic science to translational use.

Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology.

Metabolic reprogramming in cancer and immune cells occurs to support their increasing energy needs in biological tissues. Here we propose Single Cell Spatially resolved Metabolic (scSpaMet) framework for joint protein-metabolite profiling of single immune and cancer cells in male human tissues by incorporating untargeted spatial metabolomics and targeted multiplexed protein imaging in a single pipeline. We utilized the scSpaMet to profile cell types and spatial metabolomic maps of 19507, 31156, and 8215 single cells in human lung cancer, tonsil, and endometrium tissues, respectively. The scSpaMet analysis revealed cell type-dependent metabolite profiles and local metabolite competition of neighboring single cells in human tissues. Deep learning-based joint embedding revealed unique metabolite states within cell types. Trajectory inference showed metabolic patterns along cell differentiation paths. Here we show scSpaMet's ability to quantify and visualize the cell-type specific and spatially resolved metabolic-protein mapping as an emerging tool for systems-level understanding of tissue biology.

Spatial subcellular organelle networks in single cells.

Organelles play important roles in human health and disease, such as maintaining homeostasis, regulating growth and aging, and generating energy. Organelle diversity in cells not only exists between cell types but also between individual cells. Therefore, studying the distribution of organelles at the single-cell level is important to understand cellular function. Mesenchymal stem cells are multipotent cells that have been explored as a therapeutic method for treating a variety of diseases. Studying how organelles are structured in these cells can answer questions about their characteristics and potential. Herein, rapid multiplexed immunofluorescence (RapMIF) was performed to understand the spatial organization of 10 organelle proteins and the interactions between them in the bone marrow (BM) and umbilical cord (UC) mesenchymal stem cells (MSCs). Spatial correlations, colocalization, clustering, statistical tests, texture, and morphological analyses were conducted at the single cell level, shedding light onto the interrelations between the organelles and comparisons of the two MSC subtypes. Such analytics toolsets indicated that UC MSCs exhibited higher organelle expression and spatially spread distribution of mitochondria accompanied by several other organelles compared to BM MSCs. This data-driven single-cell approach provided by rapid subcellular proteomic imaging enables personalized stem cell therapeutics.

Multiplexed protein profiling reveals spatial subcellular signaling networks.

Protein-protein interaction networks are altered in multi-gene dysregulations in many disorders. Image-based protein multiplexing sheds light on signaling pathways to dissect cell-to-cell heterogeneity, previously masked by the bulk assays. Herein, we present a rapid multiplexed immunofluorescence (RapMIF) method measuring up to 25-plex spatial protein maps from cultures and tissues at subcellular resolution, providing combinatorial 272 pairwise and 1,360 tri-protein signaling states across 33 multiplexed pixel-level clusters. The RapMIF pipeline automated staining, bleaching, and imaging of the biospecimens in a single platform. RapMIF showed that WNT/ß-catenin signaling upregulated upon the inhibition of the AKT/mTOR pathway. Subcellular protein images demonstrated translocation patterns, spatial receptor discontinuity, and subcellular signaling clusters in single cells. Signaling networks exhibited spatial redistribution of signaling proteins in drug-responsive cultures. Machine learning analysis predicted the phosphorylated ß-catenin expression from interconnected signaling protein images. RapMIF is an ideal signaling discovery approach for precision therapy design.

Extracellular Matrix in Synthetic Hydrogel-Based Prostate Cancer Organoids Regulate Therapeutic Response to EZH2 and DRD2 Inhibitors.

Following treatment with androgen receptor (AR) pathway inhibitors, ≈20% of prostate cancer patients progress by shedding their AR-dependence. These tumors undergo epigenetic reprogramming turning castration-resistant prostate cancer adenocarcinoma (CRPC-Adeno) into neuroendocrine prostate cancer (CRPC-NEPC). No targeted therapies are available for CRPC-NEPCs, and there are minimal organoid models to discover new therapeutic targets against these aggressive tumors. Here, using a combination of patient tumor proteomics, RNA sequencing, spatial-omics, and a synthetic hydrogel-based organoid, putative extracellular matrix (ECM) cues that regulate the phenotypic, transcriptomic, and epigenetic underpinnings of CRPC-NEPCs are defined. Short-term culture in tumor-expressed ECM differentially regulated DNA methylation and mobilized genes in CRPC-NEPCs. The ECM type distinctly regulates the response to small-molecule inhibitors of epigenetic targets and Dopamine Receptor D2 (DRD2), the latter being an understudied target in neuroendocrine tumors. In vivo patient-derived xenograft in immunocompromised mice showed strong anti-tumor response when treated with a DRD2 inhibitor. Finally, we demonstrate that therapeutic response in CRPC-NEPCs under drug-resistant ECM conditions can be overcome by first cellular reprogramming with epigenetic inhibitors, followed by DRD2 treatment. The synthetic organoids suggest the regulatory role of ECM in therapeutic response to targeted therapies in CRPC-NEPCs and enable the discovery of therapies to overcome resistance.

Spatially visualized single-cell pathology of highly multiplexed protein profiles in health and disease.

Deep molecular profiling of biological tissues is an indicator of health and disease. We used imaging mass cytometry (IMC) to acquire spatially resolved 20-plex protein data in tissue sections from normal and chronic tonsillitis cases. We present SpatialViz, a suite of algorithms to explore spatial relationships in multiplexed tissue images by visualizing and quantifying single-cell granularity and anatomical complexity in diverse multiplexed tissue imaging data. Single-cell and spatial maps confirmed that CD68+ cells were correlated with the enhanced Granzyme B expression and CD3+ cells exhibited enrichment of CD4+ phenotype in chronic tonsillitis. SpatialViz revealed morphological distributions of cellular organizations in distinct anatomical areas, spatially resolved single-cell associations across anatomical categories, and distance maps between the markers. Spatial topographic maps showed the unique organization of different tissue layers. The spatial reference framework generated network-based comparisons of multiplex data from healthy and diseased tonsils. SpatialViz is broadly applicable to multiplexed tissue biology.

Spatially resolved 3D metabolomic profiling in tissues.

Spatially resolved RNA and protein molecular analyses have revealed unexpected heterogeneity of cells. Metabolic analysis of individual cells complements these single-cell studies. Here, we present a three-dimensional spatially resolved metabolomic profiling framework (3D-SMF) to map out the spatial organization of metabolic fragments and protein signatures in immune cells of human tonsils. In this method, 3D metabolic profiles were acquired by time-of-flight secondary ion mass spectrometry to profile up to 189 compounds. Ion beams were used to measure sub-5-nanometer layers of tissue across 150 sections of a tonsil. To incorporate cell specificity, tonsil tissues were labeled by an isotope-tagged antibody library. To explore relations of metabolic and cellular features, we carried out data reduction, 3D spatial correlations and classifications, unsupervised K-means clustering, and network analyses. Immune cells exhibited spatially distinct lipidomic fragment distributions in lymphatic tissue. The 3D-SMF pipeline affects studying the immune cells in health and disease.

Multiplex bioimaging of single-cell spatial profiles for precision cancer diagnostics and therapeutics.

Cancers exhibit functional and structural diversity in distinct patients. In this mass, normal and malignant cells create tumor microenvironment that is heterogeneous among patients. A residue from primary tumors leaks into the bloodstream as cell clusters and single cells, providing clues about disease progression and therapeutic response. The complexity of these hierarchical microenvironments needs to be elucidated. Although tumors comprise ample cell types, the standard clinical technique is still the histology that is limited to a single marker. Multiplexed imaging technologies open new directions in pathology. Spatially resolved proteomic, genomic, and metabolic profiles of human cancers are now possible at the single-cell level. This perspective discusses spatial bioimaging methods to decipher the cascade of microenvironments in solid and liquid biopsies. A unique synthesis of top-down and bottom-up analysis methods is presented. Spatial multi-omics profiles can be tailored to precision oncology through artificial intelligence. Data-driven patient profiling enables personalized medicine and beyond.

Multiplex Spatial Bioimaging for Combination Therapy Design.

Multiplex spatial analyses dissect the heterogeneous cellular abundances and interactions in tumors. Single-cell bioimaging profiles many disease-associated protein biomarkers in patient biopsies to inform the design of cancer therapies. Guided by the mechanistic insights from spatial cellular maps, combination therapy can efficiently eliminate cancers with reduced off-targets, resistance, and relapse.

COVID-19 Diagnostics, Tools, and Prevention.

The Coronavirus Disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), outbreak from Wuhan City, Hubei province, China in 2019 has become an ongoing global health emergency. The emerging virus, SARS-CoV-2, causes coughing, fever, muscle ache, and shortness of breath or dyspnea in symptomatic patients. The pathogenic particles that are generated by coughing and sneezing remain suspended in the air or attach to a surface to facilitate transmission in an aerosol form. This review focuses on the recent trends in pandemic biology, diagnostics methods, prevention tools, and policies for COVID-19 management. To meet the growing demand for medical supplies during the COVID-19 era, a variety of personal protective equipment (PPE) and ventilators have been developed using do-it-yourself (DIY) manufacturing. COVID-19 diagnosis and the prediction of virus transmission are analyzed by machine learning algorithms, simulations, and digital monitoring. Until the discovery of a clinically approved vaccine for COVID-19, pandemics remain a public concern. Therefore, technological developments, biomedical research, and policy development are needed to decipher the coronavirus mechanism and epidemiological characteristics, prevent transmission, and develop therapeutic drugs.