Multi-Niche Human Bone Marrow On-A-Chip for Studying the Interactions of Adoptive CAR-T Cell Therapies with Multiple Myeloma.

Multiple myeloma (MM), a cancer of bone marrow plasma cells, is the second-most common hematological malignancy. However, despite immunotherapies like chimeric antigen receptor (CAR)-T cells, relapse is nearly universal. The bone marrow (BM) microenvironment influences how MM cells survive, proliferate, and resist treatment. Yet, it is unclear which BM niches give rise to MM pathophysiology. Here, we present a 3D microvascularized culture system, which models the endosteal and perivascular bone marrow niches, allowing us to study MM-stroma interactions in the BM niche and model responses to therapeutic CAR-T cells. We demonstrated the prolonged survival of cell line-based and patient-derived multiple myeloma cells within our in vitro system and successfully flowed in donor-matched CAR-T cells. We then measured T cell survival, differentiation, and cytotoxicity against MM cells using a variety of analysis techniques. Our MM-on-a-chip system could elucidate the role of the BM microenvironment in MM survival and therapeutic evasion and inform the rational design of next-generation therapeutics. TEASER: A multiple myeloma model can study why the disease is still challenging to treat despite options that work well in other cancers.

Spatial Morphoproteomic Features Predict Uniqueness of Immune Microarchitectures and Responses in Lymphoid Follicles.

Multiplex imaging technologies allow the characterization of single cells in their cellular environments. Understanding the organization of single cells within their microenvironment and quantifying disease-status related biomarkers is essential for multiplex datasets. Here we proposed SNOWFLAKE, a graph neural network framework pipeline for the prediction of disease-status from combined multiplex cell expression and morphology in human B-cell follicles. We applied SNOWFLAKE to a multiplex dataset related to COVID-19 infection in humans and showed better predictive power of the SNOWFLAKE pipeline compared to other machine learning and deep learning methods. Moreover, we combined morphological features inside graph edge features to utilize attribution methods for extracting disease-relevant motifs from single-cell spatial graphs. The underlying subgraphs were further analyzed and associated with disease status across the dataset. We showed that SNOWFLAKE successfully extracted significant low dimensional embedding from subgraphs with a clear separation between disease status and helped characterize unique cellular interactions in the subgraphs. SNOWFLAKE is a generalizable pipeline for the analysis of multiplex imaging data modality by extracting disease-relevant subgraphs guided by graph-level prediction.

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.

Subcellular spatially resolved gene neighborhood networks in single cells.

Image-based spatial omics methods such as fluorescence in situ hybridization (FISH) generate molecular profiles of single cells at single-molecule resolution. Current spatial transcriptomics methods focus on the distribution of single genes. However, the spatial proximity of RNA transcripts can play an important role in cellular function. We demonstrate a spatially resolved gene neighborhood network (spaGNN) pipeline for the analysis of subcellular gene proximity relationships. In spaGNN, machine-learning-based clustering of subcellular spatial transcriptomics data yields subcellular density classes of multiplexed transcript features. The nearest-neighbor analysis produces heterogeneous gene proximity maps in distinct subcellular regions. We illustrate the cell-type-distinguishing capability of spaGNN using multiplexed error-robust FISH data of fibroblast and U2-OS cells and sequential FISH data of mesenchymal stem cells (MSCs), revealing tissue-source-specific MSC transcriptomics and spatial distribution characteristics. Overall, the spaGNN approach expands the spatial features that can be used for cell-type classification tasks.

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.

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.

Combinatorial treatment rescues tumour-microenvironment-mediated attenuation of MALT1 inhibitors in B-cell lymphomas.

Activated B-cell-like diffuse large B-cell lymphomas (ABC-DLBCLs) are characterized by constitutive activation of nuclear factor κB driven by the B-cell receptor (BCR) and Toll-like receptor (TLR) pathways. However, BCR-pathway-targeted therapies have limited impact on DLBCLs. Here we used >1,100 DLBCL patient samples to determine immune and extracellular matrix cues in the lymphoid tumour microenvironment (Ly-TME) and built representative synthetic-hydrogel-based B-cell-lymphoma organoids accordingly. We demonstrate that Ly-TME cellular and biophysical factors amplify the BCR-MYD88-TLR9 multiprotein supercomplex and induce cooperative signalling pathways in ABC-DLBCL cells, which reduce the efficacy of compounds targeting the BCR pathway members Bruton tyrosine kinase and mucosa-associated lymphoid tissue lymphoma translocation protein 1 (MALT1). Combinatorial inhibition of multiple aberrant signalling pathways induced higher antitumour efficacy in lymphoid organoids and implanted ABC-DLBCL patient tumours in vivo. Our studies define the complex crosstalk between malignant ABC-DLBCL cells and Ly-TME, and provide rational combinatorial therapies that rescue Ly-TME-mediated attenuation of treatment response to MALT1 inhibitors.

Spatially variant immune infiltration scoring in human cancer tissues.

The Immunoscore is a method to quantify the immune cell infiltration within cancers to predict the disease prognosis. Previous immune profiling approaches relied on limited immune markers to establish patients' tumor immunity. However, immune cells exhibit a higher-level complexity that is typically not obtained by the conventional immunohistochemistry methods. Herein, we present a spatially variant immune infiltration score, termed as SpatialVizScore, to quantify immune cells infiltration within lung tumor samples using multiplex protein imaging data. Imaging mass cytometry (IMC) was used to target 26 markers in tumors to identify stromal, immune, and cancer cell states within 26 human tissues from lung cancer patients. Unsupervised clustering methods dissected the spatial infiltration of cells in tissue using the high-dimensional analysis of 16 immune markers and other cancer and stroma enriched labels to profile alterations in the tumors' immune infiltration patterns. Spatially resolved maps of distinct tumors determined the spatial proximity and neighborhoods of immune-cancer cell pairs. These SpatialVizScore maps provided a ranking of patients' tumors consisting of immune inflamed, immune suppressed, and immune cold states, demonstrating the tumor's immune continuum assigned to three distinct infiltration score ranges. Several inflammatory and suppressive immune markers were used to establish the cell-based scoring schemes at the single-cell and pixel-level, depicting the cellular spectra in diverse lung tissues. Thus, SpatialVizScore is an emerging quantitative method to deeply study tumor immunology in cancer tissues.

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.

Virtual and augmented reality for biomedical applications.

3D visualization technologies such as virtual reality (VR), augmented reality (AR), and mixed reality (MR) have gained popularity in the recent decade. Digital extended reality (XR) technologies have been adopted in various domains ranging from entertainment to education because of their accessibility and affordability. XR modalities create an immersive experience, enabling 3D visualization of the content without a conventional 2D display constraint. Here, we provide a perspective on XR in current biomedical applications and demonstrate case studies using cell biology concepts, multiplexed proteomics images, surgical data for heart operations, and cardiac 3D models. Emerging challenges associated with XR technologies in the context of adverse health effects and a cost comparison of distinct platforms are discussed. The presented XR platforms will be useful for biomedical education, medical training, surgical guidance, and molecular data visualization to enhance trainees' and students' learning, medical operation accuracy, and the comprehensibility of complex biological systems.

Subcellular localization of biomolecules and drug distribution by high-definition ion beam imaging.

Simultaneous visualization of the relationship between multiple biomolecules and their ligands or small molecules at the nanometer scale in cells will enable greater understanding of how biological processes operate. We present here high-definition multiplex ion beam imaging (HD-MIBI), a secondary ion mass spectrometry approach capable of high-parameter imaging in 3D of targeted biological entities and exogenously added structurally-unmodified small molecules. With this technology, the atomic constituents of the biomolecules themselves can be used in our system as the "tag" and we demonstrate measurements down to ~30 nm lateral resolution. We correlated the subcellular localization of the chemotherapy drug cisplatin simultaneously with five subnuclear structures. Cisplatin was preferentially enriched in nuclear speckles and excluded from closed-chromatin regions, indicative of a role for cisplatin in active regions of chromatin. Unexpectedly, cells surviving multi-drug treatment with cisplatin and the BET inhibitor JQ1 demonstrated near total cisplatin exclusion from the nucleus, suggesting that selective subcellular drug relocalization may modulate resistance to this important chemotherapeutic treatment. Multiplexed high-resolution imaging techniques, such as HD-MIBI, will enable studies of biomolecules and drug distributions in biologically relevant subcellular microenvironments by visualizing the processes themselves in concert, rather than inferring mechanism through surrogate analyses.

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.

Single-cell analysis reveals effective siRNA delivery in brain tumors with microbubble-enhanced ultrasound and cationic nanoparticles.

RNA-based therapies offer unique advantages for treating brain tumors. However, tumor penetrance and uptake are hampered by RNA therapeutic size, charge, and need to be "packaged" in large carriers to improve bioavailability. Here, we have examined delivery of siRNA, packaged in 50-nm cationic lipid-polymer hybrid nanoparticles (LPHs:siRNA), combined with microbubble-enhanced focused ultrasound (MB-FUS) in pediatric and adult preclinical brain tumor models. Using single-cell image analysis, we show that MB-FUS in combination with LPHs:siRNA leads to more than 10-fold improvement in siRNA delivery into brain tumor microenvironments of the two models. MB-FUS delivery of Smoothened (SMO) targeting siRNAs reduces SMO protein production and markedly increases tumor cell death in the SMO-activated medulloblastoma model. Moreover, our analysis reveals that MB-FUS and nanoparticle properties can be optimized to maximize delivery in the brain tumor microenvironment, thereby serving as a platform for developing next-generation tunable delivery systems for RNA-based therapy in brain tumors.

Nanoscopic subcellular imaging enabled by ion beam tomography.

Multiplexed ion beam imaging (MIBI) has been previously used to profile multiple parameters in two dimensions in single cells within tissue slices. Here, a mathematical and technical framework for three-dimensional (3D) subcellular MIBI is presented. Ion-beam tomography (IBT) compiles ion beam images that are acquired iteratively across successive, multiple scans, and later assembled into a 3D format without loss of depth resolution. Algorithmic deconvolution, tailored for ion beams, is then applied to the transformed ion image series, yielding 4-fold enhanced ion beam data cubes. To further generate 3D sub-ion-beam-width precision visuals, isolated ion molecules are localized in the raw ion beam images, creating an approach coined as SILM, secondary ion beam localization microscopy, providing sub-25 nm accuracy in original ion images. Using deep learning, a parameter-free reconstruction method for ion beam tomograms with high accuracy is developed for low-density targets. In cultured cancer cells and tissues, IBT enables accessible visualization of 3D volumetric distributions of genomic regions, RNA transcripts, and protein factors with 5 nm axial resolution using isotope-enrichments and label-free elemental analyses. Multiparameter imaging of subcellular features at near macromolecular resolution is implemented by the IBT tools as a general biocomputation pipeline for imaging mass spectrometry.

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.

Cellular sociology regulates the hierarchical spatial patterning and organization of cells in organisms.

Advances in single-cell biotechnology have increasingly revealed interactions of cells with their surroundings, suggesting a cellular society at the microscale. Similarities between cells and humans across multiple hierarchical levels have quantitative inference potential for reaching insights about phenotypic interactions that lead to morphological forms across multiple scales of cellular organization, namely cells, tissues and organs. Here, the functional and structural comparisons between how cells and individuals fundamentally socialize to give rise to the spatial organization are investigated. Integrative experimental cell interaction assays and computational predictive methods shape the understanding of societal perspective in the determination of the cellular interactions that create spatially coordinated forms in biological systems. Emerging quantifiable models from a simpler biological microworld such as bacterial interactions and single-cell organisms are explored, providing a route to model spatio-temporal patterning of morphological structures in humans. This analogical reasoning framework sheds light on structural patterning principles as a result of biological interactions across the cellular scale and up.

Isotopically Encoded Nanotags for Multiplexed Ion Beam Imaging.

High-dimensional profiling of markers and analytes using approaches, such as barcoded fluorescent imaging with repeated labeling and mass cytometry has allowed visualization of biological processes at the single-cell level. To address limitations of sensitivity and mass-channel capacity, a nanobarcoding platform is developed for multiplexed ion beam imaging (MIBI) using secondary ion beam spectrometry that utilizes fabricated isotopically encoded nanotags. Use of combinatorial isotope distributions in 100 nm sized nanotags expands the labeling palette to overcome the spectral bounds of mass channels. As a proof-of-principle, a four-digit (i.e., 0001-1111) barcoding scheme is demonstrated to detect 16 variants of 2H, 19F, 79/81Br, and 127I elemental barcode sets that are encoded in silica nanoparticle matrices. A computational debarcoding method and an automated machine learning analysis approach are developed to extract barcodes for accurate quantification of spatial nanotag distributions in large ion beam imaging areas up to 0.6 mm2. Isotopically encoded nanotags should boost the performance of mass imaging platforms, such as MIBI and other elemental-based bioimaging approaches.