Graph Fourier transform for spatial omics representation and analyses of complex organs.

Spatial omics technologies decipher functional components of complex organs at cellular and subcellular resolutions. We introduce Spatial Graph Fourier Transform (SpaGFT) and apply graph signal processing to a wide range of spatial omics profiling platforms to generate their interpretable representations. This representation supports spatially variable gene identification and improves gene expression imputation, outperforming existing tools in analyzing human and mouse spatial transcriptomics data. SpaGFT can identify immunological regions for B cell maturation in human lymph nodes Visium data and characterize variations in secondary follicles using in-house human tonsil CODEX data. Furthermore, it can be integrated seamlessly into other machine learning frameworks, enhancing accuracy in spatial domain identification, cell type annotation, and subcellular feature inference by up to 40%. Notably, SpaGFT detects rare subcellular organelles, such as Cajal bodies and Set1/COMPASS complexes, in high-resolution spatial proteomics data. This approach provides an explainable graph representation method for exploring tissue biology and function.

Automated spatial omics landscape analysis approach reveals novel tissue architectures in ulcerative colitis.

The utility of spatial omics in leveraging cellular interactions in normal and diseased states for precision medicine is hampered by a lack of strategies for matching disease states with spatial heterogeneity-guided cellular annotations. Here we use a spatial context-dependent approach that matches spatial pattern detection to cell annotation. Using this approach in existing datasets from ulcerative colitis patient colonic biopsies, we identified architectural complexities and associated difficult-to-detect rare cell types in ulcerative colitis germinal-center B cell follicles. Our approach deepens our understanding of health and disease pathogenesis, illustrates a strategy for automating nested architecture detection for highly multiplexed spatial biology data, and informs precision diagnosis and therapeutic strategies.

Soft robotic platform for progressive and reversible aortic constriction in a small-animal model.

Our understanding of cardiac remodeling processes due to left ventricular pressure overload derives largely from animal models of aortic banding. However, these studies fail to enable control over both disease progression and reversal, hindering their clinical relevance. Here, we describe a method for progressive and reversible aortic banding based on an implantable expandable actuator that can be finely tuned to modulate aortic banding and debanding in a rat model. Through catheterization, imaging, and histologic studies, we demonstrate that our platform can recapitulate the hemodynamic and structural changes associated with pressure overload in a controllable manner. We leveraged soft robotics to enable noninvasive aortic debanding, demonstrating that these changes can be partly reversed because of cessation of the biomechanical stimulus. By recapitulating longitudinal disease progression and reversibility, this animal model could elucidate fundamental mechanisms of cardiac remodeling and optimize timing of intervention for pressure overload.

The tidyomics ecosystem: enhancing omic data analyses.

The growth of omic data presents evolving challenges in data manipulation, analysis and integration. Addressing these challenges, Bioconductor provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming offers a revolutionary data organization and manipulation standard. Here we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analyzing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas, spanning six data frameworks and ten analysis tools.

Investigating diversity and similarity between CBM13 modules and ricin-B lectin domains using sequence similarity networks.

BACKGROUND: The CBM13 family comprises carbohydrate-binding modules that occur mainly in enzymes and in several ricin-B lectins. The ricin-B lectin domain resembles the CBM13 module to a large extent. Historically, ricin-B lectins and CBM13 proteins were considered completely distinct, despite their structural and functional similarities. RESULTS: In this data mining study, we investigate structural and functional similarities of these intertwined protein groups. Because of the high structural and functional similarities, and differences in nomenclature usage in several databases, confusion can arise. First, we demonstrate how public protein databases use different nomenclature systems to describe CBM13 modules and putative ricin-B lectin domains. We suggest the introduction of a novel CBM13 domain identifier, as well as the extension of CAZy cross-references in UniProt to guard the distinction between CAZy and non-CAZy entries in public databases. Since similar problems may occur with other lectin families and CBM families, we suggest the introduction of novel CBM InterPro domain identifiers to all existing CBM families. Second, we investigated phylogenetic, nomenclatural and structural similarities between putative ricin-B lectin domains and CBM13 modules, making use of sequence similarity networks. We concluded that the ricin-B/CBM13 superfamily may be larger than initially thought and that several putative ricin-B lectin domains may display CAZyme functionalities, although biochemical proof remains to be delivered. CONCLUSIONS: Ricin-B lectin domains and CBM13 modules are associated groups of proteins whose database semantics are currently biased towards ricin-B lectins. Revision of the CAZy cross-reference in UniProt and introduction of a dedicated CBM13 domain identifier in InterPro may resolve this issue. In addition, our analyses show that several proteins with putative ricin-B lectin domains show very strong structural similarity to CBM13 modules. Therefore ricin-B lectin domains and CBM13 modules could be considered distant members of a larger ricin-B/CBM13 superfamily.

The tidyomics ecosystem: Enhancing omic data analyses.

The growth of omic data presents evolving challenges in data manipulation, analysis, and integration. Addressing these challenges, Bioconductor1 provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming2 offers a revolutionary standard for data organisation and manipulation. Here, we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning, and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analysing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas3, spanning six data frameworks and ten analysis tools.

AI and immunology.

AI is rapidly becoming part of many aspects of daily life, with an impact that reaches all fields of research. We asked investigators to share their thoughts on how AI is changing immunology research, what is necessary to move forward, the potential and the pitfalls, and what will remain unchanged as the field journeys into a new era.

Cross-domain information fusion for enhanced cell population delineation in single-cell spatial-omics data.

Cell population delineation and identification is an essential step in single-cell and spatial-omics studies. Spatial-omics technologies can simultaneously measure information from three complementary domains related to this task: expression levels of a panel of molecular biomarkers at single-cell resolution, relative positions of cells, and images of tissue sections, but existing computational methods for performing this task on single-cell spatial-omics datasets often relinquish information from one or more domains. The additional reliance on the availability of "atlas" training or reference datasets limits cell type discovery to well-defined but limited cell population labels, thus posing major challenges for using these methods in practice. Successful integration of all three domains presents an opportunity for uncovering cell populations that are functionally stratified by their spatial contexts at cellular and tissue levels: the key motivation for employing spatial-omics technologies in the first place. In this work, we introduce Cell Spatio- and Neighborhood-informed Annotation and Patterning (CellSNAP), a self-supervised computational method that learns a representation vector for each cell in tissue samples measured by spatial-omics technologies at the single-cell or finer resolution. The learned representation vector fuses information about the corresponding cell across all three aforementioned domains. By applying CellSNAP to datasets spanning both spatial proteomic and spatial transcriptomic modalities, and across different tissue types and disease settings, we show that CellSNAP markedly enhances de novo discovery of biologically relevant cell populations at fine granularity, beyond current approaches, by fully integrating cells' molecular profiles with cellular neighborhood and tissue image information.

Exploring therapy transport from implantable medical devices using experimentally informed computational methods.

Implantable medical devices that can facilitate therapy transport to localized sites are being developed for a number of diverse applications, including the treatment of diseases such as diabetes and cancer, and tissue regeneration after myocardial infraction. These implants can take the form of an encapsulation device which encases therapy in the form of drugs, proteins, cells, and bioactive agents, in semi-permeable membranes. Such implants have shown some success but the nature of these devices pose a barrier to the diffusion of vital factors, which is further exacerbated upon implantation due to the foreign body response (FBR). The FBR results in the formation of a dense hypo-permeable fibrous capsule around devices and is a leading cause of failure in many implantable technologies. One potential method for overcoming this diffusion barrier and enhancing therapy transport from the device is to incorporate local fluid flow. In this work, we used experimentally informed inputs to characterize the change in the fibrous capsule over time and quantified how this impacts therapy release from a device using computational methods. Insulin was used as a representative therapy as encapsulation devices for Type 1 diabetes are among the most-well characterised. We then explored how local fluid flow may be used to counteract these diffusion barriers, as well as how a more practical pulsatile flow regimen could be implemented to achieve similar results to continuous fluid flow. The generated model is a versatile tool toward informing future device design through its ability to capture the expected decrease in insulin release over time resulting from the FBR and investigate potential methods to overcome these effects.

Integrating multiplexed imaging and multiscale modeling identifies tumor phenotype conversion as a critical component of therapeutic T cell efficacy.

Cancer progression is a complex process involving interactions that unfold across molecular, cellular, and tissue scales. These multiscale interactions have been difficult to measure and to simulate. Here, we integrated CODEX multiplexed tissue imaging with multiscale modeling software to model key action points that influence the outcome of T cell therapies with cancer. The initial phenotype of therapeutic T cells influences the ability of T cells to convert tumor cells to an inflammatory, anti-proliferative phenotype. This T cell phenotype could be preserved by structural reprogramming to facilitate continual tumor phenotype conversion and killing. One takeaway is that controlling the rate of cancer phenotype conversion is critical for control of tumor growth. The results suggest new design criteria and patient selection metrics for T cell therapies, call for a rethinking of T cell therapeutic implementation, and provide a foundation for synergistically integrating multiplexed imaging data with multiscale modeling of the cancer-immune interface. A record of this paper's transparent peer review process is included in the supplemental information.

A Spatial Multi-Modal Dissection of Host-Microbiome Interactions within the Colitis Tissue Microenvironment.

The intricate and dynamic interactions between the host immune system and its microbiome constituents undergo dynamic shifts in response to perturbations to the intestinal tissue environment. Our ability to study these events on the systems level is significantly limited by in situ approaches capable of generating simultaneous insights from both host and microbial communities. Here, we introduce Microbiome Cartography (MicroCart), a framework for simultaneous in situ probing of host features and its microbiome across multiple spatial modalities. We demonstrate MicroCart by comprehensively investigating the alterations in both gut host and microbiome components in a murine model of colitis by coupling MicroCart with spatial proteomics, transcriptomics, and glycomics platforms. Our findings reveal a global but systematic transformation in tissue immune responses, encompassing tissue-level remodeling in response to host immune and epithelial cell state perturbations, and bacterial population shifts, localized inflammatory responses, and metabolic process alterations during colitis. MicroCart enables a deep investigation of the intricate interplay between the host tissue and its microbiome with spatial multiomics.

Epstein-Barr Virus Orchestrates Spatial Reorganization and Immunomodulation within the Classic Hodgkin Lymphoma Tumor Microenvironment.

Classic Hodgkin Lymphoma (cHL) is a tumor composed of rare malignant Hodgkin and Reed-Sternberg (HRS) cells nested within a T-cell rich inflammatory immune infiltrate. cHL is associated with Epstein-Barr Virus (EBV) in 25% of cases. The specific contributions of EBV to the pathogenesis of cHL remain largely unknown, in part due to technical barriers in dissecting the tumor microenvironment (TME) in high detail. Herein, we applied multiplexed ion beam imaging (MIBI) spatial pro-teomics on 6 EBV-positive and 14 EBV-negative cHL samples. We identify key TME features that distinguish between EBV-positive and EBV-negative cHL, including the relative predominance of memory CD8 T cells and increased T-cell dysfunction as a function of spatial proximity to HRS cells. Building upon a larger multi-institutional cohort of 22 EBV-positive and 24 EBV-negative cHL samples, we orthogonally validated our findings through a spatial multi-omics approach, coupling whole transcriptome capture with antibody-defined cell types for tu-mor and T-cell populations within the cHL TME. We delineate contrasting transcriptomic immunological signatures between EBV-positive and EBV-negative cases that differently impact HRS cell proliferation, tumor-immune interactions, and mecha-nisms of T-cell dysregulation and dysfunction. Our multi-modal framework enabled a comprehensive dissection of EBV-linked reorganization and immune evasion within the cHL TME, and highlighted the need to elucidate the cellular and molecular fac-tors of virus-associated tumors, with potential for targeted therapeutic strategies.

Spatially Segregated Macrophage Populations Predict Distinct Outcomes in Colon Cancer.

Tumor-associated macrophages are transcriptionally heterogeneous, but the spatial distribution and cell interactions that shape macrophage tissue roles remain poorly characterized. Here, we spatially resolve five distinct human macrophage populations in normal and malignant human breast and colon tissue and reveal their cellular associations. This spatial map reveals that distinct macrophage populations reside in spatially segregated micro-environmental niches with conserved cellular compositions that are repeated across healthy and diseased tissue. We show that IL4I1+ macrophages phagocytose dying cells in areas with high cell turnover and predict good outcome in colon cancer. In contrast, SPP1+ macrophages are enriched in hypoxic and necrotic tumor regions and portend worse outcome in colon cancer. A subset of FOLR2+ macrophages is embedded in plasma cell niches. NLRP3+ macrophages co-localize with neutrophils and activate an inflammasome in tumors. Our findings indicate that a limited number of unique human macrophage niches function as fundamental building blocks in tissue. Significance: This work broadens our understanding of the distinct roles different macrophage populations may exert on cancer growth and reveals potential predictive markers and macrophage population-specific therapy targets.

Diversity of sugar-diphospholipid-utilizing glycosyltransferase families.

Peptidoglycan polymerases, enterobacterial common antigen polymerases, O-antigen ligases, and other bacterial polysaccharide polymerases (BP-Pols) are glycosyltransferases (GTs) that build bacterial surface polysaccharides. These integral membrane enzymes share the particularity of using diphospholipid-activated sugars and were previously missing in the carbohydrate-active enzymes database (CAZy; www.cazy.org ). While the first three classes formed well-defined families of similar proteins, the sequences of BP-Pols were so diverse that a single family could not be built. To address this, we developed a new clustering method using a combination of a sequence similarity network and hidden Markov model comparisons. Overall, we have defined 17 new GT families including 14 of BP-Pols. We find that the reaction stereochemistry appears to be conserved in each of the defined BP-Pol families, and that the BP-Pols within the families transfer similar sugars even across Gram-negative and Gram-positive bacteria. Comparison of the new GT families reveals three clans of distantly related families, which also conserve the reaction stereochemistry.

Subcortical-cortical white matter connectivity in adults with autism spectrum disorder and schizophrenia patients.

Autism spectrum disorder (ASD) and schizophrenia (SZ) are neuropsychiatric disorders that overlap in symptoms associated with social-cognitive impairment. Alterations of the cingulate cortex, subcortical, medial-temporal, and orbitofrontal structures are frequently reported in both disorders. In this study, we examined white-matter connectivity between these structures in adults with ASD and SZ patients compared with their respective neurotypical controls and indirectly with each other, using probabilistic and local DTI tractography. This exploratory study utilized publicly available neuroimaging databases, of adults with ASD (ABIDE II; n = 28) and SZ (COBRE; n = 38), age-gender matched neurotypicals (NT) and associated phenotypic data. Tractography was performed using Freesurfer and MRtrix software, and diffusion metrics of white-matter tracts between cingulate-, orbitofrontal- cortices, subcortical structures, parahippocampal, entorhinal cortex were assessed. In ASD, atypical diffusivity parameters were found in the isthmus cingulate and parahippocampal connectivity to subcortical and rostral-anterior cingulate, which were also associated with IQ and social skills (SRS). In contrast, atypical diffusivity parameters were observed between the medial-orbitofrontal cortex and subcortical structures in SZ, and were associated with executive function (i.e., IQ, processing speed) and emotional regulation. Overall, the results suggest that defects in the isthmus cingulate, medial-orbitofrontal, and striato-limbic white matter connectivity may help unravel the neural underpinnings of executive and social-emotional dysfunction at the core of neuropsychiatric disorders.

Multimodal and spatially resolved profiling identifies distinct patterns of T cell infiltration in nodal B cell lymphoma entities.

The redirection of T cells has emerged as an attractive therapeutic principle in B cell non-Hodgkin lymphoma (B-NHL). However, a detailed characterization of lymphoma-infiltrating T cells across B-NHL entities is missing. Here we present an in-depth T cell reference map of nodal B-NHL, based on cellular indexing of transcriptomes and epitopes, T cell receptor sequencing, flow cytometry and multiplexed immunofluorescence applied to 101 lymph nodes from patients with diffuse large B cell, mantle cell, follicular or marginal zone lymphoma, and from healthy controls. This multimodal resource revealed quantitative and spatial aberrations of the T cell microenvironment across and within B-NHL entities. Quantitative differences in PD1+ TCF7- cytotoxic T cells, T follicular helper cells or IKZF3+ regulatory T cells were linked to their clonal expansion. The abundance of PD1+ TCF7- cytotoxic T cells was associated with poor survival. Our study portrays lymphoma-infiltrating T cells with unprecedented comprehensiveness and provides a unique resource for the investigation of lymphoma biology and prognosis.

MAPS: pathologist-level cell type annotation from tissue images through machine learning.

Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for typically challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension.

Retention of Virus Versus Surrogate, by Ultrafiltration in Seawater: Case Study of Norovirus Versus Tulane.

In the field of chemical engineering and water treatment, the study of viruses, included surrogates, is well documented. Often, surrogates are used to study viruses and their behavior because they can be produced in larger quantities in safer conditions and are easier to handle. In fact, surrogates allow studying microorganisms which are non-infectious to humans but share some properties similar to pathogenic viruses: structure, composition, morphology, and size. Human noroviruses, recognized as the leading cause of epidemics and sporadic cases of gastroenteritis across all age groups, may be mimicked by the Tulane virus. The objectives of this work were to study (i) the ultrafiltration of Tulane virus and norovirus to validate that Tulane virus can be used as a surrogate for norovirus in water treatment process and (ii) the retention of norovirus and the surrogate as a function of water quality to better understand the use of the latter pathogenic viruses. Ultrafiltration tests showed significant logarithmic reduction values (LRV) in viral RNA: around 2.5 for global LRV (i.e., based on the initial and permeate average concentrations) and between 2 and 6 for average LRV (i.e., retention rate considering the increase of viral concentration in the retentate), both for norovirus and the surrogate Tulane virus. Higher reduction rates (from 2 to 6 log genome copies) are obtained for higher initial concentrations (from 101 to 107 genome copies per mL) due to virus aggregation in membrane lumen. Tulane virus appears to be a good surrogate for norovirus retention by membrane processes.

Intermittent actuation attenuates fibrotic behaviour of myofibroblasts.

The foreign body response (FBR) to implanted materials culminates in the deposition of a hypo-permeable, collagen rich fibrotic capsule by myofibroblast cells at the implant site. The fibrotic capsule can be deleterious to the function of some medical implants as it can isolate the implant from the host environment. Modulation of fibrotic capsule formation has been achieved using intermittent actuation of drug delivery implants, however the mechanisms underlying this response are not well understood. Here, we use analytical, computational, and in vitro models to understand the response of human myofibroblasts (WPMY-1 stromal cell line) to intermittent actuation using soft robotics and investigate how actuation can alter the secretion of collagen and pro/anti-inflammatory cytokines by these cells. Our findings suggest that there is a mechanical loading threshold that can modulate the fibrotic behaviour of myofibroblasts, by reducing the secretion of soluble collagen, transforming growth factor beta-1 and interleukin 1-beta, and upregulating the anti-inflammatory interleukin-10. By improving our understanding of how cells involved in the FBR respond to mechanical actuation, we can harness this technology to improve functional outcomes for a wide range of implanted medical device applications including drug delivery and cell encapsulation platforms. STATEMENT OF SIGNIFICANCE: A major barrier to the successful clinical translation of many implantable medical devices is the foreign body response (FBR) and resultant deposition of a hypo-permeable fibrotic capsule (FC) around the implant. Perturbation of the implant site using intermittent actuation (IA) of soft-robotic implants has previously been shown to modulate the FBR and reduce FC thickness. However, the mechanisms of action underlying this response were largely unknown. Here, we investigate how IA can alter the activity of myofibroblast cells, and ultimately suggest that there is a mechanical loading threshold within which their fibrotic behaviour can be modulated. These findings can be harnessed to improve functional outcomes for a wide range of medical implants, particularly drug delivery and cell encapsulation devices.

BCG vaccination stimulates integrated organ immunity by feedback of the adaptive immune response to imprint prolonged innate antiviral resistance.

Bacille Calmette-Guérin (BCG) vaccination can confer nonspecific protection against heterologous pathogens. However, the underlying mechanisms remain mysterious. We show that mice vaccinated intravenously with BCG exhibited reduced weight loss and/or improved viral clearance when challenged with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 B.1.351) or PR8 influenza. Protection was first evident between 14 and 21 d post-vaccination and lasted ∼3 months. Notably, BCG induced a biphasic innate response and robust antigen-specific type 1 helper T cell (TH1 cell) responses in the lungs. MyD88 signaling was essential for innate and TH1 cell responses, and protection against SARS-CoV-2. Depletion of CD4+ T cells or interferon (IFN)-γ activity before infection obliterated innate activation and protection. Single-cell and spatial transcriptomics revealed CD4-dependent expression of IFN-stimulated genes in lung myeloid and epithelial cells. Notably, BCG also induced protection against weight loss after mouse-adapted SARS-CoV-2 BA.5, SARS-CoV and SHC014 coronavirus infections. Thus, BCG elicits integrated organ immunity, where CD4+ T cells feed back on tissue myeloid and epithelial cells to imprint prolonged and broad innate antiviral resistance.