Organization of the human intestine at single-cell resolution.

The intestine is a complex organ that promotes digestion, extracts nutrients, participates in immune surveillance, maintains critical symbiotic relationships with microbiota and affects overall health1. The intesting has a length of over nine metres, along which there are differences in structure and function2. The localization of individual cell types, cell type development trajectories and detailed cell transcriptional programs probably drive these differences in function. Here, to better understand these differences, we evaluated the organization of single cells using multiplexed imaging and single-nucleus RNA and open chromatin assays across eight different intestinal sites from nine donors. Through systematic analyses, we find cell compositions that differ substantially across regions of the intestine and demonstrate the complexity of epithelial subtypes, and find that the same cell types are organized into distinct neighbourhoods and communities, highlighting distinct immunological niches that are present in the intestine. We also map gene regulatory differences in these cells that are suggestive of a regulatory differentiation cascade, and associate intestinal disease heritability with specific cell types. These results describe the complexity of the cell composition, regulation and organization for this organ, and serve as an important reference map for understanding human biology and disease.

Organ Mapping Antibody Panels: a community resource for standardized multiplexed tissue imaging.

Multiplexed antibody-based imaging enables the detailed characterization of molecular and cellular organization in tissues. Advances in the field now allow high-parameter data collection (>60 targets); however, considerable expertise and capital are needed to construct the antibody panels employed by these methods. Organ mapping antibody panels are community-validated resources that save time and money, increase reproducibility, accelerate discovery and support the construction of a Human Reference Atlas.

Annotation of spatially resolved single-cell data with STELLAR.

Accurate cell-type annotation from spatially resolved single cells is crucial to understand functional spatial biology that is the basis of tissue organization. However, current computational methods for annotating spatially resolved single-cell data are typically based on techniques established for dissociated single-cell technologies and thus do not take spatial organization into account. Here we present STELLAR, a geometric deep learning method for cell-type discovery and identification in spatially resolved single-cell datasets. STELLAR automatically assigns cells to cell types present in the annotated reference dataset and discovers novel cell types and cell states. STELLAR transfers annotations across different dissection regions, different tissues and different donors, and learns cell representations that capture higher-order tissue structures. We successfully applied STELLAR to CODEX multiplexed fluorescent microscopy data and multiplexed RNA imaging datasets. Within the Human BioMolecular Atlas Program, STELLAR has annotated 2.6 million spatially resolved single cells with dramatic time savings.

Spatial mapping of protein composition and tissue organization: a primer for multiplexed antibody-based imaging.

Tissues and organs are composed of distinct cell types that must operate in concert to perform physiological functions. Efforts to create high-dimensional biomarker catalogs of these cells have been largely based on single-cell sequencing approaches, which lack the spatial context required to understand critical cellular communication and correlated structural organization. To probe in situ biology with sufficient depth, several multiplexed protein imaging methods have been recently developed. Though these technologies differ in strategy and mode of immunolabeling and detection tags, they commonly utilize antibodies directed against protein biomarkers to provide detailed spatial and functional maps of complex tissues. As these promising antibody-based multiplexing approaches become more widely adopted, new frameworks and considerations are critical for training future users, generating molecular tools, validating antibody panels, and harmonizing datasets. In this Perspective, we provide essential resources, key considerations for obtaining robust and reproducible imaging data, and specialized knowledge from domain experts and technology developers.

SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis.

MOTIVATION: Spatial omics data demand computational analysis but many analysis tools have computational resource requirements that increase with the number of cells analyzed. This presents scalability challenges as researchers use spatial omics technologies to profile millions of cells. RESULTS: To enhance the scalability of spatial omics data analysis, we developed a rasterization preprocessing framework called SEraster that aggregates cellular information into spatial pixels. We apply SEraster to both real and simulated spatial omics data prior to spatial variable gene expression analysis to demonstrate that such preprocessing can reduce computational resource requirements while maintaining high performance, including as compared to other down-sampling approaches. We further integrate SEraster with existing analysis tools to characterize cell-type spatial co-enrichment across length scales. Finally, we apply SEraster to enable analysis of a mouse pup spatial omics dataset with over a million cells to identify tissue-level and cell-type-specific spatially variable genes as well as spatially co-enriched cell-types that recapitulate expected organ structures. AVAILABILITY: SEraster is implemented as an R package on GitHub (https://github.com/JEFworks-Lab/SEraster) with additional tutorials at https://JEF.works/SEraster. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Highly multiplexed tissue imaging using repeated oligonucleotide exchange reaction.

Multiparameter tissue imaging enables analysis of cell-cell interactions in situ, the cellular basis for tissue structure, and novel cell types that are spatially restricted, giving clues to biological mechanisms behind tissue homeostasis and disease. Here, we streamlined and simplified the multiplexed imaging method CO-Detection by indEXing (CODEX) by validating 58 unique oligonucleotide barcodes that can be conjugated to antibodies. We showed that barcoded antibodies retained their specificity for staining cognate targets in human tissue. Antibodies were visualized one at a time by adding a fluorescently labeled oligonucleotide complementary to oligonucleotide barcode, imaging, stripping, and repeating this cycle. With this we developed a panel of 46 antibodies that was used to stain five human lymphoid tissues: three tonsils, a spleen, and a LN. To analyze the data produced, an image processing and analysis pipeline was developed that enabled single-cell analysis on the data, including unsupervised clustering, that revealed 31 cell types across all tissues. We compared cell-type compositions within and directly surrounding follicles from the different lymphoid organs and evaluated cell-cell density correlations. This sequential oligonucleotide exchange technique enables a facile imaging of tissues that leverages pre-existing imaging infrastructure to decrease the barriers to broad use of multiplexed imaging.

Adaptive Nanoparticle Platforms for High Throughput Expansion and Detection of Antigen-Specific T cells.

T cells are critical players in disease; yet, their antigen-specificity has been difficult to identify, as current techniques are limited in terms of sensitivity, throughput, or ease of use. To address these challenges, we increased the throughput and translatability of magnetic nanoparticle-based artificial antigen presenting cells (aAPCs) to enrich and expand (E+E) murine or human antigen-specific T cells. We streamlined enrichment, expansion, and aAPC production processes by enriching CD8+ T cells directly from unpurified immune cells, increasing parallel processing capacity of aAPCs in a 96-well plate format, and designing an adaptive aAPC that enables multiplexed aAPC construction for E+E and detection. We applied these adaptive platforms to process and detect CD8+ T cells specific for rare cancer neoantigens, commensal bacterial cross-reactive epitopes, and human viral and melanoma antigens. These innovations dramatically increase the multiplexing ability and decrease the barrier to adopt for investigating antigen-specific T cell responses.

Separating T Cell Targeting Components onto Magnetically Clustered Nanoparticles Boosts Activation.

T cell activation requires the coordination of a variety of signaling molecules including T cell receptor-specific signals and costimulatory signals. Altering the composition and distribution of costimulatory molecules during stimulation greatly affects T cell functionality for applications such as adoptive cell therapy (ACT), but the large diversity in these molecules complicates these studies. Here, we develop and validate a reductionist T cell activation platform that enables streamlined customization of stimulatory conditions. This platform is useful for the optimization of ACT protocols as well as the more general study of immune T cell activation. Rather than decorating particles with both signal 1 antigen and signal 2 costimulus, we use distinct, monospecific, paramagnetic nanoparticles, which are then clustered on the cell surface by a magnetic field. This allows for rapid synthesis and characterization of a small number of single-signal nanoparticles which can be systematically combined to explore and optimize T cell activation. By increasing cognate T cell enrichment and incorporating additional costimulatory molecules using this platform, we find significantly higher frequencies and numbers of cognate T cells stimulated from an endogenous population. The magnetic field-induced association of separate particles thus provides a tool for optimizing T cell activation for adoptive immunotherapy and other immunological studies.

Biologically Inspired Design of Nanoparticle Artificial Antigen-Presenting Cells for Immunomodulation.

Particles engineered to engage and interact with cell surface ligands and to modulate cells can be harnessed to explore basic biological questions as well as to devise cellular therapies. Biology has inspired the design of these particles, such as artificial antigen-presenting cells (aAPCs) for use in immunotherapy. While much has been learned about mimicking antigen presenting cell biology, as we decrease the size of aAPCs to the nanometer scale, we need to extend biomimetic design to include considerations of T cell biology-including T-cell receptor (TCR) organization. Here we describe the first quantitative analysis of particle size effect on aAPCs with both Signals 1 and 2 based on T cell biology. We show that aAPCs, larger than 300 nm, activate T cells more efficiently than smaller aAPCs, 50 nm. The 50 nm aAPCs require saturating doses or require artificial magnetic clustering to activate T cells. Increasing ligand density alone on the 50 nm aAPCs did not increase their ability to stimulate CD8+ T cells, confirming the size-dependent phenomenon. These data support the need for multireceptor ligation and activation of T-cell receptor (TCR) nanoclusters of similar sizes to 300 nm aAPCs. Quantitative analysis and modeling of a nanoparticle system provides insight into engineering constraints of aAPCs for T cell immunotherapy applications and offers a case study for other cell-modulating particles.

Prevention and removal of lipid deposits by lens care solutions and rubbing.

PURPOSE: Despite the prevalence of silicone hydrogel (SiHy) contact lenses, there are relatively few studies that evaluate the efficacy of multipurpose lens care solutions (MPSs) in reducing lipid deposition on these lenses and the effect of rubbing on the removal. Therefore, we used an in vitro soaking and rubbing model to compare the effectiveness of borate buffered saline (BBS) and two commercial MPSs, PureMoist and Biotrue, in preventing sorption of representative polar and nonpolar lipids. METHODS: Radiolabeled cholesterol (CH) and dipalmitoylphosphatidylcholine (DPPC) were sorbed on two SiHy lenses (senofilcon A and balafilcon A) from an artificial tear fluid. Deposition and removal were evaluated by quantitative solvent extraction and scintillation counting. RESULTS: The efficiencies of the MPSs in reducing lipid deposition are somewhat dependent on lens material. Both DPPC and CH sorption on senofilcon A are greater when lenses are preconditioned in BBS compared with preconditioning in either MPS (p < 0.05). However, neither MPS affects lipid sorption on balafilcon A lenses (p > 0.05). As for removal of presorbed lipids, neither PureMoist, Biotrue, nor BBS removed CH in the absence of rubbing. When a simulated rubbing protocol was used, minimal but detectible CH was removed (p < 0.05) from senofilcon A and balafilcon A lenses (likely only from the lens surface). These commercial solutions were not substantially better than BBS in removing DPPC, with or without rubbing (p > 0.05). CONCLUSIONS: These data suggest that MPSs do not appreciably alter lipid sorption. Rubbing lenses removes a small amount of sorbed lipids. Yet, we recommend that MPSs be used as they may disinfect SiHy lenses and may clean their surfaces of large particles.

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.

Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage.

The Human BioMolecular Atlas Program (HuBMAP) aims to construct a reference 3D structural, cellular, and molecular atlas of the healthy adult human body. The HuBMAP Data Portal (https://portal.hubmapconsortium.org) serves experimental datasets and supports data processing, search, filtering, and visualization. The Human Reference Atlas (HRA) Portal (https://humanatlas.io) provides open access to atlas data, code, procedures, and instructional materials. Experts from more than 20 consortia are collaborating to construct the HRA's Common Coordinate Framework (CCF), knowledge graphs, and tools that describe the multiscale structure of the human body (from organs and tissues down to cells, genes, and biomarkers) and to use the HRA to understand changes that occur at each of these levels with aging, disease, and other perturbations. The 6th release of the HRA v2.0 covers 36 organs with 4,499 unique anatomical structures, 1,195 cell types, and 2,089 biomarkers (e.g., genes, proteins, lipids) linked to ontologies. In addition, three workflows were developed to map new experimental data into the HRA's CCF. This paper describes the HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interface (APIs), flexible hybrid cloud infrastructure, and demonstrates first atlas usage applications and previews.

In Vivo Stimulation of Therapeutic Antigen-Specific T Cells in an Artificial Lymph Node Matrix.

T cells are critical mediators of antigen-specific immune responses and are common targets for immunotherapy. Biomaterial scaffolds have previously been used to stimulate antigen-presenting cells to elicit antigen-specific immune responses; however, structural and molecular features that directly stimulate and expand naïve, endogenous, tumor-specific T cells in vivo have not been defined. Here, an artificial lymph node (aLN) matrix is created, which consists of an extracellular matrix hydrogel conjugated with peptide-loaded-MHC complex (Signal 1), the co-stimulatory signal anti-CD28 (Signal 2), and a tethered IL-2 (Signal 3), that can bypass challenges faced by other approaches to activate T cells in situ such as vaccines. This dynamic immune-stimulating platform enables direct, in vivo antigen-specific CD8+ T cell stimulation, as well as recruitment and coordination of host immune cells, providing an immuno-stimulatory microenvironment for antigen-specific T cell activation and expansion. Co-injecting the aLN with naïve, wild-type CD8+ T cells results in robust activation and expansion of tumor-targeted T cells that kill target cells and slow tumor growth in several distal tumor models. The aLN platform induces potent in vivo antigen-specific CD8+ T cell stimulation without the need for ex vivo priming or expansion and enables in situ manipulation of antigen-specific responses for immunotherapies.

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.

Harmonizing the Generation and Pre-publication Stewardship of FAIR Image data.

Together with the molecular knowledge of genes and proteins, biological images promise to significantly enhance the scientific understanding of complex cellular systems and to advance predictive and personalized therapeutic products for human health. For this potential to be realized, quality-assured image data must be shared among labs at a global scale to be compared, pooled, and reanalyzed, thus unleashing untold potential beyond the original purpose for which the data was generated. There are two broad sets of requirements to enable image data sharing in the life sciences. One set of requirements is articulated in the companion White Paper entitled "Enabling Global Image Data Sharing in the Life Sciences," which is published in parallel and addresses the need to build the cyberinfrastructure for sharing the digital array data (arXiv:2401.13023 [q-bio.OT], https://doi.org/10.48550/arXiv.2401.13023). In this White Paper, we detail a broad set of requirements, which involves collecting, managing, presenting, and propagating contextual information essential to assess the quality, understand the content, interpret the scientific implications, and reuse image data in the context of the experimental details. We start by providing an overview of the main lessons learned to date through international community activities, which have recently made considerable progress toward generating community standard practices for imaging Quality Control (QC) and metadata. We then provide a clear set of recommendations for amplifying this work. The driving goal is to address remaining challenges, and democratize access to common practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.

Integrating Multiplexed Imaging and Multiscale Modeling Identifies Tumor Phenotype Transformation 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.

Integration of spatial and single-cell data across modalities with weak linkage.

single-cell sequencing methods have enabled the profiling of multiple types of molecular readouts at cellular resolution, and recent developments in spatial barcoding, in situ hybridization, and in situ sequencing allow such molecular readouts to retain their spatial context. Since no technology can provide complete characterization across all layers of biological modalities within the same cell, there is pervasive need for computational cross-modal integration (also called diagonal integration) of single-cell and spatial omics data. For current methods, the feasibility of cross-modal integration relies on the existence of highly correlated, a priori "linked" features. When such linked features are few or uninformative, a scenario that we call "weak linkage", existing methods fail. We developed MaxFuse, a cross-modal data integration method that, through iterative co-embedding, data smoothing, and cell matching, leverages all information in each modality to obtain high-quality integration. MaxFuse is modality-agnostic and, through comprehensive benchmarks on single-cell and spatial ground-truth multiome datasets, demonstrates high robustness and accuracy in the weak linkage scenario. A prototypical example of weak linkage is the integration of spatial proteomic data with single-cell sequencing data. On two example analyses of this type, we demonstrate how MaxFuse enables the spatial consolidation of proteomic, transcriptomic and epigenomic information at single-cell resolution on the same tissue section.

Segmentation of human functional tissue units in support of a Human Reference Atlas.

The Human BioMolecular Atlas Program (HuBMAP) aims to compile a Human Reference Atlas (HRA) for the healthy adult body at the cellular level. Functional tissue units (FTUs), relevant for HRA construction, are of pathobiological significance. Manual segmentation of FTUs does not scale; highly accurate and performant, open-source machine-learning algorithms are needed. We designed and hosted a Kaggle competition that focused on development of such algorithms and 1200 teams from 60 countries participated. We present the competition outcomes and an expanded analysis of the winning algorithms on additional kidney and colon tissue data, and conduct a pilot study to understand spatial location and density of FTUs across the kidney. The top algorithm from the competition, Tom, outperforms other algorithms in the expanded study, while using fewer computational resources. Tom was added to the HuBMAP infrastructure to run kidney FTU segmentation at scale-showcasing the value of Kaggle competitions for advancing research.

Integration of spatial and single-cell data across modalities with weakly linked features.

Although single-cell and spatial sequencing methods enable simultaneous measurement of more than one biological modality, no technology can capture all modalities within the same cell. For current data integration methods, the feasibility of cross-modal integration relies on the existence of highly correlated, a priori 'linked' features. We describe matching X-modality via fuzzy smoothed embedding (MaxFuse), a cross-modal data integration method that, through iterative coembedding, data smoothing and cell matching, uses all information in each modality to obtain high-quality integration even when features are weakly linked. MaxFuse is modality-agnostic and demonstrates high robustness and accuracy in the weak linkage scenario, achieving 20~70% relative improvement over existing methods under key evaluation metrics on benchmarking datasets. A prototypical example of weak linkage is the integration of spatial proteomic data with single-cell sequencing data. On two example analyses of this type, MaxFuse enabled the spatial consolidation of proteomic, transcriptomic and epigenomic information at single-cell resolution on the same tissue section.

Shape matters: Biodegradable anisotropic nanoparticle artificial antigen presenting cells for cancer immunotherapy.

Artificial antigen presenting cells are biomimetic particles that recapitulate the signals presented by natural antigen presenting cells in order to stimulate T cells in an antigen-specific manner using an acellular platform. We have engineered an enhanced nanoscale biodegradable artificial antigen presenting cell by modulating particle shape to achieve a nanoparticle geometry that allows for increased radius of curvature and surface area for T cell contact. The non-spherical nanoparticle artificial antigen presenting cells developed here have reduced nonspecific uptake and improved circulation time compared both to spherical nanoparticles and to traditional microparticle technologies. Additionally, the anisotropic nanoparticle artificial antigen presenting cells efficiently engage with and activate T cells, ultimately leading to a marked anti-tumor effect in a mouse melanoma model that their spherical counterparts were unable to achieve. STATEMENT OF SIGNIFICANCE: Artificial antigen presenting cells (aAPC) can activate antigen-specific CD8+ T cells but have largely been limited to microparticle-based platforms and ex vivo T cell expansion. Although more amenable to in vivo use, nanoscale aAPC have traditionally been ineffective due to limited surface area available for T cell interaction. In this work, we engineered non-spherical biodegradable nanoscale aAPC to investigate the role of particle geometry and develop a translatable platform for T cell activation. The non-spherical aAPC developed here have increased surface area and a flatter surface for T cell engagement and, therefore, can more effectively stimulate antigen-specific T cells, resulting in anti-tumor efficacy in a mouse melanoma model.