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Background: Advances in instrumented mouthguards (iMGs) allow for accurate quantification of single high-acceleration head impacts and cumulative head acceleration exposure in collision sports. However, relationships between these measures and risk of brain cell injury remain unclear., Aim: The purpose of this study was to quantify measures of non-concussive head impact exposure and assess their association with blood glial fibrillary acidic protein (GFAP), neurofilament light (NfL) and phosphorylated-tau-181 (p-tau-181) levels in male Australian football players., Methods: A total of 31 athletes underwent in-season (24 h post-match) and post-season (> 5 weeks) blood collections and/or wore HITIQ Nexus A9 iMGs measuring peak linear (PLA) and rotational (PRA) acceleration. Match footage was used to verify and code impacts. Blood GFAP, NfL, and p-tau-181 were quantified using Simoa and natural log transformed for analysis. Associations between post-match biomarkers and within match maximum single impact and cumulative PLA/PRA were assessed with linear mixed models., Results: In-season versus post-season elevations were found for GFAP (mean difference 0.14, 95% CI 0.01-0.26, p = 0.033), NfL (mean difference = 0.21, 95% CI 0.09-0.32, p = 0.001) and p-tau-181 (mean difference = 0.49, 95% CI 0.33-0.65, p < 0.001). Post-match GFAP was associated with maximum single impact PLA (B = 0.003, 95% CI 0.0002-0.005, p = 0.036), cumulative PLA (B = 0.001, 95% CI 0.0002-0.002, p = 0.017), cumulative PRA (B = 0.01, 95% CI 0.002-0.02, p = 0.014), and impact number (B = 0.03, 95% CI 0.003-0.05, p = 0.029) within a single match. Change in NfL levels between two-matches correlated with cumulative PLA (r = 0.80, 95% CI 0.38-0.95, p = 0.005), PRA (r = 0.71, 95% CI 0.19-0.92, p = 0.019) and impact number (r = 0.63, 95% CI 0.05-0.89, p = 0.038)., Conclusion: Maximum and cumulative head accelerations in Australian football, measured by iMGs, were associated with elevated blood biomarkers of brain injury, highlighting the potential of both technologies for head impact management in collision sports., Competing Interests: Declarations Conflict of interest T.J.O. has had research support from Australian and US Government research agencies (MRFF, DoD, NINDS), and research and consultancy support to his institution from Eisai, UCB Pharma, ES Therapeutics, Biogen, Liva Nova, and Kinoxis Therapeutics. L.J.E., W.T.O., G.S., S.M., B.X., L.P.G., B.P.M., J.W.H., S.S.H.R., B.M., S.R.S. and S.J.M. have no conflicts of interest to disclose. Ethics Approval and Consent to Participate The Monash University Human Research Ethics Committee approved study procedures (MUHREC project: 36556) and all participants provided written informed consent., (© 2024. The Author(s).)

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 bioimage 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 bioimage 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 bioimage data (arXiv:2401.13023 [q-bio.OT], https://doi.org/10.48550/arXiv.2401.13023). Here, 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 bioimage 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 generating community standard practices for imaging Quality Control (QC) and metadata (Faklaris et al., 2022; Hammer et al., 2021; Huisman et al., 2021; Microscopy Australia , 2016; Montero Llopis et al., 2021; Rigano et al., 2021; Sarkans et al., 2021). 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., Competing Interests: Conflict of Interest Statements Below are the statements shared by contributors indicating potential conflict of interest. NameStatementUlrike BoehmUB’s contribution to this manuscript is a result of her voluntary involvement with QUAREP-LiMi and BINA, and does not reflect the position of Carl Zeiss AG on this matter.Josh Mooreholds equity in Glencoe Software.Denis SchapiroDS reports funding from GSK and received honorariums from Immunai, Noetik, Alpenglow and Lunaphore.Damir SudarDSu is employed by Quantitative Imaging Systems, a commercial entity developing imaging software.Siyuan (Steven) WangFounder, shareholder, consultant of Translura, Inc

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 and 2D/3D reference objects. New experimental data can be mapped into the HRA using (1) three cell type annotation tools (e.g., Azimuth) or (2) validated antibody panels (OMAPs), or (3) by registering tissue data spatially. 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 previews atlas usage applications.

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., (©2024 The Authors; Published by the American Association for Cancer Research.)

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 and Implementation: SEraster is implemented as an R package on GitHub (https://github.com/JEFworks-Lab/SEraster) with additional tutorials at https://JEF.works/SEraster., (© The Author(s) 2024. Published by Oxford University Press.)

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., (© 2023. The Author(s).)

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., (© 2024 Wiley‐VCH GmbH.)

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., Competing Interests: Declaration of interests G.P.N. has equity in and is a scientific advisory board member of Akoya Biosciences, Inc., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Antigen-specific T cells traffic to, are influenced by, and create unique cellular microenvironments. Here we characterize these microenvironments over time with multiplexed imaging in a melanoma model of adoptive T cell therapy and human patients with melanoma treated with checkpoint inhibitor therapy. Multicellular neighborhood analysis reveals dynamic immune cell infiltration and inflamed tumor cell neighborhoods associated with CD8 + T cells. T cell-focused analysis indicates T cells are found along a continuum of neighborhoods that reflect the progressive steps coordinating the anti-tumor immune response. More effective anti-tumor immune responses are characterized by inflamed tumor-T cell neighborhoods, flanked by dense immune infiltration neighborhoods. Conversely, ineffective T cell therapies express anti-inflammatory cytokines, resulting in regulatory neighborhoods, spatially disrupting productive T cell-immune and -tumor interactions. Our study provides in situ mechanistic insights into temporal tumor microenvironment changes, cell interactions critical for response, and spatial correlates of immunotherapy outcomes, informing cellular therapy evaluation and engineering., Competing Interests: Declaration of interests G.P.N. has equity in and is a scientific advisory board member of Akoya Biosciences, Inc. A.J.R. is an advisor for Affini-T Therapeutics. G.P.N. and J.W.H. share a patent related to this work. The other authors declare no competing interests., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

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., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)

The Human BioMolecular Atlas Program (HuBMAP) aims to create a multi-scale spatial atlas of the healthy human body at single-cell resolution by applying advanced technologies and disseminating resources to the community. As the HuBMAP moves past its first phase, creating ontologies, protocols and pipelines, this Perspective introduces the production phase: the generation of reference spatial maps of functional tissue units across many organs from diverse populations and the creation of mapping tools and infrastructure to advance biomedical research., (© 2023. Springer Nature Limited.)

Hematopoietic stem cells (HSCs) are a rare type of hematopoietic cell that can entirely reconstitute the blood and immune system after transplantation. Allogeneic HSC transplantation (HSCT) is used clinically as a curative therapy for a range of hematolymphoid diseases; however, it remains a high-risk therapy because of its potential side effects, including poor graft function and graft-versus-host disease (GVHD). Ex vivo HSC expansion has been suggested as an approach to improve hematopoietic reconstitution in low-cell dose grafts. Here, we demonstrate that the selectivity of polyvinyl alcohol (PVA)-based mouse HSC cultures can be improved using physioxic culture conditions. Single-cell transcriptomic analysis helped confirm the inhibition of lineage-committed progenitor cells in physioxic cultures. Long-term physioxic expansion also afforded culture-based ex vivo HSC selection from whole bone marrow, spleen, and embryonic tissues. Furthermore, we provide evidence that HSC-selective ex vivo cultures deplete GVHD-causing T cells and that this approach can be combined with genotoxic-free antibody-based conditioning HSCT approaches. Our results offer a simple approach to improve PVA-based HSC cultures and the underlying molecular phenotype, and highlight the potential translational implications of selective HSC expansion systems for allogeneic HSCT., (© 2023 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved.)

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., (© 2023. The Author(s).)

The intestine is a complex organ that promotes digestion, extracts nutrients, participates in immune surveillance, maintains critical symbiotic relationships with microbiota and affects overall health 1 . The intesting has a length of over nine metres, along which there are differences in structure and function 2 . 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., (© 2023. The Author(s).)

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., Competing Interests: Declaration of Competing Interest Patents related to technology discussed in the manuscript have been filed by Johns Hopkins University with EBA, JWH, RAM, KRR, AKK, SYT, JPS, and JJG as co-inventors. Any potential conflicts of interest are managed by the Johns Hopkins University Committee on Outside Interests. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.)

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., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Highly multiplexed, single-cell imaging has revolutionized our understanding of spatial cellular interactions associated with health and disease. With ever-increasing numbers of antigens, region sizes, and sample sizes, multiplexed fluorescence imaging experiments routinely produce terabytes of data. Fast and accurate processing of these large-scale, high-dimensional imaging data is essential to ensure reliable segmentation and identification of cell types and for characterization of cellular neighborhoods and inference of mechanistic insights. Here, we describe RAPID, a Real-time, GPU-Accelerated Parallelized Image processing software for large-scale multiplexed fluorescence microscopy Data. RAPID deconvolves large-scale, high-dimensional fluorescence imaging data, stitches and registers images with axial and lateral drift correction, and minimizes tissue autofluorescence such as that introduced by erythrocytes. Incorporation of an open source CUDA-driven, GPU-assisted deconvolution produced results similar to fee-based commercial software. RAPID reduces data processing time and artifacts and improves image contrast and signal-to-noise compared to our previous image processing pipeline, thus providing a useful tool for accurate and robust analysis of large-scale, multiplexed, fluorescence imaging data., Competing Interests: Author GPN received research support from Pfizer, Vaxart, Celgene, and Juno Therapeutics during the course of this work. Authors YG and GPN are inventors on US patent 9909167, granted to Stanford University, that covers some aspects of the technology described in this paper. Authors YG and GPN have equity in and/or are scientific advisory board members of Akoya Biosciences, Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest, (Copyright © 2022 Lu, Baertsch, Hickey, Goltsev, Rech, Mani, Forgó, Kong, Jiang, Nolan and Rosenthal.)

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., (© 2021. Springer Nature America, Inc.)

The complex determinants of health and disease can be determined when approached as a system of interactions of biological agents at different scales. Similar to the physicochemical properties that govern nucleic acids and proteins, there should be a finite set of rules that dictate the behavior of cells to form tissues. Thus, the occurrence of disease can be seen as flaws in processes that are governed by rules pertaining to multicellular structures. Multiplexed imaging is a technology that connects information that bridges multiple biological scales (i.e., molecules, cells, and tissues) and enables elucidation of rules associated with the formation of multicellular structures. Uncovering important multicellular structures associated with disease will propel a wave of development of new categories of diagnostics and therapeutics., Competing Interests: Declarations of interest G.P.N. received research support from Pfizer, Vaxart, Celgene, and Juno Therapeutics during the course of this work. G.P.N. is an inventor on US patent 9909167, granted to Stanford University, which covers some aspects of CODEX multiplexed imaging technology. G.P.N. has equity in, and is on the scientific advisory board of, Akoya Biosciences, Inc., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Non-human primate (NHP) animal models are an integral part of the drug research and development process. For some biothreat pathogens, animal model challenge studies may offer the only possibility to evaluate medical countermeasure efficacy. A thorough understanding of host immune responses in such NHP models is therefore vital. However, applying antibody-based immune characterization techniques to NHP models requires extensive reagent development for species compatibility. In the case of studies involving high consequence pathogens, further optimization for use of inactivated samples may be required. Here, we describe the first optimized CO-Detection by indEXing (CODEX) multiplexed tissue imaging antibody panel for deep profiling of spatially resolved single-cell immune responses in rhesus macaques. This 21-marker panel is composed of a set of 18 antibodies that stratify major immune cell types along with a set three Ebola virus (EBOV)-specific antibodies. We validated these two sets of markers using immunohistochemistry and CODEX in fully inactivated Formalin-Fixed Paraffin-Embedded (FFPE) tissues from mock and EBOV challenged macaques respectively and provide an efficient framework for orthogonal validation of multiple antibody clones using CODEX multiplexed tissue imaging. We also provide the antibody clones and oligonucleotide tag sequences as a valuable resource for other researchers to recreate this reagent set for future studies of tissue immune responses to EBOV infection and other diseases., Competing Interests: GN and YG are co-founders and stockholders of Akoya Biosciences, Inc. GN, YG, and DM are inventors on CODEX-related patents US9909167 and US9909167B2, respectively. CS is a scientific advisor to Enable Medicine, Inc. CS, DP, and GN are inventors on a patent application related to CODEX (PCT/US2021/016641). EFM has previously consulted for Ionpath, Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Jiang, Mukherjee, Bennett, Chen, Logue, Dighero-Kemp, Kurtz, Adams, Phillips, Schürch, Goltsev, Hickey, McCaffrey, Delmastro, Chu, Reader, Keesler, Galván, Zlobec, Van Rompay, Liu, Hensley, Nolan and McIlwain.)

Multiplexed imaging is a recently developed and powerful single-cell biology research tool. However, it presents new sources of technical noise that are distinct from other types of single-cell data, necessitating new practices for single-cell multiplexed imaging processing and analysis, particularly regarding cell-type identification. Here we created single-cell multiplexed imaging datasets by performing CODEX on four sections of the human colon (ascending, transverse, descending, and sigmoid) using a panel of 47 oligonucleotide-barcoded antibodies. After cell segmentation, we implemented five different normalization techniques crossed with four unsupervised clustering algorithms, resulting in 20 unique cell-type annotations for the same dataset. We generated two standard annotations: hand-gated cell types and cell types produced by over-clustering with spatial verification. We then compared these annotations at four levels of cell-type granularity. First, increasing cell-type granularity led to decreased labeling accuracy; therefore, subtle phenotype annotations should be avoided at the clustering step. Second, accuracy in cell-type identification varied more with normalization choice than with clustering algorithm. Third, unsupervised clustering better accounted for segmentation noise during cell-type annotation than hand-gating. Fourth, Z-score normalization was generally effective in mitigating the effects of noise from single-cell multiplexed imaging. Variation in cell-type identification will lead to significant differential spatial results such as cellular neighborhood analysis; consequently, we also make recommendations for accurately assigning cell-type labels to CODEX multiplexed imaging., Competing Interests: GPN received research support from Pfizer, Vaxart, Celgene, and Juno Therapeutics during the course of this work. YG and GPN are inventors on US patent 9909167, granted to Stanford University, that covers some aspects of the technology described in this paper. YG and GPN have equity in and/or are scientific advisory board members of Akoya Biosciences, Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Hickey, Tan, Nolan and Goltsev.)

Advances in multiplexed imaging technologies have drastically improved our ability to characterize healthy and diseased tissues at the single-cell level. Co-detection by indexing (CODEX) relies on DNA-conjugated antibodies and the cyclic addition and removal of complementary fluorescently labeled DNA probes and has been used so far to simultaneously visualize up to 60 markers in situ. CODEX enables a deep view into the single-cell spatial relationships in tissues and is intended to spur discovery in developmental biology, disease and therapeutic design. Herein, we provide optimized protocols for conjugating purified antibodies to DNA oligonucleotides, validating the conjugation by CODEX staining and executing the CODEX multicycle imaging procedure for both formalin-fixed, paraffin-embedded (FFPE) and fresh-frozen tissues. In addition, we describe basic image processing and data analysis procedures. We apply this approach to an FFPE human tonsil multicycle experiment. The hands-on experimental time for antibody conjugation is ~4.5 h, validation of DNA-conjugated antibodies with CODEX staining takes ~6.5 h and preparation for a CODEX multicycle experiment takes ~8 h. The multicycle imaging and data analysis time depends on the tissue size, number of markers in the panel and computational complexity., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

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., (© 2021 The Authors. European Journal of Immunology published by Wiley-VCH GmbH.)

Biomimetic biomaterials are being actively explored in the context of cancer immunotherapy because of their ability to directly engage the immune system to generate antitumor responses. Unlike cellular therapies, biomaterial-based immunotherapies can be precisely engineered to exhibit defined characteristics including biodegradability, physical size, and tuned surface presentation of immunomodulatory signals. In particular, modulating the interface between the biomaterial surface and the target biological cell is key to enabling biological functions. Synthetic artificial antigen presenting cells (aAPCs) are promising as a cancer immunotherapy but are limited in clinical translation by the requirement of ex vivo cell manipulation and adoptive transfer of antigen-specific CD8+ T cells. To move toward acellular aAPC technology for in vivo use, we combine poly(lactic- co -glycolic acid) (PLGA) and cationic poly(beta-amino-ester) (PBAE) to form a biodegradable blend based on the hypothesis that therapeutic aAPCs fabricated from a cationic blend may have improved functions. PLGA/PBAE aAPCs demonstrate enhanced surface interactions with antigen-specific CD8+ T cells that increase T cell activation and expansion ex vivo , associated with significantly increased conjugation efficiency of T cell stimulatory signals to the aAPCs. Critically, these PLGA/PBAE aAPCs also expand antigen-specific cytotoxic CD8+ T cells in vivo without the need of adoptive transfer . Treatment with PLGA/PBAE aAPCs in combination with checkpoint therapy decreases tumor growth and extends survival in a B16-F10 melanoma mouse model. These results demonstrate the potential of PLGA/PBAE aAPCs as a biocompatible, directly injectable acellular therapy for cancer immunotherapy.

Biological processes are incredibly complex-integrating molecular signaling networks involved in multicellular communication and function, thus maintaining homeostasis. Dysfunction of these processes can result in the disruption of homeostasis, leading to the development of several disease processes including atherosclerosis. We have significantly advanced our understanding of bioprocesses in atherosclerosis, and in doing so, we are beginning to appreciate the complexities, intricacies, and heterogeneity atherosclerosi. We are also now better equipped to acquire, store, and process the vast amount of biological data needed to shed light on the biological circuitry involved. Such data can be analyzed within machine learning frameworks to better tease out such complex relationships. Indeed, there has been an increasing number of studies applying machine learning methods for patient risk stratification based on comorbidities, multi-modality image processing, and biomarker discovery pertaining to atherosclerotic plaque formation. Here, we focus on current applications of machine learning to provide insight into atherosclerotic plaque formation and better understand atherosclerotic plaque progression in patients with cardiovascular disease., (© 2021 Author(s).)

In order to provide undergraduate students with a full, rich online learning experience we adapted pre-existing online content including graduate courses from Johns Hopkins University Engineering for Professionals (JHU EP) program. These online courses were designed using published methodologies and held to a high level of rigor of a Masters-level curriculum. Adapting pre-existing online course material enabled us to more rapidly adapt to the COVID-19 shutdown of in-person education. We adapted content to meet the majority of lab-based learning objectives rather than generating self-recorded lecture material and allowing us to focus faculty time on addressing student needs. Here we discuss benefits, challenges, and methods for replicating these courses, and lessons to be applied in future offerings from this experience., (© Biomedical Engineering Society 2021.)

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.

Recent studies show gut microbiota modulate antitumor immune responses; one proposed mechanism is cross-reactivity between antigens expressed in commensal bacteria and neoepitopes. We found that T cells targeting an epitope called SVYRYYGL (SVY), expressed in the commensal bacterium Bifidobacterium breve (B. breve), cross-react with a model neoantigen, SIYRYYGL (SIY). Mice lacking B. breve had decreased SVY-reactive T cells compared with B. breve-colonized mice, and the T cell response was transferable by SVY immunization or by cohousing mice without Bifidobacterium with ones colonized with Bifidobacterium. Tumors expressing the model SIY neoantigen also grew faster in mice lacking B. breve compared with Bifidobacterium-colonized animals. B. breve colonization also shaped the SVY-reactive TCR repertoire. Finally, SVY-specific T cells recognized SIY-expressing melanomas in vivo and led to decreased tumor growth and extended survival. Our work demonstrates that commensal bacteria can stimulate antitumor immune responses via cross-reactivity and how bacterial antigens affect the T cell landscape.

Lymphocyte motility is governed by a complex array of mechanisms, and highly dependent on external microenvironmental cues. Tertiary lymphoid sites in particular have unique physical structure such as collagen fiber alignment, due to matrix deposition and remodeling. Three dimensional studies of human lymphocytes in such environments are lacking. We hypothesized that aligned collagenous environment modulates CD8+ T cells motility. We encapsulated activated CD8+ T cells in collagen hydrogels of distinct fiber alignment, a characteristic of tumor microenvironments. We found that human CD8+ T cells move faster and more persistently in aligned collagen fibers compared with nonaligned collagen fibers. Moreover, CD8+ T cells move along the axis of collagen alignment. We showed that myosin light chain kinase (MLCK) inhibition could nullify the effect of aligned collagen on CD8+ T cell motility patterns by decreasing T cell turning in unaligned collagen fiber gels. Finally, as an example of a tertiary lymphoid site, we found that xenograft prostate tumors exhibit highly aligned collagen fibers. We observed CD8+ T cells alongside aligned collagen fibers, and found that they are mostly concentrated in the periphery of tumors. Overall, using an in vitro controlled hydrogel system, we show that collagen fiber organization modulates CD8+ T cells movement via MLCK activation thus providing basis for future studies into relevant therapeutics., (Copyright © 2019 Elsevier B.V. All rights reserved.)

T cell therapies require the removal and culture of T cells ex vivo to expand several thousand-fold. However, these cells often lose the phenotype and cytotoxic functionality for mediating effective therapeutic responses. The extracellular matrix (ECM) has been used to preserve and augment cell phenotype; however, it has not been applied to cellular immunotherapies. Here, a hyaluronic acid (HA)-based hydrogel is engineered to present the two stimulatory signals required for T-cell activation-termed an artificial T-cell stimulating matrix (aTM). It is found that biophysical properties of the aTM-stimulatory ligand density, stiffness, and ECM proteins-potentiate T cell signaling and skew phenotype of both murine and human T cells. Importantly, the combination of the ECM environment and mechanically sensitive TCR signaling from the aTM results in a rapid and robust expansion of rare, antigen-specific CD8+ T cells. Adoptive transfer of these tumor-specific cells significantly suppresses tumor growth and improves animal survival compared with T cells stimulated by traditional methods. Beyond immediate immunotherapeutic applications, demonstrating the environment influences the cellular therapeutic product delineates the importance of the ECM and provides a case study of how to engineer ECM-mimetic materials for therapeutic immune stimulation in the future., (© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Magnetic particles can enrich desired cell populations to aid in understanding cell-type functions and mechanisms, diagnosis, and therapy. As cells are heterogeneous in ligand type, location, expression, and density, careful consideration of magnetic particle design for positive isolation is necessary. Antigen-specific immune cells have low frequencies, which has made studying, identifying, and utilizing these cells for therapy a challenge. Here we demonstrate the importance of magnetic particle design based on the biology of T cells. We create magnetic particles which recognize rare antigen-specific T cells and quantitatively investigate important particle properties including size, concentration, ligand density, and ligand choice in enriching these rare cells. We observe competing optima among particle parameters, with 300 nm particles functionalized with a high density of antigen-specific ligand achieving the highest enrichment and recovery of target cells. In enriching and then activating an endogenous response, 300 nm aAPCs generate nearly 65% antigen-specific T cells with at least 450-fold expansion from endogenous precursors and a 5-fold increase in numbers of antigen-specific cells after only seven days. This systematic study of particle properties in magnetic enrichment provides a case study for the engineering design principles of particles for the isolation of rare cells through biological ligands., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

We have developed a tool to both enrich and expand antigen-specific T cells. This can be helpful in cases such as to A) detect the existence of antigen-specific T cells, B) probe the dynamics of antigen-specific responses, C) understand how antigen-specific responses affect disease state such as autoimmunity, D) demystify heterogeneous responses for antigen-specific T cells, or E) utilize antigen-specific cells for therapy. The tool is based on a magnetic particle that we conjugate antigen-specific and T cell co-stimulatory signals, and that we term as artificial antigen presenting cells (aAPCs). Consequently, since the technology is simple to produce, it can easily be adopted by other laboratories; thus, our purpose here is to describe in detail the fabrication and subsequent use of the aAPCs. We explain how to attach antigen-specific and co-stimulatory signals to the aAPCs, how to utilize them to enrich for antigen-specific T cells, and how to expand antigen-specific T cells. Furthermore, we will highlight engineering design considerations based on experimental and biological information of our experience with characterizing antigen-specific T cells.

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.

T cells are crucial contributors to mounting an effective immune response and increasingly the focus of therapeutic interventions in cancer, infectious disease, and autoimmunity. Translation of current T cell immunotherapies has been hindered by off-target toxicities, limited efficacy, biological variability, and high costs. As T cell therapeutics continue to develop, the application of engineering concepts to control their delivery and presentation will be critical for their success. Here, we outline the engineer's toolbox and contextualize it with the biology of T cells. We focus on the design principles of T cell modulation platforms regarding size, shape, material, and ligand choice. Furthermore, we review how application of these design principles has already impacted T cell immunotherapies and our understanding of T cell biology. Recent, salient examples from protein engineering, synthetic particles, cellular and genetic engineering, and scaffolds and surfaces are provided to reinforce the importance of design considerations. Our aim is to provide a guide for immunologists, engineers, clinicians, and the pharmaceutical sector for the design of T cell-targeting platforms., (Copyright © 2018 Elsevier Inc. All rights reserved.)

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.

Biomimetic materials that target the immune system and generate an anti-tumor responses hold promise in augmenting cancer immunotherapy. These synthetic materials can be engineered and optimized for their biodegradability, physical parameters such as shape and size, and controlled release of immune-modulators. As these new platforms enter the playing field, it is imperative to understand their interaction with existing immunotherapies since single-targeted approaches have limited efficacy. Here, we investigate the synergy between a PLGA-based artificial antigen presenting cell (aAPC) and a checkpoint blockade molecule, anti-PD1 monoclonal antibody (mAb). The combination of antigen-specific aAPC-based activation and anti-PD-1 mAb checkpoint blockade induced the greatest IFN-γ secretion by CD8+ T cells in vitro. Combination treatment also acted synergistically in an in vivo murine melanoma model to result in delayed tumor growth and extended survival, while either treatment alone had no effect. This was shown mechanistically to be due to decreased PD-1 expression and increased antigen-specific proliferation of CD8+ T cells within the tumor microenvironment and spleen. Thus, biomaterial-based therapy can synergize with other immunotherapies and motivates the translation of biomimetic combinatorial treatments., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

As nanoparticle (NP)-mediated drug delivery research continues to expand, understanding parameters that govern NP interactions with the biological environment becomes paramount. The principles identified from the study of these parameters can be used to engineer new NPs, impart unique functionalities, identify novel utilities, and improve the clinical translation of NP formulations. One key design parameter is NP size. New methods have been developed to produce NPs with increased control of NP size between 10 and 200nm, a size range most relevant to physical and biochemical targeting through both intravascular and site-specific deliveries. Three notable techniques best suited for generating polymeric NPs with narrow size distributions are highlighted in this review: self-assembly, microfluidics-based preparation, and flash nanoprecipitation. Furthermore, the effect of NP size on the biological fate and transport properties at the molecular scale (protein-NP interactions) and the tissue and systemic scale (convective and diffusive transport of NPs) are analyzed here. These analyses underscore the importance of NP size control in considering clinical translation and assessment of therapeutic outcomes of NP delivery vehicles., (Copyright © 2015 Elsevier B.V. All rights reserved.)

The sorption and desorption of radiolabeled dipalmitoylphosphatidylcholine (DPPC) and cholesterol (CH) were measured on 5 types of commercial contact lenses. The lenses were soaked in vitro in an artificial tear fluid for 16h. The effects of borate buffered saline and two commercial multi-purpose lens-care solutions (MPSs) on reducing the lipid (DPPC and CH) sorption and increasing the lipid removal were examined. The results showed that silicone hydrogel (SiHy) lenses accumulated the most lipids, sorbing over an order of magnitude more than polymacon, a conventional hydrogel lens. Pre-soaking the SiHy lenses for 16h in MPSs reduced the DPPC sorption by up to 13% and the CH sorption by up to 11%, compared to controls that were not pre-soaked. However neither these reductions nor those on polymacon were statistically significant (p>0.05). In sorption experiments without presoaking, subsequent exposure to the MPSs removed some DPPC from the lenses (0-3.1% for SiHy lenses and 14-55% for polymacon), but CH removal was 0.0-0.8% for SiHy lenses and 0.6-28% for polymacon lenses. Some of these removals were statistically significant (p<0.05)., (Copyright © 2014 British Contact Lens Association. Published by Elsevier Ltd. All rights reserved.)

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.