Effective intercellular communication distances are determined by the relative time constants for cyto/chemokine secretion and diffusion

A mathematical model of signalling molecule-mediated processes during regeneration of osteochondral defects after chondrocyte implantation

Treating bone-cartilage defects is a fundamental clinical problem. The ability of damaged cartilage to self-repair is limited due to its avascularity. Left untreated, these defects can lead to osteoarthritis. Details of osteochondral defect repair are elusive, but animal models indicate healing occurs via an endochondral ossification-like process, similar to that in the growth plate. In the growth plate, the signalling molecules parathyroid hormone-related protein (PTHrP) and Indian Hedgehog (Ihh) form a feedback loop regulating chondrocyte hypertrophy, with Ihh inducing and PTHrP suppressing hypertrophy. To better understand this repair process and to explore the regulatory role of signalling molecules on the regeneration process, we formulate a reaction-diffusion mathematical model of osteochondral defect regeneration after chondrocyte implantation. The drivers of healing are assumed to be chondrocytes and osteoblasts, and their interaction via signalling molecules. We model cell proliferation, migration and chondrocyte hypertrophy, and matrix production and conversion, spatially and temporally. We further model nutrient and signalling molecule diffusion and their interaction with the cells. We consider the PTHrP-Ihh feedback loop as the backbone mechanisms but the model is flexible to incorporate extra signalling mechanisms if needed. Our mathematical model is able to represent repair of osteochondral defects, starting with cartilage formation throughout the defect. This is followed by chondrocyte hypertrophy, matrix calcification and bone formation deep inside the defect, while cartilage at the surface is maintained and eventually separated from the deeper bone by a thin layer of calcified cartilage. The complete process requires around 48 months. A key highlight of the model demonstrates that the PTHrP-Ihh loop alone is insufficient and an extra mechanism is required to initiate chondrocyte hypertrophy, represented by a critical cartilage density. A parameter sensitivity study reveals that the timing of the repair process crucially depends on parameters, such as the critical cartilage density, and those describing the actions of PTHrP to suppress hypertrophy, such as its diffusion coefficient, threshold concentration and degradation rate.

TWCOM: an R package for inference of cell-cell communication on spatially resolved transcriptomics data

Summary

The inference of cell-cell communication is important, as it unveils the intricate cellular behaviors at the molecular level, providing crucial insights essential for understanding complex biological processes and informing targeted interventions in various pathological contexts. Here, we present TWCOM, an R package that implements a Tweedie distribution-based model for accurate cell-cell communication inference. Operating under a generalized additive model framework, TWCOM adeptly handles both single-cell resolution and spot-based spatially resolved transcriptomics data, providing a versatile tool for robust biological sample analysis.

Availability and implementation

The R package TWCOM is available at https://github.com/dongyuanwu/TWCOM. Comprehensive documentation is included with the package.

Spatiotemporal Regulation of Cell Fate in Living Systems Using Photoactivatable Artificial DNA Membraneless Organelles

Coacervates formed by liquid-liquid phase separation emerge as important biomimetic models for studying the dynamic behaviors of membraneless organelles and synchronously motivating the creation of smart architectures with the regulation of cell fate. Despite continuous progress, it remains challenging to balance the trade-offs among structural stability, versatility, and molecular communication for regulation of cell fate and systemic investigation in a complex physiological system. Herein, we present a self-stabilizing and fastener-bound gain-of-function methodology to create a new type of synthetic DNA membraneless organelle (MO) with high stability and controlled bioactivity on the basis of DNA coacervates. Specifically, long single-strand DNA generated by rolling circle amplification (RCA) is selected as the scaffold that assembles into membraneless coacervates via phase separation. Intriguingly, the as-formed DNA MO can recruit RCA byproducts and other components to achieve self-stabilization, nanoscale condensation, and function encoding. As a proof of concept, photoactivatable DNA MO is constructed and successfully employed for time-dependent accumulation and spatiotemporal management of cancer in a mouse model. This study offers new, important insights into synthetic membraneless organelles for the basic understanding and manipulation of important life processes.

Nuclei-level prior knowledge constrained multiple instance learning for breast histopathology whole slide image classification

New breast cancer cases have surpassed lung cancer, becoming the world's most prevalent cancer. Despite advancing medical image analysis, deep learning's lack of interpretability limits its adoption among pathologists. Hence, a nuclei-level prior knowledge constrained multiple instance learning (MIL) (NPKC-MIL) for breast whole slide image (WSI) classification is proposed. NPKC-MIL primarily involves the following steps: Initially, employing the transfer learning to extract patch-level features and aggregate them into slide-level features through attention pooling. Subsequently, abstract the extracted nuclei as nodes, establish nucleus topology using the K-NN (K-Nearest Neighbors, K-NN) algorithm, and create handcrafted features for nodes. Finally, combine patch-level deep learning features with nuclei-level handcrafted features to fine-tune classification results generated by slide-level deep learning features. The experimental results demonstrate that NPKC-MIL outperforms current comparable deep learning models. NPKC-MIL expands the analytical dimension of WSI classification tasks and integrates prior knowledge into deep learning models to improve interpretability.

Day length regulates gonadotrope proliferation and reproduction via an intra-pituitary pathway in the model vertebrate Oryzias latipes

In seasonally breeding mammals and birds, the production of the hormones that regulate reproduction (gonadotropins) is controlled by a complex pituitary-brain-pituitary pathway. Indeed, the pituitary thyroid-stimulating hormone (TSH) regulates gonadotropin expression in pituitary gonadotropes, via dio2-expressing tanycytes, hypothalamic Kisspeptin, RFamide-related peptide, and gonadotropin-releasing hormone neurons. However, in fish, how seasonal environmental signals influence gonadotropins remains unclear. In addition, the seasonal regulation of gonadotrope (gonadotropin-producing cell) proliferation in the pituitary is, to the best of our knowledge, not elucidated in any vertebrate group. Here, we show that in the vertebrate model Japanese medaka (Oryzias latipes), a long day seasonally breeding fish, photoperiod (daylength) not only regulates hormone production by the gonadotropes but also their proliferation. We also reveal an intra-pituitary pathway that regulates gonadotrope cell number and hormone production. In this pathway, Tsh regulates gonadotropes via folliculostellate cells within the pituitary. This study suggests the existence of an alternative regulatory mechanism of seasonal gonadotropin production in fish.

Resource allocation in mammalian systems

Cells execute biological functions to support phenotypes such as growth, migration, and secretion. Complementarily, each function of a cell has resource costs that constrain phenotype. Resource allocation by a cell allows it to manage these costs and optimize their phenotypes. In fact, the management of resource constraints (e.g., nutrient availability, bioenergetic capacity, and macromolecular machinery production) shape activity and ultimately impact phenotype. In mammalian systems, quantification of resource allocation provides important insights into higher-order multicellular functions; it shapes intercellular interactions and relays environmental cues for tissues to coordinate individual cells to overcome resource constraints and achieve population-level behavior. Furthermore, these constraints, objectives, and phenotypes are context-dependent, with cells adapting their behavior according to their microenvironment, resulting in distinct steady-states. This review will highlight the biological insights gained from probing resource allocation in mammalian cells and tissues.

PathoGraph: An Attention-Based Graph Neural Network Capable of Prognostication Based on CD276 Labelling of Malignant Glioma Cells

Computerized methods have been developed that allow quantitative morphological analyses of whole slide images (WSIs), e.g., of immunohistochemical stains. The latter are attractive because they can provide high-resolution data on the distribution of proteins in tissue. However, many immunohistochemical results are complex because the protein of interest occurs in multiple locations (in different cells and also extracellularly). We have recently established an artificial intelligence framework, PathoFusion which utilises a bifocal convolutional neural network (BCNN) model for detecting and counting arbitrarily definable morphological structures. We have now complemented this model by adding an attention-based graph neural network (abGCN) for the advanced analysis and automated interpretation of such data. Classical convolutional neural network (CNN) models suffer from limitations when handling global information. In contrast, our abGCN is capable of creating a graph representation of cellular detail from entire WSIs. This abGCN method combines attention learning with visualisation techniques that pinpoint the location of informative cells and highlight cell-cell interactions. We have analysed cellular labelling for CD276, a protein of great interest in cancer immunology and a potential marker of malignant glioma cells/putative glioma stem cells (GSCs). We are especially interested in the relationship between CD276 expression and prognosis. The graphs permit predicting individual patient survival on the basis of GSC community features. Our experiments lay a foundation for the use of the BCNN-abGCN tool chain in automated diagnostic prognostication using immunohistochemically labelled histological slides, but the method is essentially generic and potentially a widely usable tool in medical research and AI based healthcare applications.

Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Bone marrow trephine biopsy is crucial for the diagnosis of multiple myeloma. However, the complexity of bone marrow cellular, morphologic, and spatial architecture preserved in trephine samples hinders comprehensive evaluation. To dissect the diverse cellular communities and mosaic tissue habitats, we developed a superpixel-inspired deep learning method (MoSaicNet) that adapts to complex tissue architectures and a cell imbalance aware deep learning pipeline (AwareNet) to enable accurate detection and classification of rare cell types in multiplex immunohistochemistry images. MoSaicNet and AwareNet achieved an AUC of >0.98 for tissue and cellular classification on separate test datasets. Application of MoSaicNet and AwareNet enabled investigation of bone heterogeneity and thickness as well as spatial histology analysis of bone marrow trephine samples from monoclonal gammopathies of undetermined significance (MGUS) and from paired newly diagnosed and posttreatment multiple myeloma. The most significant difference between MGUS and newly diagnosed multiple myeloma (NDMM) samples was not related to cell density but to spatial heterogeneity, with reduced spatial proximity of BLIMP1+ tumor cells to CD8+ cells in MGUS compared with NDMM samples. Following treatment of patients with multiple myeloma, there was a reduction in the density of BLIMP1+ tumor cells, effector CD8+ T cells, and regulatory T cells, indicative of an altered immune microenvironment. Finally, bone heterogeneity decreased following treatment of patients with multiple myeloma. In summary, deep learning-based spatial mapping of bone marrow trephine biopsies can provide insights into the cellular topography of the myeloma marrow microenvironment and complement aspirate-based techniques.

SIGNIFICANCE

Spatial analysis of bone marrow trephine biopsies using histology, deep learning, and tailored algorithms reveals the bone marrow architectural heterogeneity and evolution during myeloma progression and treatment.

Microfluidic-based human prostate-cancer-on-chip

Lack of adequate models significantly hinders advances in prostate cancer treatment, where resistance to androgen-deprivation therapies and bone metastasis remain as major challenges. Current in vitro models fail to faithfully mimic the complex prostate physiology. In vivo animal models can shed light on the oncogenes involved in prostate cancer development and progression; however, the animal prostate gland is fundamentally different from that of human, and the underlying genetic mechanisms are different. To address this problem, we developed the first in vitro microfluidic human Prostate-Cancer-on-Chip (PCoC) model, where human prostate cancer and stromal fibroblast cells were co-cultivated in two channels separated by a porous membrane under culture medium flow. The established microenvironment enables soluble signaling factors secreted by each culture to locally diffuse through the membrane pores affecting the neighboring culture. We particularly explored the conversion of the stromal fibroblasts into cancer-associated fibroblasts (CAFs) due to the interaction between the 2 cell types. Immunofluorescence microscopy revealed that tumor cells induced CAF biomarkers, αSMA and COL1A1, in stromal fibroblasts. The stromal CAF conversion level was observed to increase along the flow direction in response to diffusion agents, consistent with simulations of solute concentration gradients. The tumor cells also downregulated androgen receptor (AR) expression in stromal fibroblasts, while an adequate level of stromal AR expression is maintained in normal prostate homeostasis. We further investigated tumor invasion into the stroma, an early step in the metastatic cascade, in devices featuring a serpentine channel with orthogonal channel segments overlaying a straight channel and separated by an 8 µm-pore membrane. Both tumor cells and stromal CAFs were observed to cross over into their neighboring channel, and the stroma's role seemed to be proactive in promoting cell invasion. As control, normal epithelial cells neither induced CAF conversion nor promoted cell invasion. In summary, the developed PCoC model allows spatiotemporal analysis of the tumor-stroma dynamic interactions, due to bi-directional signaling and physical contact, recapitulating tissue-level multicellular responses associated with prostate cancer in vivo. Hence, it can serve as an in vitro model to dissect mechanisms in human prostate cancer development and seek advanced therapeutic strategies.

Information Transmission via Molecular Communication in Astrobiological Environments

The ubiquity of information transmission via molecular communication between cells is comprehensively documented on Earth; this phenomenon might even have played a vital role in the origin(s) and early evolution of life. Motivated by these considerations, a simple model for molecular communication entailing the diffusion of signaling molecules from transmitter to receiver is elucidated. The channel capacity C (maximal rate of information transmission) and an optimistic heuristic estimate of the actual information transmission rate ℐ are derived for this communication system; the two quantities, especially the latter, are demonstrated to be broadly consistent with laboratory experiments and more sophisticated theoretical models. The channel capacity exhibits a potentially weak dependence on environmental parameters, whereas the actual information transmission rate may scale with the intercellular distance d as ℐ ∝ d-4 and could vary substantially across settings. These two variables are roughly calculated for diverse astrobiological environments, ranging from Earth's upper oceans (C ∼ 3.1 × 103 bits/s; ℐ ∼ 4.7 × 10-2 bits/s) and deep sea hydrothermal vents (C ∼ 4.2 × 103 bits/s; ℐ ∼ 1.2 × 10-1 bits/s) to the hydrocarbon lakes and seas of Titan (C ∼ 3.8 × 103 bits/s; ℐ ∼ 2.6 × 10-1 bits/s).

Single-cell morphological and topological atlas reveals the ecosystem diversity of human breast cancer

Digital pathology allows computerized analysis of tumor ecosystem using whole slide images (WSIs). Here, we present single-cell morphological and topological profiling (sc-MTOP) to characterize tumor ecosystem by extracting the features of nuclear morphology and intercellular spatial relationship for individual cells. We construct a single-cell atlas comprising 410 million cells from 637 breast cancer WSIs and dissect the phenotypic diversity within tumor, inflammatory and stroma cells respectively. Spatially-resolved analysis identifies recurrent micro-ecological modules representing locoregional multicellular structures and reveals four breast cancer ecotypes correlating with distinct molecular features and patient prognosis. Further analysis with multiomics data uncovers clinically relevant ecosystem features. High abundance of locally-aggregated inflammatory cells indicates immune-activated tumor microenvironment and favorable immunotherapy response in triple-negative breast cancers. Morphological intratumor heterogeneity of tumor nuclei correlates with cell cycle pathway activation and CDK inhibitors responsiveness in hormone receptor-positive cases. sc-MTOP enables using WSIs to characterize tumor ecosystems at the single-cell level.

Self-supervised deep learning for highly efficient spatial immunophenotyping

BACKGROUND

Efficient biomarker discovery and clinical translation depend on the fast and accurate analytical output from crucial technologies such as multiplex imaging. However, reliable cell classification often requires extensive annotations. Label-efficient strategies are urgently needed to reveal diverse cell distribution and spatial interactions in large-scale multiplex datasets.

METHODS

This study proposed Self-supervised Learning for Antigen Detection (SANDI) for accurate cell phenotyping while mitigating the annotation burden. The model first learns intrinsic pairwise similarities in unlabelled cell images, followed by a classification step to map learnt features to cell labels using a small set of annotated references. We acquired four multiplex immunohistochemistry datasets and one imaging mass cytometry dataset, comprising 2825 to 15,258 single-cell images to train and test the model.

FINDINGS

With 1% annotations (18-114 cells), SANDI achieved weighted F1-scores ranging from 0.82 to 0.98 across the five datasets, which was comparable to the fully supervised classifier trained on 1828-11,459 annotated cells (-0.002 to -0.053 of averaged weighted F1-score, Wilcoxon rank-sum test, P = 0.31). Leveraging the immune checkpoint markers stained in ovarian cancer slides, SANDI-based cell identification reveals spatial expulsion between PD1-expressing T helper cells and T regulatory cells, suggesting an interplay between PD1 expression and T regulatory cell-mediated immunosuppression.

INTERPRETATION

By striking a fine balance between minimal expert guidance and the power of deep learning to learn similarity within abundant data, SANDI presents new opportunities for efficient, large-scale learning for histology multiplex imaging data.

FUNDING

This study was funded by the Royal Marsden/ICR National Institute of Health Research Biomedical Research Centre.

Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.

A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell-Cell Communication

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular biology at an unprecedented resolution, enabling the characterization of cellular heterogeneity, identification of rare but significant cell types, and exploration of cell-cell communications and interactions. Its broad applications span both basic and clinical research domains. In this comprehensive review, we survey the current landscape of scRNA-seq analysis methods and tools, focusing on count modeling, cell-type annotation, data integration, including spatial transcriptomics, and the inference of cell-cell communication. We review the challenges encountered in scRNA-seq analysis, including issues of sparsity or low expression, reliability of cell annotation, and assumptions in data integration, and discuss the potential impact of suboptimal clustering and differential expression analysis tools on downstream analyses, particularly in identifying cell subpopulations. Finally, we discuss recent advancements and future directions for enhancing scRNA-seq analysis. Specifically, we highlight the development of novel tools for annotating single-cell data, integrating and interpreting multimodal datasets covering transcriptomics, epigenomics, and proteomics, and inferring cellular communication networks. By elucidating the latest progress and innovation, we provide a comprehensive overview of the rapidly advancing field of scRNA-seq analysis.

An agent-based model of monocyte differentiation into tumour-associated macrophages in chronic lymphocytic leukemia

Monocyte-derived macrophages help maintain tissue homeostasis and defend the organism against pathogens. In tumors, recent studies have uncovered complex macrophage populations, including tumor-associated macrophages, which support tumorigenesis through cancer hallmarks such as immunosuppression, angiogenesis, or matrix remodeling. In the case of chronic lymphocytic leukemia, these macrophages are known as nurse-like cells (NLCs) and they protect leukemic cells from spontaneous apoptosis, contributing to their chemoresistance. We propose an agent-based model of monocyte differentiation into NLCs upon contact with leukemic B cells in vitro. We performed patient-specific model optimization using cultures of peripheral blood mononuclear cells from patients. Using our model, we were able to reproduce the temporal survival dynamics of cancer cells in a patient-specific manner and to identify patient groups related to distinct macrophage phenotypes. Our results show a potentially important role of phagocytosis in the polarization process of NLCs and in promoting cancer cells' enhanced survival.

Cell-Cell Interactions Enhance Cartilage Zonal Development in 3D Gradient Hydrogels

Cartilage tissue is characterized by zonal organization with gradual transitions of biochemical and mechanical cues from superficial to deep zones. We previously reported that 3D gradient hydrogels made of polyethylene glycol and chondroitin sulfate can induce zonal-specific responses of chondrocytes, resulting in zonal cartilage formation that mimics native tissues. While the role of cell-matrix interactions has been studied extensively, how cell-cell interactions across different zones influence cartilage zonal development remains unknown. The goal of this study is to harness gradient hydrogels as a tool to elucidate the role of cell-cell interactions in driving cartilage zonal development. When encapsulated in intact gradient hydrogels, chondrocytes exhibited strong zonal-specific responses that mimic native cartilage zonal organization. However, the separate culture of each zone of gradient hydrogels resulted in a significant decrease in cell proliferation and cartilage matrix deposition across all zones, while the trend of zonal dependence remains. Unexpectedly, mixing the coculture of all five zones of hydrogels in the same culture well largely abolished the zonal differences, with all zones behaving similarly to the softest zone. These results suggest that paracrine signal exchange among cells in different zones is essential in driving cartilage zonal development, and a spatial organization of zones is required for proper tissue zonal development. Intact, separate, or coculture groups resulted in distinct gene expression patterns in mechanosensing and cartilage-specific markers, suggesting that cell-cell interactions can also modulate mechanosensing. We further showed that 7 days of priming in intact gradient culture was sufficient to instruct the cells to complete the zonal development, and the separate or mixed coculture after 7 days of intact culture had minimal effects on cartilage formation. This study highlights the important role of cell-cell interactions in driving cartilage zonal development and validates gradient hydrogels as a useful tool to elucidate the role of cell-matrix and cell-cell interactions in driving zonal development during tissue morphogenesis and regeneration.

Artificial intelligence techniques for neuropathological diagnostics and research

Artificial intelligence (AI) research began in theoretical neurophysiology, and the resulting classical paper on the McCulloch-Pitts mathematical neuron was written in a psychiatry department almost 80 years ago. However, the application of AI in digital neuropathology is still in its infancy. Rapid progress is now being made, which prompted this article. Human brain diseases represent distinct system states that fall outside the normal spectrum. Many differ not only in functional but also in structural terms, and the morphology of abnormal nervous tissue forms the traditional basis of neuropathological disease classifications. However, only a few countries have the medical specialty of neuropathology, and, given the sheer number of newly developed histological tools that can be applied to the study of brain diseases, a tremendous shortage of qualified hands and eyes at the microscope is obvious. Similarly, in neuroanatomy, human observers no longer have the capacity to process the vast amounts of connectomics data. Therefore, it is reasonable to assume that advances in AI technology and, especially, whole-slide image (WSI) analysis will greatly aid neuropathological practice. In this paper, we discuss machine learning (ML) techniques that are important for understanding WSI analysis, such as traditional ML and deep learning, introduce a recently developed neuropathological AI termed PathoFusion, and present thoughts on some of the challenges that must be overcome before the full potential of AI in digital neuropathology can be realized.

Expansion of fibroblast cell sheets using a modified MEEK micrografting technique for wound healing applications

Cell sheet engineering, a scaffold-free approach to fabricate functional tissue constructs from several cell monolayers, has shown promise in tissue regeneration and wound healing. Unfortunately, these cell sheets are often too small to provide sufficient wound area coverage. In this study, we describe a process to enlarge cell sheets using MEEK micrografting, a technique extensively used to expand skin autografts for large burn treatments. Human dermal fibroblast cell sheets were placed on MEEK's prefolded gauze without any use of adhesive, cut along the premarked lines and stretched out at various expansion ratios (1:3, 1:6 and 1:9), resulting in regular distribution of many square islands of fibroblasts at a much larger surface area. The cellular processes essential for wound healing, including reattachment, proliferation, and migration, of the fibroblasts on expanded MEEK gauze were superior to those on nylon dressing which served as a control. The optimal expansion ratio with the highest migration rate was 1:6, possibly due to the activation of chemical signals caused by mechanical stretching and an effective intercellular communication distance. Therefore, the combination of cell sheet engineering with the MEEK micrografting technique could provide high quality cells with a large coverage area, which would be particularly beneficial in wound care applications.

Inferring a spatial code of cell-cell interactions across a whole animal body

Cell-cell interactions shape cellular function and ultimately organismal phenotype. Interacting cells can sense their mutual distance using combinations of ligand-receptor pairs, suggesting the existence of a spatial code, i.e., signals encoding spatial properties of cellular organization. However, this code driving and sustaining the spatial organization of cells remains to be elucidated. Here we present a computational framework to infer the spatial code underlying cell-cell interactions from the transcriptomes of the cell types across the whole body of a multicellular organism. As core of this framework, we introduce our tool cell2cell, which uses the coexpression of ligand-receptor pairs to compute the potential for intercellular interactions, and we test it across the Caenorhabditis elegans' body. Leveraging a 3D atlas of C. elegans' cells, we also implement a genetic algorithm to identify the ligand-receptor pairs most informative of the spatial organization of cells across the whole body. Validating the spatial code extracted with this strategy, the resulting intercellular distances are negatively correlated with the inferred cell-cell interactions. Furthermore, for selected cell-cell and ligand-receptor pairs, we experimentally confirm the communicatory behavior inferred with cell2cell and the genetic algorithm. Thus, our framework helps identify a code that predicts the spatial organization of cells across a whole-animal body.

Computational Approaches and Challenges in Spatial Transcriptomics

The development of spatial transcriptomics (ST) technologies has transformed genetic research from a single-cell data level to a two-dimensional spatial coordinate system and facilitated the study of the composition and function of various cell subsets in different environments and organs. The large-scale data generated by these ST technologies, which contain spatial gene expression information, have elicited the need for spatially resolved approaches to meet the requirements of computational and biological data interpretation. These requirements include dealing with the explosive growth of data to determine the cell-level and gene-level expression, correcting the inner batch effect and loss of expression to improve the data quality, conducting efficient interpretation and in-depth knowledge mining both at the single-cell and tissue-wide levels, and conducting multi-omics integration analysis to provide an extensible framework toward the in-depth understanding of biological processes. However, algorithms designed specifically for ST technologies to meet these requirements are still in their infancy. Here, we review computational approaches to these problems in light of corresponding issues and challenges, and present forward-looking insights into algorithm development.

A Graph Based Neural Network Approach to Immune Profiling of Multiplexed Tissue Samples

Multiplexed immunofluorescence provides an un-precedented opportunity for studying specific cell-to-cell and cell microenvironment interactions. We employ graph neural networks to combine features obtained from tissue morphology with measurements of protein expression to profile the tumour microenvironment associated with different tumour stages. Our framework presents a new approach to analysing and processing these complex multi-dimensional datasets that overcomes some of the key challenges in analysing these data and opens up the opportunity to abstract biologically meaningful interactions.

Neutrophil and natural killer cell imbalances prevent muscle stem cell-mediated regeneration following murine volumetric muscle loss

Skeletal muscle is one of the largest tissues in the body and can regenerate when damaged through a population of resident muscle stem cells. A type of muscle trauma called volumetric muscle loss overwhelms the regenerative capacity of muscle stem cells and engenders fibrotic supplantation. A comparison of muscle injuries resulting in regeneration or fibrosis revealed that intercellular communication between neutrophils and natural killer cells impacts muscle stem cell-mediated repair. Perturbation of neutrophil–natural killer cell interactions resulted in a variation of healing outcomes and suggested that immunomodulatory interventions can be effective to prevent aberrant healing outcomes.

Volumetric muscle loss (VML) overwhelms the innate regenerative capacity of mammalian skeletal muscle (SkM), leading to numerous disabilities and reduced quality of life. Immune cells are critical responders to muscle injury and guide tissue resident stem cell– and progenitor-mediated myogenic repair. However, how immune cell infiltration and intercellular communication networks with muscle stem cells are altered following VML and drive pathological outcomes remains underexplored. Herein, we contrast the cellular and molecular mechanisms of VML injuries that result in the fibrotic degeneration or regeneration of SkM. Following degenerative VML injuries, we observed the heightened infiltration of natural killer (NK) cells as well as the persistence of neutrophils beyond 2 wk postinjury. Functional validation of NK cells revealed an antagonistic role in neutrophil accumulation in part via inducing apoptosis and CCR1-mediated chemotaxis. The persistent infiltration of neutrophils in degenerative VML injuries was found to contribute to impairments in muscle stem cell regenerative function, which was also attenuated by transforming growth factor beta 1 (TGFβ1). Blocking TGFβ signaling reduced neutrophil accumulation and fibrosis and improved muscle-specific force. Collectively, these results enhance our understanding of immune cell–stem cell cross talk that drives regenerative dysfunction and provide further insight into possible avenues for fibrotic therapy exploration.

CellComm infers cellular crosstalk that drives haematopoietic stem and progenitor cell development

Intercellular communication orchestrates a multitude of physiologic and pathologic conditions. Algorithms to infer cell-cell communication and predict downstream signalling and regulatory networks are needed to illuminate mechanisms of stem cell differentiation and tissue development. Here, to fill this gap, we developed and applied CellComm to investigate how the aorta-gonad-mesonephros microenvironment dictates haematopoietic stem and progenitor cell emergence. We identified key microenvironmental signals and transcriptional networks that regulate haematopoietic development, including Stat3, Nr0b2, Ybx1 and App, and confirmed their roles using zebrafish, mouse and human models. Notably, CellComm revealed extensive crosstalk among signalling pathways and convergence on common transcriptional regulators, indicating a resilient developmental programme that ensures dynamic adaptation to changes in the embryonic environment. Our work provides an algorithm and data resource for the scientific community.

Spatially arranged encapsulation of stem cell spheroids within hydrogels for the regulation of spheroid fusion and cell migration

Mesenchymal stem cell spheroids have been encapsulated in hydrogels for various applications because spheroids demonstrate higher cell activity than individual cells in suspension. However, there is limited information on the effect of distance between spheroids (inter-spheroid distance) on fusion or migration in a hydrogel. In this study, we developed temperature-responsive hydrogels with surface microwell patterns to culture adipose-derived stem cell (ASC) spheroids and deliver them into a Matrigel for the investigation of the effect of inter-spheroid distance on spheroid behavior. The ASC spheroids were encapsulated successfully in a Matrigel, denoted as sandwich culture, with a specific inter-spheroid distance ranging from 100 to 400 µm. Interestingly, ASCs migrated from the host spheroid and formed a bridge-like structure between spheroids, denoted as a cellular bridge, only when the inter-spheroid distance was 200 µm. Thus, we performed a sandwich culture of human umbilical vein endothelial cells (HUVECs) and ASCs in co-cultured spheroids in the Matrigel to create a homogeneous endothelial cell network in the hydrogel. The HUVECs sprouted through the ASC cellular bridge and directly interacted with the adjacent spheroid when the inter-spheroid distance was 200 µm. Similar results were obtained from an in vivo study. Thus, our study suggests the appropriate inter-spheroid distance for effective spheroid encapsulation in a hydrogel. STATEMENT OF SIGNIFICANCE: Recently, spheroid-based 3D tissue culture techniques such as spheroid encapsulation or 3D printing are being intensively investigated for various purposes. However, there is limited research regarding the effect of the inter-spheroid distance on spheroid communication. Here, we demonstrate a spatially arranged spheroid encapsulation method within a Matrigel by using a temperature-responsive hydrogel. Human adipose-derived stem cell spheroids are encapsulated with a precisely controlled inter-spheroid distance from 100 to 400 µm and show different tendencies in cell migration and spheroid fusion. Our results suggest that the inter-spheroid distance affects spheroid communication, and thus, the inter-spheroid distance needs to be considered carefully according to the purpose.

Effects of tumor derived exosomes on T cells markers expression

Exosomes are 30-120nm bio particles transferred from donor to recipient cells leading to modification in their regulatory mechanisms depending upon the coded message in the form of loaded biomolecule. Cancer cells derived exosomes the true representatives of the parent cells have been found to modify the tumor surrounding/distinct regions and participate in metastasis, angiogenesis and immune suppression. Tis study was aimed to study the effects of tumor mice derived exosomes on the normal mice spleen isolated T cells by using co-culture experiments and flow cytometer analysis. We mainly focused on some of the T cells population and cytokines including IFN-γ, FOXP3+ regulatory T (Treg) cells and KI67 (proliferation marker). Overall results indicated random changes in different set of experiments, where the cancer derived exosomes reduced the IFN-γ expression in both CD4 and CD8 T cells, similarly the Treg cells were also found decreased in the presence of cancer exosomes. No significant changes were observed on the Ki67 marker expression. Such studies are helpful in understanding the role of cancer exosomes in immune cells suppression in tumor microenvironment. Cancer exosomes will need to be validated in vivo and in vitro on a molecular scale in detail for clinical applications.

Female Prostate Development: Morphological Analysis of the Budding Dynamic

The presence of the prostate in female mammals has long been known. However, pieces of information related to its development are still lacking. The aim of this study was to explore the budding dynamic during the initial prostate development in female gerbils. Pregnant females were timed, the fetuses were euthanized, and the urogenital sinus was dissected out between the embryonic days 20 and 24 (E20-E24 groups). Newborn pups (1-day-old; P1 group) underwent the same procedures. The female prostate development was based on epithelial buds which arose far from the paraurethral mesenchyme (PAM). The epithelial buds reached the PAM at prenatal day 24, crossing a small gap in the smooth muscle layer between the periurethral mesenchyme (PEM) and the PAM. Steroid nuclear receptors such as the androgen receptor and estrogen receptor alpha were localized in the PEM through the urethral wall, although some epithelial labeling was also present in the urogenital sinus epithelium (UGE). P63-positive cells were found only in the UGE, becoming restricted to the basal compartment after the 23rd prenatal day. The results showed that the gerbil female prostate exhibits a distinct budding pattern as compared to the male prostate development.

Proximity of immune and tumor cells underlies response to BRAF/MEK-targeted therapies in metastatic melanoma patients

Acquired resistance to BRAF/MEK-targeted therapy occurs in the majority of melanoma patients that harbor BRAF mutated tumors, leading to relapse or progression and the underlying mechanism is unclear in many cases. Using multiplex immunohistochemistry and spatial imaging analysis of paired tumor sections obtained from 11 melanoma patients prior to BRAF/MEK-targeted therapy and when the disease progressed on therapy, we observed a significant increase of tumor cellularity in the progressed tumors and the close association of SOX10⁺ melanoma cells with CD8⁺ T cells negatively correlated with patient's progression-free survival (PFS). In the TCGA-melanoma dataset (n = 445), tumor cellularity exhibited additive prognostic value in the immune score signature to predict overall survival in patients with early-stage melanoma. Moreover, tumor cellularity prognoses OS independent of immune score in patients with late-stage melanoma.

Spatial Metrics of Interaction between CD163-Positive Macrophages and Cancer Cells and Progression-Free Survival in Chemo-Treated Breast Cancer

Tumor-associated macrophages (TAMs) promote progression of breast cancer and other solid malignancies via immunosuppressive, pro-angiogenic and pro-metastatic effects. Tumor-promoting TAMs tend to express M2-like macrophage markers, including CD163. Histopathological assessments suggest that the density of CD163-positive TAMs within the tumor microenvironment is associated with reduced efficacy of chemotherapy and unfavorable prognosis. However, previous analyses have required research-oriented pathologists to visually enumerate CD163+ TAMs, which is both laborious and subjective and hampers clinical implementation. Objective, operator-independent image analysis methods to quantify TAM-associated information are needed. In addition, since M2-like TAMs exert local effects on cancer cells through direct juxtacrine cell-to-cell interactions, paracrine signaling, and metabolic factors, we hypothesized that spatial metrics of adjacency of M2-like TAMs to breast cancer cells will have further information value. Immunofluorescence histo-cytometry of CD163+ TAMs was performed retrospectively on tumor microarrays of 443 cases of invasive breast cancer from patients who subsequently received adjuvant chemotherapy. An objective and automated algorithm was developed to phenotype CD163+ TAMs and calculate their density within the tumor stroma and derive several spatial metrics of interaction with cancer cells. Shorter progression-free survival was associated with a high density of CD163+ TAMs, shorter median cancer-to-CD163+ nearest neighbor distance, and a high number of either directly adjacent CD163+ TAMs (within juxtacrine proximity <12 μm to cancer cells) or communicating CD163+ TAMs (within paracrine communication distance <250 μm to cancer cells) after multivariable adjustment for clinical and pathological risk factors and correction for optimistic bias due to dichotomization.

Hierarchical graph representations in digital pathology

Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological entities is imperative for computer aided cancer patient care. To this end, several approaches have leveraged cell-graphs, capturing the cell-microenvironment, to depict the tissue. These allow for utilizing graph theory and machine learning to map the tissue representation to tissue functionality, and quantify their relationship. Though cellular information is crucial, it is incomplete alone to comprehensively characterize complex tissue structure. We herein treat the tissue as a hierarchical composition of multiple types of histological entities from fine to coarse level, capturing multivariate tissue information at multiple levels. We propose a novel multi-level hierarchical entity-graph representation of tissue specimens to model the hierarchical compositions that encode histological entities as well as their intra- and inter-entity level interactions. Subsequently, a hierarchical graph neural network is proposed to operate on the hierarchical entity-graph and map the tissue structure to tissue functionality. Specifically, for input histology images, we utilize well-defined cells and tissue regions to build HierArchical Cell-to-Tissue (HACT) graph representations, and devise HACT-Net, a message passing graph neural network, to classify the HACT representations. As part of this work, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of Haematoxylin & Eosin stained breast tumor regions-of-interest, to evaluate and benchmark our proposed methodology against pathologists and state-of-the-art computer-aided diagnostic approaches. Through comparative assessment and ablation studies, our proposed method is demonstrated to yield superior classification results compared to alternative methods as well as individual pathologists. The code, data, and models can be accessed at https://github.com/histocartography/hact-net.

Clustering of catalytic nanocompartments for enhancing an extracellular non-native cascade reaction

Compartmentalization is fundamental in nature, where the spatial segregation of biochemical reactions within and between cells ensures optimal conditions for the regulation of cascade reactions. While the distance between compartments or their interaction are essential parameters supporting the efficiency of bio-reactions, so far they have not been exploited to regulate cascade reactions between bioinspired catalytic nanocompartments. Here, we generate individual catalytic nanocompartments (CNCs) by encapsulating within polymersomes or attaching to their surface enzymes involved in a cascade reaction and then, tether the polymersomes together into clusters. By conjugating complementary DNA strands to the polymersomes' surface, DNA hybridization drove the clusterization process of enzyme-loaded polymersomes and controlled the distance between the respective catalytic nanocompartments. Owing to the close proximity of CNCs within clusters and the overall stability of the cluster architecture, the cascade reaction between spatially segregated enzymes was significantly more efficient than when the catalytic nanocompartments were not linked together by DNA duplexes. Additionally, residual DNA single strands that were not engaged in clustering, allowed for an interaction of the clusters with the cell surface as evidenced by A549 cells, where clusters decorating the surface endowed the cells with a non-native enzymatic cascade. The self-organization into clusters of catalytic nanocompartments confining different enzymes of a cascade reaction allows for a distance control of the reaction spaces which opens new avenues for highly efficient applications in domains such as catalysis or nanomedicine.

In situ functional cell phenotyping reveals microdomain networks in colorectal cancer recurrence

Tumors are dynamic ecosystems comprising localized niches (microdomains), possessing distinct compositions and spatial configurations of cancer and non-cancer cell populations. Microdomain-specific network signaling coevolves with a continuum of cell states and functional plasticity associated with disease progression and therapeutic responses. We present LEAPH, an unsupervised machine learning algorithm for identifying cell phenotypes, which applies recursive steps of probabilistic clustering and spatial regularization to derive functional phenotypes (FPs) along a continuum. Combining LEAPH with pointwise mutual information and network biology analyses enables the discovery of outcome-associated microdomains visualized as distinct spatial configurations of heterogeneous FPs. Utilization of an immunofluorescence-based (51 biomarkers) image dataset of colorectal carcinoma primary tumors (n = 213) revealed microdomain-specific network dysregulation supporting cancer stem cell maintenance and immunosuppression that associated selectively with the recurrence phenotype. LEAPH enables an explainable artificial intelligence platform providing insights into pathophysiological mechanisms and novel drug targets to inform personalized therapeutic strategies.

Surface activation with oxygen plasma promotes osteogenesis with enhanced extracellular matrix formation in three-dimensional microporous scaffolds

Various types of synthetic polyesters have been developed as biomaterials for tissue engineering. These materials commonly possess biodegradability, biocompatibility, and formability, which are preferable properties for bone regeneration. The major challenge of using synthetic polyesters is the result of low cell affinity due to their hydrophobic nature, which hinders efficient cell seeding and active cell dynamics. To improve wettability, plasma treatment is widely used in industry. Here, we performed surface activation with oxygen plasma to hydrophobic copolymers, poly(l-lactide-co-trimethylene carbonate), which were shaped in 2D films and 3D microporous scaffolds, and then we evaluated the resulting surface properties and the cellular responses of rat bone marrow stem cells (rBMSC) to the material. Using scanning electron microscopy and Fourier-transform infrared spectroscopy, we demonstrated that short-term plasma treatment increased nanotopographical surface roughness and wettability with minimal change in surface chemistry. On treated surfaces, initial cell adhesion and elongation were significantly promoted, and seeding efficiency was improved. In an osteoinductive environment, rBMSC on plasma-treated scaffolds exhibited accelerated osteogenic differentiation with osteogenic markers including RUNX2, osterix, bone sialoprotein, and osteocalcin upregulated, and a greater amount of collagen matrix and mineral deposition were found. This study shows the utility of plasma surface activation for polymeric scaffolds in bone tissue engineering.

Organoid modeling of Zika and herpes simplex virus 1 infections reveals virus-specific responses leading to microcephaly

Viral infection in early pregnancy is a major cause of microcephaly. However, how distinct viruses impair human brain development remains poorly understood. Here we use human brain organoids to study the mechanisms underlying microcephaly caused by Zika virus (ZIKV) and herpes simplex virus (HSV-1). We find that both viruses efficiently replicate in brain organoids and attenuate their growth by causing cell death. However, transcriptional profiling reveals that ZIKV and HSV-1 elicit distinct cellular responses and that HSV-1 uniquely impairs neuroepithelial identity. Furthermore, we demonstrate that, although both viruses fail to potently induce the type I interferon system, the organoid defects caused by their infection can be rescued by distinct type I interferons. These phenotypes are not seen in 2D cultures, highlighting the superiority of brain organoids in modeling viral infections. These results uncover virus-specific mechanisms and complex cellular immune defenses associated with virus-induced microcephaly.

Secretome and Tunneling Nanotubes: A Multilevel Network for Long Range Intercellular Communication between Endothelial Cells and Distant Cells

As a cellular interface between the blood and tissues, the endothelial cell (EC) monolayer is involved in the control of key functions including vascular tone, permeability and homeostasis, leucocyte trafficking and hemostasis. EC regulatory functions require long-distance communications between ECs, circulating hematopoietic cells and other vascular cells for efficient adjusting thrombosis, angiogenesis, inflammation, infection and immunity. This intercellular crosstalk operates through the extracellular space and is orchestrated in part by the secretory pathway and the exocytosis of Weibel Palade Bodies (WPBs), secretory granules and extracellular vesicles (EVs). WPBs and secretory granules allow both immediate release and regulated exocytosis of messengers such as cytokines, chemokines, extracellular membrane proteins, coagulation or growth factors. The ectodomain shedding of transmembrane protein further provide the release of both receptor and ligands with key regulatory activities on target cells. Thin tubular membranous channels termed tunneling nanotubes (TNTs) may also connect EC with distant cells. EVs, in particular exosomes, and TNTs may contain and transfer different biomolecules (e.g., signaling mediators, proteins, lipids, and microRNAs) or pathogens and have emerged as a major triggers of horizontal intercellular transfer of information.

Human NK cells prime inflammatory DC precursors to induce Tc17 differentiation

Adaptive immune responses are acknowledged to evolve from innate immunity. However, limited information exists regarding whether encounters between innate cells direct the generation of specialized T-cell subsets. We aim to understand how natural killer (NK) cells modulate cell-mediated immunity in humans. We found that human CD14+CD16- monocytes that differentiate into inflammatory dendritic cells (DCs) are shaped at the early stages of differentiation by cell-to-cell interactions with NK cells. Although a fraction of monocytes is eliminated by NK-cell-mediated cytotoxicity, the polarization of interferon-γ (IFN-γ) at the NKp30-stabilized synapses triggers a stable IFN-γ signature in surviving monocytes that persists after their differentiation into DCs. Notably, NK-cell-instructed DCs drive the priming of type 17 CD8+ T cells (Tc17) with the capacity to produce IFN-γ and interleukin-17A. Compared with healthy donors, this cellular network is impaired in patients with classical NK-cell deficiency driven by mutations in the GATA2 gene. Our findings reveal a previously unrecognized connection by which Tc17-mediated immunity might be regulated by NK-cell-mediated tuning of antigen-presenting cells.

The Mystery of Red Blood Cells Extracellular Vesicles in Sleep Apnea with Metabolic Dysfunction

Sleep is very important for overall health and quality of life, while sleep disorder has been associated with several human diseases, namely cardiovascular, metabolic, cognitive, and cancer-related alterations. Obstructive sleep apnea (OSA) is the most common respiratory sleep-disordered breathing, which is caused by the recurrent collapse of the upper airway during sleep. OSA has emerged as a major public health problem and increasing evidence suggests that untreated OSA can lead to the development of various diseases including neurodegenerative diseases. In addition, OSA may lead to decreased blood oxygenation and fragmentation of the sleep cycle. The formation of free radicals or reactive oxygen species (ROS) can emerge and react with nitric oxide (NO) to produce peroxynitrite, thereby diminishing the bioavailability of NO. Hypoxia, the hallmark of OSA, refers to a decline of tissue oxygen saturation and affects several types of cells, playing cell-to-cell communication a vital role in the outcome of this interplay. Red blood cells (RBCs) are considered transporters of oxygen and nutrients to the tissues, and these RBCs are important interorgan communication systems with additional functions, including participation in the control of systemic NO metabolism, redox regulation, blood rheology, and viscosity. RBCs have been shown to induce endothelial dysfunction and increase cardiac injury. The mechanistic links between changes of RBC functional properties and cardiovascular are largely unknown. Extracellular vesicles (EVs) are secreted by most cell types and released in biological fluids both under physiological and pathological conditions. EVs are involved in intercellular communication by transferring complex cargoes including proteins, lipids, and nucleic acids from donor cells to recipient cells. Advancing our knowledge about mechanisms of RBC-EVs formation and their pathophysiological relevance may help to shed light on circulating EVs and to translate their application to clinical practice. We will focus on the potential use of RBC-EVs as valuable diagnostic and prognostic biomarkers and state-specific cargoes, and possibilities as therapeutic vehicles for drug and gene delivery. The use of RBC-EVs as a precision medicine for the diagnosis and treatment of the patient with sleep disorder will improve the prognosis and the quality of life in patients with cardiovascular disease (CVD).

Deciphering cell-cell interactions and communication from gene expression

Cell-cell interactions orchestrate organismal development, homeostasis and single-cell functions. When cells do not properly interact or improperly decode molecular messages, disease ensues. Thus, the identification and quantification of intercellular signalling pathways has become a common analysis performed across diverse disciplines. The expansion of protein-protein interaction databases and recent advances in RNA sequencing technologies have enabled routine analyses of intercellular signalling from gene expression measurements of bulk and single-cell data sets. In particular, ligand-receptor pairs can be used to infer intercellular communication from the coordinated expression of their cognate genes. In this Review, we highlight discoveries enabled by analyses of cell-cell interactions from transcriptomic data and review the methods and tools used in this context.

Insights into the mechanism of vascular endothelial cells on bone biology

In the skeletal system, blood vessels not only function as a conduit system for transporting gases, nutrients, metabolic waste, or cells but also provide multifunctional signal molecules regulating bone development, regeneration, and remodeling. Endothelial cells (ECs) in bone tissues, unlike in other organ tissues, are in direct contact with the pericytes of blood vessels, resulting in a closer connection with peripheral connective tissues. Close-contact ECs contribute to osteogenesis and osteoclastogenesis by secreting various cytokines in the paracrine or juxtacrine pathways. An increasing number of studies have revealed that extracellular vesicles (EVs) derived from ECs can directly regulate maturation process of osteoblasts and osteoclasts. The different pathways focus on targets at different distances, forming the basis of the intimate spatial and temporal link between bone tissue and blood vessels. Here, we provide a systematic review to elaborate on the function of ECs in bone biology and its underlying mechanisms based on three aspects: paracrine, EVs, and juxtacrine. This review proposes the possibility of a therapeutic strategy targeting blood vessels, as an adjuvant treatment for bone disorders.

Spatially Resolved Analytical Chemistry in Intact, Living Tissues

Tissues are an exciting frontier for bioanalytical chemistry, one in which spatial distribution is just as important as total content. Intact tissue preserves the native cellular and molecular organization and the cell-cell contacts found in vivo. Live tissue, in particular, offers the potential to analyze dynamic events in a spatially resolved manner, leading to fundamental biological insights and translational discoveries. In this Perspective, we provide a tutorial on the four fundamental challenges for the bioanalytical chemist working in living tissue samples as well as best practices for mitigating them. The challenges include (i) the complexity of the sample matrix, which contributes myriad interfering species and causes nonspecific binding of reagents; (ii) hindered delivery and mixing; (iii) the need to maintain physiological conditions; and (iv) tissue reactivity. This framework is relevant to a variety of methods for spatially resolved chemical analysis, including optical imaging, inserted sensors and probes such as electrodes, and surface analyses such as sensing arrays. The discussion focuses primarily on ex vivo tissues, though many considerations are relevant in vivo as well. Our goal is to convey the exciting potential of analytical chemistry to contribute to understanding the functions of live, intact tissues.

Light-Activated Signaling in DNA-Encoded Sender-Receiver Architectures

Collective decision making by living cells is facilitated by exchange of diffusible signals where sender cells release a chemical signal that is interpreted by receiver cells. A variety of nonliving artificial cell models have been developed in recent years that mimic various aspects of diffusion-based intercellular communication. However, localized secretion of diffusive signals from individual protocells, which is critical for mimicking biological sender-receiver systems, has remained challenging to control precisely. Here, we engineer light-responsive, DNA-encoded sender-receiver architectures, where protein-polymer microcapsules act as cell mimics and molecular communication occurs through diffusive DNA signals. We prepare spatial distributions of sender and receiver protocells using a microfluidic trapping array and set up a signaling gradient from a single sender cell using light, which activates surrounding receivers through DNA strand displacement. Our systematic analysis reveals how the effective signal range of a single sender is determined by various factors including the density and permeability of receivers, extracellular signal degradation, signal consumption, and catalytic regeneration. In addition, we construct a three-population configuration where two sender cells are embedded in a dense array of receivers that implement Boolean logic and investigate spatial integration of nonidentical input cues. The results offer a means for studying diffusion-based sender-receiver topologies and present a strategy to achieve the congruence of reaction-diffusion and positional information in chemical communication systems that have the potential to reconstitute collective cellular patterns.

Forced to communicate: Integration of mechanical and biochemical signaling in morphogenesis

Morphogenesis is a physical process that requires the generation of mechanical forces to achieve dynamic changes in cell position, tissue shape, and size as well as biochemical signals to coordinate these events. Mechanical forces are also used by the embryo to transmit detailed information across space and detected by target cells, leading to downstream changes in cellular properties and behaviors. Indeed, forces provide signaling information of complementary quality that can both synergize and diversify the functional outputs of biochemical signaling. Here, we discuss recent findings that reveal how mechanical signaling and biochemical signaling are integrated during morphogenesis and the possible context-specific advantages conferred by the interactions between these signaling mechanisms.

Exosomes-based cell-free cancer therapy: a novel strategy for targeted therapy

Exosomes are cell-derived nano-sized, phospholipid vehicles that function to transport prodigious amounts of bioactive molecules to specific recipient tissues. Several hallmarks of cancer are impacted by this exosome-mediated cell-to-cell communication, including, remodeling the architecture of the extracellular matrix, endowing cancer cells with characteristics of drug resistance, or even modulating immune responses. Owing to their immunomodulatory potential and endogenous functionalities, exosomes may also be exploited in various innovative immunological approaches to activate adaptive and innate immune effector cells to mount an effective anticancer immunosurveillance. The review highlights the recent advances in state-of-the-art technologies and protocols for using exosomes as an effective and promising application for cancer immunotherapy.

Geospatial immune variability illuminates differential evolution of lung adenocarcinoma

Remarkable progress in molecular analyses has improved our understanding of the evolution of cancer cells toward immune escape1-5. However, the spatial configurations of immune and stromal cells, which may shed light on the evolution of immune escape across tumor geographical locations, remain unaddressed. We integrated multiregion exome and RNA-sequencing (RNA-seq) data with spatial histology mapped by deep learning in 100 patients with non-small cell lung cancer from the TRACERx cohort6. Cancer subclones derived from immune cold regions were more closely related in mutation space, diversifying more recently than subclones from immune hot regions. In TRACERx and in an independent multisample cohort of 970 patients with lung adenocarcinoma, tumors with more than one immune cold region had a higher risk of relapse, independently of tumor size, stage and number of samples per patient. In lung adenocarcinoma, but not lung squamous cell carcinoma, geometrical irregularity and complexity of the cancer-stromal cell interface significantly increased in tumor regions without disruption of antigen presentation. Decreased lymphocyte accumulation in adjacent stroma was observed in tumors with low clonal neoantigen burden. Collectively, immune geospatial variability elucidates tumor ecological constraints that may shape the emergence of immune-evading subclones and aggressive clinical phenotypes.

Bioinformatics analysis of whole slide images reveals significant neighborhood preferences of tumor cells in Hodgkin lymphoma

In pathology, tissue images are evaluated using a light microscope, relying on the expertise and experience of pathologists. There is a great need for computational methods to quantify and standardize histological observations. Computational quantification methods become more and more essential to evaluate tissue images. In particular, the distribution of tumor cells and their microenvironment are of special interest. Here, we systematically investigated tumor cell properties and their spatial neighborhood relations by a new application of statistical analysis to whole slide images of Hodgkin lymphoma, a tumor arising in lymph nodes, and inflammation of lymph nodes called lymphadenitis. We considered properties of more than 400, 000 immunohistochemically stained, CD30-positive cells in 35 whole slide images of tissue sections from subtypes of the classical Hodgkin lymphoma, nodular sclerosis and mixed cellularity, as well as from lymphadenitis. We found that cells of specific morphology exhibited significantly favored and unfavored spatial neighborhood relations of cells in dependence of their morphology. This information is important to evaluate differences between Hodgkin lymph nodes infiltrated by tumor cells (Hodgkin lymphoma) and inflamed lymph nodes, concerning the neighborhood relations of cells and the sizes of cells. The quantification of neighborhood relations revealed new insights of relations of CD30-positive cells in different diagnosis cases. The approach is general and can easily be applied to whole slide image analysis of other tumor types.

In pathology, histological diagnosis is still challenging, in particular, for tumor diseases. Pathologists diagnose the disease and its stage of development on the basis of evaluation and interpretation of images of tissue sections. The quantification of experimental data to support decisions of diagnosis and prognosis, applying bioinformatics methods, is an important issue. Here, we introduce a new, general approach to analyze tissue images of tumor and non-tumor patients and to evaluate the distribution of tumor cells in the tissue. Moreover, we consider neighborhood relations between immunostained cells of different cell morphology. We focus on a special type of lymph node tumor, the Hodgkin lymphoma, exploring the two main types of the classical Hodgkin lymphoma, the nodular sclerosis and the mixed cellularity, and the non-tumor case, the lymphadenitis, representing an inflammation of the lymph node. We considered more than 400, 000 cells immunohistochemically stained with CD30 in 35 whole slide images of tissue sections. We found that cells of specific morphology exhibited significant relations to cells of certain morphology as spatial nearest neighbor. We could show different neighborhood patterns of CD30-positive cells between tumor and non-tumor. The approach is general and can easily be applied to other tumor types.

Game of neutrophils: modeling the balance between apoptosis and necrosis

BACKGROUND

Neutrophils are one of the key players in the human innate immune system (HIIS). In the event of an insult where the body is exposed to inflammation triggering moieties (ITMs), neutrophils are mobilized towards the site of insult and antagonize the inflammation. If the inflammation is cleared, neutrophils go into a programmed death called apoptosis. However, if the insult is intense or persistent, neutrophils take on a violent death pathway called necrosis, which involves the rupture of their cytoplasmic content into the surrounding tissue that causes local tissue damage, thus further aggravating inflammation. This seemingly paradoxical phenomenon fuels the inflammatory process by triggering the recruitment of additional neutrophils to the site of inflammation, aimed to contribute to the complete neutralization of severe inflammation. This delicate balance between the cost and benefit of the neutrophils' choice of death pathway has been optimized during the evolution of the innate immune system. The goal of our work is to understand how the tradeoff between the cost and benefit of the different death pathways of neutrophils, in response to various levels of insults, has been optimized over evolutionary time by using the concepts of evolutionary game theory.

RESULTS

We show that by using evolutionary game theory, we are able to formulate a game that predicts the percentage of necrosis and apoptosis when exposed to various levels of insults.

CONCLUSION

By adopting an evolutionary perspective, we identify the driving mechanisms leading to the delicate balance between apoptosis and necrosis in neutrophils' cell death in response to different insults. Using our simple model, we verify that indeed, the global cost of remaining ITMs is the driving mechanism that reproduces the percentage of necrosis and apoptosis observed in data and neutrophils need sufficient information of the overall inflammation to be able to pick a death pathway that presumably increases the survival of the organism.

Modeling cell-cell communication for immune systems across space and time

Communicating is crucial for cells to coordinate their behaviors. Immunological processes, involving diverse cytokines and cell types, are ideal for developing frameworks for modeling coordinated behaviors of cells. Here, we review recent studies that combine modeling and experiments to reveal how immune systems use autocrine, paracrine, and juxtacrine signals to achieve behaviors such as controlling population densities and hair regenerations. We explain that models are useful because one can computationally vary numerous parameters, in experimentally infeasible ways, to evaluate alternate immunological responses. For each model, we focus on the length-scales and time-scales involved and explain why integrating multiple length-scales and time-scales in a model remain challenging. We suggest promising modeling strategies for meeting this challenge and their practical consequences.

Quantitative Histopathology of Stained Tissues using Color Spatial Light Interference Microscopy (cSLIM)

Tissue biopsy evaluation in the clinic is in need of quantitative disease markers for diagnosis and, most importantly, prognosis. Among the new technologies, quantitative phase imaging (QPI) has demonstrated promise for histopathology because it reveals intrinsic tissue nanoarchitecture through the refractive index. However, a vast majority of past QPI investigations have relied on imaging unstained tissues, which disrupts the established specimen processing. Here we present color spatial light interference microscopy (cSLIM) as a new whole-slide imaging modality that performs interferometric imaging on stained tissue, with a color detector array. As a result, cSLIM yields in a single scan both the intrinsic tissue phase map and the standard color bright-field image, familiar to the pathologist. Our results on 196 breast cancer patients indicate that cSLIM can provide stain-independent prognostic information from the alignment of collagen fibers in the tumor microenvironment. The effects of staining on the tissue phase maps were corrected by a mathematical normalization. These characteristics are likely to reduce barriers to clinical translation for the new cSLIM technology.

Receptor-Ligand Rebinding Kinetics in Confinement

Rebinding kinetics of molecular ligands plays a key role in the operation of biomachinery, from regulatory networks to protein transcription, and is also a key factor in design of drugs and high-precision biosensors. In this study, we investigate initial release and rebinding of ligands to their binding sites grafted on a planar surface, a situation commonly observed in single-molecule experiments and that occurs in vivo, e.g., during exocytosis. Via scaling arguments and molecular dynamic simulations, we analyze the dependence of nonequilibrium rebinding kinetics on two intrinsic length scales: the average separation distance between the binding sites and the total diffusible volume (i.e., height of the experimental reservoir in which diffusion takes place or average distance between receptor-bearing surfaces). We obtain time-dependent scaling laws for on rates and for the cumulative number of rebinding events. For diffusion-limited binding, the (rebinding) on rate decreases with time via multiple power-law regimes before the terminal steady-state (constant on-rate) regime. At intermediate times, when particle density has not yet become uniform throughout the diffusible volume, the cumulative number of rebindings exhibits a novel, to our knowledge, plateau behavior because of the three-dimensional escape process of ligands from binding sites. The duration of the plateau regime depends on the average separation distance between binding sites. After the three-dimensional diffusive escape process, a one-dimensional diffusive regime describes on rates. In the reaction-limited scenario, ligands with higher affinity to their binding sites (e.g., longer residence times) delay entry to the power-law regimes. Our results will be useful for extracting hidden timescales in experiments such as kinetic rate measurements for ligand-receptor interactions in microchannels, as well as for cell signaling via diffusing molecules.

Tunneling nanotubes, a novel mode of tumor cell-macrophage communication in tumor cell invasion

The interaction between tumor cells and macrophages is crucial in promoting tumor invasion and metastasis. In this study, we examined a novel mechanism of intercellular communication, namely membranous actin-based tunneling nanotubes (TNTs), that occurs between macrophages and tumor cells in the promotion of macrophage-dependent tumor cell invasion. The presence of heterotypic TNTs between macrophages and tumor cells induced invasive tumor cell morphology, which was dependent on EGF-EGFR signaling. Furthermore, reduction of a protein involved in TNT formation, M-Sec (TNFAIP2), in macrophages inhibited tumor cell elongation, blocked the ability of tumor cells to invade in 3D and reduced macrophage-dependent long-distance tumor cell streaming in vitro Using an in vivo zebrafish model that recreates macrophage-mediated tumor cell invasion, we observed TNT-mediated macrophage-dependent tumor cell invasion, distant metastatic foci and areas of metastatic spread. Overall, our studies support a role for TNTs as a novel means of interaction between tumor cells and macrophages that leads to tumor progression and metastasis.