Deciphering cell-cell interactions and communication from gene expression

DiSiR: fast and robust method to identify ligand-receptor interactions at subunit level from single-cell RNA-sequencing data

Most cell-cell interactions and crosstalks are mediated by ligand-receptor interactions. The advent of single-cell RNA-sequencing (scRNA-seq) techniques has enabled characterizing tissue heterogeneity at single-cell level. In the past few years, several methods have been developed to study ligand-receptor interactions at cell type level using scRNA-seq data. However, there is still no easy way to query the activity of a specific user-defined signaling pathway in a targeted way or to map the interactions of the same subunit with different ligands as part of different receptor complexes. Here, we present DiSiR, a fast and easy-to-use permutation-based software framework to investigate how individual cells are interacting with each other by analyzing signaling pathways of multi-subunit ligand-activated receptors from scRNA-seq data, not only for available curated databases of ligand-receptor interactions, but also for interactions that are not listed in these databases. We show that, when utilized to infer ligand-receptor interactions from both simulated and real datasets, DiSiR outperforms other well-known permutation-based methods, e.g. CellPhoneDB and ICELLNET. Finally, to demonstrate DiSiR's utility in exploring data and generating biologically relevant hypotheses, we apply it to COVID lung and rheumatoid arthritis (RA) synovium scRNA-seq datasets and highlight potential differences between inflammatory pathways at cell type level for control versus disease samples.

Inferring neuron-neuron communications from single-cell transcriptomics through NeuronChat

Neural communication networks form the fundamental basis for brain function. These communication networks are enabled by emitted ligands such as neurotransmitters, which activate receptor complexes to facilitate communication. Thus, neural communication is fundamentally dependent on the transcriptome. Here we develop NeuronChat, a method and package for the inference, visualization and analysis of neural-specific communication networks among pre-defined cell groups using single-cell expression data. We incorporate a manually curated molecular interaction database of neural signaling for both human and mouse, and benchmark NeuronChat on several published datasets to validate its ability in predicting neural connectivity. Then, we apply NeuronChat to three different neural tissue datasets to illustrate its functionalities in identifying interneural communication networks, revealing conserved or context-specific interactions across different biological contexts, and predicting communication pattern changes in diseased brains with autism spectrum disorder. Finally, we demonstrate NeuronChat can utilize spatial transcriptomics data to infer and visualize neural-specific cell-cell communication.

The promise and challenges of extracellular vesicles in the diagnosis of neurodegenerative diseases

Extracellular vesicles (EVs) have emerged as essential means of intercommunication for all cell types, and their role in CNS physiology is increasingly appreciated. Accumulating evidence has demonstrated that EVs play important roles in neural cell maintenance, plasticity, and growth. However, EVs have also been demonstrated to spread amyloids and inflammation characteristic of neurodegenerative disease. Such dual roles suggest that EVs may be prime candidates for neurodegenerative disease biomarker analysis. This is supported by several intrinsic properties of EVs: Populations can be enriched by capturing surface proteins from their cell of origin, their diverse cargo represent the complex intracellular states of the cells they derive from, and they can pass the blood-brain barrier. Despite this promise, there are important questions outstanding in this young field that will need to be answered before it can fulfill its potential. Namely, overcoming the technical challenges of isolating rare EV populations, the difficulties inherent in detecting neurodegeneration, and the ethical considerations of diagnosing asymptomatic individuals. Although daunting, succeeding to answer these questions has the potential to provide unprecedented insight and improved treatment of neurodegenerative disease in the future.

A role for fibroblast-derived SASP factors in the activation of pyroptotic cell death in mammary epithelial cells

In normal tissue homeostasis, bidirectional communication between different cell types can shape numerous biological outcomes. Many studies have documented instances of reciprocal communication between fibroblasts and cancer cells that functionally change cancer cell behavior. However, less is known about how these heterotypic interactions shape epithelial cell function in the absence of oncogenic transformation. Furthermore, fibroblasts are prone to undergo senescence, which is typified by an irreversible cell cycle arrest. Senescent fibroblasts are also known to secrete various cytokines into the extracellular space; a phenomenon that is termed the senescence-associated secretory phenotype (SASP). While the role of fibroblast derived SASP factors on cancer cells has been well studied, the impact of these factors on normal epithelial cells remains poorly understood. We discovered that treatment of normal mammary epithelial cells with conditioned media (CM) from senescent fibroblasts (SASP CM) results in a caspase-dependent cell death. This capacity of SASP CM to cause cell death is maintained across multiple senescence-inducing stimuli. However, the activation of oncogenic signaling in mammary epithelial cells mitigates the ability of SASP CM to induce cell death. Despite the reliance of this cell death on caspase activation, we discovered that SASP CM does not cause cell death by the extrinsic or intrinsic apoptotic pathway. Instead, these cells die by an NLRP3, caspase-1, and gasdermin D (GSDMD)-dependent induction of pyroptosis. Taken together, our findings reveal that senescent fibroblasts can cause pyroptosis in neighboring mammary epithelial cells, which has implications for therapeutic strategies that perturb the behavior of senescent cells.

Spherical rotary cell seeding system for production of small-caliber tissue-engineered blood vessels with complex geometry

Entirely biological human tissue-engineered blood vessels (TEBV) were previously developed for clinical use. Tissue-engineered models have also proven to be valuable tools in disease modelling. Moreover, there is a need for complex geometry TEBV for study of multifactorial vascular pathologies, such as intracranial aneurysms. The main goal of the work reported in this article was to produce an entirely human branched small-caliber TEBV. The use of a novel spherical rotary cell seeding system allows effective and uniform dynamic cell seeding for a viable in vitro tissue-engineered model. In this report, the design and fabrication of an innovative seeding system with random spherical 360° rotation is described. Custom made seeding chambers are placed inside the system and hold Y-shaped polyethylene terephthalate glycol (PETG) scaffolds. The seeding conditions, such as cell concentration, seeding speed and incubation time were optimized via count of cells adhered on the PETG scaffolds. This spheric seeding method was compared to other approaches, such as dynamic and static seeding, and clearly shows uniform cell distribution on PETG scaffolds. With this simple to use spherical system, fully biological branched TEBV constructs were also produced by seeding human fibroblasts directly on custom-made complex geometry PETG mandrels. The production of patient-derived small-caliber TEBVs with complex geometry and optimized cellular distribution all along the vascular reconstructed may be an innovative way to model various vascular diseases such as intracranial aneurysms.

Visualization and quality control tools for large-scale multiplex tissue analysis in TissUUmaps3

Large-scale multiplex tissue analysis aims to understand processes such as development and tumor formation by studying the occurrence and interaction of cells in local environments in, for example, tissue samples from patient cohorts. A typical procedure in the analysis is to delineate individual cells, classify them into cell types, and analyze their spatial relationships. All steps come with a number of challenges, and to address them and identify the bottlenecks of the analysis, it is necessary to include quality control tools in the analysis workflow. This makes it possible to optimize the steps and adjust settings in order to get better and more precise results. Additionally, the development of automated approaches for tissue analysis requires visual verification to reduce skepticism with regard to the accuracy of the results. Quality control tools could be used to build users' trust in automated approaches. In this paper, we present three plugins for visualization and quality control in large-scale multiplex tissue analysis of microscopy images. The first plugin focuses on the quality of cell staining, the second one was made for interactive evaluation and comparison of different cell classification results, and the third one serves for reviewing interactions of different cell types.

Light-Activated Assembly of Connexon Nanopores in Synthetic Cells

During developmental processes and wound healing, activation of living cells occurs with spatiotemporal precision and leads to rapid release of soluble molecular signals, allowing communication and coordination between neighbors. Nonliving systems capable of similar responsive release hold great promise for information transfer in materials and site-specific drug delivery. One nonliving system that offers a tunable platform for programming release is synthetic cells. Encased in a lipid bilayer structure, synthetic cells can be outfitted with molecular conduits that span the bilayer and lead to material exchange. While previous work expressing membrane pore proteins in synthetic cells demonstrated content exchange, user-defined control over release has remained elusive. In mammalian cells, connexon nanopore structures drive content release and have garnered significant interest since they can direct material exchange through intercellular contacts. Here, we focus on connexon nanopores and present activated release of material from synthetic cells in a light-sensitive fashion. To do this, we re-engineer connexon nanopores to assemble after post-translational processing by a protease. By encapsulating proteases in light-sensitive liposomes, we show that assembly of nanopores can be triggered by illumination, resulting in rapid release of molecules encapsulated within synthetic cells. Controlling connexon nanopore activity provides an opportunity for initiating communication with extracellular signals and for transferring molecular agents to the cytoplasm of living cells in a rapid, light-guided manner.

Single-cell and spatial transcriptomics reveal aberrant lymphoid developmental programs driving granuloma formation

Granulomas are lumps of immune cells that can form in various organs. Most granulomas appear unstructured, yet they have some resemblance to lymphoid organs. To better understand granuloma formation, we performed single-cell sequencing and spatial transcriptomics on granulomas from patients with sarcoidosis and bioinformatically reconstructed the underlying gene regulatory networks. We discovered an immune stimulatory environment in granulomas that repurposes transcriptional programs associated with lymphoid organ development. Granuloma formation followed characteristic spatial patterns and involved genes linked to immunometabolism, cytokine and chemokine signaling, and extracellular matrix remodeling. Three cell types emerged as key players in granuloma formation: metabolically reprogrammed macrophages, cytokine-producing Th17.1 cells, and fibroblasts with inflammatory and tissue-remodeling phenotypes. Pharmacological inhibition of one of the identified processes attenuated granuloma formation in a sarcoidosis mouse model. We show that human granulomas adopt characteristic aspects of normal lymphoid organ development in aberrant combinations, indicating that granulomas constitute aberrant lymphoid organs.

Manifold epigenetics: A conceptual model that guides engineering strategies to improve whole-body regenerative health

Despite the promising advances in regenerative medicine, there is a critical need for improved therapies. For example, delaying aging and improving healthspan is an imminent societal challenge. Our ability to identify biological cues as well as communications between cells and organs are keys to enhance regenerative health and improve patient care. Epigenetics represents one of the major biological mechanisms involving in tissue regeneration, and therefore can be viewed as a systemic (body-wide) control. However, how epigenetic regulations concertedly lead to the development of biological memories at the whole-body level remains unclear. Here, we review the evolving definitions of epigenetics and identify missing links. We then propose our Manifold Epigenetic Model (MEMo) as a conceptual framework to explain how epigenetic memory arises and discuss what strategies can be applied to manipulate the body-wide memory. In summary we provide a conceptual roadmap for the development of new engineering approaches to improve regenerative health.

Cell-cell communications shape tumor microenvironment and predict clinical outcomes in clear cell renal carcinoma

BACKGROUND

Cell-cell communications of various cell populations within tumor microenvironment play an essential role in primary tumor growth, metastasis evolution, and immune escape. Nevertheless, comprehensive investigation of cell-cell communications in the ccRCC (Clear cell renal carcinoma) microenvironment and how this interplay affects prognosis still remains limited.

METHODS

Intercellular communications were characterized by single-cell data. Firstly, we employed "CellChat" package to characterize intercellular communications across all types of cells in microenvironment in VHL mutated and non-mutated samples from 8 patients, respectively. And pseudotime trajectory analyses were performed with monocle analyses. Finally clinical prognosis and immunotherapy efficacy with different landscapes of intercellular interplay are evaluated by TCGA-KIRC and immunotherapy cohort.

RESULTS

Firstly, the VHL phenotype may be related to the intercellular communication landscape. And trajectory analysis reveals the potential relationship of cell-cell communication molecules with T cells and Myeloid cells differentiation. Furthermore, those molecules also correlate with the infiltration of T cells and Myeloid cells. A tumor cluster with highly expressed ligands was defined by quantitative analysis and transcription factor enrichment analysis, which was identified to be pivotal for intercellular communications in tumor microenvironment. Finally, bulk data indicates bulk that different clusters with different intercellular communications have significant predictive value for prognosis and distinguished immunotherapy efficiency.

CONCLUSIONS

The intercellular communication landscapes of VHL wild and VHL mutant ccRCC vary. Intercellular communications within the tumor microenvironment also influence T cell and myeloid cell development and infiltration, as well as predict clinical prognosis and immunotherapy efficacy in ccRCC.

Innate and adaptive immune abnormalities underlying autoimmune diseases: the genetic connections

With the exception of an extremely small number of cases caused by single gene mutations, most autoimmune diseases result from the complex interplay between environmental and genetic factors. In a nutshell, etiology of the common autoimmune disorders is unknown in spite of progress elucidating certain effector cells and molecules responsible for pathologies associated with inflammatory and tissue damage. In recent years, population genetics approaches have greatly enriched our knowledge regarding genetic susceptibility of autoimmunity, providing us with a window of opportunities to comprehensively re-examine autoimmunity-associated genes and possible pathways. In this review, we aim to discuss etiology and pathogenesis of common autoimmune disorders from the perspective of human genetics. An overview of the genetic basis of autoimmunity is followed by 3 chapters detailing susceptibility genes involved in innate immunity, adaptive immunity and inflammatory cell death processes respectively. With such attempts, we hope to expand the scope of thinking and bring attention to lesser appreciated molecules and pathways as important contributors of autoimmunity beyond the 'usual suspects' of a limited subset of validated therapeutic targets.

Estimation of cell lineages in tumors from spatial transcriptomics data

Spatial transcriptomics (ST) technology through in situ capturing has enabled topographical gene expression profiling of tumor tissues. However, each capturing spot may contain diverse immune and malignant cells, with different cell densities across tissue regions. Cell type deconvolution in tumor ST data remains challenging for existing methods designed to decompose general ST or bulk tumor data. We develop the Spatial Cellular Estimator for Tumors (SpaCET) to infer cell identities from tumor ST data. SpaCET first estimates cancer cell abundance by integrating a gene pattern dictionary of copy number alterations and expression changes in common malignancies. A constrained regression model then calibrates local cell densities and determines immune and stromal cell lineage fractions. SpaCET provides higher accuracy than existing methods based on simulation and real ST data with matched double-blind histopathology annotations as ground truth. Further, coupling cell fractions with ligand-receptor coexpression analysis, SpaCET reveals how intercellular interactions at the tumor-immune interface promote cancer progression.

Complex Analysis of Single-Cell RNA Sequencing Data

Single-cell RNA sequencing (scRNA-seq) is a revolutionary tool for studying the physiology of normal and pathologically altered tissues. This approach provides information about molecular features (gene expression, mutations, chromatin accessibility, etc.) of cells, opens up the possibility to analyze the trajectories/phylogeny of cell differentiation and cell-cell interactions, and helps in discovery of new cell types and previously unexplored processes. From a clinical point of view, scRNA-seq facilitates deeper and more detailed analysis of molecular mechanisms of diseases and serves as a basis for the development of new preventive, diagnostic, and therapeutic strategies. The review describes different approaches to the analysis of scRNA-seq data, discusses the advantages and disadvantages of bioinformatics tools, provides recommendations and examples of their successful use, and suggests potential directions for improvement. We also emphasize the need for creating new protocols, including multiomics ones, for the preparation of DNA/RNA libraries of single cells with the purpose of more complete understanding of individual cells.

Screening cell-cell communication in spatial transcriptomics via collective optimal transport

Spatial transcriptomic technologies and spatially annotated single-cell RNA sequencing datasets provide unprecedented opportunities to dissect cell-cell communication (CCC). However, incorporation of the spatial information and complex biochemical processes required in the reconstruction of CCC remains a major challenge. Here, we present COMMOT (COMMunication analysis by Optimal Transport) to infer CCC in spatial transcriptomics, which accounts for the competition between different ligand and receptor species as well as spatial distances between cells. A collective optimal transport method is developed to handle complex molecular interactions and spatial constraints. Furthermore, we introduce downstream analysis tools to infer spatial signaling directionality and genes regulated by signaling using machine learning models. We apply COMMOT to simulation data and eight spatial datasets acquired with five different technologies to show its effectiveness and robustness in identifying spatial CCC in data with varying spatial resolutions and gene coverages. Finally, COMMOT identifies new CCCs during skin morphogenesis in a case study of human epidermal development.

Creating an atlas of the bone microenvironment during oral inflammatory-related bone disease using single-cell profiling

Oral inflammatory diseases such as apical periodontitis are common bacterial infectious diseases that may affect the periapical alveolar bone tissues. A protective process occurs simultaneously with the inflammatory tissue destruction, in which mesenchymal stem cells (MSCs) play a primary role. However, a systematic and precise description of the cellular and molecular composition of the microenvironment of bone affected by inflammation is lacking. In this study, we created a single-cell atlas of cell populations that compose alveolar bone in healthy and inflammatory disease states. We investigated changes in expression frequency and patterns related to apical periodontitis, as well as the interactions between MSCs and immunocytes. Our results highlight an enhanced self-supporting network and osteogenic potential within MSCs during apical periodontitis-associated inflammation. MSCs not only differentiated toward osteoblast lineage cells but also expressed higher levels of osteogenic-related markers, including Sparc and Col1a1. This was confirmed by lineage tracing in transgenic mouse models and human samples from oral inflammatory-related alveolar bone lesions. In summary, the current study provides an in-depth description of the microenvironment of MSCs and immunocytes in both healthy and disease states. We also identified key apical periodontitis-associated MSC subclusters and their biomarkers, which could further our understanding of the protective process and the underlying mechanisms of oral inflammatory-related bone disease. Taken together, these results enhance our understanding of heterogeneity and cellular interactions of alveolar bone cells under pathogenic and inflammatory conditions. We provide these data as a tool for investigators not only to better appreciate the repertoire of progenitors that are stress responsive but importantly to help design new therapeutic targets to restore bone lesions caused by apical periodontitis and other inflammatory-related bone diseases.

Multiomic Spatial Mapping of Myocardial Infarction and Implications for Personalized Therapy

Ischemic heart disease including myocardial infarction is still the leading cause of death worldwide. Although the survival early after myocardial infarction has been significantly improved by the introduction of percutaneous coronary intervention, long-term morbidity and mortality remain high. The elevated long-term mortality is mainly driven by cardiac remodeling processes triggering ischemic heart failure and electric instability. Despite the new developments in pharmaco-therapy of heart failure, we still lack targeted therapies for cardiac remodeling and fibrosis. Single-cell and genomic technologies allow us to map the human heart at unprecedented resolution and allow to gain insights into cellular and molecular heterogeneity. However, these technologies rely on digested tissue and isolated cells or nuclei and thus lack spatial information. Spatial information is critical to understand tissue homeostasis and disease and can be utilized to identify disease-driving cell populations and mechanisms including cellular cross-talk. Here, we discuss recent advances in single-cell and spatial genomic technologies that give insights into cellular and molecular mechanisms of cardiac remodeling after injury and can be utilized to identify novel therapeutic targets and pave the way toward new therapies in heart failure.

Innate lymphoid cells: potential targets for cancer therapeutics

Innate lymphoid cells (ILCs) comprise a number of different subsets, including natural killer (NK) cells, ILC1s, ILC2s, ILC3s, and lymphoid tissue-inducer (LTi) cells that express receptors and signaling pathways that are highly responsive to continuously changing microenvironmental cues. In this Review, we highlight the key features of innate cells that define their capacity to respond rapidly to different environments, how this ability can drive both tumor protection (limiting tumor development) or, alternatively, tumor progression, promoting tumor dissemination and resistance to immunotherapy. We discuss how understanding the regulation of ILCs that can detect tumor cells early in a response opens the possibility of exploiting this functional plasticity to develop rational therapeutic strategies to bolster adaptive immune responses and improve patient outcomes.

Inhibition of 2-arachidonoylglycerol degradation enhances glial immunity by single-cell transcriptomic analysis

BACKGROUND

2-Arachidonoylglycerol (2-AG) is the most abundant endogenous cannabinoid. Inhibition of 2-AG metabolism by inactivation of monoacylglycerol lipase (MAGL), the primary enzyme that degrades 2-AG in the brain, produces anti-inflammatory and neuroprotective effects in neurodegenerative diseases. However, the molecular mechanisms underlying these beneficial effects are largely unclear.

METHODS

Hippocampal and cortical cells were isolated from cell type-specific MAGL knockout (KO) mice. Single-cell RNA sequencing was performed by 10 × Genomics platform. Cell Ranger, Seurat (v3.2) and CellChat (1.1.3) packages were used to carry out data analysis.

RESULTS

Using single-cell RNA sequencing analysis, we show here that cell type-specific MAGL KO mice display distinct gene expression profiles in the brain. Inactivation of MAGL results in robust changes in expression of immune- and inflammation-related genes in microglia and astrocytes. Remarkably, upregulated expression of chemokines in microglia is more pronounced in mice lacking MAGL in astrocytes. In addition, expression of genes that regulate other cellular functions and Wnt signaling in astrocytes is altered in MAGL KO mice.

CONCLUSIONS

Our results provide transcriptomic evidence that cell type-specific inactivation of MAGL induces differential expression of immune-related genes and other fundamental cellular pathways in microglia and astrocytes. Upregulation of the immune/inflammatory genes suggests that tonic levels of immune/inflammatory vigilance are enhanced in microglia and astrocytes, particularly in microglia, by inhibition of 2-AG metabolism, which likely contribute to anti-inflammatory and neuroprotective effects produced by inactivation of MAGL in neurodegenerative diseases.

Epigenetic control of cellular crosstalk defines gastrointestinal organ fate and function

Epithelial-mesenchymal signaling in the gastrointestinal system is vital in establishing regional identity during organogenesis and maintaining adult stem cell homeostasis. Although recent work has demonstrated that Wnt ligands expressed by mesenchymal cells are required during gastrointestinal development and stem cell homeostasis, epigenetic mechanisms driving spatiotemporal control of crosstalk remain unknown. Here, we demonstrate that gastrointestinal mesenchymal cells control epithelial fate and function through Polycomb Repressive Complex 2-mediated chromatin bivalency. We find that while key lineage-determining genes possess tissue-specific chromatin accessibility, Polycomb Repressive Complex 2 controls Wnt expression in mesenchymal cells without altering accessibility. We show that reduction of mesenchymal Wnt secretion rescues gastrointestinal fate and proliferation defects caused by Polycomb Repressive Complex 2 loss. We demonstrate that mesenchymal Polycomb Repressive Complex 2 also regulates niche signals to maintain stem cell function in the adult intestine. Our results highlight a broadly permissive chromatin architecture underlying regionalization in mesenchymal cells, then demonstrate further how chromatin architecture in niches can influence the fate and function of neighboring cells.

Bioinformatic analysis of single-cell RNA sequencing dataset dissects cellular heterogeneity of triple-negative breast cancer in transcriptional profile, splicing event and crosstalk network

BACKGROUND

Triple-negative breast cancer (TNBC) is a subtype of breast cancer with high tumoral heterogeneity, while the detailed regulatory network is not well known.

METHODS

Via single-cell RNA-sequencing (scRNA-seq) data analysis, we comprehensively investigated the transcriptional profile of different subtypes of TNBC epithelial cells with gene regulatory network (GRN) and alternative splicing (AS) event analysis, as well as the crosstalk between epithelial and non-epithelial cells.

RESULTS

Of note, we found that luminal progenitor subtype exhibited the most complex GRN and splicing events. Besides, hnRNPs negatively regulates AS events in luminal progenitor subtype. In addition, we explored the cellular crosstalk among endothelial cells, stromal cells and immune cells in TNBC and discovered that NOTCH4 was a key receptor and prognostic marker in endothelial cells, which provide potential biomarker and target for TNBC intervention.

CONCLUSIONS

In summary, our study elaborates on the cellular heterogeneity of TNBC, revealing that NOTCH4 in endothelial cells was critical for TNBC intervention. This in-depth understanding of epithelial cell and non-epithelial cell network would provide theoretical basis for the development of new drugs targeting this sophisticated network in TNBC.

Graph embedding and Gaussian mixture variational autoencoder network for end-to-end analysis of single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) is a revolutionary technology to determine the precise gene expression of individual cells and identify cell heterogeneity and subpopulations. However, technical limitations of scRNA-seq lead to heterogeneous and sparse data. Here, we present autoCell, a deep-learning approach for scRNA-seq dropout imputation and feature extraction. autoCell is a variational autoencoding network that combines graph embedding and a probabilistic depth Gaussian mixture model to infer the distribution of high-dimensional, sparse scRNA-seq data. We validate autoCell on simulated datasets and biologically relevant scRNA-seq. We show that interpolation of autoCell improves the performance of existing tools in identifying cell developmental trajectories of human preimplantation embryos. We identify disease-associated astrocytes (DAAs) and reconstruct DAA-specific molecular networks and ligand-receptor interactions involved in cell-cell communications using Alzheimer's disease as a prototypical example. autoCell provides a toolbox for end-to-end analysis of scRNA-seq data, including visualization, clustering, imputation, and disease-specific gene network identification.

spaCI: deciphering spatial cellular communications through adaptive graph model

Cell-cell communications are vital for biological signalling and play important roles in complex diseases. Recent advances in single-cell spatial transcriptomics (SCST) technologies allow examining the spatial cell communication landscapes and hold the promise for disentangling the complex ligand-receptor (L-R) interactions across cells. However, due to frequent dropout events and noisy signals in SCST data, it is challenging and lack of effective and tailored methods to accurately infer cellular communications. Herein, to decipher the cell-to-cell communications from SCST profiles, we propose a novel adaptive graph model with attention mechanisms named spaCI. spaCI incorporates both spatial locations and gene expression profiles of cells to identify the active L-R signalling axis across neighbouring cells. Through benchmarking with currently available methods, spaCI shows superior performance on both simulation data and real SCST datasets. Furthermore, spaCI is able to identify the upstream transcriptional factors mediating the active L-R interactions. For biological insights, we have applied spaCI to the seqFISH+ data of mouse cortex and the NanoString CosMx Spatial Molecular Imager (SMI) data of non-small cell lung cancer samples. spaCI reveals the hidden L-R interactions from the sparse seqFISH+ data, meanwhile identifies the inconspicuous L-R interactions including THBS1-ITGB1 between fibroblast and tumours in NanoString CosMx SMI data. spaCI further reveals that SMAD3 plays an important role in regulating the crosstalk between fibroblasts and tumours, which contributes to the prognosis of lung cancer patients. Collectively, spaCI addresses the challenges in interrogating SCST data for gaining insights into the underlying cellular communications, thus facilitates the discoveries of disease mechanisms, effective biomarkers and therapeutic targets.

Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST

Spatially resolved transcriptomics involves a set of emerging technologies that enable the transcriptomic profiling of tissues with the physical location of expressions. Although a variety of methods have been developed for data integration, most of them are for single-cell RNA-seq datasets without consideration of spatial information. Thus, methods that can integrate spatial transcriptomics data from multiple tissue slides, possibly from multiple individuals, are needed. Here, we present PRECAST, a data integration method for multiple spatial transcriptomics datasets with complex batch effects and/or biological effects between slides. PRECAST unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, while requiring only partially shared cell/domain clusters across datasets. Using both simulated and four real datasets, we show improved cell/domain detection with outstanding visualization, and the estimated aligned embeddings and cell/domain labels facilitate many downstream analyses. We demonstrate that PRECAST is computationally scalable and applicable to spatial transcriptomics datasets from different platforms.

Inferring neuron-neuron communications from single-cell transcriptomics through NeuronChat

Neural communication networks form the fundamental basis for brain function. These communication networks are enabled by emitted ligands such as neurotransmitters, which activate receptor complexes to facilitate communication. Thus, neural communication is fundamentally dependent on the transcriptome. Here we develop NeuronChat, a method and package for the inference, visualization and analysis of neural-specific communication networks among pre-defined cell groups using single-cell expression data. We incorporate a manually curated molecular interaction database of neural signaling for both human and mouse, and benchmark NeuronChat on several published datasets to validate its ability in predicting neural connectivity. Then, we apply NeuronChat to three different neural tissue datasets to illustrate its functionalities in identifying interneural communication networks, revealing conserved or context-specific interactions across different biological contexts, and predicting communication pattern changes in diseased brains with autism spectrum disorder. Finally, we demonstrate NeuronChat can utilize spatial transcriptomics data to infer and visualize neural-specific cell-cell communication.

Nanoscopic Characterization of Cell Migration under Flow Using Optical and Electron Microscopy

Hematopoietic stem/progenitor cell (HSPC) and leukemic cell homing is an important biological phenomenon that takes place through essential interactions with adhesion molecules on an endothelial cell layer. The homing process of HSPCs begins with the tethering and rolling of the cells on the endothelial layer, which is achieved by the interaction between selectins on the endothelium to the ligands on HSPC/leukemic cells under shear stress of the blood flow. Although many studies have been based on in vitro conditions of the cells rolling over recombinant proteins, significant challenges remain when imaging HSPC/leukemic cells on the endothelium, a necessity when considering characterizing cell-to-cell interaction and rolling dynamics during cell migration. Here, we report a new methodology that enables imaging of stem-cell-intrinsic spatiotemporal details during its migration on an endothelium-like cell monolayer. We developed optimized protocols that preserve transiently appearing structures on HSPCs/leukemic cells during its rolling under shear stress for fluorescence and scanning electron microscopy characterization. Our new experimental platform is closer to in vivo conditions and will contribute to indepth understanding of stem-cell behavior during its migration and cell-to-cell interaction during the process of homing.

TCDD dysregulation of lncRNA expression, liver zonation and intercellular communication across the liver lobule

The persistent environmental aryl hydrocarbon receptor agonist and hepatotoxin TCDD (2,3,7,8-tetrachlorodibenzo- p -dioxin) induces hepatic lipid accumulation (steatosis), inflammation (steatohepatitis) and fibrosis. Thousands of liver-expressed, nuclear-localized lncRNAs with regulatory potential have been identified; however, their roles in TCDD-induced hepatoxicity and liver disease are unknown. We analyzed single nucleus (sn)RNA-seq data from control and chronic TCDD-exposed mouse liver to determine liver cell-type specificity, zonation and differential expression profiles for thousands of IncRNAs. TCDD dysregulated >4,000 of these lncRNAs in one or more liver cell types, including 684 lncRNAs specifically dysregulated in liver non-parenchymal cells. Trajectory inference analysis revealed major disruption by TCDD of hepatocyte zonation, affecting >800 genes, including 121 IncRNAs, with strong enrichment for lipid metabolism genes. TCDD also dysregulated expression of >200 transcription factors, including 19 Nuclear Receptors, most notably in hepatocytes and Kupffer cells. TCDD-induced changes in cellâ€"cell communication patterns included marked decreases in EGF signaling from hepatocytes to non-parenchymal cells and increases in extracellular matrix-receptor interactions central to liver fibrosis. Gene regulatory networks constructed from the snRNA-seq data identified TCDD-exposed liver network-essential lncRNA regulators linked to functions such as fatty acid metabolic process, peroxisome and xenobiotic metabolic. Networks were validated by the striking enrichments that predicted regulatory IncRNAs showed for specific biological pathways. These findings highlight the power of snRNA-seq to discover functional roles for many xenobiotic-responsive lncRNAs in both hepatocytes and liver non-parenchymal cells and to elucidate novel aspects of foreign chemical-induced hepatotoxicity and liver disease, including dysregulation of intercellular communication within the liver lobule.

HUSCH: an integrated single-cell transcriptome atlas for human tissue gene expression visualization and analyses

Understanding gene expression patterns across different human cell types is crucial for investigating mechanisms of cell type differentiation, disease occurrence and progression. The recent development of single-cell RNA-seq (scRNA-seq) technologies significantly boosted the characterization of cell type heterogeneities in different human tissues. However, the huge number of datasets in the public domain also posed challenges in data integration and reuse. We present Human Universal Single Cell Hub (HUSCH, http://husch.comp-genomics.org), an atlas-scale curated database that integrates single-cell transcriptomic profiles of nearly 3 million cells from 185 high-quality human scRNA-seq datasets from 45 different tissues. All the data in HUSCH were uniformly processed and annotated with a standard workflow. In the single dataset module, HUSCH provides interactive gene expression visualization, differentially expressed genes, functional analyses, transcription regulators and cell-cell interaction analyses for each cell type cluster. Besides, HUSCH integrated different datasets in the single tissue module and performs data integration, batch correction, and cell type harmonization. This allows a comprehensive visualization and analysis of gene expression within each tissue based on single-cell datasets from multiple sources and platforms. HUSCH is a flexible and comprehensive data portal that enables searching, visualizing, analyzing, and downloading single-cell gene expression for the human tissue atlas.

SPASCER: spatial transcriptomics annotation at single-cell resolution

In recent years, the explosive growth of spatial technologies has enabled the characterization of spatial heterogeneity of tissue architectures. Compared to traditional sequencing, spatial transcriptomics reserves the spatial information of each captured location and provides novel insights into diverse spatially related biological contexts. Even though two spatial transcriptomics databases exist, they provide limited analytical information. Information such as spatial heterogeneity of genes and cells, cell-cell communication activities in space, and the cell type compositions in the microenvironment are critical clues to unveil the mechanism of tumorigenesis and embryo differentiation. Therefore, we constructed a new spatial transcriptomics database, named SPASCER (https://ccsm.uth.edu/SPASCER), designed to help understand the heterogeneity of tissue organizations, region-specific microenvironment, and intercellular interactions across tissue architectures at multiple levels. SPASCER contains datasets from 43 studies, including 1082 sub-datasets from 16 organ types across four species. scRNA-seq was integrated to deconvolve/map spatial transcriptomics, and processed with spatial cell-cell interaction, gene pattern and pathway enrichment analysis. Cell-cell interactions and gene regulation network of scRNA-seq from matched spatial transcriptomics were performed as well. The application of SPASCER will provide new insights into tissue architecture and a solid foundation for the mechanistic understanding of many biological processes in healthy and diseased tissues.

Single-cell-led drug repurposing for Alzheimer's disease

Alzheimer's disease is the most common form of dementia. Notwithstanding the huge investments in drug development, only one disease-modifying treatment has been recently approved. Here we present a single-cell-led systems biology pipeline for the identification of drug repurposing candidates. Using single-cell RNA sequencing data of brain tissues from patients with Alzheimer's disease, genome-wide association study results, and multiple gene annotation resources, we built a multi-cellular Alzheimer's disease molecular network that we leveraged for gaining cell-specific insights into Alzheimer's disease pathophysiology and for the identification of drug repurposing candidates. Our computational approach pointed out 54 candidate drugs, mainly targeting MAPK and IGF1R signaling pathways, which could be further evaluated for their potential as Alzheimer's disease therapy.

Comprehensive bulk and single-cell transcriptome profiling give useful insights into the characteristics of osteoarthritis associated synovial macrophages

Background

Osteoarthritis (OA) is a common chronic joint disease, but the association between molecular and cellular events and the pathogenic process of OA remains unclear.

Objective

The study aimed to identify key molecular and cellular events in the processes of immune infiltration of the synovium in OA and to provide potential diagnostic and therapeutic targets.

Methods

To identify the common differential expression genes and function analysis in OA, we compared the expression between normal and OA samples and analyzed the protein-protein interaction (PPI). Additionally, immune infiltration analysis was used to explore the differences in common immune cell types, and Gene Set Variation Analysis (GSVA) analysis was applied to analyze the status of pathways between OA and normal groups. Furthermore, the optimal diagnostic biomarkers for OA were identified by least absolute shrinkage and selection operator (LASSO) models. Finally, the key role of biomarkers in OA synovitis microenvironment was discussed through single cell and Scissor analysis.

Results

A total of 172 DEGs (differentially expressed genes) associated with osteoarticular synovitis were identified, and these genes mainly enriched eight functional categories. In addition, immune infiltration analysis found that four immune cell types, including Macrophage, B cell memory, B cell, and Mast cell were significantly correlated with OA, and LASSO analysis showed that Macrophage were the best diagnostic biomarkers of immune infiltration in OA. Furthermore, using scRNA-seq dataset, we also analyzed the cell communication patterns of Macrophage in the OA synovial inflammatory microenvironment and found that CCL, MIF, and TNF signaling pathways were the mainly cellular communication pathways. Finally, Scissor analysis identified a population of M2-like Macrophages with high expression of CD163 and LYVE1, which has strong anti-inflammatory ability and showed that the TNF gene may play an important role in the synovial microenvironment of OA.

Conclusion

Overall, Macrophage is the best diagnostic marker of immune infiltration in osteoarticular synovitis, and it can communicate with other cells mainly through CCL, TNF, and MIF signaling pathways in microenvironment. In addition, TNF gene may play an important role in the development of synovitis.

Single-cell computational machine learning approaches to immune-mediated inflammatory disease: New tools uncover novel fibroblast and macrophage interactions driving pathogenesis

Recent advances in single-cell sequencing technologies call for greater computational scalability and sensitivity to analytically decompose diseased tissues and expose meaningful biological relevance in individual cells with high resolution. And while fibroblasts, one of the most abundant cell types in tissues, were long thought to display relative homogeneity, recent analytical and technical advances in single-cell sequencing have exposed wide variation and sub-phenotypes of fibroblasts of potential and apparent clinical significance to inflammatory diseases. Alongside anticipated improvements in single cell spatial sequencing resolution, new computational biology techniques have formed the technical backbone when exploring fibroblast heterogeneity. More robust models are required, however. This review will summarize the key advancements in computational techniques that are being deployed to categorize fibroblast heterogeneity and their interaction with the myeloid compartments in specific biological and clinical contexts. First, typical machine-learning-aided methods such as dimensionality reduction, clustering, and trajectory inference, have exposed the role of fibroblast subpopulations in inflammatory disease pathologies. Second, these techniques, coupled with single-cell predicted computational methods have raised novel interactomes between fibroblasts and macrophages of potential clinical significance to many immune-mediated inflammatory diseases such as rheumatoid arthritis, ulcerative colitis, lupus, systemic sclerosis, and others. Third, recently developed scalable integrative methods have the potential to map cross-cell-type spatial interactions at the single-cell level while cross-tissue analysis with these models reveals shared biological mechanisms between disease contexts. Finally, these advanced computational omics approaches have the potential to be leveraged toward therapeutic strategies that target fibroblast-macrophage interactions in a wide variety of inflammatory diseases.

Modeling human liver organ development and diseases with pluripotent stem cell-derived organoids

The discoveries of human pluripotent stem cells (PSCs) including embryonic stem cells and induced pluripotent stem cells (iPSCs) has led to dramatic advances in our understanding of basic human developmental and cell biology and has also been applied to research aimed at drug discovery and development of disease treatments. Research using human PSCs has been largely dominated by studies using two-dimensional cultures. In the past decade, however, ex vivo tissue "organoids," which have a complex and functional three-dimensional structure similar to human organs, have been created from PSCs and are now being used in various fields. Organoids created from PSCs are composed of multiple cell types and are valuable models with which it is better to reproduce the complex structures of living organs and study organogenesis through niche reproduction and pathological modeling through cell-cell interactions. Organoids derived from iPSCs, which inherit the genetic background of the donor, are helpful for disease modeling, elucidation of pathophysiology, and drug screening. Moreover, it is anticipated that iPSC-derived organoids will contribute significantly to regenerative medicine by providing treatment alternatives to organ transplantation with which the risk of immune rejection is low. This review summarizes how PSC-derived organoids are used in developmental biology, disease modeling, drug discovery, and regenerative medicine. Highlighted is the liver, an organ that play crucial roles in metabolic regulation and is composed of diverse cell types.

Profiling Cellular Ecosystems at Single-Cell Resolution and at Scale with EcoTyper

Tissues are composed of diverse cell types and cellular states that organize into distinct ecosystems with specialized functions. EcoTyper is a collection of machine learning tools for the large-scale delineation of cellular ecosystems and their constituent cell states from bulk, single-cell, and spatially resolved gene expression data. In this chapter, we provide a primer on EcoTyper and demonstrate its use for the discovery and recovery of cell states and ecosystems from healthy and diseased tissue specimens.

Immune profiling of experimental murine mastitis reveals conserved response to mammary pathogenic Escherichia coli, Mycoplasma bovis, and Streptococcus uberis

Mastitis is one of the most prevalent and economically important diseases of dairy animals. The disease is caused by ascending bacterial infection through the teat canal. Among the most common mastitis-causing bacteria are Gram-negative coliforms, Gram-positive streptococci and staphylococci, and mycoplasma. The most prominent cellular hallmark of acute mammary infection is a massive recruitment of blood neutrophils into the tubular and alveolar milk spaces. The complex biological processes of leukocyte recruitment, activation, adhesion, and migration in the mammary gland remain largely elusive to date. While field research of mastitis in dairy animals contributed a lot to the development of mitigation, control, and even eradication programs, little progress was made toward understanding the molecular mechanisms underlying the pathogenesis of the disease. We report here experimental mastitis model systems in lactating mice challenged with field strains of common udder pathogens in dairy cows. We used these model systems to apply recently developed multiplex gene expression technology (Nanostring nCounter), which enabled us to study the expression of over 700 immune genes. Our analysis revealed a core of 100 genes that are similarly regulated and functionally or physically interacting in E. coli, M. bovis, and Strep uberis murine mastitis. Common significantly enriched gene sets include TNFɑ signaling via NFkB, Interferon gamma and alpha response, and IL6-JAK-STAT3 signaling. In addition, we show a significantly enriched expression of genes associated with neutrophil extracellular traps (NET) in glands challenged by the three pathogens. Ligand-receptor analysis revealed interactions shared by the three pathogens, including the interaction of the cytokines IL1β, IL1ɑ, and TNFɑ with their receptors, and proteins involved in immune cell recruitment such as complement C3 and ICAM1 (with CD11b), chemokines CCL3 and CCL4 (with CCR1), and CSF3 (with CSF3R). Taken together, our results show that mammary infection with E. coli, M. bovis, and Strep uberis culminated in the activation of a conserved core of immune genes and pathways including NET formation.

Comprehensive visualization of cell-cell interactions in single-cell and spatial transcriptomics with NICHES

MOTIVATION

Recent years have seen the release of several toolsets that reveal cell-cell interactions from single-cell data. However, all existing approaches leverage mean celltype gene expression values, and do not preserve the single-cell fidelity of the original data. Here, we present NICHES (Niche Interactions and Communication Heterogeneity in Extracellular Signaling), a tool to explore extracellular signaling at the truly single-cell level.

RESULTS

NICHES allows embedding of ligand-receptor signal proxies to visualize heterogeneous signaling archetypes within cell clusters, between cell clusters and across experimental conditions. When applied to spatial transcriptomic data, NICHES can be used to reflect local cellular microenvironment. NICHES can operate with any list of ligand-receptor signaling mechanisms, is compatible with existing single-cell packages, and allows rapid, flexible analysis of cell-cell signaling at single-cell resolution.

AVAILABILITY AND IMPLEMENTATION

NICHES is an open-source software implemented in R under academic free license v3.0 and it is available at http://github.com/msraredon/NICHES. Use-case vignettes are available at https://msraredon.github.io/NICHES/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

SPICEMIX enables integrative single-cell spatial modeling of cell identity

Spatial transcriptomics can reveal spatially resolved gene expression of diverse cells in complex tissues. However, the development of computational methods that can use the unique properties of spatial transcriptome data to unveil cell identities remains a challenge. Here we introduce SPICEMIX, an interpretable method based on probabilistic, latent variable modeling for joint analysis of spatial information and gene expression from spatial transcriptome data. Both simulation and real data evaluations demonstrate that SPICEMIX markedly improves on the inference of cell types and their spatial patterns compared with existing approaches. By applying to spatial transcriptome data of brain regions in human and mouse acquired by seqFISH+, STARmap and Visium, we show that SPICEMIX can enhance the inference of complex cell identities, reveal interpretable spatial metagenes and uncover differentiation trajectories. SPICEMIX is a generalizable analysis framework for spatial transcriptome data to investigate cell-type composition and spatial organization of cells in complex tissues.

Recent advances in the crosstalk between adipose, muscle and bone tissues in fish

Control of tissue metabolism and growth involves interactions between organs, tissues, and cell types, mediated by cytokines or direct communication through cellular exchanges. Indeed, over the past decades, many peptides produced by adipose tissue, skeletal muscle and bone named adipokines, myokines and osteokines respectively, have been identified in mammals playing key roles in organ/tissue development and function. Some of them are released into the circulation acting as classical hormones, but they can also act locally showing autocrine/paracrine effects. In recent years, some of these cytokines have been identified in fish models of biomedical or agronomic interest. In this review, we will present their state of the art focusing on local actions and inter-tissue effects. Adipokines reported in fish adipocytes include adiponectin and leptin among others. We will focus on their structure characteristics, gene expression, receptors, and effects, in the adipose tissue itself, mainly regulating cell differentiation and metabolism, but in muscle and bone as target tissues too. Moreover, lipid metabolites, named lipokines, can also act as signaling molecules regulating metabolic homeostasis. Regarding myokines, the best documented in fish are myostatin and the insulin-like growth factors. This review summarizes their characteristics at a molecular level, and describes both, autocrine effects and interactions with adipose tissue and bone. Nonetheless, our understanding of the functions and mechanisms of action of many of these cytokines is still largely incomplete in fish, especially concerning osteokines (i.e., osteocalcin), whose potential cross talking roles remain to be elucidated. Furthermore, by using selective breeding or genetic tools, the formation of a specific tissue can be altered, highlighting the consequences on other tissues, and allowing the identification of communication signals. The specific effects of identified cytokines validated through in vitro models or in vivo trials will be described. Moreover, future scientific fronts (i.e., exosomes) and tools (i.e., co-cultures, organoids) for a better understanding of inter-organ crosstalk in fish will also be presented. As a final consideration, further identification of molecules involved in inter-tissue communication will open new avenues of knowledge in the control of fish homeostasis, as well as possible strategies to be applied in aquaculture or biomedicine.

Single-cell and spatial sequencing application in pathology

Traditionally, diagnostic pathology uses histology representing structural alterations in a disease's cells and tissues. In many cases, however, it is supplemented by other morphology-based methods such as immunohistochemistry and fluorescent in situ hybridization. Single-cell RNA sequencing (scRNA-seq) is one of the strategies that may help tackle the heterogeneous cells in a disease, but it does not usually provide histologic information. Spatial sequencing is designed to assign cell types, subtypes, or states according to the mRNA expression on a histological section by RNA sequencing. It can provide mRNA expressions not only of diseased cells, such as cancer cells but also of stromal cells, such as immune cells, fibroblasts, and vascular cells. In this review, we studied current methods of spatial transcriptome sequencing based on their technical backgrounds, tissue preparation, and analytic procedures. With the pathology examples, useful recommendations for pathologists who are just getting started to use spatial sequencing analysis in research are provided here. In addition, leveraging spatial sequencing by integration with scRNA-seq is reviewed. With the advantages of simultaneous histologic and single-cell information, spatial sequencing may give a molecular basis for pathological diagnosis, improve our understanding of diseases, and have potential clinical applications in prognostics and diagnostic pathology.

Extracellular Vesicles and Cellular Ageing

Ageing is a complex process characterized by deteriorated performance at multiple levels, starting from cellular dysfunction to organ degeneration. Stem cell-based therapies aim to administrate stem cells that eventually migrate to the injured site to replenish the damaged tissue and recover tissue functionality. Stem cells can be easily obtained and cultured in vitro, and display several qualities such as self-renewal, differentiation, and immunomodulation that make them suitable candidates for stem cell-based therapies. Current animal studies and clinical trials are being performed to assess the safety and beneficial effects of stem cell engraftments for regenerative medicine in ageing and age-related diseases.Since alterations in cell-cell communication have been associated with the development of pathophysiological processes, new research is focusing on the modulation of the microenvironment. Recent research has highlighted the important role of some microenvironment components that modulate cell-cell communication, thus spreading signals from damaged ageing cells to neighbor healthy cells, thereby promoting systemic ageing. Extracellular vesicles (EVs) are small-rounded vesicles released by almost every cell type. EVs cargo includes several bioactive molecules, such as lipids, proteins, and genetic material. Once internalized by target cells, their specific cargo can induce epigenetic modifications and alter the fate of the recipient cells. Also, EV's content is dependent on the releasing cells, thus, EVs can be used as biomarkers for several diseases. Moreover, EVs have been proposed to be used as cell-free therapies that focus on their administration to slow or even reverse some hallmarks of physiological ageing. It is not surprising that EVs are also under study as next-generation therapies for age-related diseases.

Highly multiplexed spatial profiling with CODEX: bioinformatic analysis and application in human disease

Multiplexed imaging, which enables spatial localization of proteins and RNA to cells within tissues, complements existing multi-omic technologies and has deepened our understanding of health and disease. CODEX, a multiplexed single-cell imaging technology, utilizes a microfluidics system that incorporates DNA barcoded antibodies to visualize 50 + cellular markers at the single-cell level. Here, we discuss the latest applications of CODEX to studies of cancer, autoimmunity, and infection as well as current bioinformatics approaches for analysis of multiplexed imaging data from preprocessing to cell segmentation and marker quantification to spatial analysis techniques. We conclude with a commentary on the challenges and future developments for multiplexed spatial profiling.

Diagnosis and prognosis prediction model for digestive system tumors based on immunologic gene sets

According to 2020 global cancer statistics, digestive system tumors (DST) are ranked first in both incidence and mortality. This study systematically investigated the immunologic gene set (IGS) to discover effective diagnostic and prognostic biomarkers. Gene set variation (GSVA) analysis was used to calculate enrichment scores for 4,872 IGSs in patients with digestive system tumors. Using the machine learning algorithm XGBoost to build a classifier that distinguishes between normal samples and cancer samples, it shows high specificity and sensitivity on both the validation set and the overall dataset (area under the receptor operating characteristic curve [AUC]: validation set = 0.993, overall dataset = 0.999). IGS-based digestive system tumor subtypes (IGTS) were constructed using a consistent clustering approach. A risk prediction model was developed using the Least Absolute Shrinkage and Selection Operator (LASSO) method. DST is divided into three subtypes: subtype 1 has the best prognosis, subtype 3 is the second, and subtype 2 is the worst. The prognosis model constructed using nine gene sets can effectively predict prognosis. Prognostic models were significantly associated with tumor mutational burden (TMB), tumor immune microenvironment (TIME), immune checkpoints, and somatic mutations. A composite nomogram was constructed based on the risk score and the patient's clinical information, with a well-fitted calibration curve (AUC = 0.762). We further confirmed the reliability and validity of the diagnostic and prognostic models using other cohorts from the Gene Expression Omnibus database. We identified diagnostic and prognostic models based on IGS that provide a strong basis for early diagnosis and effective treatment of digestive system tumors.

Single-cell RNA-seq methods to interrogate virus-host interactions

The twenty-first century has seen the emergence of many epidemic and pandemic viruses, with the most recent being the SARS-CoV-2-driven COVID-19 pandemic. As obligate intracellular parasites, viruses rely on host cells to replicate and produce progeny, resulting in complex virus and host dynamics during an infection. Single-cell RNA sequencing (scRNA-seq), by enabling broad and simultaneous profiling of both host and virus transcripts, represents a powerful technology to unravel the delicate balance between host and virus. In this review, we summarize technological and methodological advances in scRNA-seq and their applications to antiviral immunity. We highlight key scRNA-seq applications that have enabled the understanding of viral genomic and host response heterogeneity, differential responses of infected versus bystander cells, and intercellular communication networks. We expect further development of scRNA-seq technologies and analytical methods, combined with measurements of additional multi-omic modalities and increased availability of publicly accessible scRNA-seq datasets, to enable a better understanding of viral pathogenesis and enhance the development of antiviral therapeutics strategies.

Susceptibilities of CNS Cells towards Rabies Virus Infection Is Linked to Cellular Innate Immune Responses

Rabies is caused by neurotropic rabies virus (RABV), contributing to 60,000 human deaths annually. Even though rabies leads to major public health concerns worldwide, we still do not fully understand factors determining RABV tropism and why glial cells are unable to clear RABV from the infected brain. Here, we compare susceptibilities and immune responses of CNS cell types to infection with two RABV strains, Tha and its attenuated variant Th2P-4M, mutated on phospho- (P-protein) and matrix protein (M-protein). We demonstrate that RABV replicates in human stem cell-derived neurons and astrocytes but fails to infect human iPSC-derived microglia. Additionally, we observed major differences in transcription profiles and quantification of intracellular protein levels between antiviral immune responses mediated by neurons, astrocytes (IFNB1, CCL5, CXCL10, IL1B, IL6, and LIF), and microglia (CCL5, CXCL10, ISG15, MX1, and IL6) upon Tha infection. We also show that P- and M-proteins of Tha mediate evasion of NF-κB- and JAK-STAT-controlled antiviral host responses in neuronal cell types in contrast to glial cells, potentially explaining the strong neuron-specific tropism of RABV. Further, Tha-infected astrocytes and microglia protect neurons from Tha infection via a filtrable and transferable agent. Overall, our study provides novel insights into RABV tropism, showing the interest in studying the interplay of CNS cell types during RABV infection.

Identification of cellular heterogeneity and key signaling pathways associated with vascular remodeling and calcification in young and old primate aortas based on single-cell analysis

Aging of the vascular system is the main cause of many cardiovascular diseases. The structure and function of the blood vessel wall change with aging. To prevent age-related cardiovascular diseases, it is essential to understand the cellular heterogeneity of vascular wall and changes of cellular communication among cell subpopulations during aging. Here, using published single-cell RNA sequencing datasets of young and old monkey aortas, we analyzed the heterogeneity of vascular endothelial cells and smooth muscle cells in detail and identified a distinct endothelial cell subpopulation that involved in vascular remodeling and calcification. Moreover, cellular communication that changed with aging was analyzed and we identified a number of signaling pathways that associated with vascular aging. We found that EGF signaling pathway play an essential role in vascular remodeling and calcification of aged aortas. This work provided a better understanding of vascular aging and laid the foundation for prevention of age-related vascular pathologies.

Use of intercellular proximity labeling to quantify and decipher cell-cell interactions directed by diversified molecular pairs

FucoID is an intercellular proximity labeling technique for studying cell-cell interactions (CCIs) via fucosyltransferase (FT)-meditated fucosyl-biotinylation, which has been applied to probe antigen-specific dendritic cell (DC)-T cell interactions. In this system, bait cells of interest with cell surface-anchored FT are used to capture the interacting prey cells by transferring a biotin-modified substrate to prey cells. Here, we leveraged FucoID to study CCIs directed by different molecular pairs, e.g., programmed cell death protein-1(PD-1)/programmed cell death protein-ligand-1 (PD-L1), and identify unknown or little studied CCIs, e.g., the interaction of DCs and B cells. To expand the application of FucoID to complex systems, we also synthesized site-specific antibody-based FT conjugate, which substantially improves the ability of FucoID to probe molecular signatures of specific CCI when cells of interest (bait cells) cannot be purified, e.g., in clinical samples. Collectively, these studies demonstrate the general applicability of FucoID to study unknown CCIs in complex systems at a molecular resolution.

Controlled Transport of Magnetic Particles and Cells Using C-Shaped Magnetic Thin Films in Microfluidic Chips

Single-cell analysis is an emerging discipline that has shown a transformative impact in cell biology in the last decade. Progress in this field requires systems capable of accurately moving the cells and particles in a controlled manner. Here, we present a microfluidic platform equipped with C-shaped magnetic thin films to precisely transport magnetic particles in a tri-axial rotating magnetic field. This innovative system, compared to the other rivals, offers numerous advantages. The magnetic particles repel each other to prevent undesired cluster formation. Many particles move synced with the external rotating magnetic field, which results in highly parallel controlled particle transport. We show that the particle transport in this system is analogous to electron transport and Ohm's law in electrical circuits. The proposed magnetic transport pattern is carefully studied using both simulations and experiments for various parameters, including the magnetic field characteristics, particle size, and gap size in the design. We demonstrate the appropriate transport of both magnetic beads and magnetized living cells. We also show a pilot mRNA-capturing experiment with barcode-carrying magnetic beads. The introduced chip offers fundamental potential applications in the fields of single-cell biology and bioengineering.

Glioblastoma cells have increased capacity to repair radiation-induced DNA damage after migration to the olfactory bulb

BACKGROUND

The invasive nature of GBM combined with the diversity of brain microenvironments creates the potential for a topographic heterogeneity in GBM radioresponse. Investigating the mechanisms responsible for a microenvironment-induced differential GBM response to radiation may provide insights into the molecules and processes mediating GBM radioresistance.

METHODS

Using a model system in which human GBM stem-like cells implanted into the right striatum of nude mice migrate throughout the right hemisphere (RH) to the olfactory bulb (OB), the radiation-induced DNA damage response was evaluated in each location according to γH2AX and 53BP1 foci and cell cycle phase distribution as determined by flow cytometry and immunohistochemistry. RNAseq was used to compare transcriptomes of tumor cells growing in the OB and the RH. Protein expression and neuron-tumor interaction were defined by immunohistochemistry and confocal microscopy.

RESULTS

After irradiation, there was a more rapid dispersal of γH2AX and 53BP1 foci in the OB versus in the RH, indicative of increased double strand break repair capacity in the OB and consistent with the OB providing a radioprotective niche. With respect to the cell cycle, by 6 h after irradiation there was a significant loss of mitotic tumor cells in both locations suggesting a similar activation of the G2/M checkpoint. However, by 24 h post-irradiation there was an accumulation of G2 phase cells in the OB, which continued out to at least 96 h. Transcriptome analysis showed that tumor cells in the OB had higher expression levels of DNA repair genes involved in non-homologous end joining and genes related to the spindle assembly checkpoint. Tumor cells in the OB were also found to have an increased frequency of soma-soma contact with neurons.

CONCLUSION

GBM cells that have migrated to the OB have an increased capacity to repair radiation-induced double strand breaks and altered cell cycle regulation. These results correspond to an upregulation of genes involved in DNA damage repair and cell cycle control. Because the murine OB provides a source of radioresistant tumor cells not evident in other experimental systems, it may serve as a model for investigating the mechanisms mediating GBM radioresistance.