The diversification of methods for studying cell-cell interactions and communication

Cellular deconstruction of the human skeletal muscle microenvironment identifies an exercise-induced histaminergic crosstalk

Plasticity of skeletal muscle is induced by transcriptional and translational events in response to exercise, leading to multiple health and performance benefits. The skeletal muscle microenvironment harbors myofibers and mononuclear cells, but the rich cell diversity has been largely ignored in relation to exercise adaptations. Using our workflow of transcriptome profiling of individual myofibers, we observed that their exercise-induced transcriptional response was surprisingly modest compared with the bulk muscle tissue response. Through the integration of single-cell data, we identified a small mast cell population likely responsible for histamine secretion during exercise and for targeting myeloid and vascular cells rather than myofibers. We demonstrated through histamine H1 or H2 receptor blockade in humans that this paracrine histamine signaling cascade drives muscle glycogen resynthesis and coordinates the transcriptional exercise response. Altogether, our cellular deconstruction of the human skeletal muscle microenvironment uncovers a histamine-driven intercellular communication network steering muscle recovery and adaptation to exercise.

Neutrophils: a Central Point of Interaction Between Immune Cells and Nonimmune Cells in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a systemic autoimmune disease involving activation of the immune system and the infiltration of immune cells. As the first immune cells to reach the site of inflammation, neutrophils perform their biological functions by releasing many active substances and forming neutrophil extracellular traps (NETs). The overactivated neutrophils in patients with RA not only directly damage tissues but also, more importantly, interact with various other immune cells and broadly activate innate and adaptive immunity, leading to irreversible joint damage. However, owing to the pivotal role and complex influence of neutrophils in maintaining homoeostasis, the treatment of RA by targeting neutrophils is very difficult. Therefore, a comprehensive understanding of the interaction pathways between neutrophils and various other immune cells is crucial for the development of neutrophils as a new therapeutic target for RA. In this study, the important role of neutrophils in the pathogenesis of RA through their crosstalk with various other immune cells and nonimmune cells is highlighted. The potential of epigenetic modification of neutrophils for exploring the pathogenesis of RA and developing therapeutic approaches is also discussed. In addition, several models for studying cell‒cell interactions are summarized to support further studies of neutrophils in the context of RA.

CellPhoneDB v5: inferring cell-cell communication from single-cell multiomics data

Cell-cell communication is essential for tissue development, function and regeneration. The revolution of single-cell genomics technologies offers an unprecedented opportunity to uncover how cells communicate in vivo within their tissue niches and how disruption of these niches can lead to diseases and developmental abnormalities. CellPhoneDB is a bioinformatics toolkit designed to infer cell-cell communication by combining a curated repository of bona fide ligand-receptor interactions with methods to integrate these interactions with single-cell genomics data. Here we present a protocol for the latest version of CellPhoneDB (v5), offering several new features. First, the repository has been expanded by one-third with the addition of new interactions, including ~1,000 interactions mediated by nonpeptidic ligands such as steroidogenic hormones, neurotransmitters and small G-protein-coupled receptor (GPCR)-binding ligands. Second, we outline a new way of using the database that allows users to tailor queries to their experimental designs. Third, the update incorporates novel strategies to prioritize specific cell-cell interactions, leveraging information from other modalities such as tissue microenvironments derived from spatial transcriptomics technologies or transcription factor activities derived from a single-cell assay for transposase accessible chromatin assays. Finally, we describe the new CellPhoneDBViz module to interactively visualize and share results. Altogether, CellPhoneDB v5 enhances the precision of cell-cell communication inference, offering new insights into tissue biology in physiological microenvironments. This protocol typically takes ~15 min and requires basic knowledge of python.

Lignature: A Comprehensive Database of Ligand Signatures to Predict Cell-Cell Communication

Ligand-receptor interactions mediate intercellular communication, inducing transcriptional changes that regulate physiological and pathological processes. Ligand-induced transcriptomic signatures can be used to predict active ligands; however, the absence of a comprehensive set of ligand-response signatures has limited their practical application in predicting ligand-receptor interactions. To bridge this gap, we developed Lignature, a curated database encompassing intracellular transcriptomic signatures for 362 human ligands, significantly expanding the repertoire of ligands with available intracellular response signatures. Lignature compiles signatures from published transcriptomic datasets and established resources such as CytoSig and ImmuneDictionary, generating both gene- and pathway-based signatures for each ligand. We applied Lignature to predict active ligands driving transcriptomic changes in controlled in vitro experiments and real-world single-cell sequencing datasets. Lignature outperformed existing methods such as NicheNet, achieving higher accuracy in identifying active ligands at both the gene and pathway levels. These results establish Lignature as a robust platform for ligand signaling inference, providing a powerful tool to explore ligand-receptor interactions across diverse experimental and physiological contexts.

Reconstructing signaling histories of single cells via perturbation screens and transfer learning

Manipulating the signaling environment is an effective approach to alter cellular states for broad-ranging applications, from engineering tissues to treating diseases. Such manipulation requires knowing the signaling states and histories of the cells in situ , for which high-throughput discovery methods are lacking. Here, we present an integrated experimental-computational framework that learns signaling response signatures from a high-throughput in vitro perturbation atlas and infers combinatorial signaling activities in in vivo cell types with high accuracy and temporal resolution. Specifically, we generated signaling perturbation atlas across diverse cell types/states through multiplexed sequential combinatorial screens on human pluripotent stem cells. Using the atlas to train IRIS, a neural network-based model, and predicting on mouse embryo scRNAseq atlas, we discovered global features of combinatorial signaling code usage over time, identified biologically meaningful heterogeneity of signaling states within each cell type, and reconstructed signaling histories along diverse cell lineages. We further demonstrated that IRIS greatly accelerates the optimization of stem cell differentiation protocols by drastically reducing the combinatorial space that needs to be tested. This framework leads to the revelation that different cell types share robust signal response signatures, and provides a scalable solution for mapping complex signaling interactions in vivo to guide targeted interventions.

Spatiotemporally resolved mapping of extracellular proteomes via in vivo-compatible TyroID

Extracellular proteins play pivotal roles in both intracellular signaling and intercellular communications in health and disease. While recent advancements in proximity labeling (PL) methods, such as peroxidase- and photocatalyst-based approaches, have facilitated the resolution of extracellular proteomes, their in vivo compatibility remains limited. Here, we report TyroID, an in vivo-compatible PL method for the unbiased mapping of extracellular proteins with high spatiotemporal resolution. TyroID employs plant- and bacteria-derived tyrosinases to produce reactive o-quinone intermediates, enabling the labeling of multiple residues on endogenous proteins with bioorthogonal handles, thereby allowing for their identification via chemical proteomics. We validate TyroID's specificity by mapping extracellular proteomes and HER2-neighboring proteins using affibody-directed recombinant tyrosinases. Demonstrating its superiority over other PL methods, TyroID enables in vivo mapping of extracellular proteomes, including mapping HER2-proximal proteins in tumor xenografts, quantifying the turnover of plasma proteins and labeling hippocampal-specific proteomes in live mouse brains. TyroID emerges as a potent tool for investigating protein localization and molecular interactions within living organisms.

Quantifying and interpreting biologically meaningful spatial signatures within tumor microenvironments

The tumor microenvironment (TME) plays a crucial role in orchestrating tumor cell behavior and cancer progression. Recent advances in spatial profiling technologies have uncovered novel spatial signatures, including univariate distribution patterns, bivariate spatial relationships, and higher-order structures. These signatures have the potential to revolutionize tumor mechanism and treatment. In this review, we summarize the current state of spatial signature research, highlighting computational methods to uncover spatially relevant biological significance. We discuss the impact of these advances on fundamental cancer biology and translational research, address current challenges and future research directions.

Unraveling cell-cell communication with NicheNet by inferring active ligands from transcriptomics data

Ligand-receptor interactions constitute a fundamental mechanism of cell-cell communication and signaling. NicheNet is a well-established computational tool that infers ligand-receptor interactions that potentially regulate gene expression changes in receiver cell populations. Whereas the original publication delves into the algorithm and validation, this paper describes a best practices workflow cultivated over four years of experience and user feedback. Starting from the input single-cell expression matrix, we describe a 'sender-agnostic' approach that considers ligands from the entire microenvironment and a 'sender-focused' approach that considers ligands only from cell populations of interest. As output, users will obtain a list of prioritized ligands and their potential target genes, along with multiple visualizations. We include further developments made in NicheNet v2, in which we have updated the data sources and implemented a downstream procedure for prioritizing cell type-specific ligand-receptor pairs. Although a standard NicheNet analysis takes <10 min to run, users often invest additional time in making decisions about the approach and parameters that best suit their biological question. This paper serves to aid in this decision-making process by describing the most appropriate workflow for common experimental designs like case-control and cell-differentiation studies. Finally, in addition to the step-by-step description of the code, we also provide wrapper functions that enable the analysis to be run in one line of code, thus tailoring the workflow to users at all levels of computational proficiency.

Decoding the blueprints of embryo development with single-cell and spatial omics

Embryonic development is a complex and intricately regulated process that encompasses precise control over cell differentiation, morphogenesis, and the underlying gene expression changes. Recent years have witnessed a remarkable acceleration in the development of single-cell and spatial omic technologies, enabling high-throughput profiling of transcriptomic and other multi-omic information at the individual cell level. These innovations offer fresh and multifaceted perspectives for investigating the intricate cellular and molecular mechanisms that govern embryonic development. In this review, we provide an in-depth exploration of the latest technical advancements in single-cell and spatial multi-omic methodologies and compile a systematic catalog of their applications in the field of embryonic development. We deconstruct the research strategies employed by recent studies that leverage single-cell sequencing techniques and underscore the unique advantages of spatial transcriptomics. Furthermore, we delve into both the current applications, data analysis algorithms and the untapped potential of these technologies in advancing our understanding of embryonic development. With the continuous evolution of multi-omic technologies, we anticipate their widespread adoption and profound contributions to unraveling the intricate molecular foundations underpinning embryo development in the foreseeable future.

Cell signaling pathways discovery from multi-modal data

Deciphering cell signaling pathways is essential for advancing our understanding of basic biology, disease mechanisms, and the development of innovative therapeutic interventions. Recent advancements in multi-omics technologies enable us to capture cell signaling information in a more meaningful context. However, omics data is inherently complex-high-dimensional, heterogeneous, and extensive-making it challenging for human interpretation. Currently, computational tools capable of inferring cell signaling pathways from multi-omics data are very limited, underscoring the urgent need to develop such methods. To address this challenge, we developed Incytr, a method that facilitates the efficient discovery of cell signaling pathways by integrating diverse data modalities, including transcriptomics, proteomics, phosphoproteomics, and kinomics. We demonstrate Incytr's application in elucidating cell signaling within the contexts of COVID-19, Alzheimer's disease, and cancer. Incytr successfully rediscovered known subpathways in these diseases and generated novel hypotheses for cell-type-specific signaling pathways supported by multiple data modalities. We illustrate how overlaying Incytr-identified pathways with prior knowledge from biomarker and small molecule drug databases can be used to facilitate target and drug discovery. Overall, as we demonstrated here, with the use of simple natural language processing AI models, these pathways could serve as a discovery tool to deepen our understanding of cell-cell communication semantics and co-evolution.

The neuroimmune connectome in health and disease

The nervous and immune systems have complementary roles in the adaptation of organisms to environmental changes. However, the mechanisms that mediate cross-talk between the nervous and immune systems, called neuroimmune interactions, are poorly understood. In this Review, we summarize advances in the understanding of neuroimmune communication, with a principal focus on the central nervous system (CNS): its response to immune signals and the immunological consequences of CNS activity. We highlight these themes primarily as they relate to neurological diseases, the control of immunity, and the regulation of complex behaviours. We also consider the importance and challenges linked to the study of the neuroimmune connectome, which is defined as the totality of neuroimmune interactions in the body, because this provides a conceptual framework to identify mechanisms of disease pathogenesis and therapeutic approaches. Finally, we discuss how the latest techniques can advance our understanding of the neuroimmune connectome, and highlight the outstanding questions in the field.

FastCCC: A permutation-free framework for scalable, robust, and reference-based cell-cell communication analysis in single cell transcriptomics studies

Detecting cell-cell communications (CCCs) in single-cell transcriptomics studies is fundamental for understanding the function of multicellular organisms. Here, we introduce FastCCC, a permutation-free framework that enables scalable, robust, and reference-based analysis for identifying critical CCCs and uncovering biological insights. FastCCC relies on fast Fourier transformation-based convolution to compute p -values analytically without permutations, introduces a modular algebraic operation framework to capture a broad spectrum of CCC patterns, and can leverage atlas-scale single cell references to enhance CCC analysis on user-collected datasets. To support routine reference-based CCC analysis, we constructed the first human CCC reference panel, encompassing 19 distinct tissue types, over 450 unique cell types, and approximately 16 million cells. We demonstrate the advantages of FastCCC across multiple datasets, most of which exceed the analytical capabilities of existing CCC methods. In real datasets, FastCCC reliably captures biologically meaningful CCCs, even in highly complex tissue environments, including differential interactions between endothelial and immune cells linked to COVID-19 severity, dynamic communications in thymic tissue during T-cell development, as well as distinct interactions in reference-based CCC analysis.

Arginine Metabolism Reprogramming in Perfluorooctanoic Acid (PFOA)-Induced Liver Injury

Perfluorooctanoic acid (PFOA) is a persistent pollutant that has gained worldwide attention, owing to its widespread presence in the environment. Previous studies have reported that PFOA upregulates lipid metabolism and is associated with liver injury in humans. However, when the fatty acid degradation pathway is activated, lipid accumulation still occurs, suggesting the presence of unknown pathways and mechanisms that remain to be elucidated. In this study, adult C57BL/6N mice were exposed to PFOA at 0.1, 1, and 10 mg/kg/day. Using integrated metabolomics and transcriptomics, it was uncovered that arginine metabolism was differentially downregulated in all three groups. In vitro studies confirmed the downregulation of arginine metabolism in MIHA cell lines treated with PFOA. Supplementation of arginine could effectively rescue liver injury and downregulate the chemokine levels caused by PFOA. This finding highlights the contribution of arginine metabolism in maintaining liver health following PFOA exposure and suggests potential mechanisms of metabolic and immune modulation.

MetaLigand: A database for predicting non-peptide ligand mediated cell-cell communication

Non-peptide ligands (NPLs), including lipids, amino acids, carbohydrates, and non-peptide neurotransmitters and hormones, play a critical role in ligand-receptor-mediated cell-cell communication, driving diverse physiological and pathological processes. To facilitate the study of NPL-dependent intercellular interactions, we introduce MetaLigand, an R-based and web-accessible tool designed to infer NPL production and predict NPL-receptor interactions using transcriptomic data. MetaLigand compiles data for 233 NPLs, including their biosynthetic enzymes, transporter genes, and receptor genes, through a combination of automated pipelines and manual curation from comprehensive databases. The tool integrates both de novo and salvage synthesis pathways, incorporating multiple biosynthetic steps and transport mechanisms to improve prediction accuracy. Comparisons with existing tools demonstrate MetaLigand's superior ability to account for complex biogenesis pathways and model NPL abundance across diverse tissues and cell types. Furthermore, analysis of single-nucleus RNA-seq datasets from age-related macular degeneration samples revealed that distinct retinal cell types exhibit unique NPL profiles and participate in specific NPL-mediated pathological cell-cell interactions. Finally, MetaLigand supports single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data, enabling the visualization of predicted NPL production levels and heterogeneity at single-cell resolution.

Painting Cell-Cell Interactions by Horseradish Peroxidase and Endogenously Generated Hydrogen Peroxide

Cell-cell interactions are fundamental in biology for maintaining physiological conditions with direct contact being the most straightforward mode of interaction. Recent advancements have led to the development of various chemical tools for detecting or identifying these interactions. However, the use of exogenous cues, such as toxic reagents, bulky probes, and light irradiation, can disrupt normal cell physiology. For example, the toxicity of hydrogen peroxide (H2O2) limits the applications of peroxidases in the proximity labeling field. In this study, we aimed to address this limitation by demonstrating that membrane-localized horseradish peroxidase (HRP-TM) efficiently utilizes endogenously generated extracellular H2O2. By harnessing endogenous H2O2, we observed that HRP-TM-expressing cells can effectively label contacting cells without the need for exogenous H2O2 treatment. Furthermore, we confirmed that HRP-TM labels proximal cells in an interaction-dependent manner. These findings offer a novel approach for studying cell-cell interactions under more physiological conditions without the confounding effects of exogenous stimuli. Our study contributes to elucidating cell-cell interaction networks in various model organisms, providing valuable insights into the dynamic interplay between cells in their native network.

Aptamer-based Immune Checkpoint Inhibition for Cancer Immunotherapy

Cancer has long been a significant threat to human life and health. The advent of immune checkpoint blockade strategies has reversed cancer-induced immune suppression, advanced the development of immunotherapy, and offered new hope in the fight against cancer. Aptamers, which possess the same specificity and affinity as antibodies, are advantageous due to their synthetic accessibility and ease of modification, providing novel insights for immune checkpoint research. In this review, we outline the key aptamers currently developed for immune checkpoints such as CTLA-4, PD-1, PD-L1 and Siglec-15. We explore their potential in therapeutic strategies, including functionalizing or engineering aptamers for covalent binding, valency control, and nanostructure assembly, as well as investigating molecular mechanisms such as glycosylated protein functions and cell-cell interactions. Finally, the future applications of aptamers in immunotherapy are discussed.

CellChat for systematic analysis of cell-cell communication from single-cell transcriptomics

Recent advances in single-cell sequencing technologies offer an opportunity to explore cell-cell communication in tissues systematically and with reduced bias. A key challenge is integrating known molecular interactions and measurements into a framework to identify and analyze complex cell-cell communication networks. Previously, we developed a computational tool, named CellChat, that infers and analyzes cell-cell communication networks from single-cell transcriptomic data within an easily interpretable framework. CellChat quantifies the signaling communication probability between two cell groups using a simplified mass-action-based model, which incorporates the core interaction between ligands and receptors with multisubunit structure along with modulation by cofactors. Importantly, CellChat performs a systematic and comparative analysis of cell-cell communication using a variety of quantitative metrics and machine-learning approaches. CellChat v2 is an updated version that includes additional comparison functionalities, an expanded database of ligand-receptor pairs along with rich functional annotations, and an Interactive CellChat Explorer. Here we provide a step-by-step protocol for using CellChat v2 on single-cell transcriptomic data, including inference and analysis of cell-cell communication from one dataset and identification of altered intercellular communication, signals and cell populations from different datasets across biological conditions. The R implementation of CellChat v2 toolkit and its tutorials together with the graphic outputs are available at https://github.com/jinworks/CellChat . This protocol typically takes ~5 min depending on dataset size and requires a basic understanding of R and single-cell data analysis but no specialized bioinformatics training for its implementation.

Sonic hedgehog medulloblastoma cells in co-culture with cerebellar organoids converge towards in vivo malignant cell states

Background

In the malignant brain tumor sonic hedgehog medulloblastoma (SHH-MB) the properties of cancer cells are influenced by their microenvironment, but the nature of those effects and the phenotypic consequences for the tumor are poorly understood. The aim of this study was to identify the phenotypic properties of SHH-MB cells that were driven by the nonmalignant tumor microenvironment.

Methods

Human induced pluripotent cells (iPSC) were differentiated to cerebellar organoids to simulate the nonmaliganant tumor microenvironment. Tumor spheroids were generated from 2 distinct, long-established SHH-MB cell lines which were co-cultured with cerebellar organoids. We profiled the cellular transcriptomes of malignant and nonmalignant cells by performing droplet-based single-cell RNA sequencing (scRNA-seq). The transcriptional profiles of tumor cells in co-culture were compared with those of malignant cell monocultures and with public SHH-MB datasets of patient tumors and patient-derived orthotopic xenograft (PDX) mouse models.

Results

SHH-MB cell lines in organoid co-culture adopted patient tumor-associated phenotypes and showed increased heterogeneity compared to monocultures. Subpopulations of co-cultured SHH-MB cells activated a key marker of differentiating granule cells, NEUROD1 that was not observed in tumor monocultures. Other subpopulations expressed transcriptional determinants consistent with a cancer stem cell-like state that resembled cell states identified in vivo.

Conclusions

For SHH-MB cell lines in co-culture, there was a convergence of malignant cell states towards patterns of heterogeneity in patient tumors and PDX models implying these states were non-cell autonomously induced by the microenvironment. Therefore, we have generated an advanced, novel in vitro model of SHH-MB with potential translational applications.

Detecting global and local hierarchical structures in cell-cell communication using CrossChat

Cell-cell communication (CCC) occurs across different biological scales, ranging from interactions between large groups of cells to interactions between individual cells, forming a hierarchical structure. Globally, CCC may exist between clusters or only subgroups of a cluster with varying size, while locally, a group of cells as sender or receiver may exhibit distinct signaling properties. Current existing methods infer CCC from single-cell RNA-seq or Spatial Transcriptomics only between predefined cell groups, neglecting the existing hierarchical structure within CCC that are determined by signaling molecules, in particular, ligands and receptors. Here, we develop CrossChat, a novel computational framework designed to infer and analyze the hierarchical cell-cell communication structures using two complementary approaches: a global hierarchical structure using a multi-resolution clustering method, and multiple local hierarchical structures using a tree detection method. This framework provides a comprehensive approach to understand the hierarchical relationships within CCC that govern complex tissue functions. By applying our method to two nonspatial scRNA-seq datasets sampled from COVID-19 patients and mouse embryonic skin, and two spatial transcriptomics datasets generated from Stereo-seq of mouse embryo and 10x Visium of mouse wounded skin, we showcase CrossChat's functionalities for analyzing both global and local hierarchical structures within cell-cell communication.

Unraveling immune cell heterogeneity in autoimmune arthritis: insights from single-cell RNA sequencing

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of immune-mediated arthritis, which comprises rheumatoid arthritis and spondyloarthritis. This review outlines the key findings and advancements in scRNA-seq studies focused on the pathogenesis of autoimmune arthritis and its clinical application. In rheumatoid arthritis, scRNA-seq has elucidated the heterogeneity among synovial fibroblasts and immune cell subsets in inflammatory sites, offering insights into disease mechanisms and the differences in treatment responses. Various studies have identified distinct synovial fibroblast subpopulations, such as THY1+ inflammatory and THY1- destructive fibroblasts. Furthermore, scRNA-seq has revealed diverse T cell profiles in the synovium, including peripheral helper T cells and clonally expanded CD8+ T cells, shedding light on potential therapeutic targets and predictive markers of treatment response. Similarly, in spondyloarthritis, particularly psoriatic arthritis and ankylosing spondylitis, scRNA-seq studies have identified distinct cellular profiles associated with disease pathology. Challenges such as cost and sample size limitations persist, but collaborative efforts and utilization of public databases hold promise for overcoming these obstacles. Overall, scRNA-seq emerges as a powerful tool for dissecting cellular heterogeneity and driving precision medicine in immune-mediated arthritis.

Integrated-omics profiling unveils the disparities of host defense to ECM scaffolds during wound healing in aged individuals

Extracellular matrix (ECM) scaffold membranes have exhibited promising potential to better the outcomes of wound healing by creating a regenerative microenvironment around. However, when compared to the application in younger individuals, the performance of the same scaffold membrane in promoting re-epithelialization and collagen deposition was observed dissatisfying in aged mice. To comprehensively explore the mechanisms underlying this age-related disparity, we conducted the integrated analysis, combing single-cell RNA sequencing (scRNA-Seq) with spatial transcriptomics, and elucidated six functionally and spatially distinctive macrophage groups and lymphocytes surrounding the ECM scaffolds. Through intergroup comparative analysis and cell-cell communication, we characterized the dysfunction of Spp1+ macrophages in aged mice impeded the activation of the type Ⅱ immune response, thus inhibiting the repair ability of epidermal cells and fibroblasts around the ECM scaffolds. These findings contribute to a deeper understanding of biomaterial applications in varied physiological contexts, thereby paving the way for the development of precision-based biomaterials tailored specifically for aged individuals in future therapeutic strategies.

Crosstalk of T cells within the ovarian cancer microenvironment

Ovarian cancer (OC) represents ecosystems of highly diverse tumor microenvironments (TMEs). The presence of tumor-infiltrating lymphocytes (TILs) is linked to enhanced immune responses and long-term survival. In this review we present emerging evidence suggesting that cellular crosstalk tightly regulates the distribution of TILs within the TME, underscoring the need to better understand key cellular networks that promote or impede T cell infiltration in OC. We also capture the emergent methodologies and computational techniques that enable the dissection of cell-cell crosstalk. Finally, we present innovative ex vivo TME models that can be leveraged to map and perturb cellular communications to enhance T cell infiltration and immune reactivity.

Spatial mapping of the HCC landscape identifies unique intratumoral perivascular-immune neighborhoods

BACKGROUND

HCC develops in the context of chronic inflammation; however, the opposing roles the immune system plays in both the development and control of tumors are not fully understood. Mapping immune cell interactions across the distinct tissue regions could provide greater insight into the role individual immune populations have within tumors.

METHODS

A 39-parameter imaging mass cytometry panel was optimized with markers targeting immune cells, stromal cells, endothelial cells, hepatocytes, and tumor cells. We mapped the immune landscape of tumor, invasive margin, and adjacent nontumor regions across 16 resected tumors comprising 144 regions of interest. X-shift clustering and manual gating were used to characterize cell subsets, and Spectre quantified the spatial environment to identify cellular neighborhoods. Ligand-receptor communication was quantified on 2 single-cell RNA-sequencing data sets and 1 spatial transcriptomic data set.

RESULTS

We show immune cell densities remain largely consistent across these 3 regions, except for subsets of monocyte-derived macrophages, which are enriched within the tumors. Mapping cellular interactions across these regions in an unbiased manner identifies immune neighborhoods comprised of tissue-resident T cells, dendritic cells, and various macrophage populations around perivascular spaces. Importantly, we identify multiple immune cells within these neighborhoods interacting with VEGFA+ perivascular macrophages. VEGFA was further identified as a ligand for communication between perivascular macrophages and CD34+ endothelial cells.

CONCLUSIONS

Immune cell neighborhood interactions, but not cell densities, differ between intratumoral and adjacent nontumor regions in HCC. Unique intratumoral immune neighborhoods around the perivascular space point to an altered landscape within tumors. Enrichment of VEGFA+ perivascular macrophages within these tumors could play a key role in angiogenesis and vascular permeability.

Mapping cellular interactions from spatially resolved transcriptomics data

Cell-cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently through the introduction of spatially resolved transcriptomics (SRT) technologies, especially those that achieve single-cell resolution. Nevertheless, substantial challenges remain to analyze such highly complex data properly. Here, we introduce a multiple-instance learning framework, Spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight Spacia's power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand-receptor relationships and prior interaction databases, high false positive rates and, most importantly, the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of Spacia for three commercialized single-cell resolution SRT technologies: MERSCOPE/Vizgen, CosMx/NanoString and Xenium/10x. Overall, Spacia represents a notable step in advancing quantitative theories of cellular communications.

Cross-expression analysis reveals patterns of coordinated gene expression in spatial transcriptomics

Spatial transcriptomics promises to transform our understanding of tissue biology by molecularly profiling individual cells in situ. A fundamental question they allow us to ask is how nearby cells orchestrate their gene expression. To investigate this, we introduce cross-expression, a novel framework for discovering gene pairs that coordinate their expression across neighboring cells. Just as co-expression quantifies synchronized gene expression within the same cells, cross-expression measures coordinated gene expression between spatially adjacent cells, allowing us to understand tissue gene expression programs with single cell resolution. Using this framework, we recover ligand-receptor partners and discover gene combinations marking anatomical regions. More generally, we create cross-expression networks to find gene modules with orchestrated expression patterns. Finally, we provide an efficient R package to facilitate cross-expression analysis, quantify effect sizes, and generate novel visualizations to better understand spatial gene expression programs.

LIANA+ provides an all-in-one framework for cell-cell communication inference

The growing availability of single-cell and spatially resolved transcriptomics has led to the development of many approaches to infer cell-cell communication, each capturing only a partial view of the complex landscape of intercellular signalling. Here we present LIANA+, a scalable framework built around a rich knowledge base to decode coordinated inter- and intracellular signalling events from single- and multi-condition datasets in both single-cell and spatially resolved data. By extending and unifying established methodologies, LIANA+ provides a comprehensive set of synergistic components to study cell-cell communication via diverse molecular mediators, including those measured in multi-omics data. LIANA+ is accessible at https://github.com/saezlab/liana-py with extensive vignettes ( https://liana-py.readthedocs.io/ ) and provides an all-in-one solution to intercellular communication inference.

Uncovering underlying physical principles and driving forces of cell differentiation and reprogramming from single-cell transcriptomics

Recent advances in single-cell sequencing technology have revolutionized our ability to acquire whole transcriptome data. However, uncovering the underlying transcriptional drivers and nonequilibrium driving forces of cell function directly from these data remains challenging. We address this by learning cell state vector fields from discrete single-cell RNA velocity to quantify the single-cell global nonequilibrium driving forces as landscape and flux. From single-cell data, we quantified the Waddington landscape, showing that optimal paths for differentiation and reprogramming deviate from the naively expected landscape gradient paths and may not pass through landscape saddles at finite fluctuations, challenging conventional transition state estimation of kinetic rate for cell fate decisions due to the presence of the flux. A key insight from our study is that stem/progenitor cells necessitate greater energy dissipation for rapid cell cycles and self-renewal, maintaining pluripotency. We predict optimal developmental pathways and elucidate the nucleation mechanism of cell fate decisions, with transition states as nucleation sites and pioneer genes as nucleation seeds. The concept of loop flux quantifies the contributions of each cycle flux to cell state transitions, facilitating the understanding of cell dynamics and thermodynamic cost, and providing insights into optimizing biological functions. We also infer cell-cell interactions and cell-type-specific gene regulatory networks, encompassing feedback mechanisms and interaction intensities, predicting genetic perturbation effects on cell fate decisions from single-cell omics data. Essentially, our methodology validates the landscape and flux theory, along with its associated quantifications, offering a framework for exploring the physical principles underlying cellular differentiation and reprogramming and broader biological processes through high-throughput single-cell sequencing experiments.

New tools to study renal fibrogenesis

PURPOSE OF REVIEW

Kidney fibrosis is a key pathological aspect and outcome of chronic kidney disease (CKD). The advent of multiomic analyses using human kidney tissue, enabled by technological advances, marks a new chapter of discovery in fibrosis research of the kidney. This review highlights the rapid advancements of single-cell and spatial multiomic techniques that offer new avenues for exploring research questions related to human kidney fibrosis development.

RECENT FINDINGS

We recently focused on understanding the origin and transition of myofibroblasts in kidney fibrosis using single-cell RNA sequencing (scRNA-seq) [1] . We analysed cells from healthy human kidneys and compared them to patient samples with CKD. We identified PDGFRα+/PDGFRβ+ mesenchymal cells as the primary cellular source of extracellular matrix (ECM) in human kidney fibrosis. We found several commonly shared cell states of fibroblasts and myofibroblasts and provided insights into molecular regulators. Novel single-cell and spatial multiomics tools are now available to shed light on cell lineages, the plasticity of kidney cells and cell-cell communication in fibrosis.

SUMMARY

As further single-cell and spatial multiomic approaches are being developed, opportunities to apply these methods to human kidney tissues expand similarly. Careful design and optimisation of the multiomic experiments are needed to answer questions related to cell lineages, plasticity and cell-cell communication in kidney fibrosis.

Revisiting Cardiac Biology in the Era of Single Cell and Spatial Omics

Throughout our lifetime, each beat of the heart requires the coordinated action of multiple cardiac cell types. Understanding cardiac cell biology, its intricate microenvironments, and the mechanisms that govern their function in health and disease are crucial to designing novel therapeutical and behavioral interventions. Recent advances in single-cell and spatial omics technologies have significantly propelled this understanding, offering novel insights into the cellular diversity and function and the complex interactions of cardiac tissue. This review provides a comprehensive overview of the cellular landscape of the heart, bridging the gap between suspension-based and emerging in situ approaches, focusing on the experimental and computational challenges, comparative analyses of mouse and human cardiac systems, and the rising contextualization of cardiac cells within their niches. As we explore the heart at this unprecedented resolution, integrating insights from both mouse and human studies will pave the way for novel diagnostic tools and therapeutic interventions, ultimately improving outcomes for patients with cardiovascular diseases.

MetalinksDB: a flexible and contextualizable resource of metabolite-protein interactions

From the catalytic breakdown of nutrients to signaling, interactions between metabolites and proteins play an essential role in cellular function. An important case is cell-cell communication, where metabolites, secreted into the microenvironment, initiate signaling cascades by binding to intra- or extracellular receptors of neighboring cells. Protein-protein cell-cell communication interactions are routinely predicted from transcriptomic data. However, inferring metabolite-mediated intercellular signaling remains challenging, partially due to the limited size of intercellular prior knowledge resources focused on metabolites. Here, we leverage knowledge-graph infrastructure to integrate generalistic metabolite-protein with curated metabolite-receptor resources to create MetalinksDB. MetalinksDB is an order of magnitude larger than existing metabolite-receptor resources and can be tailored to specific biological contexts, such as diseases, pathways, or tissue/cellular locations. We demonstrate MetalinksDB's utility in identifying deregulated processes in renal cancer using multi-omics bulk data. Furthermore, we infer metabolite-driven intercellular signaling in acute kidney injury using spatial transcriptomics data. MetalinksDB is a comprehensive and customizable database of intercellular metabolite-protein interactions, accessible via a web interface (https://metalinks.omnipathdb.org/) and programmatically as a knowledge graph (https://github.com/biocypher/metalinks). We anticipate that by enabling diverse analyses tailored to specific biological contexts, MetalinksDB will facilitate the discovery of disease-relevant metabolite-mediated intercellular signaling processes.

Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples

In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. Here, we integrate two tools, LIANA and Tensor-cell2cell, which, when combined, can deploy multiple existing methods and resources to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this work, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step by step in both Python and R and provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/. This workflow typically takes ∼1.5 h to complete from installation to downstream visualizations on a graphics processing unit-enabled computer for a dataset of ∼63,000 cells, 10 cell types, and 12 samples.

Interactions between immune cells recorded

Direct interactions between cells in tissue are incompletely understood because the advanced technologies required to examine them are still in their infancy. A new method can decipher cell-cell interactions on a large scale.