Context-aware deconvolution of cell-cell communication with Tensor-cell2cell

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.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

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.

Uncovering disease-related multicellular pathway modules on large-scale single-cell transcriptomes with scPAFA

Pathway analysis is a crucial analytical phase in disease research on single-cell RNA sequencing (scRNA-seq) data, offering biological interpretations based on prior knowledge. However, currently available tools for generating cell-level pathway activity scores (PAS) exhibit computational inefficacy in large-scale scRNA-seq datasets. Additionally, disease-related pathways are often identified through cross-condition comparisons within specific cell types, overlooking potential patterns that involve multiple cell types. Here, we present single-cell pathway activity factor analysis (scPAFA), a Python library designed for large-scale single-cell datasets allowing rapid PAS computation and uncovering biologically interpretable disease-related multicellular pathway modules, which are low-dimensional representations of disease-related PAS alterations in multiple cell types. Application on colorectal cancer (CRC) datasets and large-scale lupus atlas over 1.2 million cells demonstrated that scPAFA can achieve over 40-fold reductions in the runtime of PAS computation and further identified reliable and interpretable multicellular pathway modules that capture the heterogeneity of CRC and transcriptional abnormalities in lupus patients, respectively. Overall, scPAFA presents a valuable addition to existing research tools in disease research, with the potential to reveal complex disease mechanisms and support biomarker discovery at the pathway level.

The single-cell transcriptomic atlas iPain identifies senescence of nociceptors as a therapeutical target for chronic pain treatment

Chronic pain remains a significant medical challenge with complex underlying mechanisms, and an urgent need for new treatments. Our research built and utilized the iPain single-cell atlas to study chronic pain progression in dorsal root and trigeminal ganglia. We discovered that senescence of a small subset of pain-sensing neurons may be a driver of chronic pain. This mechanism was observed in animal models after nerve injury and in human patients diagnosed with chronic pain or diabetic painful neuropathy. Notably, treatment with senolytics, drugs that remove senescent cells, reversed pain symptoms in mice post-injury. These findings highlight the role of cellular senescence in chronic pain development, demonstrate the therapeutic potential of senolytic treatments, and underscore the value of the iPain atlas for future pain research.

Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics

From single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST), one can extract high-dimensional gene expression patterns that can be described by intercellular communication networks or decoupled gene modules. These two descriptions of information flow are often assumed to occur independently. However, intercellular communication drives directed flows of information that are mediated by intracellular gene modules, in turn triggering outflows of other signals. Methodologies to describe such intercellular flows are lacking. We present FlowSig, a method that infers communication-driven intercellular flows from scRNA-seq or ST data using graphical causal modeling and conditional independence. We benchmark FlowSig using newly generated experimental cortical organoid data and synthetic data generated from mathematical modeling. We demonstrate FlowSig's utility by applying it to various studies, showing that FlowSig can capture stimulation-induced changes to paracrine signaling in pancreatic islets, demonstrate shifts in intercellular flows due to increasing COVID-19 severity and reconstruct morphogen-driven activator-inhibitor patterns in mouse embryogenesis.

Spatial and Single Cell Mapping of Castleman Disease Reveals Key Stromal Cell Types and Cytokine Pathways

Castleman disease (CD) is inflammatory lymphoproliferative disorder of unclear etiology. To determine the cellular and molecular basis of CD, we analyzed the spatial proteome of 4,485,009 single cells, transcriptome of 50,117 single nuclei, immune repertoire of 8187 single nuclei, and pathogenic mutations in Unicentric CD, idiopathic Multicentric CD, HHV8-associated MCD, and reactive lymph nodes. CD was characterized by increased non-lymphoid and stromal cells that formed unique microenvironments where they interacted with lymphoid cells. Interaction of activated follicular dendritic cell (FDC) cytoplasmic meshworks with mantle zone B cells was associated with B cell activation and differentiation. VEGF, IL-6, MAPK, and extracellular matrix pathways were elevated in stromal cells of CD. CXCL13+ FDCs, PDGFRA+ T-zone reticular cells (TRC), and ACTA2-positive perivascular reticular cells (PRC) were identified as the predominant source of increased VEGF expression and IL-6 signaling in CD. VEGF expression by FDCs was associated with peri-follicular neovascularization. FDC, TRC and PRC of CD activated JAK-STAT, TGFβ, and MAPK pathways via ligand-receptor interactions involving collagen, integrins, complement components, and VEGF receptors. T, B and plasma cells were polyclonal but showed class-switched and somatically hypermutated IgG1+ plasma cells consistent with stromal cell-driven germinal center activation. In conclusion, our findings show that stromal cell activation and associated B-cell activation and differentiation, neovascularization and stromal remodeling underlie CD and suggest new targets for treatment.

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.

The structure is the message: Preserving experimental context through tensor decomposition

Recent biological studies have been revolutionized in scale and granularity by multiplex and high-throughput assays. Profiling cell responses across several experimental parameters, such as perturbations, time, and genetic contexts, leads to richer and more generalizable findings. However, these multidimensional datasets necessitate a reevaluation of the conventional methods for their representation and analysis. Traditionally, experimental parameters are merged to flatten the data into a two-dimensional matrix, sacrificing crucial experiment context reflected by the structure. As Marshall McLuhan famously stated, "the medium is the message." In this work, we propose that the experiment structure is the medium in which subsequent analysis is performed, and the optimal choice of data representation must reflect the experiment structure. We review how tensor-structured analyses and decompositions can preserve this information. We contend that tensor methods are poised to become integral to the biomedical data sciences toolkit.

A gene regulatory network-aware graph learning method for cell identity annotation in single-cell RNA-seq data

Cell identity annotation for single-cell transcriptome data is a crucial process for constructing cell atlases, unraveling pathogenesis, and inspiring therapeutic approaches. Currently, the efficacy of existing methodologies is contingent upon specific data sets. Nevertheless, such data are often sourced from various batches, sequencing technologies, tissues, and even species. Notably, the gene regulatory relationship remains unaffected by the aforementioned factors, highlighting the extensive gene interactions within organisms. Therefore, we propose scHGR, an automated annotation tool designed to leverage gene regulatory relationships in constructing gene-mediated cell communication graphs for single-cell transcriptome data. This strategy helps reduce noise from diverse data sources while establishing distant cellular connections, yielding valuable biological insights. Experiments involving 22 scenarios demonstrate that scHGR precisely and consistently annotates cell identities, benchmarked against state-of-the-art methods. Crucially, scHGR uncovers novel subtypes within peripheral blood mononuclear cells, specifically from CD4+ T cells and cytotoxic T cells. Furthermore, by characterizing a cell atlas comprising 56 cell types for COVID-19 patients, scHGR identifies vital factors like IL1 and calcium ions, offering insights for targeted therapeutic interventions.

Cell-cell communication: new insights and clinical implications

Multicellular organisms are composed of diverse cell types that must coordinate their behaviors through communication. Cell-cell communication (CCC) is essential for growth, development, differentiation, tissue and organ formation, maintenance, and physiological regulation. Cells communicate through direct contact or at a distance using ligand-receptor interactions. So cellular communication encompasses two essential processes: cell signal conduction for generation and intercellular transmission of signals, and cell signal transduction for reception and procession of signals. Deciphering intercellular communication networks is critical for understanding cell differentiation, development, and metabolism. First, we comprehensively review the historical milestones in CCC studies, followed by a detailed description of the mechanisms of signal molecule transmission and the importance of the main signaling pathways they mediate in maintaining biological functions. Then we systematically introduce a series of human diseases caused by abnormalities in cell communication and their progress in clinical applications. Finally, we summarize various methods for monitoring cell interactions, including cell imaging, proximity-based chemical labeling, mechanical force analysis, downstream analysis strategies, and single-cell technologies. These methods aim to illustrate how biological functions depend on these interactions and the complexity of their regulatory signaling pathways to regulate crucial physiological processes, including tissue homeostasis, cell development, and immune responses in diseases. In addition, this review enhances our understanding of the biological processes that occur after cell-cell binding, highlighting its application in discovering new therapeutic targets and biomarkers related to precision medicine. This collective understanding provides a foundation for developing new targeted drugs and personalized treatments.

Integrative, high-resolution analysis of single cells across experimental conditions with PARAFAC2

Effective tools for exploration and analysis are needed to extract insights from large-scale single-cell measurement data. However, current techniques for handling single-cell studies performed across experimental conditions (e.g., samples, perturbations, or patients) require restrictive assumptions, lack flexibility, or do not adequately deconvolute condition-to-condition variation from cell-to-cell variation. Here, we report that the tensor decomposition method PARAFAC2 (Pf2) enables the dimensionality reduction of single-cell data across conditions. We demonstrate these benefits across two distinct contexts of single-cell RNA-sequencing (scRNA-seq) experiments of peripheral immune cells: pharmacologic drug perturbations and systemic lupus erythematosus (SLE) patient samples. By isolating relevant gene modules across cells and conditions, Pf2 enables straightforward associations of gene variation patterns across specific patients or perturbations while connecting each coordinated change to certain cells without pre-defining cell types. The theoretical grounding of Pf2 suggests a unified framework for many modeling tasks associated with single-cell data. Thus, Pf2 provides an intuitive universal dimensionality reduction approach for multi-sample single-cell studies across diverse biological contexts.

scHyper: reconstructing cell-cell communication through hypergraph neural networks

Cell-cell communications is crucial for the regulation of cellular life and the establishment of cellular relationships. Most approaches of inferring intercellular communications from single-cell RNA sequencing (scRNA-seq) data lack a comprehensive global network view of multilayered communications. In this context, we propose scHyper, a new method that can infer intercellular communications from a global network perspective and identify the potential impact of all cells, ligand, and receptor expression on the communication score. scHyper designed a new way to represent tripartite relationships, by extracting a heterogeneous hypergraph that includes the source (ligand expression), the target (receptor expression), and the relevant ligand-receptor (L-R) pairs. scHyper is based on hypergraph representation learning, which measures the degree of match between the intrinsic attributes (static embeddings) of nodes and their observed behaviors (dynamic embeddings) in the context (hyperedges), quantifies the probability of forming hyperedges, and thus reconstructs the cell-cell communication score. Additionally, to effectively mine the key mechanisms of signal transmission, we collect a rich dataset of multisubunit complex L-R pairs and propose a nonparametric test to determine significant intercellular communications. Comparing with other tools indicates that scHyper exhibits superior performance and functionality. Experimental results on the human tumor microenvironment and immune cells demonstrate that scHyper offers reliable and unique capabilities for analyzing intercellular communication networks. Therefore, we introduced an effective strategy that can build high-order interaction patterns, surpassing the limitations of most methods that can only handle low-order interactions, thus more accurately interpreting the complexity of intercellular communications.

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

No cell lives in a vacuum, and the molecular interactions between cells define most phenotypes. Transcriptomics provides rich information to infer cell-cell interactions and communication, thus accelerating the discovery of the roles of cells within their communities. Such research relies heavily on algorithms that infer which cells are interacting and the ligands and receptors involved. Specific pressures on different research niches are driving the evolution of next-generation computational tools, enabling new conceptual opportunities and technological advances. More sophisticated algorithms now account for the heterogeneity and spatial organization of cells, multiple ligand types and intracellular signalling events, and enable the use of larger and more complex datasets, including single-cell and spatial transcriptomics. Similarly, new high-throughput experimental methods are increasing the number and resolution of interactions that can be analysed simultaneously. Here, we explore recent progress in cell-cell interaction research and highlight the diversification of the next generation of tools, which have yielded a rich ecosystem of tools for different applications and are enabling invaluable discoveries.

Reading the palimpsest of cell interactions: What questions may we ask of the data?

Biological function depends on the composition and structure of the organism, the latter describing the organization of interactions between parts. While cells in multicellular organisms are capable of a remarkable degree of autonomy, most functions do require cell communication: the coordination of functions (growth, differentiation, and apoptosis), the compartmentalization of cellular processes, and the integration of cells into higher levels of structural organization. A wealth of data on putative cell interactions has become available, yet its biological interpretation depends on our expectations about the structure of interaction networks. Here, we attempt to formulate basic questions to ask when interpreting cell interaction data. We build on the understanding that cells fulfill two general functions: the integrity-maintaining and the organismal service function. We derive the expected patterns of cell interactions considering two intertwined aspects: the functional and the evolutionary. Based on these, we propose guidelines for analysis and interpretation of transcriptional cell-interactome data.

Complementing Cell Taxonomies with a Multicellular Analysis of Tissues

The application of single-cell molecular profiling coupled with spatial technologies has enabled charting of cellular heterogeneity in reference tissues and in disease. This new wave of molecular data has highlighted the expected diversity of single-cell dynamics upon shared external queues and spatial organizations. However, little is known about the relationship between single-cell heterogeneity and the emergence and maintenance of robust multicellular processes in developed tissues and its role in (patho)physiology. Here, we present emerging computational modeling strategies that use increasingly available large-scale cross-condition single-cell and spatial datasets to study multicellular organization in tissues and complement cell taxonomies. This perspective should enable us to better understand how cells within tissues collectively process information and adapt synchronized responses in disease contexts and to bridge the gap between structural changes and functions in tissues.

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.

Resource allocation in mammalian systems

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

Integrating single-cell multi-omics and prior biological knowledge for a functional characterization of the immune system

The immune system comprises diverse specialized cell types that cooperate to defend the host against a wide range of pathogenic threats. Recent advancements in single-cell and spatial multi-omics technologies provide rich information about the molecular state of immune cells. Here, we review how the integration of single-cell and spatial multi-omics data with prior knowledge-gathered from decades of detailed biochemical studies-allows us to obtain functional insights, focusing on gene regulatory processes and cell-cell interactions. We present diverse applications in immunology and critically assess underlying assumptions and limitations. Finally, we offer a perspective on the ongoing technological and algorithmic developments that promise to get us closer to a systemic mechanistic understanding of the immune system.

Computational cell-cell interaction technologies drive mechanistic and biomarker discovery in the tumor microenvironment

Complex networks of cell-cell interactions (CCIs) within the tumor microenvironment (TME) play a crucial role in cancer persistence. These communication axes represent prime targets for therapeutic intervention, but our incomplete understanding of the cellular heterogeneity and interacting partners within the TME remains a stubborn barrier to complete drug responses. This review outlines recent advances in the study of CCIs that leverage single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) technologies that can clarify TME dynamics. We anticipate that these strategies will promote discovery of CCIs critical to the tumor-immune interface and will, by extension, expand the repertoire of druggable tumor biomarkers.

STACCato: Supervised Tensor Analysis tool for studying Cell-cell Communication using scRNA-seq data across multiple samples and conditions

Research on cell-cell communication (CCC) is crucial for understanding biology and diseases. Many existing CCC inference tools neglect potential confounders, such as batch and demographic variables, when analyzing multi-sample, multi-condition scRNA-seq datasets. To address this significant gap, we introduce STACCato, a Supervised Tensor Analysis tool for studying Cell-cell Communication, that identifies CCC events and estimates the effects of biological conditions (e.g., disease status, tissue types) on such events, while adjusting for potential confounders. Application of STACCato to both simulated data and real scRNA-seq data of lupus and autism studies demonstrate that incorporating sample-level variables into CCC inference consistently provides more accurate estimations of disease effects and cell type activity patterns than existing methods that ignore sample-level variables. A computational tool implementing the STACCato framework is available on GitHub.

Cell Membrane-Anchored SERS Biosensor for the Monitoring of Cell-Secreted MMP-9 during Cell-Cell Communication

Matrix metalloproteinase-9 (MMP-9), a proteolytic enzyme, degrades the extracellular matrix and plays a key role in cell communication. However, the real-time monitoring of cell-secreted MMP-9 during cell-cell communication remains a challenge. Herein, we developed a cell-based membrane-anchored surface-enhanced Raman scattering (SERS) biosensor using a Au@4-mercaptobenzonitrile (4-MBN) @Ag@peptide nanoprobe for the monitoring of cell-secreted MMP-9 during cell communication. The multifunctional nanoprobe was created with Au@4-MBN@Ag acting as an interference-free SERS substrate with high enhancement in which the peptide not only serves to anchor the cell membrane but also provides MMP-9-activatable cleaved peptide chains. MMP-9-mediated cleavage resulted in the detachment of the Au@4-MBN@Ag nanoparticles from the cell membrane, thereby decreasing the SERS signals of cancer cells. The cell membrane-anchored SERS biosensor enables the real-time monitoring of cell-secreted MMP-9 during the interaction of MCF-7 and HUVEC cells. This study successfully demonstrates the dynamic change of cell-secreted MMP-9 during the communication between MCF-7 cells and HUVEC cells. The proposed nanoprobe was also utilized to precisely evaluate the breast and hepatoma cancer cell aggressiveness. This study provides a novel strategy for real-time monitoring of MMP-9 secretion during cell communication, which is promising for the investigation of the mechanisms underlying different tumor processes.

Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease

Biomedical single-cell atlases describe disease at the cellular level. However, analysis of this data commonly focuses on cell-type-centric pairwise cross-condition comparisons, disregarding the multicellular nature of disease processes. Here, we propose multicellular factor analysis for the unsupervised analysis of samples from cross-condition single-cell atlases and the identification of multicellular programs associated with disease. Our strategy, which repurposes group factor analysis as implemented in multi-omics factor analysis, incorporates the variation of patient samples across cell-types or other tissue-centric features, such as cell compositions or spatial relationships, and enables the joint analysis of multiple patient cohorts, facilitating the integration of atlases. We applied our framework to a collection of acute and chronic human heart failure atlases and described multicellular processes of cardiac remodeling, independent to cellular compositions and their local organization, that were conserved in independent spatial and bulk transcriptomics datasets. In sum, our framework serves as an exploratory tool for unsupervised analysis of cross-condition single-cell atlases and allows for the integration of the measurements of patient cohorts across distinct data modalities.

Sctensor detects many-to-many cell-cell interactions from single cell RNA-sequencing data

BACKGROUND

Complex biological systems are described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing studies focus on CCIs based on ligand-receptor (L-R) gene co-expression but the analytical methods are not appropriate to detect many-to-many CCIs.

RESULTS

In this work, we propose scTensor, a novel method for extracting representative triadic relationships (or hypergraphs), which include ligand-expression, receptor-expression, and related L-R pairs.

CONCLUSIONS

Through extensive studies with simulated and empirical datasets, we have shown that scTensor can detect some hypergraphs that cannot be detected using conventional CCI detection methods, especially when they include many-to-many relationships. scTensor is implemented as a freely available R/Bioconductor package.

ESICCC as a systematic computational framework for evaluation, selection, and integration of cell-cell communication inference methods

Cell-cell communication (CCC) is critical for determining cell fates and functions in multicellular organisms. With the advent of single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST), an increasing number of CCC inference methods have been developed. Nevertheless, a thorough comparison of their performances is yet to be conducted. To fill this gap, we developed a systematic benchmark framework called ESICCC to evaluate 18 ligand-receptor (LR) inference methods and five ligand/receptor-target inference methods using a total of 116 data sets, including 15 ST data sets, 15 sets of cell line perturbation data, two sets of cell type-specific expression/proteomics data, and 84 sets of sampled or unsampled scRNA-seq data. We evaluated and compared the agreement, accuracy, robustness, and usability of these methods. Regarding accuracy evaluation, RNAMagnet, CellChat, and scSeqComm emerge as the three best-performing methods for intercellular ligand-receptor inference based on scRNA-seq data, whereas stMLnet and HoloNet are the best methods for predicting ligand/receptor-target regulation using ST data. To facilitate the practical applications, we provide a decision-tree-style guideline for users to easily choose best tools for their specific research concerns in CCC inference, and develop an ensemble pipeline CCCbank that enables versatile combinations of methods and databases. Moreover, our comparative results also uncover several critical influential factors for CCC inference, such as prior interaction information, ligand-receptor scoring algorithm, intracellular signaling complexity, and spatial relationship, which may be considered in the future studies to advance the development of new methodologies.

CellEnBoost: A Boosting-Based Ligand-Receptor Interaction Identification Model for Cell-to-Cell Communication Inference

Cell-to-cell communication (CCC) plays important roles in multicellular organisms. The identification of communication between cancer cells themselves and one between cancer cells and normal cells in tumor microenvironment helps understand cancer genesis, development and metastasis. CCC is usually mediated by Ligand-Receptor Interactions (LRIs). In this manuscript, we developed a Boosting-based LRI identification model (CellEnBoost) for CCC inference. First, potential LRIs are predicted by data collection, feature extraction, dimensional reduction, and classification based on an ensemble of Light gradient boosting machine and AdaBoost combining convolutional neural network. Next, the predicted LRIs and known LRIs are filtered. Third, the filtered LRIs are applied to CCC elucidation by combining CCC strength measurement and single-cell RNA sequencing data. Finally, CCC inference results are visualized using heatmap view, Circos plot view, and network view. The experimental results show that CellEnBoost obtained the best AUCs and AUPRs on the collected four LRI datasets. Case study in human head and neck squamous cell carcinoma (HNSCC) tissues demonstrates that fibroblasts were more likely to communicate with HNSCC cells, which is in accord with the results from iTALK. We anticipate that this work can contribute to the diagnosis and treatment of cancers.

Epithelial Yap/Taz are required for functional alveolar regeneration following acute lung injury

A hallmark of idiopathic pulmonary fibrosis (IPF) and other interstitial lung diseases is dysregulated repair of the alveolar epithelium. The Hippo pathway effector transcription factors YAP and TAZ are implicated as essential for type 1 and type 2 alveolar epithelial cell (AT1 and AT2) differentiation in the developing lung, yet aberrant activation of YAP/TAZ is a prominent feature of the dysregulated alveolar epithelium in IPF. In these studies, we sought to define the functional role of YAP/TAZ activity during alveolar regeneration. We demonstrated that Yap and Taz were normally activated in AT2 cells shortly after injury, and deletion of Yap/Taz in AT2 cells led to pathologic alveolar remodeling, failure of AT2-to-AT1 cell differentiation, increased collagen deposition, exaggerated neutrophilic inflammation, and increased mortality following injury induced by a single dose of bleomycin. Loss of Yap/Taz activity prior to an LPS injury prevented AT1 cell regeneration, led to intraalveolar collagen deposition, and resulted in persistent innate inflammation. These findings establish that AT2 cell Yap/Taz activity is essential for functional alveolar epithelial repair and prevention of fibrotic remodeling.

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

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

Whole-body gene expression atlas of an adult metazoan

Gene activity defines cell identity, drives intercellular communication, and underlies the functioning of multicellular organisms. We present the single-cell resolution atlas of gene activity of a fertile adult metazoan: Caenorhabditis elegans. This compendium comprises 180 distinct cell types and 19,657 expressed genes. We predict 7541 transcription factor expression profile associations likely responsible for defining cellular identity. We predict thousands of intercellular interactions across the C. elegans body and the ligand-receptor pairs that mediate them, some of which we experimentally validate. We identify 172 genes that show consistent expression across cell types, are involved in basic and essential functions, and are conserved across phyla; therefore, we present them as experimentally validated housekeeping genes. We developed the WormSeq application to explore these data. In addition to the integrated gene-to-systems biology, we present genome-scale single-cell resolution testable hypotheses that we anticipate will advance our understanding of the molecular mechanisms, underlying the functioning of a multicellular organism and the perturbations that lead to its malfunction.

Identification of CD8 + T-Cell-Immune Cell Communications in Ileal Crohn's Disease

INTRODUCTION

Crohn's disease (CD) is a major subtype of inflammatory bowel disease (IBD), a spectrum of chronic intestinal disorders caused by dysregulated immune responses to gut microbiota. Although transcriptional and functional changes in a number of immune cell types have been implicated in the pathogenesis of IBD, the cellular interactions and signals that drive these changes have been less well-studied.

METHODS

We performed Cellular Indexing of Transcriptomes and Epitopes by sequencing on peripheral blood, colon, and ileal immune cells derived from healthy subjects and patients with CD. We applied a previously published computational approach, NicheNet, to predict immune cell types interacting with CD8 + T-cell subsets, revealing putative ligand-receptor pairs and key transcriptional changes downstream of these cell-cell communications.

RESULTS

As a number of recent studies have revealed a potential role for CD8 + T-cell subsets in the pathogenesis of IBD, we focused our analyses on identifying the interactions of CD8 + T-cell subsets with other immune cells in the intestinal tissue microenvironment. We identified ligands and signaling pathways that have implicated in IBD, such as interleukin-1β, supporting the validity of the approach, along with unexpected ligands, such as granzyme B, which may play previously unappreciated roles in IBD.

DISCUSSION

Overall, these findings suggest that future efforts focused on elucidating cell-cell communications among immune and nonimmune cell types may further our understanding of IBD pathogenesis.

Tensor-based insights into systems immunity and infectious disease

Profiling immune responses across several dimensions, including time, patients, molecular features, and tissue sites, can deepen our understanding of immunity as an integrated system. These studies require new analytical approaches to realize their full potential. We highlight recent applications of tensor methods and discuss several future opportunities.

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. While multiple cell-cell communication tools exist, results are specific to the tool of choice, due to the diverse assumptions made across computational frameworks. Moreover, tools are often limited to analyzing single samples or to performing pairwise comparisons. As experimental design complexity and sample numbers continue to increase in single-cell datasets, so does the need for generalizable methods to decipher cell-cell communication in such scenarios. Here, we integrate two tools, LIANA and Tensor-cell2cell, which 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 protocol, 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 we provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/. This protocol typically takes ~1.5h to complete from installation to downstream visualizations on a GPU-enabled computer, for a dataset of ~63k cells, 10 cell types, and 12 samples.

Single-cell pair-wise relationships untangled by composite embedding model

In multicellular organisms, cell identity and functions are primed and refined through interactions with other surrounding cells. Here, we propose a scalable machine learning method, termed SPRUCE, which is designed to systematically ascertain common cell-cell communication patterns embedded in single-cell RNA-seq data. We applied our approach to investigate tumor microenvironments consolidating multiple breast cancer datasets and found seven frequently observed interaction signatures and underlying gene-gene interaction networks. Our results implicate that a part of tumor heterogeneity, especially within the same subtype, is better understood by differential interaction patterns rather than the static expression of known marker genes.

Integrated analysis of cell-specific gene expression in peripheral blood using ISG15 as a marker of rejection in kidney transplantation

Background

Allograft kidney rejection can lead to graft dysfunction and graft loss. Protocol biopsy poses additional risk for recipients with normal renal function. The transcriptome of peripheral blood mononuclear cells (PBMCs) contains tremendous information and has potential application value for non-invasive diagnosis.

Methods

From the Gene Expression Omnibus database, we collected three datasets containing 109 rejected samples and 215 normal controls. After data filter and normalization, we performed deconvolution of bulk RNA sequencing data to predict cell type and cell-type specific gene expression. Subsequently, we calculated cell communication analysis by Tensor-cell2cell and conducted the least absolute shrinkage and selection operator (LASSO) logistic regression to screen the robust differentially expressed genes (DEGs). These gene expression levels were validated in mice kidney transplantation acute rejection model. The function of the novel gene ISG15 in monocytes was further confirmed by gene knockdown and lymphocyte-stimulated assay.

Results

The bulk RNA-seq hardly predicted kidney transplant rejection accurately. Seven types of immune cells and transcriptomic characteristics were predicted from the gene expression data. The monocytes showed significant differences in amount and gene expression of rejection. The cell-to-cell communication indicated the enrichment of antigen presentation and T cell activation ligand-receptor pairs. Then 10 robust genes were found by Lasso regression and a novel gene ISG15 remained differential expression in monocytes between rejection samples and normal control both in public data and animal model. Furthermore, ISG15 also showed a critical role in promoting the proliferation of T cells.

Conclusion

This study identified and validated a novel gene ISG15 associated with rejection in peripheral blood after kidney transplantation, which is a significant non-invasive diagnosis and a potential therapeutic target.