Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data

A systematic review of single-cell RNA sequencing applications and innovations

Bulk RNA sequencing is one type of RNA sequencing technique, as well as targeted RNA sequencing and whole transcriptome sequencing. It provides valuable insights into gene expression in specific cell populations or regions. However, these methods often miss the diversity of cells within complex tissues. This restriction is overcome by single-cell RNA sequencing, which records gene expression at the single-cell level. It offers a detailed picture of the diversity of cells. It is essential to study glucose homeostasis. It offers thorough explanations of cellular variation. Networks and Governance Dynamics The use of scRNA-seq in islet cells is reviewed in this study, along with sample preparation, sequencing, and computational analysis. It highlights advances in understanding cell types. Gene activity and cell interactions. Along with the challenges and limitations of scRNA-seq, this review highlights the importance of scRNA-seq in understanding complex biological processes and diseases. It is an essential resource for future research and method development in this field, which will help to build personalized treatment.

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.

Profiling immune cell tissue niches in the spatial -omics era

Immune responses require complex, spatially coordinated interactions between immune cells and their tissue environment. For decades, we have imaged tissue sections to visualize a limited number of immune-related macromolecules in situ, functioning as surrogates for cell types or processes of interest. However, this inevitably provides a limited snapshot of the tissue's immune landscape. Recent developments in high-throughput spatial -omics technologies, particularly spatial transcriptomics, and its application to human samples has facilitated a more comprehensive understanding of tissue immunity by mapping fine-grained immune cell states to their precise tissue location while providing contextual information about their immediate cellular and tissue environment. These data provide opportunities to investigate mechanisms underlying the spatial distribution of immune cells and its functional implications, including the identification of immune niches, although the criteria used to define this term have been inconsistent. Here, we review recent technological and analytic advances in multiparameter spatial profiling, focusing on how these methods have generated new insights in translational immunology. We propose a 3-step framework for the definition and characterization of immune niches, which is powerfully facilitated by new spatial profiling methodologies. Finally, we summarize current approaches to analyze adaptive immune repertoires and lymphocyte clonal expansion in a spatially resolved manner.

Multiple Sclerosis: Glial Cell Diversity in Time and Space

Multiple sclerosis (MS) is the most prevalent human inflammatory disease of the central nervous system with demyelination and glial scar formation as pathological hallmarks. Glial cells are key drivers of lesion progression in MS with roles in both tissue damage and repair depending on the surrounding microenvironment and the functional state of the individual glial subtype. In this review, we describe recent developments in the context of glial cell diversity in MS summarizing key findings with respect to pathological and maladaptive functions related to disease-associated glial subtypes. A particular focus is on the spatial and temporal dynamics of glial cells including subtypes of microglia, oligodendrocytes, and astrocytes. We contextualize recent high-dimensional findings suggesting that glial cells dynamically change with respect to epigenomic, transcriptomic, and metabolic features across the inflamed rim and during the progression of MS lesions. In summary, detailed knowledge of spatially restricted glial subtype functions is critical for a better understanding of MS pathology and its pathogenesis as well as the development of novel MS therapies targeting specific glial cell types.

4D marmoset brain map reveals MRI and molecular signatures for onset of multiple sclerosis-like lesions

Inferring cellular and molecular dynamics of multiple sclerosis (MS) lesions from postmortem tissue collected decades after onset is challenging. Using magnetic resonance image (MRI)-guided spatiotemporal RNA profiling in marmoset experimental autoimmune encephalitis (EAE), we mapped lesion dynamics and modeled molecular perturbations relevant to MS. Five distinct lesion microenvironments emerged, involving neuroglial responses, tissue destruction and repair, and brain border regulation. Before demyelination, MRI identified a high ratio of proton density-weighted signal to T1 relaxation time, capturing early hypercellularity, and elevated astrocytic and ependymal senescence signals marked perivascular and periventricular areas that later became demyelination hotspots. As lesions expanded, concentric glial barriers formed, initially dominated by proliferating and diversifying microglia and oligodendrocyte precursors, later replaced by monocytes and lymphocytes. We highlight SERPINE1+ astrocytes as a signaling hub underlying lesion onset in both marmoset EAE and MS.

Ligand-receptor interactions combined with histopathology for improved prognostic modeling in HPV-negative head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSC) is a prevalent malignancy, with HPV-negative tumors exhibiting aggressive behavior and poor prognosis. Understanding the intricate interactions within the tumor microenvironment (TME) is crucial for improving prognostic models and identifying therapeutic targets. Using BulkSignalR, we identified ligand-receptor interactions in HPV-negative TCGA-HNSC cohort (n = 395). A prognostic model incorporating 14 ligand-receptor pairs was developed using random forest survival analysis and LASSO-penalized Cox regression based on overall survival and progression-free interval of HPV-negative tumors from TCGA-HNSC. Multi-omics analysis revealed distinct molecular features between risk groups, including differences in extracellular matrix remodeling, angiogenesis, immune infiltration, and APOBEC enzyme activity. Deep learning-based tissue morphology analysis on HE-stained whole slide images further improved risk stratification, with region selection via Silicon enhancing accuracy. The integration of routine histopathology with deep learning and multi-omics data offers a clinically accessible tool for precise risk stratification, facilitating personalized treatment strategies in HPV-negative HNSC.

Mathematically mapping the network of cells in the tumor microenvironment

Cell-cell interaction (CCI) networks are key to understanding disease progression and treatment response. However, existing methods for inferring these networks often aggregate data across patients or focus on cell-type level interactions, providing a generalized overview but overlooking patient heterogeneity and local network structures. To address this, we introduce "random cell-cell interaction generator" (RaCInG), a model based on random graphs to derive personalized networks leveraging prior knowledge on ligand-receptor interactions and bulk RNA sequencing data. We applied RaCInG to 8,683 cancer patients to extract 643 network features related to the tumor microenvironment and unveiled associations with immune response and subtypes, enabling prediction and explanation of immunotherapy responses. RaCInG demonstrated robustness and showed consistencies with state-of-the-art methods. Our findings highlight RaCInG's potential to elucidate patient-specific network dynamics, offering insights into cancer biology and treatment responses. RaCInG is poised to advance our understanding of complex CCI s in cancer and other biomedical domains.

Single-nucleus analysis of thoracic perivascular adipose tissue reveals critical changes in cell composition, communication, and gene regulatory networks induced by a high fat hypertensive diet

Cardiovascular disease (CVD) is the leading cause of death worldwide, with hypertension being its primary causal factor. Most blood vessels are surrounded by perivascular adipose tissue (PVAT), which regulates blood vessel tone through the secretion of vasoactive factors. PVAT is recognized as a key mediator of vascular function and dysfunction in CVD, although the underlying mechanisms remain poorly understood. To investigate PVAT's mechanistic role in hypertension, we performed single nucleus RNA-Sequencing analysis of thoracic aortic PVAT from Dahl SS rats fed a high-fat, hypertensive diet. Computational analysis revealed extensive diet-induced changes in cell-type composition, cell-type specific gene expression, cell-cell communication pathways, and intracellular gene regulatory networks within PVAT. Furthermore, we identified key transcription factors mediating these networks and demonstrated through virtual knock-out experiments that these factors could serve as potential therapeutic targets for preventing or reversing PVAT's hypertensive state.

Hallmarks of aging: A user's guide for comparative biologists

Since the first description of a set of characteristics of aging as so-called hallmarks or pillars in 2013/2014, these characteristics have served as guideposts for the research in aging biology. They have been examined in a range of contexts, across tissues, in response to disease conditions or environmental factors, and served as a benchmark for various anti-aging interventions. While the hallmarks of aging were intended to capture generalizable characteristics of aging, they are derived mostly from studies of rodents and humans. Comparative studies of aging including species from across the animal tree of life have great promise to reveal new insights into the mechanistic foundations of aging, as there is a great diversity in lifespan and age-associated physiological changes. However, it is unclear how well the defined hallmarks of aging apply across diverse species. Here, we review each of the twelve hallmarks of aging defined by Lopez-Otin in 2023 with respect to the availability of data from diverse species. We evaluate the current methods used to assess these hallmarks for their potential to be adapted for comparative studies. Not unexpectedly, we find that the data supporting the described hallmarks of aging are restricted mostly to humans and a few model systems and that no data are available for many animal clades. Similarly, not all hallmarks can be easily assessed in diverse species. However, for at least half of the hallmarks, there are methods available today that can be employed to fill this gap in knowledge, suggesting that these studies can be prioritized while methods are developed for comparative study of the remaining hallmarks.

Towards the Next Generation of Data-Driven Therapeutics Using Spatially Resolved Single-Cell Technologies and Generative AI

Recent advances in multi-omics and spatially resolved single-cell technologies have revolutionised our ability to profile millions of cellular states, offering unprecedented opportunities to understand the complex molecular landscapes of human tissues in both health and disease. These developments hold immense potential for precision medicine, particularly in the rational design of novel therapeutics for treating inflammatory and autoimmune diseases. However, the vast, high-dimensional data generated by these technologies present significant analytical challenges, such as distinguishing technical variation from biological variation or defining relevant questions that leverage the added spatial dimension to improve our understanding of tissue organisation. Generative artificial intelligence (AI), specifically variational autoencoder- or transformer-based latent variable models, provides a powerful and flexible approach to addressing these challenges. These models make inferences about a cell's intrinsic state by effectively identifying complex patterns, reducing data dimensionality and modelling the biological variability in single-cell datasets. This review explores the current landscape of single-cell and spatial multi-omics technologies, the application of generative AI in data analysis and modelling and their transformative impact on our understanding of autoimmune diseases. By combining spatial and single-cell data with advanced AI methodologies, we highlight novel insights into the pathogenesis of autoimmune disorders and outline future directions for leveraging these technologies to achieve the goal of AI-powered personalised medicine.

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.

Single-nucleus and spatial landscape of the sub-ventricular zone in human glioblastoma

The sub-ventricular zone (SVZ) is the most well-characterized neurogenic area in the mammalian brain. We previously showed that in 65% of patients with glioblastoma (GBM), the SVZ is a reservoir of cancer stem-like cells that contribute to treatment resistance and the emergence of recurrence. Here, we build a single-nucleus RNA-sequencing-based microenvironment landscape of the tumor mass and the SVZ of 15 patients and two histologically normal SVZ samples as controls. We identify a ZEB1-centered mesenchymal signature in the tumor cells of the SVZ. Moreover, the SVZ microenvironment is characterized by tumor-supportive microglia, which spatially coexist and establish crosstalks with tumor cells. Last, differential gene expression analyses, predictions of ligand-receptor and incoming/outgoing interactions, and functional assays reveal that the interleukin (IL)-1β/IL-1RAcP and Wnt-5a/Frizzled-3 pathways represent potential therapeutic targets in the SVZ. Our data provide insights into the biology of the SVZ in patients with GBM and identify potential targets of this microenvironment.

Age-dependent differences in breast tumor microenvironment: challenges and opportunities for efficacy studies in preclinical models

Immunity suffers a function deficit during aging, and the incidence of cancer is increased in the elderly. However, most cancer models employ young mice, which are poorly representative of adult cancer patients. We have previously reported that Triple-Therapy (TT), involving antigen-presenting-cell activation by vinorelbine and generation of TCF1+-stem-cell-like T cells (scTs) by cyclophosphamide significantly improved anti-PD-1 efficacy in anti-PD1-resistant models like Triple-Negative Breast Cancer (TNBC) and Non-Hodgkin's Lymphoma (NHL), due to T-cell-mediated tumor killing. Here, we describe the effect of TT on TNBC growth and on tumor-microenvironment (TME) of young (6-8w, representative of human puberty) versus adult (12 m, representative of 40y-humans) mice. TT-efficacy was similar in young and adults, as CD8+ scTs were only marginally reduced in adults. However, single-cell analyses revealed major differences in the TME: adults had fewer CD4+ scTs, B-naïve and NK-cells, and more memory-B-cells. Cancer-associated-fibroblasts (CAF) with an Extracellular Matrix (ECM) deposition-signature (Matrix-CAFs) were more common in young mice, while pro-inflammatory stromal populations and myofibroblasts were more represented in adults. Matrix-CAFs in adult mice displayed decreased ECM-remodeling abilities, reduced collagen deposition, and a different pattern of interactions with the other cells of the TME. Taken together, our results suggest that age-dependent differences in the TME should be considered when designing preclinical studies.

Genome-scale spatial mapping of the Hodgkin lymphoma microenvironment identifies tumor cell survival factors

A key challenge in cancer research is to identify the secreted factors that contribute to tumor cell survival. Nowhere is this more evident than in Hodgkin lymphoma, where malignant Hodgkin Reed Sternberg (HRS) cells comprise only 1-5% of the tumor mass, the remainder being infiltrating immune cells that presumably are required for the survival of the HRS cells. Until now, there has been no way to characterize the complex Hodgkin lymphoma tumor microenvironment at genome scale. Here, we performed genome-wide transcriptional profiling with spatial and single-cell resolution. We show that the neighborhood surrounding HRS cells forms a distinct niche involving 31 immune and stromal cell types and is enriched in CD4+ T cells, myeloid and follicular dendritic cells, while being depleted of plasma cells. Moreover, we used machine learning to nominate ligand-receptor pairs enriched in the HRS cell niche. Specifically, we identified IL13 as a candidate survival factor. In support of this hypothesis, recombinant IL13 augmented the proliferation of HRS cells in vitro. In addition, genome-wide CRISPR/Cas9 loss-of-function studies across more than 1,000 human cancer cell lines showed that IL4R and IL13RA1, the heterodimeric partners that constitute the IL13 receptor, were uniquely required for the survival of HRS cells. Moreover, monoclonal antibodies targeting either IL4R or IL13R phenocopied the genetic loss of function studies. IL13-targeting antibodies are already FDA-approved for atopic dermatitis, suggesting that clinical trials testing such agents should be explored in patients with Hodgkin lymphoma.

Convergent flow-mediated mesenchymal force drives embryonic foregut constriction and splitting

The transformation of a two-dimensional epithelial sheet into various three-dimensional structures is a critical process in generating the diversity of animal forms. Previous studies of epithelial folding have revealed diverse mechanisms driven by epithelium-intrinsic or -extrinsic forces. Yet little is known about the biomechanical basis of epithelial splitting, which involves extreme folding and eventually a topological transition breaking the epithelial tube. Here, we leverage tracheal-esophageal separation (TES), a critical and highly conserved morphogenetic event during tetrapod embryogenesis, as a model system for interrogating epithelial tube splitting both in vivo and ex vivo. Comparing TES in chick and mouse embryos, we identified an evolutionarily conserved, compressive force exerted by the mesenchyme surrounding the epithelium, as being necessary to drive epithelial constriction and splitting. The compressive force is mediated by localized convergent flow of mesenchymal cells towards the epithelium. We further found that Sonic Hedgehog (SHH) secreted by the epithelium functions as an attractive cue for mesenchymal cells. Removal of the mesenchyme, inhibition of cell migration, or loss of SHH signaling all abrogate TES, which can be rescued by externally applied pressure. These results unveil the biomechanical basis of epithelial splitting and suggest a mesenchymal origin of tracheal-esophageal birth defects.

Monocytes generated by interleukin-6-treated human hematopoietic stem and progenitor cells secrete calprotectin that inhibits erythropoiesis

Elevated circulating levels of calprotectin (CAL), the S100A8/A9 heterodimer, are biomarkers of severe systemic inflammation. Here, we investigate the effects of CAL on early human hematopoiesis. CAL demonstrates limited impact on gene expression in stem and progenitor cells, in contrast with interleukin-6 (IL6), which promotes the expression of the S100A8 and S100A9 genes in hematopoietic progenitors and the generation of monocytes that release CAL. The main target of CAL is an erythroid-megakaryocyte progenitor (EMP) subset. CAL prevents both erythropoietin-driven differentiation of healthy progenitors and JAK2-V617F-driven erythropoiesis. In the context of JAK2-V617F, CAL also promotes the expression of S100A8 and S100A9 genes in monocytes. The signature of CAL effects is detected in the bone marrow progenitors of patients with myeloid malignancy or severe infection. These results position CAL as a mediator of IL6 effects on triggering anemia during inflammation, an effect that is amplified in the context of JAK2-V617F-driven hematopoiesis.

InTiCAR: Network-based identification of significant inter-tissue communicators for autoimmune diseases

Inter-tissue communicators (ITCs) are intricate and essential aspects of our body, as they are the keepers of homeostatic equilibrium. It is no surprise that the dysregulation of the exchange between tissues are at the core of various disorders. Among such conditions, autoimmune diseases (AIDs) refer to a collection of pathological conditions where the miscommunication drives the immune system to mistakenly attack one's own body. Due to their myriad and diverse pathophysiologies, AIDs cannot be easily diagnosed or treated, and continuous efforts are required to seek for potential diagnostic markers or therapeutic targets. The identification of ITCs with significant involvement in the disease states is therefore crucial. Here, we present InTiCAR, Inter-Tissue Communicators for Autoimmune diseases by Random walk with restart, which is a network exploration-based analysis method that suggests disease-specific ITCs based on prior knowledge of disease genes, without the need for the external expression data. We first show that distinct ITC profile s can be acquired for various diseases by InTiCAR. We further illustrate that, for autoimmune diseases (AIDs) specifically, the disease-specific ITCs outperform disease genes in diagnosing patients using the UK Biobank plasma proteome dataset. Also, through CMap LINCS dataset, we find that high perturbation on the AIDs genes can be observed by the disease-specific ITCs. Our results provide and highlight unique perspectives on biological network analysis by focusing on the entities of extracellular communications.

Single-cell analysis of the multiple myeloma microenvironment after γ-secretase inhibition and CAR T-cell therapy

ABSTRACT

Chimeric antigen receptor (CAR) T cells and bispecific antibodies targeting B-cell maturation antigen (BCMA) have significantly advanced the treatment of relapsed and refractory multiple myeloma. Resistance to BCMA-targeting therapies, nonetheless, remains a significant challenge. BCMA shedding by γ-secretase is a known resistance mechanism, and preclinical studies suggest that inhibition may improve anti-BCMA therapy. Leveraging a phase 1 clinical trial of the γ-secretase inhibitor (GSI), crenigacestat, with anti-BCMA CAR T cells (FCARH143), we used single-nuclei RNA sequencing and assay for transposase-accessible chromatin sequencing to characterize the effects of GSI on the tumor microenvironment. The most significant impacts of GSI involved effects on monocytes, which are known to promote tumor growth. In addition to observing a reduction in the frequency of nonclassical monocytes, we also detected significant changes in gene expression, chromatin accessibility, and inferred cell-cell interactions after exposure to GSI. Although many genes with altered expression are associated with γ-secretase-dependent signaling, such as Notch, other pathways were affected, indicating GSI has far-reaching effects. Finally, we detected monoallelic deletion of the BCMA locus in some patients with prior exposure to anti-BCMA therapy, which significantly correlated with reduced progression-free survival (PFS; median PFS, 57 vs 861 days). GSIs are being explored in combination with the full spectrum of BCMA-targeting agents, and our results reveal widespread effects of GSI on both tumor and immune cell populations, providing insight into mechanisms for enhancing BCMA-directed therapies.

Detection of reproducible liver cancer specific ligand-receptor signaling in blood

Cell-cell communication mediated by ligand-receptor interactions (LRI) is critical to coordinating diverse biological processes in homeostasis and disease. Lately, our understanding of these processes has greatly expanded through the inference of cellular communication, utilizing RNA extracted from bulk tissue or individual cells. Considering the challenge of obtaining tissue biopsies for these approaches, we considered the potential of studying cell-free RNA obtained from blood. To test the feasibility of this approach, we used the BulkSignalR algorithm across 295 cell-free RNA samples and compared the LRI profiles across multiple cancer types and healthy donors. Interestingly, we detected specific and reproducible LRIs particularly in the blood of liver cancer patients compared to healthy donors. We found an increase in the magnitude of hepatocyte interactions, notably hepatocyte autocrine interactions in liver cancer patients. Additionally, a robust panel of 30 liver cancer-specific LRIs presents a bridge linking liver cancer pathogenesis to discernible blood markers. In summary, our approach shows the plausibility of detecting liver LRIs in blood and builds upon the biological understanding of cell-free transcriptomes.

Deciphering normal and cancer stem cell niches by spatial transcriptomics: opportunities and challenges

Cancer stem cells (CSCs) often exhibit stem-like attributes that depend on an intricate stemness-promoting cellular ecosystem within their niche. The interplay between CSCs and their niche has been implicated in tumor heterogeneity and therapeutic resistance. Normal stem cells (NSCs) and CSCs share stemness features and common microenvironmental components, displaying significant phenotypic and functional plasticity. Investigating these properties across diverse organs during normal development and tumorigenesis is of paramount research interest and translational potential. Advancements in next-generation sequencing (NGS), single-cell transcriptomics, and spatial transcriptomics have ushered in a new era in cancer research, providing high-resolution and comprehensive molecular maps of diseased tissues. Various spatial technologies, with their unique ability to measure the location and molecular profile of a cell within tissue, have enabled studies on intratumoral architecture and cellular cross-talk within the specific niches. Moreover, delineation of spatial patterns for niche-specific properties such as hypoxia, glucose deprivation, and other microenvironmental remodeling are revealed through multilevel spatial sequencing. This tremendous progress in technology has also been paired with the advent of computational tools to mitigate technology-specific bottlenecks. Here we discuss how different spatial technologies are used to identify NSCs and CSCs, as well as their associated niches. Additionally, by exploring related public data sets, we review the current challenges in characterizing such niches, which are often hindered by technological limitations, and the computational solutions used to address them.

Intercellular signaling stabilizes single-cell level phenotypic transitions and accelerates the reestablishment of equilibrium of heterogeneous cancer cell populations

Cancer cells within tumors exhibit a wide range of phenotypic states driven by non-genetic mechanisms in addition to extensively studied genetic alterations. Conversions among cancer cell states can result in intratumoral heterogeneity which contributes to metastasis and development of drug resistance. However, mechanisms underlying the initiation and/or maintenance of such phenotypic plasticity are poorly understood. In particular, the role of intercellular communications in phenotypic plasticity remains elusive. In this study, we employ a multiscale inference-based approach using single-cell RNA sequencing (scRNA-seq) data to explore how intercellular interactions influence phenotypic dynamics of cancer cells, particularly cancers undergoing epithelial-mesenchymal transition. Our inference approach reveals that signaling interactions between cancerous cells in small cell lung cancer (SCLC) result in seemingly contradictory behaviors-reinforcing the cellular phenotypes and maintaining population-level intratumoral heterogeneity. Additionally, we find a recurring signaling pattern across multiple types of cancer in which the mesenchymal-like subtypes utilize signals from other subtypes to reinforce its phenotype, further promoting the intratumoral heterogeneity. We use a mathematical model based on ordinary differential equations to show that inter-subtype communication accelerates the development of heterogeneous tumor populations. Our work highlights the critical role of intercellular signaling in sustaining intratumoral heterogeneity, and our approach of computational analysis of scRNA-seq data can infer inter- and intra-cellular signaling networks in a holistic manner.

Pan-cancer drivers of metastasis

Metastasis remains a leading cause of cancer-related mortality, irrespective of the primary tumour origin. However, the core gene regulatory program governing distinct stages of metastasis across cancers remains poorly understood. We investigate this through single-cell transcriptome analysis encompassing over two hundred patients with metastatic and non-metastatic tumours across six cancer types. Our analysis revealed a prognostic core gene signature that provides insights into the intricate cellular dynamics and gene regulatory networks driving metastasis progression at the pan-cancer and single-cell level. Notably, the dissection of transcription factor networks active across different stages of metastasis, combined with functional perturbation, identified SP1 and KLF5 as key regulators, acting as drivers and suppressors of metastasis, respectively, at critical steps of this transition across multiple cancer types. Through in vivo and in vitro loss of function of SP1 in cancer cells, we revealed its role in driving cancer cell survival, invasive growth, and metastatic colonisation. Furthermore, tumour cells and the microenvironment increasingly engage in communication through WNT signalling as metastasis progresses, driven by SP1. Further validating these observations, a drug repurposing analysis identified distinct FDA-approved drugs with anti-metastasis properties, including inhibitors of WNT signalling across various cancers.

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.

Spatial-Omics Methods and Applications

Traditional tissue profiling approaches have evolved from bulk studies to single-cell analysis over the last decade; however, the spatial context in tissues and microenvironments has always been lost. Over the last 5 years, spatial technologies have emerged that enabled researchers to investigate tissues in situ for proteins and transcripts without losing anatomy and histology. The field of spatial-omics enables highly multiplexed analysis of biomolecules like RNAs and proteins in their native spatial context-and has matured from initial proof-of-concept studies to a thriving field with widespread applications from basic research to translational and clinical studies. While there has been wide adoption of spatial technologies, there remain challenges with the standardization of methodologies, sample compatibility, throughput, resolution, and ease of use. In this chapter, we discuss the current state of the field and highlight technological advances and limitations.

Encompassing view of spatial and single-cell RNA sequencing renews the role of the microvasculature in human atherosclerosis

Atherosclerosis is a pervasive contributor to ischemic heart disease and stroke. Despite the advance of lipid-lowering therapies and anti-hypertensive agents, the residual risk of an atherosclerotic event remains high, and developing therapeutic strategies has proven challenging. This is due to the complexity of atherosclerosis with a spatial interplay of multiple cell types within the vascular wall. In this study, we generated an integrative high-resolution map of human atherosclerotic plaques combining single-cell RNA sequencing from multiple studies and spatial transcriptomics data from 12 human specimens with different stages of atherosclerosis. Here we show cell-type-specific and atherosclerosis-specific expression changes and spatially constrained alterations in cell-cell communication. We highlight the possible recruitment of lymphocytes via ACKR1 endothelial cells of the vasa vasorum, the migration of vascular smooth muscle cells toward the lumen by transforming into fibromyocytes and cell-cell communication in the plaque region, indicating an intricate cellular interplay within the adventitia and the subendothelial space in human atherosclerosis.

Molecular correlates for HPV-negative head and neck cancer engraftment prognosticate patient outcomes

There is a pressing need to improve risk stratification and treatment selection for HPV-negative head and neck squamous cell carcinoma (HNSCC) due to the adverse side effects of treatment. One of the most important prognostic features is lymph nodes involvement. Previously, we demonstrated that tumor formation in patient-derived xenografts (i.e. engraftment) was associated with poor clinical outcomes in patients with HPV-negative HNSCC. However, assessing engraftment is challenging in clinical settings. Here, we perform transcriptomic and proteomic profiling of 88 HNSCC patients and find the relationship between engraftment and clinical outcomes is recapitulated by molecular phenotype. We identify LAMC2 and TGM3 as candidate prognostic biomarkers and validated their utility in an independent cohort containing 404 HPV-negative HNSCC patients. Strikingly, these markers significantly improve prediction of outcomes beyond nodal status alone and can significantly stratify patients without any nodal involvement. Overall, our study demonstrates how the molecular characteristics of engraftment can inform patient prognostication.

Spns1-dependent endocardial lysosomal function drives valve morphogenesis through Notch1-signaling

Autophagy-lysosomal degradation is a conserved homeostatic process considered to be crucial for cardiac morphogenesis. However, both its cell specificity and functional role during heart development remain unclear. Here, we introduced zebrafish models to visualize autophagic vesicles in vivo and track their temporal and cellular localization in the larval heart. We observed a significant accumulation of autolysosomal and lysosomal vesicles in the atrioventricular and bulboventricular regions and their respective valves. We addressed the role of lysosomal degradation based on the Spinster homolog 1 (spns1) mutant (not really started, nrs). n rs larvae displayed morphological and functional cardiac defects, including abnormal endocardial organization, impaired valve formation and retrograde blood flow. Single-nuclear transcriptome analyses revealed endocardial-specific differences in lysosome-related genes and alterations of notch1-signalling. Endocardial-specific overexpression of spns1 and notch1 rescued features of valve formation and function. Altogether, our results reveal a cell-autonomous role of lysosomal processing during cardiac valve formation affecting notch1-signalling.

Protocol for high-resolution 3D spatial transcriptomics using Open-ST

Spatial transcriptomics (ST) is fundamental for understanding molecular mechanisms in health and disease. Here, we present a protocol for efficient and high-resolution ST in 2D/3D with Open-ST. We describe all steps for repurposing Illumina flow cells into spatially barcoded capture areas and preparing ST libraries from stained cryosections. We detail the computational workflow for generating 2D/3D molecular maps ("virtual tissue blocks"), aligned with histological data, unlocking molecular pathways in space. Open-ST is applicable to any tissue, including clinical samples. For complete details on the use and execution of this protocol, please refer to Schott et al.1.

Bone morphogenetic protein 4 induces hematopoietic stem cell development from murine hemogenic endothelial cells in culture

Hematopoietic stem cells (HSCs) develop from hemogenic endothelial cells (HECs) during mouse embryogenesis. Understanding the signaling molecules required for HSC development is crucial for the in vitro derivation of HSCs. We previously induced HSCs from embryonic HECs, isolated at embryonic day 10.5 (E10.5), in serum-free culture conditions with stem cell factor, thrombopoietin, and an endothelial feeder layer. Here, we aimed to elucidate signal requirements for inducing HSCs from earlier-stage HECs. Single-cell RNA sequencing (RNA-seq) analysis detected bone morphogenetic protein (BMP) signaling activation in E9.5 HECs. Adding BMP4 to the culture conditions led to the induction of HSCs from E9.5 HECs. Furthermore, isolating BMP4 receptor-expressing HECs from E9.5 embryos enriched progenitors with HSC-forming ability. This study identified BMP4 as an essential factor promoting the differentiation of early HECs into HSCs, opening up new possibilities for the in vitro derivation of HSCs.

Plasticity of Human Microglia and Brain Perivascular Macrophages in Aging and Alzheimer's Disease

The complex roles of myeloid cells, including microglia and perivascular macrophages, are central to the neurobiology of Alzheimer's disease (AD), yet they remain incompletely understood. Here, we profiled 832,505 human myeloid cells from the prefrontal cortex of 1,607 unique donors covering the human lifespan and varying degrees of AD neuropathology. We delineated 13 transcriptionally distinct myeloid subtypes organized into 6 subclasses and identified AD-associated adaptive changes in myeloid cells over aging and disease progression. The GPNMB subtype, linked to phagocytosis, increased significantly with AD burden and correlated with polygenic AD risk scores. By organizing AD-risk genes into a regulatory hierarchy, we identified and validated MITF as an upstream transcriptional activator of GPNMB, critical for maintaining phagocytosis. Through cell-to-cell interaction networks, we prioritized APOE-SORL1 and APOE-TREM2 ligand-receptor pairs, associated with AD progression. In both human and mouse models, TREM2 deficiency disrupted GPNMB expansion and reduced phagocytic function, suggesting that GPNMB's role in neuroprotection was TREM2-dependent. Our findings clarify myeloid subtypes implicated in aging and AD, advancing the mechanistic understanding of their role in AD and aiding therapeutic discovery.

Human heart assembloids with autologous tissue-resident macrophages recreate physiological immuno-cardiac interactions

Interactions between the developing heart and the embryonic immune system are essential for proper cardiac development and maintaining homeostasis, with disruptions linked to various diseases. While human pluripotent stem cell (hPSC)-derived organoids are valuable models for studying human organ function, they often lack critical tissue-resident immune cells. Here, we introduce an advanced human heart assembloid model, termed hHMA (human heart-macrophage assembloid), which fully integrates autologous cardiac tissue- resident macrophages (MPs) with pre-existing human heart organoids (hHOs). Through multi-omic analyses, we confirmed that these MPs are phenotypically similar to embryonic cardiac tissue-resident MPs and remain viable in the assembloids over time. The inclusion of MPs significantly impacts hHMA development, influencing cardiac cellular composition, boosting cellular communication, remodeling the extracellular matrix, promoting ventricular morphogenesis, and enhancing sarcomeric maturation. Our findings indicate that MPs contribute to homeostasis via efferocytosis, integrate into the cardiomyocyte electrical system, and support catabolic metabolism. To demonstrate the versatility of this model, we developed a platform to study cardiac arrhythmias by chronic exposure to pro-inflammatory factors linked to arrhythmogenesis in clinical settings, successfully replicating key features of inflammasome-mediated atrial fibrillation. Overall, this work introduces a robust platform for examining the role of immune cells in cardiac development, disease mechanisms, and drug discovery, bridging the gap between in vitro models and human physiology. These findings offer insights into cardiogenesis and inflammation-driven heart disease, positioning the hHMA system as an invaluable tool for future cardiovascular research and therapeutic development.

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.

Platelet life cycle during aging: function, production and clearance

Platelets are important players in hemostasis. Alterations in platelet number and/or function lead to life-threatening conditions like thrombosis, myocardial infarction and stroke. During aging, changes at the cellular, organ and systemic level occur that affect platelet counts, platelet functionality, the expression of platelet surface receptors, clearance markers as well as their interactions with immune cells. Understanding how these changes influence platelets can help to prevent the alterations of hemostasis and thrombosis we observe in the elderly. In this review, we highlight the respective changes at important sites of the platelet life cycle: bone marrow, liver and spleen, but also show how alterations in immunity contribute. We point out the necessity for further research on age-related systemic alterations in these systems and their interplay with platelets to better understand the complex processes that cause alterations in the platelet life cycle during aging.

Spatial and single-nucleus transcriptomic analysis of genetic and sporadic forms of Alzheimer's disease

The pathogenesis of Alzheimer's disease (AD) depends on environmental and heritable factors, with its molecular etiology still unclear. Here we present a spatial transcriptomic (ST) and single-nucleus transcriptomic survey of late-onset sporadic AD and AD in Down syndrome (DSAD). Studying DSAD provides an opportunity to enhance our understanding of the AD transcriptome, potentially bridging the gap between genetic mouse models and sporadic AD. We identified transcriptomic changes that may underlie cortical layer-preferential pathology accumulation. Spatial co-expression network analyses revealed transient and regionally restricted disease processes, including a glial inflammatory program dysregulated in upper cortical layers and implicated in AD genetic risk and amyloid-associated processes. Cell-cell communication analysis further contextualized this gene program in dysregulated signaling networks. Finally, we generated ST data from an amyloid AD mouse model to identify cross-species amyloid-proximal transcriptomic changes with conformational context.

Cell type mapping reveals tissue niches and interactions in subcortical multiple sclerosis lesions

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system. Inflammation is gradually compartmentalized and restricted to specific tissue niches such as the lesion rim. However, the precise cell type composition of such niches, their interactions and changes between chronic active and inactive stages are incompletely understood. We used single-nucleus and spatial transcriptomics from subcortical MS and corresponding control tissues to map cell types and associated pathways to lesion and nonlesion areas. We identified niches such as perivascular spaces, the inflamed lesion rim or the lesion core that are associated with the glial scar and a cilia-forming astrocyte subtype. Focusing on the inflamed rim of chronic active lesions, we uncovered cell-cell communication events between myeloid, endothelial and glial cell types. Our results provide insight into the cellular composition, multicellular programs and intercellular communication in tissue niches along the conversion from a homeostatic to a dysfunctional state underlying lesion progression in MS.

Single-cell transcriptomics reveal distinctive patterns of fibroblast activation in heart failure with preserved ejection fraction

Inflammation, fibrosis and metabolic stress critically promote heart failure with preserved ejection fraction (HFpEF). Exposure to high-fat diet and nitric oxide synthase inhibitor N[w]-nitro-l-arginine methyl ester (L-NAME) recapitulate features of HFpEF in mice. To identify disease-specific traits during adverse remodeling, we profiled interstitial cells in early murine HFpEF using single-cell RNAseq (scRNAseq). Diastolic dysfunction and perivascular fibrosis were accompanied by an activation of cardiac fibroblast and macrophage subsets. Integration of fibroblasts from HFpEF with two murine models for heart failure with reduced ejection fraction (HFrEF) identified a catalog of conserved fibroblast phenotypes across mouse models. Moreover, HFpEF-specific characteristics included induced metabolic, hypoxic and inflammatory transcription factors and pathways, including enhanced expression of Angiopoietin-like 4 (Angptl4) next to basement membrane compounds, such as collagen IV (Col4a1). Fibroblast activation was further dissected into transcriptional and compositional shifts and thereby highly responsive cell states for each HF model were identified. In contrast to HFrEF, where myofibroblast and matrifibrocyte activation were crucial features, we found that these cell states played a subsidiary role in early HFpEF. These disease-specific fibroblast signatures were corroborated in human myocardial bulk transcriptomes. Furthermore, we identified a potential cross-talk between macrophages and fibroblasts via SPP1 and TNFɑ with estimated fibroblast target genes including Col4a1 and Angptl4. Treatment with recombinant ANGPTL4 ameliorated the murine HFpEF phenotype and diastolic dysfunction by reducing collagen IV deposition from fibroblasts in vivo and in vitro. In line, ANGPTL4, was elevated in plasma samples of HFpEF patients and particularly high levels associated with a preserved global-longitudinal strain. Taken together, our study provides a comprehensive characterization of molecular fibroblast activation patterns in murine HFpEF, as well as the identification of Angiopoietin-like 4 as central mechanistic regulator with protective effects.

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.

Implementation and validation of single-cell genomics experiments in neuroscience

Single-cell or single-nucleus transcriptomics is a powerful tool for identifying cell types and cell states. However, hypotheses derived from these assays, including gene expression information, require validation, and their functional relevance needs to be established. The choice of validation depends on numerous factors. Here, we present types of orthogonal and functional validation experiment to strengthen preliminary findings obtained using single-cell and single-nucleus transcriptomics as well as the challenges and limitations of these approaches.

CellMsg: graph convolutional networks for ligand-receptor-mediated cell-cell communication analysis

The role of cell-cell communications (CCCs) is increasingly recognized as being important to differentiation, invasion, metastasis, and drug resistance in tumoral tissues. Developing CCC inference methods using traditional experimental methods are time-consuming, labor-intensive, cannot handle large amounts of data. To facilitate inference of CCCs, we proposed a computational framework, called CellMsg, which involves two primary steps: identifying ligand-receptor interactions (LRIs) and measuring the strength of LRIs-mediated CCCs. Specifically, CellMsg first identifies high-confident LRIs based on multimodal features of ligands and receptors and graph convolutional networks. Then, CellMsg measures the strength of intercellular communication by combining the identified LRIs and single-cell RNA-seq data using a three-point estimation method. Performance evaluation on four benchmark LRI datasets by five-fold cross validation demonstrated that CellMsg accurately captured the relationships between ligands and receptors, resulting in the identification of high-confident LRIs. Compared with other methods of identifying LRIs, CellMsg has better prediction performance and robustness. Furthermore, the LRIs identified by CellMsg were successfully validated through molecular docking. Finally, we examined the overlap of LRIs between CellMsg and five other classical CCC databases, as well as the intercellular crosstalk among seven cell types within a human melanoma tissue. In summary, CellMsg establishes a complete, reliable, and well-organized LRI database and an effective CCC strength evaluation method for each single-cell RNA-seq data. It provides a computational tool allowing researchers to decipher intercellular communications. CellMsg is freely available at https://github.com/pengsl-lab/CellMsg.

scDCA: deciphering the dominant cell communication assembly of downstream functional events from single-cell RNA-seq data

Cell-cell communications (CCCs) involve signaling from multiple sender cells that collectively impact downstream functional processes in receiver cells. Currently, computational methods are lacking for quantifying the contribution of pairwise combinations of cell types to specific functional processes in receiver cells (e.g. target gene expression or cell states). This limitation has impeded understanding the underlying mechanisms of cancer progression and identifying potential therapeutic targets. Here, we proposed a deep learning-based method, scDCA, to decipher the dominant cell communication assembly (DCA) that have a higher impact on a particular functional event in receiver cells from single-cell RNA-seq data. Specifically, scDCA employed a multi-view graph convolution network to reconstruct the CCCs landscape at single-cell resolution, and then identified DCA by interpreting the model with the attention mechanism. Taking the samples from advanced renal cell carcinoma as a case study, the scDCA was successfully applied and validated in revealing the DCA affecting the crucial gene expression in immune cells. The scDCA was also applied and validated in revealing the DCA responsible for the variation of 14 typical functional states of malignant cells. Furthermore, the scDCA was applied and validated to explore the alteration of CCCs under clinical intervention by comparing the DCA for certain cytotoxic factors between patients with and without immunotherapy. In summary, scDCA provides a valuable and practical tool for deciphering the cell type combinations with the most dominant impact on a specific functional process of receiver cells, which is of great significance for precise cancer treatment. Our data and code are free available at a public GitHub repository: https://github.com/pengsl-lab/scDCA.git.

Deciphering craniopharyngioma subtypes: Single-cell analysis of tumor microenvironment and immune networks

Craniopharyngiomas, including adamantinomatous (ACP) and squamous papillary (PCP) types, are challenging to treat because of their proximity to crucial pituitary structures. This study aimed to characterize the cellular composition, tumor tissue diversity, and cell-cell interactions in ACPs and PCPs using single-cell RNA sequencing. Single-cell clustering revealed diverse cell types, further classified into developing epithelial, calcification, and immune response for ACP and developing epithelial, cell cycle, and immune response for PCP, based on gene expression patterns. Subclustering revealed the enrichment of classical M1 and M2 macrophages in ACP and PCP, respectively, with high expression of pro-inflammatory markers in classical M1 macrophages. The classical M1 and M2 macrophage ratio significantly correlated with the occurrence of diabetes insipidus and panhypopituitarism. Cell-cell interactions, particularly involving CD44-SPP, were identified between tumor cells. Thus, we developed a comprehensive cell atlas that elucidated the molecular characteristics and immune cell inter-networking in ACP and PCP tumor microenvironments.

Application of spatial-omics to the classification of kidney biopsy samples in transplantation

Improvement of long-term outcomes through targeted treatment is a primary concern in kidney transplant medicine. Currently, the validation of a rejection diagnosis and subsequent treatment depends on the histological assessment of allograft biopsy samples, according to the Banff classification system. However, the lack of (early) disease-specific tissue markers hinders accurate diagnosis and thus timely intervention. This challenge mainly results from an incomplete understanding of the pathophysiological processes underlying late allograft failure. Integration of large-scale multimodal approaches for investigating allograft biopsy samples might offer new insights into this pathophysiology, which are necessary for the identification of novel therapeutic targets and the development of tailored immunotherapeutic interventions. Several omics technologies - including transcriptomic, proteomic, lipidomic and metabolomic tools (and multimodal data analysis strategies) - can be applied to allograft biopsy investigation. However, despite their successful application in research settings and their potential clinical value, several barriers limit the broad implementation of many of these tools into clinical practice. Among spatial-omics technologies, mass spectrometry imaging, which is under-represented in the transplant field, has the potential to enable multi-omics investigations that might expand the insights gained with current clinical analysis technologies.

Blood-generating heart-forming organoids recapitulate co-development of the human haematopoietic system and the embryonic heart

Despite the biomedical importance of haematopoietic stem cells and haematopoietic progenitor cells, their in vitro stabilization in a developmental context has not been achieved due to limited knowledge of signals and markers specifying the multiple haematopoietic waves as well as ethically restricted access to the human embryo. Thus, an in vitro approach resembling aspects of haematopoietic development in the context of neighbouring tissues is of interest. Our established human pluripotent stem cell-derived heart-forming organoids (HFOs) recapitulate aspects of heart, vasculature and foregut co-development. Modulating HFO differentiation, we here report the generation of blood-generating HFOs. While maintaining a functional ventricular-like heart anlagen, blood-generating HFOs comprise a mesenchyme-embedded haemogenic endothelial layer encompassing multiple haematopoietic derivatives and haematopoietic progenitor cells with erythro-myeloid and lymphoid potential, reflecting aspects of primitive and definitive haematopoiesis. The model enables the morphologically structured co-development of cardiac, endothelial and multipotent haematopoietic tissues equivalent to the intra-embryonic haematopoietic region in vivo, promoting research on haematopoiesis in vitro.

Spatially resolved analysis of pancreatic cancer identifies therapy-associated remodeling of the tumor microenvironment

In combination with cell-intrinsic properties, interactions in the tumor microenvironment modulate therapeutic response. We leveraged single-cell spatial transcriptomics to dissect the remodeling of multicellular neighborhoods and cell-cell interactions in human pancreatic cancer associated with neoadjuvant chemotherapy and radiotherapy. We developed spatially constrained optimal transport interaction analysis (SCOTIA), an optimal transport model with a cost function that includes both spatial distance and ligand-receptor gene expression. Our results uncovered a marked change in ligand-receptor interactions between cancer-associated fibroblasts and malignant cells in response to treatment, which was supported by orthogonal datasets, including an ex vivo tumoroid coculture system. We identified enrichment in interleukin-6 family signaling that functionally confers resistance to chemotherapy. Overall, this study demonstrates that characterization of the tumor microenvironment using single-cell spatial transcriptomics allows for the identification of molecular interactions that may play a role in the emergence of therapeutic resistance and offers a spatially based analysis framework that can be broadly applied to other contexts.

Generation of Mammalian Astrocyte Functional Heterogeneity

Mammalian astrocytes have regional roles within the brain parenchyma. Indeed, the notion that astrocytes are molecularly heterogeneous could help explain how the central nervous system (CNS) retains embryonic positional information through development into specialized regions into adulthood. A growing body of evidence supports the concept of morphological and molecular differences between astrocytes in different brain regions, which might relate to their derivation from regionally patterned radial glia and/or local neuron inductive cues. Here, we review evidence for regionally encoded functions of astrocytes to provide an integrated concept on lineage origins and heterogeneity to understand regional brain organization, as well as emerging technologies to identify and further investigate novel roles for astrocytes.

A New Graph Autoencoder-Based Multi-Level Kernel Subspace Fusion Framework for Single-Cell Type Identification

The advent of single-cell RNA sequencing (scRNA-seq) technology offers the opportunity to conduct biological research at the cellular level. Single-cell type identification based on unsupervised clustering is one of the fundamental tasks of scRNA-seq data analysis. Although many single-cell clustering methods have been developed recently, few can fully exploit the deep potential relationships between cells, resulting in suboptimal clustering. In this paper, we propose scGAMF, a graph autoencoder-based multi-level kernel subspace fusion framework for scRNA-seq data analysis. Based on multiple top feature sets, scGAMF unifies deep feature embedding and kernel space analysis into a single framework to learn an accurate clustering affinity matrix. First, we construct multiple top feature sets to avoid the high variability caused by single feature set learning. Second, scGAMF uses a graph autoencoder (GAEs) to extract deep information embedded in the data, and learn embeddings including gene expression patterns and cell-cell relationships. Third, to fully explore the deep potential relationships between cells, we design a multi-level kernel space fusion strategy. This strategy uses a kernel expression model with adaptive similarity preservation to learn a self-expression matrix shared by all embedding spaces of a given feature set, and a consensus affinity matrix across multiple top feature sets. Finally, the consensus affinity matrix is used for spectral clustering, visualization, and identification of gene markers. Extensive validation on real datasets shows that scGAMF achieves higher clustering accuracy than many popular single-cell analysis methods.

Mapping intratumoral myeloid-T cell interactomes at single-cell resolution reveals targets for overcoming checkpoint inhibitor resistance

Effective cancer immunotherapies restore anti-tumor immunity by rewiring cell-cell communication. Treatment-induced changes in communication can be inferred from single-cell RNA-sequencing (scRNA-seq) data, but current methods do not effectively manage heterogeneity within cell types. Here we developed a computational approach to efficiently analyze scRNA-seq-derived, single-cell-resolved cell-cell interactomes, which we applied to determine how agonistic CD40 (CD40ag) alters immune cell crosstalk alone, across tumor models, and in combination with immune checkpoint blockade (ICB). Our analyses suggested that CD40ag improves responses to ICB by targeting both immuno-stimulatory and immunosuppressive macrophage subsets communicating with T cells, and we experimentally validated a spatial basis for these subsets with immunofluorescence and spatial transcriptomics. Moreover, treatment with CD40ag and ICB established coordinated myeloid-T cell interaction hubs that are critical for reestablishing antitumor immunity. Our work advances the biological significance of hypotheses generated from scRNA-seq-derived cell-cell interactomes and supports the clinical translation of myeloid-targeted therapies for ICB-resistant tumors.

Astrocyte regional specialization is shaped by postnatal development

Astrocytes are an abundant class of glial cells with critical roles in neural circuit assembly and function. Though many studies have uncovered significant molecular distinctions between astrocytes from different brain regions, how this regionalization unfolds over development is not fully understood. We used single-nucleus RNA sequencing to characterize the molecular diversity of brain cells across six developmental stages and four brain regions in the mouse and marmoset brain. Our analysis of over 170,000 single astrocyte nuclei revealed striking regional heterogeneity among astrocytes, particularly between telencephalic and diencephalic regions, at all developmental time points surveyed in both species. At the stages sampled, most of the region patterning was private to astrocytes and not shared with neurons or other glial types. Though astrocytes were already regionally patterned in late embryonic stages, this region-specific astrocyte gene expression signature changed dramatically over postnatal development, and its composition suggests that regional astrocytes further specialize postnatally to support their local neuronal circuits. Comparing across species, we found divergence in the expression of astrocytic region- and age-differentially expressed genes and the timing of astrocyte maturation relative to birth between mouse and marmoset, as well as hundreds of species differentially expressed genes. Finally, we used expansion microscopy to show that astrocyte morphology is largely conserved across gray matter regions of prefrontal cortex, striatum, and thalamus in the mouse, despite substantial molecular divergence.

Hypertrophic cardiomyopathy-associated mutations drive stromal activation via EGFR-mediated paracrine signaling

Hypertrophic cardiomyopathy (HCM) is characterized by thickening of the left ventricular wall, diastolic dysfunction, and fibrosis, and is associated with mutations in genes encoding sarcomere proteins. While in vitro studies have used human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) to study HCM, these models have not examined the multicellular interactions involved in fibrosis. Using engineered cardiac microtissues (CMTs) composed of HCM-causing MYH7-variant hiPSC-CMs and wild-type fibroblasts, we observed cell-cell cross-talk leading to increased collagen deposition, tissue stiffening, and decreased contractility dependent on fibroblast proliferation. hiPSC-CM conditioned media and single-nucleus RNA sequencing data suggested that fibroblast proliferation is mediated by paracrine signals from MYH7-variant cardiomyocytes. Furthermore, inhibiting epidermal growth factor receptor tyrosine kinase with erlotinib hydrochloride attenuated stromal activation. Last, HCM-causing MYBPC3-variant CMTs also demonstrated increased stromal activation and reduced contractility, but with distinct characteristics. Together, these findings establish a paracrine-mediated cross-talk potentially responsible for fibrotic changes observed in HCM.