Evaluation of cell-cell interaction methods by integrating single-cell RNA sequencing data with spatial information

Understanding psoriatic disease at single-cell resolution: an update

PURPOSE OF REVIEW

This review examines recent advancements in psoriasis research through single-cell technologies, including single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics. These methods have uncovered the cellular diversity underlying psoriasis, identifying immune cell, keratinocyte, and fibroblast subtypes that play pivotal roles in disease progression. Such insights are vital for addressing the complexity and heterogeneity of psoriasis, paving the way for targeted therapies.

RECENT FINDINGS

Recent studies emphasize the roles of IL-17-producing T cells (T17), keratinocytes, and fibroblasts in driving inflammation. T-cell cytokines, including IL-17A and IL-17F, induce keratinocyte hyperproliferation and amplify inflammation through an IL-36 feed-forward loop. Fibroblast subsets, such as SFRP2+ and WNT5A+/IL24+ fibroblasts, contribute to extracellular matrix remodeling and cytokine release, worsening the inflammatory environment. These studies also reveal the intricate fibroblast-keratinocyte crosstalk via the IL-17/IL-36 and PRSS3-F2R pathways. More recently, advancement with spatial transcriptomics has uncovered metabolic dysregulation in psoriatic keratinocytes, highlighting HIF1α-driven glycolysis and lactate production as critical in sustaining chronic inflammation. Furthermore, nonlesional skin from severe psoriasis patients exhibits transcriptomic changes resembling lesional skin, suggesting systemic "prelesional" state with the upregulation of lipid metabolism genes.

SUMMARY

These discoveries have significant clinical implications. Integrating single-cell and spatial technologies into psoriasis research offers promising avenues for developing tailored treatments and improving patient outcomes. Specifically, with spatial transcriptomics revealing immune signatures and cell-cell colocalization that may serve as early indicators of disease severity and systemic involvement. Targeting metabolic pathways in keratinocytes and localized immune microenvironments may enhance precision therapies for psoriasis.

Advancing Tissue Engineering Through a Portable Perfusion and Incubation System

Perfusion offers unique benefits to tissue-engineered systems, enhancing oxygen and nutrient transport, which improves tissue formation and growth. In this study, we present a novel and integrated portable perfusion system. Weighing < 10 lbs, the system can maintain continuous flow in a standard incubation environment (37 °C, 5% CO2), effectively functioning as a portable perfusion and tissue culturing system. To characterize the perfusion system's flow parameters, we measured the volumetric flow rate across a range of pressures and found that the system could achieve flow velocities between 1.69 to 4.6 μm/s, which is similar to in vivo interstitial flow. Computational fluid dynamics revealed fully developed laminar flow within the sample-containing region of the perfusion system, helping ensure even fluid and nutrient distribution. To study the system's compatibility with live tissues, bioengineered tissue patches were created and perfused. After 24 h of perfusion, no significant difference in cell viability was observed between the perfused samples and static controls, indicating no adverse effects on cell health. Perfusion also facilitated enhanced spatial organization within tissue patches, reducing the inter-spheroids distance. Furthermore, perfusion strengthened the tissue matrix and reduced the degradation rate of the hydrogel scaffold. Complemented by its ability to provide mobile perfusion and incubation, this novel integrated portable perfusion system holds promise for promoting tissue maturation and advancing tissue bioengineering studies.

The integrated single-cell analysis interpret the lactate metabolism-driven immune suppression in triple-negative breast cancer

BACKGROUND

Individuals with triple-negative breast cancer (TNBC) exhibit elevated lactate levels, which offers a valuable lead for investigating the molecular mechanisms underlying the tumor microenvironment (TME) and identifying more efficacious treatments.

METHODS

TNBC samples were classified based on lactate-associated genes. A single-cell transcriptomic approach was employed to examine functional differences across cells with varying lactate metabolism. Immunohistochemistry was used to explore the relationship between lactate metabolism and the CXCL12/CXCR4 signaling axis. In addition, utilizing machine learning techniques, we constructed a prognostic model based on lactic acid phenotype genes.

RESULTS

Lactate-associated gene-based stratification revealed increased immune cell infiltration and immune checkpoint expression in Lactate Cluster 1. Elevated lactate metabolism scores were observed in both cancer-associated fibroblasts (CAFs) and malignant cells. CAFs with high lactate metabolism exhibited immune suppression through the CXCL12/CXCR4 axis. Immunohistochemistry confirmed elevated LDHA, LDHB, CXCL12, and CXCR4 levels in the high lactate group.

CONCLUSION

This study elucidates the complex interplay between lactate and immune cells in TNBC and highlights the CXCL12/CXCR4 axis as a key pathway through which lactate mediates immune suppression, offering new insights into metabolic regulation within the TME. Furthermore, we developed a prognostic model based on lactate metabolism phenotype genes to predict the prognosis of TNBC patients and guide immunotherapy.

Cardiac regeneration: Unraveling the complex network of intercellular crosstalk

The heart is composed of multiple cell types, including cardiomyocytes, endothelial/endocardial cells, fibroblasts, resident immune cells and epicardium and crosstalk between these cell types is crucial for proper cardiac function and homeostasis. In response to cardiac injury or disease, cell-cell interactions and intercellular crosstalk contribute to remodeling to compensate reduced heart function. In some vertebrates, the heart can regenerate following cardiac injury. While cardiomyocytes play a crucial role in this process, additional cell types are necessary to create a pro-regenerative microenvironment in the injured heart. Here, we review recent literature regarding the importance of cellular crosstalk in promoting cardiac regeneration and provide insight into emerging technologies to investigate cell-cell interactions in vivo. Lastly, we explore recent studies highlighting the importance of inter-organ communication in response to injury and promotion of cardiac regeneration. Importantly, understanding how intercellular and inter-organ crosstalk promote cardiac regeneration is essential for the development of therapeutic strategies to stimulate regeneration in the human heart.

Unravelling tumour spatiotemporal heterogeneity using spatial multimodal data

Analysing the genome, epigenome, transcriptome, proteome, and metabolome within the spatial context of cells has transformed our understanding of tumour spatiotemporal heterogeneity. Advances in spatial multi-omics technologies now reveal complex molecular interactions shaping cellular behaviour and tissue dynamics. This review highlights key technologies and computational methods that have advanced spatial domain identification and their pseudo-relations, as well as inference of intra- and inter-cellular molecular networks that drive disease progression. We also discuss strategies to address major challenges, including data sparsity, high-dimensionality, scalability, and heterogeneity. Furthermore, we outline how spatial multi-omics enables novel insights into disease mechanisms, advancing precision medicine and informing targeted therapies. KEY POINTS: Advancements in spatial multi-omics facilitate our understanding of tumour spatiotemporal heterogeneity. AI-driven multimodal models uncover complex molecular interactions that underlie cellular behaviours and tissue dynamics. Combining multi-omics technologies and AI-enabled bioinformatics tools helps predict critical disease stages, such as pre-cancer, advancing precision medicine, and informing targeted therapeutic strategies.

scMultiSim: simulation of single-cell multi-omics and spatial data guided by gene regulatory networks and cell-cell interactions

Simulated single-cell data are essential for designing and evaluating computational methods in the absence of experimental ground truth. Here we present scMultiSim, a comprehensive simulator that generates multimodal single-cell data encompassing gene expression, chromatin accessibility, RNA velocity and spatial cell locations while accounting for the relationships between modalities. Unlike existing tools that focus on limited biological factors, scMultiSim simultaneously models cell identity, gene regulatory networks, cell-cell interactions and chromatin accessibility while incorporating technical noise. Moreover, it allows users to adjust each factor's effect easily. Here we show that scMultiSim generates data with expected biological effects, and demonstrate its applications by benchmarking a wide range of computational tasks, including multimodal and multi-batch data integration, RNA velocity estimation, gene regulatory network inference and cell-cell interaction inference using spatially resolved gene expression data. Compared to existing simulators, scMultiSim can benchmark a much broader range of existing computational problems and even new potential tasks.

An overview of computational methods in single-cell transcriptomic cell type annotation

The rapid accumulation of single-cell RNA sequencing data has provided unprecedented computational resources for cell type annotation, significantly advancing our understanding of cellular heterogeneity. Leveraging gene expression profiles derived from transcriptomic data, researchers can accurately infer cell types, sparking the development of numerous innovative annotation methods. These methods utilize a range of strategies, including marker genes, correlation-based matching, and supervised learning, to classify cell types. In this review, we systematically examine these annotation approaches based on transcriptomics-specific gene expression profiles and provide a comprehensive comparison and categorization of these methods. Furthermore, we focus on the main challenges in the annotation process, especially the long-tail distribution problem arising from data imbalance in rare cell types. We discuss the potential of deep learning techniques to address these issues and enhance model capability in recognizing novel cell types within an open-world framework.

snRNA-Seq and Spatial Transcriptome Reveal Cell-Cell Crosstalk Mediated Metabolic Regulation in Porcine Skeletal Muscle

BACKGROUND

Cell-cell crosstalk between myogenic, adipogenic and immune cells in skeletal muscle to regulate energy metabolism and lipid deposition has received considerable attention. The specific mechanisms of interaction between the different cells in skeletal muscle are still unclear.

METHODS

Using integrated analysis of snRNA-seq and spatial transcriptome, the gene expression profile of longissimus dorsi (LD) muscle was compared between adult Taoyuan black (TB, obese, native Chinese breed) and Duroc (lean) pigs.

RESULTS

TB pig had more intramuscular fat (IMF) deposition (3.91%, p = 0.0244) and higher slow myofiber proportion (17.13%, p < 0.0001) compared with Duroc pig (IMF, 2.38%; slow myofiber, 6.92%) at the age of 180 days. We identified eight cell populations in porcine LD muscle. Five subpopulations of myonuclei and 10 subclusters of fibro/adipogenic progenitors (FAPs) were defined by marker genes. CellChat analysis revealed that communication between immune cells and other cells via the BMP and EGF signalling pathway was only observed in Duroc and not in TB pig. Both snRNA-seq and spatial transcriptome pointed out that FAPs are the important source of secretory proteins. A total of 35 upregulated and 23 downregulated differentially expressed genes (DEGs) were annotated as secretory, one upregulated and 36 downregulated secretory DEGs were identified between TB and Duroc pigs in FAPs by snRNA-seq and FAPs-high regions by spatial transcriptome, respectively. The distribution of FAPs was accompanied by the divergent myofiber-type composition. The expression level of slow myofiber marker gene (MYH7) was higher in both FAPs-high and FAPs-low regions of TB compared with Duroc pig (p < 0.0001), and expression level of fast myofiber maker gene (MYH1) was upregulated in FAPs-high region of Duroc compared with FAPs-high region of TB (p < 0.0001) and FAPs-low region of Duroc pig (p = 0.0002). The metabolic differences of myofibers between TB and Duroc pigs were mainly concentrated in energy, lipid and nitrogen metabolism-related pathway (p < 0.05). The significant correlation (R > 0.4, p < 0.05) between secretory and metabolism-related DEGs with spatial aggregation was verified by regression analysis for random region extraction (area of 25 spots, n = 400) from spatial transcriptome, and we speculated that the alteration of secretory proteins forming the microenvironment might regulate myofiber metabolism via target genes such as IRS1, PLPP1 and SLC38A2.

CONCLUSIONS

Our study provides new insights into skeletal muscle microenvironment that contributes to metabolic regulation and new methods and resources to study cell-cell communication in skeletal muscle.

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

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

Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective

The development of single-cell and spatial transcriptomics has revolutionized our capacity to investigate cellular properties, functions, and interactions in both cellular and spatial contexts. Despite this progress, the analysis of single-cell and spatial omics data remains challenging. First, single-cell sequencing data are high-dimensional and sparse, and are often contaminated by noise and uncertainty, obscuring the underlying biological signal. Second, these data often encompass multiple modalities, including gene expression, epigenetic modifications, metabolite levels, and spatial locations. Integrating these diverse data modalities is crucial for enhancing prediction accuracy and biological interpretability. Third, while the scale of single-cell sequencing has expanded to millions of cells, high-quality annotated datasets are still limited. Fourth, the complex correlations of biological tissues make it difficult to accurately reconstruct cellular states and spatial contexts. Traditional feature engineering approaches struggle with the complexity of biological networks, while deep learning, with its ability to handle high-dimensional data and automatically identify meaningful patterns, has shown great promise in overcoming these challenges. Besides systematically reviewing the strengths and weaknesses of advanced deep learning methods, we have curated 21 datasets from nine benchmarks to evaluate the performance of 58 computational methods. Our analysis reveals that model performance can vary significantly across different benchmark datasets and evaluation metrics, providing a useful perspective for selecting the most appropriate approach based on a specific application scenario. We highlight three key areas for future development, offering valuable insights into how deep learning can be effectively applied to transcriptomic data analysis in biological, medical, and clinical settings.

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

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

Single-cell transcriptomics reveals the multidimensional dynamic heterogeneity from primary to metastatic gastric cancer

Reprogramming of the tumor microenvironment (TME) plays a critical role in gastric cancer (GC) progression and metastasis. However, the multidimensional features between primary tumors and organ-specific metastases remain poorly understood. In this study, we characterized the dynamic heterogeneity of GC from primary to metastatic stages. We identified seven major cell types and 27 immune and stromal subsets. Immune cells decreased, while immunosuppressive cells increased in ovarian and peritoneal metastases. A 30-gene signature for ovarian metastasis was validated in GC cohorts. Additionally, critical ligand-receptor interactions, including LGALS9-MET in liver metastasis and PVR-TIGIT in lymph node metastasis, were identified as potential therapeutic targets. Furthermore, CLOCK, a transcription factor, was associated with poor prognosis and influenced immune cell interactions and migration. Collectively, this study provides valuable insights into TME dynamics in GC and highlights potential avenues for targeted therapies.

SpaCcLink: exploring downstream signaling regulations with graph attention network for systematic inference of spatial cell-cell communication

BACKGROUND

Cellular communication is vital for the proper functioning of multicellular organisms. A comprehensive analysis of cellular communication demands the consideration not only of the binding between ligands and receptors but also of a series of downstream signal transduction reactions within cells. Thanks to the advancements in spatial transcriptomics technology, we are now able to better decipher the process of cellular communication within the cellular microenvironment. Nevertheless, the majority of existing spatial cell-cell communication algorithms fail to take into account the downstream signals within cells.

RESULTS

In this study, we put forward SpaCcLink, a cell-cell communication analysis method that takes into account the downstream influence of individual receptors within cells and systematically investigates the spatial patterns of communication as well as downstream signal networks. Analyses conducted on real datasets derived from humans and mice have demonstrated that SpaCcLink can help in identifying more relevant ligands and receptors, thereby enabling us to systematically decode the downstream genes and signaling pathways that are influenced by cell-cell communication. Comparisons with other methods suggest that SpaCcLink can identify downstream genes that are more closely associated with biological processes and can also discover reliable ligand-receptor relationships.

CONCLUSIONS

By means of SpaCcLink, a more profound and all-encompassing comprehension of the mechanisms underlying cellular communication can be achieved, which in turn promotes and deepens our understanding of the intricate complexity within organisms.

Multimodal transcriptomics reveal neurogenic aging trajectories and age-related regional inflammation in the dentate gyrus

The mammalian dentate gyrus (DG) is involved in certain forms of learning and memory, and DG dysfunction has been implicated in age-related diseases. Although neurogenic potential is maintained throughout life in the DG as neural stem cells (NSCs) continue to generate new neurons, neurogenesis decreases with advancing age, with implications for age-related cognitive decline and disease. In this study, we used single-cell RNA sequencing to characterize transcriptomic signatures of neurogenic cells and their surrounding DG niche, identifying molecular changes associated with neurogenic aging from the activation of quiescent NSCs to the maturation of fate-committed progeny. By integrating spatial transcriptomics data, we identified the regional invasion of inflammatory cells into the hippocampus with age and show here that early-onset neuroinflammation decreases neurogenic activity. Our data reveal the lifelong molecular dynamics of NSCs and their surrounding neurogenic DG niche with age and provide a powerful resource to understand age-related molecular alterations in the aging hippocampus.

Elucidating cardiomyocyte heterogeneity and maturation dynamics through integrated single-cell and spatial transcriptomics

The intricate development and functionality of the mammalian heart are influenced by the heterogeneous nature of cardiomyocytes (CMs). In this study, single-cell and spatial transcriptomics were utilized to analyze cells from neonatal mouse hearts, resulting in a comprehensive atlas delineating the transcriptional profiles of distinct CM subsets. A continuum of maturation states was elucidated, emphasizing a progressive developmental trajectory rather than discrete stages. This approach enabled the mapping of these states across various cardiac regions, illuminating the spatial organization of CM development and the influence of the cellular microenvironment. Notably, a subset of transitional CMs was identified, characterized by a transcriptional signature marking a pivotal maturation phase, presenting a promising target for therapeutic strategies aimed at enhancing cardiac regeneration. This atlas not only elucidates fundamental aspects of cardiac development but also serves as a valuable resource for advancing research into cardiac physiology and pathology, with significant implications for regenerative medicine.

Brain aging and rejuvenation at single-cell resolution

Brain aging leads to a decline in cognitive function and a concomitant increase in the susceptibility to neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. A key question is how changes within individual cells of the brain give rise to age-related dysfunction. Developments in single-cell "omics" technologies, such as single-cell transcriptomics, have facilitated high-dimensional profiling of individual cells. These technologies have led to new and comprehensive characterizations of brain aging at single-cell resolution. Here, we review insights gleaned from single-cell omics studies of brain aging, starting with a cell-type-centric overview of age-associated changes and followed by a discussion of cell-cell interactions during aging. We highlight how single-cell omics studies provide an unbiased view of different rejuvenation interventions and comment on the promise of combinatorial rejuvenation approaches for the brain. Finally, we propose new directions, including models of brain aging and neural stem cells as a focal point for rejuvenation.

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.

Follicular lymphoma research: an open dialogue for a collaborative roadmap

Follicular lymphoma (FL) is the second most common type of lymphoma (20% of all non-Hodgkin lymphomas), derived from germinal centre (GC) B cells, and is characterised by its significant clinical, prognostic and biological heterogeneity, leading to complexity in management. Despite significant biological investigation and indisputable clinical progress since the advent of the immunotherapy era more than 20 years ago, much remains to be done to understand and cure this lymphoma. Today, FL is metaphorically a giant puzzle on the table with patches of sky, landscape and foliage clearly appearing. However, many of the remaining pieces are held by various stakeholders (e.g. clinicians, pathologists, researchers, drug developers) without global agreement on what the gaps are, or any clear blueprint on how to solve the puzzle of understanding the heterogeneity of this disease and create curative and tailored therapies. With the advent of new investigation and drug technologies, together with recent advances in our capacity to manage big data, the time seems ripe for a change of scale. More than ever, this will require collaboration between and within all stakeholders to overcome the current bottlenecks in the field. As for every investigator, we acknowledge that this first draft is necessarily biased, incomplete and some FL expert readers might recognise some remaining gaps not addressed. We hope they will reply to make this effort a collaborative one to assemble all the pieces in the most ideal fashion. As such, this review intends to be a first step and an interactive platform to a collaborative roadmap towards better understanding and care of FL.

Single-cell RNA sequencing identifies the expression of hemoglobin in chondrocyte cell subpopulations in osteoarthritis

In recent years, chondrocytes have been found to contain hemoglobin, which might be an alternative strategy for adapting to the hypoxic environment, while the potential mechanisms of that is still unclear. Here, we report the expression characteristics and potential associated pathways of hemoglobin in chondrocytes using single-cell RNA sequencing (scRNA-seq). We downloaded data of normal people and patients with osteoarthritis (OA) from the Gene Expression Omnibus (GEO) database and cells are unbiased clustered based on gene expression pattern. We determined the expression levels of hemoglobin in various chondrocyte subpopulations. Meanwhile, we further explored the difference in the enriched signaling pathways and the cell-cell interaction in chondrocytes of the hemoglobin high-expression and low-expression groups. Specifically, we found that SPP1 was closely associated with the expression of hemoglobin in OA progression. Our findings provide new insights into the distribution characteristics of hemoglobin in chondrocytes and provide potential clues to the underlying role of hemoglobin in OA and the mechanisms related to that, providing potential new ideas for the treatment of OA.

FABP5+ macrophages contribute to lipid metabolism dysregulation in type A aortic dissection

Type A aortic dissection (TAAD) is an acute onset disease with a high mortality rate. TAAD is caused by a tear in the aortic intima and subsequent blood infiltration. The most prominent characteristics of TAAD are aortic media degeneration and inflammatory cell infiltration, which disturb the structural integrity and function of nonimmune and immune cells in the aortic wall. However, to date, there is no systematic evaluation of the interactions between nonimmune cells and immune cells and their effects on metabolism in the context of aortic dissection. Here, multiomics, including bulk RNA-seq, single-cell RNA-seq, and lipid metabolomics, was applied to elucidate the comprehensive TAAD lipid metabolism landscape. Normally, monocytes in the stress response state secrete a variety of cytokines. Injured fibroblasts lack the ability to degrade lipids, which is suspected to contribute to a high lipid environment. Macrophages differentiate into fatty acid binding protein 5-positive (FABP5+) macrophages under the stimulation of metabolic substrates. Moreover, the upregulation of Fabp5+ macrophages were retrospectively validated in TAAD model mice and TAAD patients. Finally, Fabp5+ macrophages can generate a wide range of proinflammatory cytokines, which possibly contribute to TAAD pathogenesis.

CCL4L2 participates in tendinopathy progression by promoting macrophage inflammatory responses: a single-cell analysis

BACKGROUND

Tendinopathy is very common in clinical practice, which is highly prevalent in athletes, sports enthusiasts and other people involved in high-load weight-bearing activities. Common types of tendinopathy include rotator cuff injury, Achilles tendinitis, tennis elbow and so on. Macrophages (Macs) are key immune cells in the pathogenesis of tendinopathy. In this study, CCL4L2+ M1-related signaling pathways were screened by combining single-cell RNA sequencing (scRNA-seq) to explore their significance in tendinopathy treatment.

METHODS

Immune cell populations were screened by Uniform Manifold Approximation and Projection (UMAP) downscaling, and Mac cell subsets were annotated using cell marker genes. The cellular communication mechanism between different cellular subsets such as Macs and tendon stem/progenitor cells (TSPCs) was demonstrated by cellular communication analysis. Based on cell marker genes of CCL4L2 + M1, Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed to compare the expression differences in M1 and M2 between the Disease and Healthy groups. Associations between CCL4L2+ M1 and TSPCs were inferred by cell-cell communication analysis. The effects of CCL4L2 on Mac polarization and TSPCs were verified by enzyme-linked immunosorbent assay (ELISA) and real-time fluorescence quantitative PCR (qPCR).

RESULTS

The proportions of TSPCs, endothelial cells (ECs), smooth muscle cells (SMCs), and immune cells were significantly elevated in the Disease group. The proportion of M1 cells in the Disease group was higher than that in the Healthy group, while the proportion of M2 cells was lower than that in the Healthy group. M1 differentially expressed genes (DEGs) were mainly enriched to disease-related and immunoinflammation-related signaling pathways. Signaling intensities between M1 and TSPCs in pathways related to immunoinflammation and ischemic injury were significantly increased in the Disease group. The proportion of CCL4L2 + M1 in the Disease group was significantly higher than in the Healthy group, and communications between CCL4L2 + M1 and TSPCs varied significantly. Compared with the Control group, the expression levels of inflammatory cytokines were higher in the CCL4L2 group, and the expression levels of tendon differentiation markers (Egr1, Mkx, Scx, Type 1 collagen, Tnmd) were significantly down-regulated.

CONCLUSION

The present study analyzed the heterogeneous alterations in the Healthy and Disease groups by scRNA-seq data and found that there was a significant inflammatory infiltrate in the Disease group with markedly increased Mac activity, which was associated with activation of the CCL4L2 + M1-associated signaling pathways. CCL4L2 promotes M1 polarization and inhibits TSPC differentiation through activating M1-related inflammatory signaling pathways. These findings contribute to a more comprehensive understanding of tendon injury progression and provide potential targets for tendinopathy treatment.

VGAE-CCI: variational graph autoencoder-based construction of 3D spatial cell-cell communication network

Cell-cell communication plays a critical role in maintaining normal biological functions, regulating development and differentiation, and controlling immune responses. The rapid development of single-cell RNA sequencing and spatial transcriptomics sequencing (ST-seq) technologies provides essential data support for in-depth and comprehensive analysis of cell-cell communication. However, ST-seq data often contain incomplete data and systematic biases, which may reduce the accuracy and reliability of predicting cell-cell communication. Furthermore, other methods for analyzing cell-cell communication mainly focus on individual tissue sections, neglecting cell-cell communication across multiple tissue layers, and fail to comprehensively elucidate cell-cell communication networks within three-dimensional tissues. To address the aforementioned issues, we propose VGAE-CCI, a deep learning framework based on the Variational Graph Autoencoder, capable of identifying cell-cell communication across multiple tissue layers. Additionally, this model can be applied to spatial transcriptomics data with missing or partially incomplete data and can clustered cells at single-cell resolution based on spatial encoding information within complex tissues, thereby enabling more accurate inference of cell-cell communication. Finally, we tested our method on six datasets and compared it with other state of art methods for predicting cell-cell communication. Our method outperformed other methods across multiple metrics, demonstrating its efficiency and reliability in predicting cell-cell communication.

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.

Development and Validation of an AI-Driven System for Automatic Literature Analysis and Molecular Regulatory Network Construction

Decoding gene regulatory networks is essential for understanding the mechanisms underlying many complex diseases. GENET is developed, an automated system designed to extract and visualize extensive molecular relationships from published biomedical literature. Using natural language processing, entities and relations are identified from a randomly selected set of 1788 scientific articles, and visualized in a filterable knowledge graph. The performance of GENET is evaluated and compared with existing methods. The named entity recognition model has achieved an overall precision of 94.23% (4835/5131; 93.56-94.84%), recall of 97.72% (4835/4948; 97.27-98.10%), and an F1 score of 95.94%. The relation extraction model has demonstrated an overall precision of 91.63% (2593/2830; 90.55-92.59%), recall of 89.17% (2593/2908; 87.99-90.25%), and an F1 score of 90.38%. GENET significantly outperforms existing methods in extracting molecular relationships (P < 0.001). Additionally, GENET has successfully predicted WNT family member 4 regulates insulin-like growth factor 2 via signal transducer and activator of transcription 3 in colon cancer. With RNA sequencing data and multiple immunofluorescence, the authenticity of this prediction is validated, supporting the promising feasibility of GENET.

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.

Single-cell spatial transcriptomics in cardiovascular development, disease, and medicine

Cardiovascular diseases (CVDs) impose a significant burden worldwide. Despite the elucidation of the etiology and underlying molecular mechanisms of CVDs by numerous studies and recent discovery of effective drugs, their morbidity, disability, and mortality are still high. Therefore, precise risk stratification and effective targeted therapies for CVDs are warranted. Recent improvements in single-cell RNA sequencing and spatial transcriptomics have improved our understanding of the mechanisms and cells involved in cardiovascular phylogeny and CVDs. Single-cell RNA sequencing can facilitate the study of the human heart at remarkably high resolution and cellular and molecular heterogeneity. However, this technique does not provide spatial information, which is essential for understanding homeostasis and disease. Spatial transcriptomics can elucidate intracellular interactions, transcription factor distribution, cell spatial localization, and molecular profiles of mRNA and identify cell populations causing the disease and their underlying mechanisms, including cell crosstalk. Herein, we introduce the main methods of RNA-seq and spatial transcriptomics analysis and highlight the latest advances in cardiovascular research. We conclude that single-cell RNA sequencing interprets disease progression in multiple dimensions, levels, perspectives, and dynamics by combining spatial and temporal characterization of the clinical phenome with multidisciplinary techniques such as spatial transcriptomics. This aligns with the dynamic evolution of CVDs (e.g., "angina-myocardial infarction-heart failure" in coronary artery disease). The study of pathways for disease onset and mechanisms (e.g., age, sex, comorbidities) in different patient subgroups should improve disease diagnosis and risk stratification. This can facilitate precise individualized treatment of CVDs.

A distinct immune landscape in anti-synthetase syndrome profiled by a single-cell genomic study

Objectives

The objective of this study was to profile the transcriptional profiles of peripheral blood mononuclear cells (PBMCs) and their immune repertoires affected by anti-synthetase syndrome (ASS) at the single-cell level.

Methods

We performed single-cell RNA sequencing (scRNA-seq) analysis of PBMCs and bulk RNA sequencing for patients with ASS (N=3) and patients with anti-melanoma differentiation-associated gene 5-positive dermatomyositis (MDA5+ DM, N=3) along with healthy controls (HCs, N=4). As ASS and MDA5+ DM have similar organ involvements, MDA5+ DM was used as a disease control. The immune repertoire was constructed by reusing the same scRNA-seq datasets. Importantly, flow cytometry was performed to verify the results from the scRNA-seq analysis.

Results

After meticulous annotation of PBMCs, we noticed a significant decrease in the proportion of mucosal-associated invariant T (MAIT) cells in ASS patients compared to HCs, while there was a notable increase in the proportion of proliferative NKT cells. Compared with MDA5+ DM patients, in their PBMCs ASS patients presented substantial enrichment of interferon pathways, which were primarily mediated by IFN-II, and displayed a weak immune response. Furthermore, ASS patients exhibited more pronounced metabolic abnormalities, which may in turn affect oxidative phosphorylation pathways. Monocytes from ASS patients appear to play a crucial role as receptive signaling cells for the TNF pathway. Immunophenotyping analysis of PBMCs from ASS patients revealed an increasing trend for the clone type CQQSYSTPWTF.

Conclusion

Using single-cell genomic datasets of ASS PBMCs, we revealed a distinctive profile in the immune system of individuals with ASS, compared to that with MDA5+ DM or healthy controls.

Cell-cell interactions in the heart: advanced cardiac models and omics technologies

A healthy heart comprises various cell types, including cardiomyocytes, endothelial cells, fibroblasts, immune cells, and among others, which work together to maintain optimal cardiac function. These cells engage in complex communication networks, known as cell-cell interactions (CCIs), which are essential for homeostasis, cardiac structure, and efficient function. However, in the context of cardiac diseases, the heart undergoes damage, leading to alterations in the cellular composition. Such pathological conditions trigger significant changes in CCIs, causing cell rearrangement and the transition between cell types. Studying these interactions can provide valuable insights into cardiac biology and disease mechanisms, enabling the development of new therapeutic strategies. While the development of cardiac organoids and advanced 3D co-culture technologies has revolutionized in vitro studies of CCIs, recent advancements in single-cell and spatial multi-omics technologies provide researchers with powerful and convenient tools to investigate CCIs at unprecedented resolution. This article provides a concise overview of CCIs observed in both normal and injured heart, with an emphasis on the cutting-edge methods used to study these interactions. It highlights recent advancements such as 3D co-culture systems, single-cell and spatial omics technologies, that have enhanced the understanding of CCIs. Additionally, it summarizes the practical applications of CCI research in advancing cardiovascular therapies, offering potential solutions for treating heart disease by targeting intercellular communication.

Neuronal subtype-specific transcriptomic changes in the cerebral neocortex associated with sleep pressure

Sleep is homeostatically regulated by sleep pressure, which increases during wakefulness and dissipates during sleep. Recent studies have suggested that the cerebral neocortex, a six-layered structure composed of various layer- and projection-specific neuronal subtypes, is involved in the representation of sleep pressure governed by transcriptional regulation. Here, we examined the transcriptomic changes in neuronal subtypes in the neocortex upon increased sleep pressure using single-nucleus RNA sequencing datasets and predicted the putative intracellular and intercellular molecules involved in transcriptome alterations. We revealed that sleep deprivation (SD) had the greatest effect on the transcriptome of layer 2 and 3 intratelencephalic (L2/3 IT) neurons among the neocortical glutamatergic neuronal subtypes. The expression of mutant SIK3 (SLP), which is known to increase sleep pressure, also induced profound changes in the transcriptome of L2/3 IT neurons. We identified Junb as a candidate transcription factor involved in the alteration of the L2/3 IT neuronal transcriptome by SD and SIK3 (SLP) expression. Finally, we inferred putative intercellular ligands, including BDNF, LSAMP, and PRNP, which may be involved in SD-induced alteration of the transcriptome of L2/3 IT neurons. We suggest that the transcriptome of L2/3 IT neurons is most impacted by increased sleep pressure among neocortical glutamatergic neuronal subtypes and identify putative molecules involved in such transcriptional alterations.

collectNET: a web server for integrated inference of cell-cell communication network

Cell-cell communication (CCC) through ligand-receptor (L-R) pairs forms the cornerstone for complex functionalities in multicellular organisms. Deciphering such intercellular signaling can contribute to unraveling disease mechanisms and enable targeted therapy. Nonetheless, notable biases and inconsistencies are evident among the inferential outcomes generated by current methods for inferring CCC network. To fill this gap, we developed collectNET (http://health.tsinghua.edu.cn/collectnet) as a comprehensive web platform for analyzing CCC network, with efficient calculation, hierarchical browsing, comprehensive statistics, advanced searching, and intuitive visualization. collectNET provides a reliable online inference service with prior knowledge of three public L-R databases and systematic integration of three mainstream inference methods. Additionally, collectNET has assembled a human CCC atlas, including 126 785 significant communication pairs based on 343 023 cells. We anticipate that collectNET will benefit researchers in gaining a more holistic understanding of cell development and differentiation mechanisms. Database URL: http://health.tsinghua.edu.cn/collectnet.

Intracellular spatial transcriptomic analysis toolkit (InSTAnT)

Imaging-based spatial transcriptomics technologies such as Multiplexed error-robust fluorescence in situ hybridization (MERFISH) can capture cellular processes in unparalleled detail. However, rigorous and robust analytical tools are needed to unlock their full potential for discovering subcellular biological patterns. We present Intracellular Spatial Transcriptomic Analysis Toolkit (InSTAnT), a computational toolkit for extracting molecular relationships from spatial transcriptomics data at single molecule resolution. InSTAnT employs specialized statistical tests and algorithms to detect gene pairs and modules exhibiting intriguing patterns of co-localization, both within individual cells and across the cellular landscape. We showcase the toolkit on five different datasets representing two different cell lines, two brain structures, two species, and three different technologies. We perform rigorous statistical assessment of discovered co-localization patterns, find supporting evidence from databases and RNA interactions, and identify associated subcellular domains. We uncover several cell type and region-specific gene co-localizations within the brain. Intra-cellular spatial patterns discovered by InSTAnT mirror diverse molecular relationships, including RNA interactions and shared sub-cellular localization or function, providing a rich compendium of testable hypotheses regarding molecular functions.

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.

Spatial multi-omics analysis of the microenvironment in traumatic spinal cord injury: a narrative review

Traumatic spinal cord injury (tSCI) is a severe injury to the central nervous system that is categorized into primary and secondary injuries. Among them, the local microenvironmental imbalance in the spinal cord caused by secondary spinal cord injury includes accumulation of cytokines and chemokines, reduced angiogenesis, dysregulation of cellular energy metabolism, and dysfunction of immune cells at the site of injury, which severely impedes neurological recovery from spinal cord injury (SCI). In recent years, single-cell techniques have revealed the heterogeneity of multiple immune cells at the genomic, transcriptomic, proteomic, and metabolomic levels after tSCI, further deepening our understanding of the mechanisms underlying tSCI. However, spatial information about the tSCI microenvironment, such as cell location and cell-cell interactions, is lost in these approaches. The application of spatial multi-omics technology can solve this problem by combining the data obtained from immunohistochemistry and multiparametric analysis to reveal the changes in the microenvironment at different times of secondary injury after SCI. In this review, we systematically review the progress of spatial multi-omics techniques in the study of the microenvironment after SCI, including changes in the immune microenvironment and discuss potential future therapeutic strategies.

Deciphering cell-cell communication at single-cell resolution for spatial transcriptomics with subgraph-based graph attention network

The inference of cell-cell communication (CCC) is crucial for a better understanding of complex cellular dynamics and regulatory mechanisms in biological systems. However, accurately inferring spatial CCCs at single-cell resolution remains a significant challenge. To address this issue, we present a versatile method, called DeepTalk, to infer spatial CCC at single-cell resolution by integrating single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics (ST) data. DeepTalk utilizes graph attention network (GAT) to integrate scRNA-seq and ST data, which enables accurate cell-type identification for single-cell ST data and deconvolution for spot-based ST data. Then, DeepTalk can capture the connections among cells at multiple levels using subgraph-based GAT, and further achieve spatially resolved CCC inference at single-cell resolution. DeepTalk achieves excellent performance in discovering meaningful spatial CCCs on multiple cross-platform datasets, which demonstrates its superior ability to dissect cellular behavior within intricate biological processes.

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.

Immune disease dialogue of chemokine-based cell communications as revealed by single-cell RNA sequencing meta-analysis

Immune-mediated diseases are characterized by aberrant immune responses, posing significant challenges to global health. In both inflammatory and autoimmune diseases, dysregulated immune reactions mediated by tissue-residing immune and non-immune cells precipitate chronic inflammation and tissue damage that is amplified by peripheral immune cell extravasation into the tissue. Chemokine receptors are pivotal in orchestrating immune cell migration, yet deciphering the signaling code across cell types, diseases and tissues remains an open challenge. To delineate disease-specific cell-cell communications involved in immune cell migration, we conducted a meta-analysis of publicly available single-cell RNA sequencing (scRNA-seq) data across diverse immune diseases and tissues. Our comprehensive analysis spanned multiple immune disorders affecting major organs: atopic dermatitis and psoriasis (skin), chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis (lung), ulcerative colitis (colon), IgA nephropathy and lupus nephritis (kidney). By interrogating ligand-receptor (L-R) interactions, alterations in cell proportions, and differential gene expression, we unveiled intricate disease-specific and common immune cell chemoattraction and extravasation patterns. Our findings delineate disease-specific L-R networks and shed light on shared immune responses across tissues and diseases. Insights gleaned from this analysis hold promise for the development of targeted therapeutics aimed at modulating immune cell migration to mitigate inflammation and tissue damage. This nuanced understanding of immune cell dynamics at the single-cell resolution opens avenues for precision medicine in immune disease management.

Integrating Single-Cell and Spatial Transcriptomics to Uncover and Elucidate GP73-Mediated Pro-Angiogenic Regulatory Networks in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) was characterized as being hypervascular. In the present study, we generated a single-cell spatial transcriptomic landscape of the vasculogenic etiology of HCC and illustrated overexpressed Golgi phosphoprotein 73 (GP73) HCC cells exerting cellular communication with vascular endothelial cells with high pro-angiogenesis potential via multiple receptor-ligand interactions in the process of tumor vascular development. Specifically, we uncovered an interactive GP73-mediated regulatory network coordinated with c-Myc, lactate, Janus kinase 2/signal transducer and activator of transcription 3 (JAK2/STAT3) pathway, and endoplasmic reticulum stress (ERS) signals in HCC cells and elucidated its pro-angiogenic roles in vitro and in vivo. Mechanistically, we found that GP73, the pivotal hub gene, was activated by histone lactylation and c-Myc, which stimulated the phosphorylation of downstream STAT3 by directly binding STAT3 and simultaneously enhancing glucose-regulated protein 78 (GRP78)-induced ERS. STAT3 potentiates GP73-mediated pro-angiogenic functions. Clinically, serum GP73 levels were positively correlated with HCC response to anti-angiogenic regimens and were essential for a prognostic nomogram showing good predictive performance for determining 6-month and 1-year survival in patients with HCC treated with anti-angiogenic therapy. Taken together, the aforementioned data characterized the pro-angiogenic roles and mechanisms of a GP73-mediated network and proved that GP73 is a crucial tumor angiogenesis niche gene with favorable anti-angiogenic potential in the treatment of HCC.

Dissecting tumor microenvironment from spatially resolved transcriptomics data by heterogeneous graph learning

Spatially resolved transcriptomics (SRT) has enabled precise dissection of tumor-microenvironment (TME) by analyzing its intracellular molecular networks and intercellular cell-cell communication (CCC). However, lacking computational exploration of complicated relations between cells, genes, and histological regions, severely limits the ability to interpret the complex structure of TME. Here, we introduce stKeep, a heterogeneous graph (HG) learning method that integrates multimodality and gene-gene interactions, in unraveling TME from SRT data. stKeep leverages HG to learn both cell-modules and gene-modules by incorporating features of diverse nodes including genes, cells, and histological regions, allows for identifying finer cell-states within TME and cell-state-specific gene-gene relations, respectively. Furthermore, stKeep employs HG to infer CCC for each cell, while ensuring that learned CCC patterns are comparable across different cell-states through contrastive learning. In various cancer samples, stKeep outperforms other tools in dissecting TME such as detecting bi-potent basal populations, neoplastic myoepithelial cells, and metastatic cells distributed within the tumor or leading-edge regions. Notably, stKeep identifies key transcription factors, ligands, and receptors relevant to disease progression, which are further validated by the functional and survival analysis of independent clinical data, thereby highlighting its clinical prognostic and immunotherapy applications.

Single-cell and spatiotemporal profile of ovulation in the mouse ovary

Ovulation is a spatiotemporally coordinated process that involves several tightly controlled events, including oocyte meiotic maturation, cumulus expansion, follicle wall rupture and repair, and ovarian stroma remodeling. To date, no studies have detailed the precise window of ovulation at single-cell resolution. Here, we performed parallel single-cell RNA-seq and spatial transcriptomics on paired mouse ovaries across an ovulation time course to map the spatiotemporal profile of ovarian cell types. We show that major ovarian cell types exhibit time-dependent transcriptional states enriched for distinct functions and have specific localization profiles within the ovary. We also identified gene markers for ovulation-dependent cell states and validated these using orthogonal methods. Finally, we performed cell-cell interaction analyses to identify ligand-receptor pairs that may drive ovulation, revealing previously unappreciated interactions. Taken together, our data provides a rich and comprehensive resource of murine ovulation that can be mined for discovery by the scientific community.

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.

Advance in Multi-omics Research Strategies on Cholesterol Metabolism in Psoriasis

The skin is a complex and dynamic organ where homeostasis is maintained through the intricate interplay between the immune system and metabolism, particularly cholesterol metabolism. Various factors such as cytokines, inflammatory mediators, cholesterol metabolites, and metabolic enzymes play crucial roles in facilitating these interactions. Dysregulation of this delicate balance contributes to the pathogenic pathways of inflammatory skin conditions, notably psoriasis. In this article, we provide an overview of omics biomarkers associated with psoriasis in relation to cholesterol metabolism. We explore multi-omics approaches that reveal the communication between immunometabolism and psoriatic inflammation. Additionally, we summarize the use of multi-omics strategies to uncover the complexities of multifactorial and heterogeneous inflammatory diseases. Finally, we highlight potential future perspectives related to targeted drug therapies and research areas that can advance precise medicine. This review aims to serve as a valuable resource for those investigating the role of cholesterol metabolism in psoriasis.

Influence of intersignaling crosstalk on the intracellular localization of YAP/TAZ in lung cells

A cell is a dynamic system in which various processes occur simultaneously. In particular, intra- and intercellular signaling pathway crosstalk has a significant impact on a cell's life cycle, differentiation, proliferation, growth, regeneration, and, consequently, on the normal functioning of an entire organ. Hippo signaling and YAP/TAZ nucleocytoplasmic shuttling play a pivotal role in normal development, homeostasis, and tissue regeneration, particularly in lung cells. Intersignaling communication has a significant impact on the core components of the Hippo pathway and on YAP/TAZ localization. This review describes the crosstalk between Hippo signaling and key lung signaling pathways (WNT, SHH, TGFβ, Notch, Rho, and mTOR) using lung cells as an example and highlights the remaining unanswered questions.

Challenges and best practices in omics benchmarking

Technological advances enabling massively parallel measurement of biological features - such as microarrays, high-throughput sequencing and mass spectrometry - have ushered in the omics era, now in its third decade. The resulting complex landscape of analytical methods has naturally fostered the growth of an omics benchmarking industry. Benchmarking refers to the process of objectively comparing and evaluating the performance of different computational or analytical techniques when processing and analysing large-scale biological data sets, such as transcriptomics, proteomics and metabolomics. With thousands of omics benchmarking studies published over the past 25 years, the field has matured to the point where the foundations of benchmarking have been established and well described. However, generating meaningful benchmarking data and properly evaluating performance in this complex domain remains challenging. In this Review, we highlight some common oversights and pitfalls in omics benchmarking. We also establish a methodology to bring the issues that can be addressed into focus and to be transparent about those that cannot: this takes the form of a spreadsheet template of guidelines for comprehensive reporting, intended to accompany publications. In addition, a survey of recent developments in benchmarking is provided as well as specific guidance for commonly encountered difficulties.

Towards early diagnosis of Alzheimer's disease: advances in immune-related blood biomarkers and computational approaches

Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. Here, we give a background on recent advances in research on brain-immune system cross-talk in Alzheimer's disease and review machine learning approaches, which can combine multiple biomarkers with further information (e.g. age, sex, APOE genotype) into predictive models supporting an earlier diagnosis. In addition, mechanistic modeling approaches, such as agent-based modeling open the possibility to model and analyze cell dynamics over time. This review aims to provide an overview of the current state of immune-system related blood-based biomarkers and their potential for the early diagnosis of Alzheimer's disease.

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.

Single-Cell Informatics for Tumor Microenvironment and Immunotherapy

Cancer comprises malignant cells surrounded by the tumor microenvironment (TME), a dynamic ecosystem composed of heterogeneous cell populations that exert unique influences on tumor development. The immune community within the TME plays a substantial role in tumorigenesis and tumor evolution. The innate and adaptive immune cells "talk" to the tumor through ligand-receptor interactions and signaling molecules, forming a complex communication network to influence the cellular and molecular basis of cancer. Such intricate intratumoral immune composition and interactions foster the application of immunotherapies, which empower the immune system against cancer to elicit durable long-term responses in cancer patients. Single-cell technologies have allowed for the dissection and characterization of the TME to an unprecedented level, while recent advancements in bioinformatics tools have expanded the horizon and depth of high-dimensional single-cell data analysis. This review will unravel the intertwined networks between malignancy and immunity, explore the utilization of computational tools for a deeper understanding of tumor-immune communications, and discuss the application of these approaches to aid in diagnosis or treatment decision making in the clinical setting, as well as the current challenges faced by the researchers with their potential future improvements.

Investigation of cellular communication and signaling pathways in tumor microenvironment for high TP53-expressing osteosarcoma cells through single-cell RNA sequencing

Osteosarcoma (OS) stands as the most prevalent primary bone cancer in children and adolescents, and its limited treatment options often result in unsatisfactory outcomes, particularly for metastatic cases. The tumor microenvironment (TME) has been recognized as a crucial determinant in OS progression. However, the intercellular dynamics between high TP53-expressing OS cells and neighboring cell types within the TME are yet to be thoroughly understood. In our study, we harnessed the single-cell RNA sequencing (scRNA-seq) technology in combination with the computational tool-Cellchat, aiming to elucidate the intercellular communication networks present within OS. Through meticulous quantitative inference and subsequent analysis of these networks, we succeeded in identifying significant signaling pathways connecting high TP53-expressing OS cells with proximate cell types, namely Macrophages, Monocytes, Endothelial Cells, and PVLs. This research brings forth a nuanced understanding of the intricate patterns and coordination involved in the TME's intercellular communication signals. These findings not only provide profound insights into the molecular mechanisms underpinning OS but also indicate potential therapeutic targets that could revolutionize treatment strategies.

ICELLNET v2: a versatile method for cell-cell communication analysis from human transcriptomic data

SUMMARY

Several methods have been developed in the past years to infer cell-cell communication networks from transcriptomic data based on ligand and receptor expression. Among them, ICELLNET is one of the few approaches to consider the multiple subunits of ligands and receptors complexes to infer and quantify cell communication. In here, we present a major update of ICELLNET. As compared to its original implementation, we (i) drastically expanded the ICELLNET ligand-receptor database from 380 to 1669 biologically curated interactions, (ii) integrated important families of communication molecules involved in immune crosstalk, cell adhesion, and Wnt pathway, (iii) optimized ICELLNET framework for single-cell RNA sequencing data analyses, (iv) provided new visualizations of cell-cell communication results to facilitate prioritization and biological interpretation. This update will broaden the use of ICELLNET by the scientific community in different biological fields.

AVAILABILITY AND IMPLEMENTATION

ICELLNET package is implemented in R. Source code, documentation and tutorials are available on GitHub (https://github.com/soumelis-lab/ICELLNET).

Navigating the landscapes of spatial transcriptomics: How computational methods guide the way

Spatially resolved transcriptomics has been dramatically transforming biological and medical research in various fields. It enables transcriptome profiling at single-cell, multi-cellular, or sub-cellular resolution, while retaining the information of geometric localizations of cells in complex tissues. The coupling of cell spatial information and its molecular characteristics generates a novel multi-modal high-throughput data source, which poses new challenges for the development of analytical methods for data-mining. Spatial transcriptomic data are often highly complex, noisy, and biased, presenting a series of difficulties, many unresolved, for data analysis and generation of biological insights. In addition, to keep pace with the ever-evolving spatial transcriptomic experimental technologies, the existing analytical theories and tools need to be updated and reformed accordingly. In this review, we provide an overview and discussion of the current computational approaches for mining of spatial transcriptomics data. Future directions and perspectives of methodology design are proposed to stimulate further discussions and advances in new analytical models and algorithms. This article is categorized under: RNA Methods > RNA Analyses in Cells RNA Evolution and Genomics > Computational Analyses of RNA RNA Export and Localization > RNA Localization.

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.