An unsupervised method for physical cell interaction profiling of complex tissues

Unveiling contact-mediated cellular crosstalk

Cell-cell interactions orchestrate complex functions in multicellular organisms, forming a regulatory network for diverse biological processes. Their disruption leads to disease states. Recent advancements - including single-cell sequencing and spatial transcriptomics, coupled with powerful bioengineering and molecular tools - have revolutionized our understanding of how cells respond to each other. Notably, spatial transcriptomics allows us to analyze gene expression changes based on cell proximity, offering a unique window into the impact of cell-cell contact. Additionally, computational approaches are being developed to decipher how cell contact governs the symphony of cellular responses. This review explores these cutting-edge approaches, providing valuable insights into deciphering the intricate cellular changes influenced by cell-cell communication.

Single-cell transcriptomic analysis identifies infiltrating plasmacytoid dendritic cells in psoriasis epidermis

Our study unveils the cellular and molecular dynamics in psoriasis epidermis, revealing diverse cell states and their interactions driving inflammation and altered developmental trajectories. Importantly, a previously overlooked plasmacytoid dendritic cell cluster with inflammatory properties was identified in the epidermis of chronic psoriasis lesions, suggesting a potential role for these cells in chronic psoriasis.

ST-SCSR: identifying spatial domains in spatial transcriptomics data via structure correlation and self-representation

Recent advances in spatial transcriptomics (ST) enable measurements of transcriptome within intact biological tissues by preserving spatial information, offering biologists unprecedented opportunities to comprehensively understand tissue micro-environment, where spatial domains are basic units of tissues. Although great efforts are devoted to this issue, they still have many shortcomings, such as ignoring local information and relations of spatial domains, requiring alternatives to solve these problems. Here, a novel algorithm for spatial domain identification in Spatial Transcriptomics data with Structure Correlation and Self-Representation (ST-SCSR), which integrates local information, global information, and similarity of spatial domains. Specifically, ST-SCSR utilzes matrix tri-factorization to simultaneously decompose expression profiles and spatial network of spots, where expressional and spatial features of spots are fused via the shared factor matrix that interpreted as similarity of spatial domains. Furthermore, ST-SCSR learns affinity graph of spots by manipulating expressional and spatial features, where local preservation and sparse constraints are employed, thereby enhancing the quality of graph. The experimental results demonstrate that ST-SCSR not only outperforms state-of-the-art algorithms in terms of accuracy, but also identifies many potential interesting patterns.

MNMST: topology of cell networks leverages identification of spatial domains from spatial transcriptomics data

Advances in spatial transcriptomics provide an unprecedented opportunity to reveal the structure and function of biology systems. However, current algorithms fail to address the heterogeneity and interpretability of spatial transcriptomics data. Here, we present a multi-layer network model for identifying spatial domains in spatial transcriptomics data with joint learning. We demonstrate that spatial domains can be precisely characterized and discriminated by the topological structure of cell networks, facilitating identification and interpretability of spatial domains, which outperforms state-of-the-art baselines. Furthermore, we prove that network model offers an effective and efficient strategy for integrative analysis of spatial transcriptomics data from various platforms.

Spatial Biology of Breast Cancer

Spatial findings have shaped on our understanding of breast cancer. In this review, we discuss how spatial methods, including spatial transcriptomics and proteomics and the resultant understanding of spatial relationships, have contributed to concepts regarding cancer progression and treatment. In addition to discussing traditional approaches, we examine how emerging multiplex imaging technologies have contributed to the field and how they might influence future research.

Adult tissue-specific stem cell interaction: novel technologies and research advances

Adult tissue-specific stem cells play a dominant role in tissue homeostasis and regeneration. Various in vivo markers of adult tissue-specific stem cells have been increasingly reported by lineage tracing in genetic mouse models, indicating that marked cells differentiation is crucial during homeostasis and regeneration. How adult tissue-specific stem cells with indicated markers contact the adjacent lineage with indicated markers is of significance to be studied. Novel methods bring future findings. Recent advances in lineage tracing, synthetic receptor systems, proximity labeling, and transcriptomics have enabled easier and more accurate cell behavior visualization and qualitative and quantitative analysis of cell-cell interactions than ever before. These technological innovations have prompted researchers to re-evaluate previous experimental results, providing increasingly compelling experimental results for understanding the mechanisms of cell-cell interactions. This review aimed to describe the recent methodological advances of dual enzyme lineage tracing system, the synthetic receptor system, proximity labeling, single-cell RNA sequencing and spatial transcriptomics in the study of adult tissue-specific stem cells interactions. An enhanced understanding of the mechanisms of adult tissue-specific stem cells interaction is important for tissue regeneration and maintenance of homeostasis in organisms.

A global database for modeling tumor-immune cell communication

Communications between tumor cells and surrounding immune cells help shape the tumor immunity continuum. Recent breakthroughs in high-throughput technologies as well as computational algorithms had reported many important tumor-immune cell (TIC) communications, which were scattered in thousands of published studies and impeded systematical characterization of the TIC communications across cancer. Here, a comprehensive database, TICCom, was developed to model TIC communications, containing 739 experimentally-validated or manually-curated interactions collected from more than 3,000 literatures as well as 4,537,709 predicted interactions inferred via six computational algorithms by reanalyzing 32 scRNA-seq datasets and bulk RNA-seq data across 25 cancer types. The communications between tumor cells and 14 types of immune cells were characterized, and the involved ligand-receptor interactions were further integrated. 14190 human and 3650 mouse integrated ligand-receptor interactions with supplemented corresponding function information were also stored in the TICCom database. Our database would serve as a valuable resource for investigating TIC communications.

Cellular-scale proximity labeling for recording cell spatial organization in mouse tissues

Proximity labeling has emerged as a powerful strategy for interrogating cell-cell interactions. However, the nanometer-scale labeling radius impedes the use of current methods for indirect cell communications and makes recording cell spatial organization in tissue samples difficult. Here, we develop quinone methide-assisted identification of cell spatial organization (QMID), a chemical strategy with the labeling radius matching the cell dimension. The activating enzyme is installed on the surface of bait cells, which produces QM electrophiles that can diffuse across micrometers and label proximal prey cells independent of cell-cell contacts. In cell coculture, QMID reveals gene expression of macrophages that are regulated by spatial proximity to tumor cells. Furthermore, QMID enables labeling and isolation of proximal cells of CD4⁺ and CD8⁺ T cells in the mouse spleen, and subsequent single-cell RNA sequencing uncovers distinctive cell populations and gene expression patterns within the immune niches of specific T cell subtypes. QMID should facilitate dissecting cell spatial organization in various tissues.

Universal recording of cell-cell contacts in vivo for interaction-based transcriptomics

Cellular interactions are essential for tissue organization and functionality. In particular, immune cells rely on direct and usually transient interactions with other immune and non-immune populations to specify and regulate their function. To study these "kiss-and-run" interactions directly in vivo, we previously developed LIPSTIC (Labeling Immune Partnerships by SorTagging Intercellular Contacts), an approach that uses enzymatic transfer of a labeled substrate between the molecular partners CD40L and CD40 to label interacting cells. Reliance on this pathway limited the use of LIPSTIC to measuring interactions between CD4+ helper T cells and antigen presenting cells, however. Here, we report the development of a universal version of LIPSTIC (uLIPSTIC), which can record physical interactions both among immune cells and between immune and non-immune populations irrespective of the receptors and ligands involved. We show that uLIPSTIC can be used, among other things, to monitor the priming of CD8+ T cells by dendritic cells, reveal the cellular partners of regulatory T cells in steady state, and identify germinal center (GC)-resident T follicular helper (Tfh) cells based on their ability to interact cognately with GC B cells. By coupling uLIPSTIC with single-cell transcriptomics, we build a catalog of the immune populations that physically interact with intestinal epithelial cells (IECs) and find evidence of stepwise acquisition of the ability to interact with IECs as CD4+ T cells adapt to residence in the intestinal tissue. Thus, uLIPSTIC provides a broadly useful technology for measuring and understanding cell-cell interactions across multiple biological systems.

Technological and computational advances driving high-throughput oncology

Engineering and computational advances have opened many new avenues in cancer research, particularly when being exploited in interdisciplinary approaches. For example, the combination of microfluidics, novel sequencing technologies, and computational analyses has been crucial to enable single-cell assays, giving a detailed picture of tumor heterogeneity for the very first time. In a similar way, these 'tech' disciplines have been elementary for generating large data sets in multidimensional cancer 'omics' approaches, cell-cell interaction screens, 3D tumor models, and tissue level analyses. In this review we summarize the most important technology and computational developments that have been or will be instrumental for transitioning classical cancer research to a large data-driven, high-throughput, high-content discipline across all biological scales.

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

BACKGROUND

Cell-cell interactions are important for information exchange between different cells, which are the fundamental basis of many biological processes. Recent advances in single-cell RNA sequencing (scRNA-seq) enable the characterization of cell-cell interactions using computational methods. However, it is hard to evaluate these methods since no ground truth is provided. Spatial transcriptomics (ST) data profiles the relative position of different cells. We propose that the spatial distance suggests the interaction tendency of different cell types, thus could be used for evaluating cell-cell interaction tools.

RESULTS

We benchmark 16 cell-cell interaction methods by integrating scRNA-seq with ST data. We characterize cell-cell interactions into short-range and long-range interactions using spatial distance distributions between ligands and receptors. Based on this classification, we define the distance enrichment score and apply an evaluation workflow to 16 cell-cell interaction tools using 15 simulated and 5 real scRNA-seq and ST datasets. We also compare the consistency of the results from single tools with the commonly identified interactions. Our results suggest that the interactions predicted by different tools are highly dynamic, and the statistical-based methods show overall better performance than network-based methods and ST-based methods.

CONCLUSIONS

Our study presents a comprehensive evaluation of cell-cell interaction tools for scRNA-seq. CellChat, CellPhoneDB, NicheNet, and ICELLNET show overall better performance than other tools in terms of consistency with spatial tendency and software scalability. We recommend using results from at least two methods to ensure the accuracy of identified interactions. We have packaged the benchmark workflow with detailed documentation at GitHub ( https://github.com/wanglabtongji/CCI ).

Gut Epithelial Inositol Polyphosphate Multikinase Alleviates Experimental Colitis via Governing Tuft Cell Homeostasis

BACKGROUND & AIMS

Inositol polyphosphate multikinase (IPMK), an essential enzyme for inositol phosphate metabolism, has been known to mediate major biological events such as growth. Recent studies have identified single-nucleotide polymorphisms in the IPMK gene associated with inflammatory bowel disease predisposition. Therefore, we aimed to investigate the functional significance of IPMK in gut epithelium.

METHODS

We generated intestinal epithelial cell (IEC)-specific Ipmk knockout (IPMK^ΔIEC) mice, and assessed their vulnerability against dextran sulfate sodium-induced experimental colitis. Both bulk and single-cell RNA sequencing were performed to analyze IPMK-deficient colonic epithelial cells and colonic tuft cells.

RESULTS

Although IPMK^ΔIEC mice developed normally and showed no intestinal abnormalities during homeostasis, Ipmk deletion aggravated dextran sulfate sodium-induced colitis, with higher clinical colitis scores, and increased epithelial barrier permeability. Surprisingly, Ipmk deletion led to a significant decrease in the number of tuft cells without influencing other IECs. Single-cell RNA sequencing of mouse colonic tuft cells showed 3 distinct populations of tuft cells, and further showed that a transcriptionally inactive population was expanded markedly in IPMK^ΔIEC mice, while neuronal-related cells were relatively decreased.

CONCLUSIONS

Cholinergic output from tuft cells is known to be critical for the restoration of intestinal architecture upon damage, supporting that tuft cell-defective IPMK^ΔIEC mice are more prone to colitis. Thus, intestinal epithelial IPMK is a critical regulator of colonic integrity and tissue regeneration by determining tuft cell homeostasis and affecting cholinergic output.

MTG16 regulates colonic epithelial differentiation, colitis, and tumorigenesis by repressing E protein transcription factors

Aberrant epithelial differentiation and regeneration contribute to colon pathologies, including inflammatory bowel disease (IBD) and colitis-associated cancer (CAC). Myeloid translocation gene 16 (MTG16, also known as CBFA2T3) is a transcriptional corepressor expressed in the colonic epithelium. MTG16 deficiency in mice exacerbates colitis and increases tumor burden in CAC, though the underlying mechanisms remain unclear. Here, we identified MTG16 as a central mediator of epithelial differentiation, promoting goblet and restraining enteroendocrine cell development in homeostasis and enabling regeneration following dextran sulfate sodium-induced (DSS-induced) colitis. Transcriptomic analyses implicated increased Ephrussi box-binding transcription factor (E protein) activity in MTG16-deficient colon crypts. Using a mouse model with a point mutation that attenuates MTG16:E protein interactions (Mtg16P209T), we showed that MTG16 exerts control over colonic epithelial differentiation and regeneration by repressing E protein-mediated transcription. Mimicking murine colitis, MTG16 expression was increased in biopsies from patients with active IBD compared with unaffected controls. Finally, uncoupling MTG16:E protein interactions partially phenocopied the enhanced tumorigenicity of Mtg16-/- colon in the azoxymethane/DSS-induced model of CAC, indicating that MTG16 protects from tumorigenesis through additional mechanisms. Collectively, our results demonstrate that MTG16, via its repression of E protein targets, is a key regulator of cell fate decisions during colon homeostasis, colitis, and cancer.

Reconstructing physical cell interaction networks from single-cell data using Neighbor-seq

Cell-cell interactions are the fundamental building blocks of tissue organization and multicellular life. We developed Neighbor-seq, a method to identify and annotate the architecture of direct cell–cell interactions and relevant ligand–receptor signaling from the undissociated cell fractions in massively parallel single cell sequencing data. Neighbor-seq accurately identifies microanatomical features of diverse tissue types such as the small intestinal epithelium, terminal respiratory tract, and splenic white pulp. It also captures the differing topologies of cancer-immune-stromal cell communications in pancreatic and skin tumors, which are consistent with the patterns observed in spatial transcriptomic data. Neighbor-seq is fast and scalable. It draws inferences from routine single-cell data and does not require prior knowledge about sample cell-types or multiplets. Neighbor-seq provides a framework to study the organ-level cellular interactome in health and disease, bridging the gap between single-cell and spatial transcriptomics.

Spatially resolved transcriptomics of high-grade serous ovarian carcinoma

Bulk and single-cell RNA sequencing do not provide full characterization of tissue spatial diversity in cancer samples, and currently available in situ techniques (multiplex immunohistochemistry and imaging mass cytometry) allow for only limited analysis of a small number of targets. The current study represents the first comprehensive approach to spatial transcriptomics of high-grade serous ovarian carcinoma using intact tumor tissue. We selected a small cohort of patients with highly annotated high-grade serous ovarian carcinoma, categorized them by response to neoadjuvant chemotherapy (poor or excellent), and analyzed pre-treatment tumor tissue specimens. Our study uncovered extensive differences in tumor composition between the poor responders and excellent responders to chemotherapy, related to cell cluster organization and localization. This in-depth characterization of high-grade serous ovarian carcinoma tumor tissue from poor and excellent responders showed that spatial interactions between cell clusters may influence chemo-responsiveness more than cluster composition alone.

Inflammatory Microenvironment of Skin Wounds

Wound healing is a dynamic and highly regulated process that can be separated into three overlapping and interdependent phases: inflammation, proliferation, and remodelling. This review focuses on the inflammation stage, as it is the key stage of wound healing and plays a vital role in the local immune response and determines the progression of wound healing. Inflammatory cells, the main effector cells of the inflammatory response, have been widely studied, but little attention has been paid to the immunomodulatory effects of wound healing in non-inflammatory cells and the extracellular matrix. In this review, we attempt to deepen our understanding of the wound-healing microenvironment in the inflammatory stage by focusing on the interactions between cells and the extracellular matrix, as well as their role in regulating the immune response during the inflammatory stage. We hope our findings will provide new ideas for promoting tissue regeneration through immune regulation.

Articulating the "stem cell niche" paradigm through the lens of non-model aquatic invertebrates

Stem cells (SCs) in vertebrates typically reside in "stem cell niches" (SCNs), morphologically restricted tissue microenvironments that are important for SC survival and proliferation. SCNs are broadly defined by properties including physical location, but in contrast to vertebrates and other "model" organisms, aquatic invertebrate SCs do not have clearly documented niche outlines or properties. Life strategies such as regeneration or asexual reproduction may have conditioned the niche architectural variability in aquatic or marine animal groups. By both establishing the invertebrates SCNs as independent types, yet allowing inclusiveness among them, the comparative analysis will allow the future functional characterization of SCNs.

Integrating single-cell genomics pipelines to discover mechanisms of stem cell differentiation

Pluripotent stem cells underpin a growing sector that leverages their differentiation potential for research, industry, and clinical applications. This review evaluates the landscape of methods in single-cell transcriptomics that are enabling accelerated discovery in stem cell science. We focus on strategies for scaling stem cell differentiation through multiplexed single-cell analyses, for evaluating molecular regulation of cell differentiation using new analysis algorithms, and methods for integration and projection analysis to classify and benchmark stem cell derivatives against in vivo cell types. By discussing the available methods, comparing their strengths, and illustrating strategies for developing integrated analysis pipelines, we provide user considerations to inform their implementation and interpretation.