Deciphering cell-cell interactions and communication from gene expression

A global view of altered ligand-receptor interactions in bone marrow aging based on single-cell sequencing

Altered cell-cell communication is a hallmark of aging, but its impact on bone marrow aging remains poorly understood. Based on a common and effective pipeline and single-cell transcriptome sequencing, we detected 384,124 interactions including 2575 ligand-receptor pairs and 16 non-adherent bone marrow cell types in old and young mouse and identified a total of 5560 significantly different interactions, which were then verified by flow cytometry and quantitative real-time PCR. These differential ligand-receptor interactions exhibited enrichment for the senescence-associated secretory phenotypes. Further validation demonstrated supplementing specific extracellular ligands could modify the senescent signs of hematopoietic stem cells derived from old mouse. Our work provides an effective procedure to detect the ligand-receptor interactions based on single-cell sequencing, which contributes to understand mechanisms and provides a potential strategy for intervention of bone marrow aging.

Cancer marker TNFRSF1A: From single‑cell heterogeneity of renal cell carcinoma to functional validation

During the progression of renal cell carcinoma (RCC), tumor growth, metastasis and treatment response heterogeneity are regulated by both the tumor itself and the tumor microenvironment (TME). The aim of the present study was to investigate the role of the TME in RCC and construct a crosstalk network for clear cell RCC (ccRCC). An additional aim was to evaluate whether TNF receptor superfamily member 1A (TNFRSF1A) is a potential therapeutic target for ccRCC. Single-cell data analysis of RCC was performed using the GSE152938 dataset, focusing on key cellular components and their involvement in the ccRCC TME. Additionally, cell-cell communication was analyzed to elucidate the complex network of the ccRCC microenvironment. Analyses of data from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium databases were performed to further mine the key TNF receptor genes, with a particular focus on the prediction and assessment of the cancer-associated features of TNFRSF1A. In addition, following the silencing of TNFRSF1A using small interfering RNA in the 786-O ccRCC cell line, a number of in vitro experiments were conducted to further investigate the cancer-promoting characteristics of TNFRSF1A. These included 5-ethynyl-2'-deoxyuridine incorporation, Cell Counting Kit-8, colony formation, Transwell, cell cycle and apoptosis assays. The TNF signaling pathway was found to have a critical role in the development of ccRCC. Based on the specific crosstalk identified between TNF and TNFRSF1A, the communication of this signaling pathway within the TME was elucidated. The results of the cellular phenotype experiments indicated that TNFRSF1A promotes the proliferation, migration and invasion of ccRCC cells. Consequently, it is proposed that targeting TNFRSF1A may disrupt tumor progression and serve as a therapeutic strategy. In conclusion, by understanding the TME and identifying significant crosstalk within the TNF signaling pathway, the potential of TNFRSF1A as a therapeutic target is highlighted. This may facilitate an advance in precision medicine and improve the prognosis for patients with RCC.

Spatial transcriptomic characterization of pathologic niches in IPF

Despite advancements in antifibrotic therapy, idiopathic pulmonary fibrosis (IPF) remains a medical condition with unmet needs. Single-cell RNA sequencing (scRNA-seq) has enhanced our understanding of IPF but lacks the cellular tissue context and gene expression localization that spatial transcriptomics provides. To bridge this gap, we profiled IPF and control patient lung tissue using spatial transcriptomics, integrating the data with an IPF scRNA-seq atlas. We identified three disease-associated niches with unique cellular compositions and localizations. These include a fibrotic niche, consisting of myofibroblasts and aberrant basaloid cells, located around airways and adjacent to an airway macrophage niche in the lumen, containing SPP1+ macrophages. In addition, we identified an immune niche, characterized by distinct lymphoid cell foci in fibrotic tissue, surrounded by remodeled endothelial vessels. This spatial characterization of IPF niches will facilitate the identification of drug targets that disrupt disease-driving niches and aid in the development of disease relevant in vitro models.

Reversible Modulation of Cell-Cell Interactions Using Electrochemistry

Cell-cell interactions play an important role in many biological processes, and various methods have been developed for controlling the cell-cell interactions. However, the effective and rapid control of intercellular interactions remains challenging. Herein, we report a novel, rapid, and effective electrochemical strategy without destroying the basic life processes for the dynamic control of intercellular interactions via liposome fusion. In the proposed system, bioorthogonal chemical groups and hydroquinone (HQ)- and aminooxy (AO)-tethered ligands were modified on the surface of living cells on the basis of the liposome fusion, enabling dynamical intercellular assemblies. Upon application of the corresponding oxidative potential, the "off-state" HQ could be oxidized to the "on-state" quinone (Q), which subsequently reacts with AO-tethered ligands to form stable oxime linkages under physiological conditions. This reaction effectively shortens the distance between cells, promoting the formation of cell clusters. When the corresponding reverse reductive potential is applied, the oxime linkage is cleaved, resulting in the release of the cells. Furthermore, we employed HQ- and AO-tethered ligands to modify mitochondria, inducing mitochondrial aggregation. This noninvasive and label-free strategy allows for the dynamic reversible regulation of intercellular interactions, enhancing our understanding of intercellular communication networks, and has the potential for improving the antitumor therapy efficacy.

trans-Interacting Plasma Membrane Proteins and Binding Partner Identification

Plasma membrane proteins (PMPs) play critical roles in a myriad of physiological and disease conditions. A unique subset of PMPs functions through interacting with each other in trans at the interface between two contacting cells. These trans-interacting PMPs (tiPMPs) include adhesion molecules and ligands/receptors that facilitate cell-cell contact and direct communication between cells. Among the tiPMPs, a significant number have apparent extracellular binding domains but remain orphans with no known binding partners. Identification of their potential binding partners is therefore important for the understanding of processes such as organismal development and immune cell activation. While a number of methods have been developed for the identification of protein binding partners in general, very few are applicable to tiPMPs, which interact in a two-dimensional fashion with low intrinsic binding affinities. In this review, we present the significance of tiPMP interactions, the challenges of identifying binding partners for tiPMPs, and the landscape of method development. We describe current avidity-based screening approaches for identifying novel tiPMP binding partners and discuss their advantages and limitations. We conclude by highlighting the importance of developing novel methods of identifying new tiPMP interactions for deciphering the complex protein interactome and developing targeted therapeutics for diseases.

Histopathological-based analysis of human kidney spatial transcriptomics data: toward precision pathology

The application of spatial transcriptomic (ST) technologies is booming, and has already yielded important insights across many different tissues and disease models. In nephrology, ST technologies have helped to decipher the cellular and molecular mechanisms at work in kidney diseases and have allowed the recent creation of spatially anchored human kidney atlases in healthy and diseased kidney tissues. During ST data analysis, the obtained computationally-annotated clusters are often superimposed on a histological image without their initial identification being based on the morphological and spatial analysis of the tissues and lesions. In this study, we conduct a histopathological-based analysis of spatial transcriptomics data on a human kidney sample corresponding as closely as possible to the reality of the interpretation of a kidney biopsy in a healthcare or research context. Our work demonstrates the feasibility of a morphological-based approach to interpreting ST data, helping to improve our understanding of the lesional phenomena at work in CKD, at both cellular and molecular levels. Finally, we show that our newly identified pathological-based clusters can be accurately projected onto other slides from nephrectomy or needle biopsies samples and thus serve as a reference for analyzing other kidney tissues paving the way for the future of molecular microscopy and precision pathology.

Interactions between immune cell types facilitate the evolution of immune traits

An essential prerequisite for evolution by natural selection is variation among individuals in traits that affect fitness1. The ability of a system to produce selectable variation, known as evolvability2, thus markedly affects the rate of evolution. Although the immune system is among the fastest-evolving components in mammals3, the sources of variation in immune traits remain largely unknown4,5. Here we show that an important determinant of the immune system's evolvability is its organization into interacting modules represented by different immune cell types. By profiling immune cell variation in bone marrow of 54 genetically diverse mouse strains from the Collaborative Cross6, we found that variation in immune cell frequencies is polygenic and that many associated genes are involved in homeostatic balance through cell-intrinsic functions of proliferation, migration and cell death. However, we also found genes associated with the frequency of a particular cell type that are expressed in a different cell type, exerting their effect in what we term cyto-trans. The vertebrate evolutionary record shows that genes associated in cyto-trans have faced weaker negative selection, thus increasing the robustness and hence evolvability2,7,8 of the immune system. This phenomenon is similarly observable in human blood. Our findings suggest that interactions between different components of the immune system provide a phenotypic space in which mutations can produce variation with little detriment, underscoring the role of modularity in the evolution of complex systems9.

Imputing abundance of over 2500 surface proteins from single-cell transcriptomes with context-agnostic zero-shot deep ensembles

Cell surface proteins serve as primary drug targets and cell identity markers. The emergence of techniques like CITE-seq has enabled simultaneous quantification of surface protein abundance and transcript expression for multimodal data analysis within individual cells. The published data have been utilized to train machine learning models for predicting surface protein abundance based solely from transcript expression. However, the small scale of proteins predicted and the poor generalization ability for these computational approaches across diverse contexts, such as different tissues or disease states, impede their widespread adoption. Here we propose SPIDER (surface protein prediction using deep ensembles from single-cell RNA-seq), a context-agnostic zero-shot deep ensemble model, which enables the large-scale prediction of cell surface protein abundance and generalizes better to various contexts. Comprehensive benchmarking shows that SPIDER outperforms other state-of-the-art methods. Using the predicted surface abundance of >2500 proteins from single-cell transcriptomes, we demonstrate the broad applications of SPIDER including cell type annotation, biomarker/target identification, and cell-cell interaction analysis in hepatocellular carcinoma and colorectal cancer.

Whole brain alignment of spatial transcriptomics between humans and mice with BrainAlign

The increasing utilization of mouse models in human neuroscience research places higher demands on computational methods to translate findings from the mouse brain to the human one. In this study, we develop BrainAlign, a self-supervised learning approach, for the whole brain alignment of spatial transcriptomics (ST) between humans and mice. BrainAlign encodes spots and genes simultaneously in two separated shared embedding spaces by a heterogeneous graph neural network. We demonstrate that BrainAlign could integrate cross-species spots into the embedding space and reveal the conserved brain regions supported by ST information, which facilitates the detection of homologous regions between humans and mice. Genomic analysis further presents gene expression connections between humans and mice and reveals similar expression patterns for marker genes. Moreover, BrainAlign can accurately map spatially similar homologous regions or clusters onto a unified spatial structural domain while preserving their relative positions.

A cuproptosis-related lncRNAs signature predicts prognosis and reveals pivotal interactions between immune cells in colon cancer

Copper-mediated cell death presents distinct pathways from established apoptosis processes, suggesting alternative therapeutic approaches for colon cancer. Our research aims to develop a predictive framework utilizing long-noncoding RNAs (lncRNAs) related to cuproptosis to predict colon cancer outcomes while examining immune interactions and intercellular signaling. We obtained colon cancer-related human mRNA expression profiles and clinical information from the Cancer Genome Atlas repository. To isolate lncRNAs involved in cuproptosis, we applied Cox proportional hazards modeling alongside the least absolute shrinkage and selection operator technique. We elucidated the underlying mechanisms by examining the tumor mutational burden, the extent of immune cell penetration, and intercellular communication dynamics. Based on the model, drugs were predicted and validated with cytological experiments. A 13 lncRNA-cuproptosis-associated risk model was constructed. Two colon cancer cell lines were used to validate the predicted representative mRNAs with high correlation coefficients with copper-induced cell death. Survival enhancement in the low-risk cohort was evidenced by the trends in Kaplan-Meier survival estimates. Analysis of immune cell infiltration suggested that survival was induced by the increased infiltration of naïve CD4+ T cells and a reduction of M2 macrophages within the low-risk faction. Decreased infiltration of naïve B cells, resting NK cells, and M0 macrophages was significantly associated with better overall survival. Combined single-cell analysis suggested that CCL5-ACKR1, CCL2-ACKR1, and CCL5-CCR1 pathways play key roles in mediating intercellular dialogues among immune constituents within the neoplastic microhabitat. We identified three drugs with a high sensitivity in the high-risk group. In summary, this discovery establishes the possibility of using 13 cuproptosis-associated lncRNAs as a risk model to assess the prognosis, unravel the immune mechanisms and cell communication, and improve treatment options, which may provide a new idea for treating colon cancer.

scCross: a deep generative model for unifying single-cell multi-omics with seamless integration, cross-modal generation, and in silico exploration

Single-cell multi-omics data reveal complex cellular states, providing significant insights into cellular dynamics and disease. Yet, integration of multi-omics data presents challenges. Some modalities have not reached the robustness or clarity of established transcriptomics. Coupled with data scarcity for less established modalities and integration intricacies, these challenges limit our ability to maximize single-cell omics benefits. We introduce scCross, a tool leveraging variational autoencoders, generative adversarial networks, and the mutual nearest neighbors (MNN) technique for modality alignment. By enabling single-cell cross-modal data generation, multi-omics data simulation, and in silico cellular perturbations, scCross enhances the utility of single-cell multi-omics studies.

xSiGra: explainable model for single-cell spatial data elucidation

Recent advancements in spatial imaging technologies have revolutionized the acquisition of high-resolution multichannel images, gene expressions, and spatial locations at the single-cell level. Our study introduces xSiGra, an interpretable graph-based AI model, designed to elucidate interpretable features of identified spatial cell types, by harnessing multimodal features from spatial imaging technologies. By constructing a spatial cellular graph with immunohistology images and gene expression as node attributes, xSiGra employs hybrid graph transformer models to delineate spatial cell types. Additionally, xSiGra integrates a novel variant of gradient-weighted class activation mapping component to uncover interpretable features, including pivotal genes and cells for various cell types, thereby facilitating deeper biological insights from spatial data. Through rigorous benchmarking against existing methods, xSiGra demonstrates superior performance across diverse spatial imaging datasets. Application of xSiGra on a lung tumor slice unveils the importance score of cells, illustrating that cellular activity is not solely determined by itself but also impacted by neighboring cells. Moreover, leveraging the identified interpretable genes, xSiGra reveals endothelial cell subset interacting with tumor cells, indicating its heterogeneous underlying mechanisms within complex cellular interactions.

The shared role of neutrophils in ankylosing spondylitis and ulcerative colitis

This study aimed to analyze single-cell sequencing data to investigate immune cell interactions in ankylosing spondylitis (AS) and ulcerative colitis (UC). Vertebral bone marrow blood was collected from three AS patients for 10X single-cell sequencing. Analysis of single-cell data revealed distinct cell types in AS and UC patients. Cells significantly co-expressing immune cells (P < 0.05) were subjected to communication analysis. Overlapping genes of these co-expressing immune cells were subjected to GO and KEGG analyses. Key genes were identified using STRING and Cytoscape to assess their correlation with immune cell expression. The results showed the significance of neutrophils in both diseases (P < 0.01), with notable interactions identified through communication analysis. XBP1 emerged as a Hub gene for both diseases, with AUC values of 0.760 for AS and 0.933 for UC. Immunohistochemistry verified that the expression of XBP1 was significantly lower in the AS group and significantly greater in the UC group than in the control group (P < 0.01). This finding highlights the critical role of neutrophils in both AS and UC, suggesting the presence of shared immune response elements. The identification of XBP1 as a potential therapeutic target offers promising intervention avenues for both diseases.

TWCOM: an R package for inference of cell-cell communication on spatially resolved transcriptomics data

Summary

The inference of cell-cell communication is important, as it unveils the intricate cellular behaviors at the molecular level, providing crucial insights essential for understanding complex biological processes and informing targeted interventions in various pathological contexts. Here, we present TWCOM, an R package that implements a Tweedie distribution-based model for accurate cell-cell communication inference. Operating under a generalized additive model framework, TWCOM adeptly handles both single-cell resolution and spot-based spatially resolved transcriptomics data, providing a versatile tool for robust biological sample analysis.

Availability and implementation

The R package TWCOM is available at https://github.com/dongyuanwu/TWCOM. Comprehensive documentation is included with the package.

Insulin receptor orchestrates kidney antibacterial defenses

Urinary tract infection (UTI) commonly afflicts people with diabetes. This augmented infection risk is partly due to deregulated insulin receptor (IR) signaling in the kidney collecting duct. The collecting duct is composed of intercalated cells (ICs) and principal cells (PCs). Evidence suggests that ICs contribute to UTI defenses. Here, we interrogate how IR deletion in ICs impacts antibacterial defenses against uropathogenic Escherichia coli. We also explore how IR deletion affects immune responses in neighboring PCs with intact IR expression. To accomplish this objective, we profile the transcriptomes of IC and PC populations enriched from kidneys of wild-type and IC-specific IR knock-out mice that have increased UTI susceptibility. Transcriptomic analysis demonstrates that IR deletion suppresses IC-integrated stress responses and innate immune defenses. To define how IR shapes these immune defenses, we employ murine and human kidney cultures. When challenged with bacteria, murine ICs and human kidney cells with deregulated IR signaling cannot engage central components of the integrated stress response-including activating transcriptional factor 4 (ATF4). Silencing ATF4 impairs NFkB activation and promotes infection. In turn, NFkB silencing augments infection and suppresses antimicrobial peptide expression. In diabetic mice and people with diabetes, collecting duct cells show reduced IR expression, impaired integrated stress response engagement, and compromised immunity. Collectively, these translational data illustrate how IR orchestrates collecting duct antibacterial responses and the communication between ICs and PCs.

scSemiProfiler: Advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning

Single-cell sequencing is a crucial tool for dissecting the cellular intricacies of complex diseases. Its prohibitive cost, however, hampers its application in expansive biomedical studies. Traditional cellular deconvolution approaches can infer cell type proportions from more affordable bulk sequencing data, yet they fall short in providing the detailed resolution required for single-cell-level analyses. To overcome this challenge, we introduce "scSemiProfiler", an innovative computational framework that marries deep generative models with active learning strategies. This method adeptly infers single-cell profiles across large cohorts by fusing bulk sequencing data with targeted single-cell sequencing from a few rigorously chosen representatives. Extensive validation across heterogeneous datasets verifies the precision of our semi-profiling approach, aligning closely with true single-cell profiling data and empowering refined cellular analyses. Originally developed for extensive disease cohorts, "scSemiProfiler" is adaptable for broad applications. It provides a scalable, cost-effective solution for single-cell profiling, facilitating in-depth cellular investigation in various biological domains.

Ion Signaling in Cell Motility and Development in Dictyostelium discoideum

Cell-to-cell communication is fundamental to the organization and functionality of multicellular organisms. Intercellular signals orchestrate a variety of cellular responses, including gene expression and protein function changes, and contribute to the integrated functions of individual tissues. Dictyostelium discoideum is a model organism for cell-to-cell interactions mediated by chemical signals and multicellular formation mechanisms. Upon starvation, D. discoideum cells exhibit coordinated cell aggregation via cyclic adenosine 3',5'-monophosphate (cAMP) gradients and chemotaxis, which facilitates the unicellular-to-multicellular transition. During this process, the calcium signaling synchronizes with the cAMP signaling. The resulting multicellular body exhibits organized collective migration and ultimately forms a fruiting body. Various signaling molecules, such as ion signals, regulate the spatiotemporal differentiation patterns within multicellular bodies. Understanding cell-to-cell and ion signaling in Dictyostelium provides insight into general multicellular formation and differentiation processes. Exploring cell-to-cell and ion signaling enhances our understanding of the fundamental biological processes related to cell communication, coordination, and differentiation, with wide-ranging implications for developmental biology, evolutionary biology, biomedical research, and synthetic biology. In this review, I discuss the role of ion signaling in cell motility and development in D. discoideum.

Predicting intercellular communication based on metabolite-related ligand-receptor interactions with MRCLinkdb

BACKGROUND

Metabolite-associated cell communications play critical roles in maintaining human biological function. However, most existing tools and resources focus only on ligand-receptor interaction pairs where both partners are proteinaceous, neglecting other non-protein molecules. To address this gap, we introduce the MRCLinkdb database and algorithm, which aggregates and organizes data related to non-protein L-R interactions in cell-cell communication, providing a valuable resource for predicting intercellular communication based on metabolite-related ligand-receptor interactions.

RESULTS

Here, we manually curated the metabolite-ligand-receptor (ML-R) interactions from the literature and known databases, ultimately collecting over 790 human and 670 mouse ML-R interactions. Additionally, we compiled information on over 1900 enzymes and 260 transporter entries associated with these metabolites. We developed Metabolite-Receptor based Cell Link Database (MRCLinkdb) to store these ML-R interactions data. Meanwhile, the platform also offers extensive information for presenting ML-R interactions, including fundamental metabolite information and the overall expression landscape of metabolite-associated gene sets (such as receptor, enzymes, and transporter proteins) based on single-cell transcriptomics sequencing (covering 35 human and 26 mouse tissues, 52 human and 44 mouse cell types) and bulk RNA-seq/microarray data (encompassing 62 human and 39 mouse tissues). Furthermore, MRCLinkdb introduces a web server dedicated to the analysis of intercellular communication based on ML-R interactions. MRCLinkdb is freely available at https://www.cellknowledge.com.cn/mrclinkdb/ .

CONCLUSIONS

In addition to supplementing ligand-receptor databases, MRCLinkdb may provide new perspectives for decoding the intercellular communication and advancing related prediction tools based on ML-R interactions.

Extracellular Vesicles: The Next Generation of Biomarkers and Treatment for Central Nervous System Diseases

It has been widely established that the characterization of extracellular vesicles (EVs), particularly small EVs (sEVs), shed by different cell types into biofluids, helps to identify biomarkers and therapeutic targets in neurological and neurodegenerative diseases. Recent studies are also exploring the efficacy of mesenchymal stem cell-derived extracellular vesicles naturally enriched with therapeutic microRNAs and proteins for treating various diseases. In addition, EVs released by various neural cells play a crucial function in the modulation of signal transmission in the brain in physiological conditions. However, in pathological conditions, such EVs can facilitate the spread of pathological proteins from one brain region to the other. On the other hand, the analysis of EVs in biofluids can identify sensitive biomarkers for diagnosis, prognosis, and disease progression. This review discusses the potential therapeutic use of stem cell-derived EVs in several central nervous system diseases. It lists their differences and similarities and confers various studies exploring EVs as biomarkers. Further advances in EV research in the coming years will likely lead to the routine use of EVs in therapeutic settings.

Proteomics Analysis of Interactions between Drug-Resistant and Drug-Sensitive Cancer Cells: Comparative Studies of Monoculture and Coculture Cell Systems

Cell-cell interactions, which allow cells to communicate with each other through molecules in their microenvironment, are critical for the growth, health, and functions of cells. Previous studies show that drug-resistant cells can interact with drug-sensitive cells to elevate their drug resistance level, which is partially responsible for cancer recurrence. Studying protein targets and pathways involved in cell-cell communication provides essential information for fundamental cell biology studies and therapeutics of human diseases. In the current studies, we performed direct coculture and indirect coculture of drug-resistant and drug-sensitive cell lines, aiming to investigate intracellular proteins responsible for cell communication. Comparative studies were carried out using monoculture cells. Shotgun bottom-up proteomics results indicate that the P53 signaling pathway has a strong association with drug resistance mechanisms, and multiple TP53-related proteins were upregulated in both direct and indirect coculture systems. In addition, cell-cell communication pathways, including the phagosome and the HIF-signaling pathway, contribute to both direct and indirect coculture systems. Consequently, AK3 and H3-3A proteins were identified as potential targets for cell-cell interactions that are relevant to drug resistance mechanisms. We propose that the P53 signaling pathway, in which mitochondrial proteins play an important role, is responsible for inducing drug resistance through communication between drug-resistant and drug-sensitive cancer cells.

Combining TIGIT Blockade with MDSC Inhibition Hinders Breast Cancer Bone Metastasis by Activating Antitumor Immunity

Bone is the most common site of breast cancer metastasis. Bone metastasis is incurable and is associated with severe morbidity. Utilizing an immunocompetent mouse model of spontaneous breast cancer bone metastasis, we profiled the immune transcriptome of bone metastatic lesions and peripheral bone marrow at distinct metastatic stages, revealing dynamic changes during the metastatic process. We show that cross-talk between granulocytes and T cells is central to shaping an immunosuppressive microenvironment. Specifically, we identified the PD-1 and TIGIT signaling axes and the proinflammatory cytokine IL1β as central players in the interactions between granulocytes and T cells. Targeting these pathways in vivo resulted in attenuated bone metastasis and improved survival, by reactivating antitumor immunity. Analysis of patient samples revealed that TIGIT and IL1β are prominent in human bone metastasis. Our findings suggest that cotargeting immunosuppressive granulocytes and dysfunctional T cells may be a promising novel therapeutic strategy to inhibit bone metastasis. Significance: Temporal transcriptome profiling of the immune microenvironment in breast cancer bone metastasis revealed key communication pathways between dysfunctional T cells and myeloid derived suppressor cells. Cotargeting of TIGIT and IL1β inhibited bone metastasis and improved survival. Validation in patient data implicated these targets as a novel promising approach to treat human bone metastasis.

Send it, receive it, quick erase it: A mouse model to decipher chemokine communication

A method to precisely determine which cells respond to chemokines in vivo is currently lacking. A novel class of dual fluorescence reporter mice could help identify cells that produce and/or sense a given chemokine in vitro and in vivo (Rodrigo et al. 2024. J. Exp. Med.https://doi.org/10.1084/jem.20231814).

Dual fluorescence reporter mice for Ccl3 transcription, translation, and intercellular communication

Chemokines guide immune cells during their response against pathogens and tumors. Various techniques exist to determine chemokine production, but none to identify cells that directly sense chemokines in vivo. We have generated CCL3-EASER (ErAse, SEnd, Receive) mice that simultaneously report for Ccl3 transcription and translation, allow identifying Ccl3-sensing cells, and permit inducible deletion of Ccl3-producing cells. We infected these mice with murine cytomegalovirus (mCMV), where Ccl3 and NK cells are critical defense mediators. We found that NK cells transcribed Ccl3 already in homeostasis, but Ccl3 translation required type I interferon signaling in infected organs during early infection. NK cells were both the principal Ccl3 producers and sensors of Ccl3, indicating auto/paracrine communication that amplified NK cell response, and this was essential for the early defense against mCMV. CCL3-EASER mice represent the prototype of a new class of dual fluorescence reporter mice for analyzing cellular communication via chemokines, which may be applied also to other chemokines and disease models.

A count-based model for delineating cell-cell interactions in spatial transcriptomics data

MOTIVATION

Cell-cell interactions (CCIs) consist of cells exchanging signals with themselves and neighboring cells by expressing ligand and receptor molecules and play a key role in cellular development, tissue homeostasis, and other critical biological functions. Since direct measurement of CCIs is challenging, multiple methods have been developed to infer CCIs by quantifying correlations between the gene expression of the ligands and receptors that mediate CCIs, originally from bulk RNA-sequencing data and more recently from single-cell or spatially resolved transcriptomics (SRT) data. SRT has a particular advantage over single-cell approaches, since ligand-receptor correlations can be computed between cells or spots that are physically close in the tissue. However, the transcript counts of individual ligands and receptors in SRT data are generally low, complicating the inference of CCIs from expression correlations.

RESULTS

We introduce Copulacci, a count-based model for inferring CCIs from SRT data. Copulacci uses a Gaussian copula to model dependencies between the expression of ligands and receptors from nearby spatial locations even when the transcript counts are low. On simulated data, Copulacci outperforms existing CCI inference methods based on the standard Spearman and Pearson correlation coefficients. Using several real SRT datasets, we show that Copulacci discovers biologically meaningful ligand-receptor interactions that are lowly expressed and undiscoverable by existing CCI inference methods.

AVAILABILITY AND IMPLEMENTATION

Copulacci is implemented in Python and available at https://github.com/raphael-group/copulacci.

Protocol for unsupervised inference of cell-cell communication using matrix decomposition

Exploring cell-cell communication is pivotal for understanding biological processes in multicellular life forms. Here, we present a protocol that details the use of matrix decomposition to infer cell-cell communication (MDIC3) for unsupervised cell-cell communication inference. We describe steps for using the MDIC3 Python scripts to deduce cell-cell communication and identify key ligand-receptor pairs between a specific cell type pair from a single-cell gene expression dataset. This protocol has potential application in cell-cell communication inference on any species. For complete details on the use and execution of this protocol, please refer to Liu et al.1.

Quantitative live-cell imaging of secretion activity reveals dynamic immune responses

Quantification of cytokine secretion has facilitated advances in the field of immunology, yet the dynamic and varied secretion profiles of individual cells, particularly those obtained from limited human samples, remain obscure. Herein, we introduce a technology for quantitative live-cell imaging of secretion activity (qLCI-S) that enables high-throughput and dual-color monitoring of secretion activity at the single-cell level over several days, followed by transcriptome analysis of individual cells based on their phenotype. The efficacy of qLCI-S was demonstrated by visualizing the characteristic temporal pattern of cytokine secretion of group 2 innate lymphoid cells, which constitute less than 0.01% of human peripheral blood mononuclear cells, and by revealing minor subpopulations with enhanced cytokine production. The underlying mechanism of this feature was linked to the gene expression of stimuli receptors. This technology paves the way for exploring gene expression signatures linked to the spatiotemporal dynamic nature of various secretory functions.

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.

Tissue module discovery in single-cell-resolution spatial transcriptomics data via cell-cell interaction-aware cell embedding

Computational methods are desired for single-cell-resolution spatial transcriptomics (ST) data analysis to uncover spatial organization principles for how individual cells exert tissue-specific functions. Here, we present ST data analysis via interaction-aware cell embedding (SPACE), a deep-learning method for cell-type identification and tissue module discovery from single-cell-resolution ST data by learning a cell representation that captures its gene expression profile and interactions with its spatial neighbors. SPACE identified spatially informed cell subtypes defined by their special spatial distribution patterns and distinct proximal-interacting cell types. SPACE also automatically discovered "cell communities"-tissue modules with discernible boundaries and a uniform spatial distribution of constituent cell types. For each cell community, SPACE outputs a characteristic proximal cell-cell interaction network associated with physiological processes, which can be used to refine ligand-receptor-based intercellular signaling analyses. We envision that SPACE can be used in large-scale ST projects to understand how proximal cell-cell interactions contribute to emergent biological functions within cell communities. A record of this paper's transparent peer review process is included in the supplemental information.

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.

Chemomechanical Communication between Liposomes Based on Enzyme Cascades

Communication between cells is crucial to the survival of both uni- and multicellular organisms. The primary mode of communication involves chemical cues. There is great current interest in mimicking this behavior in synthetic cells to understand the physical basis of intercellular communication and design collective functional behavior. Using liposomal cell mimics, we demonstrate how a chemical input can elicit a mechanical response (enhanced motility). We employed a single substrate to trigger enzyme cascade-induced control of the diffusion of up to three different liposome populations. Furthermore, substrate competition allows temporal control over enhanced diffusion. The use of enzyme cascades to propagate chemical signals provides a robust and efficient mechanism for diverse populations of protocells to coordinate their motion in response to signals from each other.

The migrasome, an organelle for cell-cell communication

Migrasomes, newly identified extracellular organelles produced by migrating cells, are observed widely across both in vivo and in vitro studies. These organelles, rich in signaling and bioactive molecules, are pivotal in a range of physiological functions. This opinion summarizes current understanding of migrasomes, highlighting their importance as a versatile mechanism for cell-cell communication. Furthermore, it examines their roles in health and disease and potential diagnostic and therapeutic applications, and addresses the emerging challenges and open questions in this developing field.

Surface Crystal and Degradability of Shape Memory Scaffold Essentialize Osteochondral Regeneration

The minimally invasive deployment of scaffolds is a key safety factor for the regeneration of cartilage and bone defects. Osteogenesis relies primarily on cell-matrix interactions, whereas chondrogenesis relies on cell-cell aggregation. Bone matrix expansion requires osteoconductive scaffold degradation. However, chondrogenic cell aggregation is promoted on the repellent scaffold surface, and minimal scaffold degradation supports the avascular nature of cartilage regeneration. Here, a material satisfying these requirements for osteochondral regeneration is developed by integrating osteoconductive hydroxyapatite (HAp) with a chondroconductive shape memory polymer (SMP). The shape memory function-derived fixity and recovery of the scaffold enabled minimally invasive deployment and expansion to fill irregular defects. The crystalline phases on the SMP surface inhibited cell aggregation by suppressing water penetration and subsequent protein adsorption. However, HAp conjugation SMP (H-SMP) enhanced surface roughness and consequent cell-matrix interactions by limiting cell aggregation using crystal peaks. After mouse subcutaneous implantation, hydrolytic H-SMP accelerated scaffold degradation compared to that by the minimal degradation observed for SMP alone for two months. H-SMP and SMP are found to promote osteogenesis and chondrogenesis, respectively, in vitro and in vivo, including the regeneration of rat osteochondral defects using the binary scaffold form, suggesting that this material is promising for osteochondral regeneration.

Discovery and Characterization of the ddx41 Gene in Atlantic Salmon: Evolutionary Implications, Structural Functions, and Innate Immune Responses to Piscirickettsia salmonis and Renibacterium salmoninarum Infections

The innate immune response in Salmo salar, mediated by pattern recognition receptors (PRRs), is crucial for defending against pathogens. This study examined DDX41 protein functions as a cytosolic/nuclear sensor for cyclic dinucleotides, RNA, and DNA from invasive intracellular bacteria. The investigation determined the existence, conservation, and functional expression of the ddx41 gene in S. salar. In silico predictions and experimental validations identified a single ddx41 gene on chromosome 5 in S. salar, showing 83.92% homology with its human counterpart. Transcriptomic analysis in salmon head kidney confirmed gene transcriptional integrity. Proteomic identification through mass spectrometry characterized three unique peptides with 99.99% statistical confidence. Phylogenetic analysis demonstrated significant evolutionary conservation across species. Functional gene expression analysis in SHK-1 cells infected by Piscirickettsia salmonis and Renibacterium salmoninarum indicated significant upregulation of DDX41, correlated with increased proinflammatory cytokine levels and activation of irf3 and interferon signaling pathways. In vivo studies corroborated DDX41 activation in immune responses, particularly when S. salar was challenged with P. salmonis, underscoring its potential in enhancing disease resistance. This is the first study to identify the DDX41 pathway as a key component in S. salar innate immune response to invading pathogens, establishing a basis for future research in salmonid disease resistance.

Bioorthogonal Quinone Methide Decaging Enables Live-Cell Quantification of Tumor-Specific Immune Interactions

Effective antitumor immunity hinges on the specific engagement between tumor and cytotoxic immune cells, especially cytotoxic T cells. Although investigating these intercellular interactions is crucial for characterizing immune responses and guiding immunotherapeutic applications, direct and quantitative detection of tumor-T cell interactions within a live-cell context remains challenging. We herein report a photocatalytic live-cell interaction labeling strategy (CAT-Cell) relying on the bioorthogonal decaging of quinone methide moieties for sensitive and selective investigation and quantification of tumor-T cell interactions. By developing quinone methide-derived probes optimized for capturing cell-cell interactions (CCIs), we demonstrated the capacity of CAT-Cell for detecting CCIs directed by various types of receptor-ligand pairs (e.g., CD40-CD40L, TCR-pMHC) and further quantified the strengths of tumor-T cell interactions that are crucial for evaluating the antitumor immune responses. We further applied CAT-Cell for ex vivo quantification of tumor-specific T cell interactions on splenocyte and solid tumor samples from mouse models. Finally, the broad compatibility and utility of CAT-Cell were demonstrated by integrating it with the antigen-specific targeting system as well as for tumor-natural killer cell interaction detection. By leveraging the bioorthogonal photocatalytic decaging chemistry on quinone methide, CAT-Cell provides a sensitive, tunable, universal, and noninvasive toolbox for unraveling and quantifying the crucial but delicate tumor-immune interactions under live-cell settings.

Impeller: a path-based heterogeneous graph learning method for spatial transcriptomic data imputation

MOTIVATION

Recent advances in spatial transcriptomics allow spatially resolved gene expression measurements with cellular or even sub-cellular resolution, directly characterizing the complex spatiotemporal gene expression landscape and cell-to-cell interactions in their native microenvironments. Due to technology limitations, most spatial transcriptomic technologies still yield incomplete expression measurements with excessive missing values. Therefore, gene imputation is critical to filling in missing data, enhancing resolution, and improving overall interpretability. However, existing methods either require additional matched single-cell RNA-seq data, which is rarely available, or ignore spatial proximity or expression similarity information.

RESULTS

To address these issues, we introduce Impeller, a path-based heterogeneous graph learning method for spatial transcriptomic data imputation. Impeller has two unique characteristics distinct from existing approaches. First, it builds a heterogeneous graph with two types of edges representing spatial proximity and expression similarity. Therefore, Impeller can simultaneously model smooth gene expression changes across spatial dimensions and capture similar gene expression signatures of faraway cells from the same type. Moreover, Impeller incorporates both short- and long-range cell-to-cell interactions (e.g. via paracrine and endocrine) by stacking multiple GNN layers. We use a learnable path operator in Impeller to avoid the over-smoothing issue of the traditional Laplacian matrices. Extensive experiments on diverse datasets from three popular platforms and two species demonstrate the superiority of Impeller over various state-of-the-art imputation methods.

AVAILABILITY AND IMPLEMENTATION

The code and preprocessed data used in this study are available at https://github.com/aicb-ZhangLabs/Impeller and https://zenodo.org/records/11212604.

Cell communication pathway prognostic model identified detrimental neurodevelopmental pathways in neuroblastoma

Neurodevelopmental cell communication plays a crucial role in neuroblastoma prognosis. However, determining the impact of these communication pathways on prognosis is challenging due to limited sample sizes and patchy clinical survival information of single cell RNA-seq data. To address this, we have developed the cell communication pathway prognostic model (CCPPM) in this study. CCPPM involves the identification of communication pathways through single-cell RNA-seq data, screening of prognosis-significant pathways using bulk RNA-seq data, conducting functional and attribute analysis of these pathways, and analyzing the post-effects of communication within these pathways. By employing the CCPPM, we have identified ten communication pathways significantly influencing neuroblastoma, all related to axongenesis and neural projection development, especially the BMP7-(BMPR1B-ACVR2B) communication pathway was found to promote tumor cell migration by activating the transcription factor SMAD1 and regulating UNK and MYCBP2. Notably, BMP7 expression was higher in neuroblastoma samples with distant metastases. In summary, CCPPM offers a novel approach to studying the influence of cell communication pathways on disease prognosis and identified detrimental communication pathways related to neurodevelopment.

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.

Bridging systems biology and tissue engineering: Unleashing the full potential of complex 3D in vitro tissue models of disease

Rapid advances in tissue engineering have resulted in more complex and physiologically relevant 3D in vitro tissue models with applications in fundamental biology and therapeutic development. However, the complexity provided by these models is often not leveraged fully due to the reductionist methods used to analyze them. Computational and mathematical models developed in the field of systems biology can address this issue. Yet, traditional systems biology has been mostly applied to simpler in vitro models with little physiological relevance and limited cellular complexity. Therefore, integrating these two inherently interdisciplinary fields can result in new insights and move both disciplines forward. In this review, we provide a systematic overview of how systems biology has been integrated with 3D in vitro tissue models and discuss key application areas where the synergies between both fields have led to important advances with potential translational impact. We then outline key directions for future research and discuss a framework for further integration between fields.

Dissecting Spatiotemporal Structures in Spatial Transcriptomics via Diffusion-Based Adversarial Learning

Recent advancements in spatial transcriptomics (ST) technologies offer unprecedented opportunities to unveil the spatial heterogeneity of gene expression and cell states within tissues. Despite these capabilities of the ST data, accurately dissecting spatiotemporal structures (e.g., spatial domains, temporal trajectories, and functional interactions) remains challenging. Here, we introduce a computational framework, PearlST (partial differential equation [PDE]-enhanced adversarial graph autoencoder of ST), for accurate inference of spatiotemporal structures from the ST data using PDE-enhanced adversarial graph autoencoder. PearlST employs contrastive learning to extract histological image features, integrates a PDE-based diffusion model to enhance characterization of spatial features at domain boundaries, and learns the latent low-dimensional embeddings via Wasserstein adversarial regularized graph autoencoders. Comparative analyses across multiple ST datasets with varying resolutions demonstrate that PearlST outperforms existing methods in spatial clustering, trajectory inference, and pseudotime analysis. Furthermore, PearlST elucidates functional regulations of the latent features by linking intercellular ligand-receptor interactions to most contributing genes of the low-dimensional embeddings, as illustrated in a human breast cancer dataset. Overall, PearlST proves to be a powerful tool for extracting interpretable latent features and dissecting intricate spatiotemporal structures in ST data across various biological contexts.

Identification of prognostic risk model based on plasma cell markers in hepatocellular carcinoma through single-cell sequencing analysis

Hepatocellular carcinoma (HCC) represents a substantial global health burden. Tumorinfiltrating B lymphocytes (TIL-Bs) contribute to tumor progression and significantly impact the efficacy of tumor therapy. However, the characteristics of TIL-Bs in HCC and their effect on HCC therapy remain elusive. Single-cell RNA sequencing (scRNAseq) was applied to investigate the heterogeneity, cellular differentiation and cell-cell communication of TIL-Bs in HCC. Further, the Cancer Genome Atlas-liver hepatocellular carcinoma (TCGA-LIHC) and liver cancer institutes (LCI) cohorts were applied to construct and validate the plasma cell marker-based prognostic risk model. The relationship between the prognostic risk model and the responsiveness of immunotherapy and chemotherapy in patients with HCC were estimated by OncoPredict and tumor immune dysfunction and exclusion (TIDE) algorithm. Finally, we established nomogram and calibration curves to evaluate the precision of the risk score in predicating survival probability. Our data identified five subtypes of TIL-Bs in HCC, each exhibiting varying levels of infiltration in tumor tissues. The interactions between TIL-Bs and other cell types contributed to shaping distinct tumor microenvironments (TME). Moreover, we found that TIL-Bs subtypes had disparate prognostic values in HCC patients. The prognostic risk model demonstrated exceptional predictive accuracy for overall survival and exhibited varying sensitivities to immunotherapy and chemotherapy among patients with HCC. Our data demonstrated that the risk score stood as an independent prognostic predictor and the nomogram results further affirmed its strong prognostic capability. This study reveals the heterogeneity of TIL-Bs and provides a prognostic risk model based on plasma cell markers in HCC, which could prove valuable in predicting prognosis and guiding the choice of suitable therapies for patients with HCC.

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.

Spatial transcriptomic brain imaging reveals the effects of immunomodulation therapy on specific regional brain cells in a mouse dementia model

Increasing evidence of brain-immune crosstalk raises expectations for the efficacy of novel immunotherapies in Alzheimer's disease (AD), but the lack of methods to examine brain tissues makes it difficult to evaluate therapeutics. Here, we investigated the changes in spatial transcriptomic signatures and brain cell types using the 10x Genomics Visium platform in immune-modulated AD models after various treatments. To proceed with an analysis suitable for barcode-based spatial transcriptomics, we first organized a workflow for segmentation of neuroanatomical regions, establishment of appropriate gene combinations, and comprehensive review of altered brain cell signatures. Ultimately, we investigated spatial transcriptomic changes following administration of immunomodulators, NK cell supplements and an anti-CD4 antibody, which ameliorated behavior impairment, and designated brain cells and regions showing probable associations with behavior changes. We provided the customized analytic pipeline into an application named STquantool. Thus, we anticipate that our approach can help researchers interpret the real action of drug candidates by simultaneously investigating the dynamics of all transcripts for the development of novel AD therapeutics.

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

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

Characterizing efficient feature selection for single-cell expression analysis

Unsupervised feature selection is a critical step for efficient and accurate analysis of single-cell RNA-seq data. Previous benchmarks used two different criteria to compare feature selection methods: (i) proportion of ground-truth marker genes included in the selected features and (ii) accuracy of cell clustering using ground-truth cell types. Here, we systematically compare the performance of 11 feature selection methods for both criteria. We first demonstrate the discordance between these criteria and suggest using the latter. We then compare the distribution of selected genes in their means between feature selection methods. We show that lowly expressed genes exhibit seriously high coefficients of variation and are mostly excluded by high-performance methods. In particular, high-deviation- and high-expression-based methods outperform the widely used in Seurat package in clustering cells and data visualization. We further show they also enable a clear separation of the same cell type from different tissues as well as accurate estimation of cell trajectories.

Accurately deciphering spatial domains for spatially resolved transcriptomics with stCluster

Spatial transcriptomics provides valuable insights into gene expression within the native tissue context, effectively merging molecular data with spatial information to uncover intricate cellular relationships and tissue organizations. In this context, deciphering cellular spatial domains becomes essential for revealing complex cellular dynamics and tissue structures. However, current methods encounter challenges in seamlessly integrating gene expression data with spatial information, resulting in less informative representations of spots and suboptimal accuracy in spatial domain identification. We introduce stCluster, a novel method that integrates graph contrastive learning with multi-task learning to refine informative representations for spatial transcriptomic data, consequently improving spatial domain identification. stCluster first leverages graph contrastive learning technology to obtain discriminative representations capable of recognizing spatially coherent patterns. Through jointly optimizing multiple tasks, stCluster further fine-tunes the representations to be able to capture complex relationships between gene expression and spatial organization. Benchmarked against six state-of-the-art methods, the experimental results reveal its proficiency in accurately identifying complex spatial domains across various datasets and platforms, spanning tissue, organ, and embryo levels. Moreover, stCluster can effectively denoise the spatial gene expression patterns and enhance the spatial trajectory inference. The source code of stCluster is freely available at https://github.com/hannshu/stCluster.

Resident and recruited macrophages differentially contribute to cardiac healing after myocardial ischemia

Cardiac macrophages are heterogenous in phenotype and functions, which has been associated with differences in their ontogeny. Despite extensive research, our understanding of the precise role of different subsets of macrophages in ischemia/reperfusion (I/R) injury remains incomplete. We here investigated macrophage lineages and ablated tissue macrophages in homeostasis and after I/R injury in a CSF1R-dependent manner. Genomic deletion of a fms-intronic regulatory element (FIRE) in the Csf1r locus resulted in specific absence of resident homeostatic and antigen-presenting macrophages, without affecting the recruitment of monocyte-derived macrophages to the infarcted heart. Specific absence of homeostatic, monocyte-independent macrophages altered the immune cell crosstalk in response to injury and induced proinflammatory neutrophil polarization, resulting in impaired cardiac remodeling without influencing infarct size. In contrast, continuous CSF1R inhibition led to depletion of both resident and recruited macrophage populations. This augmented adverse remodeling after I/R and led to an increased infarct size and deterioration of cardiac function. In summary, resident macrophages orchestrate inflammatory responses improving cardiac remodeling, while recruited macrophages determine infarct size after I/R injury. These findings attribute distinct beneficial effects to different macrophage populations in the context of myocardial infarction.

Utilizing a Proximity Dependent Labeling Strategy to Study Cancer-Immune Intercellular Interactions In Vitro and In Vivo

Immune cells play a critical role in surveilling and defending against cancer, emphasizing the importance of understanding how they interact and communicate with cancer cells to determine cancer status, treatment response, and the formation of the tumor microenvironment (TME). To this end, we conducted a study demonstrating the effectiveness of an enzyme-mediated intercellular proximity labeling (EXCELL) method, which utilizes a modified version of the sortase A enzyme known as mgSrtA, in detecting and characterizing immune-tumor cell interactions. The mgSrtA enzyme is expressed on the membrane of tumor cells, which is able to label immune cells that interact with tumor cells in a proximity-dependent manner. Our research indicates that the EXCELL technique can detect and characterize immune-tumor cell interactions in a time- and concentration-dependent manner, both in vitro and in vivo, without requiring pre-engineering of the immune cells. We also highlight its ability to detect various types of immune cell subpopulations in vivo that have migrated out of the tumor into the spleen, providing insights into the role of peripheral T-cell recruitment in tumor progression. Overall, our findings suggest that the EXCELL method has great potential for improving our understanding of immune cell dynamics within the TME, ultimately leading to more potent pharmacological effects and cancer immunotherapy strategies. SIGNIFICANCE STATEMENT: The enzyme-mediated intercellular proximity labeling method holds promise for detecting immune cell interactions with cancer cells, both in vitro and in vivo. It has important implications for studying immune tumor cell dynamics and potentially uncovering novel subtypes of immune cells within the tumor microenvironment, both prior to and during immunotherapeutic interventions.

Identification of ligand and receptor interactions in CKD and MASH through the integration of single cell and spatial transcriptomics

BACKGROUND

Chronic Kidney Disease (CKD) and Metabolic dysfunction-associated steatohepatitis (MASH) are metabolic fibroinflammatory diseases. Combining single-cell (scRNAseq) and spatial transcriptomics (ST) could give unprecedented molecular disease understanding at single-cell resolution. A more comprehensive analysis of the cell-specific ligand-receptor (L-R) interactions could provide pivotal information about signaling pathways in CKD and MASH. To achieve this, we created an integrative analysis framework in CKD and MASH from two available human cohorts.

RESULTS

The analytical framework identified L-R pairs involved in cellular crosstalk in CKD and MASH. Interactions between cell types identified using scRNAseq data were validated by checking the spatial co-presence using the ST data and the co-expression of the communicating targets. Multiple L-R protein pairs identified are known key players in CKD and MASH, while others are novel potential targets previously observed only in animal models.

CONCLUSION

Our study highlights the importance of integrating different modalities of transcriptomic data for a better understanding of the molecular mechanisms. The combination of single-cell resolution from scRNAseq data, combined with tissue slide investigations and visualization of cell-cell interactions obtained through ST, paves the way for the identification of future potential therapeutic targets and developing effective therapies.

DNA Reaction Circuits to Establish Designated Biological Functions in Multicellular Community

In multicellular organisms, individual cells are coordinated through complex communication networks to accomplish various physiological tasks. Aiming to establish new biological functions in the multicellular community, we used DNA as the building block to develop a cascade of nongenetic reaction circuits to establish a dynamic cell-cell communication network. Utilizing membrane-anchored amphiphilic DNA tetrahedra (TDN) as the nanoscaffold, reaction circuits were incorporated into three unrelated cells in order to uniquely regulate their sense-and-response behaviors. As a proof-of-concept, this step enabled these cells to simulate significant biological events involved in T cell-mediated anticancer immunity. Such events included cancer-associated antigen recognition and the presentation of antigen-presenting cells (APCs), APC-facilitated T cell activation and dissociation, and T cell-mediated cancer targeting and killing. By combining the excellent programmability and molecular recognition ability of DNA, our cell-surface reaction circuits hold promise for mimicking and manipulating many biological processes.

The landscape of cancer-rewired GPCR signaling axes

We explored the dysregulation of G-protein-coupled receptor (GPCR) ligand systems in cancer transcriptomics datasets to uncover new therapeutics opportunities in oncology. We derived an interaction network of receptors with ligands and their biosynthetic enzymes. Multiple GPCRs are differentially regulated together with their upstream partners across cancer subtypes and are associated to specific transcriptional programs and to patient survival patterns. The expression of both receptor-ligand (or enzymes) partners improved patient stratification, suggesting a synergistic role for the activation of GPCR networks in modulating cancer phenotypes. Remarkably, we identified many such axes across several cancer molecular subtypes, including many involving receptor-biosynthetic enzymes for neurotransmitters. We found that GPCRs from these actionable axes, including, e.g., muscarinic, adenosine, 5-hydroxytryptamine, and chemokine receptors, are the targets of multiple drugs displaying anti-growth effects in large-scale, cancer cell drug screens, which we further validated. We have made the results generated in this study freely available through a webapp (gpcrcanceraxes.bioinfolab.sns.it).