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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.

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Diversity, a hallmark of G protein-coupled receptor (GPCR) signaling, partly stems from alternative splicing of a single gene generating more than one isoform for a receptor. Additionally, receptor responses to ligands can be attenuated by desensitization upon prolonged or repeated ligand exposure. Both phenomena have been demonstrated and exemplified by the deuterostome tachykinin signaling system, although the role of phosphorylation in desensitization remains a subject of debate. Here, we describe the signaling system for tachykinin-related peptides (TKRPs) in a protostome, mollusk Aplysia. We cloned the Aplysia TKRP precursor, which encodes three TKRPs (apTKRP-1, apTKRP-2a, and apTKRP-2b) containing the FXGXR-amide motif. In situ hybridization and immunohistochemistry showed predominant expression of TKRP mRNA and peptide in the cerebral ganglia. TKRPs and their posttranslational modifications were observed in extracts of central nervous system ganglia using mass spectrometry. We identified two Aplysia TKRP receptors (apTKRPRs), named apTKRPR-A and apTKRPR-B. These receptors are two isoforms generated through alternative splicing of the same gene and differ only in their intracellular C termini. Structure-activity relationship analysis of apTKRP-2b revealed that both C-terminal amidation and conserved residues of the ligand are critical for receptor activation. C-terminal truncates and mutants of apTKRPRs suggested that there is a C-terminal phosphorylation-independent desensitization for both receptors. Moreover, apTKRPR-B also exhibits phosphorylation-dependent desensitization through the phosphorylation of C-terminal Ser/Thr residues. This comprehensive characterization of the Aplysia TKRP signaling system underscores the evolutionary conservation of the TKRP and TK signaling systems, while highlighting the intricacies of receptor regulation through alternative splicing and differential desensitization mechanisms.

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The WHO reported an estimated 249 million malaria cases and 608,000 malaria deaths in 85 countries in 2022. A total of 94% of malaria deaths occurred in Africa, 80% of which were children under 5. In other words, one child dies every minute from malaria. The RTS,S/AS01 malaria vaccine, which uses the Plasmodium falciparum circumsporozoite protein (CSP) to target sporozoite infection of the liver, achieved modest efficacy. The Malaria Vaccine Implementation Program (MVIP), coordinated by the WHO and completed at the end of 2023, found that immunization reduced mortality by only 13%. To further reduce malaria death, the development of a more effective malaria vaccine is a high priority. Three malaria vaccine targets being considered are the sporozoite liver infection (pre-erythrocytic stage), the merozoite red blood cell infection (asexual erythrocytic stage), and the gamete/zygote mosquito infection (sexual/transmission stage). These targets involve specific ligand-receptor interactions. However, most current malaria vaccine candidates that target two major parasite population bottlenecks, liver infection, and mosquito midgut infection, do not focus on such parasite ligands. Here, we evaluate the potential of newly identified parasite ligands with a phage peptide-display technique as novel malaria vaccine antigens.

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Multicellular organisms have dense affinity with the coordination of cellular activities, which severely depend on communication across diverse cell types. Cell-cell communication (CCC) is often mediated via ligand-receptor interactions (LRIs). Existing CCC inference methods are limited to known LRIs. To address this problem, we developed a comprehensive CCC analysis tool SEnSCA by integrating single cell RNA sequencing and proteome data. SEnSCA mainly contains potential LRI acquisition and CCC strength evaluation. For acquiring potential LRIs, it first extracts LRI features and reduces the feature dimension, subsequently constructs negative LRI samples through K-means clustering, finally acquires potential LRIs based on Stacking ensemble comprising support vector machine, 1D-convolutional neural networks and multi-head attention mechanism. During CCC strength evaluation, SEnSCA conducts LRI filtering and then infers CCC by combining the three-point estimation approach and single cell RNA sequencing data. SEnSCA computed better precision, recall, accuracy, F1 score, AUC and AUPR under most of conditions when predicting possible LRIs. To better illustrate the inferred CCC network, SEnSCA provided three visualization options: heatmap, bubble diagram and network diagram. Its application on human melanoma tissue demonstrated its reliability in CCC detection. In summary, SEnSCA offers a useful CCC inference tool and is freely available at https://github.com/plhhnu/SEnSCA.

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Small extracellular vesicles (sEVs) are important mediators of intercellular communication by transferring of functional components (proteins, RNAs, and lipids) to recipient cells. Some PTMs, including phosphorylation and N-glycosylation, have been reported to play important role in EV biology, such as biogenesis, protein sorting and uptake of sEVs. MS-based proteomic technology has been applied to identify proteins and PTM modifications in sEVs. Previous proteomic studies of sEVs from C2C12 myoblasts, an important skeletal muscle cell line, focused on identification of proteins, but no PTM information on sEVs proteins is available.In this study, we systematically analyzed the proteome, phosphoproteome, and N-glycoproteome of sEVs from C2C12 myoblasts with LC-MS/MS. In-depth analyses of the three proteomic datasets revealed that the three proteomes identified different catalogues of proteins, and PTMomic analysis could expand the identification of cargos in sEVs. At the proteomic level, a high percentage of membrane proteins, especially tetraspanins, was identified. The sEVs-derived phosphoproteome had a remarkably high level of tyrosine-phosphorylated sites. The tyrosine-phosphorylated proteins might be involved with EPH-Ephrin signaling pathway. At the level of N-glycoproteomics, several glycoforms, such as complex N-linked glycans and sialic acids on glycans, were enriched in sEVs. Retrieving of the ligand-receptor interaction in sEVs revealed that extracellular matrix (ECM) and cell adhesion molecule (CAM) represented the most abundant ligand-receptor pairs in sEVs. Mapping the PTM information on the ligands and receptors revealed that N-glycosylation mainly occurred on ECM and CAM proteins, while phosphorylation occurred on different categories of receptors and ligands. A comprehensive PTM map of ECM-receptor interaction and their components is also provided.In summary, we conducted a comprehensive proteomic and PTMomic analysis of sEVs of C2C12 myoblasts. Integrated proteomic, phosphoproteomic, and N-glycoproteomic analysis of sEVs might provide some insights about their specific uptake mechanism.

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Colorectal cancer (CRC) is characterized by high morbidity, high mortality, and limited response to immunotherapies. The peripheral immune system is an important component of tumor immunity, and enhancements of peripheral immunity help to suppress tumor progression. However, the functional alterations of the peripheral immune system in CRC are unclear. Here, we used mass spectrometry-based quantitative proteomics to establish a protein expression atlas for the peripheral immune system in CRC, including plasma and five types of immune cells (CD4+ T cells, CD8+ T cells, monocytes, natural killer cells, and B cells). Synthesizing the results of the multidimensional analysis, we observed an enhanced inflammatory phenotype in CRC, including elevated expression of plasma inflammatory proteins, activation of the inflammatory pathway in monocytes, and increased inflammation-related ligand-receptor interactions. Notably, we observed tumor effects on peripheral T cells, including altered cell subpopulation ratios and suppression of cell function. Suppression of CD4+ T cell function is mainly mediated by high expression levels of protein tyrosine phosphatases. Among them, the expression of protein tyrosine phosphatase receptor type J (PTPRJ) gradually increased with CRC progression; knockdown of PTPRJ in vitro could promote T cell activation, thereby enhancing peripheral immunity. We also found that the combination of leucine-rich α-2 glycoprotein 1 (LRG1) and apolipoprotein A4 (APOA4) had the best predictive ability for colorectal cancer and has the potential to be a biomarker. Overall, this study provides a comprehensive understanding of the peripheral immune system in CRC. It also offers insights regarding the potential clinical utilities of these peripheral immune characteristics as diagnostic indicators and therapeutic targets.

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Introduction: Leishmaniasis comprises a complex group of diseases caused by protozoan parasites from the Leishmania genus, presenting a significant threat to human health. Infection starts by the release into the skin of metacyclic promastigote (MP) form of the parasite by an infected sand fly. Soon after their release, the MPs enter a phagocytic host cell. This study focuses on finding peptides that can inhibit MP-phagocytic host cell interaction. Methods: We used a phage display library to screen for peptides that bind to the surface of L. amazonensis (causative agent for cutaneous leishmaniasis) and L. infantum (causative agent for cutaneous and visceral leishmaniasis) MPs. Candidate peptide binding to the MP surface and inhibition of parasite-host cell interaction were tested in vitro. Peptide Inhibition of visceral leishmaniasis development was assessed in BALB/c mice. Results: The selected L. amazonensis binding peptide (La1) and the L. infantum binding peptide (Li1) inhibited 44% of parasite internalization into THP-1 macrophage-like cells in vitro. While inhibition of internalization by La1 was specific to L. amazonensis, Li1 was effective in inhibiting internalization of both parasite species. Importantly, Li1 inhibited L. infantum spleen and liver infection of BALB/c mice by 84%. Conclusion: We identified one peptide that specifically inhibits L. amazonensis MP infection of host cells and another that inhibits both, L. amazonensis and L. infantum, MP infection. Our findings suggest a promising path for the development of new treatments and prevention of leishmaniasis.

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Translocator protein (TSPO) is an 18 kDa transmembrane protein, localized primarily on the outer mitochondrial membrane. It has been found to be involved in various physiological processes and pathophysiological conditions. Though studies on its structure have been performed only recently, there is little information on the nature of dynamics and doubts about some structures referenced in the literature, especially the NMR structure of mouse TSPO. In the present work, we thoroughly study the dynamics of mouse TSPO protein by means of atomistic molecular dynamics simulations, in presence as well as in absence of the diagnostic ligand PKA. We considered two starting structures: the NMR structure and a homology model (HM) generated on the basis of X-ray structures from bacterial TSPO. We examine the conformational landscape in both the modes for both starting points, in presence and absence of the ligand, in order to measure its impact for both structures. The analysis highlights high flexibility of the protein globally, but NMR simulations show a surprisingly flexibility even in the presence of the ligand. Interestingly, this is not the case for HM calculations, to the point that the ligand seems not so stable as in the NMR system and an unbinding event process is partially sampled. All those results tend to show that the NMR structure of mTSPO seems not deficient but is just in another portion of the global conformation space of TSPO.

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The mannose receptor (MR, CD 206) is an endocytic receptor primarily expressed by macrophages and dendritic cells, which plays a critical role in both endocytosis and antigen processing and presentation. MR carbohydrate recognition domains (CRDs) exhibit a high binding affinity for branched and linear oligosaccharides. Furthermore, multivalent mannose presentation on the various templates like peptides, proteins, polymers, micelles, and dendrimers was proven to be a valuable approach for the selective and efficient delivery of various therapeutically active agents to MR. This review provides a detailed account of the most relevant and recent aspects of the synthesis and application of mannosylated bioactive formulations for MR-mediated delivery in treatments of cancer and other infectious diseases. It further highlights recent findings related to the necessary structural features of the mannose-containing ligands for successful binding to the MR.

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Cell-cell communication is essential to many key biological processes. Intercellular communication is generally mediated by ligand-receptor interactions (LRIs). Thus, building a comprehensive and high-quality LRI resource can significantly improve intercellular communication analysis. Meantime, due to lack of a "gold standard" dataset, it remains a challenge to evaluate LRI-mediated intercellular communication results. Here, we introduce CellGiQ, a high-confident LRI prediction framework for intercellular communication analysis. Highly confident LRIs are first inferred by LRI feature extraction with BioTriangle, LRI selection using LightGBM, and LRI classification based on ensemble of gradient boosted neural network and interpretable boosting machine. Subsequently, known and identified high-confident LRIs are filtered by combining single-cell RNA sequencing (scRNA-seq) data and further applied to intercellular communication inference through a quartile scoring strategy. To validation the predictions, CellGiQ exploited several evaluation strategies: using AUC and AUPR, it surpassed six competing LRI prediction models on four LRI datasets; through Venn diagrams and molecular docking, its predicted LRIs were validated by five other popular intercellular communication inference methods; based on the overlapping LRIs, it computed high Jaccard index with six other state-of-the-art intercellular communication prediction tools within human HNSCC tissues; by comparing with classical models and literature retrieve, its inferred HNSCC-related intercellular communication results was further validated. The novelty of this study is to identify high-confident LRIs based on machine learning as well as design several LRI validation ways, providing reference for computational LRI prediction. CellGiQ provides an open-source and useful tool to decompose LRI-mediated intercellular communication at single cell resolution. CellGiQ is freely available at https://github.com/plhhnu/CellGiQ.

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Sequencing-based spatial transcriptomics (ST) methods allow unbiased capturing of RNA molecules at barcoded spots, charting the distribution and localization of cell types and transcripts across a tissue. While the coarse resolution of these techniques is considered a disadvantage, we argue that the inherent proximity of transcriptomes captured on spots can be leveraged to reconstruct cellular networks. To this end, we developed ISCHIA (Identifying Spatial Co-occurrence in Healthy and InflAmed tissues), a computational framework to analyze the spatial co-occurrence of cell types and transcript species within spots. Co-occurrence analysis is complementary to differential gene expression, as it does not depend on the abundance of a given cell type or on the transcript expression levels, but rather on their spatial association in the tissue. We applied ISCHIA to analyze co-occurrence of cell types, ligands and receptors in a Visium dataset of human ulcerative colitis patients, and validated our findings at single-cell resolution on matched hybridization-based data. We uncover inflammation-induced cellular networks involving M cell and fibroblasts, as well as ligand-receptor interactions enriched in the inflamed human colon, and their associated gene signatures. Our results highlight the hypothesis-generating power and broad applicability of co-occurrence analysis on spatial transcriptomics data.

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The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.

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AIMS: TAFA2, a cytokine specifically expressed in the central nervous system, plays a vital role in neuronal cell survival. TAFA2 deficiency has been correlated to various neurological disorders in mice and humans. However, the underlying mechanism remains elusive, especially its membrane-binding receptor through which TAFA2 functions. This study aimed to identify the specific binding receptor responsible for the anti-apoptotic effects of TAFA2. MAIN METHOD: Co-immunoprecipitation (Co-IP) and quantitative mass spectrometry-based proteomic analysis were employed to identify potential TAFA2 binding proteins in V5 knockin mouse brain lysates. Subsequent validation involved in vitro and in vivo Co-IP and pull-down using specific antibodies. The functional analysis included evaluating the effects of ADGRL1 knockout, overexpression, and Lectin-like domain (Lec) deletion mutant on TAFA2's anti-apoptotic activity and analyzing the intracellular signaling pathways mediated by TAFA2 through ADGRL1. KEY FINDINGS: Our study identified ADGRL1 as a potential receptor for TAFA2, which directly binds to TAFA2 through its lectin-like domain. Overexpression ADGRL1, but not ADGRL1ΔLec, induced apoptosis, which could be effectively suppressed by recombinant TAFA2 (rTAFA2). In ADGRL1-/- cells or re-introducing with ADGRL1ΔLec, responses to rTAFA2 in suppressing cell apoptosis were compromised. Increased cAMP, p-PKA, p-CREB, and BCL2 levels were also observed in response to rTAFA2 treatment, with these responses attenuated in ADGRL1-/- or ADGRL1ΔLec-expressing cells. SIGNIFICANCE: Our results demonstrated that TAFA2 directly binds to the lectin-like domain of ADGRL1, activating cAMP/PKA/CREB/BCL2 signaling pathway, which is crucial in preventing cell death. These results implicate TAFA2 and its receptor ADGRL1 as potential therapeutic targets for neurological disorders.

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The cytokine interleukin (IL)-11 has been shown to play a role in promoting fibrosis and cancer, including lung adenocarcinoma, garnering interest as an attractive target for therapeutic intervention. We used combinatorial methods to engineer an IL-11 variant that binds with higher affinity to the IL-11 receptor and stimulates enhanced receptor-mediated cell signaling. Introduction of two additional point mutations ablates IL-11 ligand/receptor association with the gp130 coreceptor signaling complex, resulting in a high-affinity receptor antagonist. Unlike wild-type IL-11, this engineered variant potently blocks IL-11-mediated cell signaling and slows tumor growth in a mouse model of lung cancer. Our approach highlights a strategy where native ligands can be engineered and exploited to create potent receptor antagonists.

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The intricate association of oncogenic markers negatively impacts accurate gastric cancer diagnosis and leads to the proliferation of mortality rate. Molecular heterogeneity is inevitable in determining gastric cancer's progression state with multiple cell types involved. Identification of pathogenic gene signatures is imperative to understand the disease's etiology. This study demonstrates a systematic approach to identifying oncogenic gastric cancer genes linked with different cell types. The raw counts of adjacent normal and gastric cancer samples are subjected to a quality control step. The dimensionality reduction and multidimensional clustering are performed using Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) techniques. The adjacent normal and gastric cancer sample cell clusters are annotated with the Human Primary Cell Atlas database using the "SingleR." Cellular state transition between the distinct groups is characterized using trajectory analysis. The ligand-receptor interaction between Vascular Endothelial Growth Factor (VEGF) and cell clusters unveils crucial molecular pathways in gastric cancer progression. Chondrocytes, Smooth muscle cells, and fibroblast cell clusters contain genes contributing to poor survival rates based on hazard ratio during survival analysis. The GC-related oncogenic signatures are isolated by comparing the gene set with the DisGeNET database. Twelve gastric cancer biomarkers (SPARC, KLF5, HLA-DRB1, IGFBP3, TIMP3, LGALS1, IGFBP6, COL18A1, F3, COL4A1, PDGFRB, COL5A2) are linked with gastric cancer and further validated through gene set enrichment analysis. Drug-gene interaction found PDGFRB, interacting with various anti-cancer drugs, as a potential inhibitor for gastric cancer. Further investigations on these molecular signatures will assist the development of precision therapeutics, promising longevity among gastric cancer patients.

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Integral membrane proteins are an important class of cellular proteins. These take part in key cellular processes such as signaling transducing receptors to transporters, many operating within the plasma membrane. More than half of the FDA-approved protein-targeting drugs operate via interaction with proteins that contain at least one membrane-spanning region, yet the characterization and study of their native interactions with therapeutic agents remains a significant challenge. This challenge is due in part to such proteins often being present in small quantities within a cell. Effective solubilization of membrane proteins is also problematic, with the detergents typically employed in solubilizing membranes leading to a loss of functional activity and key interacting partners. In recent years, alternative methods to extract membrane proteins within their native lipid environment have been investigated, with the aim of producing functional nanodiscs, maintaining protein-protein and protein-lipid interactions. A promising approach involves extracting membrane proteins in the form of styrene maleic acid lipid particles (SMALPs) that allow the retention of their native conformation. This extraction method offers many advantages for further protein analysis and allows the study of the protein interactions with other molecules, such as drugs. Here, we describe a protocol for efficient SMALP extraction of functionally active membrane protein complexes within nanodiscs. We showcase the method on the isolation of a low copy number plasma membrane receptor complex, the nicotinic acetylcholine receptor (nAChR), from adult Drosophila melanogaster heads. We demonstrate that these nanodiscs can be used to study native receptor-ligand interactions. This protocol can be applied across many biological scenarios to extract the native conformations of low copy number integral membrane proteins.

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Opioids are a mainstay in acute pain management and produce their effects and side effects (e.g., tolerance, opioid-use disorder and immune suppression) by interaction with opioid receptors. I will discuss opioid pharmacology in some controversial areas of enquiry of anaesthetic relevance. The main opioid target is the µ (mu,MOP) receptor but other members of the opioid receptor family, δ (delta; DOP) and κ (kappa; KOP) opioid receptors also produce analgesic actions. These are naloxone-sensitive. There is important clinical development relating to the Nociceptin/Orphanin FQ (NOP) receptor, an opioid receptor that is not naloxone-sensitive. Better understanding of the drivers for opioid effects and side effects may facilitate separation of side effects and production of safer drugs. Opioids bind to the receptor orthosteric site to produce their effects and can engage monomer or homo-, heterodimer receptors. Some ligands can drive one intracellular pathway over another. This is the basis of biased agonism (or functional selectivity). Opioid actions at the orthosteric site can be modulated allosterically and positive allosteric modulators that enhance opioid action are in development. As well as targeting ligand-receptor interaction and transduction, modulating receptor expression and hence function is also tractable. There is evidence for epigenetic associations with different types of pain and also substance misuse. As long as the opioid narrative is defined by the 'opioid crisis' the drive to remove them could gather pace. This will deny use where they are effective, and access to morphine for pain relief in low income countries.

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BACKGROUND: The potential mechanism underlying the association between Homologous recombination deficiency (HRD) and immunotherapy in colon cancer has not been investigated. METHODS: The exon sequencing data and transcriptome data of 456 colon adenocarcinoma (COAD) patients were obtained from the TCGA database. Pathway activity score was calculated by GSVA methods and engaged in further survival analysis. The prognostic value of the candidate pathways was validated in an external GEO cohort and an immunotherapy cohort. RESULTS: Patients with high HRD were associated with poor prognosis, lower tumor mutation burden and microsatellite instability, higher fraction genome alteration, and less sensitivity to immunotherapy in COAD. And then, the neuroactive ligand-receptor interaction pathway was over-activated in high-HRD tumors and associated with immunosuppression in colon cancer with high HRD. Besides, the pathway was associated with prognosis and immunotherapy response in COAD. Moreover, genes in this pathway such as LTB4R2 can be used as a novel target for therapy development in colon cancer. CONCLUSION: Our study not only revealed the potential mechanism of HRD and the function of the neuroactive ligand-receptor interaction pathway in colon cancer but also provided new clues for the improvement of immunotherapy response in colon cancer.

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Bidirectional communication between the developing conceptus and endometrium is essential for pregnancy recognition and establishment in ruminants. We dissect the transcriptomic dynamics of sheep conceptus and corresponding endometrium at pre- and peri-implantation stages using single-cell RNA sequencing. Spherical blastocysts contain five cell types, with 68.62% trophectoderm cells. Strikingly, elongated conceptuses differentiate into 17 cell types, indicating dramatic cell fate specifications. Cell-type-specific gene expression delineates the features of distinctive trophectoderm lineages and indicates that the transition from polar trophectoderm to trophoblast increases interferon-tau expression and likely drives elongation initiation. We identify 13 endometrium-derived cell types and elucidate their molecular responses to conceptus development. Integrated analyses uncover multiple paired transcripts mediating the dialogues between extraembryonic membrane and endometrium, including IGF2-IGF1R, FGF19-FGFR1, NPY-NPY1R, PROS1-AXL, and ADGRE5-CD55. These data provide insight into the molecular regulation of conceptus elongation and represent a valuable resource for functional investigations of pre- and peri-implantation ruminant development.

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BACKGROUND: Cell-cell communication in a tumor microenvironment is vital to tumorigenesis, tumor progression and therapy. Intercellular communication inference helps understand molecular mechanisms of tumor growth, progression and metastasis. METHODS: Focusing on ligand-receptor co-expressions, in this study, we developed an ensemble deep learning framework, CellComNet, to decipher ligand-receptor-mediated cell-cell communication from single-cell transcriptomic data. First, credible LRIs are captured by integrating data arrangement, feature extraction, dimension reduction, and LRI classification based on an ensemble of heterogeneous Newton boosting machine and deep neural network. Next, known and identified LRIs are screened based on single-cell RNA sequencing (scRNA-seq) data in certain tissues. Finally, cell-cell communication is inferred by incorporating scRNA-seq data, the screened LRIs, a joint scoring strategy that combines expression thresholding and expression product of ligands and receptors. RESULTS: The proposed CellComNet framework was compared with four competing protein-protein interaction prediction models (PIPR, XGBoost, DNNXGB, and OR-RCNN) and obtained the best AUCs and AUPRs on four LRI datasets, elucidating the optimal LRI classification ability. CellComNet was further applied to analyze intercellular communication in human melanoma and head and neck squamous cell carcinoma (HNSCC) tissues. The results demonstrate that cancer-associated fibroblasts highly communicate with melanoma cells and endothelial cells strong communicate with HNSCC cells. CONCLUSIONS: The proposed CellComNet framework efficiently identified credible LRIs and significantly improved cell-cell communication inference performance. We anticipate that CellComNet can contribute to anticancer drug design and tumor-targeted therapy.

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