RNAi-dependent Polycomb repression controls transposable elements in Tetrahymena.

RNAi and Polycomb repression play evolutionarily conserved and often coordinated roles in transcriptional silencing. Here, we show that, in the protozoan Tetrahymena thermophila, germline-specific internally eliminated sequences (IESs)-many related to transposable elements (TEs)-become transcriptionally activated in mutants deficient in the RNAi-dependent Polycomb repression pathway. Germline TE mobilization also dramatically increases in these mutants. The transition from noncoding RNA (ncRNA) to mRNA production accompanies transcriptional activation of TE-related sequences and vice versa for transcriptional silencing. The balance between ncRNA and mRNA production is potentially affected by cotranscriptional processing as well as RNAi and Polycomb repression. We posit that interplay between RNAi and Polycomb repression is a widely conserved phenomenon, whose ancestral role is epigenetic silencing of TEs.

Annotation and evaluation of base editing outcomes in multiple cell types using CRISPRbase.

CRISPR-Cas base editing (BE) system is a powerful tool to expand the scope and efficiency of genome editing with single-nucleotide resolution. The editing efficiency, product purity, and off-target effect differ among various BE systems. Herein, we developed CRISPRbase (http://crisprbase.maolab.org), by integrating 1 252 935 records of base editing outcomes in more than 50 cell types from 17 species. CRISPRbase helps to evaluate the putative editing precision of different BE systems by integrating multiple annotations, functional predictions and a blasting system for single-guide RNA sequences. We systematically assessed the editing window, editing efficiency and product purity of various BE systems. Intensive efforts were focused on increasing the editing efficiency and product purity of base editors since the byproduct could be detrimental in certain applications. Remarkably, more than half of cancer-related off-target mutations were non-synonymous and extremely damaging to protein functions in most common tumor types. Luckily, most of these cancer-related mutations were passenger mutations (4840/5703, 84.87%) rather than cancer driver mutations (863/5703, 15.13%), indicating a weak effect of off-target mutations on carcinogenesis. In summary, CRISPRbase is a powerful and convenient tool to study the outcomes of different base editors and help researchers choose appropriate BE designs for functional studies.

Risk modeling of single-cell transcriptomes reveals the heterogeneity of immune infiltration in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common primary hepatic malignancies. E2F transcription factors play an important role in the tumorigenesis and progression of HCC, mainly through the RB/E2F pathway. Prognostic models for HCC based on gene signatures have been developed rapidly in recent years; however, their discriminating ability at the single-cell level remains elusive, which could reflect the underlying mechanisms driving the sample bifurcation. In this study, we constructed and validated a predictive model based on E2F expression, successfully stratifying patients with HCC into two groups with different survival risks. Then we used a single-cell dataset to test the discriminating ability of the predictive model on infiltrating T cells, demonstrating remarkable cellular heterogeneity as well as altered cell fates. We identified distinct cell subpopulations with diverse molecular characteristics. We also found that the distribution of cell subpopulations varied considerably across onset stages among patients, providing a fundamental basis for patient-oriented precision evaluation. Moreover, single-sample gene set enrichment analysis revealed that subsets of CD8+ T cells with significantly different cell adhesion levels could be associated with different patterns of tumor cell dissemination. Therefore, our findings linked the conventional prognostic gene signature to the immune microenvironment and cellular heterogeneity at the single-cell level, thus providing deeper insights into the understanding of HCC tumorigenesis.

Comprehensive evaluation of computational methods for predicting cancer driver genes.

Optimal methods could effectively improve the accuracy of predicting and identifying candidate driver genes. Various computational methods based on mutational frequency, network and function approaches have been developed to identify mutation driver genes in cancer genomes. However, a comprehensive evaluation of the performance levels of network-, function- and frequency-based methods is lacking. In the present study, we assessed and compared eight performance criteria for eight network-based, one function-based and three frequency-based algorithms using eight benchmark datasets. Under different conditions, the performance of approaches varied in terms of network, measurement and sample size. The frequency-based driverMAPS and network-based HotNet2 methods showed the best overall performance. Network-based algorithms using protein-protein interaction networks outperformed the function- and the frequency-based approaches. Precision, F1 score and Matthews correlation coefficient were low for most approaches. Thus, most of these algorithms require stringent cutoffs to correctly distinguish driver and non-driver genes. We constructed a website named Cancer Driver Catalog (http://159.226.67.237/sun/cancer_driver/), wherein we integrated the gene scores predicted by the foregoing software programs. This resource provides valuable guidance for cancer researchers and clinical oncologists prioritizing cancer driver gene candidates by using an optimal tool.

CircleBase: an integrated resource and analysis platform for human eccDNAs.

Rapid advances in high-throughput sequencing technologies have led to the discovery of thousands of extrachromosomal circular DNAs (eccDNAs) in the human genome. Loss-of-function experiments are difficult to conduct on circular and linear chromosomes, as they usually overlap. Hence, it is challenging to interpret the molecular functions of eccDNAs. Here, we present CircleBase (http://circlebase.maolab.org), an integrated resource and analysis platform used to curate and interpret eccDNAs in multiple cell types. CircleBase identifies putative functional eccDNAs by incorporating sequencing datasets, computational predictions, and manual annotations. It classifies them into six sections including targeting genes, epigenetic regulations, regulatory elements, chromatin accessibility, chromatin interactions, and genetic variants. The eccDNA targeting and regulatory networks are displayed by informative visualization tools and then prioritized. Functional enrichment analyses revealed that the top-ranked cancer cell eccDNAs were enriched in oncogenic pathways such as the Ras and PI3K-Akt signaling pathways. In contrast, eccDNAs from healthy individuals were not significantly enriched. CircleBase provides a user-friendly interface for searching, browsing, and analyzing eccDNAs in various cell/tissue types. Thus, it is useful to screen for potential functional eccDNAs and interpret their molecular mechanisms in human cancers and other diseases.

OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers.

The prevalence of neutral mutations in cancer cell population impedes the distinguishing of cancer-causing driver mutations from passenger mutations. To systematically prioritize the oncogenic ability of somatic mutations and cancer genes, we constructed a useful platform, OncoVar (https://oncovar.org/), which employed published bioinformatics algorithms and incorporated known driver events to identify driver mutations and driver genes. We identified 20 162 cancer driver mutations, 814 driver genes and 2360 pathogenic pathways with high-confidence by reanalyzing 10 769 exomes from 33 cancer types in The Cancer Genome Atlas (TCGA) and 1942 genomes from 18 cancer types in International Cancer Genome Consortium (ICGC). OncoVar provides four points of view, 'Mutation', 'Gene', 'Pathway' and 'Cancer', to help researchers to visualize the relationships between cancers and driver variants. Importantly, identification of actionable driver alterations provides promising druggable targets and repurposing opportunities of combinational therapies. OncoVar provides a user-friendly interface for browsing, searching and downloading somatic driver mutations, driver genes and pathogenic pathways in various cancer types. This platform will facilitate the identification of cancer drivers across individual cancer cohorts and helps to rank mutations or genes for better decision-making among clinical oncologists, cancer researchers and the broad scientific community interested in cancer precision medicine.

A Polycomb repressive complex is required for RNAi-mediated heterochromatin formation and dynamic distribution of nuclear bodies.

Polycomb group (PcG) proteins are widely utilized for transcriptional repression in eukaryotes. Here, we characterize, in the protist Tetrahymena thermophila, the EZL1 (E(z)-like 1) complex, with components conserved in metazoan Polycomb Repressive Complexes 1 and 2 (PRC1 and PRC2). The EZL1 complex is required for histone H3 K27 and K9 methylation, heterochromatin formation, transposable element control, and programmed genome rearrangement. The EZL1 complex interacts with EMA1, a helicase required for RNA interference (RNAi). This interaction is implicated in co-transcriptional recruitment of the EZL1 complex. Binding of H3K27 and H3K9 methylation by PDD1-another PcG protein interacting with the EZL1 complex-reinforces its chromatin association. The EZL1 complex is an integral part of Polycomb bodies, which exhibit dynamic distribution in Tetrahymena development: Their dispersion is driven by chromatin association, while their coalescence by PDD1, likely via phase separation. Our results provide a molecular mechanism connecting RNAi and Polycomb repression, which coordinately regulate nuclear bodies and reorganize the genome.

ER-associated degradation preserves hematopoietic stem cell quiescence and self-renewal by restricting mTOR activity.

Hematopoietic stem cells (HSC) self-renew to sustain stem cell pools and differentiate to generate all types of blood cells. HSCs remain in quiescence to sustain their long-term self-renewal potential. It remains unclear whether protein quality control is required for stem cells in quiescence when RNA content, protein synthesis, and metabolic activities are profoundly reduced. Here, we report that protein quality control via endoplasmic reticulum-associated degradation (ERAD) governs the function of quiescent HSCs. The Sel1L/Hrd1 ERAD genes are enriched in the quiescent and inactive HSCs, and conditional knockout of Sel1L in hematopoietic tissues drives HSCs to hyperproliferation, which leads to complete loss of HSC self-renewal and HSC depletion. Mechanistically, ERAD deficiency via Sel1L knockout leads to activation of mammalian target of rapamycin (mTOR) signaling. Furthermore, we identify Ras homolog enriched in brain (Rheb), an activator of mTOR, as a novel protein substrate of Sel1L/Hrd1 ERAD, which accumulates upon Sel1L deletion and HSC activation. Importantly, inhibition of mTOR, or Rheb, rescues HSC defects in Sel1L knockout mice. Protein quality control via ERAD is, therefore, a critical checkpoint that governs HSC quiescence and self-renewal by Rheb-mediated restriction of mTOR activity.

Prevalence and architecture of posttranscriptionally impaired synonymous mutations in 8,320 genomes across 22 cancer types.

Somatic synonymous mutations are one of the most frequent genetic variants occurring in the coding region of cancer genomes, while their contributions to cancer development remain largely unknown. To assess whether synonymous mutations involved in post-transcriptional regulation contribute to the genetic etiology of cancers, we collected whole exome data from 8,320 patients across 22 cancer types. By employing our developed algorithm, PIVar, we identified a total of 22,948 posttranscriptionally impaired synonymous SNVs (pisSNVs) spanning 2,042 genes. In addition, 35 RNA binding proteins impacted by these identified pisSNVs were significantly enriched. Remarkably, we discovered markedly elevated ratio of somatic pisSNVs across all 22 cancer types, and a high pisSNV ratio was associated with worse patient survival in five cancer types. Intriguing, several well-established cancer genes, including PTEN, RB1 and PIK3CA, appeared to contribute to tumorigenesis at both protein function and posttranscriptional regulation levels, whereas some pisSNV-hosted genes, including UBR4, EP400 and INTS1, exerted their function during carcinogenesis mainly via posttranscriptional mechanisms. Moreover, we predicted three drugs associated with two pisSNVs, and numerous compounds associated with expression signature of pisSNV-hosted genes. Our study reveals the prevalence and clinical relevance of pisSNVs in cancers, and emphasizes the importance of considering posttranscriptional impaired synonymous mutations in cancer biology.

OncoBase: a platform for decoding regulatory somatic mutations in human cancers.

Whole-exome and whole-genome sequencing have revealed millions of somatic mutations associated with different human cancers, and the vast majority of them are located outside of coding sequences, making it challenging to directly interpret their functional effects. With the rapid advances in high-throughput sequencing technologies, genome-scale long-range chromatin interactions were detected, and distal target genes of regulatory elements were determined using three-dimensional (3D) chromatin looping. Herein, we present OncoBase (http://www.oncobase.biols.ac.cn/), an integrated database for annotating 81 385 242 somatic mutations in 68 cancer types from more than 120 cancer projects by exploring their roles in distal interactions between target genes and regulatory elements. OncoBase integrates local chromatin signatures, 3D chromatin interactions in different cell types and reconstruction of enhancer-target networks using state-of-the-art algorithms. It employs informative visualization tools to display the integrated local and 3D chromatin signatures and effects of somatic mutations on regulatory elements. Enhancer-promoter interactions estimated from chromatin interactions are integrated into a network diffusion system that quantitatively prioritizes somatic mutations and target genes from a large pool. Thus, OncoBase is a useful resource for the functional annotation of regulatory noncoding regions and systematically benchmarking the regulatory effects of embedded noncoding somatic mutations in human carcinogenesis.

EpiDenovo: a platform for linking regulatory de novo mutations to developmental epigenetics and diseases.

De novo mutations (DNMs) have been shown to be a major cause of severe early-onset genetic disorders such as autism spectrum disorder and intellectual disability. Over one million DNMs have been identified in developmental disorders by next generation sequencing, but linking these DNMs to the genes that they impact remains a challenge, as the majority of them are embedded in non-coding regions. As most developmental diseases occur in the early stages of development or during childhood, it is crucial to clarify the details of epigenetic regulation in early development in order to interpret the mechanisms underlying developmental disorders. Here, we develop EpiDenovo, a database that is freely available at http://www.epidenovo.biols.ac.cn/, and which provides the associations between embryonic epigenomes and DNMs in developmental disorders, including several neuropsychiatric disorders and congenital heart disease. EpiDenovo provides an easy-to-use web interface allowing users rapidly to find the epigenetic signatures of DNMs and the expression patterns of the genes that they regulate during embryonic development. In summary, EpiDenovo is a useful resource for selecting candidate genes for further functional studies in embryonic development, and for investigating regulatory DNMs as well as other genetic variants causing or underlying developmental disorders.

CirGRDB: a database for the genome-wide deciphering circadian genes and regulators.

Circadian rhythms govern various kinds of physiological and behavioral functions of the living organisms, and disruptions of the rhythms are highly detrimental to health. Although several databases have been built for circadian genes, a resource for comprehensive post-transcriptional regulatory information of circadian RNAs and expression patterns of disease-related circadian RNAs is still lacking. Here, we developed CirGRDB (http://cirgrdb.biols.ac.cn) by integrating more than 4936 genome-wide assays, with the aim of fulfilling the growing need to understand the rhythms of life. CirGRDB presents a friendly web interface that allows users to search and browse temporal expression patterns of interested genes in 37 human/mouse tissues or cell lines, and three clinical disorders including sleep disorder, aging and tumor. More importantly, eight kinds of potential transcriptional and post-transcriptional regulators involved in the rhythmic expression of the specific genes, including transcription factors, histone modifications, chromatin accessibility, enhancer RNAs, miRNAs, RNA-binding proteins, RNA editing and RNA methylation, can also be retrieved. Furthermore, a regulatory network could be generated based on the regulatory information. In summary, CirGRDB offers a useful repository for exploring disease-related circadian RNAs, and deciphering the transcriptional and post-transcriptional regulation of circadian rhythms.

Genetic landscape of papillary thyroid carcinoma in the Chinese population.

Improvement in the clinical outcome of human cancers requires characterization of the genetic alterations underlying their pathogenesis. Large-scale genomic and transcriptomic characterization of papillary thyroid carcinomas (PTCs) in Western populations has revealed multiple oncogenic drivers which are essential for understanding pathogenic mechanisms of this disease, while, so far, the genetic landscape in Chinese patients with PTC remains uncharacterized. Here, we conducted a large-scale genetic analysis of PTCs from patients in China to determine the mutational landscape of this cancer. By performing targeted DNA amplicon and targeted RNA deep-sequencing, we elucidated the landscape of somatic genetic alterations in 355 Chinese patients with PTC. A total of 88.7% of PTCs were found to harbor at least one candidate oncogenic driver genetic alteration. Among them, around 72.4% of the cases carried BRAF mutations; 2.8% of cases harbored RAS mutations; and 13.8% of cases were characterized with in-frame gene fusions, including seven newly identified kinase gene fusions. TERT promoter mutations were likely to occur in a sub-clonal manner in our PTC cohort. The prevalence of somatic genetic alterations in PTC was significantly different between our Chinese cohort and TCGA datasets for American patients. Additionally, combined analyses of genetic alterations and clinicopathologic features demonstrated that kinase gene fusion was associated with younger age at diagnosis, larger tumor size, and lymph node metastasis in PTC. With the analyses of DNA rearrangement sites of RET gene fusions in PTC, signatures of chromosome translocations related to RET fusion events were also depicted. Collectively, our results provide fundamental insight into the pathogenesis of PTC in the Chinese population. Copyright © 2017 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

TET1 modulates H4K16 acetylation by controlling auto-acetylation of hMOF to affect gene regulation and DNA repair function.

The Ten Eleven Translocation 1 (TET1) protein is a DNA demethylase that regulates gene expression through altering statue of DNA methylation. However, recent studies have demonstrated that TET1 could modulate transcriptional expression independent of its DNA demethylation activity; yet, the detailed mechanisms underlying TET1's role in such transcriptional regulation remain not well understood. Here, we uncovered that Tet1 formed a chromatin complex with histone acetyltransferase Mof and scaffold protein Sin3a in mouse embryonic stem cells by integrative genomic analysis using publicly available ChIP-seq data sets and a series of in vitro biochemical studies in human cell lines. Mechanistically, the TET1 facilitated chromatin affinity and enzymatic activity of hMOF against acetylation of histone H4 at lysine 16 via preventing auto-acetylation of hMOF, to regulate expression of the downstream genes, including DNA repair genes. We found that Tet1 knockout MEF cells exhibited an accumulation of DNA damage and genomic instability and Tet1 deficient mice were more sensitive to x-ray exposure. Taken together, our findings reveal that TET1 forms a complex with hMOF to modulate its function and the level of H4K16Ac ultimately affect gene expression and DNA repair.

Population Genomics Reveals Speciation and Introgression between Brown Norway Rats and Their Sibling Species.

Murine rodents are excellent models for study of adaptive radiations and speciation. Brown Norway rats (Rattus norvegicus) are successful global colonizers and the contributions of their domesticated laboratory strains to biomedical research are well established. To identify nucleotide-based speciation timing of the rat and genomic information contributing to its colonization capabilities, we analyzed 51 whole-genome sequences of wild-derived Brown Norway rats and their sibling species, R. nitidus, and identified over 20 million genetic variants in the wild Brown Norway rats that were absent in the laboratory strains, which substantially expand the reservoir of rat genetic diversity. We showed that divergence of the rat and its siblings coincided with drastic climatic changes that occurred during the Middle Pleistocene. Further, we revealed that there was a geographically widespread influx of genes between Brown Norway rats and the sibling species following the divergence, resulting in numerous introgressed regions in the genomes of admixed Brown Norway rats. Intriguing, genes related to chemical communications among these introgressed regions appeared to contribute to the population-specific adaptations of the admixed Brown Norway rats. Our data reveals evolutionary history of the Brown Norway rat, and offers new insights into the role of climatic changes in speciation of animals and the effect of interspecies introgression on animal adaptation.

RBP-Var: a database of functional variants involved in regulation mediated by RNA-binding proteins.

Transcription factors bind to the genome by forming specific contacts with the primary DNA sequence; however, RNA-binding proteins (RBPs) have greater scope to achieve binding specificity through the RNA secondary structure. It has been revealed that single nucleotide variants (SNVs) that alter RNA structure, also known as RiboSNitches, exhibit 3-fold greater local structure changes than replicates of the same DNA sequence, demonstrated by the fact that depletion of RiboSNitches could result in the alteration of specific RNA shapes at thousands of sites, including 3' UTRs, binding sites of microRNAs and RBPs. However, the network between SNVs and post-transcriptional regulation remains unclear. Here, we developed RBP-Var, a database freely available at http://www.rbp-var.biols.ac.cn/, which provides annotation of functional variants involved in post-transcriptional interaction and regulation. RBP-Var provides an easy-to-use web interface that allows users to rapidly find whether SNVs of interest can transform the secondary structure of RNA and identify RBPs whose binding may be subsequently disrupted. RBP-Var integrates DNA and RNA biology to understand how various genetic variants and post-transcriptional mechanisms cooperate to orchestrate gene expression. In summary, RBP-Var is useful in selecting candidate SNVs for further functional studies and exploring causal SNVs underlying human diseases.

Ras-induced epigenetic inactivation of the RRAD (Ras-related associated with diabetes) gene promotes glucose uptake in a human ovarian cancer model.

RRAD (Ras-related associated with diabetes) is a small Ras-related GTPase that is frequently inactivated by DNA methylation of the CpG island in its promoter region in cancer tissues. However, the role of the methylation-induced RRAD inactivation in tumorigenesis remains unclear. In this study, the Ras-regulated transcriptome and epigenome were profiled by comparing T29H (a Ras(V12)-transformed human ovarian epithelial cell line) with T29 (an immortalized but non-transformed cell line) through reduced representation bisulfite sequencing and digital gene expression. We found that Ras(V12)-mediated oncogenic transformation was accompanied by RRAD promoter hypermethylation and a concomitant loss of RRAD expression. In addition, we found that the RRAD promoter was hypermethylated, and its transcription was reduced in ovarian cancer versus normal ovarian tissues. Treatment with the DNA methyltransferase inhibitor 5-aza-2'-deoxycytidine resulted in demethylation in the RRAD promoter and restored RRAD expression in T29H cells. Additionally, treatment with farnesyltransferase inhibitor FTI277 resulted in restored RRAD expression and inhibited DNA methytransferase expression and activity in T29H cells. By employing knockdown and overexpression techniques in T29 and T29H, respectively, we found that RRAD inhibited glucose uptake and lactate production by repressing the expression of glucose transporters. Finally, RRAD overexpression in T29H cells inhibited tumor formation in nude mice, suggesting that RRAD is a tumor suppressor gene. Our results indicate that Ras(V12)-mediated oncogenic transformation induces RRAD epigenetic inactivation, which in turn promotes glucose uptake and may contribute to ovarian cancer tumorigenesis.

A Bayesian framework to identify methylcytosines from high-throughput bisulfite sequencing data.

High-throughput bisulfite sequencing technologies have provided a comprehensive and well-fitted way to investigate DNA methylation at single-base resolution. However, there are substantial bioinformatic challenges to distinguish precisely methylcytosines from unconverted cytosines based on bisulfite sequencing data. The challenges arise, at least in part, from cell heterozygosis caused by multicellular sequencing and the still limited number of statistical methods that are available for methylcytosine calling based on bisulfite sequencing data. Here, we present an algorithm, termed Bycom, a new Bayesian model that can perform methylcytosine calling with high accuracy. Bycom considers cell heterozygosis along with sequencing errors and bisulfite conversion efficiency to improve calling accuracy. Bycom performance was compared with the performance of Lister, the method most widely used to identify methylcytosines from bisulfite sequencing data. The results showed that the performance of Bycom was better than that of Lister for data with high methylation levels. Bycom also showed higher sensitivity and specificity for low methylation level samples (<1%) than Lister. A validation experiment based on reduced representation bisulfite sequencing data suggested that Bycom had a false positive rate of about 4% while maintaining an accuracy of close to 94%. This study demonstrated that Bycom had a low false calling rate at any methylation level and accurate methylcytosine calling at high methylation levels. Bycom will contribute significantly to studies aimed at recalibrating the methylation level of genomic regions based on the presence of methylcytosines.

Innovative insights into extrachromosomal circular DNAs in gynecologic tumors and reproduction.

Originating but free from chromosomal DNA, extrachromosomal circular DNAs (eccDNAs) are organized in circular form and have long been found in unicellular and multicellular eukaryotes. Their biogenesis and function are poorly understood as they are characterized by sequence homology with linear DNA, for which few detection methods are available. Recent advances in high-throughput sequencing technologies have revealed that eccDNAs play crucial roles in tumor formation, evolution, and drug resistance as well as aging, genomic diversity, and other biological processes, bringing it back to the research hotspot. Several mechanisms of eccDNA formation have been proposed, including the breakage-fusion-bridge (BFB) and translocation-deletion-amplification models. Gynecologic tumors and disorders of embryonic and fetal development are major threats to human reproductive health. The roles of eccDNAs in these pathological processes have been partially elucidated since the first discovery of eccDNA in pig sperm and the double minutes in ovarian cancer ascites. The present review summarized the research history, biogenesis, and currently available detection and analytical methods for eccDNAs and clarified their functions in gynecologic tumors and reproduction. We also proposed the application of eccDNAs as drug targets and liquid biopsy markers for prenatal diagnosis and the early detection, prognosis, and treatment of gynecologic tumors. This review lays theoretical foundations for future investigations into the complex regulatory networks of eccDNAs in vital physiological and pathological processes.

Cell-cell communication: new insights and clinical implications.

Multicellular organisms are composed of diverse cell types that must coordinate their behaviors through communication. Cell-cell communication (CCC) is essential for growth, development, differentiation, tissue and organ formation, maintenance, and physiological regulation. Cells communicate through direct contact or at a distance using ligand-receptor interactions. So cellular communication encompasses two essential processes: cell signal conduction for generation and intercellular transmission of signals, and cell signal transduction for reception and procession of signals. Deciphering intercellular communication networks is critical for understanding cell differentiation, development, and metabolism. First, we comprehensively review the historical milestones in CCC studies, followed by a detailed description of the mechanisms of signal molecule transmission and the importance of the main signaling pathways they mediate in maintaining biological functions. Then we systematically introduce a series of human diseases caused by abnormalities in cell communication and their progress in clinical applications. Finally, we summarize various methods for monitoring cell interactions, including cell imaging, proximity-based chemical labeling, mechanical force analysis, downstream analysis strategies, and single-cell technologies. These methods aim to illustrate how biological functions depend on these interactions and the complexity of their regulatory signaling pathways to regulate crucial physiological processes, including tissue homeostasis, cell development, and immune responses in diseases. In addition, this review enhances our understanding of the biological processes that occur after cell-cell binding, highlighting its application in discovering new therapeutic targets and biomarkers related to precision medicine. This collective understanding provides a foundation for developing new targeted drugs and personalized treatments.