CellCommuNet: an atlas of cell-cell communication networks from single-cell RNA sequencing of human and mouse tissues in normal and disease states

scMMO-atlas: a single cell multimodal omics atlas and portal for exploring fine cell heterogeneity and cell dynamics

Single-cell multimodal sequencing parallelly captures multiple modalities of the same cell, providing unparalleled insights into cell heterogeneity and cell dynamics. For example, joint profiling of chromatin accessibility and transcriptome from the same single cell (scATAC + RNA) identified new cell subsets within the well-defined clusters. However, lack of single-cell multimodal omics (scMMO) database has led to data fragmentation, seriously hindering access, utilization and mining of scMMO data. Here, we constructed a scMMO atlas by collecting and integrating various scMMO data, then constructed scMMO database and portal called scMMO-atlas (https://www.biosino.org/scMMO-atlas/). scMMO-atlas includes scATAC + RNA (ISSAAS-seq, SNARE-seq, paired-seq, sci-CAR, scCARE-seq, 10X Multiome and so on), scRNA + protein, scATAC + protein and scTri-modal omics data, with 3 168 824 cells from 27 cell tissues/organs. scMMO-atlas offered an interactive portal for visualization and featured analysis for each modality and the integrated data. Integrated analysis of scATAC + RNA data of mouse cerebral cortex in scMMO-atlas identified more cell subsets compared with unimodal omics data. Among these new cell subsets, there is an early astrocyte subset highly expressed Grm3, called Astro-Grm3. Furthermore, we identified Ex-L6-Tle4-Nrf1, a progenitor of Ex-L6-Tle4, indicating the statistical power provided by the big data in scMMO-atlas. In summary, scMMO-atlas offers cell atlas, database and portal to facilitate data utilization and biological insight.

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

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

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.

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.

Exploring wound management in dental pulp: Utilizing single-cell RNA sequencing for global transcriptomic analysis in healthy and inflamed pulpal tissues

The process of wound healing in the dental pulp is characterized by intricate interplay of signalling cascades, cellular responses, and extracellular matrix (ECM). The objective of this research was to examine the intricate interaction between signalling cascades, cellular responses, and extracellular matrix (ECM) dynamics that comprise the wound healing process of dental pulp. We conducted a controlled laboratory analysis of transcriptomic landscape of dental pulp tissues, including both healthy and inflamed samples, utilizing single-cell RNA sequencing. We identified significant change in cellular composition under carious conditions by analysing samples from 50 patients. Specifically, the proportion of immune cells increased from 25% to 40%, while the proportion of fibroblasts decreased from 20% to 10%. A transition towards ECM remodelling and fibrosis was indicated by this change. In addition, substantial increase inexpression of critical genes including COL1A1, FN1, IL-1B, IL-6 and TNC was detected, indicating that the extracellular matrix (ECM) was actively remodelled and that a robust inflammatory response was present, both of which are vital for tissue repair. Increased cell-cell interactions among B cells, plasma cells, macrophages and MSCs, and fibroblasts were highlighted in our study, demonstrating the intricate cellular dynamics that occur in response to dental pulp injury. The knowledge gained regarding the cellular and molecular processes underlying pulp wound healing contributed to the advancement of knowledge regarding pulp pathology and regeneration. Moreover, it established a foundation for creation of targeted therapeutic interventions that seek to maximize pulp repair and regeneration. This study represented noteworthy achievement in the field of dental surgery, establishing a solid groundwork for subsequent investigations into regenerative medicine, wound healing, and dental tissue restoration.

The 2024 Nucleic Acids Research database issue and the online molecular biology database collection

The 2024 Nucleic Acids Research database issue contains 180 papers from across biology and neighbouring disciplines. There are 90 papers reporting on new databases and 83 updates from resources previously published in the Issue. Updates from databases most recently published elsewhere account for a further seven. Nucleic acid databases include the new NAKB for structural information and updates from Genbank, ENA, GEO, Tarbase and JASPAR. The Issue's Breakthrough Article concerns NMPFamsDB for novel prokaryotic protein families and the AlphaFold Protein Structure Database has an important update. Metabolism is covered by updates from Reactome, Wikipathways and Metabolights. Microbes are covered by RefSeq, UNITE, SPIRE and P10K; viruses by ViralZone and PhageScope. Medically-oriented databases include the familiar COSMIC, Drugbank and TTD. Genomics-related resources include Ensembl, UCSC Genome Browser and Monarch. New arrivals cover plant imaging (OPIA and PlantPAD) and crop plants (SoyMD, TCOD and CropGS-Hub). The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). Over the last year the NAR online Molecular Biology Database Collection has been updated, reviewing 1060 entries, adding 97 new resources and eliminating 388 discontinued URLs bringing the current total to 1959 databases. It is available at http://www.oxfordjournals.org/nar/database/c/.