SPEED: Single-cell Pan-species atlas in the light of Ecology and Evolution for Development and Diseases

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.

Single-nucleus RNA and multiomics in situ pairwise sequencing reveals cellular heterogeneity of the abnormal ligamentum teres in patients with developmental dysplasia of the hip

Developmental dysplasia of the hip (DDH) is the most common hip deformity in pediatric orthopedics. One of the common pathological changes in DDH is the thickening and hypertrophy of the ligamentum teres. However, the underlying pathogenic mechanism responsible for these changes remains unclear. This study represents the first time that the heterogeneity of cell subsets in the abnormal ligamentum teres of patients with DDH has been resolved at the single-cell and spatial levels by snRNA-Seq and MiP-Seq. Through gene set enrichment and intercellular communication network analyses, we found that receptor-like cells and ligament stem cells may play an essential role in the pathological changes resulting in ligamentum teres thickening and hypertrophy. Eight ligand-receptor pairs related to the ECM-receptor pathway were observed to be closely associated with DDH. Further, using the Monocle R package, we predicted a differentiation trajectory of pericytes into two branches, leading to junctional ligament stem cells or fibroblasts. The expression of extracellular matrix-related genes along pseudotemporal trajectories was also investigated. Using MiP-Seq, we determined the expression distribution of marker genes specific to different cell types within the ligamentum teres, as well as differentially expressed DDH-associated genes at the spatial level.

SCAN: Spatiotemporal Cloud Atlas for Neural cells

The nervous system is one of the most complicated and enigmatic systems within the animal kingdom. Recently, the emergence and development of spatial transcriptomics (ST) and single-cell RNA sequencing (scRNA-seq) technologies have provided an unprecedented ability to systematically decipher the cellular heterogeneity and spatial locations of the nervous system from multiple unbiased aspects. However, efficiently integrating, presenting and analyzing massive multiomic data remains a huge challenge. Here, we manually collected and comprehensively analyzed high-quality scRNA-seq and ST data from the nervous system, covering 10 679 684 cells. In addition, multi-omic datasets from more than 900 species were included for extensive data mining from an evolutionary perspective. Furthermore, over 100 neurological diseases (e.g. Alzheimer's disease, Parkinson's disease, Down syndrome) were systematically analyzed for high-throughput screening of putative biomarkers. Differential expression patterns across developmental time points, cell types and ST spots were discerned and subsequently subjected to extensive interpretation. To provide researchers with efficient data exploration, we created a new database with interactive interfaces and integrated functions called the Spatiotemporal Cloud Atlas for Neural cells (SCAN), freely accessible at http://47.98.139.124:8799 or http://scanatlas.net. SCAN will benefit the neuroscience research community to better exploit the spatiotemporal atlas of the neural system and promote the development of diagnostic strategies for various neurological disorders.

SCAR: Single-cell and Spatially-resolved Cancer Resources

Advances in sequencing and imaging technologies offer a unique opportunity to unravel cell heterogeneity and develop new immunotherapy strategies for cancer research. There is an urgent need for a resource that effectively integrates a vast amount of transcriptomic profiling data to comprehensively explore cancer tissue heterogeneity and the tumor microenvironment. In this context, we developed the Single-cell and Spatially-resolved Cancer Resources (SCAR) database, a combined tumor spatial and single-cell transcriptomic platform, which is freely accessible at http://8.142.154.29/SCAR2023 or http://scaratlas.com. SCAR contains spatial transcriptomic data from 21 tumor tissues and single-cell transcriptomic data from 11 301 352 cells encompassing 395 cancer subtypes and covering a wide variety of tissues, organoids, and cell lines. This resource offers diverse functional modules to address key cancer research questions at multiple levels, including the screening of tumor cell types, metabolic features, cell communication and gene expression patterns within the tumor microenvironment. Moreover, SCAR enables the analysis of biomarker expression patterns and cell developmental trajectories. SCAR also provides a comprehensive analysis of multi-dimensional datasets based on 34 state-of-the-art omics techniques, serving as an essential tool for in-depth mining and understanding of cell heterogeneity and spatial location. The implications of this resource extend to both cancer biology research and cancer immunotherapy development.

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

Cell-cell communication, as a basic feature of multicellular organisms, is crucial for maintaining the biological functions and microenvironmental homeostasis of cells, organs, and whole organisms. Alterations in cell-cell communication contribute to many diseases, including cancers. Single-cell RNA sequencing (scRNA-seq) provides a powerful method for studying cell-cell communication by enabling the analysis of ligand-receptor interactions. Here, we introduce CellCommuNet (http://www.inbirg.com/cellcommunet/), a comprehensive data resource for exploring cell-cell communication networks in scRNA-seq data from human and mouse tissues in normal and disease states. CellCommuNet currently includes 376 single datasets from multiple sources, and 118 comparison datasets between disease and normal samples originating from the same study. CellCommuNet provides information on the strength of communication between cells and related signalling pathways and facilitates the exploration of differences in cell-cell communication between healthy and disease states. Users can also search for specific signalling pathways, ligand-receptor pairs, and cell types of interest. CellCommuNet provides interactive graphics illustrating cell-cell communication in different states, enabling differential analysis of communication strength between disease and control samples. This comprehensive database aims to be a valuable resource for biologists studying cell-cell communication networks.

scPlantDB: a comprehensive database for exploring cell types and markers of plant cell atlases

Recent advancements in single-cell RNA sequencing (scRNA-seq) technology have enabled the comprehensive profiling of gene expression patterns at the single-cell level, offering unprecedented insights into cellular diversity and heterogeneity within plant tissues. In this study, we present a systematic approach to construct a plant single-cell database, scPlantDB, which is publicly available at https://biobigdata.nju.edu.cn/scplantdb. We integrated single-cell transcriptomic profiles from 67 high-quality datasets across 17 plant species, comprising approximately 2.5 million cells. The data underwent rigorous collection, manual curation, strict quality control and standardized processing from public databases. scPlantDB offers interactive visualization of gene expression at the single-cell level, facilitating the exploration of both single-dataset and multiple-dataset analyses. It enables systematic comparison and functional annotation of markers across diverse cell types and species while providing tools to identify and compare cell types based on these markers. In summary, scPlantDB serves as a comprehensive database for investigating cell types and markers within plant cell atlases. It is a valuable resource for the plant research community.

Uncovering the prominent role of satellite cells in paravertebral muscle development and aging by single-nucleus RNA sequencing

To uncover the role of satellite cells (SCs) in paravertebral muscle development and aging, we constructed a single-nucleus transcriptomic atlas of mouse paravertebral muscle across seven timepoints spanning the embryo (day 16.5) to old (month 24) stages. Eight cell types, including SCs, fast muscle cells, and slow muscle cells, were identified. An energy metabolism-related gene set, TCA CYCLE IN SENESCENCE, was enriched in SCs. Forty-two skeletal muscle disease-related genes were highly expressed in SCs and exhibited similar expression patterns. Among them, Pdha1 was the core gene in the TCA CYCLE IN SENESCENCE; Pgam2, Sod1, and Suclg1 are transcription factors closely associated with skeletal muscle energy metabolism. Transcription factor enrichment analysis of the 42 genes revealed that Myod1 and Mef2a were also highly expressed in SCs, which regulated Pdha1 expression and were associated with skeletal muscle development. These findings hint that energy metabolism may be pivotal in SCs development and aging. Three ligand-receptor pairs of extracellular matrix (ECM)-receptor interactions, Lamc1-Dag1, Lama2-Dag1, and Hspg2-Dag1, may play a vital role in SCs interactions with slow/fast muscle cells and SCs self-renewal. Finally, we built the first database of a skeletal muscle single-cell transcriptome, the Musculoskeletal Cell Atlas (http://www.mskca.tech), which lists 630,040 skeletal muscle cells and provides interactive visualization, a useful resource for revealing skeletal muscle cellular heterogeneity during development and aging. Our study could provide new targets and ideas for developing drugs to inhibit skeletal muscle aging and treat skeletal muscle diseases.

Constructing an extracellular matrix-related prognostic model for idiopathic pulmonary fibrosis based on machine learning

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive interstitial lung disease. Multiple research has revealed that the extracellular matrix (ECM) may be associated with the development and prognosis of IPF, however, the underlying mechanisms remain incompletely understood.

METHODS

We included GSE70866 dataset from the GEO database and established an ECM-related prognostic model utilizing LASSO, Random forest and Support vector machines algorithms. To compare immune cell infiltration levels between the high and low risk groups, we employed the ssGSEA algorithm. Enrichment analysis was conducted to explore pathway differences between the high-risk and low-risk groups. Finally, the model genes were validated using an external validation set consisting of IPF cases, as well as single-cell data analysis.

RESULTS

Based on machine learning algorithms, we constructed an ECM-related risk model. IPF patients in the high-risk group had a worse overall survival rate than those in the low-risk group. The model's AUC predictive values were 0.786, 0.767, and 0.768 for the 1-, 2-, and 3-year survival rates, respectively. The validation cohort validated these findings, demonstrating our model's effective prognostication. Chemokine-related pathways were enriched through enrichment analysis. Moreover, immune cell infiltration varied significantly between the two groups. Finally, the validation results indicate that the expression levels of all the model genes exhibited significant differential expression.

CONCLUSIONS

Based on CST6, PPBP, CSPG4, SEMA3B, LAMB2, SERPINB4 and CTF1, our study developed and validated an ECM-related risk model that accurately predicts the outcome of IPF patients.

Identification of cuproptosis-related molecular subtypes and a novel predictive model of COVID-19 based on machine learning

Background

To explicate the pathogenic mechanisms of cuproptosis, a newly observed copper induced cell death pattern, in Coronavirus disease 2019 (COVID-19).

Methods

Cuproptosis-related subtypes were distinguished in COVID-19 patients and associations between subtypes and immune microenvironment were probed. Three machine algorithms, including LASSO, random forest, and support vector machine, were employed to identify differentially expressed genes between subtypes, which were subsequently used for constructing cuproptosis-related risk score model in the GSE157103 cohort to predict the occurrence of COVID-19. The predictive values of the cuproptosis-related risk score were verified in the GSE163151 cohort, GSE152418 cohort and GSE171110 cohort. A nomogram was created to facilitate the clinical use of this risk score, and its validity was validated through a calibration plot. Finally, the model genes were validated using lung proteomics data from COVID-19 cases and single-cell data.

Results

Patients with COVID-19 had higher significantly cuproptosis level in blood leukocytes compared to patients without COVID-19. Two cuproptosis clusters were identified by unsupervised clustering approach and cuproptosis cluster A characterized by T cell receptor signaling pathway had a better prognosis than cuproptosis cluster B. We constructed a cuproptosis-related risk score, based on PDHA1, PDHB, MTF1 and CDKN2A, and a nomogram was created, which both showed excellent predictive values for COVID-19. And the results of proteomics showed that the expression levels of PDHA1 and PDHB were significantly increased in COVID-19 patient samples.

Conclusion

Our study constructed and validated an cuproptosis-associated risk model and the risk score can be used as a powerful biomarker for predicting the existence of SARS-CoV-2 infection.

Single cell and immunity: Better understanding immune cell heterogeneities with single cell sequencing

Single-cell sequencing has scientific impacts on better understanding the immunity. There is a rapid development in single cell-based databases and analytic tools to provide the potential of clinical and translational discovery. The understanding of single-cell based immunity needs a strong program and solid evidence of preclinical and clinical validation and evaluation. The current special topic issue on single cell and immunity aimed to provide a strong communication for the progress of single cell-based studies on immune cell functional diversity in development and disease. The topic has a clear scope on the application of single cell sequencing to better understand immune cell heterogeneities, functions, cell-cell interactions, responses and regulatory roles in systems immunology and diseases.