Cell Taxonomy: a curated repository of cell types with multifaceted characterization

Maternal COVID-19 infection associated with offspring neurodevelopmental disorders

Maternal COVID-19 infection increases the incidence of neurodevelopmental disorders (NDDs) in offspring, although the underlying mechanisms have not been elucidated. This study demonstrated that COVID-19 infection during pregnancy disrupted the balance of maternal and fetal immune environments, driving alterations in astrocytes, endothelial cells, and excitatory neurons. A risk score was established using 47 unique genes in the single-cell transcriptome of gestational mothers. The high risk score in CD4 proliferating T cell level served as an indicator for increased risk of offspring NDDs. Summary-based Mendelian randomization and phenome-wide association study analyses were conducted to identify the causal association of the transcriptional changes with the increased risk of offspring NDDs. Additionally, 10 drugs were identified as potential therapeutic candidates. Our findings support a model where the maternal COVID-19 infection changed the levels of CD4 proliferating T cells, leading to the alterations of astrocytes, endothelial cells, and excitatory neurons in offspring, contributing to the increased risk of NDDs in these individuals.

Exploring BIRC family genes as prognostic biomarkers and therapeutic targets in prostate cancer

The potential oncogenic role of Baculoviral inhibitor of apoptosis (IAP) Repeat-Containing (BIRC) genes in prostate cancer (PCa) has yet to be fully investigated. Two genes associated with disease recurrence, BIRC5 and BIRC7, were identified through survival analysis, and prostate cancer patients were categorized into two subtypes, C1 and C2, based on these genes. We performed survival analyses to assess the relationship between subtypes and the prognosis of PCa. Single-cell dataset analysis was used to identify specific cell types with enriched expression of BIRC family genes. Our findings show that BIRC5 and BIRC7 exhibit higher expression in PCa tissues compared to non-cancerous tissues. High expression of BIRC5 and BIRC7 independently correlates with an adverse prognosis in PCa. The analysis of mechanisms reveals that the differentially expressed genes impact signaling pathways associated with cancer and immunity. BIRC5/BIRC7 correlate with several immune cells infiltrating levels including T cells and macrophages. Furthermore, our research indicates that elevated expression of BIRC5 is associated with immune infiltration in PCa. These findings highlight the potential of BIRC5/BIRC7 or C1 subtype as prognostic biomarkers, offering new insights into possible targets for the development of therapeutic biomarkers and immunotherapeutic for PCa.

Therapeutic implications of endoplasmic reticulum stress gene CCL3 in cervical squamous cell carcinoma

This study investigated ERS-related gene expressions in CESC, identifying two molecular subtypes, P1 and P2, and constructing a precise prognostic model based on these subtypes. TCGA's whole-genome expression profiles were used to recognize these subtypes through the ConsensusClusterPlus method, further refining prognostic models with univariate and Lasso Cox regression analyses validated by the GSE39001 dataset. The study analyzed the expression distribution of ERS marker genes within T cell subgroups using scRNA-seq data (GSE168652), highlighting T cell diversity. The critical role of the CCL3 gene in prognostic models was examined explicitly in CD8 + T cells from healthy individuals and CESC patients. Elevated CCL3 levels were observed in patients' CD8 + T cells compared to healthy controls. Functional experiments involving CCL3 knockdown and overexpression in HeLa and SiHa CESC cell lines were conducted to investigate its impact on cell proliferation, migration, and invasion. These findings were subsequently validated in a nude mouse model. The results demonstrated that suppressing CCL3 inhibited cell proliferation, migration, and invasion significantly, while its overexpression promoted these processes. In the mouse model, CCL3 silencing reduced tumor growth and decreased Ki-67 labeling within the tumor tissues, indicating the therapeutic potential of targeting CCL3 in CESC treatment, possibly through CD8 + T cell regulation. This study contributes new prognostic assessment tools and personalized treatment options for CESC patients, paving the way for more targeted therapies in CESC by discovering the CCL3 gene, presenting significant clinical implications.

Molecular and Functional Convergences Associated with Complex Multicellularity in Eukarya

A key trait of Eukarya is the independent evolution of complex multicellularity in animals, land plants, fungi, brown algae, and red algae. This phenotype is characterized by the initial exaptation of cell-cell adhesion genes followed by the emergence of mechanisms for cell-cell communication, together with the expansion of transcription factor gene families responsible for cell and tissue identity. The number of cell types is commonly used as a quantitative proxy for biological complexity in comparative genomics studies. While expansions of individual gene families have been associated with variations in the number of cell types within individual complex multicellular lineages, the molecular and functional roles responsible for the independent evolution of complex multicellular across Eukarya remain poorly understood. We employed a phylogeny-aware strategy to conduct a genomic-scale search for associations between the number of cell types and the abundance of genomic components across a phylogenetically diverse set of 81 eukaryotic species, including species from all complex multicellular lineages. Our annotation schemas represent 2 complimentary aspects of genomic information: homology, represented by conserved sequences, and function, represented by Gene Ontology terms. We found many gene families sharing common biological themes that define complex multicellular to be independently expanded in 2 or more complex multicellular lineages, such as components of the extracellular matrix, cell-cell communication mechanisms, and developmental pathways. Additionally, we describe many previously unknown associations of biological themes and biological complexity, such as expansions of genes playing roles in wound response, immunity, cell migration, regulatory processes, and response to natural rhythms. Together, our findings unveil a set of functional and molecular convergences independently expanded in complex multicellular lineages likely due to the common selective pressures in their lifestyles.

Editome Disease Knowledgebase v2.0: an updated resource of editome-disease associations through literature curation and integrative analysis

Motivation

Editome Disease Knowledgebase (EDK) is a curated resource of knowledge between RNA editome and human diseases. Since its first release in 2018, a number of studies have discovered previously uncharacterized editome-disease associations and generated an abundance of RNA editing datasets. Thus, it is desirable to make significant updates for EDK by incorporating more editome-disease associations as well as their related editing profiles.

Results

Here, we present EDK v2.0, an updated version of editome-disease associations based on both literature curation and integrative analysis. EDK v2.0 incorporates a curated collection of 1097 editome-disease associations involving 115 diseases from 321 publications. Meanwhile, based on a standardized pipeline, EDK v2.0 provides RNA editing profiles from 48 datasets covering 2536 samples across 55 diseases. Through differential analysis on RNA editing, it further identifies a total of 7190 differential edited genes and 86 242 differential editing sites (DESs), leading to 266 339 DES-disease associations. Moreover, a curated list of 28 160 cis-RNA editing QTL associations, 458 187 DES-RNA binding protein associations, and 21 DES-RNA secondary structure associations are annotated and added to EDK v2.0. Additionally, it is equipped with a series of user-friendly tools to facilitate RNA editing online analysis.

Availability and implementation

https://ngdc.cncb.ac.cn/edk/.

CancerSCEM 2.0: an updated data resource of single-cell expression map across various human cancers

The field of single-cell RNA sequencing (scRNA-seq) has advanced rapidly in the past decade, generating significant amounts of valuable data for researchers to study complex tumor profiles. This data is crucial for gaining innovative insights into cancer biology. CancerSCEM (https://ngdc.cncb.ac.cn/cancerscem) is a public resource that integrates, analyzes and visualizes scRNA-seq data related to cancer, and it provides invaluable support to numerous cancer-related studies. With CancerSCEM 2.0, scRNA-seq data have increased from 208 to 1466 datasets, covering tumor, matching-normal and peripheral blood samples across 127 research projects and 74 cancer types. The new version of this resource enhances transcriptome analysis by adding copy number variation evaluation, transcription factor enrichment, pseudotime trajectory construction, and diverse biological feature scoring. It also introduces a new cancer metabolic map at the single-cell level, providing an intuitive understanding of metabolic regulation across different cancer types. CancerSCEM 2.0 has a more interactive analysis platform, including four modules and 14 analytical functions, allowing researchers to perform cancer scRNA-seq data analyses in various dimensions. These enhancements are expected to expand the usability of CancerSCEM 2.0 to a broader range of cancer research and clinical applications, potentially revolutionizing our understanding of cancer mechanisms and treatments.

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.

EGFR and MUC1 as dual-TAA drug targets for lung cancer and colorectal cancer

Background

Epidermal growth factor receptor (EGFR) is a key protein in cellular signaling that is overexpressed in many human cancers, making it a compelling therapeutic target. On-target severe skin toxicity has limited its clinical application. Dual-targeting therapy represents a novel approach to overcome the challenges of EGFR-targeted therapies.

Methods

A single-cell tumor-normal RNA transcriptomic meta-atlas of lung adenocarcinoma (LUAD) and normal lung tissues was constructed from published data. Tumor associated antigens (TAAs) were screened from the genes which were expressed on cell surface and could distinguish cancer cells from normal cells. Expression of MUC1 and EGFR in tumors and normal tissues was detected by immunohistochemistry (IHC), bulk transcriptomic and single-cell transcriptomic analyses. RNA cut-off values were calculated using paired analysis of RNA sequencing and IHC in patient-derived tumor xenograft samples. They were used to estimate the abundance of EGFR- and MUC-positive subjects in The Cancer Genome Atlas Program (TCGA) database. Survival analysis of EGFR and MUC1 expression was carried out using the transcription and clinical data from TCGA.

Results

A candidate TAA target, transmembrane glycoprotein mucin 1 (MUC1), showed strong expression in cancer cells and low expression in normal cells. Single-cell analysis suggested EGFR and MUC1 together had better tumor specificity than the combination of EGFR with other drug targets. IHC data confirmed that EGFR and MUC1 were highly expressed on LUAD and colorectal cancer (CRC) clinical samples but not on various normal tissues. Notably, co-expression of EGFR and MUC1 was observed in 98.4% (n=64) of patients with LUAD and in 91.6% (n=83) of patients with CRC. It was estimated that EGFR and MUC1 were expressed in 97.5% of LUAD samples in the TCGA dataset. Besides, high expression of EGFR and MUC1 was significantly associated with poor prognosis of LUAD and CRC patients.

Conclusions

Single-cell RNA, bulk RNA and IHC data demonstrated the high expression levels and co-expression patterns of EGFR and MUC1 in tumors but not normal tissues. Therefore, it is a promising TAA combination for therapeutic targeting which could enhance on-tumor efficacy while reducing off-tumor toxicity.

Potential molecular mechanisms of ETV6-RUNX1-positive B progenitor cell cluster in acute lymphoblastic leukemia revealed by single-cell RNA sequencing

Aim

This study was to explore role of immune landscape and the immune cells in acute lymphoblastic leukemia (ALL) progression.

Background

The most prevalent genetic alteration in childhood ALL is the ETV6-RUNX1 fusion. The increased proliferation of B progenitor cells could expedite the disease's progression due to irregularities in the cell cycle. Nevertheless, the mechanisms by which particular cell clusters influence the cell cycle and promote the advancement of ALL are still not well understood.

Objective

This study was to explore role of immune landscape and the immune cells in ALL progression.

Methods

Single-cell RNA sequencing (scRNA-seq) data of ETV6-RUNX1 and healthy pediatric samples obtained from GSE132509 were clustered and annotated using the Seurat package, and differentially highly expressed genes identified in each cluster were analyzed using DAVID for pathway annotation. Chromosome amplification and deletion were analyzed using the inferCNV package. SCENIC evaluated the regulation of transcription factors and target gene formation in cells. cellphoneDB and CellChat were served to infer ligand-receptor pairs that mediate interactions between subpopulations. The role of the target gene in regulating ALL progression was assessed using RT-qPCR, Transwell and scratch healing assays.

Results

The bone marrow mononuclear cells (BMMCs) from ETV6-RUNX1 and healthy pediatric samples in GSE132509 were divided into 11 clusters, and B cell cluster 1 was identified as B progenitor cell, which was amplified on chromosome 6p. B progenitor cells were divided into seven clusters. Expression levels of amplified genes in chromosome 6p of B progenitor cell cluster 5 were the highest, and its specific highly expressed genes were annotated to pathways promoting cell cycle progression. Regulons formed in B progenitor cell cluster 5 were all involved in promoting cell cycle progression, so it was regarded as the B progenitor cell cluster that drives cell cycle progression. The key regulator of the B progenitor cell is E2F1, which promotes the migration and invasion ability of the cell line HAP1. The major ligand-receptor pairs that mediate the communication of B progenitor cell cluster 5 with cytotoxic NK/T cells or naive T cells included FAM3C-CLEC2D, CD47-SIRPG, HLAE-KLRC2, and CD47-KLRC2. HLAE-KLRC1 and TGFB1-(TGFBR1+TGFBR2).

Conclusion

This study outlined the immune cell landscape of ETV6-RUNX1 ALL and identified chromosome 6p amplification in B progenitor cells, described the major B progenitor cell cluster driving cell cycle progression and its potential regulatory mechanisms on NK cells and T cells, providing cellular and molecular insights into ETV6-RUNX1 ALL.

Exploring IRGs as a Biomarker of Pulmonary Hypertension Using Multiple Machine Learning Algorithms

BACKGROUND

Pulmonary arterial hypertension (PAH) is a severe disease with poor prognosis and high mortality, lacking simple and sensitive diagnostic biomarkers in clinical practice. This study aims to identify novel diagnostic biomarkers for PAH using genomics research.

METHODS

We conducted a comprehensive analysis of a large transcriptome dataset, including PAH and inflammatory response genes (IRGs), integrated with 113 machine learning models to assess diagnostic potential. We developed a clinical diagnostic model based on hub genes, evaluating their effectiveness through calibration curves, clinical decision curves, and ROC curves. An animal model of PAH was also established to validate hub gene expression patterns.

RESULTS

Among the 113 machine learning algorithms, the Lasso + LDA model achieved the highest AUC of 0.741. Differential expression profiles of hub genes CTGF, DDR2, FGFR2, MYH10, and YAP1 were observed between the PAH and normal control groups. A diagnostic model utilizing these hub genes was developed, showing high accuracy with an AUC of 0.87. MYH10 demonstrated the most favorable diagnostic performance with an AUC of 0.8. Animal experiments confirmed the differential expression of CTGF, DDR2, FGFR2, MYH10, and YAP1 between the PAH and control groups (p < 0.05); Conclusions: We successfully established a diagnostic model for PAH using IRGs, demonstrating excellent diagnostic performance. CTGF, DDR2, FGFR2, MYH10, and YAP1 may serve as novel molecular diagnostic markers for PAH.

Key platelet genes play important roles in predicting the prognosis of sepsis

Sepsis is a life-threatening organ malfunction induced by an imbalanced immunological reaction to infection in the host. Many studies have utilized traditional RNA sequencing (RNA-seq) data to identify important biological targets to predict sepsis prognosis. However, alterations in core cells and functional status cannot be effectively detected in sepsis patients. The goal of this study was to identify key cells through single-cell RNA-seq (scRNA-seq), and combine bulk RNA-seq data and multiple algorithm analysis to construct a stable prognostic model for sepsis. The scRNA-seq and bulk RNA-seq data from sepsis patients were collected from the Gene Expression Omnibus (GEO) database. The R package "Seurat" was used to process the scRNA-seq data. Cell communication was investigated using the R package "CellChat". The pseudo-time of the cells was calculated using the R package "monocle". The R package "limma" was used to identify differentially expressed genes (DEGs) between the sepsis group and the control group. Weighted gene correlation network analysis (WGCNA) was used to identify critical modules. Eight kinds of machine learning and 90 algorithm combinations were used to construct the prognostic model for sepsis. Quantitative real-time PCR (qRT‒PCR) was performed to determine the expression of key genes in the cecal ligation and puncture (CLP)-induced sepsis mouse model. The immunological status and related properties of DEGs were then investigated in the high- and low-risk groups delineated by the model. By combining the scRNA-seq data from nine samples, 13 clusters and 9 cell types were identified. CellChat analysis revealed that the number and strength of interactions between platelets and a variety of cells increased. We identified key platelet genes from the scRNA-seq data and combined these genes and the results of differential analysis and WGCNA of the bulk RNA-seq data. After univariate Cox regression analysis, we calculated the Cindex of the model constructed by the combination of 90 algorithms, and we finally determined the "CoxBoost + Lasso" combination. Multivariate Cox regression was used to construct the final prognostic model. The qRT-PCR results revealed significant differences in five key prognostic genes between the CLP and sham groups. The data was classified into high- and low-risk groups based on the model score. The high-risk group had a poorer survival rate and less immune infiltration. We identified the importance of platelets in sepsis patients through scRNA-seq, and established prognostic models with key genes that were identified via scRNA-seq combined with bulk RNA-seq analysis. The results of this model were closely associated with patient survival rates and immunological status and this model is useful for the prognostic management of sepsis.

OrganogenesisDB: A Comprehensive Database Exploring the Cell-Type Identities and Gene Expression Dynamics during Organogenesis

Organogenesis, the phase of embryonic development that starts at the end of gastrulation and continues until birth is the critical process for understanding cellular differentiation and maturation during organ development. The rapid development of single-cell transcriptomics technology has led to many novel discoveries in understanding organogenesis while also accumulating a large quantity of data. To fill this gap, OrganogenesisDB (http://organogenesisdb.com/), which is a comprehensive database dedicated to exploring cell-type identification and gene expression dynamics during organogenesis, is developed. OrganogenesisDB contains single-cell RNA sequencing data for more than 1.4 million cells from 49 published datasets spanning various developmental stages. Additionally, 3324 cell markers are manually curated for 1120 cell types across 9 human organs and 4 mouse organs. OrganogenesisDB leverages various analysis tools to assist users in annotating and understanding cell types at different developmental stages and helps in mining and presenting genes that exhibit specific patterns and play key regulatory roles during cell maturation and differentiation. This work provides a critical resource and useful tool for deciphering cell lineage determination and uncovering the mechanisms underlying organogenesis.

Biophysically interpretable inference of cell types from multimodal sequencing data

Multimodal, single-cell genomics technologies enable simultaneous measurement of multiple facets of DNA and RNA processing in the cell. This creates opportunities for transcriptome-wide, mechanistic studies of cellular processing in heterogeneous cell populations, such as regulation of cell fate by transcriptional stochasticity or tumor proliferation through aberrant splicing dynamics. However, current methods for determining cell types or 'clusters' in multimodal data often rely on ad hoc approaches to balance or integrate measurements, and assumptions ignoring inherent properties of the data. To enable interpretable and consistent cell cluster determination, we present meK-means (mechanistic K-means) which integrates modalities through a unifying model of transcription to learn underlying, shared biophysical states. With meK-means we can cluster cells with nascent and mature mRNA measurements, utilizing the causal, physical relationships between these modalities. This identifies shared transcription dynamics across cells, which induce the observed molecule counts, and provides an alternative definition for 'clusters' through the governing parameters of cellular processes.

Targeting Liver Epsins Ameliorates Dyslipidemia in Atherosclerosis

Rationale

Low density cholesterol receptor (LDLR) in the liver is critical for the clearance of low-density lipoprotein cholesterol (LDL-C) in the blood. In atherogenic conditions, proprotein convertase subtilisin/kexin 9 (PCSK9) secreted by the liver, in a nonenzymatic fashion, binds to LDLR on the surface of hepatocytes, preventing its recycling and enhancing its degradation in lysosomes, resulting in reduced LDL-C clearance. Our recent studies demonstrate that epsins, a family of ubiquitin-binding endocytic adaptors, are critical regulators of atherogenicity. Given the fundamental contribution of circulating LDL-C to atherosclerosis, we hypothesize that liver epsins promote atherosclerosis by controlling LDLR endocytosis and degradation.

Objective

We will determine the role of liver epsins in promoting PCSK9-mediated LDLR degradation and hindering LDL-C clearance to propel atherosclerosis.

Methods and Results

We generated double knockout mice in which both paralogs of epsins, namely, epsin-1 and epsin-2, are specifically deleted in the liver (Liver-DKO) on an ApoE -/- background. We discovered that western diet (WD)-induced atherogenesis was greatly inhibited, along with diminished blood cholesterol and triglyceride levels. Mechanistically, using scRNA-seq analysis on cells isolated from the livers of ApoE-/- and ApoE-/- /Liver-DKO mice on WD, we found lipogenic Alb hi hepatocytes to glycogenic HNF4α hi hepatocytes transition in ApoE-/- /Liver-DKO. Subsequently, gene ontology analysis of hepatocyte-derived data revealed elevated pathways involved in LDL particle clearance and very-low-density lipoprotein (VLDL) particle clearance under WD treatment in ApoE-/- /Liver-DKO, which was coupled with diminished plasma LDL-C levels. Further analysis using the MEBOCOST algorithm revealed enhanced communication score between LDLR and cholesterol, suggesting elevated LDL-C clearance in the ApoE-/- Liver-DKO mice. In addition, we showed that loss of epsins in the liver upregulates of LDLR protein level. We further showed that epsins bind LDLR via the ubiquitin-interacting motif (UIM), and PCSK9-triggered LDLR degradation was abolished by depletion of epsins, preventing atheroma progression. Finally, our therapeutic strategy, which involved targeting liver epsins with nanoparticle-encapsulated siRNAs, was highly efficacious at inhibiting dyslipidemia and impeding atherosclerosis.

Conclusions

Liver epsins promote atherogenesis by mediating PCSK9-triggered degradation of LDLR, thus raising the circulating LDL-C levels. Targeting epsins in the liver may serve as a novel therapeutic strategy to treat atherosclerosis by suppression of PCSK9-mediated LDLR degradation.

SingleCellGGM enables gene expression program identification from single-cell transcriptomes and facilitates universal cell label transfer

Gene co-expression analysis of single-cell transcriptomes, aiming to define functional relationships between genes, is challenging due to excessive dropout values. Here, we developed a single-cell graphical Gaussian model (SingleCellGGM) algorithm to conduct single-cell gene co-expression network analysis. When applied to mouse single-cell datasets, SingleCellGGM constructed networks from which gene co-expression modules with highly significant functional enrichment were identified. We considered the modules as gene expression programs (GEPs). These GEPs enable direct cell-type annotation of individual cells without cell clustering, and they are enriched with genes required for the functions of the corresponding cells, sometimes at levels greater than 10-fold. The GEPs are conserved across datasets and enable universal cell-type label transfer across different studies. We also proposed a dimension-reduction method through averaging by GEPs for single-cell analysis, enhancing the interpretability of results. Thus, SingleCellGGM offers a unique GEP-based perspective to analyze single-cell transcriptomes and reveals biological insights shared by different single-cell datasets.

Development of prognostic signatures and risk index related to lipid metabolism in ccRCC

Background

Clear cell renal cell carcinoma (ccRCC) is a metabolic disorder characterized by abnormal lipid accumulation in the cytoplasm. Lipid metabolism-related genes may have important clinical significance for prognosis prediction and individualized treatment.

Methods

We collected bulk and single-cell transcriptomic data of ccRCC and normal samples to identify key lipid metabolism-related prognostic signatures. qPCR was used to confirm the expression of signatures in cancer cell lines. Based on the identified signatures, we developed a lipid metabolism risk score (LMRS) as a risk index. We explored the potential application value of prognostic signatures and LMRS in precise treatment from multiple perspectives.

Results

Through comprehensive analysis, we identified five lipid metabolism-related prognostic signatures (ACADM, ACAT1, ECHS1, HPGD, DGKZ). We developed a risk index LMRS, which was significantly associated with poor prognosis in patients. There was a significant correlation between LMRS and the infiltration levels of multiple immune cells. Patients with high LMRS may be more likely to respond to immunotherapy. The different LMRS groups were suitable for different anticancer drug treatment regimens.

Conclusion

Prognostic signatures and LMRS we developed may be applied to the risk assessment of ccRCC patients, which may have potential guiding significance in the diagnosis and precise treatment of ccRCC patients.

Deciphering smooth muscle cell heterogeneity in atherosclerotic plaques and constructing model: a multi-omics approach with focus on KLF15/IGFBP4 axis

BACKGROUND

Ruptured atherosclerotic plaques often precipitate severe ischemic events, such as stroke and myocardial infarction. Unraveling the intricate molecular mechanisms governing vascular smooth muscle cell (VSMC) behavior in plaque stabilization remains a formidable challenge.

METHODS

In this study, we leveraged single-cell and transcriptomic datasets from atherosclerotic plaques retrieved from the gene expression omnibus (GEO) database. Employing a combination of single-cell population differential analysis, weighted gene co-expression network analysis (WGCNA), and transcriptome differential analysis techniques, we identified specific genes steering the transformation of VSMCs in atherosclerotic plaques. Diagnostic models were developed and validated through gene intersection, utilizing the least absolute shrinkage and selection operator (LASSO) and random forest (RF) methods. Nomograms for plaque assessment were constructed. Tissue localization and expression validation were performed on specimens from animal models, utilizing immunofluorescence co-localization, western blot, and reverse-transcription quantitative-polymerase chain reaction (RT-qPCR). Various online databases were harnessed to predict transcription factors (TFs) and their interacting compounds, with determination of the cell-specific localization of TF expression using single-cell data.

RESULTS

Following rigorous quality control procedures, we obtained a total of 40,953 cells, with 6,261 representing VSMCs. The VSMC population was subsequently clustered into 5 distinct subpopulations. Analyzing inter-subpopulation cellular communication, we focused on the SMC2 and SMC5 subpopulations. Single-cell subpopulation and WGCNA analyses revealed significant module enrichments, notably in collagen-containing extracellular matrix and cell-substrate junctions. Insulin-like growth factor binding protein 4 (IGFBP4), apolipoprotein E (APOE), and cathepsin C (CTSC) were identified as potential diagnostic markers for early and advanced plaques. Notably, gene expression pattern analysis suggested that IGFBP4 might serve as a protective gene, a hypothesis validated through tissue localization and expression analysis. Finally, we predicted TFs capable of binding to IGFBP4, with Krüppel-like family 15 (KLF15) emerging as a prominent candidate showing relative specificity within smooth muscle cells. Predictions about compounds associated with affecting KLF15 expression were also made.

CONCLUSION

Our study established a plaque diagnostic and assessment model and analyzed the molecular interaction mechanisms of smooth muscle cells within plaques. Further analysis revealed that the transcription factor KLF15 may regulate the biological behaviors of smooth muscle cells through the KLF15/IGFBP4 axis, thereby influencing the stability of advanced plaques via modulation of the PI3K-AKT signaling pathway. This could potentially serve as a target for plaque stability assessment and therapy, thus driving advancements in the management and treatment of atherosclerotic plaques.

scATAC-Ref: a reference of scATAC-seq with known cell labels in multiple species

Chromatin accessibility profiles at single cell resolution can reveal cell type-specific regulatory programs, help dissect highly specialized cell functions and trace cell origin and evolution. Accurate cell type assignment is critical for effectively gaining biological and pathological insights, but is difficult in scATAC-seq. Hence, by extensively reviewing the literature, we designed scATAC-Ref (https://bio.liclab.net/scATAC-Ref/), a manually curated scATAC-seq database aimed at providing a comprehensive, high-quality source of chromatin accessibility profiles with known cell labels across broad cell types. Currently, scATAC-Ref comprises 1 694 372 cells with known cell labels, across various biological conditions, >400 cell/tissue types and five species. We used uniform system environment and software parameters to perform comprehensive downstream analysis on these chromatin accessibility profiles with known labels, including gene activity score, TF enrichment score, differential chromatin accessibility regions, pathway/GO term enrichment analysis and co-accessibility interactions. The scATAC-Ref also provided a user-friendly interface to query, browse and visualize cell types of interest, thereby providing a valuable resource for exploring epigenetic regulation in different tissues and cell types.

Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2024

The National Genomics Data Center (NGDC), which is a part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support the global academic and industrial communities. With the rapid accumulation of multi-omics data at an unprecedented pace, CNCB-NGDC continuously expands and updates core database resources through big data archiving, integrative analysis and value-added curation. Importantly, NGDC collaborates closely with major international databases and initiatives to ensure seamless data exchange and interoperability. Over the past year, significant efforts have been dedicated to integrating diverse omics data, synthesizing expanding knowledge, developing new resources, and upgrading major existing resources. Particularly, several database resources are newly developed for the biodiversity of protists (P10K), bacteria (NTM-DB, MPA) as well as plant (PPGR, SoyOmics, PlantPan) and disease/trait association (CROST, HervD Atlas, HALL, MACdb, BioKA, BioKA, RePoS, PGG.SV, NAFLDkb). All the resources and services are publicly accessible at https://ngdc.cncb.ac.cn.

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.

Aging-related genes revealed Neuroinflammatory mechanisms in ischemic stroke by bioinformatics

Ischemic stroke (IS) is a leading cause of disability, morbidity, and mortality globally. Aging affects immune function and contributes to poor outcomes of IS in elderly individuals. However, little is known about how aging-related genes (ARGs) are involved in IS. In this study, the relationship between ARGs and IS immune microenvironment biomarkers was explored by bioinformatics. Two IS microarray datasets (GSE22255, GSE16561) from human blood samples were analyzed and 502 ARGs were identified, from which 29 differentially expressed ARGs were selected. Functional analysis revealed that 7 of these ARGs (IL1B, FOS, JUN, CXCL5, PTGS2, TNFAIP3 and TLR4) were involved in five top enriched pathways (IL-17 signaling pathway, TNF signaling pathway, Rheumatoid arthritis, NF-kappa B signaling pathway and Pertussis) related to immune responses and inflammation. Five hub DE-ARGs (IL2RB, FOS, IL7R, ALDH2 and BIRC2) were identified using machine learning algorithms, and their association with immune-related characteristics was confirmed by additional tests. Single-cell sequencing dataset GSE129788 was retrieved to analyze aging molecular-related features, which was in accordance with microarray datasets. Clustering analysis revealed two subtypes of IS, which were distinguished by their differential expression of genes related to the NF-kappa B signaling pathway. These findings highlight the importance of ARGs in regulating immune responses in IS and suggest potential prevention and treatment strategies as well as guidelines for future research.

Biophysically Interpretable Inference of Cell Types from Multimodal Sequencing Data

Multimodal, single-cell genomics technologies enable simultaneous capture of multiple facets of DNA and RNA processing in the cell. This creates opportunities for transcriptome-wide, mechanistic studies of cellular processing in heterogeneous cell types, with applications ranging from inferring kinetic differences between cells, to the role of stochasticity in driving heterogeneity. However, current methods for determining cell types or 'clusters' present in multimodal data often rely on ad hoc or independent treatment of modalities, and assumptions ignoring inherent properties of the count data. To enable interpretable and consistent cell cluster determination from multimodal data, we present meK-Means (mechanistic K-Means) which integrates modalities and learns underlying, shared biophysical states through a unifying model of transcription. In particular, we demonstrate how meK-Means can be used to cluster cells from unspliced and spliced mRNA count modalities. By utilizing the causal, physical relationships underlying these modalities, we identify shared transcriptional kinetics across cells, which induce the observed gene expression profiles, and provide an alternative definition for 'clusters' through the governing parameters of cellular processes.

HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting

Medical decision-making processes can be enhanced by comprehensive biomedical knowledge bases, which require fusing knowledge graphs constructed from different sources via a uniform index system. The index system often organizes biomedical terms in a hierarchy to provide the aligned entities with fine-grained granularity. To address the challenge of scarce supervision in the biomedical knowledge fusion (BKF) task, researchers have proposed various unsupervised methods. However, these methods heavily rely on ad-hoc lexical and structural matching algorithms, which fail to capture the rich semantics conveyed by biomedical entities and terms. Recently, neural embedding models have proved effective in semantic-rich tasks, but they rely on sufficient labeled data to be adequately trained. To bridge the gap between the scarce-labeled BKF and neural embedding models, we propose HiPrompt, a supervision-efficient knowledge fusion framework that elicits the few-shot reasoning ability of large language models through hierarchy-oriented prompts. Empirical results on the collected KG-Hi-BKF benchmark datasets demonstrate the effectiveness of HiPrompt.