Systematic single-cell analysis reveals dynamic control of transposable element activity orchestrating the endothelial-to-hematopoietic transition.

BACKGROUND: The endothelial-to-hematopoietic transition (EHT) process during definitive hematopoiesis is highly conserved in vertebrates. Stage-specific expression of transposable elements (TEs) has been detected during zebrafish EHT and may promote hematopoietic stem cell (HSC) formation by activating inflammatory signaling. However, little is known about how TEs contribute to the EHT process in human and mouse. RESULTS: We reconstructed the single-cell EHT trajectories of human and mouse and resolved the dynamic expression patterns of TEs during EHT. Most TEs presented a transient co-upregulation pattern along the conserved EHT trajectories, coinciding with the temporal relaxation of epigenetic silencing systems. TE products can be sensed by multiple pattern recognition receptors, triggering inflammatory signaling to facilitate HSC emergence. Interestingly, we observed that hypoxia-related signals were enriched in cells with higher TE expression. Furthermore, we constructed the hematopoietic cis-regulatory network of accessible TEs and identified potential TE-derived enhancers that may boost the expression of specific EHT marker genes. CONCLUSIONS: Our study provides a systematic vision of how TEs are dynamically controlled to promote the hematopoietic fate decisions through transcriptional and cis-regulatory networks, and pre-train the immunity of nascent HSCs.

HALD, a human aging and longevity knowledge graph for precision gerontology and geroscience analyses.

Human aging is a natural and inevitable biological process that leads to an increased risk of aging-related diseases. Developing anti-aging therapies for aging-related diseases requires a comprehensive understanding of the mechanisms and effects of aging and longevity from a multi-modal and multi-faceted perspective. However, most of the relevant knowledge is scattered in the biomedical literature, the volume of which reached 36 million in PubMed. Here, we presented HALD, a text mining-based human aging and longevity dataset of the biomedical knowledge graph from all published literature related to human aging and longevity in PubMed. HALD integrated multiple state-of-the-art natural language processing (NLP) techniques to improve the accuracy and coverage of the knowledge graph for precision gerontology and geroscience analyses. Up to September 2023, HALD had contained 12,227 entities in 10 types (gene, RNA, protein, carbohydrate, lipid, peptide, pharmaceutical preparations, toxin, mutation, and disease), 115,522 relations, 1,855 aging biomarkers, and 525 longevity biomarkers from 339,918 biomedical articles in PubMed. HALD is available at https://bis.zju.edu.cn/hald .

Characterization of human pluripotent stem cell differentiation by single-cell dual-omics analyses.

In vivo differentiation of human pluripotent stem cells (hPSCs) has unique advantages, such as multilineage differentiation, angiogenesis, and close cell-cell interactions. To systematically investigate multilineage differentiation mechanisms of hPSCs, we constructed the in vivo hPSC differentiation landscape containing 239,670 cells using teratoma models. We identified 43 cell types, inferred 18 cell differentiation trajectories, and characterized common and specific gene regulation patterns during hPSC differentiation at both transcriptional and epigenetic levels. Additionally, we developed the developmental single-cell Basic Local Alignment Search Tool (dscBLAST), an R-based cell identification tool, to simplify the identification processes of developmental cells. Using dscBLAST, we aligned cells in multiple differentiation models to normally developing cells to further understand their differentiation states. Overall, our study offers new insights into stem cell differentiation and human embryonic development; dscBLAST shows favorable cell identification performance, providing a powerful identification tool for developmental cells.

An analytical framework for decoding cell type-specific genetic variation of gene regulation.

A deeper understanding of genetic regulation and functional mechanisms underlying genetic associations with complex traits and diseases is impeded by cellular heterogeneity and linkage disequilibrium. To address these limits, we introduce Huatuo, a framework to decode genetic variation of gene regulation at cell type and single-nucleotide resolutions by integrating deep-learning-based variant predictions with population-based association analyses. We apply Huatuo to generate a comprehensive cell type-specific genetic variation landscape across human tissues and further evaluate their potential roles in complex diseases and traits. Finally, we show that Huatuo's inferences permit prioritizations of driver cell types associated with complex traits and diseases and allow for systematic insights into the mechanisms of phenotype-causal genetic variation.

Construction of a cross-species cell landscape at single-cell level.

Individual cells are basic units of life. Despite extensive efforts to characterize the cellular heterogeneity of different organisms, cross-species comparisons of landscape dynamics have not been achieved. Here, we applied single-cell RNA sequencing (scRNA-seq) to map organism-level cell landscapes at multiple life stages for mice, zebrafish and Drosophila. By integrating the comprehensive dataset of > 2.6 million single cells, we constructed a cross-species cell landscape and identified signatures and common pathways that changed throughout the life span. We identified structural inflammation and mitochondrial dysfunction as the most common hallmarks of organism aging, and found that pharmacological activation of mitochondrial metabolism alleviated aging phenotypes in mice. The cross-species cell landscape with other published datasets were stored in an integrated online portal-Cell Landscape. Our work provides a valuable resource for studying lineage development, maturation and aging.

How many cell types are there in nature? How do they change during the life cycle? These are two fundamental questions that researchers have been trying to understand in the area of biology. In this study, single-cell mRNA sequencing data were used to profile over 2.6 million individual cells from mice, zebrafish and Drosophila at different life stages, 1.3 million of which were newly collected. The comprehensive datasets allow investigators to construct a cross-species cell landscape that helps to reveal the conservation and diversity of cell taxonomies at genetic and regulatory levels. The resources in this study are assembled into a publicly available website at http://bis.zju.edu.cn/cellatlas/.

Cell landscape of larval and adult Xenopus laevis at single-cell resolution.

The rapid development of high-throughput single-cell RNA sequencing technology offers a good opportunity to dissect cell heterogeneity of animals. A large number of organism-wide single-cell atlases have been constructed for vertebrates such as Homo sapiens, Macaca fascicularis, Mus musculus and Danio rerio. However, an intermediate taxon that links mammals to vertebrates of more ancient origin is still lacking. Here, we construct the first Xenopus cell landscape to date, including larval and adult organs. Common cell lineage-specific transcription factors have been identified in vertebrates, including fish, amphibians and mammals. The comparison of larval and adult erythrocytes identifies stage-specific hemoglobin subtypes, as well as a common type of cluster containing both larval and adult hemoglobin, mainly at NF59. In addition, cell lineages originating from all three layers exhibits both antigen processing and presentation during metamorphosis, indicating a common regulatory mechanism during metamorphosis. Overall, our study provides a large-scale resource for research on Xenopus metamorphosis and adult organs.

Construction of the axolotl cell landscape using combinatorial hybridization sequencing at single-cell resolution.

The Mexican axolotl (Ambystoma mexicanum) is a well-established tetrapod model for regeneration and developmental studies. Remarkably, neotenic axolotls may undergo metamorphosis, a process that triggers many dramatic changes in diverse organs, accompanied by gradually decline of their regeneration capacity and lifespan. However, the molecular regulation and cellular changes in neotenic and metamorphosed axolotls are still poorly investigated. Here, we develop a single-cell sequencing method based on combinatorial hybridization to generate a tissue-based transcriptomic landscape of the neotenic and metamorphosed axolotls. We perform gene expression profiling of over 1 million single cells across 19 tissues to construct the first adult axolotl cell landscape. Comparison of single-cell transcriptomes between the tissues of neotenic and metamorphosed axolotls reveal the heterogeneity of non-immune parenchymal cells in different tissues and established their regulatory network. Furthermore, we describe dynamic gene expression patterns during limb development in neotenic axolotls. This system-level single-cell analysis of molecular characteristics in neotenic and metamorphosed axolotls, serves as a resource to explore the molecular identity of the axolotl and facilitates better understanding of metamorphosis.

Simulating the Dynamic Intra-Tumor Heterogeneity and Therapeutic Responses.

A tumor is a complex tissue comprised of heterogeneous cell subpopulations which exhibit substantial diversity at morphological, genetic and epigenetic levels. Under the selective pressure of cancer therapies, a minor treatment-resistant subpopulation could survive and repopulate. Therefore, the intra-tumor heterogeneity is recognized as a major obstacle to effective treatment. In this paper, we propose a stochastic clonal expansion model to simulate the dynamic evolution of tumor subpopulations and the therapeutic effect at different times during tumor progression. The model is incorporated in the CES webserver, for the convenience of simulation according to initial user input. Based on this model, we investigate the influence of various factors on tumor progression and treatment consequences and present conclusions drawn from observations, highlighting the importance of treatment timing. The model provides an intuitive illustration to deepen the understanding of temporal intra-tumor heterogeneity dynamics and treatment responses, thus helping the improvement of personalized diagnostic and therapeutic strategies.

DeepTrio: a ternary prediction system for protein-protein interaction using mask multiple parallel convolutional neural networks.

MOTIVATION: Protein-protein interaction (PPI), as a relative property, is determined by two binding proteins, which brings a great challenge to design an expert model with an unbiased learning architecture and a superior generalization performance. Additionally, few efforts have been made to allow PPI predictors to discriminate between relative properties and intrinsic properties. RESULTS: We present a sequence-based approach, DeepTrio, for PPI prediction using mask multiple parallel convolutional neural networks. Experimental evaluations show that DeepTrio achieves a better performance over several state-of-the-art methods in terms of various quality metrics. Besides, DeepTrio is extended to provide additional insights into the contribution of each input neuron to the prediction results. AVAILABILITY AND IMPLEMENTATION: We provide an online application at http://bis.zju.edu.cn/deeptrio. The DeepTrio models and training data are deposited at https://github.com/huxiaoti/deeptrio.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In situ dissecting the evolution of gene duplication with different histone modification patterns based on high-throughput data analysis in Arabidopsis thaliana.

BACKGROUND: Genetic regulation is known to contribute to the divergent expression of duplicate genes; however, little is known about how epigenetic modifications regulate the expression of duplicate genes in plants. METHODS: The histone modification (HM) profile patterns of different modes of gene duplication, including the whole genome duplication, proximal duplication, tandem duplication and transposed duplication were characterized based on ChIP-chip or ChIP-seq datasets. In this study, 10 distinct HM marks including H2Bub, H3K4me1, H3K4me2, H3K4me3, H3K9ac, H3K9me2, H3K27me1, H3K27me3, H3K36me3 and H3K14ac were analyzed. Moreover, the features of gene duplication with different HM patterns were characterized based on 88 RNA-seq datasets of Arabidopsis thaliana. RESULTS: This study showed that duplicate genes in Arabidopsis have a more similar HM pattern than single-copy genes in both their promoters and protein-coding regions. The evolution of HM marks is found to be coupled with coding sequence divergence and expression divergence after gene duplication. We found that functionally selective constraints may impose on epigenetic evolution after gene duplication. Furthermore, duplicate genes with distinct functions have more divergence in histone modification compared with the ones with the same function, while higher expression divergence is found with mutations of chromatin modifiers. This study shows the role of epigenetic marks in regulating gene expression and functional divergence after gene duplication in plants based on sequencing data.

Bioinformatics resources facilitate understanding and harnessing clinical research of SARS-CoV-2.

The coronavirus disease 2019 (COVID-19) pandemic, caused by the coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has created an unprecedented threat to public health. The pandemic has been sweeping the globe, impacting more than 200 countries, with more outbreaks still lurking on the horizon. At the time of the writing, no approved drugs or vaccines are available to treat COVID-19 patients, prompting an urgent need to decipher mechanisms underlying the pathogenesis and develop curative treatments. To fight COVID-19, researchers around the world have provided specific tools and molecular information for SARS-CoV-2. These pieces of information can be integrated to aid computational investigations and facilitate clinical research. This paper reviews current knowledge, the current status of drug development and various resources for key steps toward effective treatment of COVID-19, including the phylogenetic characteristics, genomic conservation and interaction data. The final goal of this paper is to provide information that may be utilized in bioinformatics approaches and aid target prioritization and drug repurposing. Several SARS-CoV-2-related tools/databases were reviewed, and a web-portal named OverCOVID (http://bis.zju.edu.cn/overcovid/) is constructed to provide a detailed interpretation of SARS-CoV-2 basics and share a collection of resources that may contribute to therapeutic advances. These information could improve researchers' understanding of SARS-CoV-2 and help to accelerate the development of new antiviral treatments.

Genome-wide analysis of long non-coding RNAs responsive to multiple nutrient stresses in Arabidopsis thaliana.

Nutrient stress is the most important environmental stress that limits plant growth and development. Although recent evidence highlights the vital functions of long non-coding RNAs (lncRNA) in response to single nutrient stress in some model plants, a comprehensive investigation of the effect of lncRNAs in response to nutrient stress has not been performed in Arabidopsis thaliana. Here, we presented the identification and characterization of lncRNAs under seven nutrient stress conditions. The expression pattern analysis revealed that aberrant expression of lncRNAs is a stress-specific manner under nutrient stress conditions and that lncRNAs are more sensitive to nutrient stress than protein-coding genes (PCGs). Moreover, competing endogenous RNA (ceRNA) network and lncRNA-mRNA co-expression network (CEN) were constructed to explore the potential function of these lncRNAs under nutrient stress conditions. We further combined different expressed lncRNAs with ceRNA network and CEN to select key lncRNAs in response to nutrient stress. Together, our study provides important information for further insights into the role of lncRNAs in response to stress in plants.

Construction of a human cell landscape at single-cell level.

Single-cell analysis is a valuable tool for dissecting cellular heterogeneity in complex systems1. However, a comprehensive single-cell atlas has not been achieved for humans. Here we use single-cell mRNA sequencing to determine the cell-type composition of all major human organs and construct a scheme for the human cell landscape (HCL). We have uncovered a single-cell hierarchy for many tissues that have not been well characterized. We established a 'single-cell HCL analysis' pipeline that helps to define human cell identity. Finally, we performed a single-cell comparative analysis of landscapes from human and mouse to identify conserved genetic networks. We found that stem and progenitor cells exhibit strong transcriptomic stochasticity, whereas differentiated cells are more distinct. Our results provide a useful resource for the study of human biology.

PncStress: a manually curated database of experimentally validated stress-responsive non-coding RNAs in plants.

Non-coding RNAs (ncRNAs) are recognized as key regulatory molecules in many biological processes. Accumulating evidence indicates that ncRNA-related mechanisms play important roles in plant stress responses. Although abundant plant stress-responsive ncRNAs have been identified, these experimentally validated results have not been gathered into a single public domain archive. Therefore, we established PncStress by curating experimentally validated stress-responsive ncRNAs in plants, including microRNAs, long non-coding RNAs and circular RNAs. The current version of PncStress contains 4227 entries from 114 plants covering 48 biotic and 91 abiotic stresses. For each entry, PncStress has biological information and network visualization. Serving as a manually curated database, PncStress will become a valuable resource in support of plant stress response research.

scMCA: A Tool to Define Mouse Cell Types Based on Single-Cell Digital Expression.

For decades, people have been trying to define cell type with the combination of expressed genes. The choice of the limited number of genes for the classification limits the precision of this system. Here, we build a "single-cell Mouse Cell Atlas (scMCA) analysis" pipeline based on scRNA-seq datasets covering all mouse cell types. We build the scMCA reference and then use the tool "scMCA" to match single-cell digital expression to its closest cell type.

Dissecting LncRNA Roles in Renal Cell Carcinoma Metastasis and Characterizing Genomic Heterogeneity by Single-Cell RNA-seq.

Long noncoding RNAs (lncRNA) have recently emerged as important regulators in cancer cell proliferation and metastasis. However, the role of lncRNAs in metastatic clear cell renal cell carcinoma (ccRCC) remains unclear. Here, single-cell RNA sequencing data were analyzed from primary renal cell carcinoma and paired metastatic renal cell carcinoma specimens, and characterized the expression profiles of over 10,000 genes, including 1,874 lncRNAs. Further analysis revealed that lncRNAs exhibit cancer type- and tissue-specific expression across ccRCC cells. Interestingly, a number of lncRNAs (n = 173) associated with ccRCC metastasis, termed ccRCC metastasis-associated lncRNAs (CMAL). Moreover, functional analysis based on a CMAL-PCG coexpression network revealed that CMALs contribute to cell adhesion, immune response, and cell proliferation. In combination with survival analysis, 12 CMALs were identified that participate in TNF and hypoxia-inducible factor 1 signaling to promote ccRCC metastasis. Further investigation on intratumoral heterogeneity showed that some CMALs are selectively expressed in different subpopulations. IMPLICATIONS: To explore ccRCC metastasis, the current study performed a global dissection of lncRNAs and a complex genomic analysis of ccRCC tumor heterogeneity. The data shed light on the discovery of potential lncRNA biomarkers and lncRNA therapeutic targets.

Mapping the Mouse Cell Atlas by Microwell-Seq.

Single-cell RNA sequencing (scRNA-seq) technologies are poised to reshape the current cell-type classification system. However, a transcriptome-based single-cell atlas has not been achieved for complex mammalian systems. Here, we developed Microwell-seq, a high-throughput and low-cost scRNA-seq platform using simple, inexpensive devices. Using Microwell-seq, we analyzed more than 400,000 single cells covering all of the major mouse organs and constructed a basic scheme for a mouse cell atlas (MCA). We reveal a single-cell hierarchy for many tissues that have not been well characterized previously. We built a web-based "single-cell MCA analysis" pipeline that accurately defines cell types based on single-cell digital expression. Our study demonstrates the wide applicability of the Microwell-seq technology and MCA resource.

FPD: A comprehensive phosphorylation database in fungi.

Protein phosphorylation, one of the most classic post-translational modification, plays a critical role in diverse cellular processes including cell cycle, growth, and signal transduction pathways. However, the available information about phosphorylation in fungi is limited. Here, we provided a Fungi Phosphorylation Database (FPD) that comprises high-confidence in vivo phosphosites identified by MS-based proteomics in various fungal species. This comprehensive phosphorylation database contains 62 272 non-redundant phosphorylation sites in 11 222 proteins across eight organisms, including Aspergillus flavus, Aspergillus nidulans, Fusarium graminearum, Magnaporthe oryzae, Neurospora crassa, Saccharomyces cerevisiae, Schizosaccharomyces pombe, and Cryptococcus neoformans. A fungi-specific phosphothreonine motif and several conserved phosphorylation motifs were discovered by comparatively analysing the pattern of phosphorylation sites in plants, animals, and fungi.