HervD Atlas: a curated knowledgebase of associations between human endogenous retroviruses and diseases.

Human endogenous retroviruses (HERVs), as remnants of ancient exogenous retrovirus infected and integrated into germ cells, comprise ∼8% of the human genome. These HERVs have been implicated in numerous diseases, and extensive research has been conducted to uncover their specific roles. Despite these efforts, a comprehensive source of HERV-disease association still needs to be added. To address this gap, we introduce the HervD Atlas (https://ngdc.cncb.ac.cn/hervd/), an integrated knowledgebase of HERV-disease associations manually curated from all related published literature. In the current version, HervD Atlas collects 60 726 HERV-disease associations from 254 publications (out of 4692 screened literature), covering 21 790 HERVs (21 049 HERV-Terms and 741 HERV-Elements) belonging to six types, 149 diseases and 610 related/affected genes. Notably, an interactive knowledge graph that systematically integrates all the HERV-disease associations and corresponding affected genes into a comprehensive network provides a powerful tool to uncover and deduce the complex interplay between HERVs and diseases. The HervD Atlas also features a user-friendly web interface that allows efficient browsing, searching, and downloading of all association information, research metadata, and annotation information. Overall, the HervD Atlas is an essential resource for comprehensive, up-to-date knowledge on HERV-disease research, potentially facilitating the development of novel HERV-associated diagnostic and therapeutic strategies.

STAB2: an updated spatio-temporal cell atlas of the human and mouse brain.

The brain is constituted of heterogeneous types of neuronal and non-neuronal cells, which are organized into distinct anatomical regions, and show precise regulation of gene expression during development, aging and function. In the current database release, STAB2 provides a systematic cellular map of the human and mouse brain by integrating recently published large-scale single-cell and single-nucleus RNA-sequencing datasets from diverse regions and across lifespan. We applied a hierarchical strategy of unsupervised clustering on the integrated single-cell transcriptomic datasets to precisely annotate the cell types and subtypes in the human and mouse brain. Currently, STAB2 includes 71 and 61 different cell subtypes defined in the human and mouse brain, respectively. It covers 63 subregions and 15 developmental stages of human brain, and 38 subregions and 30 developmental stages of mouse brain, generating a comprehensive atlas for exploring spatiotemporal transcriptomic dynamics in the mammalian brain. We also augmented web interfaces for querying and visualizing the gene expression in specific cell types. STAB2 is freely available at https://mai.fudan.edu.cn/stab2.

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.

A comparative atlas of single-cell chromatin accessibility in the human brain.

Recent advances in single-cell transcriptomics have illuminated the diverse neuronal and glial cell types within the human brain. However, the regulatory programs governing cell identity and function remain unclear. Using a single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq), we explored open chromatin landscapes across 1.1 million cells in 42 brain regions from three adults. Integrating this data unveiled 107 distinct cell types and their specific utilization of 544,735 candidate cis-regulatory DNA elements (cCREs) in the human genome. Nearly a third of the cCREs demonstrated conservation and chromatin accessibility in the mouse brain cells. We reveal strong links between specific brain cell types and neuropsychiatric disorders including schizophrenia, bipolar disorder, Alzheimer's disease (AD), and major depression, and have developed deep learning models to predict the regulatory roles of noncoding risk variants in these disorders.

Comprehensive cell atlas of the first-trimester developing human brain.

The adult human brain comprises more than a thousand distinct neuronal and glial cell types, a diversity that emerges during early brain development. To reveal the precise sequence of events during early brain development, we used single-cell RNA sequencing and spatial transcriptomics and uncovered cell states and trajectories in human brains at 5 to 14 postconceptional weeks (pcw). We identified 12 major classes that are organized as ~600 distinct cell states, which map to precise spatial anatomical domains at 5 pcw. We described detailed differentiation trajectories of the human forebrain and midbrain and found a large number of region-specific glioblasts that mature into distinct pre-astrocytes and pre-oligodendrocyte precursor cells. Our findings reveal the establishment of cell types during the first trimester of human brain development.

Single-cell DNA methylation and 3D genome architecture in the human brain.

Delineating the gene-regulatory programs underlying complex cell types is fundamental for understanding brain function in health and disease. Here, we comprehensively examined human brain cell epigenomes by probing DNA methylation and chromatin conformation at single-cell resolution in 517 thousand cells (399 thousand neurons and 118 thousand non-neurons) from 46 regions of three adult male brains. We identified 188 cell types and characterized their molecular signatures. Integrative analyses revealed concordant changes in DNA methylation, chromatin accessibility, chromatin organization, and gene expression across cell types, cortical areas, and basal ganglia structures. We further developed single-cell methylation barcodes that reliably predict brain cell types using the methylation status of select genomic sites. This multimodal epigenomic brain cell atlas provides new insights into the complexity of cell-type-specific gene regulation in adult human brains.

Lifelong restructuring of 3D genome architecture in cerebellar granule cells.

The cerebellum contains most of the neurons in the human brain and exhibits distinctive modes of development and aging. In this work, by developing our single-cell three-dimensional (3D) genome assay-diploid chromosome conformation capture, or Dip-C-into population-scale (Pop-C) and virus-enriched (vDip-C) modes, we resolved the first 3D genome structures of single cerebellar cells, created life-spanning 3D genome atlases for both humans and mice, and jointly measured transcriptome and chromatin accessibility during development. We found that although the transcriptome and chromatin accessibility of cerebellar granule neurons mature in early postnatal life, 3D genome architecture gradually remodels throughout life, establishing ultra-long-range intrachromosomal contacts and specific interchromosomal contacts that are rarely seen in neurons. These results reveal unexpected evolutionarily conserved molecular processes that underlie distinctive features of neural development and aging across the mammalian life span.

What is a cell type?

A next step for cell atlases should be to chart perturbations in human model systems.

Yolk sac cell atlas reveals multiorgan functions during human early development.

The extraembryonic yolk sac (YS) ensures delivery of nutritional support and oxygen to the developing embryo but remains ill-defined in humans. We therefore assembled a comprehensive multiomic reference of the human YS from 3 to 8 postconception weeks by integrating single-cell protein and gene expression data. Beyond its recognized role as a site of hematopoiesis, we highlight roles in metabolism, coagulation, vascular development, and hematopoietic regulation. We reconstructed the emergence and decline of YS hematopoietic stem and progenitor cells from hemogenic endothelium and revealed a YS-specific accelerated route to macrophage production that seeds developing organs. The multiorgan functions of the YS are superseded as intraembryonic organs develop, effecting a multifaceted relay of vital functions as pregnancy proceeds.

JGI Plant Gene Atlas: an updateable transcriptome resource to improve functional gene descriptions across the plant kingdom.

Gene functional descriptions offer a crucial line of evidence for candidate genes underlying trait variation. Conversely, plant responses to environmental cues represent important resources to decipher gene function and subsequently provide molecular targets for plant improvement through gene editing. However, biological roles of large proportions of genes across the plant phylogeny are poorly annotated. Here we describe the Joint Genome Institute (JGI) Plant Gene Atlas, an updateable data resource consisting of transcript abundance assays spanning 18 diverse species. To integrate across these diverse genotypes, we analyzed expression profiles, built gene clusters that exhibited tissue/condition specific expression, and tested for transcriptional response to environmental queues. We discovered extensive phylogenetically constrained and condition-specific expression profiles for genes without any previously documented functional annotation. Such conserved expression patterns and tightly co-expressed gene clusters let us assign expression derived additional biological information to 64 495 genes with otherwise unknown functions. The ever-expanding Gene Atlas resource is available at JGI Plant Gene Atlas (https://plantgeneatlas.jgi.doe.gov) and Phytozome (https://phytozome.jgi.doe.gov/), providing bulk access to data and user-specified queries of gene sets. Combined, these web interfaces let users access differentially expressed genes, track orthologs across the Gene Atlas plants, graphically represent co-expressed genes, and visualize gene ontology and pathway enrichments.

Percepción de los estudiantes de las carreras de la salud sobre la utilización de metodologías activas para la enseñanza y aprendizaje de la histología / Perception of students of health careers on the use of active methodologies for teaching and learning histology

Introducción:Las metodologías activas tienen como protagonista al estudiante y, al ser enriquecidas con las tecnologías de la información y las comunicaciones, optimizan el aprendizaje activo, la participación y el trabajo colaborativo. El objetivo de este trabajo fue identificar la percepción de la satisfacción de los estudiantes de segundo año de Medicina y Tecnología Médica frente a la implementación de metodologías activas para el aprendizaje de la histología. Sujetos y métodos:Se utilizó un diseño cuantitativo, de corte transversal, descriptivo-comparativo. La percepción de los estudiantes se obtuvo a través de la aplicación de un cuestionario de satisfacción sobre metodologías activas, como el atlas digital y el póster científico, que incluyó cuatro dimensiones: metodología, atmósfera de aprendizaje, autorregulación y evaluación. Resultados:Las puntuaciones promedio obtenidas en cada dimensión fueron: metodología, media = 4,29; atmósfera de aprendizaje, media = 4,46; autorregulación, media = 4,37; y evaluación, media = 4,31, equivalentes a un porcentaje de satisfacción del 87,6, el 91,2, el 88,5 y el 83,7%, respectivamente. Las mejores percepciones se obtuvieron de los estudiantes de la carrera de Tecnología Médica. Conclusión:Los estudiantes percibieron positivamente las metodologías activas en beneficio de su aprendizaje en histología. Este tipo de metodologías favoreció principalmente la percepción de un buen ambiente para el aprendizaje y el logro de la autorregulación.(AU)

Introduction: Active methodologies have the student as their protagonist and, when enriched with Information and Communication Technologies (ICT), optimize active learning, participation and collaborative work. The objective of this work was to identify the perception of satisfaction of second-year students of medicine and medical technology, regarding the implementation of active methodologies for learning histology. Subjects and methods: A cross-sectional, descriptive-comparative quantitative design was used. The perception of the students was obtained through the application of a satisfaction questionnaire on active methodologies such as digital atlas and scientific poster, which included 4 dimensions: methodology, learning atmosphere, self-regulation and evaluation. Results: The average scores obtained in each dimension were: methodology M = 4.29, learning atmosphere M = 4.46, self-regulation M = 4.37 and evaluation M = 4.31, equivalent to a percentage of satisfaction of 87.6%, 91.2%, 88.5% and 83.7%, respectively. The best perceptions were obtained from the students of the medical technology career. Conclusion: The students positively perceived the active methodologies for the benefit of their learning in histology. This type of methodologies mainly favored the perception of a good environment for learning and the achievement of self-regulation.(AU)

AtOM, an ontology model to standardize use of brain atlases in tools, workflows, and data infrastructures.

Brain atlases are important reference resources for accurate anatomical description of neuroscience data. Open access, three-dimensional atlases serve as spatial frameworks for integrating experimental data and defining regions-of-interest in analytic workflows. However, naming conventions, parcellation criteria, area definitions, and underlying mapping methodologies differ considerably between atlases and across atlas versions. This lack of standardized description impedes use of atlases in analytic tools and registration of data to different atlases. To establish a machine-readable standard for representing brain atlases, we identified four fundamental atlas elements, defined their relations, and created an ontology model. Here we present our Atlas Ontology Model (AtOM) and exemplify its use by applying it to mouse, rat, and human brain atlases. We discuss how AtOM can facilitate atlas interoperability and data integration, thereby increasing compliance with the FAIR guiding principles. AtOM provides a standardized framework for communication and use of brain atlases to create, use, and refer to specific atlas elements and versions. We argue that AtOM will accelerate analysis, sharing, and reuse of neuroscience data.

Aging Fly Cell Atlas identifies exhaustive aging features at cellular resolution.

Aging is characterized by a decline in tissue function, but the underlying changes at cellular resolution across the organism remain unclear. Here, we present the Aging Fly Cell Atlas, a single-nucleus transcriptomic map of the whole aging Drosophila. We characterized 163 distinct cell types and performed an in-depth analysis of changes in tissue cell composition, gene expression, and cell identities. We further developed aging clock models to predict fly age and show that ribosomal gene expression is a conserved predictive factor for age. Combining all aging features, we find distinctive cell type-specific aging patterns. This atlas provides a valuable resource for studying fundamental principles of aging in complex organisms.

Decoding foreign antigen tolerance.

Cell atlases of human tolerogenic milieus guide transformative immunotherapies.

Using single cell atlas data to reconstruct regulatory networks.

Inference of global gene regulatory networks from omics data is a long-term goal of systems biology. Most methods developed for inferring transcription factor (TF)-gene interactions either relied on a small dataset or used snapshot data which is not suitable for inferring a process that is inherently temporal. Here, we developed a new computational method that combines neural networks and multi-task learning to predict RNA velocity rather than gene expression values. This allows our method to overcome many of the problems faced by prior methods leading to more accurate and more comprehensive set of identified regulatory interactions. Application of our method to atlas scale single cell data from 6 HuBMAP tissues led to several validated and novel predictions and greatly improved on prior methods proposed for this task.

A transcription factor atlas of directed differentiation.

Transcription factors (TFs) regulate gene programs, thereby controlling diverse cellular processes and cell states. To comprehensively understand TFs and the programs they control, we created a barcoded library of all annotated human TF splice isoforms (>3,500) and applied it to build a TF Atlas charting expression profiles of human embryonic stem cells (hESCs) overexpressing each TF at single-cell resolution. We mapped TF-induced expression profiles to reference cell types and validated candidate TFs for generation of diverse cell types, spanning all three germ layers and trophoblasts. Targeted screens with subsets of the library allowed us to create a tailored cellular disease model and integrate mRNA expression and chromatin accessibility data to identify downstream regulators. Finally, we characterized the effects of combinatorial TF overexpression by developing and validating a strategy for predicting combinations of TFs that produce target expression profiles matching reference cell types to accelerate cellular engineering efforts.

IEAtlas: an atlas of HLA-presented immune epitopes derived from non-coding regions.

Cancer-related epitopes can engage the immune system against tumor cells, thus exploring epitopes derived from non-coding regions is emerging as a fascinating field in cancer immunotherapies. Here, we described a database, IEAtlas (http://bio-bigdata.hrbmu.edu.cn/IEAtlas), which aims to provide and visualize the comprehensive atlas of human leukocyte antigen (HLA)-presented immunogenic epitopes derived from non-coding regions. IEAtlas reanalyzed publicly available mass spectrometry-based HLA immunopeptidome datasets against our integrated benchmarked non-canonical open reading frame information. The current IEAtlas identified 245 870 non-canonical epitopes binding to HLA-I/II allotypes across 15 cancer types and 30 non-cancerous tissues, greatly expanding the cancer immunopeptidome. IEAtlas further evaluates the immunogenicity via several commonly used immunogenic features, including HLA binding affinity, stability and T-cell receptor recognition. In addition, IEAtlas provides the biochemical properties of epitopes as well as the clinical relevance of corresponding genes across major cancer types and normal tissues. Several flexible tools were also developed to aid retrieval and to analyze the epitopes derived from non-coding regions. Overall, IEAtlas will serve as a valuable resource for investigating the immunogenic capacity of non-canonical epitopes and the potential as therapeutic cancer vaccines.

QUADRatlas: the RNA G-quadruplex and RG4-binding proteins database.

RNA G-quadruplexes (RG4s) are non-canonical, disease-associated post-transcriptional regulators of gene expression whose functions are driven by RNA-binding proteins (RBPs). Being able to explore transcriptome-wide RG4 formation and interaction with RBPs is thus paramount to understanding how they are regulated and exploiting them as potential therapeutic targets. Towards this goal, we present QUADRatlas (https://rg4db.cibio.unitn.it), a database of experimentally-derived and computationally predicted RG4s in the human transcriptome, enriched with biological function and disease associations. As RBPs are key to their function, we mined known interactions of RG4s with such proteins, complemented with an extensive RBP binding sites dataset. Users can thus intersect RG4s with their potential regulators and effectors, enabling the formulation of novel hypotheses on RG4 regulation, function and pathogenicity. To support this capability, we provide analysis tools for predicting whether an RBP can bind RG4s, RG4 enrichment in a gene set, and de novo RG4 prediction. Genome-browser and table views allow exploring, filtering, and downloading the data quickly for individual genes and in batch. QUADRatlas is a significant step forward in our ability to understand the biology of RG4s, offering unmatched data content and enabling the integrated analysis of RG4s and their interactions with RBPs.

GWAS Atlas: an updated knowledgebase integrating more curated associations in plants and animals.

GWAS Atlas (https://ngdc.cncb.ac.cn/gwas/) is a manually curated resource of genome-wide genotype-to-phenotype associations for a wide range of species. Here, we present an updated implementation of GWAS Atlas by curating and incorporating more high-quality associations, with significant improvements and advances over the previous version. Specifically, the current release of GWAS Atlas incorporates a total of 278,109 curated genotype-to-phenotype associations for 1,444 different traits across 15 species (10 plants and 5 animals) from 830 publications and 3,432 studies. A collection of 6,084 lead SNPs of 439 traits and 486 experiment-validated causal variants of 157 traits are newly added. Moreover, 1,056 trait ontology terms are newly defined, resulting in 1,172 and 431 terms for Plant Phenotype and Trait Ontology and Animal Phenotype and Trait Ontology, respectively. Additionally, it is equipped with four online analysis tools and a submission platform, allowing users to perform data analysis and data submission. Collectively, as a core resource in the National Genomics Data Center, GWAS Atlas provides valuable genotype-to-phenotype associations for a diversity of species and thus plays an important role in agronomic trait study and molecular breeding.

DrugMAP: molecular atlas and pharma-information of all drugs.

The efficacy and safety of drugs are widely known to be determined by their interactions with multiple molecules of pharmacological importance, and it is therefore essential to systematically depict the molecular atlas and pharma-information of studied drugs. However, our understanding of such information is neither comprehensive nor precise, which necessitates the construction of a new database providing a network containing a large number of drugs and their interacting molecules. Here, a new database describing the molecular atlas and pharma-information of drugs (DrugMAP) was therefore constructed. It provides a comprehensive list of interacting molecules for >30 000 drugs/drug candidates, gives the differential expression patterns for >5000 interacting molecules among different disease sites, ADME (absorption, distribution, metabolism and excretion)-relevant organs and physiological tissues, and weaves a comprehensive and precise network containing >200 000 interactions among drugs and molecules. With the great efforts made to clarify the complex mechanism underlying drug pharmacokinetics and pharmacodynamics and rapidly emerging interests in artificial intelligence (AI)-based network analyses, DrugMAP is expected to become an indispensable supplement to existing databases to facilitate drug discovery. It is now fully and freely accessible at: https://idrblab.org/drugmap/.