Combinatorial genetic strategies for dissecting cell lineages, cell types, and gene function in the mouse brain DOI
Qi Zhang,

Xue Liu,

Ling Gong

et al.

Development Growth & Differentiation, Journal Year: 2023, Volume and Issue: 65(9), P. 546 - 553

Published: Nov. 14, 2023

Research in neuroscience has greatly benefited from the development of genetic approaches that enable lineage tracing, cell type targeting, and conditional gene regulation. Recent advances combinatorial strategies, which integrate multiple cellular features, have significantly enhanced spatiotemporal precision flexibility these manipulations. In this minireview, we introduce concept design strategies provide a few examples their application fate mapping, reversible These advancements facilitated in-depth investigation into developmental principles underlying assembly brain circuits, granting experimental access to highly specific lineages subtypes, as well offering valuable new tools for modeling studying neurological diseases. Additionally, discuss future directions aimed at expanding improving existing toolkit better understanding development, structure, function healthy diseased brains.

Language: Английский

Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models DOI Creative Commons
Chenling Xu, Romain Lopez, Edouard Mehlman

et al.

Molecular Systems Biology, Journal Year: 2021, Volume and Issue: 17(1)

Published: Jan. 1, 2021

As the number of single-cell transcriptomics datasets grows, natural next step is to integrate accumulating data achieve a common ontology cell types and states. However, it not straightforward compare gene expression levels across automatically assign type labels in new dataset based on existing annotations. In this manuscript, we demonstrate that our previously developed method, scVI, provides an effective fully probabilistic approach for joint representation analysis scRNA-seq data, while accounting uncertainty caused by biological measurement noise. We also introduce ANnotation using Variational Inference (scANVI), semi-supervised variant scVI designed leverage state scANVI favorably state-of-the-art methods integration annotation terms accuracy, scalability, adaptability challenging settings. contrast methods, multiple with single generative model can be directly used downstream tasks, such as differential expression. Both are easily accessible through scvi-tools.

Language: Английский

Citations

402
CZ CELL×GENE Discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data DOI Creative Commons

Shibla Abdulla,

Brian D. Aevermann, Pedro Assis

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Nov. 2, 2023

Abstract Hundreds of millions single cells have been analyzed to date using high throughput transcriptomic methods, thanks technological advances driving the increasingly rapid generation single-cell data. This provides an exciting opportunity for unlocking new insights into health and disease, made possible by meta-analysis that span diverse datasets building on recent in large language models other machine learning approaches. Despite promise these emerging analytical tools analyzing amounts data, a major challenge remains sheer number inconsistent format, data accessibility. Many are available via unique portals platforms often lack interoperability. Here, we present CZ CellxGene Discover ( cellxgene.cziscience.com ), platform curated interoperable resource, free-to-use online portal, hosts growing corpus community contributed spans more than 50 million cells. Curated, standardized, associated with consistent cell-level metadata, this collection is largest its kind. A suite features enables accessibility reusability both computational visual interfaces allow researchers rapidly explore individual perform cross-corpus analysis. functionality enabling meta-analyses tens across studies tissues providing global views human at resolution

Language: Английский

Citations

67
CZ CELLxGENE Discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data DOI Creative Commons

Shibla Abdulla,

Brian D. Aevermann,

Pedro Assis

et al.

Nucleic Acids Research, Journal Year: 2024, Volume and Issue: 53(D1), P. D886 - D900

Published: Nov. 28, 2024

Hundreds of millions single cells have been analyzed using high-throughput transcriptomic methods. The cumulative knowledge within these datasets provides an exciting opportunity for unlocking insights into health and disease at the level cells. Meta-analyses that span diverse building on recent advances in large language models other machine-learning approaches pose new directions to model extract insight from single-cell data. Despite promise emerging analytical tools analyzing amounts data, sheer number datasets, data accessibility remains a challenge. Here, we present CZ CELLxGENE Discover (cellxgene.cziscience.com), platform curated interoperable Available via free-to-use online portal, hosts growing corpus community-contributed over 93 million unique Curated, standardized associated with consistent cell-level metadata, this collection is largest its kind rapidly community contributions. A suite features enables reusability both computational visual interfaces allow researchers explore individual perform cross-corpus analysis, run meta-analyses tens across studies tissues resolution

Language: Английский

Citations

47
Schizophrenia genomics: genetic complexity and functional insights DOI
Patrick F. Sullivan, Shuyang Yao, Jens Hjerling‐Leffler

et al.

Nature reviews. Neuroscience, Journal Year: 2024, Volume and Issue: 25(9), P. 611 - 624

Published: July 19, 2024

Language: Английский

Citations

15
Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas construction and usage DOI Creative Commons
Katy Börner, Philip D. Blood, Jonathan C. Silverstein

et al.

Nature Methods, Journal Year: 2025, Volume and Issue: unknown

Published: March 13, 2025

Abstract The Human BioMolecular Atlas Program (HuBMAP) aims to construct a 3D Reference (HRA) of the healthy adult body. Experts from 20+ consortia collaborate develop Common Coordinate Framework (CCF), knowledge graphs and tools that describe multiscale structure human body (from organs tissues down cells, genes biomarkers) use HRA characterize changes occur with aging, disease other perturbations. v.2.0 covers 4,499 unique anatomical structures, 1,195 cell types 2,089 biomarkers (such as genes, proteins lipids) 33 ASCT+B tables 65 Objects linked ontologies. New experimental data can be mapped into using (1) type annotation (for example, Azimuth), (2) validated antibody panels or (3) by registering tissue spatially. This paper describes user stories, terminology, formats, ontology validation, unified analysis workflows, interfaces, instructional materials, application programming flexible hybrid cloud infrastructure previews atlas usage applications.

Language: Английский

Citations

1
Integrating multimodal data to understand cortical circuit architecture and function DOI
Anton Arkhipov, Nuno Maçarico da Costa, Saskia de Vries

et al.

Nature Neuroscience, Journal Year: 2025, Volume and Issue: unknown

Published: March 24, 2025

Language: Английский

Citations

1
Developmental Mouse Brain Common Coordinate Framework DOI Creative Commons

Fae A. Kronman,

Josephine K. Liwang, Rebecca Betty

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Sept. 15, 2023

3D standard reference brains serve as key resources to understand the spatial organization of brain and promote interoperability across different studies. However, unlike adult mouse brain, lack atlases for developing has hindered advancement our understanding development. Here, we present a multimodal developmental common coordinate framework (DevCCF) spanning embryonic day (E) 11.5, E13.5, E15.5, E18.5, postnatal (P) 4, P14, P56 with anatomical segmentations defined by ontology. At each age, DevCCF features undistorted morphologically averaged atlas templates created from Magnetic Resonance Imaging co-registered high-resolution light sheet fluorescence microscopy. Expert-curated at age adhere an updated prosomeric model can be explored via interactive web-visualizer. As use case, employed unveil emergence GABAergic neurons in brains. Moreover, integrated Allen CCFv3 into template stereotaxic coordinates mapped transcriptome cell-type data In summary, is openly accessible resource that used large-scale integration gain comprehensive

Language: Английский

Citations

15
Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage DOI Creative Commons
Katy Börner, Philip D. Blood, Jonathan C. Silverstein

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: April 2, 2024

The Human BioMolecular Atlas Program (HuBMAP) aims to construct a reference 3D structural, cellular, and molecular atlas of the healthy adult human body. HuBMAP Data Portal (https://portal.hubmapconsortium.org) serves experimental datasets supports data processing, search, filtering, visualization. Reference (HRA) (https://humanatlas.io) provides open access data, code, procedures, instructional materials. Experts from more than 20 consortia are collaborating HRA's Common Coordinate Framework (CCF), knowledge graphs, tools that describe multiscale structure body (from organs tissues down cells, genes, biomarkers) use HRA understand changes occur at each these levels with aging, disease, other perturbations. 6th release v2.0 covers 36 4,499 unique anatomical structures, 1,195 cell types, 2,089 biomarkers (e.g., proteins, lipids) linked ontologies 2D/3D objects. New can be mapped into using (1) three type annotation Azimuth) or (2) validated antibody panels (OMAPs), (3) by registering tissue spatially. This paper describes user stories, terminology, formats, ontology validation, unified analysis workflows, interfaces, materials, application programming interface (APIs), flexible hybrid cloud infrastructure, previews usage applications.

Language: Английский

Citations

6
The past, present and future of neuroscience data sharing: a perspective on the state of practices and infrastructure for FAIR DOI Creative Commons
Maryann E. Martone

Frontiers in Neuroinformatics, Journal Year: 2024, Volume and Issue: 17

Published: Jan. 5, 2024

Neuroscience has made significant strides over the past decade in moving from a largely closed science characterized by anemic data sharing, to open where amount of publicly available neuroscience increased dramatically. While this increase is driven part large prospective sharing studies, we are starting see long tail data, no doubt journal requirements and funder mandates. Concomitant with shift increasing support FAIR principles practices infrastructure. particularly critical for its multiplicity types, scales model systems infrastructure that serves them. As envisioned early days neuroinformatics, currently served globally distributed ecosystem neuroscience-centric repositories, specialized around types. To make findable, accessible, interoperable, reusable requires coordination across different stakeholders, including researchers who produce repositories it available, aggregators indexers field search engines community organizations help coordinate efforts develop standards FAIR. The International Neuroinformatics Coordinating Facility led move toward FAIR, fielding several resources achieve In perspective, I provide an overview components required thoughts on past, present future neuroscience, laboratory engine.

Language: Английский

Citations

5
Online conversion of reconstructed neural morphologies into standardized SWC format DOI Creative Commons
Ketan Mehta, Bengt Ljungquist,

James Ogden

et al.

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: Nov. 16, 2023

Digital reconstructions provide an accurate and reliable way to store, share, model, quantify, analyze neural morphology. Continuous advances in cellular labeling, tissue processing, microscopic imaging, automated tracing catalyzed a proliferation of software applications reconstruct These computer programs typically encode the data custom file formats. The resulting format heterogeneity severely hampers interoperability reusability these valuable data. Among many alternatives, SWC has emerged as popular community choice, coalescing rich ecosystem related neuroinformatics resources for tracing, visualization, analysis, simulation. This report presents standardized specification format. In addition, we introduce xyz2swc, free online service that converts all 26 reconstruction formats (and 72 variations) described scientific literature into standard. xyz2swc is available open source through user-friendly browser interface ( https://neuromorpho.org/xyz2swc/ui/ ) Application Programming Interface (API).

Language: Английский

Citations

11