Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs DOI Creative Commons
Caterina Carraro,

Jessica V. Montgomery,

Julien Klimmt

et al.

Frontiers in Molecular Neuroscience, Journal Year: 2024, Volume and Issue: 17

Published: June 17, 2024

Drug discovery is a generally inefficient and capital-intensive process. For neurodegenerative diseases (NDDs), the development of novel therapeutics particularly urgent considering long list late-stage drug candidate failures. Although our knowledge on pathogenic mechanisms driving neurodegeneration growing, additional efforts are required to achieve better ultimately complete understanding pathophysiological underpinnings NDDs. Beyond etiology NDDs being heterogeneous multifactorial, this process further complicated by fact that current experimental models only partially recapitulate major phenotypes observed in humans. In such scenario, multi-omic approaches have potential accelerate identification new or repurposed drugs against multitude underlying One advantage for implementation these overarching tools able disentangle disease states model perturbations through comprehensive characterization distinct molecular layers (i.e., genome, transcriptome, proteome) up single-cell resolution. Because recent advances increasing their affordability scalability, use omics technologies drive nascent, but rapidly expanding neuroscience field. Combined with increasingly advanced vitro models, which benefited from introduction human iPSCs, multi-omics shaping paradigm NDDs, prediction screening. review, we discuss examples, main advantages open challenges targets therapies

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

387
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

66
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

39
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

11
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
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
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

4
Validation of Structure Tensor Analysis for Orientation Estimation in Brain Tissue Microscopy DOI Creative Commons
Bryson D P Gray, A. W. Smith, Allan MacKenzie‐Graham

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 16, 2025

Abstract Accurate localization of white matter pathways using diffusion MRI is critical to investigating brain connectivity, but the accuracy current methods not thoroughly understood. A fruitful approach validating consider microscopy data that have been co-registered with post mortem samples. In this setting, structure tensor analysis a standard computing local orientations for validation. However, itself has well-validated and subject uncertainty in its angular resolution, selectivity specific spatial scales. work, we conducted simulation study investigate tensors estimate fibers arranged configurations without crossings. We examined range simulated conditions, focus on method’s behavior images anisotropic which particularly common acquisition. also analyzed 2D 3D optical data. Our results show parameter choice relatively little effect estimating single orientations, although decreases anisotropy. On other hand, when crossing fibers, parameters becomes critical, poor choices result orientation estimates are essentially random. This work provides set recommendations researchers seeking apply effectively axonal imaging quantifies limitations, case Highlights Structure homogeneous robust Accuracy crossing-fiber regions highly sensitive Image anisotropy large Simulations provide guidelines choosing based image characteristics

Language: Английский

Citations

0
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

0
Cutting-Edge Technologies Illuminate the Neural Landscape of Cancer: Insights into Tumor Development DOI
Yajing Wang, Zhao‐Jun Wang,

Xinyuan Mao

et al.

Cancer Letters, Journal Year: 2025, Volume and Issue: unknown, P. 217667 - 217667

Published: March 1, 2025

Language: Английский

Citations

0