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85 results for “Cell Health”
From Pixels to Phenotypes: Integrating Image-Based Profiling with Cell Health Data Improves Interpretability
<p>Code: https://github.com/srijitseal/BioMorph_Space<br> <br> Cell Painting assays generate morphological profiles that are versatile descriptors of biological systems and have been used to predict <em>in vitro</em> and <em>in vivo</em> drug effects. However, Cell Painting features are based on image statistics, and are, therefore, often not readily biologically interpretable. In this study, we introduce an approach that maps specific Cell Painting features into the BioMorph space using readouts from comprehensive Cell Health assays. We validated that the resulting BioMorph space effectively connected compounds not only with the morphological features associated with their bioactivity but with deeper insights into phenotypic characteristics and cellular processes associated with the given bioactivity. The BioMorph space revealed the mechanism of action for individual compounds, including dual-acting compounds such as emetine, an inhibitor of both protein synthesis and DNA replication. In summary, BioMorph space offers a more biologically relevant way to interpret cell morphological features from the Cell Painting assays and to generate hypotheses for experimental validation.</p> <p> </p> <p>The following datasets are released:<br> </p> <p>Cell_Health_median_357_profiles_70_labels.csv :<br> The Cell Heath dataset for CRISPR perturbations. Contains median consensus signatures for the 357 consensus profiles (119 CRISPR perturbations × 3 cell lines) Ref: Way et al.</p> <p>Cell_Painitng_CRISPR_Perturbations_357_profiles_827_features_scaled.csv:<br> The Cell Painting dataset for CRISPR perturbations. Contains 827 morphology features (and metadata annotation) for 357 consensus profiles (119 CRISPR perturbations × 3 cell lines). Ref: Way et al.</p> <p>Cell_Painting_data_658_compounds_827_Features_scaled.csv<br> The Cell Painting dataset for compound perturbations. Contains 658 structurally unique compounds with 827 Cell Painting features. Ref: Bray et al</p> <p>Endpoints_9_Mitotox_biological_activities_658_compounds.csv<br> The biological assay activity labels for compound perturbations. Contains 658 structurally unique compounds with 9 biological activity consensus hit calls. Ref: ToxCast/MoleculeNet</p> <p>BioMoprh_pvalue_658_compunds_398_BioMorph_terms.csv:<br> The dataset of standardised BioMorph term p-values. Contains 398 BioMorph terms for the 658 compounds in the biological activity dataset. <br> <br> References: <br> Way et al. Predicting cell health phenotypes using image-based morphology profiling. Mol Biol Cell. 2021;32(9):995-1005.<br> Bray et al. A dataset of images and morphological profiles of 30 000 small-molecule treatments using the Cell Painting assay. Gigascience. 2017;6(12):1-5. <br> MoleculeNet: Wu et al. MoleculeNet: A benchmark for molecular machine learning. Chem Sci. 2018;9(2):513-530. <br> ToxCast: Exploring ToxCast Data | US EPA https://www.epa.gov/chemical-research/exploring-toxcast-data (accessed Jul 9, 2023).</p>
Unsupervised neural network for single cell Multi-omics INTegration (UMINT): An application to health and disease
<p>This dataset repository corresponds to the project Unsupervised neural network for single cell Multi-omics INTegration (UMINT): An application to health and disease.</p>
Multimodal single cell analysis of the paediatric lower airway reveals novel immune cell phenotypes in early life health and disease
<p>RDS files of SingleCellExperiment objects containing raw single cell RNA-seq count data required to replicate the analyses presented at: <a href="https://oshlacklab.com/paed-cf-cite-seq/">https://oshlacklab.com/paed-cf-cite-seq/</a> and described in the pre-print titled: <em>"</em>Multimodal single cell analysis of the paediatric lower airway reveals novel immune cell phenotypes in early life health and disease<em>"</em>.<br> Instructions for how to incorporate the raw data into the analysis can be found at: <a href="https://oshlacklab.com/paed-cf-cite-seq/gettingStarted.html">https://oshlacklab.com/paed-cf-cite-seq/gettingStarted.html</a> and the complete analysis code and additional data files can be cloned/downloaded from: <a href="https://github.com/Oshlack/paed-cf-cite-seq">https://github.com/Oshlack/paed-cf-cite-seq</a>.</p>
Cell Health Data Supplementary Files
<p>This dataset contains supplementary files related to the <em><a href="https://github.com/WayScience/cell-health-data">cell-health-data</a></em> repository that were too large to upload on GitHub. Each of these files were saved to and loaded from an external hard drive during Cell Health Data Processing. These files include:</p> <ul> <li>single_cell_classification_probabilities/ : Single cell classification probabilities derived with each model trained in <a href="https://github.com/WayScience/phenotypic_profiling_model">phenotypic_profiling_model</a>. Each compressed csv file in single_cell_classification_probabilities/ corresponds to single-cell classifications from a particular plate as derived with a particular model. The model used to derive the probabilities is indicated by the file's parent folders. Each compressed csv file includes the metadata (location and perturbation) and phenotypic class probabilities for each cell in the plate.</li> <li>classification_profiles/ : This folder contains aggregated data known as "classification profiles". These profiles are generated from the single-cell classification probabilities found in the 'single_cell_classification_probabilities/' folder. Specifically, for each model, we find the mean of the single-cell classification probabilities across each perturbation and cell line to create a composite profile. This aggregated data provides a summarized view of cell behavior for each perturbation/cell line combination, as predicted by each model.</li> </ul> <p>For more information regarding the generation of this data, please see <a href="https://github.com/WayScience/cell-health-data/tree/master/4.classify-single-cell-phenotypes">cell-health-data/4.classify-single-cell-phenotypes</a>.</p>
Health Information Technology System ("Roadmap 2.0") in the Context of Hematopoietic Cell Transplantation
ClinicalTrials.gov study NCT04094844. IPD Sharing: NO. Countries: 1. Publications: 3.
Using Patient-Centered Guidelines in a Technology Platform to Improve Health Care in Adults With Sickle Cell Disease
ClinicalTrials.gov study NCT03629678. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
The Natural History of Gene Expression in the Lung Cells of Non-Smokers, Smokers and Ex-Smokers in Health and Disease
ClinicalTrials.gov study NCT00974064. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Patient and Caregiver Participation Through an Educational Health Information Technology System ("BMT Roadmap") in the Context of Hematopoietic Cell Transplantation
ClinicalTrials.gov study NCT03161665. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Remotely Monitored, Mobile Health-supported, High Intensity Interval Training Before Hematopoietic Stem Cell Transplant
ClinicalTrials.gov study NCT06953648. IPD Sharing: NO. Countries: 1. Publications: 142.
Ghrelin Effect on Beta Cell Function in Health and Disease
ClinicalTrials.gov study NCT01729299. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Study About How Blood Cell Growth Patterns Relate to Heart Health After Treatment for Hodgkin Lymphoma
ClinicalTrials.gov study NCT05705531. IPD Sharing: Not stated. Countries: 2. Publications: 1.
Physical and Emotional Health of Caregivers for People Who Have Had Stem Cell Transplants
ClinicalTrials.gov study NCT01468857. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Health Economic Analysis of Islet Cell Transplantation for the Stabilization of the Severe Forms of Type 1 Diabetes
ClinicalTrials.gov study NCT02854696. IPD Sharing: NO. Countries: 2. Publications: 5.
A Stress and Pain Self-management m-Health App for Adult Outpatients With Sickle Cell Disease
ClinicalTrials.gov study NCT04484272. IPD Sharing: NO. Countries: 1. Publications: 1.
Electronic Health Mindfulness-based Music Therapy Intervention for Patients Undergoing Allogeneic Stem Cell Transplantation
ClinicalTrials.gov study NCT05968963. IPD Sharing: NO. Countries: 1. Publications: 0.
Community Health Workers and mHealth for Sickle Cell Disease Care
ClinicalTrials.gov study NCT03648710. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Health-Related Quality of Life Outcomes in Patients With Aggressive B-Cell Lymphomas Treated With CAR-T Cell Therapy in Real Life
ClinicalTrials.gov study NCT06026644. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Raw data related to: "Transcriptional Regulators of T Helper 17 Cell Differentiation in Health and Autoimmune Diseases"
<p><strong>Introduction</strong></p> <p>T helper (Th) 17 cells are a subtype of CD4 T lymphocytes characterized by the expression of retinoic acid-receptor (RAR)-related orphan receptor (ROR)γt transcription factor, encoded by gene <em>Rorc</em>. These cells are implicated in the pathology of autoimmune inflammatory disorders as well as in the clearance of extracellular infections. The main function of Th17 cells is the production of cytokine called interleukin (IL)-17A.</p> <p><strong>Methods</strong></p> <p>We described the lineage defining transcription factor RORγt and other factors that regulate transcription of <em>Il17a, Rorc </em>and Th17-related genes, such as Il17f, Il21, Il23r, which may positively or negatively influence their expression.</p> <p><strong>Results</strong></p> <p>We discussed the potential new therapeutic approaches for Th17-related diseases targeting these transcription factors. The wide knowledge of transcriptional regulators of Th17 cells is crucial for the better understanding of the pathogenic role of these cells and for development of therapeutic strategies aimed at fighting Th17-related diseases.</p>
Self-Management Program and Survivorship Care Plan in Improving the Health of Cancer Survivors After Stem Cell Transplant
ClinicalTrials.gov study NCT03125070. IPD Sharing: NO. Countries: 1. Publications: 0.
Registry to Collect Health Information About Desmoplastic Small Round Cell Tumor
ClinicalTrials.gov study NCT04690374. IPD Sharing: YES. Countries: 1. Publications: 0.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.