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136 results for “phenotypic integration”
Cardelino: Integrating whole exomes and single-cell transcriptomes to reveal phenotypic impact of somatic variants
<p>This dataset consists of the reference data files, metadata and processed results files for the paper "Cardelino: Integrating whole exomes and single-cell transcriptomes to reveal phenotypic impact of somatic variants," which investigates clonality in normal human dermal fibroblast cell populations in 32 cell lines from distinct donors, using bulk whole-exome sequencing and single-cell RNA-sequencing data.</p> <p>This dataset contains everything required to reproduce the results presented in the paper from processed data and results of our data processing workflows. Our analyses can be reproduced using the <a href="https://github.com/davismcc/fibroblast-clonality">source code</a> and instructions available at our <a href="https://davismcc.github.io/fibroblast-clonality/">project website</a>.</p> <p>The <em>entire</em> analysis workflow from raw data to final results is also reproducible but is substantially more complicated and computationally intensive. It also requires large datasets to be obtained from other repositories. Specifically, single-cell RNA-seq data have been deposited in the ArrayExpress database at EMBL-EBI under accession number E-MTAB-7167. Whole-exome sequencing data is available through the HipSci portal (www.hipsci.org). Combined with the dataset in this repository and following the instructions on the project website, it is possible to run our entire analysis pipeline.</p> <p> </p>
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>
Supplementary information to the article by van Beijnum et al. "Integrating phenotypic search and phosphoproteomic profiling of active kinases for optimization of drug mixtures for RCC treatment"
<p>Supplementary information to the article "Integrating phenotypic search and phosphoproteomic profiling of active kinases for optimization of drug mixtures for RCC treatment".</p> <p><strong>Judy R. van Beijnum<sup>1</sup>, Andrea Weiss<sup>2, 3</sup>, Robert H. Berndsen<sup>1,2 </sup>, Tse J. Wong<sup>1</sup>, Louise C. Reckman<sup>1</sup>, Sander R. Piersma<sup>4,5</sup>, Marloes Zoetemelk<sup>2,3</sup>, Richard de Haas<sup>1,4,5</sup>, Olivier Dormond<sup>6</sup>, Axel Bex<sup>7,8</sup>, Alexander A. Henneman<sup>4,5</sup>, Connie R. Jimenez<sup>4,5</sup>, Arjan W. Griffioen<sup>1</sup>, Patrycja Nowak-Sliwinska<sup>2,3,9</sup>*</strong></p> <p> </p> <p><sup>1</sup> Angiogenesis Laboratory, Department of Medical Oncology, Amsterdam UMC, Vrije Universiteit Amsterdam, Medical Oncology, Cancer Center Amsterdam, De Boelelaan 1117, Amsterdam, Netherlands;</p> <p><sup>2</sup> Molecular Pharmacology Group, School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland*;</p> <p><sup>3 </sup>Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland</p> <p><sup>4 </sup>Department of Medical Oncology, Amsterdam UMC, Vrije Universiteit Amsterdam, Medical Oncology, Cancer Center Amsterdam, De Boelelaan 1117, Amsterdam, Netherlands</p> <p><sup>5</sup> OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands</p> <p><sup>6</sup> Department of Visceral surgery, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland<sup> </sup> </p> <p><sup>7</sup> Royal Free London NHS Foundation Trust, Renal Cancer Centre, UCL Division of Surgical and Interventional Science, London, UK</p> <p><sup>8</sup> Netherlands Cancer Institute, Amsterdam, The Netherlands</p> <p><sup>9 </sup> Translational Research Centre in Oncohaematology, Geneva, Switzerland</p> <p> Correspondence: <a href="mailto:Patrycja.Nowak-Sliwinska@unige.ch">Patrycja.Nowak-Sliwinska@unige.ch</a></p>
Weak genetic signal for phenotypic integration implicates developmental processes as major regulators of trait covariation
<p>Phenotypic integration is an important metric that describes the degree of covariation among traits in a population, and is hypothesized to arise due to selection for shared functional processes. Our ability to identify the genetic and/or developmental underpinnings of integration is marred by temporally overlapping cell-, tissue-, and structure-level processes that serve to continually 'overwrite' the structure of covariation among traits through ontogeny. Here we examine whether traits that are integrated at the phenotypic level, also exhibit a shared genetic basis (e.g., pleiotropy). We micro-CT scanned two hard tissue traits, and two soft tissue traits (mandible, pectoral girdle, atrium, and ventricle respectively) from an F<sub>5</sub> hybrid population of Lake Malawi cichlids, and used geometric morphometrics to extract 3D shape information from each trait. Given the large degree of asymmetric variation that may reflect developmental instability, we separated symmetric- from asymmetric-components of shape variation. We then performed quantitative trait loci (QTL) analysis to determine the degree of genetic overlap between shapes. While we found ubiquitous associations among traits at the phenotypic level, except for a handful of notable exceptions, our QTL analysis revealed few overlapping genetic regions. Taken together, this indicates developmental interactions can play a large role in determining the degree of phenotypic integration among traits, and likely obfuscate the genotype to phenotype map, limiting our ability to gain a comprehensive picture of the genetic contributors responsible for phenotypic divergence.</p>
Data from: Phenotypic integration in an extended phenotype: among‐individual variation in nest‐building traits of the alfalfa leafcutting bee (Megachile rotundata)
Structures such as nests and burrows are an essential component of many organisms' life-cycle and requires a complex sequence of behaviors. Because behaviors can vary consistently among individuals and be correlated with one another, we hypothesized that these structures would 1) show evidence of among-individual variation, 2) be organized into distinct functional modules, and 3) show evidence of trade-offs among functional modules due to limits on energy budgets. We tested these hypotheses using the alfalfa leafcutting bee, Megachile rotundata, a solitary bee and important crop pollinator. M. rotundata constructs complex nests by gathering leaf materials to form a linear series of cells in pre-existing cavities. In this study, we examined variation in the following nest construction traits: reproduction (number of cells per nest and nest length), nest protection (cap length and number of leaves per cap), cell construction (cell size and number of leaves per cell), and cell provisioning (cell mass) from 60 nests. We found a general decline in investment in cell construction and provisioning with each new cell built. In addition, we found evidence for both repeatability and plasticity in cell provisioning with little evidence for trade-offs among traits. Instead, most traits were positively, albeit weakly, correlated (r ~ 0.15), and traits were loosely organized into covarying modules. Our results show that individual differences in nest construction are detectable at a level similar to that of other behavioral traits and that these traits are only weakly integrated. This suggests that nest components are capable of independent evolutionary trajectories.
Data from: Sexual dimorphism, phenotypic integration, and the evolution of head structure in casque-headed lizards
Sexes can differ in features associated with differential reproduction, which can be used during courtship or aggressive encounters. Some traits tend to evolve independently between sexes and emerge as sexually dimorphic within the organismal phenotype. We characterize such a relationship by estimating the phenotypic integration of the head morphology and modularity of the crest in the casque-headed lizards (Corytophanidae). In this clade, some species show extreme sexual dimorphism (e.g., head crests in the genus Basiliscus) while in others, both sexes are monomorphic. To characterize these patterns, we define phenotypic integration at the interspecific level as a pattern or network of traits evidenced by phylogenetically adjusted correlations that persist among species. At this level, modularity is an increased connectedness (e.g., higher correlation) among sections of these networks that persist in a lineage during the evolution of complex phenotypes. To test both concepts, we used phylogenetic geomorphometrics to characterize the head structure of corytophanid lizards, based on a time-calibrated phylogeny that includes candidate fossil ancestors. We found evidence of an older diversification of corytophanids than previously reported (~67 vs. ~23.5 MYA) and show that this clade includes two morphological head architectures: (1) Sexually dimorphic crests present in males that are evolving independently from the rest of the head structure, and (2) full integration of the head morphology in monomorphic species. We propose that both architectures are optimal evolutionary trajectories of the parietal crest bones in the head of these lizards. In sexually dimorphic species, these bones are elongated and thinner, and gave rise to the extended crest used in male courtship displays. In monomorphic species, the parietal crest grew thicker in both sexes to allow for a better insertion of muscles associated with a stronger bite.
Data from: Thyroid hormone tinkering elicits integrated phenotypic changes potentially explaining rapid adaptation of color vision in cichlid fish
Vision is critical for most vertebrates, including fish. One challenge that aquatic habitats pose is the high variability in spectral properties depending on depth, turbidity and composition of the water body. By altering opsin gene expression and chromophore usage, cichlid fish modulate visual sensitivities to maximize sensory input from the available light in their respective habitat. Thyroid hormone (TH) has been proposed to play a role in governing adaptive diversification in visual sensitivity in Nicaraguan Midas cichlids, which evolved in less than ~3,100 generations. As suggested by indirect measurements of TH levels (i.e., expression of deiodinases), populations adapted to short wavelength light in clear lakes have lower TH levels than ones inhabiting turbid lakes enriched in long-wavelength light. We experimentally manipulated TH levels by exposing two-week-old Midas cichlids to exogenous TH or a TH-inhibitor and measured opsin gene expression and chromophore usage (via cyp27c1 expression). Whereas exogenous TH induces long-wavelength sensitivity by changing opsin gene expression and chromophore usage in a concerted manner, TH-inhibited fish exhibit a visual phenotype with sensitivities shifted to shorter-wavelengths. Tinkering with TH levels in eyes results in concerted phenotypic changes that can provide a rapid mechanism of adaptation to novel light environments. --
Data from: Macroevolutionary integration of phenotypes within and across ant worker castes
<p>Phenotypic traits are often integrated into evolutionary modules: sets of organismal parts that evolve together. In social insect colonies the concepts of integration and modularity apply to sets of traits both within and among functionally and phenotypically differentiated castes. On macroevolutionary timescales, patterns of integration and modularity within and across castes can be clues to the selective and ecological factors shaping their evolution and diversification. We develop a set of hypotheses describing contrasting patterns of worker integration and apply this framework in a broad (246 species) comparative analysis of major and minor worker evolution in the hyperdiverse ant genus Pheidole. Using geometric morphometrics in a phylogenetic framework, we inferred fast and tightly integrated evolution of mesosoma shape between major and minor workers, but slower and more independent evolution of head shape between the two worker castes. Thus, Pheidole workers are evolving as a mixture of intra- and inter-caste integration and rate heterogeneity. The decoupling of homologous traits across worker castes may represent an important process facilitating the rise of social complexity.</p>
Integrated Imaging Strategy to Phenotype Progression of Liver Tumors During and After Chemoembolization
ClinicalTrials.gov study NCT02471313. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Data from: Phenotypic integration between claw and toepad traits promotes microhabitat specialization in the Anolis adaptive radiation
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Phenomic data-driven biological prediction of maize through field-based high throughput phenotyping integration with genomic data
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Data from: Macroevolution of body extremities reveals an integrated phenotypic response of coloration and morphology to temperature in a large clade of Neotropical passerines (Furnariida)
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Data from: Phenotypic integration and the evolution of signal repertoires: a case study of treefrog acoustic communication
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Weak genetic signal for phenotypic integration implicates developmental processes as major regulators of trait covariation
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Data from: Thyroid hormone tinkering elicits integrated phenotypic changes potentially explaining rapid adaptation of color vision in cichlid fish
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Data from: Phenotypic integration limits the variation in plant phenotypic plasticity among traits: a meta-analysis
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Data from: Macroevolutionary integration of phenotypes within and across ant worker castes
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Data from: Phenotypic integration in an extended phenotype: among‐individual variation in nest‐building traits of the alfalfa leafcutting bee (Megachile rotundata)
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Data from: Sexual dimorphism, phenotypic integration, and the evolution of head structure in casque-headed lizards
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Incremental integration for tracking genotype-phenotype associations - dataset snapshot
<p>This dataset is an archive of data and code files used to generate figures and tables for a manuscript entitled "Incremental integration for tracking genotype-phenotype associations".</p> <p>Data files include the exact versions of raw materials as well as databases produced during the course of the analysis described in the manuscript. </p> <p>Code files include python and R components.</p> <p>Further details are available in the archive README file.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.