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Phenotypic differences between interfertile Chlamydomonas species- timelapse microscopy data, part 1

This repository contains timelapse microscopy data of two interfertile Chlamydomonas algal species. The protocol to generate this data is described in the associated publication, <a href="https://doi.org/10.57844/arcadia-35f0-3e16">"Phenotypic differences between interfertile Chlamydomonas species"</a>, and summarized here. Cells were collected from agar plates and suspended in water, then left to sit overnight to encourage gamete formation. During this time, non-motile cells settled, allowing for the enrichment of motile cells in the supernatant. These enriched cells were then loaded onto agar microchambers (100 micron diameter and 40 micron depth) for imaging. We collected videos on a Nikon Ti2-E microscope equipped with a Photometrics Kinetix digital scMos camera. We performed differential interference contrast (DIC) imaging using a Plan Apo 10× 0.45 Air objective. We collected videos with a 5.1 ms exposure with acquisition every 50 ms for three minutes. We placed a red light filter [IR longpass, 610 nm (ThorLabs)] in the light path to maintain swimming behavior of cells. The procedure was standardized and repeated four times to ensure consistency. Timelapse data of C. reinhardtii or C. smithii cells in agar microchamber wells from experiments "1" and "2" are shared here.<h4>Reference</h4><a href="https://doi.org/10.57844/arcadia-35f0-3e16">Essock-Burns T, Garcia III G, MacQuarrie CD, Mets DG, York R. (2023). Phenotypic differences between interfertile Chlamydomonas species</a><h4>Notes</h4>Experiment 1, performed on 230509: This experiment was meant to include DIC timelapse data, but a DIC polarizer was not inserted during the data collection. The resulting data was effectively brightfield data.Experiment 2, performed on 230516: DIC timelapse data of Chlamydomonas cells swimming in agar microchamber wells."Cr" indicates Chlamydomonas reinhardtii"Cs" indicates Chlamydomonas smithiiTimelapse frames: 3601 framesFrame rate: 20 frames per second (fps)Pixel size: 0.6398 microns/pixel

Fig. 6 in Phenotypic Study Of Population And Distribution Of The Poecilia Reticulata (Cyprinodontiformes, Poeciliidae) From Kyiv Sewage System (Ukraine)

Fig. 6. Dependence of the area of orange spots (stripes, %) on the body length of male guppies (L) P. reticulata.

Fig. 1 in Phenotypic Study Of Population And Distribution Of The Poecilia Reticulata (Cyprinodontiformes, Poeciliidae) From Kyiv Sewage System (Ukraine)

Fig. 1. Potential (probabilistic) model of P. reticulata current world expansion built in the Maxent program based on the CliMond climatic data and GBIF data (2021). Areas of highest habitat suitability (> 0.5) are colored in red and areas of lowest (<0.1) — in blue.

Fig. 7 in Phenotypic Study Of Population And Distribution Of The Poecilia Reticulata (Cyprinodontiformes, Poeciliidae) From Kyiv Sewage System (Ukraine)

Fig. 7. Dependence of the proportions of the tail (C1/C2) on the body length of P. reticulata: C1/C2 = 1 ("symmetrical tail") is shown by the line.

Transcriptional profiling of peripheral blood mononuclear cells identifies inflammatory phenotypes in ataxia telangiectasia

This is an AnnData object in h5ad (hdf5) format containing de-identified bulk RNA-seq gene expression matrices from PBMCs. These data are related to the study entitled "Transcriptional profiling of peripheral blood mononuclear cells identifies inflammatory phenotypes in ataxia telangiectasia". This AnnData object contains a table of sample-specific metadata (`obs`), gene-specific metadata (`var`), and multiple gene expression matrices stored as `layers`. These layers include raw counts, DEseq2 normalized counts, vst normalized counts, and rlog normalized counts. Some additional layers include regressed versions of the previously mentioned counts matrices, where sequencing batch (`cohort` in the obs table) has been regressed out using the `combat` tool. The layer `rlog_combat_regressed_batch` is recommended for downstream processing, and has been loaded into the `X` slot of the anndata object for convenience. The md5sum of this h5ad file is listed here: 04d7c6c549fb37cf730a5dca7897f86fOpening and working with AnnData objects in h5ad format requires the use of the `anndata` python library (https://github.com/scverse/anndata).

opencc-zeroNov 2023View details →

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Unveiling the genetic networks: Exploring the dynamic interaction of photosynthetic phenotypes in woody plants across varied light gradients

Background: Understanding the mechanisms by which genes control and regulate complex quantitative traits during periods of fluctuating resources remains a challenging and uncertain task in photosynthesis studies. Most studies have focused on the structure of photosynthesis, the photosynthetic response under stress, or the genetic mechanisms involved in photosynthetic effects and neglected the interactive genetic mechanism that governs various traits through significant quantitative trait loci (QTLs). Results In this study, we have developed a differential dynamic system that enables the identification of QTLs based on the photosynthetic phenotypic and genotypic data under varying levels of light intensity gradients. The framework not only allows for the assessment of the direct effects of QTLs on phenotypes but also captures how they influence interactions among phenotypes as light intensities change. We have analyzed the genetic effects and genetic variance, visualized the genetic network associated with photosynthesis interactions, and validated the effectiveness and stability of the DDS framework. Pivotal QTLs were identified individually to uncover the process and pattern of interaction. Through functional annotation, we made an intriguing discovery that seemingly unimportant QTLs can still have significant genetic effects on phenotypic changes through their regulation with other QTLs. Conclusions This finding emphasizes the significance of considering the interactive genetic architecture when seeking to understand the genetic interaction mechanism of photosynthesis in natural populations of woody plants. Moreover, our research provides a novel framework that can be extended to explore the interactive genetic architecture among organisms, contributing to a deeper understanding of stress resistance mechanisms in woody plants.

opencc-by-4.0Jan 2024View details →

From Pixels to Phenotypes: Integrating Image-Based Profiling with Cell Health Data Improves Interpretability

Code: https://github.com/srijitseal/BioMorph_Space Cell Painting assays generate morphological profiles that are versatile descriptors of biological systems and have been used to predict in vitro and in vivo 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.   The following datasets are released:   Cell_Health_median_357_profiles_70_labels.csv : 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. Cell_Painitng_CRISPR_Perturbations_357_profiles_827_features_scaled.csv: 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. Cell_Painting_data_658_compounds_827_Features_scaled.csv The Cell Painting dataset for compound perturbations. Contains 658 structurally unique compounds with 827 Cell Painting features. Ref: Bray et al Endpoints_9_Mitotox_biological_activities_658_compounds.csv The biological assay activity labels for compound perturbations. Contains 658 structurally unique compounds with 9 biological activity consensus hit calls. Ref: ToxCast/MoleculeNet BioMoprh_pvalue_658_compunds_398_BioMorph_terms.csv: The dataset of standardised BioMorph term p-values. Contains 398 BioMorph terms for the 658 compounds in the biological activity dataset.  References:  Way et al. Predicting cell health phenotypes using image-based morphology profiling. Mol Biol Cell. 2021;32(9):995-1005. 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.  MoleculeNet: Wu et al. MoleculeNet: A benchmark for molecular machine learning. Chem Sci. 2018;9(2):513-530.  ToxCast: Exploring ToxCast Data | US EPA https://www.epa.gov/chemical-research/exploring-toxcast-data (accessed Jul 9, 2023).

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Data from: In vivo functional phenotypes from a computational epistatic model of evolution

Computational models of evolution are valuable for understanding the dynamics of sequence variation, to infer phylogenetic relationships or potential evolutionary pathways, and for biomedical and industrial applications. Despite these benefits, few have validated their propensities to generate outputs with in vivo functionality, which would enhance their value as accurate and interpretable evolutionary algorithms. Utilizing the Hamiltonian of the joint probability of sequences in the family as fitness metric, we sampled and experimentally tested for in vivo beta-lactamase activity in E. coli TEM-1 variants. These variants retain family-like functionality while being more active than their WT predecessor. We found that depending on the inference method used to generate the epistatic constraints, different parameters simulate diverse selection strengths. Under weaker selection, local Hamiltonian fluctuations reliably predict relative changes to variant fitness, recapitulating neutral evolution. In this dataset, we include input datasets, simulation trajectories as well as experimental data to support the publication: "In vivo functional phenotypes from a computationa epistatic model of evolution".

opencc-zeroJan 2024View details →

FIGURE 4 in Fungia fungites (Linnaeus, 1758) (Scleractinia, Fungiidae) is a species complex that conceals large phenotypic variation and a previously unrecognized genus

FIGURE 4 Maximum likelihood (ML) tree based on ITS sequences. Numbers on main branches show percentages of bootstrap values (>50%) in neighbor-joining (NJ) and ML.

FIGURE 1 in Fungia fungites (Linnaeus, 1758) (Scleractinia, Fungiidae) is a species complex that conceals large phenotypic variation and a previously unrecognized genus

FIGURE 1 Map of the sampling sites. Downloaded from Brill.com 12/12/2023 03:06:27PM via Open Access. This is an open access article distributed under the terms of the CC-BY 4.0 License. https://creativecommons.org/licenses/by/4.0/

FIGURE 7 in Fungia fungites (Linnaeus, 1758) (Scleractinia, Fungiidae) is a species complex that conceals large phenotypic variation and a previously unrecognized genus

FIGURE 7 Specimens in Clade A (Fungia fungites). Scale bars: 1 cm for white bar, 1 mm for black bar. A. Living specimen of immature type (MUFS C307). B. Septal dentation (MUFS C307). C. Costal spine (MUFS C307). D. Living specimen of attached morph (MUFS C309). E. Septal dentation (MUFS C309). F. Costal spine (MUFS C309). G. Living specimen of unattached morph (MUFS C324). H. Septal dentation (MUFS C324). I. Costal spine (MUFS C324).

FIGURE 10 in Fungia fungites (Linnaeus, 1758) (Scleractinia, Fungiidae) is a species complex that conceals large phenotypic variation and a previously unrecognized genus

FIGURE 10 Neotype of Fungia fungites (RMNH16235). Scale bars: 1cm. A. Upper side. B. Basal side. C. Enlarged view Downloaded from Brill.com 12/12/2023 03:06:27PM of septa. D. Enlarged view of viacostal Openspines Access.. This is an open access article distributed under the terms of the CC-BY 4.0 License. https://creativecommons.org/licenses/by/4.0/

FIGURE 5 in Fungia fungites (Linnaeus, 1758) (Scleractinia, Fungiidae) is a species complex that conceals large phenotypic variation and a previously unrecognized genus

FIGURE 5 Box plot of density of septal dentation and costal spine between clades A (Fungia fungites) and B (Fungiidae sp.). The lower and upper limits of the rectangular boxes indicate the 25 to 75% range, and the horizontal line within the boxes is the median (50%).

FIGURE 6 in Fungia fungites (Linnaeus, 1758) (Scleractinia, Fungiidae) is a species complex that conceals large phenotypic variation and a previously unrecognized genus

FIGURE 6 Scatter plot of numbers of septa versus corallum diameter between clades A (Fungia fungites) and B (Fungiidae sp.). Plots for F. fungites are shown by circle whereas Fungiidae sp. are shown by triangle. Blank plots mean attached specimens and plots for solid mean unattached specimens.

FIGURE 8 in Fungia fungites (Linnaeus, 1758) (Scleractinia, Fungiidae) is a species complex that conceals large phenotypic variation and a previously unrecognized genus

FIGURE 8 Specimens in Clade B (Fungiidae sp.). Scale bars: 1 cm for white bar, 1 mm for black bar. A. Living specimen of immature type (MUFS C335). B. Septal dentation (MUFS C335). C. Costal spine (MUFS C335). D. Corallites of attached morph (MUFS C188). E. Septal dentation (MUFS C188). F. Costal spine (MUFS C188). G. Living specimen of unattached morph (MUFS C338). H. Septal dentation (MUFS C338) I. Costal spine (MUFS C338).

FIGURE 2 in Fungia fungites (Linnaeus, 1758) (Scleractinia, Fungiidae) is a species complex that conceals large phenotypic variation and a previously unrecognized genus

FIGURE 2 Schematic illustration of Fungia fungites. A. Cross section in F. fungites. B. Side view of septum. Abbreviations and symbols: CD, Corallum diameter; CoC, Center of corallum; CS, Costal spine; M, Mouth; ST, Septal tooth; TSE, Top of septal edge; a, density of septal dentation; b, density of costal spine.

FIGURE 9 in Fungia fungites (Linnaeus, 1758) (Scleractinia, Fungiidae) is a species complex that conceals large phenotypic variation and a previously unrecognized genus

FIGURE 9 Micromorphology of septal side using scanning electron microscopy. Scale bars: 0.5 cm. A. Immature type in Clade A (MUFS C307). B. Attached morph in Clade A (MUFS C309). C. Unattached morph in Clade A (MUFS C325). D. Immature type in Clade B (MUFS C166). E. Attached morph in Clade B (MUFS C188). C. Unattached morph in Clade B (MUFS C338).

FIGURE 3 in Fungia fungites (Linnaeus, 1758) (Scleractinia, Fungiidae) is a species complex that conceals large phenotypic variation and a previously unrecognized genus

FIGURE 3 Maximum likelihood (ML) tree based on COI sequences. Numbers on main branches show percentages of bootstrap values (>50%) in neighbor-joining (NJ) and ML.

opencc-by-4.0Feb 2024View details →

Images of mouse VTA and SNc sections labeled for neurotransmitter phenotype markers via RNAscope

48 coronal sections from 3 male and 3 female C57Bl/6J mice were labeled for mRNAs encoding the canonical vesicular transporters for dopamine (VMAT2), GABA (VGAT), and glutamate (VGLUT2). These sections were then imaged via confocal microscopy and saved as CZI files, editable with Zeiss Zen software. These image files include 'event marker' graphics, indicating where neurons positive for one or more of the above transporters are.  The protocols used for sample preparation, labeling, imaging, and counting are linked in the Related Works section.  Cell count data are organized by subregion (SUBREGION counts for R.csv), anterior-posterior (e.g. VGATVTA.csv), or anterior-posterior and medial-lateral distribution (e.g. 'snc triple.csv' or 'snc vmat2 AP and ML.csv'). The R code provided uses these csv files to visualize expression patterns across VTA and SNc.