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81 results for “Organelle”
Demo code for "ESPRESSO: Spatiotemporal omics based on organelle phenotyping": https://doi.org/10.1101/2024.06.13.598932
<p>The files and code provided are sufficient to run end-to-end the ESPRESSO analysis, from the microscope data to the feature analysis.<br>In the following, we provide guidance for the installation and running of the program, the output for each of the scripts are also stored in the <br>"ESPRESSO - Demo\Output of scripts" folder and can be consulted directly.</p>
Organelle Genome Utilities benchmark data
<p>test_Lamiaceae.zip: Benchmark result of Lamiaceae plastid data. Includes original sequences and analyze result from OGU.<br>test_1M_out.zip: Benchmark result of 1 million random GenBank records. Includes original sequences and analyze result from OGU.<br>test_308_angiosperm.zip: Benchmark result of 308 angiosperm family's plastid genomes. Includes original sequences and analyze result from OGU.<br>test_rodents.zip: Benchmark result of 307 mitochondria genomes from rodents. Includes original sequences and analyze result from OGU.<br>CDS and spacer tree of angiosperm plastid data.zip: Sequences and maximum likelihood trees of angiosperm plastid CDS and spacers. Built with IQTREE2.</p> <p>OGU-source code.zip: three versions of source code of OGU. </p>
Dataset for "Whole-genome de novo assemblies reveal structural variations and organelle-to-nucleus DNA transfers in Asian and African rice""
<p>DXCWR_O.rufipogon_scaffolded_anchored.fa.gz</p> <p>--Scaffolded and anchored genome assembly for <em>O. rufipogon</em> DXCWR.</p> <p>DXCWR_O.rufipogon_scaffolded_anchored.gff.gz</p> <p>--Gene annotation for the genome assembly DXCWR_O.rufipogon_scaffolded_anchored.fa.</p> <p>DXCWR_O.rufipogon_scaffolded_anchored_repeatmasker.gff.gz</p> <p>--Repeat annotation for the genome assembly DXCWR_O.rufipogon_scaffolded_anchored.fa.</p> <p>IRGC104165_O.glaberrima_scaffolded_anchored.fa.gz</p> <p>--Scaffolded and anchored genome assembly for <em>O. glaberrima</em> IRGC104165.</p> <p>IRGC104165_O.glaberrima_scaffolded_anchored.gff.gz</p> <p>--Gene annotation for the genome assembly IRGC104165_O.glaberrima_scaffolded_anchored.fa.</p> <p>IRGC104165_O.glaberrima_scaffolded_anchored_repeatmasker.gff.gz</p> <p>--Repeat annotation for the genome assembly IRGC104165_O.glaberrima_scaffolded_anchored.fa.</p> <p>W1411_O.barthii_scaffolded_anchored.fa.gz</p> <p>--Scaffolded and anchored genome assembly for <em>O. barthii</em> W1411.</p> <p>W1411_O.barthii_scaffolded_anchored.gff.gz</p> <p>--Gene annotation for the genome assembly W1411_O.barthii_scaffolded_anchored.fa.</p> <p>W1411_O.barthii_scaffolded_anchored_repeatmasker.gff.gz</p> <p>--Repeat annotation for the genome assembly W1411_O.barthii_scaffolded_anchored.fa.</p> <p>W2014_O.nivara_scaffolded_anchored.fa.gz</p> <p>--Scaffolded and anchored genome assembly for <em>O. nivara</em> W2014.</p> <p>W2014_O.nivara_scaffolded_anchored.gff.gz</p> <p>--Gene annotation for the genome assembly W2014_O.nivara_scaffolded_anchored.fa.</p> <p>W2014_O.nivara_scaffolded_anchored_repeatmasker.gff.gz</p> <p>--Repeat annotation for the genome assembly W2014_O.nivara_scaffolded_anchored.fa.</p>
Cryo-4D-STEM datasets on cells and cellular organelles for demonstrating a dose-Efficient cryo-EM technique: tilt-Corrected Scanning Transmission Electron Microscopy
<p>This upload contains three 4D-STEM datasets in .raw format for demonstrating a dose-efficient cryo-EM technique for thick samples: tilt-corrected Scanning Transmission Electron Microscopy (tcBF-STEM). The dataset dimension is 128130256*256. Data were acquired on vitrified intact E.coli cells and isolated human cell organelles. This upload also contains the EFTEM images in .mrc acqired in the same ROI as the 4D-STEM dataset. </p> <p>It also contains analysis of the manuscript's Fig 3 and Ext. data fig 8. </p>
Data on: Impact of neurite alignment on organelle motion
<p><strong>Impact of neurite alignment on organelle motion.</strong></p> <p>Maria Mytiliniou, Joeri A. J. Wondergem, Thomas Schmidt, Doris Heinrich.<br>J. R. Soc. Interface <strong>19</strong>:20210617.<br>doi: https://doi.org/10.1098/rsif.2021.0617</p> <p><strong>Abstract</strong></p> <p>Intracellular transport is pivotal for cell growth and survival. Malfunctions in this process have been associated with devastating neurodegenerative diseases, posing a need for deeper understanding of the involved mechanisms. Here, we used an experimental methodology that lead neurites of differentiated PC12 cells in either of two configurations: an one-dimensional, where the neurites align along lines, or a two-dimensional configuration, where the neurites adopt a random orientation and shape on a flat substrate. We subsequently monitored the motion of functional organelles, the lysosomes, inside the neurites. Implementing a time-resolved analysis of the mean-squared displacement, we quantitatively characterized distinct motion modes of the lysosomes. Our results indicate that neurite alignment gives rise to faster diiffusive and super-diiffusive lysosomal motion in comparison to the situation where the neurites are randomly oriented. After inducing lysosome swelling through an osmotic challenge by sucrose, we confirmed the predicted slowdown in diffusive mobility. Surprisingly we found that the swelling-induced mobility change affected each of the (sub- /super-) diiffusive motion modes differently and depended on the alignment configuration of the neurites. Our findings imply that intracellular transport is significantly and robustly dependent on cell morphology, which might be in part controlled by the extracellular matrix.</p>
Phylogenomics and biogeography of Torreya (Taxaceae) – Integrating data from three organelle genomes, morphology, and fossils and a practical method for reducing missing data from RAD-seq
<p><span>Restriction site-associated DNA sequencing (RAD-seq) enables obtaining thousands of genetic markers for phylogenomic studies. However, RAD-seq data are subject to allele dropout (ADO) due to polymorphisms at enzyme cutting sites. We developed a new pipeline, RADADOR, to mitigate the ADO in outgroups by recovering missing loci from previously published transcriptomes in our study of a gymnosperm genus </span><em>Torreya</em><span>. Using the supplemented RAD-seq data in combination with plastome and mitochondrial gene sequences, morphology, and fossil records, we reconstructed the phylogenetic and biogeographic histories of the genus and test hypotheses on diversity anomaly in eastern Asian-North American floristic disjunction. Our results showed that our pipeline recovered many loci missing from the outgroup, and the improved data yielded a more robust phylogeny for </span><em>Torreya</em><span>. Using the fossilized-birth-death model and divergence-extinction-cladogenesis method we resolved detailed biogeographic history of </span><em>Torreya</em><span> that suggested a Jurassic origin in the Laurasia and differential speciation and extinction among continents accounting for the modern diversity anomaly biased toward Eastern Asia (EA). The history also supported a vicariance origin of the modern </span><em>Torreya</em><span> from a widespread ancestor in EA and NA in the mid-Eocene, cross-Beringia exchange in the early Paleogene before the vicariant isolation, in contrast to the "Out of NA" pattern common to gymnosperms and in contrast to the "Out of EA" hypothesis previously proposed for the genus. Furthermore, we observed phylogenetic discordance between the nuclear and plastid phylogenies on </span><em>T. jackii</em><span>, suggesting differential lineage sorting of plastid genomes among </span><em>Torreya</em><span> species or plastid genome capture in </span><em>T. jackii</em><span>.</span></p>
Phylogenomics and biogeography of Torreya (Taxaceae) – Integrating data from three organelle genomes, morphology, and fossils and a practical method for reducing missing data from RAD-seq
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Phenotypic differences between interfertile Chlamydomonas species- high-resolution confocal z-stacks for visualizing organelle morphology
<p>This repository contains high-resolution confocal z-stacks of two interfertile <i>Chlamydomonas</i> algal species. The protocol to generate this data is described in the associated publication, "Phenotypic differences between interfertile <i>Chlamydomonas</i> species", and briefly summarized here. Cells were collected from agar plates with TAP medium and suspended in 500 µl of liquid TAP medium in a 1.5 ml eppendorf tube overnight. Cells were pelleted using a microcentrifuge at 2000 x g for 2 min and the supernatant removed. For staining mitochondria, PKMito orange was used at a 1:500 concentration and cells were moved to opaque black microcentrifuge tubes and placed on a tube rotator for 45 min. Cells were pelleted again and washed twice with fresh TAP medium. After the final wash and supernatant removal, cells were resuspended in 25 µl of 1.25% low gelling agar in TAP medium (kept at 45 C). Then 1 µl of the cell/agar mixture was mounted on a #1.5 coverslip with a small wax circle drawn to retain the droplet. Coverslips were flipped and placed on a slide and sealed with VALAP. </p><p>Images were collected on a Nikon CSU W-1 SoRA spinning disk confocal microscope equipped with an ORCA-Fusion BT digital scMOS camera. In order to apply deconvolution in the downstream processing, we needed to oversample (sample beyond Nyquist) in z resolution. To do this, we used a 100×/1.45 NA objective in 2.8× SoRa magnification mode, using ROIs of either 670 × 670 × 81 or 850 × 850 × 91. We imaged with a z-step size of 100 nm for sub-Nyquist sampling. We imaged bright-field first, then 640 nm excitation autofluorescence of chloroplasts, and then 561 nm excitation for PKmito orange dye, because the chloroplasts would bleach after 561 nm excitation. We set exposures to 300 ms with 30% and 50% laser power for 640 and 561, respectively.</p><p>We have included a set of demo data (10 images per species) that accompany the pub hosted on the Arcadia Science webpage (3Dmorpho_demo_data). In addition, we included all of the raw data we collected in this experiment (3Dmorpho_raw_data). Please use the point spread functions (PSF) from the zipped folders for each respective dataset (demo or raw). </p>
Supplementary data for: Encyclopaedia of family A DNA polymerases localized in organelles: Evolutionary contribution of bacteria including the proto-mitochondrion
<p><span>DNA polymerases (DNAPs) synthesize DNA from deoxyribonucleotides in a semi-conservative manner and serve as the core of DNA replication and repair machinery. In eukaryotic cells, there are two genome-containing organelles, mitochondria and plastids, that were derived from an α-proteobacterium and a cyanobacterium, respectively. Except for rare cases of genome-lacking mitochondria and plastids, both organelles must be served by nucleus-encoded DNAPs that localize and work in them to maintain their genomes. The evolution of organellar DNAPs has yet to be fully understood because of two unsettled issues. First, the diversity of organellar DNAPs has not been elucidated in the full spectrum of eukaryotes. Second, it is unclear when the DNAPs that were used originally in the endosymbiotic bacteria giving rise to mitochondria and plastids were discarded, as the organellar DNAPs known to date show no phylogenetic affinity to those of the extant α-proteobacteria or cyanobacteria. </span><span>In this study, we identified from diverse eukaryotes 134</span> <span>family A DNAP sequences, which were classified into 10 novel types, and explored their evolutionary origins. The subcellular localizations of selected DNAPs were further examined experimentally. The results presented here suggest that the diversity of organellar DNAPs has been shaped by multiple transfers of the Pol</span><span>I</span><span> gene from phylogenetically broad bacteria, and their occurrence in eukaryotes was additionally impacted by secondary plastid endosymbioses. Finally, we propose that the last eukaryotic common ancestor may have possessed two mitochondrial DNAPs, POP and a candidate of the direct descendant of the proto-mitochondrial DNAP, rdxPolA, identified in this study.</span></p>
OrgaMapper: A robust and easy-to-use workflow for analyzing organelle positioning
<p><span>Eukaryotic cells are highly compartmentalized by a variety of organelles that carry out specific cellular processes. The position of these organelles within the cell is elaborately regulated and vital for their function. For instance, the position of lysosomes relative to the nucleus controls their degradative capacity and is altered in pathophysiological conditions. The molecular components orchestrating the precise localization of organelles remain incompletely understood. A confounding factor in these studies is the fact that organelle positioning is surprisingly non-trivial to address. E.g., perturbations that affect the localization of organelles often lead to secondary phenotypes such as changes in cell or organelle size. These phenotypes could potentially mask effects or lead to the identification of false positive hits. To uncover and test potential molecular components at scale, accurate and easy-to-use analysis tools are required that allow robust measurements of organelle positioning. </span></p> <h2><span>Results</span></h2> <p><span>Here, we present an analysis workflow for the faithful, robust, and quantitative analysis of organelle positioning phenotypes. Our workflow consists of an easy-to-use Fiji plugin and an R Shiny App. These tools enable users without background in image or data analysis to (1) segment single cells and nuclei and to detect organelles, (2) to measure cell size and the distance between detected organelles and the nucleus, (3) to measure intensities in the organelle channel plus one additional channel, (4) to measure radial intensity profiles of organellar markers, and (5) to plot the results in informative graphs. Using simulated data and immunofluorescent images of cells in which the function of known factors for lysosome positioning has been perturbed, we show that the workflow is robust against common problems for the accurate assessment of organelle positioning such as changes of cell shape and size, organelle size and background.</span></p> <h2><span>Conclusion</span></h2> <p><span>OrgaMapper is a versatile, robust and easy-to-use automated image analysis workflow that can be utilized in microscopy-based hypothesis testing and screens. It effectively allows for the mapping of the intracellular space and thereby enables the discovery of novel regulators of organelle positioning. </span></p>
Measuring age-dependent viscoelasticity of organelles, cells and organisms with Time-Shared Optical Tweezer Microrheology
<p>Source data for Nature Nanotechnology, <span>DOI: 10.1038/s41565-024-01830-y</span></p>
Data From: Oatk - a de novo assembly tool for complex plant organelle genomes
<p>This reposity hosts the data for 195 plant organelle genome assemblies generated in the manuscript "Oatk: a de novo assembly tool for complex plant organelle genomes". The sequence data were produced by the Tree of Life programme at the Sanger Institute, mostly from the Darwin Tree of Life (DToL) project, including 24 monocots, 154 eudicots, 16 mosses and one liverwort. See SAMPLE_LIST file for descriptions of these species.</p> <p>In each species subfolder, below files are included.</p> <ol> <li><code>PLTD.fasta</code> Plastome assembly file in FASTA format</li> <li><code>PLTD.annot.bed</code> Plastome assembly annotation file in BED format</li> <li><code>MITO.fasta</code> Mitogenome assembly file in FASTA format</li> <li><code>MITO.annot.bed</code> Mitogenome assembly annotation file in BED format</li> <li><code>MBG.gfa</code> Genome assembly file in GFA format generated with MBG</li> <li><code>PMAT.gfa</code> Genome assembly file in GFA format generated with OATK</li> <li><code>OATK.gfa</code> Genome assembly file in GFA format generated with PMAT (may not exist)</li> </ol> <p> </p> <p>Updates in the New Version:</p> <p>In the previous version, our raw PacBio HiFi read pre-processing pipeline had screened out some reads that it erroneously thought contained HiFi adapter sequence, which led to the gaps in the Hibiscus plastomes. We now fixed this and have rerun all the assemblies that led to any linear organelle components (37 species). All plastomes remain unchanged except for the three Hibiscuses, which are now also circular. Thirteen mitogenomes changed, with six of them now becoming circular.</p>
Genotype data of 1970 Pedunculate oak trees (Quercus robur L.) in 13 European countries at 381 gene loci covering the nuclear and organelle genome
<p>The data set is the result of genetic inventory on 1970 Pedunculate oak trees from 197 locations in Europe. The samples are from the countries: Belarus, Bosnia, Bulgaria, Croatia, Finland, France, Germany, Hungary, Italy, Latvia, Poland, Russia and Ukraine. At each location ten individual trees were collected. The data set includes the location ID and geographic coordinates of each sampled tree (longitude and latitude in decimal degrees) and the genotype data. All samples were screened with a targeted sequencing approach on a set of 381 polymorphic loci (356 nuclear SNPs, 3 nuclear InDels, 17 chloroplast SNPs and five mitochondrial SNPs).</p> <p>The genotype of each individual is one row in the csv-file "genotypes". The genotypes at the nuclear markers are diploid and represented by two columns per gene marker. The genetic information at the organelle genome is haploid. For each of these gene markers one column is used. Genotypes are coded by Arabic numbers. The meaning of the numbers is explained in the table "coding genotypes" in a second csv-file.</p>
Genotype data of 970 Pedunculate oak trees (Quercus robur L.) in Russia and neighbouring countries at 385 gene loci covering the nuclear and organelle genome
<p>The data set is the result of genetic inventory on 970 Pedunculate oak trees from 97 locations in Russia, Belarus, Ukraine and Latvia representing the eastern part of the natural species range. The data set includes the sample IDs, geographic coordinates of each sampled tree (longitude and latitude in decimal degrees) and the genotype data. All samples were screened with a set of 385 gene markers covering the nuclear (n=364) and the organelle genomes (chloroplast: n=14, mitochondria: n=7) by use of a genotyping by sequencing approach. With exception of three INDELs (Insert and Deletion mutations) all gene markers are SNPs (Single nucleotide Polymorphism).</p>
Supplementary information, datasets and fluorescence images related to the article "The role of NSP6 in the biogenesis of the SARS-CoV-2 replication organelle"
<p>Supplementary information, datasets and fluorescence images related to the article "The role of NSP6 in the biogenesis of the SARS-CoV-2 replication organelle".<br> The PDF file entitled "Supplementary material" contains the uncropped original western blots and autoradiographs published in the article.<br> The PDF files entitled "Extended Data Fig.2,6,7,9,10 all panels" and "Figure 1,4 all panels" contain the original full-size confocal immunofluorescence images from which specific ROIs are published in the article.<br> The Excel files "Source Data Principal Figures" and "Source Data Extended Figures" contain all the original datasets used for calculation and graphical representation of data published in the article.</p>
Data from: Environmental DNA particle size distribution and quantity differ across taxa and organelles
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Dataset: Comparative analysis of the coordinated motion of Hsp70s from different organelles observed by single molecule three-color FRET
<p>Dataset: Comparative analysis of the coordinated motion of Hsp70s from different organelles observed by single molecule three-color FRET</p>
Data for: Photosystems in the eye-like organelles of heterotrophic warnowiid dinoflagellates
<p><span>The function of the eye-like ocelloid in warnowiid dinoflagellates remains unknown because warnowiids are rare and uncultured, and scant molecular data exists for this group. While the ocelloid resembles the camera-type eyes found in animals, it is composed of organelles, with a highly modified plastid serving as the retinal body. By performing single-cell transcriptomics on cells isolated from the environment, we generate a comprehensive molecular dataset of warnowiids that includes all four known genera and one previously undescribed genus. We show that the heterotrophic members of this group have retained and express components of the ancestral photosynthetic mechanism, seemingly without photosystem II and RuBisCo. Our findings suggest that the ocelloid retinal body has repurposed the remaining photosystem to perform a function other than photosynthesis.</span></p>
Genotype data of 970 Pedunculate oak trees (Quercus robur L.) in Russia and neighbouring countries at 385 gene loci covering the nuclear and organelle genome
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Data from: Connections across open water: A bi-organelle, genomics-scale assessment of Atlantic-wide population dynamics in a pelagic, endangered apex predator shark (Isurus oxyrinchus)
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.