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1,411 results for “Cell States”
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary:</strong> Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p><strong>Data: </strong></p> <p>MDAlab_cDC1_maturation.tar : Docker image used for the analysis</p>
Extended zenodo data figures for, "An atlas of healthy and injured cell states and niches in the human kidney"
<p>This repository includes extended data zenodo figures for the manuscript, "An atlas of healthy and injured cell states and niches in the human kidney." Figures and figure legends are included in the included pdf file.</p>
Supporting data and analysis for, "An atlas of healthy and injured cell states and niches in the human kidney"
<p>Online repository for contents associated with kidney cell state atlas: </p> <p>This combines the image datasets used for the manuscript describing an approach for the integrated tissue cytometry analysis of mesoscale confocal imaging datasets.</p> <p>Linked deposits:</p> <table> <tbody> <tr> <td>Zenodo Extended Data figures</td> <td>10.5281/zenodo.7120908</td> </tr> <tr> <td>3D Cytometry and neighborhood analysis</td> <td>10.5281/zenodo.7120941</td> </tr> <tr> <td>Github repository, "Cell-State-Atlas_2022"</td> <td>https://github.com/KPMP/Cell-State-Atlas-2022</td> </tr> </tbody> </table> <p> </p> <p> </p>
Dataset: Phytoplankton Cell-States: multiparameter lifetime flow monitoring reveals cellular heterogeneity
<h1>Dataset for Multiparameter-based photosynthetic state transitions of single phytoplankton cells data.</h1> <h2>Creating environment</h2> <p>yaml and spec file are provided of the conda environment used. Most packages are typical, with the key exception of fretbursts</p> <p>A custom/development version of fretbursts was used, which contains support for more flexible photon selection (Ph_sel) objects, necessary for the larger parameter space. This version is in the polarization branch of harripd fork of fretbursts. The simple route to install is </p> <p><code>pip install git+https://https://github.com/harripd/FRETBursts@polarization</code></p> <p>however, if you want to ensure the exact same version is installed use:</p> <p><code>pip install git+https://github.com/harripd/FRETBursts@12a5168</code></p> <h2>Data organization</h2> <p>All zip files are meant to be unziped into the same destination folder (ie if a file is in a folder in the zip file, it should be in a subfolder inside the shared highest level folder, but if two files inside different zip folders are in the root of their respective zip files, they should be places in the same folder when all files are unzipped.</p> <p>After unzipping everything your folder for this article should contain the following items (normal text indicates file, <strong>bold text indicates folder</strong>).</p> <ol> <li>Flow_PhytoCellStates_figures.ipynb</li> <li><strong>mpAlgae</strong></li> <li><strong>figures</strong></li> <li>20230905_cyanoWH8102_diurnal</li> <li>20230707_cyanolightjump</li> <li>20230609_ppurp</li> <li>20230724_redalgaelightjump</li> <li>20231211_pmicans_6umol_diurnal</li> <li><strong>20231217_pmicans_6umol_lightjump</strong></li> <li><strong>20231019_WH8102_imaging</strong></li> <li><strong>bulk_cyano_lightjump</strong></li> <li>synWH8102_di_REPO_20230905.hdf5</li> <li>synWH8102_di_REPO_20230905.csv</li> <li>synWH8102_lj_REPO_20230707.hdf5</li> <li>synWH8102_lj_REPO_20230707.csv</li> <li>ppurp_di_REPO_20230609.hdf5</li> <li>ppurp_di_REPO_20230609.csv</li> <li>ppurp_lj_REPO_20230724.hdf5</li> <li>ppurp_lj_REPO_20230724.csv</li> <li>pmicans_di_REPO_20231211.hdf5</li> <li>pmicans_di_REPO_20231211.csv</li> <li>ppurp_lj_REPO_20230724.hdf5</li> <li>ppurp_lj_REPO_20230724.csv</li> </ol> <p>Flow_API_all.ipynb (item 1) is the jupyter notebook that analyses the data and produces the figures. It relies on having mpAlgae (item 2) in the same folder, as it contains the underlying code that adapts FRETBurts to work with the cell based data, adding functionality to combine multiple data files into one experiment, dynamic gating of data, storing calculated values to disk, which also reduces the memory requirements of the system, as well as a number of advanced plotting functions. Flow_API_all.ipynb also expects a figures folder (item 3) to exist so that it can save the produced figures. It also expects items 4-9 to exist, as those folder names are recorded as where the different raw data sets are stored. Even numbered items 12-22 (all items 12-23 that are hdf5 files) are produced by Flow_API_all.ipynb, and are where processed values of burst search are stored. the mpAlgae api makes it so that if these files are present, burst search is skipped, and instead loaded from these result files. Thus if you desire to examine the data, you only need the top level hdf5 files. These files also save results of histogram fittings, so that results can be loaded instead of recalculatd. </p> <p>The csv files are export of calculated parameters to csv format. Note that these are ungated, and thus proper gating must be implemented if plots are to be recapitulated from these files, this is done so that alternative gating strategies can also be explored.</p> <p> </p>
Identifying cell states in single-cell RNA-seq data at statistically maximal resolution
<p>In this repository we provide the datasets for the results of the Cellstates method as shown in the article: “Identifying cell states in single-cell RNA-seq data at statistically maximal resolution” by Pascal Grobecker, Thomas Sakoparnig, and Erik van Nimwegen.</p> <p>A preprint is available under the following DOI: https://doi.org/10.1101/2023.10.31.564980</p> <p>There is a README file that describes the contents and formats of each of the tab-separated values files. There is one subdirectory containing files with Cellstates' results on the dataset of Zeisel et al. (DOI: 10.1016/j.cell.2018.06.021) which is accompanied by another README file describing the formats and contents of these files. </p>
Characterizing multidimensional cellular physiological states with quantitative three-dimensional shape descriptors for cell membranes
<h3>Supplementary dataset and code for the article "<em>Characterizing Cellular Physiological States with Three-Dimensional Shape Descriptors for Cell Membranes</em>"</h3> <p>CShaper Dataset.zip: The 3D cell regions reused from the previously published article <a href="https://doi.org/10.1038/s41467-020-19863-x">https://doi.org/10.1038/s41467-020-19863-x</a>.</p> <p>Cell Shape Descriptors - Code & Data.zip: The code (exemplifed by embryo Sample04) and data (including embryo Sample04-Sample20) of 12 3D shape descriptors for all 3D cell regions in the <em>CShaper</em> dataset.</p> <p>GUI.zip: The user-friendly software <em>Shape Descriptor Tool</em> is a Graphical User Interface based on <em>Matlab</em> for calculating 12 shape descriptors for a 3D cell region (exemplified by embryo Sample20, time point 14, ABpl cell in the <em>CShaper</em> dataset). The instruction guidebook is included in the Supplementary Material of the article.</p>
Unveiling the Mechanisms of Solid-State Dewetting in Solid Oxide Cells with Novel 2D Electrodes
<p>Supporting videos as a part of the Supplementary Material of the paper published by the Journal of Power Sources, Volume 420, 30 April 2019, Pages 124-133 doi: 10.1016/j.jpowsour.2019.02.068</p>
Machine readable code lists for an algorithm to identify incident non-small cell lung cancer (NSCLC) in United States healthcare claims data
<p>Machine readable code lists for an algorithm to identify incident non-small cell lung cancer (NSCLC) in United States healthcare claims data</p>
Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration
<p>Skeletal muscle repair is driven by the coordinated self-renewal and fusion of myogenic stem and progenitor cells. Single-cell gene expression analyses of myogenesis have been hampered by the poor sampling of rare and transient cell states that are critical for muscle repair, and do not inform the spatial context that is important for myogenic differentiation. Here, we demonstrate how large-scale integration of single-cell and spatial transcriptomic data can overcome these limitations. We created a single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 88 publicly available single-cell (scRNAseq) and single-nucleus (snRNAseq) RNA-sequencing datasets. The resulting dataset includes more than 365,000 cells and spans a wide range of ages, injury, and repair conditions. Together, these data enabled identification of the predominant cell types in skeletal muscle, and resolved cell subtypes, including endothelial subtypes distinguished by vessel-type of origin, fibro/adipogenic progenitors defined by functional roles, and many distinct immune populations. The representation of different experimental conditions and the depth of transcriptome coverage enabled robust profiling of sparsely expressed genes. We built a densely sampled transcriptomic model of myogenesis, from stem cell quiescence to myofiber maturation and identified rare, transitional states of progenitor commitment and fusion that are poorly represented in individual datasets. We performed spatial RNA sequencing of mouse muscle at three time points after injury and used the integrated dataset as a reference to achieve a high-resolution, local deconvolution of cell subtypes. We also used the integrated dataset to explore ligand-receptor co-expression patterns and identify dynamic cell-cell interactions in muscle injury response. We provide a public web tool to enable interactive exploration and visualization of the data. Our work supports the utility of large-scale integration of single-cell transcriptomic data as a tool for biological discovery.</p>
Frozen feather and motif files for Distinct stage-specific transcriptional states of B cells derived from human tonsillar tissue.
<p>Frozen feather and motif files for manuscript "Distinct stage-specific transcriptional states of B cells derived from human tonsillar tissue" by Espinoza DA, et al. Data was downloaded from cisTarget database from aertslab.org.</p>
Cell tracking data from: Automated timelapse data segmentation reveals in vivo cell state dynamics
<p>Embryonic development proceeds as a series of orderly cell state transitions built upon noisy molecular processes. Here, we defined gene expression and cell motion states using single cell RNA sequencing data and in vivo timelapse cell tracking data of the zebrafish tailbud. We performed a parallel identification of these states using dimensional reduction methods and a change point detection algorithm. Both types of cell states were quantitatively mapped onto embryos, and we utilized the cell motion states to study the dynamics of biological state transitions over time. The time average pattern of cell motion states is reproducible among embryos. However, individual embryos exhibit transient deviations from the time average forming left-right asymmetries in collective cell motion. Thus, the reproducible pattern of cell states and bilateral symmetry arises from temporal averaging. In addition, collective cell behavior can be a source of asymmetry rather than a buffer against noisy individual cell behavior.</p>
Single-cell chromatin accessibility profiling reveals a self-renewing muscle satellite cell state
<p><span class="TextRun SCXW101911863 BCX0"><span class="NormalTextRun SCXW101911863 BCX0">A balance between self-renewal and differentiation is critical for the regenerative capacity of tissue-resident stem cells. In skeletal muscle, successful regeneration requires the orchestrated activation, proliferation, and differentiation of muscle satellite cells (</span><span class="SpellingError SCXW101911863 BCX0">MuSCs</span><span class="NormalTextRun SCXW101911863 BCX0">) that are normally quiescent. A subset of </span><span class="SpellingError SCXW101911863 BCX0">MuSCs</span><span class="NormalTextRun SCXW101911863 BCX0"> undergoes self-renewal to replenish the stem cell pool, but the features that identify and define self-renewing </span><span class="SpellingError SCXW101911863 BCX0">MuSCs</span><span class="NormalTextRun SCXW101911863 BCX0"> remain to be elucidated. Here, through single-cell chromatin accessibility analysis, we reveal the self-renewal versus differentiation trajectories of </span><span class="SpellingError SCXW101911863 BCX0">MuSCs</span><span class="NormalTextRun SCXW101911863 BCX0"> over the course of regeneration in vivo. We identify </span><span class="SpellingError SCXW101911863 BCX0">Betaglycan</span><span class="NormalTextRun SCXW101911863 BCX0"> as a unique marker of self-renewing </span><span class="SpellingError SCXW101911863 BCX0">MuSCs</span><span class="NormalTextRun SCXW101911863 BCX0"> that can be purified and efficiently contributes to regeneration after transplantation. We also show that SMAD4 and downstream genes are genetically required for self-renewal </span></span><span class="TextRun SCXW101911863 BCX0"><span class="NormalTextRun SCXW101911863 BCX0">in vivo</span></span><span class="TextRun SCXW101911863 BCX0"><span class="NormalTextRun SCXW101911863 BCX0"> by restricting differentiation. Our study unveils the identity and mechanisms of self-renewing </span><span class="SpellingError SCXW101911863 BCX0">MuSCs</span><span class="NormalTextRun SCXW101911863 BCX0"> while providing a key resource for comprehensive analysis of muscle regeneration.</span></span></p>
Atomistic, Macromolecular Model of the Populus Secondary Cell Wall Informed by Solid-State NMR
<p>Solid-state NMR dataset used to inform molecular model of the Populus secondary cell wall.</p> <p>Contains:</p> <p>- 2D through-bond and through-space ssNMR data on 13C-enriched Populus wood. Data used to inform spectral deconvolution of selective 1D spin-diffusion data</p> <p>- 2D gelHSQC NMR data for understanding of lignin composition</p> <p>- MultiCP-1D-DARR datasets on 5 replicates. Includes raw NMR data and ascii files of processed spectra. For each replicate, three experiments are conducted: non-selective 1D MultiCP-DARR, 22ppm selective MultiCP-DARR, and 150ppm selective MultiCP-DARR. 13C-13C spin-diffusion mixing times ranged from 0.001 to 5000 ms.</p> <p>- Excel files containing tabulated deconvoluted signal areas, and averages for key signal groups.</p> <p>- Excel files containing tabulated magnetization recovery values for each signal and signal groups for all replicates. Averaged values for X2C, X2L, L2C and L2X at the longest mixing times (see manuscript) are used as key metrics for evaluating molecular models.</p> <p>- Excel files containing tabulated T1-adjusted spin-diffusion rate constants for each replicate, and key averages.</p>
Safety and Feasibility Study of Combination of State of Art Chemoimmunotherapy, Intensive Central Nervous System Prophylaxis and Scrotal Irradiation to Treat Primary Diffuse Large B-cell Lymphoma of T
ClinicalTrials.gov study NCT00945724. IPD Sharing: Not stated. Countries: 2. Publications: 1.
A Dose Escalation Study of Intravenous L-citrulline in Steady-state Sickle Cell Disease
ClinicalTrials.gov study NCT02314689. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Stochastic cell-cycle entry and cell-state-dependent fate outputs of injury-reactivated tectal radial glia in zebrafish
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Dysregulation of cell state dynamics during early stages of serous endometrial carcinogenesis
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Local nuclear to cytoplasmic ratio regulates H3.3 incorporation via cell cycle state during zygotic genome activation
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Cell tracking data from: Automated timelapse data segmentation reveals in vivo cell state dynamics
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Single-cell chromatin accessibility profiling reveals a self-renewing muscle satellite cell state
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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.