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1,237 results for “cell characterization”
Development of a high-throughput small molecule screening assay for phenotypical characterization of lysosomal storage disorder-affected cells, with infantile cystinosis as a proof of principle
<p>Together with the Pivot Park Screening Centre we performed a drug screen on CTNS-/- proximal tubule cells. For this we developed an assay to evaluate LC3-II positive puncta, and which may be applied for any disease in which autophagy plays an important role. The screen was optimized by the hotel for a 384 well format, making it useful for high throughput screening. The screen was performed with 1280 compounds from the Prestwick library.</p>
Global Characterization of Megakaryocytes in Bone Marrow, Peripheral Blood, and Cord Blood by Single-cell RNA Sequencing
<p><span><span>Megakaryocytes (MK) are mainly derived from bone marrow (BM) and are mainly involved in platelet production. Recent studies have shown that MK derived from BM may have immune function, and that MK from peripheral blood (PB) are associated with prostate cancer. We analyzed more than 1.2 million single-cell transcriptome data from 132 samples of PB, BM, and cord blood (CB) from healthy individuals and patients, and obtained 4474 MK single cell and 14 MK subtypes. We found that MK were widely distributed and the amount of MK in PB was more than that in BM and there were specificity MK subtypes in PB. We found classical MK1 with typical MK characteristics and non-classical MK2 closely related to immunity which was the most common subtype in BM and CB. Classical MK1 was closely related to Non-Small Cell Lung Cancer (NSCLC) and has diagnostic ability. MK2 may have potential adaptive immune function and play a role in tumor NSCLC and autoimmune diseases Systemic Lupus Erythematosus. This study deepened our understanding of MK and suggested that MK had potential immune functions and was involved in various diseases.</span></span></p>
Characterization of photorecruitable Guanine Exchange Factors for the study of Rho GTPases in endothelial cells
<p>Time lapses of respective figures belonging to the report titled "<strong>Characterization of photorecruitable Guanine Exchange Factors for the study of Rho GTPases in endothelial cells"</strong>. This report was written as part of the Msc programe in Biomedical Science of the University of Amsterdam. </p>
Multiplexed single-cell characterization of alternative polyadenylation regulators (HEK293FT & K562 Perturb-seq data)
<p>This site provides access to datasets from the CPA-Perturb-seq <a href="https://www.biorxiv.org/content/10.1101/2023.02.09.527751v1">manuscript</a> Kowalski*, Wessels*, Linder* et al., including processed Perturb-seq datasets from HEK293FT and K562. We release these data as Seurat objects, where each object contains single-cell quantifications of gene expression (RNA assay), and in addition, quantifications of polyA site usage (polyA site assay). To explore these data, please install the <a href="https://github.com/satijalab/PASTA">PASTA</a> (PolyA Site analysis using relative Transcript Abundance) package, which provides infrastructure and analytical tools to explore alternative polyadenylation at single-cell resolution. For each dataset, we also include a fragment file which enables visualization of read coverage plots across groups of cells. </p> <p>The files include:</p> <p>1. CPA_K562.Rds : Seurat object containing the K562 CPA-Perturb-seq dataset </p> <p>2. CPA_K562_fragments.tsv.gz : Fragment file for the K562 dataset </p> <p>3. CPA_K562_fragments.tsv.gz.tbi : Fragment file index for the K562 dataset </p> <p> </p> <p>R code below:</p> <pre><code>library(PASTA) k562 <- readRDS("CPA_K562.Rds") # Add fragments for plotting Fragments(k562) <- CreateFragmentObject(path = "download/CPA_K562_blocks.tsv.gz", cells = Cells(k562)) # visualize polyA site usage PolyACoveragePlot(k562, region ="chr7-26212195-26213351")</code></pre>
ChromoPhyloGen: characterizing copy number alteration patterns in heterogeneous tumor cell populations at Single-Cell Resolution
<p>The human liver cancer cell line Huh7 was obtained from the American Type Culture Collection (ATCC). Huh7 cells were cultivated in Dulbecco's Modified Eagle Medium (DMEM, Gibco, C11995), supplemented with 1% penicillin/streptomycin (Gibco, 15140122), and 10% fetal bovine serum (FBS, Excell, FSP500). Huh7 cell line was maintained under a 95% O2 and 5% CO2 humidified atmosphere in an incubator at 37˚C. </p> <p>The scDNA-seq library was performed using the Chromium Single cell DNA Library & Gel Bead kit (10x Genomics, PN1000040) in combination with the Chromium instrument. The samples were processed on Chromium Single cell Chip C and D (10x Genomics, 1000022 and 1000042, respectively) according to the manufacturer's user guide and subsequently run on a thermocycler. The barcoded libraries were sequenced using the Novaseq 6000 300 cycle high-output flow cells.</p> <p>The scRNA-seq library was generated using the 10x Genomics Chromium Single Cell 3' & Gel Bead Kit v3 (10x Genomics, PN100075) in combination with the Chromium instrument. The samples were processed on Chromium Single cell Chip B (10x Genomics,1000154) according to the manufacturer's protocol and subsequently run on a thermocycler. The 3' gene expression libraries were sequenced using the Novaseq 6000 300 cycle high-output flow cells.</p> <p> </p>
A multiomic characterization of the leukemia cell line REH using short- and long-read sequencing
<p>This is a public repository containing secondary datasets described in the publication <a href="https://doi.org/10.26508/lsa.202302481">"A multiomic characterization of the leukemia cell line REH using short- and long-read sequencing"</a>. Primary data for this project are available at NCBI/SRA under the BioProject accession numbers PRJNA600820 and PRJNA834955, and include the following sequencing datasets:</p> <p>REH cell line:</p> <ul> <li>PacBio WGS</li> <li>ONT Ultralong WGS</li> <li>Illumina short-read PCR-free WGS</li> <li>IsoSeq RNA-seq</li> <li>Illumina short-read RNA-seq</li> </ul> <p>GM12878 cell line:</p> <ul> <li>Illumina short-read RNA-seq</li> </ul> <p>This dataset includes the following files:</p> <p><strong>Depth of Coverage analysis</strong></p> <ul> <li>Output from `samtools coverage`: <em>samtools.coverage.illumina.txt, samtools.coverage.ont.txt, samtools.coverage.pb.txt</em></li> <li>Output from `copycat` (binned coverage): <em>copycat.ont.coverage.10kb.csv, copycat.pb.coverage.10kb.csv, copycat.pcrfree.coverage.10kb.csv</em></li> </ul> <p><strong>Structural Variant (SV) callsets</strong></p> <ul> <li><strong>Raw:</strong> <em>illumina.tiddit.vcf, ont.sniffles.vcf, pb.sniffles.vcf</em></li> <li><strong>Filtered: </strong><em>REH.svs.filtered.csv</em></li> </ul> <p><strong>SNV callsets</strong></p> <ul> <li><strong>Filtered and annotated:</strong> <em>REH.mutect.filtered.ann.vcf.gz</em></li> </ul> <p><strong>Fusion gene callsets</strong></p> <ul> <li><strong>Short-read: </strong><em>GM12878.fusionreport.txt, illumina.all.txt, illumina.filtered.csv, REH.arriba.fusions.tsv, REH.fusioncatcher.fusion-genes.txt, REH.pizzly.txt, REH.squid.fusions.annotated.txt, REH.starfusion.abridged.tsv, REH.pdf</em></li> <li><strong>Long-read: </strong><em>cupcake.long.csv, cupcake.std.csv, jaffa_results.csv</em></li> <li><strong>Filtered: </strong><em>REH.fusions.filtered.csv</em><br> </li> </ul> <p> </p>
Results of the mechanical characterization by tensile tests and the in vitro cell-biomaterial interaction analyses by WST-1 and Live/Dead of 3D-printed scaffolds generated by PLA, PCL, FF, FD and GelMA
<p>Dataset containing the quantitative results of the mechanical characterization, and the in vitro cell-biomaterial interaction analyses with neural cells after 72 hours and 7 days of cell culture of the following 3D printed scaffolds:</p> <ul> <li>Polylactic acid (PLA)</li> <li>Polycaprolactone (PCL)</li> <li>Conductive Filaflex (FF)</li> <li>Flexdym (FD)</li> <li>GelMA (G)</li> </ul> <p>The Live/Dead results were quantified using ImageJ software to determine area fractions corresponding to live (green) and dead (red) cells.</p>
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>1. Table1_full_version.docx : Marker genes for cell clusters of global Seurat analysis<br> 2. Table2_full_version.docx : List of the Immgen samples used to generate the reference compendium for CMAP signature generation<br> 3. Table5_full_version.docx : Top 20 marker genes for cell clusters of the Seurat analysis on selected cDC1s</p> <p> </p> <p> </p>
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> </p> <p><strong>Data:</strong></p> <p>1. Immgen_cell_types.cls : Microarray Phase 1 expression</p> <p>2. Immgen_norm_exp_data.gct : Microarray Phase 1 class</p> <p> </p>
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>cDC1_maturation_loom_file.rds : Loom file used for RNA Velocity Analysis</p> <p><br> </p> <p> </p> <p> </p>
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>
Investigation on anodes from anode supported cells manufactured by Solidpower and aged by IEES-BAS with electrochemical characterization.
<p>samples are mounted in epoxy-resin and polished up to 250nm using diamond paste on specific tissue (MD-NAP, Struers). Observation by SEM (ZEISS EVO 40) using backscattered electrons as the contrast method. Chemical analysis performed using EDS after calibration with Co standard and using ZAF algorithm correction. The standard magnification is 5000. The pictures are further threated using ImageJ software for quantitative image analysis. Porosity and Nickel particles are highlighted by using Coreldraw software image enhancements.</p>
Transcriptomic characterization of 2D and 3D human induced pluripotent stem cell-based in vitro models as New Approach Methodologies for developmental neurotoxicity testing
<p><strong>Abstract:</strong> The safety and developmental neurotoxicity (DNT) potential of chemicals remain critically understudied due to limitations of current in vivo testing guidelines, which are low throughput, resource-intensive, and hindered by species differences that limit their relevance to human health. To address these issues, robust new approach methodologies (NAMs) using deeply characterized cell models are essential. This study presents the comprehensive transcriptomic characterization of two advanced human-induced pluripotent stem cell (hiPSC)-derived models: a 2D adherent and a 3D neurosphere model of human neural progenitor cells (hiNPCs) differentiated up to 21 days. Using high-throughput RNA sequencing, we compared gene expression profiles of 2D and 3D models at three developmental stages (3, 14, and 21 days of differentiation). Both models exhibit maturation towards post-mitotic neurons, with the 3D model maturing faster and showing a higher prevalence of GABAergic neurons, while the 2D model is enriched with glutamatergic neurons. Both models demonstrate broad applicability domains, including excitatory and inhibitory neurons, astrocytes, and key endocrine and especially the understudied cholinergic receptors. Comparison with human fetal brain samples confirms their physiological relevance. This study provides novel in-depth applicability insights into the temporal and dimensional aspects of hiPSC-derived neural models for DNT testing. The complementary use of these two models is highlighted: the 2D model excels in synaptogenesis assessment, while the 3D model is particularly suited for neural network formation as observed as well in previous functional studies with these models. This research marks a significant advancement in developing human-relevant, high-throughput DNT assays for regulatory purposes.</p> <p><strong>This data sets contains:</strong></p> <p><strong>Tab. S1</strong> - Significant genes results</p> <p><strong>Tab. S2</strong> - Enriched pathways_GO_Biological Processes</p> <p><strong>Tab. S3</strong> - Enriched pathways_GO_Cellular Components</p> <p><strong>Tab. S4</strong> - Enriched pathways_GO_Molecular Function</p> <p><strong>Tab. S5</strong> - Enriched pathways_KEGG</p> <p><strong>Tab. S6</strong> - EnrichEnriched pathways_Panther</p> <p><strong>Tab. S7</strong> - Enriched pathways_Reactome</p> <p><strong>Tab. S8</strong> - Gene counts</p> <p><strong>Tab. S9</strong> - Gene selection for targeted analysis</p>
CXCL12-loaded-hydrogel (CLG): a new device for metastatic Circulating Tumor Cells (CTCs) capturing and characterization
<p><strong>Background</strong>: Circulating Tumor Cells (CTCs) represent a small, heterogeneous population that comprise the minority of cells able to develop metastasis. To trap and characterize CTCs with metastatic attitude, a CXCL12-loaded hyaluronic-gel (CLG) was developed. CXCR4+cells with invasive capability would infiltrate CLG.<br><strong>Methods</strong>: Human colon, renal, lung and ovarian cancer cells (HT29, A498, H460 and OVCAR8 respectively) were seeded on 150 µl Empty Gels (EG) or 300 ng/ml CXCL12 loaded gel (CLG) and allowed to infiltrate for 16 hours. Gels were then digested and fixed with 2% FA-HAse for human cancer cell enumeration or digested with HAse and cancer cells recovered. CLG-recovered cells migrated toward CXCL12 and were tested for colonies/spheres formation. Moreover, CXCR4, E-Cadherin and Vimentin expression was assessed through flow cytometry and RT-PCR. The clinical trial “TRAP4MET” recruited 48 metastatic/advanced cancer patients (8 OC, 8 LC, 8 GBM, 8 EC, 8 RCC and 8 EC). 10 cc whole blood were devoted to PBMCs extraction (7cc) and ScreenCell™ filters (3cc) CTCs evaluation. Ficoll-isolated patient’s PBMCs were seeded over CLG and allowed to infiltrate for 16 hours; gels were digested and fixed with 2% FA-HAse, cells stained and DAPI+/CD45-/pan-CK+ cells enumerated as CTCs.<br><strong>Results</strong>: Human cancer cells infiltrate CLG more efficiently than EG (CLG/EG ratio 1.25 for HT29/ 1.58 for A498/ 1.71 for H460 and 2.83 for OVCAR8). CLG- recovered HT29 cells display hybrid-mesenchymal features [low E-cadherin (40%) and high vimentin (235%) as compared to HT29], CXCR4 two-fold higher than HT29, efficiently migrate toward CXCL12 (two-fold higher than HT29) and developed higher number of colonies (171±21 for HT29-CLG vs 131±8 colonies for HT29) /larger spheres (spheroid area: 26561±6142 µm2 for HT29-CLG vs 20297±7238 for HT29). In TRAP4MET clinical trial, CLG-CTCs were isolated in 8/8 patients with OC, 6/8 with LC, 6/8 with CRC, 8/8 with EC, 8/8 with RCC cancer and 5/8 with GBM. Interestingly, in OC, LC and GBM, CLG isolated higher number of CTCs as compared to the conventional ScreenCell™ (CLG/SC ratio=1.88 for OC, 2.47 for LC and 11.89 for GBM). Bland and Altman blot analysis and Passing and Bablok regression analysis showed concordance between the methodological approaches but indicate that SC and CLG are not superimposable suggesting that the two systems select cells with different features.<br><strong>Conclusion</strong>: CLG might represent a new and easy tool to isolate invasive CTCs in multiple cancers such as OC, LC and GBM at today orphan of reliable methods to consistently detect CTCs. </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>
A quantitative approach to easily characterize and predict cell death and escape to β-lactam treatments
<p>This repository contains the raw experimental data corresponding to the paper entitled "A quantitative approach to easily characterize and predict cell death and escape to β-lactam treatments" and authored by Andreani, Gross and colleagues. </p> <p>The analysis of the data and the creation of the figures of the paper can be done using the code available at https://gitlab.inria.fr/InBio/Public/esbl-escape. A README file, briefly describing data organization, is provided.</p>
Flow Cytometry Data from "Bacterial cell surface characterization by phage display coupled to high-throughput sequencing"
<p>This record contains the flow cytometry data from the manuscript "Bacterial cell surface characterization by phage display coupled to high-throughput sequencing."</p> <p>Files are in <a href="https://docs.flowjo.com/flowjo/advanced-features/fj-acs/">Archive Cytometry Standard (ACS) format</a> . Each <code>.acs</code> file is a zip container which holds both the raw <code>.fcs</code> files and a FlowJo workspace (<code>.wsp</code>) file.</p> <p>Keywords in the workspace file identify which primary antibody (<code>primary</code>) was used and which cell genotype (<code>strain</code>) was used for each sample. The workspace also encodes the gating scheme and compensation matrix applied to each sample. Plots in the manuscript are exported from Layout views in the workspace.</p>
Data and code for "Cell shape characterization, alignment and comparison using FlowShape"
<p><strong>Code</strong></p> <p>This folder contains a snapshot of the FlowShape Python package at the time of paper submission. The latest version is available at: <a href="https://bitbucket.org/pgmsembryogenesis/flowshape/src/main/">https://bitbucket.org/pgmsembryogenesis/flowshape/src/main/</a></p> <p><strong>Data</strong></p> <p>Contain 3D meshes of <em>C. elegans</em> early embryo cells, in Wavefront .obj format. Each filename has three parts sepparated by underscores</p> <ul> <li>Embryo identifier</li> <li>Cell name</li> <li>Timestep For example: <code>wt13_ABar_18</code> Embryo = wt13 Cell = ABar Timestep = 18</li> </ul> <p>The timesteps are frames from the original timelapse, where one frame corresponds to 90 seconds. The starting point is arbitrary, so time is aligned by EMS division.</p> <p>Embryo labels:</p> <ul> <li><code>7cell01-7cell07</code>: 7 wild-type embryos imaged only around the seven-cell stage.</li> <li><code>wt01-wt19</code>: 19 wild-type embryos imaged for a longer time.</li> <li><code>dshmig01-dshmig05</code>: 5 dsh-2 / mig-5 RNAi knockdown embryos, imaged around seven-cell stage.</li> </ul> <p><code>cell_filters.csv</code> is a CSV file containing identifiers of embryos left out of the analysis because of automatically detected errors. Further, wt06 was also left out because of problems with labeling the cells.</p> <p><strong>Images</strong></p> <p>Contains raw microscopy data that was used to generate the meshes. <code>resolution.csv</code> contains a list of Z, Y and X resolutions (micron / pixel).</p> <p><strong>Changelog</strong></p> <p>0.1.0</p> <ul> <li>Initial upload</li> </ul> <p>0.2.0</p> <ul> <li>Added microscopy images.</li> <li>Removed cells 6 and 7 from dsh-2 / mig-5 dataset, which were not supposed to be included there.</li> <li>Removed Python cache files.</li> </ul>
An Interpretable Framework to Characterize Compound Treatments on Filamentous Fungi using Cell Painting and Deep Metric Learning
<p>This deposit contains:</p> <ul> <li>{train,test,val}.csv: Meta-data files</li> <li>images.tar.gz: Archive of images</li> <li>checkpoint.pth.tar: Model weights</li> <li>code.tar.gz: Code necessary to reproduce our experiments.</li> </ul>
A Study of Combination Amivantamab and Carboplatin-Pemetrexed Therapy, Compared With Carboplatin-Pemetrexed, in Participants With Advanced or Metastatic Non-Small Cell Lung Cancer Characterized by Epi
ClinicalTrials.gov study NCT04538664. IPD Sharing: YES. Countries: 26. Publications: 3.
ScienceDex guides
Understand access before you commit
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.