Find research datasets worth reusing

opencc-by-4.0Oct 2021View details →

Potential Cell Lysis Mechanisms for B. subtilis

Description: A list of potential cell lysis mechanisms for Bacillus subtilis, cured and organized as part of the partnership between iGEM UANL and UNILA teams in the design of a Kill Switch for B. subtilis. For more information on both designs, check out the projects wikis: iGEM UNILA LatAm 2021: https://2021.igem.org/Team:UNILA_LatAm iGEM FCB-UANL 2021: https://2021.igem.org/Team:FCB-UANL Columns: <ul> <li>CRISPR: CRISPR systems developed for B. subtilis <ul> <li>CRISPR Variant: Cas variant used in the system</li> <li>Editing/Disruption Efficiency: System efficiency evaluated for multiple or single editions</li> <li>Mechanisms: operating mechanism</li> <li>Application: Main objective for the developed system</li> <li>B. subtilis considerations: Particularities regarding the development of the system in B. subtilis</li> <li>Year: year of publication</li> </ul> </li> <li>TA Systems: Toxin-Antitoxin systems developed for B. subtilis  <ul> <li>Toxin-Antitoxin: Which antitoxins and toxins involved in the system  </li> <li>Type: TA System Type</li> <li>Mechanism: operating mechanism</li> <li>Application: Main objective for the developed system</li> </ul> </li> <li>Sporulation: Control of sporulation events in B. subtilis  <ul> <li>Spores Control</li> <li>Type</li> <li>Mechanism: operating mechanism</li> <li>Application: Main objective for the developed system</li> </ul> </li> <li> iGEM Applications: Kill Switch designs for B. subtilis since 2014. <ul> <li>Kill Switch System: Design description of Kill switch systems</li> <li>Type: System components</li> <li>Mechanism: operating mechanism</li> <li>Application: Main objective for the developed system</li> <li>Test: Type of assessment performed by the team</li> <li>Parts Registry: Parts submitted to the Registry of Standard Biological Parts.</li> </ul> </li> </ul> Remote Access: <a href="https://docs.google.com/spreadsheets/d/1PM7NS5JbfVLgjsa6s7L8yqBj_G1A5mSnKCPaeGmkIwg/edit#gid=0">Potential Cell Lysis Mechanisms for B. subtilis</a>

opencc-by-4.0Oct 2021View details →

Data set for "Cell type-specific membrane potential changes in dorsolateral striatum accompanying reward-based sensorimotor learning"

Data set for: Sippy T, Chaimowitz C, Crochet S, Petersen CCH (2021) Cell type-specific membrane potential changes in dorsolateral striatum accompanying reward-based sensorimotor learning. FUNCTION 2: zqab049. https://doi.org/10.1093/function/zqab049 There are 2 files in this upload: 1. The file named "2021_Sippy_FUNCTION.pdf" is the Open Access pdf of the online publication in FUNCTION. 2. The file named "Sippy_data_code.zip" (~5 GB) is a zipped version of a folder ‘Sippy_data_code’, which contains the data analyzed in the study along with the Matlab codes used to generate the published figures. To access the data and the codes, first unzip the file, add the folder with subfolders to the Matlab path and run the different codes. The current folder must be the main folder (‘Sippy_data_code’). You first need to run ‘AnalyzeDataStructure.m’ and afterwards you can run the other codes. Each code computes and plots the results used in the corresponding figure. Figures are saved in the subfolder ‘Figures’. The subfolder ‘Data’ contains the data structure ‘Data.mat’ to be analyzed, as well as a Matlab file called ‘p_value_colormap.mat’ used to plot the p value color bars in some figures. The subfolder ‘Functions’ contains functions called by the main codes. The subfolder ‘Codes’ contains the following codes: ‘AnalyzeDataStructure.m’: computes the results and saves them as a new data structure called ‘Analyzed_Data’, in the subfolder ‘Results’. ‘Figure_1.m’: computes and plots the results for the panels D, E and F of Figure 1. ‘Figure_2.m’: computes and plots the results for the panels D-G and I-K of Figure 2. ‘Figure_3.m’: computes and plots the results for the panels A-F of Figure 3. ‘SuppFigure_2.m’: computes and plots the results for the panels B, D and F of Supplementary Figure 2. ‘SuppFigure_3.m’: computes and plots the results for the panels A-D of Supplementary Figure 3. ‘SuppFigure_4.m': computes and plots the results for the panels A-C of Supplementary Figure 4.   The data structures contain the following fields: ‘Mouse_Name’: name of the mouse. ‘Mouse_RecordingDate’: date of recording (YMD). ‘Mouse_DateOfBirth’: date of birth of the mouse (YMD). ‘Mouse_Sex’: sex of the mouse (F or M). ‘Mouse_Genotype’: genotype of the mouse (strain of the two parents): A2A-Cre = Adora2a-Cre mice; D1-Cre = Drd1a-Cre mice; TdTomato = Lox-Stop-Lox-tdTomato mice; D1TdTomato = Drd1a-tdTomato mice; D2GFP = Drd2-GFP mice. ‘Mouse_Level’: Training level (NAÏVE or EXPERT). ‘Cell_Counter’: cell recorded in a given mouse. ‘Cell_Type’: type of the recorded cell (dSPN, iSPN or TAN). ‘Cell_TargetedBrainArea’: Brain area targeted (DLS). ‘Cell_Recovered’: Indicate cells that have been labelled and anatomically recovered (TRUE). ‘Cell_Coordinates’: Cell coordinates (in mm) relative to bregma (Lateral, AP, Ventro-dorsal) ‘Cell_Fluorescence’: expression of the genetically encoded fluorophore (FALSE or TRUE) and fluorophore (TdTomato or GFP). A neuron recorded in a Drd1a-tdTomato x Drd2-GFP (cf Mouse_Genotype) with Cell_Fluorescence= {TRUE, TdTomato} is considered as a dSPN (cf Cell_Type). ‘Sweep_Counter’: number of the sweep recorded for a given neuron (data were acquired across successive continuous sweeps of 30-300 s). ‘Sweep_Type’: experimental condition during that sweep (characterization = electrophysiological identification of the neurons; behavior = behavioral task). ‘Sweep_MembranePotential’: membrane potential recording (mV) after cutting of the APs. ‘Sweep_CurrentInjected’: current injected into the cell (pA). ‘Sweep_PiezoLick’: voltage signal from the piezo sensor attached to the water spout used to detect licking in behavior sweeps. ‘Sweep_Trial’: voltage command triggering the onset of each trial (both Catch and Stimulus trials) in behavior sweeps. ‘Sweep_WhiskerStim’: voltage command triggering the onset of each whisker stimulus in behavior sweeps. ‘Sweep_Valve’: voltage command triggering the opening of the valve delivering the reward in Hit trials. ‘Sweep_SamplingRate’: sampling rate (sample.s-1) of the recorded signals for each sweep. ‘Sweep_TimeStamp’: time at the beginning of the recorded sweep (H/min/s). ‘Sweep_Reward’: voltage command indicating reward availability during the response window following whisker stimulus in behavior sweeps. ‘Sweep_APThresh’: Threshold (V) used to detect action potentials (AP) during current injection.

opencc-by-4.0Oct 2021View details →

TF-Marker: A comprehensive manually curated database for transcription factors and related markers in specific cell and tissue types in human.

Here, we developed the TF-Marker database (TF-Marker, http://bio.liclab.net/TF-Marker/) which is committed to a comprehensive manual curation of TFs and related markers with experimental evidence in specific cell and tissue types in human. Currently, through reviewing 2,091 published literature, we have manually classified TFs and related markers into five types according to their functions: 1) TF: TFs, which regulate the expression of markers; 2) T Marker: markers, which are regulated by TFs (TF and T Marker pairs can identify cell types more specifically); 3) I Marker: markers, which influence the activity of TFs (I Markers can also influence the development of specific cells and tissues); 4) TFMarker: TFs, which play roles as markers (TFMarkers are cell/tissue-specific TFs used as cell markers in biology experiments); and 5) TF Pmarker: TFs, which play roles as potential markers. By curating thousands of published literature, 5,905 entries including 1,316 TFs, 1,092 T Markers, 473 I Markers, 1,600 TFMarkers and 1,424 TF Pmarkers, were annotated in 383 cell types and 95 tissue types in human. Moreover, TF-Marker divided markers into disease markers and tissue/cell-specific markers. TF-Marker is an elaborate database, which provides TFs and related markers supported by experimental evidence. We believe TF-Marker will provide strong support for research into cell/tissue-specific TFs and related markers.

Morphological profiles extracted from Human MCF7 cells – compound-profiling experiment BBBC021.

This contains morphological profiles extracted from Human MCF-7 cells from the compound-profiling experiment BBBC021 hosted at the Broad Bioimage Benchmark Collection. The profiles were extracted using the software SCIP.

Escape from NK cell tumor surveillance by NGFR-induced lipid remodeling in melanoma

Metabolomics Data used in the publication 1) raw files obtained by the FGCZ (<a href="https://fgcz.ch/">Functional Genomics Center Zurich</a>): QCpools (technical replicate of all sample pooled: p2947_o5292_SOP3_DDA5_pos_QCpool_cells Sample of M010817 CMVTOEV cells not induced: p2947_o5292_SOP3_DDA5_pos_EV_cells_noninduced_sample Sample of M010817 CMVTOEV cells induced: p2947_o5292_SOP3_DDA5_pos_EV_cells_induced_sample Sample of M010817 CMVTONGFR cells not induced: p2947_o5292_SOP3_DDA5_pos_p75_cells_noninduced_sample Sample of M010817 CMVTONGFR cells induced: p2947_o5292_SOP3_DDA5_pos_p75_cells_induced_sample 2) method description LC-MS LC-MS-based lipidomic analysis was performed with M010817 CMVTOEV and CMVTONGFR cells pretreated with 1 μg/ml doxycycline for 24 h. Cells were detached with PBS 2 mM EDTA, washed and resuspended in 1-butanol/methanol (1:1). Cells were vortexed for 15 sec and subsequently sonicated on ice with a pulse of 3x 10 sec and 1x 30 sec at an amplitude of 15% using a SONOPULS HD 2070 Ultrasonic Homogenizer (Bandelin). Cell extracts were centrifuged at 16’000 g, 20°C for 10 min to remove macromolecules and subsequently diluted (1:3) with water/methanol (1:2) solution. The dilution was vortexed and centrifuged (16,000 x g, 20 °C, 10 min). 100 μl of the supernatant was transferred to a glass vial with narrowed bottom (Total Recovery Vials, Waters) for LC-MS injection.  Lipids were separated on a nanoAcquity UPLC (Waters) equipped with a HSS T3 capillary column (150 μm x30mm, 1.8 μm particle size, Waters), applying a gradient of 5 mM ammonium acetate in water/acetonitrile 95:5 (A) and 5 mM ammonium acetate in isopropanol/acetonitrile 90:10 (B) from 5% B to 100% B over 10 min. The following 5 min conditions were kept at 100% B, followed by 5 min reequilibration to 5% B. The injection volume was 1 μL. The flow rate was constant at 2.5 μl/min. The UPLC was coupled to QExactive mass spectrometer (Thermo) by a nanoESI source. MS data was acquired using positive polarization and data-dependent acquisition (DDA). Full scan MS spectra were acquired in profile mode from 80-1200 m/z with an automatic gain control target of 1e6, an Orbitrap resolution of 70`000, and a maximum injection time of 200 ms. The 5 most intense charged (z = +1 or +2) precursor ions from each full scan were selected for collision induced dissociation fragmentation. Precursor was accumulated with an isolation window of 0.4 Da, an automatic gain control value of 5e4, a resolution of 17`500, a maximum injection time of 50 ms and fragmented with a normalized collision energy of 20, 30 and 40 (arbitrary unit). Generated fragment ions were scanned in the linear trap. Minimal signal intensity for MS2 selection was set to 500.   3) csv result files were generated by the FGCZ (<a href="https://fgcz.ch/">Functional Genomics Center Zurich</a>) adducts found: adducts_p2947_o5292_new_10K_20210910 identifications found: identifications_p2947_o5292_new_10K_20210910 abundances of features: measurements_p2947_o5292_new_10K_20210910 Metaboanalyst file statistics: Metabo_p2947_o5292_10k_cells_CVcleaned_20210916 Metaboanalyst file enrichment: Metabo_Enrichment_p2947_o5292_10k_cells_20210910 _p75 4)  method description data analysis Data sets were evaluated with Progenesis QI software (Nonlinear Dynamics), which aligns the ion intensity maps based on a reference data set, followed by a peak picking on an aggregated ion intensity map. Detected ions were identified based on accurate mass, detected adduct patterns and isotope patterns by comparing with entries in the LipidMaps Data Base (LM) and KEGG database. Considered adducts were M+H, M+NH4, 2M+H M+H-H2O. A mass accuracy tolerance of 5 ppm was set for the searches. Fragmentation patterns were considered for 600 the identifications of metabolites. Putative identifications were further ranked based on Mass error (observed mass – exact mass), isotope similarity (observed versus theoretical).

Dataset for publication "Cell design strategies for sodium-zinc chloride (Na-ZnCl2) batteries, and first demonstration of tubular cells with 38 Ah capacity"

stationary energy storage; ZEBRA battery; high-temperature metal chloride battery; molten-salt battery; molten sodium anode. Measured data to recreate Figures 1-8 in the above manuscript.

opencc-by-4.0Dec 2022View details →

Antagonism between viral infection and innate immunity at the single-cell level -- Immunostaining Imaging Dataset

This dataset accompanies the article "Antagonism between viral infection and innate immunity at the single-cell level", at the time of submission available as a <a href="https://doi.org/10.1101/2022.11.18.517110">preprint</a>.

opencc-by-4.0Dec 2022View details →

Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches

Raw experimental data associated with the manuscript "Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches", by Sosa-Carrillo and colleagues, bioRxiv, 10.1101/2022.11.02.514931. The code to analyze this raw data and generate the figures for the manuscript is available on GitLab at https://gitlab.inria.fr/InBio/Public/yeastcybersecretion.

opencc-by-4.0Dec 2022View details →

Phytoplankton list of taxa and cell biovolume from a subtropical coastal lagoon, South America

We share information about the phytoplankton community from a subtropical coastal lagoon, Laguna de Rocha, a Biosphere Reserve, located in Uruguay, South America (34°37'60" S, 54°18'0" W). The lagoon is ~ 72 km2 with a mean depth of 0.5 m, and is characterized by a strong South-North salinity gradient that reflects its intermittent connection to the Atlantic Ocean (in the South). The lagoon has been subject to eutrophication in the last several decades. The dataset presented here belongs to the publication of Bonilla et al., 2005 (doi.org/10.1007/BF02696017). We present the complete list of phytoplankton taxa identified in a survey (1996-2000) for two sites, South and North, locations described in figure 1 (Bonilla et al., 2005). We also presented the cellular linear measurements and the cell biovolume of the most representative taxa. We believe these data are useful for the scientific community interested in phytoplankton dynamics, diversity and calculation of biomass (biovolume).

opencc-by-4.0Jan 2023View details →

Simulated movies with gaussian-shaped pHluorin signal intensity on the cell surface

Synthetic data mimicking exocytic events across a wide range of features including normalized intensity, apparent size and decay mean lifetime. Numbers and spatial location of simulated events are randomly distributed over time. Events could have: * positive attribute: single exponential decay * negative attribute: constant signal for a random amount of time, damped sine decay signal +/- spatial displacement

Time-series of shoreline change for the Klamath River Littoral Cell (California)

This repository contains 35 years of tidally-corrected shoreline change data at the Klamath River Littoral Cell in northern California. This dataset was used in Warrick et al. 2023, "A Large Sediment Accretion Wave Along a Northern California Littoral Cell", to investigate and track the movement of a large sediment wave. CoastSat was used to map shoreline changes on Landsat 5, Landsat 7 and Landsat 8 imagery between 1984 and 2022. The Coastsat toolbox is publicly available at https://github.com/kvos/CoastSat and described in Vos et al. 2019, <a href="https://doi.org/10.1016/j.envsoft.2019.104528">https://doi.org/10.1016/j.envsoft.2019.104528</a>. The time-series of shoreline change were tidally-corrected along cross-shore transects using tide levels from a global tide model (FES2014) and a satellite-derived estimate of the beach slope (as described in Vos et al. 2020, "Beach slopes from satellite-derived shorelines", <a href="https://doi.org/10.1029/2020GL088365">https://doi.org/10.1029/2020GL088365</a>). The data is located in the /shoreline_data folder and structured as follows: <ul> <li>The littoral cell is divided in 4 sections (kmt_01, kmt_02, kmt_03, kmt_04)</li> <li>For each section there is a folder with 4 CSV files: <ul> <li>time_series_tidally_corrected.csv: this file contains the tidally-corrected time-series of shoreline change along each transect belonging to the site (e.g. kmt01-000, kmt01-001 etc). This is the final product used for coastal change analyses.</li> <li>time_series_raw.csv: this file contains the raw time-series of shoreline change, which have not be tidally-corrected. Note that each image is taken at a different stage of the tide.</li> <li>tide_levels_fes2014: this file contains the tide levels at the time of image acquisition extracted from FES2014 (global tide model publicly available on AVISO+).</li> <li>transect_coordinates_and_beach_slopes.csv: this file contains the coordinates (in WGS84 lat/lon coordinates) as well as the estimated beach slope for each transect.</li> </ul> </li> </ul> In addition, there are 3 geospatial layers (.GEOJSON) which contain important spatial information. All the geospatial layers are in EPSG:2163 - US National Atlas Equal Area: <ul> <li> Klamath_polygons.geojson: this layer contains the polygons that were used to run CoastSat for each section of the littoral cell.</li> <li>Klamath_shorelines.geojson: this layer contains the sandy shorelines that were used to generate the cross-shore transects (also used as reference shorelines in CoastSat).</li> <li>transects.geojson: this layer contains the cross-shore transects, which are spaced 100 m alongshore.</li> </ul> Finally, in the /animations folder, there is a clip showing the mapped shorelines on the satellite imagery.

Unit cells and resummed interactions for the calculations in "Systematic Analysis of Crystalline Phases in Bosonic Lattice Models with Algebraically Decaying Density-Density Interactions"

This directory contains all the unit cells with the respective resummed interactions used for the optimisation procedure to obtain the results in the work "Systematic Analysis of Crystalline Phases in Bosonic Lattice Models with Algebraically Decaying Density-Density Interactions[1]". To get an overview of the organization of the directory and a description of the data we recommend the README file. [1]: J. A. Koziol et al., Systematic Analysis of Crystalline Phases in Bosonic Lattice Models with Algebraically Decaying Density-Density Interactions, <a href="https://10.21468/SciPostPhys.14.5.136">10.21468/SciPostPhys.14.5.136</a>, 2023

Literature Datasets for the publication "Systematic Review: Prevalence and Practices of Immunofluorescent Cell Image Processing"

This dataset contains the CSV files returned from PubMed searches used to complete a Systematic Review of Image Processing Publication Practices for methods applied to immunofluorescent images of all CNS cells.  The file names are organized "date_supplementarytablenumber" followed by the appropriate search terms.

opencc-by-4.0Jan 2023View details →

Ultrathin a-Si:H/Oxide Transparent Solar Cells Exhibiting UV-Blue Selective-Like Absorption

Raw Dataset from which the publication of the article "Ultrathin a-Si:H/Oxide Transparent Solar Cells Exhibiting UV-Blue Selective-Like Absorption" (10.1002/solr.202200928) is derived.

Altered nanoparticle uptake by lung carcinoma cells when stimulated with epidermal growth factor

This dataset provides the raw data supporting the paper "Altered nanoparticle uptake by lung carcinoma cells when stimulated with epidermal growth factor". The focus of the study was to investigate the uptake of two different sizes of silica NPs and gold NPs in lung epithelial cells A549 in the presence of epidermal growth factor (EGF).  The data set includes: <ul> <li>Screening for EGF receptor using western blot and confocal microscopy (Figure 1 and Figure S1)</li> <li>Investigating expression of RAC1/CDC42 proteins upon EGF stimulation using Western blot (Figure 2)</li> <li>Investigating expression of RAC1 gene upon EGF stimulation using RT-qPCR (Figure S2)</li> <li>Evaluating uptake of endocytic markers upon EGF stimulation using confocal laser scanning microscopy (Figure 3, Figure S4) and flow cytometry (Figure 3)</li> <li>Nanoparticle characterization using TEM (Figure 4, Figure S6) and UV-Vis (Figure S5, Figure S6)</li> <li>Evaluating silica nanoparticle uptake upon EGF stimulation using confocal laser scanning microscopy (Figure 5, Figure S8) and flow cytometry (Figure 5)</li> <li>Evaluating gold nanoparticle uptake upon EGF stimulation using dark-field microscopy and ICP-AES (Figure 6)</li> <li>Investigating expression of c-MYC gene upon EGF stimulation using RT-qPCR (Figure 6)</li> <li>Cell viability results, analysed via lactate dehydrogenase assay (Figure S3) and MTS assay (Figure S9)</li> <li>Raw integrated density data from dark-field images (Figure S9)</li> </ul>

Integrative in situ mapping of single-cell transcriptional states and tissue histopathology in an Alzheimer disease model

Amyloid-β plaques and neurofibrillary tau tangles are the neuropathologic hallmarks of Alzheimer’s disease (AD), but the spatiotemporal cellular responses and molecular mechanisms underlying AD pathophysiology remain poorly understood. Here we introduce STARmap PLUS to simultaneously map single-cell transcriptional states and disease marker proteins in brain tissues of AD mouse models at a voxel size of 95  95  350 nm. This high-resolution spatial transcriptomics map revealed a core-shell structure where disease-associated microglia (DAM) closely contact amyloid-β plaques, whereas disease-associated astrocyte-like cells (DAA-like) and oligodendrocyte precursor cells (OPC) are enriched in the outer shells surrounding the plaque-DAM complex. Hyperphosphorylated tau emerged mainly in excitatory neurons in the CA1 region accompanied by infiltration of oligodendrocyte subtypes into the axon bundles of hippocampal alveus. The integrative STARmap PLUS method bridges single-cell gene expression profiles with tissue histopathology at subcellular resolution, providing an unprecedented roadmap to pinpoint the molecular and cellular mechanisms of AD pathology and neurodegeneration.

opencc-by-4.0Mar 2023View details →

raw data of Gut microbiota remodeling and intestinal adaptation to lipid malabsorption after enteroendocrine cell loss in adult mice

Microbiome dataset for "Gut microbiota remodeling and intestinal adaptation to lipid malabsorption after enteroendocrine cell loss in adult mice" publication https://doi.org/10.1016/j.jcmgh.2023.02.013

opencc-by-4.0Dec 2021View details →

Complement activation induces excessive T cell cytotoxicity in severe COVID-19: Analysis of single cell data cohort 1 (Berlin).

This repository contains the R Markdown files with the analysis of CyTOF and scRNA-seq data corresponding to cohort 1 (Berlin) analysed in Georg et al. 2021 "Complement activation induces excessive T cell cytotoxicity in severe COVID-19". Additionally, here we include the necessary CyTOF data to reproduce this analysis. CyTOF data: <ul> <li>The debarcoded fcs files (before batch-correction) can be found in <a href="https://flowrepository.org/id/FR-FCM-Z4P5">https://flowrepository.org/id/FR-FCM-Z4P5</a>. \</li> <li>Here you can find the necessary data to reproduce the analysis (cytof_analysis.Rmd, cytof_analysis.html): <ul> <li>data_norm_all.csv: single-cell protein expression data (after batch-normalization and in linear scale).</li> <li>data_Tcells_annotated.csv: single-cell protein expression of gated T cells with cluster annotation.</li> <li>phenograph_CD4_k30.csv, phenograph_CD8_k30.csv, phenograph_TCRgd_k30.csv: output from Louvain Clustering computed with PhenoGraph (<a href="https://github.com/jacoblevine/PhenoGraph">https://github.com/jacoblevine/PhenoGraph</a>) per T cell compartment.</li> <li>clusterannotation.csv: annotation for each cluster and metacluster</li> </ul> </li> </ul> scRNA-seq data: <ul> <li>The raw data can be found in <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175450">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175450</a></li> <li>Other files to reproduce the analysis (scRNAseq_analysis_1preprocessing.Rmd, scRNAseq_analysis_2clustering.Rmd, scRNAseq_analysis_3convalescent.Rmd): <ul> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_Sawitzki_RECAST_09_2021.xlsx">scRNAseq_Sawitzki_RECAST_09_2021.xlsx</a>: Single-cell metadata.</li> <li>scRNAseq_samples.tsv: Samples metadata.</li> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_genelist_annotation.xlsx">scRNAseq_genelist_annotation.xlsx</a>: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175450">G</a>ene list for the annotation of T cells (Also in Mendeley, see Data and Code Availability).</li> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_GO_RESPONSE_TO_TYPE_I_INTERFERON.txt">scRNAseq_GO_RESPONSE_TO_TYPE_I_INTERFERON.txt</a>, <a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_GO_DEFENSE_RESPONSE_TO_VIRUS.txt">scRNAseq_GO_DEFENSE_RESPONSE_TO_VIRUS.txt</a>, , <a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_GO_T_CELL_MEDIATED_CYTOTOXICITY.txt">scRNAseq_GO_T_CELL_MEDIATED_CYTOTOXICITY.txt</a>: Gene lists for the signatures “Response to Type I Interferon” , “Defense Response to virus” and “Cytotoxicity” used for GSEA. (Also in Table S2).</li> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_traj18_trav10.txt">scRNAseq_traj18_trav10.txt</a>,<a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_trbv25.txt">scRNAseq_trbv25.txt</a>: sequences to determine the proportion of TRAV10-TRAJ18-TRBV25 pairing T cell clones across all T cell clusters.</li> </ul> </li> </ul>

opencc-by-4.0Mar 2023View details →

Centripetal migration in Drosophila ovary IX: E-cadherin null clones pt2 & E-Cadherin germ cell RNAi pt 2

Part of data supporting Figs 6, 7, S3, S6, S16 of “Two phases for centripetal migration of Drosophila melanogaster follicle cells: initial ingression followed by epithelial migration” DOI: 10.1242/dev.200492 Data file descriptions: <ul> <li>“FRT G13 mitotic clones”                                               14.6 GB</li> </ul>             Fixed sample image data for clones of cells with E-Cadherin mutant or control mitotic clones <ul> <li>“GC RNAi flipout timelapse data pt2”                          28.92GB</li> </ul>             Timelapse image data for clones of germ cells with E-Cadherin knockdown <ul> <li> “Image analysis of ring canals-fixed G13 control”        6 KB</li> </ul>             Evaluation of fixed samples for ring canal position just prior to stage 11, nurse cell dumping, using <ul> <li>“Immuno Shg LOF clonal analysis”                                     98 KB</li> </ul>             Preliminary evaluation of sample image with clones of cells with E-Cadherin mutant or  control mitotic clones <ul> <li>“Live GC RNAi clonal data Prelim Eval”                                 25.2 MB</li> </ul>             Preliminary evaluation of germ cell E-Cadherin knockdown samples