Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,052
datasets available to search
ShareScore release 0.9.0
Dataset results
1,052 results for “Cell growth”
Data from: Intrinsic growth heterogeneity of mouse leukemia cells underlies differential susceptibility to a growth-inhibiting anticancer drug
<p>Cancer cell populations consist of phenotypically heterogeneous cells. Growing evidence suggests that pre-existing phenotypic differences among cancer cells correlate with differential susceptibility to anticancer drugs and eventually lead to a relapse. Such phenotypic differences can arise not only externally driven by the environmental heterogeneity around individual cells but also internally by the intrinsic fluctuation of cells. However, the quantitative characteristics of intrinsic phenotypic heterogeneity emerging even under constant environments and their relevance to drug susceptibility remain elusive. Here we employed a microfluidic device, mammalian mother machine, for studying the intrinsic heterogeneity of growth dynamics of mouse lymphocytic leukemia cells (L1210) across tens of generations. The generation time of this cancer cell line had a distribution with a long tail and a heritability across generations. We determined that a minority of cell lineages exist in a slow-cycling state for multiple generations. These slow-cycling cell lineages had a higher chance of survival than the fast-cycling lineages under continuous exposure to the anticancer drug Mitomycin C. This result suggests that heritable heterogeneity in cancer cells' growth in a population influences their susceptibility to anticancer drugs.</p>
Exploring Cellular Dynamics: A Comprehensive Analysis of SH-SY5Y Cell Growth in Variable Polycarbonate Chambers and Comparative Insights with Standard Petri Dishes
<div> <div> <div> <div> <div> <div> <p>The provided illustrations delineate the proliferation of SH-SY5Y cells within polycarbonate chambers with varying diameters, ranging from 3 mm to 50 mm. HBE and HeLaGFP cells serve as points of comparison. Five microscopic images were captured for each culture plate, one at the central location and four at random sites. To trace the growth trajectory across different chamber sizes, images from various culture days underwent manual binarization using GIMP and/or ImageJ software. The resulting binarized images can be accessed in the 'masks' directories within the zipped files, corresponding to the attached images. Comparative analysis of cell growth within these chambers was conducted against growth observed in standard 60 mm Petri dishes from Corning.</p> <p>For monitoring cell growth, we employed an Olympus phase-contrast microscope (CKX53) equipped with a DLT camera (DLT-Cam PRO 1080 HDMI USB).</p> <div> <div> <div> <p>The file nomenclature employed herein serves to represent the primary experimental conditions under consideration. It comprises a hierarchical structure, encompassing various parameters as follows: cellsName_passageNumber_cellSuspension_measurementDayNumber_chamberDiameter_regionNumber_hOfFluids_number.</p> <p>The cellsNames parameter encapsulates the complete nomenclature of the cells utilized in the experiment.</p> <p>The passageNumber parameter represents the passage number and is encoded as 'pNumber.'</p> <p>The cellSuspension parameter is denoted by distinct codes:</p> <ul> <li>'c1' designates a cell suspension with a concentration of 5x10^5 cells/ml.</li> <li>'c3' signifies a cell suspension with a concentration of 1x10^5 cells/ml.</li> <li>'c4' denotes a cell suspension with a concentration of 5x10^4 cells/ml.</li> </ul> <p>The measurementDayNumber parameter is encoded as 'afterXXXdays,' signifying the number of culture days.</p> <p>The 'd' followed by a number (e.g., d6, d8, d10, d12, d15) indicates the diameter of the chamber in millimeters. In a given series of files (e.g., 1day_d6_1, 1day_d6_2, 1day_d6_3, 1day_d6_4, 1day_d6_5), the trailing numeral (_1, _2, _3, _4, _5) corresponds to a photograph captured in a distinct region of the sample.</p> <p>The hOfFluids parameter is employed to signify the height ('h') of the fluid above the cells in the chamber, expressed in millimeters.</p> <div> <div> <div> <p>In adherence to the aforementioned conventions, every file name serves to distinctly characterize a particular collection of experimental conditions. This practice enhances both the comprehensibility and systematic organization of the data. It is noteworthy that the complete set of parameters may not always be provided; for instance, the height of the column of medium is assumed to be 2 mm unless otherwise specified.</p> </div> </div> </div> </div> </div> </div> <br> <p> </p> </div> </div> </div> </div> </div> </div>
Hormone receptors AR, ER, PR and growth factor receptor Her-2 expression in oral squamous cell carcinoma: Correlation with overall survival, disease-free survival and 10-year survival in a high-risk population
<p>Oral squamous cell carcinoma (OSCC) comprises most of head and neck neoplasms and is one of the highest-ranking and lethal cancers in Pakistan due to prevailing mouth habits. Growth and hormonal receptors act as prognostic markers and targets for therapy in some cancers, but their application in OSCC is largely unexplored. This study aimed to evaluate the expression of growth and hormonal receptors in OSCC patients and correlate it with 10-year, overall and disease-free survival. To achieve this objective, immunohistochemistry for Her-2, AR, ER and PR was performed on 100 formalin-fixed paraffin-embedded primary OSCC specimens. Receptor expression was correlated with mouth habits and clinicopathological features and patient survival was analyzed using Kaplan-Meier method and Cox regression univariate analysis. We observed that in 100 patients, there were 57 males and 43 females. Immunopositive Her-2 expression was observed in 21% of patients, AR in 13%, ER in 3% and 0% for PR. Patients with betel quid/areca nut mouth habits had significantly absent Her-2 expression (P=0.035). Also, Her-2 negative patients were also negative for AR expression (P=0.002). Her-2 positive patients had poor 10-year survival (P=0.041). A trend of low survival and high recurrence rate was observed in AR positive patients, but this was not significant (P=0.072). No statistically relevant correlations were seen in the case of ER and PR. In conclusion, Her-2 may be a valuable marker for predicting long-term prognosis of OSCC patients.</p>
The role of cell-envelope synthesis for envelope growth and cytoplasmic density in Bacillus subtilis
<p>All cells must increase their volumes in response to biomass growth to maintain intracellular mass density within physiologically permissive bounds. Here, we investigate the regulation of volume growth in the Gram-positive bacterium <em>Bacillus subtilis</em>. To increase volume, bacteria enzymatically expand their cell envelopes and insert new envelope material. First, we demonstrate that cell-volume growth is determined indirectly, by expanding their envelopes in proportion to mass growth, similarly to the Gram-negative <em>Escherichia coli</em>, despite their fundamentally different envelope structures. Next, we studied, which pathways might be responsible for robust surface-to-mass coupling: We found that both peptidoglycan synthesis and membrane synthesis are required for proper surface-to-mass coupling. However, surprisingly, neither pathway is solely rate-limiting, contrary to wide-spread belief, since envelope growth continues at a reduced rate upon complete inhibition of either process. To arrest cell-envelope growth completely, the simultaneous inhibition of both envelope-synthesis processes is required. Thus, we suggest that multiple envelope-synthesis pathways collectively confer an important aspect of volume regulation, the coordination between surface growth and biomass growth.</p>
Heterogeneity of RNA editing in mesothelioma and how RNA editing enzyme ADAR2 affects mesothelioma cell growth, response to chemotherapy and tumor microenvironment
<p>Raw data supporting the manuscript</p>
New protein production in primary pulmonary artery endothelial cells treated with insulin-like growth factor 1 treatment
<p>Maximum projections of flat-fielded and deconvolved epi-fluorescence imaging data for primary sheep pulmonary artery endothelial cells (PAEC) treated with vehicle or insulin-like growth factor 1 (IGF1) media.</p> <p>Two cell types: normal PAEC and persistent pulmonary hypertension of the newborn (PPHN) PAEC</p> <p>Three time points: 0 minutes, 1 hour, and 24 hours post-treatment</p> <p>Two fluorescence channels:<br> C0 - DAPI for nuclei (R37606, Life Technologies)<br> C1 - Click-IT new protein translation kit (C10428, C10429, Life Technologies)</p>
Scaling between cell cycle duration and wing growth is regulated by Fat-Dachsous signaling in Drosophila
<p>The atypical cadherins Fat and Dachsous (Ds) signal through the Hippo pathway to regulate growth of numerous organs, including the <em>Drosophila</em> wing. Here, we find that Ds-Fat signaling tunes a unique feature of cell proliferation found to control the rate of wing growth. The duration of the cell cycle increases in direct proportion to the size of the wing, leading to linear rather than exponential growth. Ds-Fat signaling enhances the rate at which the cell cycle lengthens with wing size, thus diminishing the linear rate of wing growth. We show that this results in a complex but stereotyped relative scaling of wing growth with body growth in <em>Drosophila</em>. Finally, we examine the dynamics of Fat and Ds protein distribution in the wing, observing graded distributions that change during growth. However, the significance of these dynamics is unclear since perturbations in expression have negligible impact on wing growth.</p>
Growth kinetics of the HSJD-DIPG-07 cell line in non-adherent culture
<p>The growth kinetics of the HSJD-DIPG-07 cell line when grown as neurospheres in non-adherent culture conditions.</p>
Huntingtin nanobody purification using osmotic shock cell lysis and growth in M9 media – 2018/04/09
<p>Huntingtin structure-function open lab notebook project. Huntingtin nanobody purification using osmotic shock cell lysis and growth in M9 media – 2018/04/09</p>
Data set and data processing software of: Bacterial cell size modulation along the growth curve across nutrient conditions
<div>In Repository.zip it is possible to find the following folders:</div> <div> </div> <div>ImageProcess: Shows an example of the studied phtos, the segmentation mask obtained using Ilastik and the scripts used to estimate the cell dimensions.</div> <div> </div> <div>DataProcessing: Includes the raw data for cells size in all the studied conditions, a script showing the filtering and the data processing for plotting most of the figures of the article.</div> <div> </div> <div>CFUod: Includes the dataset of CFU and OD measurements studied in the article. The inered trends over different biological replica and the data processing for plotting the Figures in the main text. </div> <div> </div> <div> </div> <div>_______________________________________________________________</div> <div> </div> <div>ImageProces:</div> <div> </div> <div>This folder contains:</div> <div> </div> <div>* IMAGES folder: Contains a 10 arbitrary folders of images, one for different OD conditions for the experiment of M9 + 0.25% CAS. Each image is a .tif file. The pixel size is 0.07 micrometers per pixel and they were obtained using bright field microscopy imaging. </div> <div> </div> <div>* SEG folder: Contains the masks for the same number of folders and photos equivalent photos in the IMAGES folder. Masks are also in .tif format.</div> <div> </div> <div>* "Dataset.csv": Is a typical dataset obtained from the images using the script of image processing. The data consists on the following columns:</div> <div>a. OD: Label of the OD measurement. Following experimental arbitrary notation, this number was the time in hours times 10. </div> <div>b. Photo: The label of the segmented photo.</div> <div>c. Area: Area of the segmenteated contour (squared micrometers).</div> <div>d. Len: Cell size length (Micrometers).</div> <div> </div> <div>* "ImageProcesing.ipynb": Jupyter notebook for procesing the images and their masks. The output is "Dataset.csv"</div> <div> </div> <div>____________________________________________________________________________________________________</div> <div> </div> <div> </div> <div>DataProcessing:</div> <div> </div> <div>This folder contains:</div> <div> </div> <div>* RawData.csv: comma separated values file with the dimensions of different cells in for the studied conditions. The data consists on the following columns:</div> <div>a. Strain: Represents the experimental condition. It has the following values:</div> <div>M9= E.coli Growth in minimal M9</div> <div>M9cas25= E.coli in M9 + 0.25% Casaminoacids</div> <div>LBSS= E.coli in LB in steady growth</div> <div>SalLB= S. enterica in LB.</div> <div>SalM9=S. enterica in M9</div> <div>M9cas50= E.coli in M9 + 0.5% Casaminoacids</div> <div>LB2= E. coli in LB</div> <div>b. Photo: label for the studied photo.</div> <div>c. Time: Time in hours after resuspension.</div> <div>d. OD: Optical density of the studied population.</div> <div>e. Len: Cell length of the situdied contour (micrometers).</div> <div>f. Area: Projected area of the cell contour (squared micrometers).</div> <div>g. Area: Volume of the cell (cubic micrometers).</div> <div>h. SAV surface/volume ratio.</div> <div>i. Width: Cell width </div> <div>j. Aspect; Aspect ratio length/width</div> <div> </div> <div>*Stats.csv: Results of the statistical moments of cell size dimensions calculated from "Rawdata.csv" using "Plotter.ipynb". These data consists on the following columns:</div> <div> </div> <div>a. Time: Time (hours)</div> <div>b. OD: Optical density </div> <div>c. MnVol: Mean cell volume (cubic micrometers)</div> <div>d. MnVolErr: 95% confidence interval of the mean volume.</div> <div>e. CV2Vol: squared coefficient of variation of the volume.</div> <div>f. CV2VolErr: 95% confidence interval squared coefficient of variation of the volume.</div> <div>g. Mnw: Mean cell width (micrometers)</div> <div>h. MnwErr: 95% confidence interval of the mean width.</div> <div>i. CV2w: squared coefficient of variation of the cell width.</div> <div>j. CV2wErr: 95% confidence interval squared coefficient of variation of the width.</div> <div>k. MnLen: Mean cell length (micrometers)</div> <div>l. MnLenErr: 95% confidence interval of the mean length.</div> <div>m. CV2Len: squared coefficient of variation of the cell length.</div> <div>n. CV2LenErr: 95% confidence interval of the squared coefficient of variation of the cell length.</div> <div>o. Strain: Nutrient conditions</div> <div> </div> <div>*Ploter.ipnyb: Jupyter notebook which using "RawData.csv" calculates the moments in "Stats.csv" and plots most of the figures of the main article. </div> <div> </div> <div> </div> <div>__________________________________________________________________________________ </div> <div> </div> <div>CFUod: </div> <div> </div> <div>This folder contains:</div> <div> </div> <div>* resultsOD.csv: OD values for different biology replicas. The columns are as follows:</div> <div>a. t: Time (hours)</div> <div>b. log(OD): Natural logarithm of the bets fit for the optical density</div> <div>c. log(OD) error: 95% confidence interval for the best fit of the natural logarithm of the optical density.</div> <div>d. gr: best fit growth rate in units of 1/hours.</div> <div>e. gr error: 95% confidence interval of the growth rate.</div> <div>f. three columns called "od": each represents the optical density for each experimental replica.</div> <div> </div> <div> </div> <div>* resultscfu.csv: cfu values for different biology replicas. The columns are as follows:</div> <div>a. t: Time (hours)</div> <div>b. log(OD): Natural logarithm of the bets fit for the cfu</div> <div>c. log(OD) error: 95% confidence interval for the best fit of the natural logarithm of the cfu.</div> <div>d. gr: best fit growth rate in units of 1/hours.</div> <div>e. gr error: 95% confidence interval of the growth rate.</div> <div>f. three columns called "od": each represents the cfu for each experimental replica.</div> <div> </div> <div> </div> <div>*ODGrowthRate.ipynb: jupyter notebook that uses "resultsOD.csv" and "resultscfu.csv" for plotting the ratio OD/cfu.</div> <div> </div> <div> </div> <div>Any question please ask cnieto@udel.edu</div> <div> </div> <div>Cesar Augusto Nieto Acuna</div> <div> </div> <div>Newark, Delaware, USA</div> <div> </div> <div>08/05/2024</div>
Dataset related to article: Cell-envelope growth of Gram-negative bacteria proceeds independently of cell-wall synthesis
<p>Single-cell data for the article: </p> <p>Enno R. Oldewurtel, Yuki Kitahara, Baptiste Cordier, Richard Wheeler, Gizem Özbaykal, Elisa Brambilla, Ivo Gomperts Boneca, Lars D. Renner, and Sven van Teeffelen</p> <p>Cell-envelope growth of Gram-negative bacteria proceeds independently of cell-wall synthesis. EMBO J (2023)</p>
Allosteric activation of cell wall synthesis during bacterial growth
<p>This repository contains single-molecule FRET data related to this manuscript organized by figure. Note that data that appear both in the main and in the supplementary figures are provided only once, in the relevant main figure folders. Each figure folder contains all relevant datasets, deposited as zipped folders with pre-processed raw trajectories in the .dat format. These trajectories list donor excitation/donor emission (column 1) and donor excitation/acceptor emission (column 2) values as a function of time, and can be visualized and further processed using either custom code or the publicly available ebFRET software (http://ebfret.github.io/).</p>
Data for "Spatial consistency of cell growth direction during organ morphogenesis requires CELLULOSE-SYNTHASE INTERACTIVE1"
<p>This is the data associated with the following publication : "Spatial consistency of cell growth direction during organ morphogenesis requires CELLULOSE-SYNTHASE INTERACTIVE1"</p>
A Study of Lazertinib in Participants With Epidermal Growth Factor Receptor (EGFR) Mutation Positive Advanced Non-Small Cell Lung Cancer (NSCLC)
ClinicalTrials.gov study NCT04075396. IPD Sharing: YES. Countries: 3. Publications: 1.
Erlotinib Hydrochloride With or Without Bevacizumab in Treating Patients With Stage IV Non-small Cell Lung Cancer With Epidermal Growth Factor Receptor Mutations
ClinicalTrials.gov study NCT01532089. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Study of Erlotinib (Tarceva) After Surgery With or Without Adjuvant Chemotherapy in Non-Small Cell Lung Carcinoma (NSCLC) Patients Who Have Epidermal Growth Factor Receptor (EGFR) Positive Tumors
ClinicalTrials.gov study NCT00373425. IPD Sharing: Not stated. Countries: 19. Publications: 1.
Stem Cell Therapy and Growth Factor Ovarian in Vitro Activation
ClinicalTrials.gov study NCT04009473. IPD Sharing: YES. Countries: 3. Publications: 1.
Oleclumab (MEDI9447) Epidermal Growth Factor Receptor Mutant (EGFRm) Non-small Cell Lung Cancer (NSCLC) Novel Combination Study
ClinicalTrials.gov study NCT03381274. IPD Sharing: YES. Countries: 3. Publications: 1.
Effects of Growth Hormone and IGF-1 on Anabolic Signals and Stem Cell Recruitment in Human Skeletal Muscle
ClinicalTrials.gov study NCT03878992. IPD Sharing: NO. Countries: 1. Publications: 12.
The Study Observes How Long Patients With Non-small Cell Lung Cancer (NSCLC) Benefit From Treatment With Epidermal Growth Factor Tyrosine Kinase Inhibitor (EGFR-TKI) When Given Either for Uncommon Mut
ClinicalTrials.gov study NCT04179890. IPD Sharing: NO. Countries: 1. Publications: 1.
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.