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160 results for “cell size”
Constriction rate modulation can drive cell size control and homeostasis in C. crescentus. Data part 2.
<p>This data corresponds to the publication "Constriction rate modulation can drive cell size control and homeostasis in <em>C. crescentus</em>".</p> <p>This is part 2 out of 2.</p> <p>Part 1: 10.5281/zenodo.1172042</p> <p>See metadata.docx for more information.</p>
Constriction rate modulation can drive cell size control and homeostasis in C. crescentus. Data part 1.
<p>This data corresponds to the publication "Constriction rate modulation can drive cell size control and homeostasis in <em>C. crescentus</em>".</p> <p>This is part 1 out of 2.</p> <p>Part 2: 10.5281/zenodo.1241005 </p> <p>See metadata.docx for more information.</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>
Single-cell burst size estimates
<p>Bacteriophage burst size is the average number of phage virions released from infected bacterial cells, and its magnitude depends on the duration of an intracellular progeny accumulation phase. Burst size is often measured at the population, not single-cell, level, and consequently, statistical moments are not commonly available. In this study, we estimated the bacteriophage lambda (λ) single-cell burst size mean and variance following different length intracellular accumulation periods by employing E. coli lysogens bearing lysis-deficient λ prophages. Single lysogens can be isolated and chemically lysed at desired times following prophage induction to quantify progeny intracellular accumulation within individual cells. Our data show that λ phage burst size initially increased exponentially with increased lysis time (i.e., period between induction and chemical lysis), and then saturated at longer lysis times. We also demonstrated that cell-to-cell variation or "noise" in lysis timing did not contribute significantly to burst size noise. The burst size noise remained constant with increasing mean burst size. The most likely explanation for the experimentally observed constant burst size noise was that cell-to-cell differences in burst size originated from intercellular heterogeneity in cellular capacities to produce phages. The mean burst size measured at different lysis times was positively correlated to cell volume, which may determine the cellular phage production capacity. However, experiments controlling for cell size indicated that there are other factors in addition to cell size that determine this cellular capacity.</p>
Data from: The fat body cortical actin network regulates Drosophila inter-organ nutrient trafficking, signaling, and adipocyte cell size
<p>Defective nutrient storage and adipocyte enlargement (hypertrophy) are emerging features of metabolic syndrome and type 2 diabetes. How the cytoskeletal network contributes to nutrient uptake, fat storage, and adipocyte size remains poorly understood. Utilizing the <em>Drosophila</em> larval fat body (FB) as a model adipose tissue, we show that a specific actin isoform—Act5C—forms the cortical actin network necessary for inter-organ lipid trafficking. Act5C also promotes FB tissue expansion during larval development so larvae can store sufficient biomass for metamorphosis. We find FB-specific loss of Act5C, but not other <em>Drosophila</em> actins, perturbs FB triglyceride (TG) storage in lipid droplets (LDs), resulting in developmentally delayed larvae that fail to develop into flies. Act5C localizes to the FB cell surface where it intimately contacts peripheral LDs (pLDs), forming a cortical actin network together with spectrins for cell architectural support. While both the cortical actin and spectrin cytoskeletons maintain FB cell surface architecture, we find that only the actin network is required for fat storage. Mechanistically, we show that FBs lacking the Act5C cortical cytoskeleton exhibit a block in lipoprotein (Lpp) secretion from FB cells, and a subsequent disruption of gut:FB inter-organ lipid transport, resulting in mid-gut fat accumulation. Utilizing temporal RNAi-depletion approaches, we also reveal that Act5C is indispensable post-embryogenesis during larval feeding to promote FB cell expansion. Act5C-deficient FBs fail to expand cell sizes, leading to lipodystrophic larvae unable to accrue sufficient biomass for metamorphosis. Collectively, we propose that the Act5C-mediated cortical actin network of <em>Drosophila</em> adipose tissue plays an essential role in post-embryonic inter-organ nutrient transport and FB cell size determination for organismal energy homeostasis and development.</p>
Use of Pediatric Size Phlebotomy Tubes in Adult Critically Ill Patients to Reduce Red Blood Cell Transfusions
ClinicalTrials.gov study NCT03286465. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Cell size-dependent species sensitivity to nanoparticles underlies changes in phytoplankton diversity and productivity
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Data from: Are acute and acclimated thermal effects on metabolic rate modulated by cell size? A comparison between diploid and triploid zebrafish larvae
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Data of cell size, cell carbon content, and biomass of dinoflagellates and diatoms in the oceanic ecosystem of the Southern Gulf of Mexico
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Data from: The fat body cortical actin network regulates Drosophila inter-organ nutrient trafficking, signaling, and adipocyte cell size
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Data for: Linking cell size, Vmax, and Km in phototrophs and chemotrophs: Insights from Bayesian inference
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Diatom cell-size composition as a novel tool for quantitative estimates of water table in peatlands
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Single-cell burst size estimates
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Supporting information for: Discrimination ability of central visual field testing using stimulus size I, II, and III and relationship with macular ganglion cell thickness in chiasmal compression
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Supporting data for: Honeybees adapt to a range of comb cell size by merging, tilting and layering their construction
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Data from: Phytoplankton growth and stoichiometric responses to warming, nutrient addition and grazing depend on lake productivity and cell size
<p>Global change involves shifts in multiple environmental factors that act in concert to shape ecological systems in ways that depend on local biotic and abiotic conditions. Little is known about the effects of combined global change stressors on phytoplankton communities, and particularly how these are mediated by distinct community properties such as productivity, grazing pressure and size distribution. Here, we tested for the effects of warming and eutrophication on phytoplankton net growth rate and C:N:P stoichiometry in two phytoplankton cell size fractions (<30 μm and >30 μm) in the presence and absence of grazing in microcosm experiments. Because effects may also depend on lake productivity, we used phytoplankton communities from three Dutch lakes spanning a trophic gradient. We measured the response of each community to multifactorial combinations of temperature, nutrient, and grazing treatments and found that nutrients elevated net growth rates and reduced carbon:nutrient ratios of all three phytoplankton communities. Warming effects on growth and stoichiometry depended on nutrient supply and lake productivity, with enhanced growth in the most productive community dominated by cyanobacteria, and strongest stoichiometric responses in the most oligotrophic community at ambient nutrient levels. Grazing effects were also most evident in the most oligotrophic community, with reduced net growth rates and phytoplankton C:P stoichiometry that suggests consumer‐driven nutrient recycling. Our experiments indicate that stoichiometric responses to warming and interactions with nutrient addition and grazing are not universal but depend on lake productivity and cell size distribution.</p>
MicroCT data to 'Maximum CO2 diffusion inside leaves is limited by the scaling of cell size and genome size'
<p>This dataset is presented in the following publication. Please cite this publication if you use the dataset.</p> <p><em>Théroux-Rancourt Guillaume, Roddy Adam B., Earles J. Mason, Gilbert Matthew E., Zwieniecki Maciej A., Boyce C. Kevin, Tholen Danny, McElrone Andrew J., Simonin Kevin A. and Brodersen Craig R. 2021. <strong>Maximum CO<sub>2</sub> diffusion inside leaves is limited by the scaling of cell size and genome size.</strong> Proceedings of the Royal Society B. 288: 20203145. doi:<a href="https://doi.org/10.1098/rspb.2020.3145">10.1098/rspb.2020.3145</a></em></p> <p>We collected leaf samples from botanical gardens, greenhouses, and field sites, which were then transported to one of three synchrotron-based microCT beamlines for imaging. To- and three-dimensional data were then extracted from the microCT images to characterize the structural anatomy of the different study species. Those data were then analyzed and correlated with genome size data available from the Kew Plant DNA C-values database (https://cvalues.science.kew.org) or newly collected by us.</p> <p>From the botanical garden collections we selected representative species from across the vascular plant phylogeny, aiming to capture as much variation in anatomical/physiological/ecological traits as possible while also sampling species that represented important divergences in the phylogeny. Within clades, we also sampled species that spanned ecological breadth (e.g. xerophytic ferns). We focused solely on C3 terrestrial vascular plants, meaning that we did not sample C4 or CAM species, which have different photosynthetic biochemistry and associated anatomy.</p> <p>In most cases, single microCT scans were used per species. This constraint was primarily due to the extremely limited amount of time available at the microCT facility, as well as the labor intensive process of producing the final volume renderings and data analysis.</p> <p>MicroCT scans were collected by GTR, JME, ABR, CRB, AJM, CKB, MJZ, and DT at one of the three microCT beamlines from fresh leaf material. Leaves were cut at the base of the petiole or short stem segment, the cut end was wrapped in wet paper towels, and the entire shoot immediately put in a plastic bag before being transported to the synchrotron and scanned within 36 h of excision. Samples were prepared before each scan (less than 30 min) by excising a small sample that was then enclosed between two pieces of Kapton (polyimide) tape to prevent desiccation while allowing high X-ray transmittance.</p> <p>All microCT data were collected at the Lawrence Berkeley National Laboratory (LBNL) tomography beamline 8.3.2, the Swiss Light Source (SLS) TOMCAT Tomography beamline of the Paul Scherrer Institute, or the Advanced Photon Source tomography beamline 2-BM-A,B of Argonne National Laboratory (ANL). MicroCT datasets were reconstructed from the raw projection images obtained using TomoPy, an open-source Python-based framework for reconstructing tomographic data (LBNL), or using the in-house reconstruction platform of the beamline (SLS, ANL). LBNL and SLS data can be reconstruction using TomoPy, and this software is available at the following link: <a href="http://microct.lbl.gov/software">http://microct.lbl.gov/software</a></p> <p>Image stacks were segmented using the open-source software ImageJ either manually or using an automated machine learning algorithm (Théroux-Rancourt et al., 2020: doi:<a href="https://doi.org/10.1002/aps3.11380">10.1002/aps3.11380</a>). Traits were extracted from the segmented image stacks using ImageJ, the BoneJ plugin of ImageJ, or with an open-source Python-program available at: <a href="https://github.com/plant-microct-tools/leaf-traits-microct">https://github.com/plant-microct-tools/leaf-traits-microct</a>. Because of the multiple synchrotron and multiple sessions involved in acquiring this dataset, and because the analysis process lasted several years and was carried out by several persons, the size of each image stack and the details in each stack vary. What is present in each stack is the airspace and the whole mesophyll (i.e. the leaf without the epidermis), and in the majority of the cases, the vasculature is also segmented.</p>
FIGURE 2 in Erythrocyte nuclear size as a better diagnostic character than cell size in the identification of live cryptic polyploid species
FIGURE 2. Comparison of nuclear areas of the individuals included in the estimation of boundary values (A) and the remaining individuals (B) of Odontophrynus cordobae and O. americanus. The dotted line represents the limit nuclear area to separate species. Upper and lower ends of boxes represent 75th and 25th percentiles. Whiskers represent the minimum and the maximum values. The center line within each box shows the locations of the sample median and the plus sign indicates the location of the sample mean.
FIGURE 1 in Erythrocyte nuclear size as a better diagnostic character than cell size in the identification of live cryptic polyploid species
FIGURE 1. Comparison of nuclear areas of the individuals included in the estimation of boundary values (A) and the remaining individuals (B) of Pleurodema kriegi and P. cordobae. The dotted line represents the limit nuclear area to separate species. Upper and lower ends of boxes represent 75th and 25th percentiles. Whiskers represent the minimum and the maximum values, except for outlier points. The center line within each box shows the locations of the sample median and the plus sign indicates the location of the sample mean.
Data Analysis for: Coupling Cell Size Regulation and Proliferation Dynamics for C. glutamicum Reveals Cell Division Based on Surface Area
<div>Data and methods of Data Analysis of: Coupling Cell Size Regulation and Proliferation Dynamics of</div> <div>C. glutamicum Reveals Cell Division Based on Surface Area</div> <div> </div> <div>Authors: Cesar Nieto and Zahra Vahdat at University of Delaware (2023)</div> <div>Correspondence: cnieto@udel.edu.</div> <div> </div> <div> </div>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.