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1,127 results for “cell cycle”
Spatiotemporal multiplexed immunofluorescence imaging of living cells and tissues with bioorthogonal cycling of fluorescent probes
<p>Raw multichannel and/or Z-stack source data from time series images in TIF format to accompany publication of:</p> <p><strong>Spatiotemporal multiplexed immunofluorescence imaging of living cells and tissues with bioorthogonal cycling of fluorescent probes</strong></p> <p>Jina Ko<sup>1</sup>, Martin Wilkovitsch<sup>2</sup>, Juhyun Oh<sup>1</sup>, Rainer Kohler<sup>1</sup>, Evangelia Bolli<sup>1,3</sup>, Mikael J. Pittet<sup>1,3,4,5</sup>, Claudio Vinegoni<sup>1</sup>, David B. Sykes<sup>6,7</sup>, Hannes Mikula<sup>2</sup>, Ralph Weissleder<sup>1,8</sup>*, Jonathan C. T. Carlson<sup>1,7</sup>*</p> <p><sup>1 </sup>Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge St, CPZN 5206, Boston, MA 02114 </p> <p><sup>2</sup> Institute of Applied Synthetic Chemistry, TU Wien, 1060 Vienna, Austria </p> <p><sup>3</sup> Department of Pathology and Immunology, University of Geneva, Geneva, Switzerland</p> <p><sup>4</sup> Ludwig Institute for Cancer Research, Lausanne Branch, Switzerland</p> <p><sup>5</sup> AGORA Cancer Center, Lausanne, Switzerland</p> <p><sup>6</sup> Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA</p> <p><sup>7 </sup>Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA</p> <p><sup>8 </sup>Department of Systems Biology, Harvard Medical School, 200 Longwood Ave, Boston, MA 02115</p>
VeloCycle-estimated cell cycle phases of single cells from a genome-scale perturb-seq performed in K562
<p>Continuous cell cycle phase position between 0 and 2π estimated by <em>VeloCycle </em>(Lederer et al., <em>Nature Methods</em> 2024) for single cells of the perturb-seq performed in the K562 CML cell line by Replogle et al., Cell 2022. </p> <p>Underlies the cell cycle imbalances inferred in Pulver & Forey et al., 2024.</p> <p>Method manuscript exerpt:</p> <p>"For analysis on the genome-wide perturb-seq dataset of K562 cells (Replogle <em>et al.</em>, 2022), a transfer learning approach was applied. Condition-independent estimation of the periodic Fourier series components would be especially challenging on Perturb-seq knockdown conditions containing either (1) very few cells or (2) cells belonging to just one phase of the cell cycle. To infer accurate cell cycle phases for these cells, we first performed manifold-learning for 5,000 training steps to estimate the gene harmonic coefficients (ν0, ν1sin, ν1cos) on a larger set of non-targeting control (NT) K562 cells (75,328 cells), which are more evenly distributed throughout the various phases of the cell cycle. Next, we ran manifold-learning again for 5,000 training steps, but on the entire perturb-seq dataset of 1,971,608 cells and 4,127 gene knockdown conditions (with at least 75 cells per condition). This time, we conditioned <em>VeloCycle</em> on the gene harmonic coefficients learned in the first step. This allowed cells belonging to each stratified gene knockdown condition to be assigned to a position on the cell cycle manifold, while restricting those assignments such that they were based on gene expression patterns earned on a larger and more informative dataset (allowing for batch effect expression differences)."</p>
Dataset and Code for Manuscript "Multi-angle pulse shape detection of scattered light in flow cytometry for label-free cell cycle classification"
<p>Dataset of measurements for cell cycle analysis with description:</p> <ul> <li>ReadMe file with explanations on the data set and analysis</li> <li>exemplary Matlab script file for analysis</li> <li>binary data files conatining the pulse shapes in all channels</li> <li>FCS data files containing common flow cytometry parameters in each channel</li> </ul> <p>Data on unsorted HEK cells, HEK cells sorted for cell cycle phases, and unsorted Jurkat cell are included.</p>
Dataset for publication "Influence of precursor morphology and cathode processing on performance and cycle life of sodium-zinc chloride (Na-ZnCl2) battery cells"
<p>High-temperature sodium-metal battery; sodium-metal halide battery (ZEBRA); molten-salt battery; zinc battery for stationary energy storage; alkali metal anode.</p> <p>Datasets used in the above manuscript. </p>
Single-cell datasets for cell cycle plasticity underlies fractional resistance to palbociclib in ER+/HER2- breast tumor cells
<p>There are 7 files uploaded in the data.</p><p>tumor_preprocessed.h5ad: Full primary tumor dataset post-feature selection and standardization across three treatment conditions (0, 10, and 100 nM palbociclib). AnnData object format. 14 cell cycle features, phase labels and other cell metadata, and two PHATE dimensions for manifold visualization.</p><p>T47D_preprocssed.h5ad: Full dataset of main text T47D dataset post-feature selection and standardization across three treatment conditions (0, 10, and 100 nM palbociclib). AnnData object format. 14 cell cycle features, phase labels and other cell metadata, and two PHATE dimensions for manifold visualization.</p><p>sketched_integrated.h5ad: After downsample 6,000 (2,000 per condition) from T47D_preprocessed and tumor_preprocessed, we integrate the two datasets into one joint latent space using TRANSACT. Now included in the data are the consensus component columns ('0',..,'13'). AnnData object.</p><p>sketched_integrated_df.csv: sketched_integrated.h5ad in .csv format.</p><p>T47D_replicate_preprocessed: Replicate experimental dataset of T47D for supplementary analysis post-feature selection and standardization across three treatment conditions (0, 10, and 100 nM palbociclib). 15 cell cycle features (same 14 but with CDK6).</p><p>sketched_rep.h5ad: Representative downsample of the T47D_replicate_preprocessed. Selecting 6,000 cells (2,000 for each of the three treatment conditions) using kernel herding sketching. AnnData object.</p><p>sketched_rep_df.csv: Same data as sketched_rep.h5ad in csv format.</p><p>T47D_triplicate_preprocessed.h5ad: T47D biological replicate sample collected in triplicate form (three wells for 0, 10, and 100 nM of palbociclib). Wells were joined and the data were sketched down to 20,000 per condition.</p><p>T47D_triplicate_preprocessed.h5ad: T47D triplicate in .csv form.</p><p>tumor_2_preprocessed.h5ad: An additional tumor sample from a new patient with the same treatment conditions of palbociclib. Sketched down to 2,000 cells per condition.</p><p>tumor_2_preprocessed.csv: The additional tumor sample in .csv form.</p><p> </p><p>Further description of sketched_integrated: This is the joint dataset between the T47D and primary tumor, after subsampling using kernel herding sketching. This is a dataset consisting of T47D and primary tumor cells resected from a consented patient. The samples were imaged using iterative indirect immunofluorescent imaging (4i) to get proteomic measurements on a single-cell level. The T47D and tumor samples were gathered, cultured, and imaged separately. Each sample was treated with three conditions of CDK4/6 inhibitor palbociclib (control, 10 nM, and 100 nM). Then, we used kernel sketching to representatively downsample each dataset, selecting 2,000 from each of the three treatment conditions (6,000 cells from each of the two sources). We used an integration method called TRANSACT to integrate the two datasets into one shared, latent space. The dataset here is consisting of these 12,000 cells. The columns ('0','1',...'13') are the principal vectors of the joint latent space. After that, there are the columns of the standardized proteomic measurements of different cell cycle effectors, and biological annotations of interest. The standardization is done for each data source separately. Well refers to the treatment condition. 'prb_ratio' is a marker of if a cell is still proliferating or arrested, found by selecting the upper modality of pRB/RB values. 'phase' are cell cycle phase labels found by unsupervised clustering done on a handful of known cell cycle markers.</p>
Data for Universal prediction of cell cycle position using transfer learning
<p>The repo contains the data for Universal prediction of cell cycle position using transfer learning (https://www.biorxiv.org/content/10.1101/2021.04.06.438463v2).</p> <p>The scripts to analyze and generate all figures could be found at https://github.com/hansenlab/tricycle_paper_figs</p> <p>v1.1 update: add neurosphere_scvelo.qs - an R SingleCellExperiment object saved as qs file that has spliced counts, unspliced counts, and all outputs from scvelo.</p>
In vitro cell cycle oscillations exhibit a robust and hysteretic response to changes in cytoplasmic density
<p>Cells control the properties of the cytoplasm to ensure proper functioning of biochemical processes. Recent studies showed that cytoplasmic density varies in both physiological and pathological states of cells undergoing growth, division, differentiation, apoptosis, senescence, and metabolic starvation. Little is known about how cellular processes cope with these cytoplasmic variations. Here, we study how a cell cycle oscillator comprising cyclin-dependent kinase (Cdk1) responds to changes in cytoplasmic density by systematically diluting or concentrating cycling <em>Xenopus</em> egg extracts in cell-like microfluidic droplets. We found that the cell cycle maintains robust oscillations over a wide range of deviations from the endogenous density: as low as 0.2× to more than 1.22× relative cytoplasmic density (RCD). A further dilution or concentration from these values arrested the system in a low or high steady state of Cdk1 activity, respectively. Interestingly, diluting an arrested cytoplasm of 1.22× RCD recovers oscillations at lower than 1× RCD. Thus, the cell cycle switches reversibly between oscillatory and stable steady states at distinct thresholds depending on the direction of tuning, forming a hysteresis loop. We propose a mathematical model which recapitulates these observations and predicts that the Cdk1/Wee1/Cdc25 positive feedback loops do not contribute to the observed robustness, supported by experiments. Our system can be applied to study how cytoplasmic density affects other cellular processes.</p>
LiBforSecUse Data Release - Impedance spectra of life cycle tests of commercial 18650 cells
<p>The EMPIR project LiBforSecUse aimed to develop empirical measurement models to estimate the residual capacity of second-use Li-ion battery cells with impedance-based measurement and evaluation methods. The models have been established based on a series of life cycle tests of commercial 18650 (graphite/NMC) cells including regular impedance spectroscopy and capacity measurements. The measured data are made publicly available here. They can be downloaded to verify the models established within the project and they may be used for further investigations. However, the user is asked to pay tribute to the project and the researchers providing the data by citing this data source. A pdf file is added to give more detailed information on the data.</p>
Dataset to: Realistic accelerated stress tests for PEM fuel cells: Test procedure development based on standardized automotive driving cycles
<p>This is the dataset to the published article "Realistic accelerated stress tests for PEM fuel cells: Test procedure development based on standardized automotive driving cycles" (DOI: 10.1016/j.ijhydene.2023.08.292) in which the degradation of two commercial PEM fuel cell stacks was analyzed. <strong>Please cite this publication if you use the dataset in a publication as follows</strong>:</p> <p>P. Thiele, Y. Yang, S. Dirkes, M. Wick, S. Pischinger, Realistic accelerated stress tests for PEM fuel cells: Test procedure development based on standardized automotive driving cycles, Int. J. Hydrogen Energy 52 (Part D) (2024) 1065–1080, https://doi.org/10.1016/j.ijhydene.2023.08.292.</p>
Supplementary data and simulation code - "ADCY10 is a key regulator of cell cycle control"
<p>Experimental FACS data of cell cycle analysis: The folder raw_data_FACS contains the FACS data for different concentrations of bicarbonate (hco3-_rawdata_all2.csv) and for the KH7 experiments (kh7_rawdata_all.csv). The folder summarized_data_input4analysis contains the csv-files that where used for the Bayesian analysis of cell cycle control. Details are given in the headings of the files.</p> <p>We also set up a webpage, where simulations of the suggested mathematical model of the cell cycle can be performed:</p> <p><a href="https://cellcycle-simulation2.herokuapp.com/">https://cellcycle-simulation2.herokuapp.com/</a></p> <p>The results may be used for prediction or experimental design.</p> <p> </p>
Data from: Automated workflow for the cell cycle analysis of (non-)adherent cells using a machine learning approach
Open the record for dataset details and reuse information.
In vitro cell cycle oscillations exhibit a robust and hysteretic response to changes in cytoplasmic density
Open the record for dataset details and reuse information.
FIB-tomography data of Ni-YSZ anodes for Solid Oxide Fuel Cells (SOFC): Comparison of pristine and degraded materials (before/after redox cycling)
<p><em>Contents: </em></p> <p>This dataset contains 3D image stacks acquired with FIB-tomography from Ni-YSZ cermet anodes for Solid Oxide Fuel Cells (SOFC).</p> <p>The data was collected from three different Ni-YSZ anodes (fine-, medium- and coarse-grained). Each of these anodes was investigated first in pristine state (after sintering and reduction) and then also in degraded state (after exposure to 8 redox cycles).</p> <p>The 6 tomographs are then presented as stacks of 2D-tiff-images in 2 different versions: as gray-scale images (raw data) and as segmented images (Ni=white, YSZ=gray and pores=black). In total this gives 12 image stacks.</p> <p><strong>Further details</strong>, such as the voxel resolutions and image window sizes are listed in the downloadable excel file (<strong>2_3D_Data_Info.xlsx</strong>).</p> <p> </p> <p><em>Scientific Context: </em></p> <p>The microstructures of the cermet anodes were investigated for the purpose of optimizing the anode performance, which depends on effective transport properties (i.e. conductivity of ions in YSZ and of electrons in Ni, as well as diffusivity of fuel/gas in the pores). Furthermore the anode performance also depends on the catalytic/electrochemical activity (i.e. Ni-surface area and three phase boundary length TPBL). The microstructure characteristics have a strong influence on effective properties, electrochemical activity and associated anode performance. Furthermore, microstructure degradation (e.g. by Ni-coarsening) may lead to performance loss over time.</p> <p>Hence, the investigations focus on a fundamental, quentitative understanding of the relationships between microstructure characteristics and effective properties. The study reveals quantitative descriptions of all relevant microstructure characteristics (porosity, tortuosity, constrictivity, surface/interface areas, TPBL) and of the corresponding effective transport porperties (electric and ionic.conductivities). The corresponding anode performance was characterized by impedance spectroscopy.</p> <p>The quantitative <strong>results of the microstructure investigation were published</strong> in:</p> <p><strong>Pecho et al</strong> 2015a (doi:10.3390/ma8095265),</p> <p><strong>Pecho et al</strong> 2015b (doi:10.3390/ma8105370),</p> <p><strong>Holzer et al</strong> 2013 (doi: 10.1016/j.jpowsour.2013.05.047),</p> <p><strong>Holzer et al</strong> 2011a (doi: 10.1016/j.jpowsour.2010.08.017) and</p> <p><strong>Holzer et al</strong> 2011b (doi: 10.1016/j.jpowsour.2010.08.006).</p>
Oscillatory dynamics of mRNA metabolism and chromatin accessibility in mESCs during the cell cycle
<p>Processed single-cell RNAseq and multiome sequencing--single-nucleus RNAseq and ATACseq for mESCs</p> <table> <tbody> <tr> <td><strong>Filename</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>scrna_deepdycle.h5ad</td> <td>single-cell RNAseq data, has information about unsplcied and spliced levels and the inferred cell cycle phase</td> </tr> <tr> <td>snrna_deepdycle_rep1.h5ad</td> <td>single-nulceus RNAseq data (replicate 1), has information about unsplcied and spliced levels and the inferred cell cycle phase</td> </tr> <tr> <td>snrna_deepdycle_rep2.h5ad</td> <td>single-nulceus RNAseq data (replicate 2), has information about unsplcied and spliced levels and the inferred cell cycle phase</td> </tr> <tr> <td>body_coverage_lif1.tsv.gz</td> <td>single nucleus ATACseq data (replicate 1), counts table of ATACseq peaks mapped to the genes</td> </tr> <tr> <td>body_coverage_lif2.tsv.gz</td> <td>single nucleus ATACseq data (replicate 2), counts table of ATACseq peaks mapped to the genes</td> </tr> <tr> <td>model_predictions.zip</td> <td>gene-wise predictions for gene expression--unspliced and spliced, and mRNA metabolism rates--synthesis, splicing, export, and degradation rates for single-cell and single-nucleus data</td> </tr> </tbody> </table>
Enabling Long-term Cycling Stability of Na3V2(PO4)3/C vs. Hard Carbon Full-cells
<p>Battery cycling data to research paper: </p><p>https://doi.org/10.26434/chemrxiv-2023-jf6mq </p><p>for questions on the data files, please contact pirmin[dot]stueble[at]kit.edu or anna[dot]smith[at]kit.edu</p>
Data from: "Lithium-ion battery degradation: comprehensive cycle ageing data and analysis for commercial 21700 cells"
<h1><strong>Intro</strong></h1> <p>Dataset from the publication "Lithium-ion battery degradation: comprehensive cycle ageing data and analysis for commercial 21700 cells", DOI: https://doi.org/10.1016/j.jpowsour.2024.234185</p> <p>Full details of the study can be found in the publication, including thorough descriptions of the experimental methods and structure. A basic desciption of the experimental procedure and data structure is included here for ease of use.</p> <p>Commercial 21700 cylindrical cells (LG M50T, LG GBM50T2170) were cycle aged under 3 different temperatures [10, 25, 40] °C and 4 different SoC ranges [0-30, 70-85, 85-100, 0-100]%, as well as a further [0-100]% SoC range experiment which utilised a drive-cycle discharge instead of constant-current. The same C-rates (0.3C / 1 C, for charge / discharge) were used in all tests; multiple cells were tested under each condition. These are listed in the table below.</p> <table> <tbody> <tr> <td> <div> <p><strong>Experiment</strong></p> </div> </td> <td> <div> <p><strong>SOC Window</strong></p> </div> </td> <td> <div> <p><strong>Cycles per ageing set</strong></p> </div> </td> <td> <div> <p><strong>Current</strong></p> </div> </td> <td> <div> <p><strong>Temperature</strong></p> </div> </td> <td> <div> <p><strong>Number of Cells</strong></p> </div> </td> </tr> <tr> <td> <div> <p>1</p> </div> </td> <td> <div> <p>0-30%</p> </div> </td> <td> <div> <p>257</p> </div> </td> <td> <div> <p>0.3C / 1D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> <div> <p> </p> </div> </td> <td> <div> <p> </p> </div> </td> <td> <div> <p> </p> </div> </td> <td> <div> <p> </p> </div> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> <div> <p>2,2</p> </div> </td> <td> <div> <p>70-85%</p> </div> </td> <td> <div> <p>515</p> </div> </td> <td> <div> <p>0.3C / 1D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> <div> <p>3</p> </div> </td> <td> <div> <p>85-100%</p> </div> </td> <td> <div> <p>515</p> </div> </td> <td> <div> <p>0.3C / 1D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> <div> <p>4</p> </div> </td> <td> <div> <p>0-100% (drive-cycle)</p> </div> </td> <td> <div> <p>78</p> </div> </td> <td> <div> <p>0.3C / noisy D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> <div> <p>5</p> </div> </td> <td> <div> <p>0-100%</p> </div> </td> <td> <div> <p>78</p> </div> </td> <td> <div> <p>0.3C / 1D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> </tbody> </table> <p>Cells were base-cooled at set temperatures using bespoke test rigs (see our linked publications for details; the supporting information file contains detailed descriptions and photographs). Cells were subject to break-in cycles prior to beginning of life (BoL) performance tests using the ‘Reference Performance Test’ (RPT) procedures. They were then alternately subject to ageing sets and RPTs until the end of testing. Full details of each of these procedures are described in the linked publication.</p> <p>The data contained in this repository is then described in the Data section below. This includes a description of the folder structure and naming conventions, file formats, and data analysis methods used for the ‘Processed Data’ which has been calculated from the raw data.</p> <p>An 'experimental_metadata' .xlsx file is included to aid parsing of data. A jupyter notebook has also been included to demonstate how to access some of the data.</p> <h1>Data</h1> <p>Data are organised according to their parent ‘Experiment’, as defined above, with a folder for each. Within each Experiment folder, there are 3 subfolders: ‘Summary Data’, ‘Processed Timeseries Data’, and ‘Raw Data’.</p> <h2>Summary Data</h2> <p>This folder contains data which has been extracted by processing the raw data in the ‘Degradation Cycling’ and ‘Performance Checks’ folders. In most cases, the data you are looking for will be stored here.</p> <p>It contains: </p> <h3>Performance Summary</h3> <p>A summary file for each cell which details key ageing metrics such as number of ageing cycles, charge throughput, cell capacity, resistance, and degradation mode analysis results. Each row of data corresponds to a different SoH.</p> <p>Degradation Mode Analysis (DMA) was also performed on the C/10 discharge data at each RPT. This analysis uses an optimisation function to determine the capacities and offset of the positive and negative electrodes by calculating a full cell voltage vs capacity curve using 1/2 cell data and comparing against the experimentally measured voltage vs capacity data from the C/10 discharge. See our <a href="https://doi.org/10.1021/acsaem.2c02047">ACS publication</a> for more details.</p> <p>Data includes:</p> <p>· Ageing Set: numbered 0 (BoL) to x, where x is the number of ageing sets the cell has been subject to.</p> <p>· Ageing Cycles: number of ageing cycles the cell has been subject to. *this is not equivalent full cycles.</p> <p>· Ageing Set Start Date/ End date: The date that each ageing set began/ ended.</p> <p>· Days of degradation: Number of days between the date of the first ageing set beginning and the current ageing set ending.</p> <p>· Age set average temperature: average recorded surface temperature of the cell during cycle ageing. Temperature was recorded approximately 1/2 way up the length of the cell (i.e. between positive and negative caps).</p> <p>· Charge throughput: total accumulated charge recorded during all cycles during ageing (i.e. sum of charge and discharge). This is the cumulative total since BoL (not including RPTs, and not including break-in cycles).</p> <p>· Energy throughput: as with "charge throughput", but for energy.</p> <p>· C/10 Capacity: the capacity recorded during the C/10 discharge test of each RPT.</p> <p>· C/2 Capacity: the capacity recorded during the C/2 discharge test of each even-numbered RPT.</p> <p>· 0.1s Resistance: The resistance calculated from the 25-pulse GITT test of each even-numbered RPT. This value is taken from the 12th pulse of the procedure (which corresponds to ~52% SoC at BoL). The resistance is calculated by dividing the voltage drop by the current at a timecale of 0.1 seconds after the current pulse is applied (the fastest timescale possible under the 10 Hz recording condition).</p> <p>· Fitting parameters: output from the DMA optimisation function; 5 parameters which detail the upper/lower SoCs of each electrode, and the capacity fraction of graphite in the negative electrode.</p> <p>· Capacity and offset data: calculated based on the fitting parameters above alongside the measured C/10 discharge capacity.</p> <p>· DM data: Quantities of LLI, LAM-PE, LAM-NE, LAM-NE-Gr, and LAM-NE-Si calculated from the change in capacities/offset of each electrode since BoL.</p> <p>· RMSE data: the root mean squared error of the optimisation function calculated from the residual between the measured and simulated voltage vs capacity profiles.</p> <h3>Ageing Sets Summary</h3> <p>Data from the ageing cycles, summarised on an average per cycle and an average per ageing set basis. Metrics include mean/ max/ min temperatures, voltages etc.</p> <h2>Processed Timeseries data</h2> <p>Timeseries data (voltage, current, temperature, etc.) from each subtest (pOCV, GITT, etc.) of the RPTs, all grouped by subtest-type and by cell ID.</p> <p>Contains the same data as in the ‘Performance Checks’ subfolder of the 'Raw Data' folder, but has been processed to slice into relevant subtests from the RPT procedure and includes only limited variables (time, voltage, current, charge, temperature). These are all saved as .csv files. In general this data will be easier to access than the raw data, but perhaps not as rich.</p> <h2>Raw Data</h2> <p>These are the raw data from the performance checks and from the degradation cycles themselves. The data from here has already been processed by me to get values of ‘energy throughput’, ‘charge throughput’, ‘average ageing temperature’, etc., which are all saved in the ‘Summary Data’ folder as described in the relevant section above.</p> <p>The data in the ‘Degradation Cycling’ folder are organised by ageing set (where an ageing set is a defined number of ageing cycles, as described in the paper). In theory, each cell should have one datafile in each ageing set subfolder. However, due to experimental issues, tests can sometimes be interrupted midway though, requiring the test to be subsequently resumed. In this case, there may be multiple datafiles for each cell in a given ageing set; during analysis, these should be concatenated according to the descriptor in the filename (e.g., ‘cycling7’ + ‘cycling7 (part 2)').</p> <p>Similarly, the unprocessed raw data from the performance checks (i.e. RPTs) is stored in the 'Performance Checks' folder, and structured in the same way.</p> <p>The raw data are saved in the .mpr format produced by the Biologic battery cycler. This is a binary format which is storage-efficient but can be more difficult to process for analysis purposes. We have therefore also exported the data into .txt files (called .mpt) for the performance checks (RPTs) which make analysis easier. However, the exported .mpt files could not be included for the degradation cycling files due to their larger size. If you require access these degradation cycle data, the .mpr binary file can be parsed using the <a href="https://github.com/echemdata/galvani">Galvani</a> package in python, or you can use Biologic’s (proprietary) BT-Lab software to export the data into .txt files.</p> <h3>File Naming Convention</h3> <p>The raw datafiles are named with a standard format. This is:</p> <p> <em>NDK - LG M50 deg - exp 1 - rig 1 - 10degC - cell A - RPT1_01_MB_CB1</em></p> <p> {NDK - LG M50 deg} - {exp 1} – {rig 1} – {10degC} – {cell A} – {RPT1}_{01}_{MB}_{CB1}</p> <p>{Standard prefix} – {experiment number} – {ID of test rig} – {control temperature} – {Cell ID} – {RPT number <em>or</em> aging cycle number}_{step number for the characterisation procedure (see above)}_{experimental technique name (will always be “MB”)}_{battery cycler channel ID used (always the same for a particular cell/experiment)}</p> <p> </p>
Cell-cycle dependent DNA repair and replication unifies patterns of chromosome instability
<p>This repository contains the data used to generate the figures in paper: Cell-cycle dependent DNA repair and replication unifies patterns of chromosome instability.</p>
The molecular architecture of cell cycle arrest
<p>The cellular decision governing the transition between proliferative and arrested states is crucial to the development and function of every tissue. While the molecular mechanisms that regulate the proliferative cell cycle are well established, we know comparatively little about what happens to cells as they diverge into cell cycle arrest. We combined hyperplexed, single-cell imaging with manifold learning to obtain a map of the molecular architecture that governs cell cycle exit and progression into reversible (“quiescent”) and irreversible (“senescent”) states of arrest. Using this map, we resolved multiple points of divergence from the proliferative cell cycle into distinct states of arrest and the molecular mechanisms governing these fate decisions, which we verified by single-cell, time-lapse imaging. We found that senescence is an obligate G1-like molecular state, regardless of the phase of cell cycle exit, and that cells can escape from this “irreversible” state of arrest through the upregulation of G1 – but not G2 – cyclins. This map of cell cycle arrest provides our first glimpse of the overall organization of the proliferation/arrest decision.</p>
Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data
<p>We proposed DCCA for accurately dissecting the cellular heterogeneity on joint-profiling multi-omics data from the same individual cell by transferring representation between each other.</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>
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.