Find research datasets worth reusing

opencc-by-4.0Jul 2018View details →

Data set for "State-dependent cell-type-specific membrane potential dynamics and unitary synaptic inputs in awake mice"

Data set for: Pala A, Petersen CCH (2018) State-dependent cell-type-specific membrane potential dynamics and unitary synaptic inputs in awake mice. eLife 7: e35869. DOI: https://doi.org/10.7554/eLife.35869. There are 12 files in this data upload: 1. '2018_Pala_eLife.pdf' - this is a pdf version of the online publication: Pala & Petersen (2018). 2. 'data.mat' - this is a Matlab data structure, which contains all the data for the publication. 3. 'DataViewer.m' - this is a Matlab code for viewing the data. 4. 'DataViewer.fig' - this is a Matlab figure file, which is the GUI layout for 'DataViewer.m'. 5. 'PalaPetersen_Plot.m' - this is a Matlab code, which plots the figures for Pala & Petersen (2018). 6. 'PalaPetersen_Analysis.m' - this is a Matlab code, which analyses the data for the figures of Pala & Petersen (2018). 7. 'blankAPs.m' - this is a Matlab code, which blanks action potentials from the membrane potential trace. 8. 'lowpassfilt.m' - this is a Matlab code, which low pass filters the LFP. 9. 'medianFiltAPs.m' - this is a Matlab code, which median filters the membrane potential trace to remove action potentials. 10. 'remTrialswithAPs.m' - this is a Matlab code, which removes trials with action potentials. 11. 'retrieveSegDur.m' - this is a Matlab code, which retrieves chunks of the recording of a given length. 12. 'suptitleAP.m' - this is a Matlab code, which puts titles above subplots.

opencc-by-4.0Aug 2021View details →

Membrane-Interacting DNA Nanotubes Induce Cancer Cell Death

This dataset contains the raw data that were used for the publication entitled, "Membrane-Interacting DNA Nanotubes Induce Cancer Cell Death" published in Nanomaterials on 4 August 2021. Abstract: DNA nanotechnology offers to build nanoscale structures with defined chemistries to precisely position biomolecules or drugs for selective cell targeting and drug delivery. Owing to the negatively charged nature of DNA, for delivery purposes DNA is frequently conjugated with hydrophobic moieties, positively charged polymers/peptides, cell surface receptor recognizing molecules or antibodies. Here, we designed and assembled cholesterol-modified DNA nanotubes to interact with cancer cells and conjugated them with cytochrome c to induce cancer cell apoptosis. By flow cytometry and confocal microscopy, we observed that DNA nanotubes efficiently bound to the plasma membrane as a function of the number of conjugated cholesterol moieties. The complex was taken up by the cells and localized to the endosomal compartment. Cholesterol-modified DNA nanotubes, but not unmodified ones, induced increased membrane permeability, caspase activation and cell death. Irreversible inhibition of caspase activity, with Z-VAD-FMK, however, only partially prevented cell death. Cytochrome c conjugated DNA nanotubes were also efficiently taken up but did not increased the rate of cell death. These results demonstrate that cholesterol-modified DNA nanotubes induce cancer cell death associated with increased cell membrane permeability and only partially dependent on caspase activity, consistent with a combined form of apoptotic and necrotic cell death. DNA nanotubes may be further developed as primary cytotoxic agents, or drug delivery vehicles, through cholesterol mediated cellular membrane interactions and uptake.

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-4.0Jul 2023View details →

Supplements for "Understanding the interaction between a human transferrin receptor aptamer-short double stranded RNA conjugate and its cell membrane target by in silico methods"

Supplements for "Understanding the interaction between a human transferrin receptor aptamer-short double stranded RNA conjugate and its cell membrane target by in silico methods".  This supplement includes the following files:   1. Structures of the most stable Protein-Aptamer complexes predicted from HADDOCK Haddock_Cluster1.pdb       Haddock_Cluster2.pdb  Haddock_Cluster3.pdb  2. Structure of the most stable conformation aligned with Protein-transferring complex PDB cluster1_aligned.pdb       transferrin_aligned.pdb  3. MM-GBSA decomposition analysis of the three replicas for Protein-Aptamer aptamer_new_rep01_Decomp.dat         aptamer_new_rep02_Decomp.dat     aptamer_new_rep03_Decomp.dat         4. MM-GBSA decomposition analysis of the three replicas for Protein-Aptamer-Conjugate conjugate_new_rep01_Decomp.dat     conjugate_new_rep02_Decomp.dat       conjugate_new_rep03_Decomp.dat

opencc-by-4.0Nov 2023View details →

Aquaporins Embedded in a Cell Membrane with O2 Diffusion

This is an illustration of an aquaporin  embedded in a cell membrane with O2 diffusion as well.

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A role for myosin II cluster and membrane energy in cortex rupture for Dictyostelium discoideum cells

Blebs, pressure driven protrusions of the cell membrane, facilitate the movement of eukaryotic cells such as the soil amoeba Dictyostelium discoideum, white blood cells and cancer cells. Blebs initiate when the cell membrane separates from the underlying cortex. A local rupture of the cortex, has been suggested as a mechanism by which blebs are initiated. However, much clarity is still needed about how cells inherently regulate rupture of the cortex in locations where blebs are expected to form. In this work, we examine the role of membrane energy and the motor protein myosin II (myosin) in facilitating the cell driven rupture of the cortex. We perform under-agarose chemotaxis experiments, using Dictyostelium discoideum cells, to visualize the dynamics of myosin and calculate changes in membrane energy in the blebbing region. To facilitate a rapid detection of blebs and analysis of the energy and myosin distribution at the cell front, we introduce an autonomous bleb detection algorithm that takes in discrete cell boundaries and returns the coordinate location of blebs with its shape characteristics. We are able to identify by microscopy naturally occurring gaps in the cortex prior to membrane detachment at sites of bleb nucleation. These gaps form at positions calculated to have high membrane energy, and are associated with areas of myosin enrichment. Myosin is also shown to accumulate in the cortex prior to bleb initiation and just before the complete disassembly of the cortex. Together our findings provide direct spatial and temporal evidence to support cortex rupture as an intrinsic bleb initiation mechanism and suggests that myosin clusters are associated with regions of high membrane energy where its contractile activity leads to a rupture of the cortex at points of maximal energy.

opencc-zeroMar 2022View details →

opencc-by-4.0Jun 2022View details →

Hippocampal CA1 pyramidal cell membrane voltage recorded in response to noise stimuli at two temperatures.

Electrophysiological recording of the membrane voltage (whole-cell patch-clamp) of three hippocampal CA1 pyramidal cells. Cells are stimulated with a current step chosen to ensure a firing rate around 5-10Hz (amplitude of the current step is given in the filenames) and a noise stimulus with zero mean (Ornstein-Uhlenbeck process with 4ms timescale). Each CSV file contains three columns, the timepoints (saved at 10000Hz), the noise stimulus, and the voltage trace recorded in response to the given noise stimulus. Voltages are recorded at low temperatures (around 32 degrees Celsius) and at high temperatures (around 37 degrees Celsius for cell 1, around 40 degrees Celsius for cells 2 and 3), exact temperatures are given in the filenames. The file metadata.csv contains additional information.

opencc-by-4.0Jun 2024View details →

Accompanying data for the paper "Accounting for the mechanical response of the cell membrane during the uptake of random nanoparticles"

<h2>Contributions</h2> <ul> <li>Iaquinta Sarah did contribute to the first draft edition, the development of the theoretical background of the algorithms and their implementation</li> <li>Khazaie Sharam did contribute to the revision and edition of the draft, and to the development of the theoretical background of the algorithms</li> <li>Jacquemin Frédéric did contribute to the project management and to the revision of the article.</li> <li>Fréour Sylvain did contribute to the project management and to the revision of the article.</li> </ul> <h2>Funding sources</h2> i-Site NExT : Grant/Award Number: ANR-16-IDEX-0007, Région Pays de la Loire and CNRS (French National Centre for Scientific Research). <h2>Data structure and information</h2> <ul> <li>code - <code>np_uptake source and data directory</code> <ul> <li>workflow - <code>scripts to reproduce figures</code></li> <li>np_uptake - <code>source code producing results and figures</code> <ul> <li>figures - <code>utility module to produce figures</code></li> <li>model - <code>see detailed description below</code></li> <li>metamodel_implementation - <code>see detailed description below</code></li> <li>sensitivity_analysis - <code>see detailed description below</code></li> </ul> </li> </ul> </li> </ul> <h3>Detailed description</h3> <h4>Abstract</h4> In order to improve the efficiency of the delivery of cancer treatments to cancer cells, the cellular uptake of nanoparticles (NPs), used as drug delivery systems, is numerically investigated through a mechanical approach. The objective is to optimize the NP's mechanical and geometrical properties to enhance their entry into cancer cells while avoiding benign ones. In previous studies, these properties are modeled as constant during the process of cellular uptake. However, recent observations of the displacement of the membrane's constituents towards the region in the cell membrane where the uptake of the NPs takes place show that the mechanical properties of the membrane vary during this process. Reason for writing The important contribution of adhesion to the wrapping process is already well documented in literature. It is therefore crucial to model this parameter properly as the conclusions made with a constant adhesion model may not be accurate compared to reality. Methodology Based on the existing knowledge on the reaction of membrane constituents to interaction with NPs, a 3-parameter sigmoidal function, accounting for the delay, amplitude, and speed of the reaction, has been used to model the evolution of adhesion. A variance-based sensitivity analysis has then been performed in order to quantify the influence of these parameters on the outputs of the model. Results It was found that the introduction of a variable adhesion tends to alter the predictions of endocytosis of NPs. The contribution of the amplitude and delay is respectively 0.32 and 0.43 times as important as that of the NP's aspect ratio, which is the prominent parameter. The influence of the slope of the transition is the least important parameter and does not appear to contribute to endocytosis. Implications Hence, models of the cellular uptake of NPs should use a variable, instead of constant, adhesion in order a representative as possible of the behavior of the cell membrane. The predictions are different from those obtained using a model with constant adhesion. <h4>Code</h4> This repository is divided into 4 folders: <ul> <li> model: contains the code used to compute the total variation of energy of the interface between a circular NP and a membrane by accounting for the mechanical accommodation of the latter. This folder also contains the routine to determine the final wrapping phase of the system. </li> <li> metamodel_implementation: contains a script to check for the representativeness of the dataset used to create a metamodel, a script to create Kriging and PCE metamodels using the Openturns opensource library, and a routine to validate the metamodel that has just been created; </li> <li> sensitivity_analysis: contains a script that allows to create samples based on the Kriging metamodels that have been created and exported as .pkl files in the metamodel folder. These samples are then used to the apply sensitivity algorithms. The user can choose among the various sensitivity algorithms provided by Openturns. For PCE metamodels, a routine is implemented to directly get the Sobol indices from the coefficients of the PCE metamodel. The indices can be plotted through plot routines; </li> <li> figures: contains a utils script to display the graphs and save them as PNG files with consistency. </li> </ul>

opencc-by-4.0Nov 2022View details →

Lattice Boltzmann simulation of liquid water transport in gas diffusion layers of proton exchange membrane fuel cells: Impact of gas diffusion layer and microporous layer degradation on effective transport properties

Underlying data to publication Sarkezi-Selsky et al., J. Pow. Sour. 556 (2023) 232415, https://doi.org/10.1016/j.jpowsour.2022.232415  Polymer Electrolyte Membrane Fuel Cells (PEMFCs) represent a promising technology for clean drivetrain solutions, in particular for heavy-duty applications. However, lifetime requirements demand high durability of each cell component. In this work, transport of liquid water through pristine and degraded gas diffusion layers (GDL) was simulated with a 3D Color-Gradient Lattice Boltzmann model. The GDL microstructure was reconstructed from high-resolution X-ray micro-computed tomography (μ-CT) of an impregnated Freudenberg H14. The effect of a microporous layer (MPL) was considered by reconstruction of an impregnated and MPL-coated H14. Aged microstructures were generated artificially, assuming loss of polytetrafluoroethylene (PTFE) within the GDL and increase of MPL macroporosity as main degradation mechanisms. Liquid water transport within aged microstructures was simulated by imposing a liquid phase flow rate until breakthrough was reached. Subsequently, the GDL microstructures were analyzed for their breakthrough characteristics by means of saturation and effective gas transport properties. When the MPL was pristine, no distinct GDL degradation effect was observable, this was attributed to the MPL dominating capillary transport. MPL aging, however, led to increased saturations and thus to a deterioration of the effective gas transport. With a partially degraded MPL, aging of the GDL then appeared to affect the breakthrough characteristics.

opencc-by-4.0Nov 2020View details →

Supplementary movies for: "The mammalian membrane microenvironment regulates the sequential attachment of bacteria to host cells"

Supplementary Movie 1: Live visualization of bacterial attachment to host cells. Microscopic visualizations of E. coli VHH adhesion to HeLa GFP. Maximum intensity projection of a 1-hour confocal microscopy time-lapse at 0.1 fps accelerated 100x. Overlay of bacteria (red) location of initial contact (circles) and their corresponding tracks (colored lines). Scale bar: 50 µm.   Supplementary Movie 2: High-speed visualization of bacterial attachment to host cells upon contact. Close-up on microscopic visualizations of E. coli VHH E. coli adhesion to HeLa GFP. Maximum intensity projection of 5-minutes confocal microscopy time-lapses at 1 fps accelerated 10x. Overlay of bacteria (red) location of initial contact (circles) and their corresponding tracks (colored lines).   Supplementary Movie 3: Attachment of E. coli VHH to GFP-coated coverslips. E. coli VHH in flow binding to the edge of a GFP-functionalized coverslip used as a substrate for a microfluidic channel. Maximum intensity projection of 5-minutes confocal microscopy time-lapse at 1 fps accelerated 10x. Scale bar: 50 µm.   Supplementary Movie 4: HeLa GFP cells actively pull bacteria towards their cell body. Maximum intensity projection of a confocal time-lapse experiment at 0.1 fps accelerated 100x of E. coli VHH (red), HeLa GFP (CD80) (green) and 3 mm/s flow. Scale bar: 10 µm.   Supplementary Movie 5: Bacteria sequester GFP upon attachment. Maximum intensity projection of 20-minutes epifluorescence microscopy time-lapse at 1 frame per minutes accelerated 60x. E. coli VHH were added on HeLa GFP under static conditions at an MOI of 200 for a couple of minutes and washed 3 times before imaging. Scale bar: 10 µm.   Supplementary Movie 6: Flagella promote unspecific transient binding. Maximum intensity projection of a confocal time-lapse experiment at 6 frames per minutes accelerated 100x of flagellated E. coli VHH (red), HeLa GFP (CD80) (green) and 3 mm/s flow. Scale bar: 10 µm.

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Data for: Exocytosis of the silicified cell wall of diatoms involves extensive membrane disintegration

Diatoms are unicellular algae, characterized by silica cell walls. The silica elements are formed intracellularly in a membrane-bound silica deposition vesicle (SDV), and are exocytosed after completion. How diatoms maintain membrane homeostasis during the exocytosis of these large and rigid silica elements is a long-standing enigma. We studied membrane dynamics during cell wall formation and exocytosis in two model diatom species, using live-cell confocal microscopy, transmission electron microscopy and cryo-electron tomography. Our results show that during the formation of the mineral phase it is in tight association with the SDV membranes, which are forming a precise mold of the delicate geometrical patterns. During exocytosis, the distal SDV membrane and the plasma membrane gradually detach from the mineral and disintegrate in the extracellular space, without any noticeable endocytic retrieval or extracellular repurposing. Within the cell, there is no evidence for the formation of a new plasma membrane, thus the proximal SDV membrane becomes the new barrier between the cell and its environment, and assumes the role of a new plasma membrane. These results provide direct structural observations of diatom silica exocytosis, and point to an extraordinary mechanism in which membrane homeostasis is maintained by discarding, rather than recycling, significant membrane patches.

opencc-zeroSep 2021View details →

opencc-by-4.0Dec 2022View details →

Data set accompanying the paper "Effective cell membrane tension is independent of polyacrylamide substrate stiffness"

This data set contains the data presented in the publication "Effective cell membrane tension is independent of polyacrylamide substrate stiffness". It consists of optical tweezers data and traction force microscopy data of 3T3 fibroblasts and Xenopus retinal ganglion cells on several different substrates.

opencc-by-4.0Jan 2023View details →

Ultra-stable self-standing Au nanowires/TiO2 nanoporous membrane system for high-performance photoelectrochemical water splitting cells - Dataset

Dataset of results presented in Mater. Horiz., 2022,9, 2797-2808: Ultra-stable self-standing Au nanowires/TiO2 nanoporous membrane system for high-performance photoelectrochemical water splitting cells. E. W. would like to acknowledge Alexander von Humboldt Foundation, Bonn, Germany, for funding the postdoctoral fellowship, and the Polish National Agency For Academic Exchange, Polish Returns Programme (Project no. BPN/PPO/ 2021/1/00002), and the National Science Centre, Poland (Project no. 2022/01/1/ST5/00019) for financial support of the project. G. S. would like to acknowledge the National Science Centre, Poland (Project no. 2016/23/B/ST5/00790). The authors thank C. Erdmann for performing the transmission electron microscopy measurements.

opencc-by-4.0Dec 2022View details →

Text-fig. 47. Synchrotron radiation X-ray tomographic microscopy (SRXTM) images of "One-seeded fruit sp. 1"; Catefica locality, Portugal. a, b) Lateral view of fruits showing slightly sinuous ventral margin and the curved stalk; c, d) Longitudinal sections perpendicular to each other through the median part of fruit and its single seed (c, orthoslice yz0652, d, xz0739) showing the bitegmic seed closely adhering to the fruit wall (fw); the several cell layer thick outer integument (oi) and the membranous inner integument (ii); note the vascular bundle (vb) branching into a dorsal and lateral bundle near the base of the fruit; e) Transverse section (orthoslice xy0600) showing fruit wall (fw) and outer (oi) and inner (ii) integuments of the seed; f) Longitudinal section (orthoslice yz0871) through the micropylar region showing micropyle (mi) formed from membranous inner integument (ii). Specimens, Catefica 49-S174927 (a), Catefica 49-S174923 (b, f), Catefica 49-S174769 (c–e). Scale bars = 300 Μm (a–d), 100 Μm (e, f). in The Early Cretaceous Mesofossil Flora Of Catefica, Portugal: Angiosperms

Text-fig. 47. Synchrotron radiation X-ray tomographic microscopy (SRXTM) images of "One-seeded fruit sp. 1"; Catefica locality, Portugal. a, b) Lateral view of fruits showing slightly sinuous ventral margin and the curved stalk; c, d) Longitudinal sections perpendicular to each other through the median part of fruit and its single seed (c, orthoslice yz0652, d, xz0739) showing the bitegmic seed closely adhering to the fruit wall (fw); the several cell layer thick outer integument (oi) and the membranous inner integument (ii); note the vascular bundle (vb) branching into a dorsal and lateral bundle near the base of the fruit; e) Transverse section (orthoslice xy0600) showing fruit wall (fw) and outer (oi) and inner (ii) integuments of the seed; f) Longitudinal section (orthoslice yz0871) through the micropylar region showing micropyle (mi) formed from membranous inner integument (ii). Specimens, Catefica 49-S174927 (a), Catefica 49-S174923 (b, f), Catefica 49-S174769 (c–e). Scale bars = 300 Μm (a–d), 100 Μm (e, f).

Dataset from "Channelrhodopsin-2 Oligomerization in Cell Membrane Revealed by Photo-Activated Localization Microscopy" (wtChR2-mEos3.2)

This dataset contains raw microscopy data from the article "Channelrhodopsin-2 Oligomerization in Cell Membrane Revealed by Photo-Activated Localization Microscopy" (doi.org/10.1002/anie.202307555) Zip file contains PALM measurements of HEK293 cells expressing ChR2WT fused with mEos3.2. Each folder in .zip file contains one PALM measurement (TIFF format) and recording settings (.xml) automatically created by acquiring software. For technical reasons, single measurements are divided into TIFF files of =<4088 frames. Detailed protocols for sample preparation and data acquisition are described in the article. Some details are listed below. SAMPLE PREPARATION cell line: Flp-In™ T-REx™ 293 transfection method: modified calcium-phosphate transient transfection [1] fixation method: 4% paraformaldehyde solution in PBS for 30 min at room temperature imaging buffer: PBS PALM ACQUISITION A custom-built setup for single-molecule localization microscopy is described in [2]. mEos3.2 was simultaneously photoconverted, imaged and photobleached by gradually increasing UV illumination (405 nm; up to 1 mW at the sample) and continuous excitation at 561 nm (approximately 50 mW at the sample). PALM movies of cell plasma membrane were recorded in total internal reflection fluorescence (TIRF) mode using an EMCCD camera: exposure time = 100 ms frame size = 512x512 pixels pixel size = 80 nm   [1] Chen C, Okayama H. High-efficiency transformation of mammalian cells by plasmid DNA. Molecular and cellular biology. 1987 Aug 1;7(8):2745-52. [2] Tang Y, Dai L, Zhang X, Li J, Hendriks J, Fan X, Gruteser N, Meisenberg A, Baumann A, Katranidis A, Gensch T. SNSMIL, a real-time single molecule identification and localization algorithm for super-resolution fluorescence microscopy. Scientific reports. 2015 Jun 22;5(1):11073

Dataset from "Channelrhodopsin-2 Oligomerization in Cell Membrane Revealed by Photo-Activated Localization Microscopy" (ChR2(C34A/C36A)-mEos3.2)

This dataset contains raw microscopy data from the article "Channelrhodopsin-2 Oligomerization in Cell Membrane Revealed by Photo-Activated Localization Microscopy" (doi.org/10.1002/anie.202307555) Zip file contains PALM measurements of HEK293 cells expressing ChR2C34A/C36A fused with mEos3.2. Each folder in .zip file contains one PALM measurement (TIFF format) and recording settings (.xml) automatically created by acquiring software. For technical reasons, single measurements are divided into TIFF files of =<4088 frames. Detailed protocols for sample preparation and data acquisition are described in the article. Some details are listed below. SAMPLE PREPARATION cell line: Flp-In™ T-REx™ 293 transfection method: modified calcium-phosphate transient transfection [1] fixation method: 4% paraformaldehyde solution in PBS for 30 min at room temperature imaging buffer: PBS PALM ACQUISITION A custom-built setup for single-molecule localization microscopy is described in [2]. mEos3.2 was simultaneously photoconverted, imaged and photobleached by gradually increasing UV illumination (405 nm; up to 1 mW at the sample) and continuous excitation at 561 nm (approximately 50 mW at the sample). PALM movies of cell plasma membrane were recorded in total internal reflection fluorescence (TIRF) mode using an EMCCD camera: exposure time = 100 ms frame size = 512x512 pixels pixel size = 80 nm   [1] Chen C, Okayama H. High-efficiency transformation of mammalian cells by plasmid DNA. Molecular and cellular biology. 1987 Aug 1;7(8):2745-52. [2] Tang Y, Dai L, Zhang X, Li J, Hendriks J, Fan X, Gruteser N, Meisenberg A, Baumann A, Katranidis A, Gensch T. SNSMIL, a real-time single molecule identification and localization algorithm for super-resolution fluorescence microscopy. Scientific reports. 2015 Jun 22;5(1):11073

Dataset from "Channelrhodopsin-2 Oligomerization in Cell Membrane Revealed by Photo-Activated Localization Microscopy" (b1AR-mEos3.2)

This dataset contains raw microscopy data from the article "Channelrhodopsin-2 Oligomerization in Cell Membrane Revealed by Photo-Activated Localization Microscopy" (doi.org/10.1002/anie.202307555) Zip file contains PALM measurements of HEK293 cells expressing ß1AR fused with mEos3.2. Each folder in .zip file contains one PALM measurement (TIFF format) and recording settings (.xml) automatically created by acquiring software. For technical reasons, single measurements are divided into TIFF files of =<4088 frames. Detailed protocols for sample preparation and data acquisition are described in the article. Some details are listed below. SAMPLE PREPARATION cell line: Flp-In™ T-REx™ 293 transfection method: modified calcium-phosphate transient transfection [1] fixation method: 4% paraformaldehyde solution in PBS for 30 min at room temperature imaging buffer: PBS PALM ACQUISITION A custom-built setup for single-molecule localization microscopy is described in [2]. mEos3.2 was simultaneously photoconverted, imaged and photobleached by gradually increasing UV illumination (405 nm; up to 1 mW at the sample) and continuous excitation at 561 nm (approximately 50 mW at the sample). PALM movies of cell plasma membrane were recorded in total internal reflection fluorescence (TIRF) mode using an EMCCD camera: exposure time = 100 ms frame size = 512x512 pixels pixel size = 80 nm   <ol> <li>Chen C, Okayama H. High-efficiency transformation of mammalian cells by plasmid DNA. Molecular and cellular biology. 1987 Aug 1;7(8):2745-52.</li> <li>Tang Y, Dai L, Zhang X, Li J, Hendriks J, Fan X, Gruteser N, Meisenberg A, Baumann A, Katranidis A, Gensch T. SNSMIL, a real-time single molecule identification and localization algorithm for super-resolution fluorescence microscopy. Scientific reports. 2015 Jun 22;5(1):11073</li> </ol>

Dataset from "Channelrhodopsin-2 Oligomerization in Cell Membrane Revealed by Photo-Activated Localization Microscopy" (CD28-mEos3.2)

This dataset contains raw microscopy data from the article "Channelrhodopsin-2 Oligomerization in Cell Membrane Revealed by Photo-Activated Localization Microscopy" (doi.org/10.1002/anie.202307555) Zip file contains PALM measurements of HEK293 cells expressing CD28 fused with mEos3.2. Each folder in .zip file contains one PALM measurement (TIFF format) and recording settings (.xml) automatically created by acquiring software. For technical reasons, single measurements are divided into TIFF files of =<4088 frames. Detailed protocols for sample preparation and data acquisition are described in the article. Some details are listed below. SAMPLE PREPARATION cell line: Flp-In™ T-REx™ 293 transfection method: modified calcium-phosphate transient transfection [1] fixation method: 4% paraformaldehyde solution in PBS for 30 min at room temperature imaging buffer: PBS PALM ACQUISITION A custom-built setup for single-molecule localization microscopy is described in [2]. mEos3.2 was simultaneously photoconverted, imaged and photobleached by gradually increasing UV illumination (405 nm; up to 1 mW at the sample) and continuous excitation at 561 nm (approximately 50 mW at the sample). PALM movies of cell plasma membrane were recorded in total internal reflection fluorescence (TIRF) mode using an EMCCD camera: exposure time = 100 ms frame size = 512x512 pixels pixel size = 80 nm   <ol> <li>Chen C, Okayama H. High-efficiency transformation of mammalian cells by plasmid DNA. Molecular and cellular biology. 1987 Aug 1;7(8):2745-52.</li> <li>Tang Y, Dai L, Zhang X, Li J, Hendriks J, Fan X, Gruteser N, Meisenberg A, Baumann A, Katranidis A, Gensch T. SNSMIL, a real-time single molecule identification and localization algorithm for super-resolution fluorescence microscopy. Scientific reports. 2015 Jun 22;5(1):11073</li> </ol>

Cell membrane buckling governs early-stage ridge formation in butterfly wing scales: data

This repository contains the raw data for: JF Totz, AD McDougal, L Wagner, S Kang, PTC So, J Dunkel, BD Wilts, and M Kolle, Cell membrane buckling governs early-stage ridge formation in butterfly wing scales, (forthcoming). The raw data is of a volumetric time series of scales growing on the wing of an individual Vanessa cardui pupa, collected with quantitative phase imaging. Additional details may be found in the Materials and Methods, as well as the SI, of the above publication.   The companion code repository may be found on Zenodo: JF Totz, AD McDougal, L Wagner, S Kang, PTC So, J Dunkel, BD Wilts, and M Kolle. (Forthcoming). "Cell membrane buckling governs early-stage ridge formation in butterfly wing scales:code" (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8369163">https://doi.org/10.5281/zenodo.8369163</a>   Note that file A-40-01_11_04_34_set_115.mat was previously released in: AD McDougal, S Kang, Z Yaqoob, PTC So, and M Kolle, Data and analysis codes for “In vivo visualization of butterfly scale cell morphogenesis in Vanessa cardui.” Zenodo. https://doi.org/10.5281/zenodo.5532941. We include it here for completeness of this time series.