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Cisplatin enhances cell stiffness and decreases invasiveness rate in prostate cancer cells by actin accumulation: Confocal and atomic force microscopy
<p><strong>Summary</strong></p> <p>Dataset of imaging data related to the publication Raudenska, M., Kratochvilova, M., Vicar, T., Gumulec, J., Balvan, J., Polanska, H. Pribyl, J. & Masarik, M.:Cisplatin enhances cell stiffness and decreases invasiveness rate in prostate cancer cells by actin accumulation. <em>Scientific Reports </em><strong>2019, </strong>9, 1660</p> <p>This dataset includes image data of <em>atomic force microcopy</em> (Young modulus) and <em>confocal microscopy</em>(staining of F-actin and β-tubulin) of prostate cell lines PNT1A, 22Rv1, and PC-3. </p> <p><strong>Materials and Methods</strong></p> <p><em>Cells, cell culture conditions</em></p> <p>Cells confluent up to 50–60% were washed with a FBS-free medium and treated with a fresh medium with FBS and required antineoplastic drug concentration (IC50 concentration for the particular cell line). The cells were treated with 93 µM (PC-3), 38 µM (PNT1A), and 24 µM (22Rv1) of cisplatin (Sigma-Aldrich, St. Louis, Missouri), respectively. IC50 concentrations used for treatment with docetaxel (Sigma-Aldrich, St. Louis, Missouri) were 200nM for PC-3, 70nM for PNT1A, and 150nM for 22Rv1. </p> <p><em>Long-term zinc (II) treatment of cell cultures</em></p> <p>Cells were cultivated in the constant presence of zinc(II) ions. Concentrations of zinc(II) sulphate in the medium were increased gradually by small changes of 25 or 50 µM. The cells were cultivated at each concentration no less than one week before harvesting and their viability was checked before adding more zinc. This process was used to select zinc resistant cells naturally and to ensure better accumulation of zinc within the cells (accumulation of zinc is usually poor during the short-term treatment of prostate cancer cells). Total time of the cultivation of cell lines in the zinc(II)-containing media exceeded one year. Resulting concentrations of zinc(II) in the media (IC50 for the particular cell line) were 50 µM for the PC-3 cell line, 150 µM for the PNT1A cell line, and 400 µM for the 22Rv1 cell line. The concentrations of zinc(II) in the media and FBS were taken into account. </p> <p><em>Actin and tubulin staining</em></p> <p>β-tubulin was labeled with anti- β tubulin antibody [EPR1330] (ab108342) at a working dilution of 1/300. The secondary antibody used was Alexa Fluor® 555 donkey anti-rabbit (ab150074) at a dilution of 1/1000. Actin was labeled with Alexa Fluor™ 488 Phalloidin (A12379, Invitrogen); 1 unit per slide. For mounting Duolink® In Situ Mounting Medium with DAPI (DUO82040) was used. The cells were fixed in 3.7% paraformaldehyde and permeabilized using 0.1% Triton X-100. </p> <p><em>Confocal microscopy</em></p> <p>The microscopy of samples was performed at the Institute of Biophysics, Czech Academy of Sciences, Brno, Czech Republic. Leica DM RXA microscope (equipped with DMSTC motorized stage, Piezzo z-movement, MicroMax CCD camera, CSU-10 confocal unit and 488, 562, and 714 nm laser diodes with AOTF) was used for acquiring detailed cell images (100× oil immersion Plan Fluotar lens, NA 1.3). Total 50 Z slices was captured with Z step size 0.3 μm.</p> <p><em>Atomic force microscopy</em></p> <p>We used the bioAFM microscope JPK NanoWizard 3 (JPK, Berlin, Germany) placed on the inverted optical microscope Olympus IX‑81 (Olympus, Tokyo, Japan) equipped with the fluorescence and confocal module, thus allowing a combined experiment (AFM‑optical combined images). The maximal scanning range of the AFM microscope in X‑Y‑Z range was 100‑100‑15 µm. The typical approach/retract settings were identical with a 15 μm extend/retract length, Setpoint value of 1 nN, a pixel rate of 2048 Hz and a speed of 30 µm/s. The system operated under closed-loop control. After reaching the selected contact force, the cantilever was retracted. The retraction length of 15 μm was sufficient to overcome any adhesion between the tip and the sample and to make sure that the cantilever had been completely retracted from the sample surface. Force‑distance (FD) curve was recorded at each point of the cantilever approach/retract movement. AFM measurements were obtained at 37°C (Petri dish heater, JPK) with force measurements recorded at a pulling speed of 30 µm/s (extension time 0.5 sec).</p> <p>The Young's modulus (E) was calculated by fitting the Hertzian‑Sneddon model on the FD curves measured as force maps (64x64 points) of the region containing either a single cell or multiple cells. JPK data evaluation software was used for the batch processing of measured data. The adjustment of the cantilever position above the sample was carried out under the microscope by controlling the position of the AFM‑head by motorized stage equipped with Petri dish heater (JPK) allowing precise positioning of the sample together with a constant elevated temperature of the sample for the whole period of the experiment. Soft uncoated AFM probes HYDRA-2R-100N (Applied NanoStructures, Mountain View, CA, USA), i.e. silicon nitride cantilevers with silicon tips are used for stiffness studies because they are maximally gentle to living cells (not causing mechanical stimulation). Moreover, as compared with coated cantilevers, these probes are very stable under elevated temperatures in liquids – thus allowing long-time measurements without nonspecific changes in the measured signal.</p> <p><em>Image analysis</em></p> <p>Fluorescence microscopy data were analyzed in ImageJ 1.52h and Python 3.7.1 as follows: cells were manually segmented using actin fluorescence channel, two regions were created for analysis: whole cell and cell periphery, lining a 4 μm thick region around cell border and including most of periphery actin cytoskeleton. In these two regions following parameters were measured for both actin and tubulin fluorescence: Integrated intensity, median intensity, and following regions were measured to describe cell morphology: Cell area, Maximum caliper (max feret diameter), roundness, and aspect ratio. Moreover, stress fibers were manually segmented in every cell and following parameters were measured: number of fibers per cell, feret angle of fiber, integrated intensity, fiber length, mean intensity. Next, a standard deviation of feret angles of individual fibers was calculated relatively to mean of feret angle using a circstd function from scipy package for Python.</p> <p><strong>Identification of files</strong></p> <p><em>Microscopy data</em></p> <p>Files are separated into individual zip files. The dataset of <em>confocal microscopy </em>is separated based on treatments: untreated control, docetaxel-treated cells, cisplatin-treated cells, zinc-treated cells. Filenames actin_tubulin_Zstack_cisplatin.zip, actin_tubulin_Zstack_untreated_control.zip, actin_tubulin_Zstack_zinc.zip, actin_tubulin_Zstack_docetaxel.zip. Files included in these ZIP archives are named as follows: "cellline_treatment_FOV". Files are 3-layer 16bit tiff files with layer sequence as follows: F-Actin (Phalloidin)/b-tubulin/Hoechst 33342. The dataset contains 242 FOVs of three cell line types/three treatments + one control, files are Z-stacks made of 50 slices.</p> <p>The dataset of <em>atomic force microscopy </em>(AFM) is included in one ZIP archive "AFM_YoungModulus_SetpointHeight.zip", which includes data on Young modulus and Setpoint Height of cell lines 22Rv1, PNT1A and PC-3 and treatments zinc, docetaxel, cisplatin (+control), i.e. identical like for confocal microscopy. The file naming is as follows: "AFM_cellline_treatment_FOV_Youngmodulus.tif" for Young modulus and "AFM_cellline_treatment_FOV_setpointheight.tif" for setpoint height. The data are filtered 32-bit tiff images, where the pixel value correspond to cell stiffness (young modulus) in Pa or setpoint height in m.</p> <p><em>Confocal microscopy analysis files</em></p> <p>Following files are csv tables including image analysis of actin/tubulin staining captured by confocal microscope:</p> <p>Cytoskeleton_fluo_analysis_Cell_Cell_periphery_morphology.csv: table includes analyzed data for actin and tubulin staining in following cellular regions: cell, cell periphery. Standard ImageJ parameters regarding intensity and morphology included.</p> <p>Cytoskeleton_fluo_analysis_Fibers.csv: table includes results of manual segmentation and consequent analysis of actin stres fibers in the cells. Apart from standard ImageJ parameters, also number of stress fibers per cell and standard deviation of fiber angle relative to the cell mean angle (for details see methods) are included.</p>
Zellige example dataset: mouse embryonic cochlea stained for F-actin
<p><strong>Mouse embryonic cochlea stained for F-actin at embryonic stage E16.5. </strong></p> <p>The z-stack image encompasses an epithelial surface and a thick mesh of mesenchymal cells. It was acquired with a swept-field confocal microscope (Bruker OpterraII) equipped with a Nikon Plan-Apochromat 60x lens (NA=1.4). Pixel size 0.133 µm, z step <1 µm. This dataset contains both the ground-truth height map and the height map generated with Zellige, along with the parameters used to generate the latter one. The Zellige parameters used are:</p> <p><span class="math-tex">\(T_{A}=1, T_{otsu}=1, S_{min}=5, \sigma_{xy}=4, \sigma_{z}=1, T_{OSE1}=0.1, R_{1}=5, C_{1}=0.7, T_{OSE2}=0.1, R_{2}=10, C_{2}=0.8.\)</span></p> <p>Nota: to compare the ground truth height map with the Zellige height map, one first needs to substrat 1 to all values of the Zellige height map.</p> <p>See the accompanying paper: Extracting multiple surfaces from 3D microscopy images in complex biological tissues with the Zellige software tool. Trébeau <em>et al.</em> 2022: <a href="https://doi.org/10.1101/2022.04.05.485876">https://doi.org/10.1101/2022.04.05.485876</a></p> <p> </p>
Structural basis of actin monomer re-charging by cyclase-associated protein
<p>1) table_of_simulations.pdf: table of simulations</p> <p>2) toppar_HIC.str: methylhistidine (HIC) topologies and parameters</p> <p> -prepared based on analogy</p> <p> -to be used with top_all36_prot.rtf and par_all36_prot.prm</p> <p>3) simulation_archive.tar.gz</p> <p> The Contents:</p> <p>1_ADP-Actin--CARP, 2_ADP-Actin--CAP1, 3_ATP-Actin--WH2, 4_ADP-Actin<br> All systems presented in the paper; see table_of_simulations.pdf<br> Each directory contains<br> 000README gromacs_topologies gromacs_tpr_files index.ndx processed_trajectories prod.mdp systems_at_t=0</p> <p>*** The rosetta models for WH2 domain and the proline-rich loop that connects it to the CARP domain can be found in 2_ADP-Actin--CAP1/rosetta_models</p> <p><br> _Topologies:<br> toppar_c36_jul16:<br> The charmm force field version used to generate topologies before conversion to gromacs; see 000README in the systems directory<br> <br> ***toppar_c36_jul16/toppar_HIC.str: The topology and parameters for methylated histidine used in the simulations.</p> <p> gromacs_topologies:<br> Contains all itp files (converted from psf file using PyTopol's psf2top utility) and parameters.<br> Note that relevant files can also be found in directories corresponding to each system ( 1_ADP-Actin--CARP 2_ADP-Actin--CAP1 3_ATP-Actin--WH2 4_ADP-Actin)</p> <p> </p>
Data set for the replication package of the paper "Constriction of actin rings by passive crosslinkers"
<p>Data set for the replication package of the paper "Constriction of actin rings by passive crosslinkers".</p>
Confocal and STED Live F-actin dataset
<p>Paired confocal and STED images of F-actin nanostructures in living neurons using the far-red fluorogenic dye SiR-Actin. This dataset was used to train and test the TA-GAN model for confocal-to-STED super-resolution of axonal and dendritic F-actin in living neurons (<a href="https://doi.org/10.1101/2021.07.19.452964">Resolution Enhancement with a Task-Assisted GAN to Guide Optical Nanoscopy Image Analysis and Acquisition</a>).</p> <p>All images : 20 nm/pixel.</p> <p>Folders:<br> - train : 753 pairs of confocal/STED images (varying sizes)<br> - valid : 47 pairs of confocal/STED images (varying sizes)<br> - test_initial & test_final : 84 confocal/STED pairs acquired before (initial) and 84 acquired after (final) control sequences where 15 confocal images of the full FOV (500 x 500 pixels = 10μm x 10μm) were acquired at 1 frame/minute. In addition to the confocal image, a sub-region (100 x 100 pixels = 2μm x 2μm) was selected outside the central ROI (300 x 300 pixels = 6μm x 6μm) and acquired with the STED modality at every time step; the signal decrease due to photobleaching effects can therefore be more prononced in the border region outside the central ROI.<br> - test_series : contains 149 series of images acquired with TA-GAN assistance using the change-based Dice coefficient threshold or the variability-based threshold. </p> <p>The test_series folder contains folders with names corresponding to "[date of acquisition]_cs[coverslip number]_ROI[selected region number]". Each folder contains three subfolders : input, full_STED, and initial_final_confocal. <br> - Input : this folder contains three-channel images for each of the 15 frames in the series. The first channel is the full FOV confocal image (10μm x 10μm), the second channel is the STED sub-region (2μm x 2μm) with zero-padding to match the shape of the FOV, and the third channel is a decision map of 0s and 1s, with the 1s indicating the position in the FOV of the STED sub-region.<br> - full_STED : this folder contains all the STED FOVs (10μm x 10μm) acquired when triggered by the TA-GAN assistance. The number of full_STED FOVs varies from 0 to 15 per region, with a mean of 2.8 STED images per series. The full FOV STED images acquired before (initialSTED.tif) and after (finalSTED.tif) the series of 15 frames are also included.<br> - initial_final_confocal : The full FOV confocal images acquired before (initialConfocal.tif) and after (finalConfocal.tif) the series of 15 frames.</p>
F-actin Imaging of Mechanically Compressed Pseudostratified Human Bronchial Epithelial Cells
<p><strong>Overview</strong></p> <p>This dataset includes immunofluorescence microscopy of pseudostratified airway epithelial cells stained for F-actin. Each field of view consists of 3 images visualizing the apical cell boundaries, basal cell boundaries, and basal cell stress fibers.</p> <p><strong>Cell Culture and Treatment</strong></p> <p>Primary human bronchial epithelial cells (from a single donor) were grown on transwells in air-liquid interface culture for 14 days to model a well-differentiated, pseudostratified airway epithelium. Cells were then exposed to mechanical compression (30 cmH2O for 3 hours) mimicking asthmatic bronchoconstriction. Cells were fixed (4% PFA for 30 minutes) at 24, 48, or 72 hours after mechanical compression. Two transwells were collected per condition and timepoint.</p> <p><strong>Immunofluorescence Imaging</strong></p> <p>Fixed transwells were stained for F-actin (Alexa fluor 488-Phalloidin, ThermoFisher Scientific, diluted 1:40, 30 minutes). Transwell membranes were cut from the plastic support and mounted on glass slides. Slides were imaged using a Zeiss Axio Observer Z1 with an apotome module controlled using Zen Blue 2.0 software. Five random fields of view were imaged from each transwell membrane in a z-stack from substrate to apical cell surface. To visualize various planes through the pseudostratified epithelial layer (apical cell boundaries, basal cell boundaries, and basal cell stress fibers), maximum intensity projections were generated from regions of interest through the z-stack.</p> <p><strong>Dataset</strong></p> <p>Each image file contains</p> <ul> <li>Timepoint: <strong>24</strong>, <strong>48</strong>, or <strong>72 </strong>hours after treatment</li> <li>Condition & Well: control <strong>(C) </strong>or mechanical compression<strong> (P) </strong>followed by a number indicating the well (1 or 2)</li> <li>Unique Z-stack ID: <strong><em>3-digit number</em></strong></li> <li><em>Miscellaneous note: MIP or MIP_ROI</em></li> <li>Region of Interest: apical cell boundaries (<strong>ACB</strong>), basal cell boundaries (<strong>BCB</strong>), or basal stress fibers (<strong>SF</strong>)</li> </ul> <p>For example, these 3 maximum intensity projection images came from the<em> same z-stack/field of view. </em>They are from the 72 hour timepoint, mechanical compression, well #2, z-stack #147:</p> <ul> <li>72hr_P2_147_MIP_ACB.tif: <em>apical cell boundaries</em></li> <li>72hr_P2_147_MIP_ROI_BCB.tif: <em>basal cell boundaries</em></li> <li>72hr_P2_147_MIP_ROI_SF.tif: <em>basal stress fiber</em></li> </ul>
Phase Contrast Time-Lapse and F-actin Imaging of Mechanically Compressed or Irradiated Pseudostratified Human Bronchial Epithelial Cells
<p><strong>Overview</strong></p> <p>This dataset includes phase contrast time-lapse imaging of <em>in vitro</em> pseudostratified airway epithelial cells to visualize their collective cellular migration after exposure to mechanical compression (mimicking bronchoconstriction) or irradiation. Additionally, the cells were fixed and stained for F-actin to visualize the apical cell boundaries, basal cell boundaries, and basal cell stress fibers.</p> <p><strong>Cell Culture and Treatment</strong></p> <p>Primary human bronchial epithelial cells (from a single donor) were grown on transwells in air-liquid interface (ALI) culture for 14 days to model a well-differentiated, pseudostratified airway epithelium. Cells were then exposed to either mechanical compression (30 cmH2O for 3 hours) mimicking asthmatic bronchoconstriction or irradiation (1Gy of ionizing radiation using a RS 2000 Biological Research Irradiator (RadSource) on ALI days 7, 10, and 14).</p> <p><strong>Phase Contrast Time-Lapse Imaging</strong></p> <p>At 24 or 72 hours after final treatment, cells were imaged to visualize collective cellular migration. For each independent experimental replicate (2 transwells per treatment per timepoint), six fields of view per well were imaged every 6 minutes over 1.5 hours. The imaging chamber was supplied with 37°C, 5% CO2, humidified air on a Zeiss Axio Observer Z1 to collect phase contrast images. <em>The image resolution is 0.586 µm/pixel.</em></p> <p><strong>Immunofluorescence Imaging</strong></p> <p>Cells were fixed (4% PFA for 30 minutes) at 24 or 72 hours after final treatment (and after phase contrast time-lapse imaging). Fixed transwells were stained for F-actin (Alexa fluor 488-Phalloidin, ThermoFisher Scientific, diluted 1:40, 30 minutes). Transwell membranes were cut from the plastic support and mounted on glass slides. Slides were imaged using a Zeiss Axio Observer Z1 with an apotome module controlled using Zen Blue 2.0 software. Five random fields of view were imaged from each transwell membrane in a z-stack from substrate to apical cell surface. To visualize various planes through the pseudostratified epithelial layer (apical cell boundaries, basal cell boundaries, and basal cell stress fibers), maximum intensity projections were generated from regions of interest through the z-stack. <em>The image resolution is 0.293 µm/pixel.</em></p> <p><strong>Dataset</strong></p> <p>Phase contrast time-lapse movies are provided as *.avi files. Immunofluorescence images are provided as *.tif files. For an individual transwell, the imaging dataset includes:</p> <ul> <li>6 phase contrast time-lapse movies</li> <li>5 immunofluorescence images of apical cell boundaries</li> <li>5 immunofluorescence images of basal cell boundaries</li> <li>5 immunofluorescence images of basal cell stress fibers</li> </ul> <p>Phase contrast time-lapse filenames contain</p> <ul> <li>Donor: U13</li> <li>Timepoint: 24 or 72 hours</li> <li>Treatment & Well: control (C), mechanical compression (P), or irradiation (R); well 1 or 2</li> <li>Field of View: (1) – (6)</li> </ul> <p>Immunofluorescence image filenames contain:</p> <ul> <li>Donor: <strong>U13</strong></li> <li>Timepoint: <strong>24</strong> or <strong>72</strong> hours</li> <li>Treatment & Well: control (<strong>C</strong>), mechanical compression (<strong>P</strong>), or irradiation (<strong>R</strong>); well <strong>1</strong> or <strong>2</strong></li> <li>Field of View: <strong>1-5</strong></li> <li>Region of Interest: apical cell boundaries (<strong>ACB</strong>), basal cell boundaries (<strong>BCB</strong>), or basal stress fibers (<strong>SF</strong>)</li> </ul> <p>Phase contrast time-lapse and immunofluorescence from the same transwell will all start with the same “Donor_Timepoint_Treatment/Well...” (i.e. U13_24_C1…). <strong>Note that the images from phase contrast and immunofluorescence are not necessarily from matched locations within the transwell and are at different spatial scales.</strong></p> <p>Immunofluorescence images from the same z-stack field of view will start with the same “Donor_Timepoint_Treatment/Well_FieldofView…” (i.e. U13_24_C1_1…).</p>
Modeling actinic flux and photolysis frequencies in dense biomass burning plumes - Data asset
<p>Dataset is related to the paper by Tirpitz et al.: Modeling actinic flux and photolysis frequencies in dense biomass burning plumes</p> <p>Contains the preprocessed model input data and the modeling results (actinic fluxes and photolysis frequencies) for the Shady wildfire on July 25, 2019. The VPC model is on a private Github-Repository. Access is provided by the authors on request. See paper and INFO.txt in Data.zip for more information.</p> <p>Correspondence:<br> Jan-Lukas Tirpitz: jltirpitz@atmos.ucla.edu<br> Jochen Stutz: jochen@atmos.ucla.edu</p> <p>Other contributors:<br> Santo Fedele Colosimo<br> Nathaniel Brockway<br> Robert Spurr<br> Matthew Christi<br> Samuel Hall<br> Kirk Ullmann <br> Johnathan Hair<br> Taylor Shingler<br> Rodney Weber<br> Jack Dibb<br> Richard Moore<br> Elizabeth Wiggins<br> Vijay Natraj<br> Nicolas Theys</p>
On discovering functions in actin filament automata
<p>This video show how mapping of 8-bit inputs to 8-bit outputs are implemented in actin filament automaton G. Projection of actin filament on z-plane is shown in grey; projection of nodes being in excited state by the moment of recording inputs are shown in red. Plots show values of activity, i.e. a number of nodes in excited state along x-coordinate.</p>
Reconstitution of Arp2/3-nucleated actin assembly with proteins CP, V-1 and CARMIL
<p><span>Actin polymerization is often associated with membrane proteins containing capping-protein-interacting (CPI) motifs, such as CARMIL, CD2AP, and WASHCAP/Fam21. CPI motifs bind directly to actin capping protein (CP), and this interaction weakens the binding of CP to barbed ends of actin filaments, lessening the ability of CP to functionally cap those ends. The protein V-1 / myotrophin binds to the F-actin binding site on CP and sterically blocks CP from binding barbed ends. CPI-motif proteins also weaken the binding between V-1 and CP, which decreases the inhibitory effects of V-1, thereby freeing CP to cap barbed ends. Here, we address the question of whether CPI-motif proteins on a surface analogous to a membrane lead to net activation or inhibition of actin assembly nucleated by Arp2/3 complex. Using reconstitution with purified components, we discovered that CARMIL at the surface promotes and enhances actin assembly, countering the inhibitory effects of V-1 and thus activating CP. The reconstitution involves the presence of an Arp2/3 activator on the surface, along with Arp2/3 complex, V-1, CP, profilin and actin monomers in solution, recreating key features of cell physiology.</span></p>
2022-actin-prediction data product
<p>Data product produce by pipeline archived at <a href="https://doi.org/10.5281/zenodo.7384386">DOI: 10.5281/zenodo.7384386</a> (https://github.com/Arcadia-Science/2022-actin-prediction/releases/tag/v1.0).</p>
Reconstitution of phase-separated signaling clusters and actin polymerization on supported lipid bilayers
<p>Liquid–liquid phase separation driven by weak interactions between multivalent molecules contributes to the cellular organization by promoting the formation of biomolecular condensates. At membranes, phase separation can promote the assembly of transmembrane proteins with their cytoplasmic binding partners into micron-sized membrane-associated condensates. For example, phase separation promotes clustering of nephrin, a transmembrane adhesion molecule, resulting in increased Arp2/3 complex-dependent actin polymerization. In vitro reconstitution is a powerful approach to understanding phase separation in biological systems. With a bottom-up approach, we can determine the molecules necessary and sufficient for phase separation, map the phase diagram by quantifying de-mixing over a range of molecular concentrations, assess the material properties of the condensed phase using fluorescence recovery after photobleaching (FRAP), and even determine how phase separation impacts downstream biochemical activity. Here, we describe a detailed protocol to reconstitute nephrin clusters on supported lipid bilayers with purified recombinant protein. We also describe how to measure Arp2/3 complex-dependent actin polymerization on bilayers using fluorescence microscopy. These different protocols can be performed independently or combined as needed. These general techniques can be applied to reconstitute and study phase-separated signaling clusters of many different receptors or to generally understand how actin polymerization is regulated at membranes.</p>
Reconstitution of phase-separated signaling clusters and actin polymerization on supported lipid bilayers
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Microscopy and biophysical data for: Synthetic control of actin polymerization and symmetry breaking in active protocells
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Data from: A multiscale biophysical model for the recruitment of actin nucleating proteins at the membrane interface
<p>The dynamics and organization of the actin cytoskeleton are crucial to many cellular events such as motility, polarization, cell shaping, and cell division. The intracellular and extracellular signaling associated with this cytoskeletal network is communicated through cell membranes. Hence the organization of membrane macromolecules and actin filament assembly are highly interdependent. Although the actin-membrane linkage is known to happen through many routes, the major class of interactions is through the direct interaction of actin-binding proteins with the lipid class containing poly-phosphatidylinositols (PPIs). Among the PPIs, phosphatidylinositol bisphosphate (PI(4,5)P<sub>2</sub>) acts as a significant factor controlling actin polymerization in the proximity of the membrane by binding to actin-associated proteins. The molecular interactions between these actin-binding proteins and the membrane lipids remain elusive. Here, using molecular modeling, analytical theory, and experimental methods, we investigate the binding of three different actin-binding proteins, mDia2, NWASP, and gelsolin, to membranes containing PI(4,5)P<sub>2</sub> lipids. We perform molecular dynamics simulations on the protein-bilayer system and analyze the membrane binding in the form of hydrogen bonds and salt bridges at various PI(4,5)P<sub>2</sub> and cholesterol concentrations. Our experimental study with PI(4,5)P<sub>2</sub>-containing large unilamellar vesicles mimics the computational experiments. Using the multivalencies of the proteins obtained in molecular simulations and the cooperative binding mechanisms of the proteins, we also propose a multivalent binding model that predicts the actin filament distributions at various PI(4,5)P<sub>2 </sub>and protein concentrations.</p>
Comparing Lifeact and Phalloidin for super-resolution imaging of actin in fixed cells
<p>Visualizing actin filaments in fixed cells is of great interest for a variety of topics in cell biology such as cell division, cell movement, and cell signaling. We investigated the possibility of replacing phalloidin, the standard reagent for super-resolution imaging of F-actin in fixed cells, with the actin binding peptide `lifeact'. We compared the labels for use in single molecule based super-resolution microscopy, where AlexaFluor 647 labeled phalloidin was used in a (d)STORM modality and Atto 655 labeled lifeact was used in a single molecule imaging, reversible binding modality. We found that imaging with lifeact had a comparable resolution in reconstructed images and provided several advantages over phalloidin including lower costs, the ability to image multiple regions of interest on a coverslip without degradation, simplified sequential super-resolution imaging, and more continuous labeling of thin filaments.</p>
Fungal Divergent Actin: Trait mapping and function hypothesis
<p>This projects investigates the potential new function of a divergent actin form mainly found in fungal species (Fungal Divergent Actin (FDA)) using trait mapping and association analysis as a novel strategy to generate hypotheses about protein function. We use the ProteinCartography pipeline (https://doi.org/10.57844/arcadia-a5a6-1068), combined with trait mapping and trait association analysis to generate hypotheses about the function of FDA. </p> <p>The overall idea is to identify a working set of fungal species for which we are able to confidently determine the presence or absence of fungal divergent actin, gather fungal trait information for these species (ecological traits, structural traits, genetic traits etc...) and determine whether there is any correlation between the presence/absence of FDA and one of the fungal trait. Any correlation between FDA presence/absence and fungal trait can then be used to infer hypothesis about the function of FDA.</p> <p>Our approach is divided into four main steps:</p> <p>- Step 1: Expanding the initial set of fungal species that possess FDA</p> <p>- Step 2: Defining the 'working set of species' (set of fungal species for which we can determine their FDA status (presence or absence))</p> <p>- Step 3: Curating fungal trait information</p> <p>- Step 4: Statistical modeling of the association of FDA and chosen fungal traits</p> <p> </p> <p>In this upload, we provide files that are parts of Steps 1 & 2:</p> <p> - ProteinCartography folder of the ProteinCartography run for the FDA proteins: fungal_divergent_actins_PC.zip</p> <p> - .csv table of all fungal proteins and associated species in Uniprot that have available structure in AlphaFold: Fungi_prot_uniprot.csv</p> <p> </p> <p><strong>ProteinCartography run: fungal_divergent_actins_PC.zip</strong></p> <p><em>(Step 1 - Expanding the initial set of fungal species that possess FDA)</em></p> <p>The aim of the first step, is to detect as many fungal species as possible that possess FDA. For this, we used ProteinCartography (https://doi.org/10.57844/arcadia-a5a6-1068), to screen for protein that have similar structures than the previously identified Fungal Divergent Actin (REF actin pub) and expand the original cluster of FDA.</p> <p>After identifying 6 representative sequences of Fungal Divergent Actins, we used each of the six proteins as input proteins for "Search Mode'' of the pipeline ProteinCartography. Full details on the ProteinCartography pipeline can be found in the GitHub repository and accompanying pub (https://doi.org/10.57844/arcadia-a5a6-1068).</p> <p>The fungal_divergent_actins_PC.zip contains all the inputs and outputs of the ProteinCartography run:</p> <ul> <li>fasta and pdb files for all 6 input proteins</li> <li>configuration file for the ProteinCatography run</li> <li>all output files (including main files presented in the Pub: similarity matrix, semantic analysis and aggregated_features_pca_umap in the subfolder output/clusteringresults</li> </ul> <p> </p> <p><strong>Uniprot list of fungal protein with available structure: </strong>Fungi_prot_uniprot.csv</p> <p><em>(Step 2 - Defining the 'working set of species')</em></p> <p>The aim of Step 2 is to identify the list of fungal species for which we can confidently determine the FDA presence/absence status. While ProteinCartography allows us to identify species that possess FDA, we also need to be able to confidently tell when a species doesn't possess FDA. Thus, the working set is defined as the set of species for which the presence or absence of FDA was putatively established.</p> <p>Because ProteinCartography relies on protein structures available in UniProt and AlphaFold, we decided to define our working set as any fungal species that has a minimum of 6000 protein structures in AlphaFold. Then we consider that any species of this set that is not part of the extended cluster does not possess FDA.</p> <p>Fungi_prot_uniprot.csv is the list of fungal proteins and associated species that have structure avaialable in AlphaFold from UniProt. This serves as the initial file to eventually count the number of proteins with available structures per species and identify the working set of fungal species - To obtain this list we conducted an 'Advanced search' in UniProt using the following query: </p> <ul> <li>'Fungi' in Taxonomy field </li> <li>' * ' for the field AlphaFoldDB cross-reference (found within the Cross reference /3D structure field)</li> </ul>
Figure, 18. Tegulaster alba (R = 5 mm, paratype, NMV F45118): a, abactinal view; b, actinal view. in A molecular and morphological revision of genera of Asterinidae (Echinodermata: Asteroidea)
Figure, 18. Tegulaster alba (R = 5 mm, paratype, NMV F45118): a, abactinal view; b, actinal view.
Data for "Freeze-tolerant crickets fortify their actin cytoskeleton in fat body tissue"
<p>These data files and code are associated with the scientific article <br>"Freeze-tolerant crickets fortify their actin cytoskeleton in fat body tissue."<br>This material is under the same copyright protections as the article itself.</p> <p>Please see the README.txt file for more information.</p>
Mapping the interaction surface between CaVβ and actin and its role in calcium channel clearance
<p><strong>HADDOCK protein-protein docking data for the Ca<sub>V</sub>β/F-actin complex models reported in "Mapping the interaction surface between Ca<sub>V</sub>β and actin and its role in calcium channel clearance".</strong></p> <p> </p> <p>The dataset is divided in four different folders:</p> <ol> <li><strong>Cavbeta2:</strong> data for the docking between dimeric actin (PDB 5OOE) and Ca<sub>V</sub>β<sub>2</sub> (PDB 5V2P) using XL-MS-derived distance restraints </li> <li><strong>Cavbeta4:</strong> data for the docking between dimeric actin (PDB 5OOE) and Ca<sub>V</sub>β<sub>4</sub> (PDB 1VYV) using XL-MS-derived distance restraints </li> <li><strong>Monomer:</strong> data for the control docking between <em>monomeric</em> actin (PDB 5OOE) and Ca<sub>V</sub>β<sub>2</sub> (PDB 5V2P) using XL-MS-derived distance restraints </li> <li><strong>Ab_initio:</strong> data for the control dockings between dimeric actin (PDB 5OOE) and Ca<sub>V</sub>β<sub>2</sub> (PDB 5V2P) <em>without</em> XL-MS-derived distance restraints and using the <em>ab initio</em> options available in HADDOCK 2.4</li> </ol> <p> </p> <p>Each of the <strong>Cavbeta</strong> folders (1-2) and the <strong>Monomer</strong> folder (3) contain:</p> <p>- Inputs: </p> <ul> <li>HADDOCK run parameter file (job_params.json)</li> <li>XL/MS-derived unambiguous distance restraints (unambig.tbl)</li> <li>Bioinformatics-derived (CPORT and NACCESS) ambiguous distance restraints (ambig.tbl)</li> </ul> <p>- Outputs:</p> <ul> <li>Top 4 models of the selected HADDOCK cluster (cluster1_1.pdb, ..., cluster1_4.pdb)</li> <li>Source data for the docking analyses presented in the Supplementary Information (Supplementary Figures 4, 6, 9, 13 and 14 and Supplementary Table 4)</li> </ul> <p> </p> <p>The <strong>Ab_initio</strong> folder (4) contains:</p> <p>- Input: </p> <ul> <li>HADDOCK run parameter files for each of the six control simulations presented in Supplementary Table 6 (job_params.json)</li> </ul> <p>- Outputs:</p> <ul> <li>Source data for the docking analyses presented in Supplementary Table 6</li> <li>Model from control simulation 1 shown in Supplementary Figure 5</li> </ul> <p> </p>
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.