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264 results for “Single molecule”
Single-molecule Fluorescent In Situ Hybridization (smFISH) for RNA detection in the fungal pathogen Candida albicans dataset
<p><strong>This dataset is connected to the protocol article titled:</strong></p> <p>Single-molecule Fluorescent <em>In Situ</em> Hybridization (smFISH) for RNA detection in the fungal pathogen <em>Candida albicans</em></p> <p><strong>Abstract:</strong></p> <p><em>Candida albicans</em> is the most prevalent human fungal pathogen. Its pathogenicity is linked to the ability of <em>C. albicans</em> to reversibly change morphology and to grow as yeast, pseudohyphal or hyphal cells in response to environmental stimuli. Understanding the molecular regulation controlling those morphological switches remains a challenge that, if solved, could help fight <em>C. albicans</em> infections.</p> <p>While numerous studies investigated gene expression changes occurring during <em>C. albicans</em> morphological switches using bulk approaches (e.g., RNA sequencing), here we describe a single-cell and single-molecule RNA imaging and analysis protocol to measure absolute mRNA counts in morphologically intact cells. To detect endogenous mRNAs in single fixed cells, we optimized a single molecule fluorescent <em>in situ</em> hybridization (smFISH) protocol for <em>C. albicans</em>, which allows one to quantify the differential expression of mRNAs in yeast, pseudohyphae or hyphal cells. We quantified the expression of two mRNAs, cell cycle-controlled mRNA (<em>CLB2)</em> and a transcription regulator (<em>EFG1</em>), which show differential expression in the different morphological cell types and in different nutrient conditions. In this protocol we described in detail the major steps of this approach: growth and fixation, hybridization, imaging, cell-segmentation and mRNA spot analysis. Raw data is provided with the protocol to favour reproducibility. This approach could benefit the molecular characterization of <em>C. albicans</em> and other filamentous fungi, pathogenic or non-pathogenic.</p> <p><strong>Data description:</strong></p> <p>This dataset consists of a FISH experiment spanning two different mRNAs, EFG1 and CLB2, and two different nutrient condition, being SPIDER37 and TSB37 in Candida albicans. For culturing, the C. albicans wildtype strain SC5314 was inoculated at 30 degrees overnight (~15 hours) in 10 mL of TSB medium in a 30 degree (celsius) shaking incubator. Next, samples were diluted to a density of 10^5 cells/ mL and inoculated for 6 hours in either 30 mL TSB medium or Spider medium at 37 degrees in falcon tubes on an orbital microplate shaker. Then, samples were fixated by adding PFA to a final concentration of 4% to the medium. For hybridization, both mRNAs were hybridized independently by specific DNA oligo labelled with a Quasar670 dye to enable the visualisation of single mRNA molecules. As both genes are labelled by the same dye, these oligos were not co-applied to the same sample but to independent samples.</p> <p><strong>Microscopy</strong></p> <p>For smFISH imaging we use an Olympus BX-63 epifluorescence microscope equipped with Ultrasonic stage and UPlanApo 100x 1.35NA oil-immersion objective (Olympus). Lumencore SOLA FISH light source, a Hamamatsu ORCA-Fusion sCMOS camera (6.5 µm-pixel size) mounted using U-CMT C-Mount Adapter, and zero-pixel shift filter sets: F36-500 DAPI HC Brightline Bandpass Filter, F36-502 FITC HC BrightLine Filter, F36-542 Cy3 HC BrightLine Filter, and F36-523 Cy5 HC BrightLine Filter. Images are acquired across 61-81 optical sections (depending on the sample thickness) with a z-step size of 0.2 μm. The CellSens software (Olympus) is used for instrument control and image acquisition. For the DAPI channel 10-50 ms of exposure was used. Whilst, for the CY5 channel, used imaging the FISH probes, 750 ms was applied. </p> <p> </p>
Single-molecule Fluorescent In Situ Hybridization (smFISH) for RNA detection in the fungal pathogen Candida albicans small example dataset
<p><strong>This small example dataset is connected to the protocol article titled:</strong></p> <p>Single-molecule Fluorescent <em>In Situ</em> Hybridization (smFISH) for RNA detection in the fungal pathogen <em>Candida albicans</em></p> <p><strong>Abstract:</strong></p> <p><em>Candida albicans</em> is the most prevalent human fungal pathogen. Its pathogenicity is linked to the ability of <em>C. albicans</em> to reversibly change morphology and to grow as yeast, pseudohyphal or hyphal cells in response to environmental stimuli. Understanding the molecular regulation controlling those morphological switches remains a challenge that, if solved, could help fight <em>C. albicans</em> infections.</p> <p>While numerous studies investigated gene expression changes occurring during <em>C. albicans</em> morphological switches using bulk approaches (e.g., RNA sequencing), here we describe a single-cell and single-molecule RNA imaging and analysis protocol to measure absolute mRNA counts in morphologically intact cells. To detect endogenous mRNAs in single fixed cells, we optimized a single molecule fluorescent <em>in situ</em> hybridization (smFISH) protocol for <em>C. albicans</em>, which allows one to quantify the differential expression of mRNAs in yeast, pseudohyphae or hyphal cells. We quantified the expression of two mRNAs, cell cycle-controlled mRNA (<em>CLB2)</em> and a transcription regulator (<em>EFG1</em>), which show differential expression in the different morphological cell types and in different nutrient conditions. In this protocol, we described in detail the major steps of this approach: growth and fixation, hybridization, imaging, cell-segmentation and mRNA spot analysis. Raw data is provided with the protocol to favour reproducibility. This approach could benefit the molecular characterization of <em>C. albicans</em> and other filamentous fungi, pathogenic or non-pathogenic.</p> <p><strong>Data description:</strong></p> <p>This dataset consists of a FISH experiment spanning two different mRNAs, EFG1 and CLB2, and one nutrient condition, SPIDER37, in Candida albicans. For culturing, the C. albicans wildtype strain SC5314 was inoculated at 30 degrees overnight (~15 hours) in 10 mL of TSB medium in a 30 °C shaking incubator. Next, samples were diluted to a density of 10^5 cells/ mL and inoculated for 6 hours in 30 mL Spider medium at 37 °C in falcon tubes on an orbital microplate shaker. Then, samples were fixated by adding PFA to a final concentration of 4% to the medium. For hybridization, both mRNAs were hybridized independently by specific DNA oligo labelled with a Quasar670 dye to enable the visualisation of single mRNA molecules. As both genes are labelled by the same dye, these oligos were not co-applied to the same sample but to independent samples.</p> <p><strong>Microscopy</strong></p> <p>For smFISH imaging we use an Olympus BX-63 epifluorescence microscope equipped with Ultrasonic stage and UPlanApo 100x 1.35NA oil-immersion objective (Olympus). Lumencore SOLA FISH light source, a Hamamatsu ORCA-Fusion sCMOS camera (6.5 µm-pixel size) mounted using U-CMT C-Mount Adapter, and zero-pixel shift filter sets: F36-500 DAPI HC Brightline Bandpass Filter, F36-502 FITC HC BrightLine Filter, F36-542 Cy3 HC BrightLine Filter, and F36-523 Cy5 HC BrightLine Filter. Images are acquired across 61-81 optical sections (depending on the sample thickness) with a z-step size of 0.2 μm. The CellSens software (Olympus) is used for instrument control and image acquisition. For the DAPI channel 10-50 ms of exposure was used. Whilst, for the CY5 channel, used for imaging the FISH probes, 750 ms was applied. </p>
Monitoring the compaction of single DNA molecules in Xenopus egg extract in real time
Open the record for dataset details and reuse information.
Absolute and arbitrary orientation of single molecule shapes
<p>All software for the kinetic, thermodynamic, and EM simulations associated with the journal paper are presented here. Additionally the AFM images and codes for analyzing the orientation of right triangles as well as e-beam and DNA origami design files associated with the paper is included.</p>
Coupled gene expression on single DNA molecules (raw single-molecule movies + description)
<p>Raw single-molecule movies and corresponding experimental details in description file</p>
Dataset: Highly sensitive single molecule detection of macromolecule ion beams
<p>Figure 6 & Figure 7.zip: Figure 6 Superconducting nanowire detector (SSPD) beam profile of a mass-selected Insulin (Ins5+) ion beam at 1000 eV of impact energy.; Figure 7 Area-normalized molecule detection quantum yield. - raw data and Origin 2022 files</p><p>Voltage Shifts.zip: Calibration of deflection electrodes with linear stage - raw data and Origin 2022 files</p><p>ESI source stability.opju: 1 Hour measurement of the source stability with a TOF-MS - Origin 2022 file</p><p>Figure 2.opju: Impact energy in ESI-QMS-SSPD vs. ESI-QMS-Phosphorscreen mass spectra. - Origin 2022 file</p><p>Figure 3.opju: Signal background and complexity in ESI-QMS-SSPD vs. ESI-TOF-MCP spectra. - Origin 2022 file</p><p>Figure 4.opju: Influence of Energy/Mass/Momentum/structure on the detection mechanism. - Origin 2022 file</p><p>Figure 5.opju: Confirmation of the hot-spot model for SSPD detector D2 - Origin 2022 file</p><p>Figure S2.opju: Identification of protein mass spectra. - Origin 2022 file</p><p>Figure S3.opju: ESI-QMS-SSPD spectrum measured with SSPD. - Origin 2022 file</p><p>Figure S5.opju: Beam profile at different deflection voltages. - Origin 2022 file</p><p>Figure S9.opju: Photo-activated ESI-QMS-SSPD spectrum of Ru-complex modified Insulin. - Origin 2022 file</p><p>Linear stage position readback.txt: Position readback when lifting the Linear Stage recorded with a laser triangulometer. - raw data</p>
Polarisation camera movie of single SYTOX Orange molecules on a cover glass
<p>This image dataset is a movie of the fluorescence of single SYTOX Orange molecules (S34861, Invitrogen) dispersed on a cover glass. The data was collected on a fluorescence microscope (Ti-U, Nikon) with a polarisation camera (CS505MUP, Thorlabs). The molecules were excited with a 532 nm diode laser with a measured power density at the sample plane of 0.36 kW/cm^2. An exposure time of 100 ms was used. The following filters were used: dichroic (Di03-R532-t1, Semrock) and emission filter (FF01-582/64, Semrock).</p>
Bayesian machine learning analysis of single-molecule fluorescence colocalization images
<p>Data files for the "Bayesian machine learning analysis of single-molecule fluorescence colocalization images" manuscript.</p>
Can DyeCycling break the photobleaching limit in single-molecule FRET?
<p><strong>Abstract. </strong>Biomolecular systems, such as proteins, crucially rely on dynamic processes at the nanoscale. Detecting biomolecular nanodynamics is therefore key to obtaining a mechanistic understanding of the energies and molecular driving forces that control biomolecular systems. Single-molecule fluorescence resonance energy transfer (smFRET) is a powerful technique to observe in real-time how a single biomolecule proceeds through its functional cycle involving a sequence of distinct structural states. Currently, this technique is fundamentally limited by irreversible photobleaching, causing the untimely end of the experiment and thus, a prohibitively narrow temporal bandwidth of ≤ 3 orders of magnitude. Here, we introduce <em>‘DyeCycling’</em>, a measurement scheme with which we aim to break the photobleaching limit in single-molecule FRET. We introduce the concept of spontaneous dye replacement by simulations, and as an experimental proof-of-concept, we demonstrate the intermittent observation of a single biomolecule for one hour with a time resolution of milliseconds. Theoretically, DyeCycling can provide >100-fold more information per single molecule than conventional smFRET. We discuss the experimental implementation of DyeCycling, its current and fundamental limitations, and specific biological use cases. Given its general simplicity and versatility, DyeCycling has the potential to revolutionize the field of time-resolved smFRET, where it may serve to unravel a wealth of biomolecular dynamics by bridging from milliseconds to the hour range.</p>
Dataset for "Ultra-strong coupling of a single molecule to a plasmonic nanocavity: A first-principles study"
<p># Data and code for "Ultra-strong coupling of a single molecule to a plasmonic nanocavity: A first-principles study," M. Kuisma, B. Rousseaux, K.M. Czajkowski, T.P. Rossi, T. Shegai, P. Erhart, T.J. Antosiewicz, ACS Photonics, doi:10.1021/acsphotonics.2c00066 (2022).</p> <p><br> ## Contents</p> <p>* *data-{type}/*: reproducible data<br> * *src/*: input scripts</p> <p><br> ## Description of the data</p> <p>The data are stored in directories *data-{type}/*. The contents of the directories<br> can be reproduced with the included input scripts.</p> <p>The data are organized in subdirectories *data-{type}/{system}/* corresponding to<br> the considered nanoparticle-molecule systems and simulation type:</p> <p>* data-fd: free energy calculations done with the finite difference mode<br> * data-lcao: strong coupling calculations done with LCAO mode<br> * data-d3: DFT-D3 calculations</p> <p>The contents of each subdirectory are:</p> <p>* *data-{fd,lcao,d3}/{system}/structure.xyz*: physical atomic structure<br> * *data-lcao/{system}/td-x/dm.dat*: delta-kick-induced time-dependent dipole moment<br> * *data-lcao/{system}/td-x/dm_abs_Lorentz_0.100.dat*: photoabsorption spectrum</p> <p>The spectrum plots in the article correspond to the first (x values) and<br> second (y values) columns of the spectrum files.</p> <p>## Reproduction of the data</p> <p>The data was produced using the Python scripts in *src/*,<br> Python version 3.7.3, GPAW version 20.1.0, libxc version 4.3.4,<br> ASE version 3.20.0, NumPy version 1.16.2, and SciPy version 1.2.1.</p> <p>The calculation of the data of a system consists of<br> the following steps (in *bash* shell with, e.g., system=rlx-ico-Al147`):</p> <p>1. Ground-state calculation:<br> * Copy the gs folder to a data-lcao/{system} folder<br> * Set up parellel calculaton parameters as necessary for the computing infrastructure (parallel.py)<br> * Select the Poisson Solver in the settings.py file by commenting out / uncommenting:<br> * for single particles or molecules use poissonsolver = PoissonSolver(eps=eps, remove_moment=9)<br> * otherwise comment out the above line and uncomment the last 8 lines<br> * Submit the gs.py calculation as appropriate for the particular system<br> 2. Time-propagation calculation:<br> * Requires finished ground-state calculation<br> * Set up parellel calculaton parameters as necessary for the computing infrastructure (parallel.py)<br> * Submit the td.py calculation as appropriate for the particular system<br> 3. Spectrum calculation:<br> * Requires finished time-propagation calculation (30 fs propagation)<br> * Run the `$ python spec.py` script</p> <p>Note that the example python scripts use variables STARTTIME and WALLTIME to define<br> allocated compuing time in HPC environments. The `WALLTIME` and `STARTTIME` environment<br> variables defined in *submit.sbatch* are required for a clean exit of the calculation<br> within the allocated time.</p> <p>If the ground-state or time-propagation calculations do not finish within the<br> allocated time, the same *gsc.py* or *tdc.py* scripts can be (re)run to continue<br> the calculation.</p>
Organizing Structural Principles of the Interleukin-17 Ligand-Receptor Axis - Single molecule tracking - raw data
<p>This dataset contains the raw image data that was analyzed in the manuscript "Organizing Structural Principles of the Interleukin-17 Ligand-Receptor Axis"</p>
Organizing Structural Principles of the Interleukin-17 Ligand-Receptor Axis - Single molecule tracking - raw data - calibration images
<p>This dataset contains the images used for channel calibration for the single molecule data that was analyzed in the manuscript "Organizing Structural Principles of the Interleukin-17 Ligand-Receptor Axis"</p>
Simulations of Single Molecule Localization Microscopy frames with scattered single emitters
<p>Datasets used in the work "Combining deep learning with SUPPOSe and Compressed Sensing for SNR-enhanced localization of overlapping emitters".</p> <p>The file <strong>Sample dataset.zip</strong> contains simulated images of frames of Single-Molecule Localization Microscopy. In the directory structure, Q is the number of emitters, d is the emitter distance (in pixels), imax is the image maximum intensity and i indexes different noise realizations. There are three images within each folder: <strong>X</strong> is a noiseless image, <strong>Y</strong> is the image with noise and background and <strong>Z</strong> is a denoised image predicted using a convolutional neural network.</p> <p>The file <strong>Train dataset.zip</strong> contains 5000 simulated pairs of images of single emitters distributed randomly that were used to train a convolutional neural network for denoising. The folder <strong>X</strong> contains noiseless images and the folder <strong>Y</strong> contains the corresponding images with noise and background.</p> <p>In all cases, a Gaussian PSF with size <span class="math-tex">\(\sigma = 3\)</span> px was used as the PSF of the imaging system, Noise is modeled as a Poisson process with a dark signal <span class="math-tex">\(i_{dark} = 10\)</span>.</p> <p>Corresponding author: Axel M. Lacapmesure (alacapmesure@fi.uba.ar)</p> <p> </p> <p><strong>CHANGELIST</strong></p> <ul> <li>Version 2: corrected all file extensions in "Train dataset.zip" that were wrong.</li> </ul>
Code and Data for "Real-time dynamic single-molecule protein sequencing on an integrated semiconductor device"
<p><strong>Code and Data for "Real-time dynamic single-molecule protein sequencing on an integrated semiconductor device".</strong></p> <pre>Code to analyze data produced by the Quantum-Si benchtop device and semiconductor chip is provided in a Python library <strong>qsi_algo</strong> under several submodules: - <strong>rs_caller.py</strong>: Algorithm for calling RS segments (also called ROI segments throughout code). - <strong>rs_caller_controller.py</strong>: Code framework for executing RS calling and property computation in a distributed manner - <strong>rs_properties</strong>: Code for computing properties of identified RS - <strong>rs_classifier</strong>: Algorithms for identifying peptide states (i.e. residue calls) associated with an RS - <strong>utils.py</strong>: shared helper code - <strong>pulse_reader</strong>: reader for binary pulse file - <strong>filters</strong>: ROI and pulse filtering utilities - <strong>plotting</strong>: functions for visualization of data relevant to the analyses presented Jupyter notebooks (<strong>.ipynb</strong>) files are named according to the manuscript figure they are associated with. Analysis code inside uses provided RS (recognition segment) data to demonstrate filtering and residue-calling techniques required to replicate analyses shown in manuscript figures. Please note: several methods rely on randomization for model initialization and/or data sampling which can cause small deviations from equivalent analyses in published figures. The raw data produced from the Quantum-Si benchtop device and semiconductor chip for the assays presented in the accompanying study is presented in a pulse-called binary file format. Pulses can be used as input for RS identification and peptide state identification. Pre-segmented (RS-identified) files are included for convenience. The data contained in the files include: <strong>{run_id}.bin</strong>: Binary format for storing pulse info. The reader provided in <strong>qsi_algo.pulse_reader</strong> produces the following columns: - <strong>aperture_index</strong>: unique aperture index on chip - <strong>start_f</strong>: index of first frame in pulse, counted from the beginning of the run - <strong>end_f</strong>: index of last frame in pulse, counted from the beginning of the run - <strong>dur_f</strong>: duration of pulse in frames - <strong>dur_s</strong>: duration of pulse in seconds - <strong>ipd_f</strong>: interpulse duration in frames (number of frames since end of preceding pulse) - <strong>ipd_s</strong>: interpulse duration in seconds (time in seconds elapsed since end of preceding pulse) - <strong>snr</strong>: signal-to-noise ratio (bin1_intensity / bin1_bg_std) - <strong>intensity</strong>: intensity of pulse (counts above baseline in bin1) - <strong>bin0_intensity</strong>: counts above baseline in bin0 - <strong>intensity_display</strong>: bin1_intensity + bin1_bg_mean - <strong>binratio</strong>: bin0_intensity / bin1_intensity - <strong>bg_mean</strong>: bin1 background mean in region of pulse - <strong>bg_std</strong>: bin1 background standard deviation in region pulse - <strong>bin0_bg_mean</strong>: bin0 background mean in region of pulse - <strong>bin0_bg_std</strong>: bin0 background standard deviation in region pulse <strong>{run_id}.csv.gz</strong>: Compressed comma-separated value file containing RS/ROI properties computed from raw pulses.bin file by included RS caller (example in <strong>rs_caller.py</strong>). - <strong>ap</strong>: unique aperture index on chip - <strong>ROI</strong>: ordinal ROI number in the aperture, 0-indexed - <strong>start_p</strong>: index (.loc) of first pulse in the ROI (inclusive) in pulse dataframe - <strong>end_p</strong>: index (.loc) of last pulse in the ROI (inclusive) in pulse dataframe - <strong>start_f</strong>: first frame of the first pulse in the ROI (inclusive) - <strong>end_f</strong>: Last frame of the last pulse in the ROI (exclusive) - <strong>start_s</strong>: Time (in seconds elapsed from beginning of run) of the start of the ROI - <strong>end_s</strong>: Time (in seconds elapsed from beginning of run) of the end of the ROI - <strong>dur_f</strong>: Duration in frames of the ROI - <strong>dur_s</strong>: Duration in seconds of the ROI - <strong>num_pulses</strong>: Number of pulses in the ROI (that also passed filtering during ROI-calling) - <strong>pw_mean</strong>: Mean pulse duration (in seconds) of pulses in the ROI - <strong>ipd_mean</strong>: Mean inter-pulse duration (in seconds) of pulses in the ROI - <strong>snr_mean</strong>: Mean signal-to-noise ratio of pulses in the ROI - <strong>intensity_mean</strong>: Mean intensity above baseline of pulses in the ROI - <strong>binratio_norm</strong>: Estimated pulse bin ratio of pulses in the ROI, according to the following equation: sum(bin0_intensity*dur_f) / np.sum(bin1_intensity*dur_f) - <strong>ROI_score</strong>: ROI quality score (0-1 from least to most likely to contain recognizer-peptide recognition pulsing) - <strong>binratio_skew</strong>: bin ratio correction factor accounting for binning signal timing differences across the chip. This factor has already been applied to the binratio_norm column</pre>
Reference genes for quantitative Arabidopsis single molecule RNA fluorescence in situ hybridization
Abstract Subcellular mRNA quantities and spatial distributions are fundamental for driving gene regulatory programmes. Single molecule RNA fluorescence in situ hybridization (smFISH) uses fluorescent probes to label individual mRNA molecules, thereby facilitating both localization and quantitative studies. Validated reference mRNAs function as positive controls and are required for calibration. Here we present selection criteria for the first set of Arabidopsis smFISH reference genes. Following sequence and transcript data assessments, four mRNA probe sets were selected for imaging. Transcript counts per cell, correlations with cell size, and corrected fluorescence intensities were all calculated for comparison. In addition to validating reference probe sets, we present sample preparation steps that can retain green fluorescent protein fluorescence, thereby providing a method for simultaneous RNA and protein detection. In summary, our reference gene analyses, modified protocol, and simplified quantification method together provide a firm foundation for future quantitative single molecule RNA studies in Arabidopsis root apical meristem cells.
Source data - Engineering Modular and Tunable Single Molecule Sensors by Decoupling Sensing from Signal Output
<p>Research data supporting the findings of "<em>Engineering Modular and Tunable Single Molecule Sensors by Decoupling Sensing from Signal Output</em>" by Lennart Grabenhorst, Martina Pfeiffer, Thea Schinkel, Mirjam Kümmerlin, Gereon A. Brüggenthies, Jasmin B. Maglic, Florian Selbach, Alexander T. Murr, Philip Tinnefeld and Viktorija Glembockyte. For questions concerning this data, please reach out to Philip Tinnefeld or Viktorija Glembockyte.</p>
Single-molecule structural and kinetic studies across sequence space
<p>Data and analysis code related to the research article:</p> <p>Single-molecule structural and kinetic studies across sequence space</p>
Single-molecule dynamics and genome-wide transcriptomics reveal that NF-kB (p65)-DNA binding times can be decoupled from transcriptional activation
<p>Data and analysis code repository for: </p> <p>Single-molecule dynamics and genome-wide transcriptomics reveal that NF-kB (p65)-DNA binding times can be decoupled from transcriptional activation</p> <p>https://doi.org/10.1101/255380</p>
Raw data for figures used in manuscript - Highly parallel single-molecule identification of proteins in zeptomole-scale mixtures
<p>Raw Image files used for generating the figures (Fig 2, Fig3, Fig4, Fig5 and Fig6, Supplementary figures, files needed for background subtraction and image processing tutorial (docker image)) in the manuscript - Highly parallel single-molecule identification of proteins in zeptomole-scale mixtures</p> <p>Use command tar xfz[v] *.tar.gz to retain the file structure. </p> <p>Docker image in image processing tutorial works on Linux platforms only.</p> <p>File structure after un-compressing each *.tar.gz is as follows - </p> <p>1. acetylated_background_signalsFiles.tar.gz</p> <p> - Folders for the different experiments with the name expt[1..30]</p> <p> - *SIGNALS.pkl - Pickle file (python encoded) containing the information of the histogram of the peptide step-drops</p> <p> - acetylated_backgroundFiles_list.csv (file formatted for performing iterative_background.py)</p> <p> - README.txt (information on the contents and the use of the files in the directory)</p> <p>2. fig2.tar.gz</p> <p> - fig2A/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig2B/ (contains raw image folders, processed_results and README.txt)</p> <p>2. fig3and4.tar.gz</p> <p> - acPeptide_label-2-5/ (contains raw image folders, processed_results)</p> <p> - bocPeptide_label-2-5/ (contains raw image folders, processed_results)</p> <p> - README.txt</p> <p>3. fig5.tar.gz</p> <p> - fig5A_panel1/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5A_panel2/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_A2/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_A3/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_B1/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_B2/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5C/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5D/ (contains raw image folders, processed_results and README.txt)</p> <p>5. fig6.tar.gz</p> <p> - fig6B_top/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig6B_bottom/ (contains raw image folders, processed_results and README.txt)</p> <p>6. fig_supplementary08.tar.gz</p> <p> - (contains raw image folders, processed_results and README.txt)</p> <p>7. fig_supplementart12.tar.gz</p> <p> - supplementary_fig14A/ (contains raw image folders, processed_results and README.txt)</p> <p> - supplementary_fig14B/ (contains raw image folders, processed_results and README.txt)</p> <p>8. imageProcessingTutorial.tar.gz</p> <p> - walkthrough_docker_image.tar.xz (contains the docker image with necessary code pre-installed. Includes small example dataset; Works only in linux docker and not macOS)</p> <p> - README.txt (information on the image processing tutorial). </p>
Revealing the Reovirus T1L-NgR1 binding interface using single-molecule approaches
<p>Initial topology, parameter and coordinate files of the molecular dynamics (MD) simulation of <em>human and mouse NgR1 in complex with two σ3 subunits</em>. We used AMBER for the equilibration phase and GROMACS (2022.3) for the production as molecular engines (input_gromacs.mdp). For each system we run three independent replicas of 2.5 µs. Water molecules and ion atoms were removed from the original trajectories and topology prior to upload. All replicas were previously aligned to chain A (NgR1):</p> <ul> <li>system 1: human NgR1 in complex with two σ3 subunits</li> <li>system 2: murine NgR1 in complex with two σ3 subunits</li> </ul>
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.