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175 results for “Single-molecule”
Systematic assessment of burst impurity in confocal-based single-molecule fluorescence detection using Brownian motion simulations - photon timetag simulation files
<p>Attached are the photon timestamp and channels simulated for different 3D diffusing molecules simulations at different conditions (simulation was performed by PyBroMo).</p> <p>Each of the files has, in its name, a code. The meaning of the codes are as following:</p> <pre>f32445 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a numerical PSF model </pre> <pre>a01f8f - 15 molecules at a concentration of 31 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a numerical PSF model</pre> <pre>9ff667 - 15 molecules at a concentration of 15.5 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a numerical PSF model</pre> <pre>71154a - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 22.5 micron^2/s - 60 second simulation using a numerical PSF model</pre> <pre>ad926d - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 5.625 micron^2/s - 60 second simulation using a numerical PSF model</pre> <pre>1ab235 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s - 180 second simulation using a numerical PSF model</pre> <pre>d00978 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 5.625 micron^2/s - 180 second simulation using a numerical PSF model</pre> <p> </p> <pre>2469bb - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a Gaussian PSF model </pre> <pre>4be121 - 15 molecules at a concentration of 31 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a Gaussian PSF model</pre> <pre>a7088f - 15 molecules at a concentration of 15.5 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a Gaussian PSF model</pre> <pre>023983 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 22.5 micron^2/s - 60 second simulation using a Gaussian PSF model</pre> <pre>653f61 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 5.625 micron^2/s - 60 second simulation using a Gaussian PSF model</pre> <pre>4f06ee - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s - 180 second simulation using a Gaussian PSF model</pre> <pre>dec32c - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 5.625 micron^2/s - 180 second simulation using a Gaussian PSF model</pre> <p> </p> <pre>85b0a1 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s , 10 of which belong to a sub-population with a mean FRET efficiency of 0.75, and the leftover 5 belong to another sub-populations with a mean FRET efficiency of 0.50 - 60 second simulation using a Numerical PSF model</pre> <pre>964ef3 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s , 10 of which belong to a sub-population with a mean FRET efficiency of 0.75, and the leftover 5 belong to another sub-populations with a mean FRET efficiency of 0.50 - 180 second simulation using a Numerical PSF model</pre> <p> </p> <pre>f28f6e - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s , 10 of which belong to a sub-population with a mean FRET efficiency of 0.75, and the leftover 5 belong to another sub-populations with a mean FRET efficiency of 0.50 - 60 second simulation using a Gaussian PSF model</pre> <pre>c311dd - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s , 10 of which belong to a sub-population with a mean FRET efficiency of 0.75, and the leftover 5 belong to another sub-populations with a mean FRET efficiency of 0.50 - 180 second simulation using a Gaussian PSF model</pre>
3D super-resolution datasets associated with the paper "Whole-cell multi-target single-molecule super-resolution imaging in 3D with microfluidics and a single-objective tilted light sheet"
<p>3D single-molecule super-resolution datasets corresponding to reconstructions shown in <em>Whole-cell multi-target single-molecule super-resolution imaging in 3D with microfluidics and a single-objective tilted light sheet</em> by Saliba & Gagliano, Gustavsson et. al.</p>
Datasets and scripts for: Single-Molecule Dynamic Structural Biology with Vertically Arranged DNA on a Fluorescence Microscope
<p>The folder contains raw datasets (.ptu files) together with scripts and relevant results to reproduce the figures' plots of: "Single-Molecule Dynamic Structural Biology with Vertically Arranged DNA on a Fluorescence Microscope". </p>
Dataset for the paper High durability and stability of 2D nanofluidic devices for long-term single-molecule sensing
<p>Information regarding the Dataset, corresponding to the paper: “Thakur, M., Cai, N., Zhang, M. et al. High durability and stability of 2D nanofluidic devices for long-term single-molecule sensing. npj 2D Mater Appl 7, 11 (2023). https://doi.org/10.1038/s41699-023-00373-5”</p> <p>This folder contains the raw data and complete package of codes used to analyze, view, save, and plot data for the publication titled "High durability and stability of 2D nanofluidic devices for long-term single-molecule sensing". The code folder, "OpenNanopore-nanopore-tools", can be used to plot raw data which corresponds to the figures in the paper and supplementary information. </p>
Datasets underlying the paper Zero-mode waveguide nanowells for single-molecule detection in living cells
<p>Different datasets underlying the paper Zero-mode waveguide nanowells for single-molecule detection in living cells. The repository contains .zip archives, mostly containing a readme file with additional information.</p> <pre>Cell imaging experiments.zip contains the raw image files acquired on arrays of version 1 or version 2 using a Nikon TI inverted microscope and used in figures 4-6. </pre> <p>Gla_0127_14.zip contains SEM images of the fabrication of arrays of version 1</p> <p>Gla_29_Pd_1.zip contains SEM images of the fabrication of arrays of version 2</p> <p>SM experiments.zip contains the raw single-molecule fluorescence data acquired on an array of version 1 using a PicoQuant Microtime microscope together with the analysis files.</p> <p>FDTD simulations.zip contains the simulation files for the use in the software Lumerical</p>
Imaging data from "Live-cell 3D single-molecule tracking reveals modulation of enhancer dynamics by NuRD"
<p>3D 20ms, 3D 500ms and 2D dCas9 raw videos, localisation, tracking and trajectory analysis data</p> <p>From 'Live-cell 3D single-molecule tracking reveals modulation of enhancer dynamics by NuRD" (2021). Biorxiv. https://doi.org/10.1101/2020.04.03.003178</p>
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>
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>
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>
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>
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>
Raw data: The NEOtrap – en route with a new single-molecule technique
<p>NEOtrap raw data shown in:</p> <p>The NEOtrap – en route with a new single-molecule technique. Schmid, Dekker (2021) iScience.</p> <p>Incl. bead trapping and ClpP trapping as specified.</p>
Single-molecule junction spontaneously restored by DNA zipper
<p>Date set for "Single-molecule junction spontaneously restored by DNA zipper" published in Nature Commun, DOI : 10.1038/s41467-021-25943-3.</p>
Observation of robust energy transfer in the photosynthetic protein allophycocyanin using single-molecule pump-probe spectroscopy - single-molecule photon stream
<p>Photon stream used in the article "<em>Observation of robust energy transfer in the photosynthetic protein allophycocyanin using single-molecule pump-probe spectroscopy" </em> to analyze single-molecule fluorescence emission. Detected emission for single-molecule pump-probe experiments with an associated instrument response function (IRF) and background fluoresence (BG). Each detected photon is described by its time within the collected photon stream and its time relative to the excitation laser. Data is organized by sample and by date. Also included is an .xlsx document with fitted timescales for all included molecules and Matalb structure titled 'FinalDataAndStatistics.mat', which includes the final data, and statistics for the data used within the paper.</p>
Force-tuned Avidity of Spike Variant-ACE2 Interactions viewed on the Single-Molecule Level - MD simulations Dataset
<p>Models of SARS-CoV-2 virus spike protein bound to 1-3 of ACE2 receptors embedded in lipid nanodisks. Systems include all files in GROMACS format needed to reproduce simulations performed in the "Force-tuned Avidity of Spike Variant-ACE2 Interactions viewed on the Single-Molecule Level" article.</p> <p> </p> <table> <caption>Details</caption> <thead> <tr> <th scope="col">system</th> <th scope="col">box size (x-y-z) [nm]</th> <th scope="col">Number of atoms</th> </tr> </thead> <tbody> <tr> <td>Spike +<br> 1x ACE2, full length</td> <td>33.44834 28.96711 57.95573</td> <td>5,665,217</td> </tr> <tr> <td>Spike +<br> 1x ACE2, truncated</td> <td>28.05757 24.29856 46.74417</td> <td>3,203,907</td> </tr> <tr> <td>Spike +<br> 2x ACE2, truncated</td> <td>28.30864 21.23141 48.40873</td> <td>2,936,398</td> </tr> <tr> <td>Spike +<br> 3x ACE2, truncated</td> <td>28.32733 21.24544 48.30436</td> <td>2,936,588</td> </tr> </tbody> </table>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.