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15 results for “eLife”
Data and Computer scripts for 'Place recognition using batlike sonar' (eLife)
<p>The paper ‘Place recognition using batlike sonar’ can be freely accessed online at http://dx.doi.org/10.7554/eLife.14188. Contents, including text, figures, and data, are free to reuse under a CC BY 4.0 license. </p> <p>The uploaded files contain all data (including raw data), scripts and supporting files used in preparing the manuscript</p> <ul> <li>360panoramas.tar: 360 panoramic pictures taken at the locations at the St Andrews site.</li> <li>PhotosSites.tar: Additional pictures taken at the different ensonification sites</li> <li>ProcessData.tar: This contains all Matlab code for processing and visualizing the data. Also, it contains the templates for all locations as used in the paper.</li> <li>RawData.7z.0xx: These files contain the raw acoustic data as recorded from the microphones for all locations at each of the three sites. This data is provided as Matlab arrays. Due to the file size limitation of Zenodo, the archive has been split into 17 parts. Your archive manager should be able to open all data by accessing RawData.7z.001.</li> <li>Latex.tar: The latex source code and images for the final manuscript.</li> </ul>
Supplemental data for "Intramolecular feedback regulation of the LRRK2 Roc G domain by a LRRK2 kinase dependent mechanism" (Gilsbach et al., eLife 2024, doi:10.7554/eLife.91083)
<p><strong>Supportive data for the eLife version of record.</strong></p> <p><strong>(1) Data used for the Michaelis Menten Kinetics.</strong></p> <p><strong>HPLC-based assay.</strong> Steady-state kinetic measurements of LRRK2-mediated GTP hydrolysis were performed as previously described (Ahmadian et al., 1997). Briefly, 0.1 µM of full-length LRRK2 was incubated with different amounts of GTP (0, 25, 75, 150, 250, 500, 1000, 2000, 3000 and 5000 µM) and production of GDP was monitored by reversed phase C18 HPLC. To this end, the samples (10 µl) were directly injected on a reversed-phase C18 column (pre-column: Hypersil Gold, 3µm particle size, 4.6x10mm; main column: Hypersil Gold, 5µm particle size, 4.6x250mm, Thermo Scientific) using an Ultimate 3000 HPLC system (Thermo Scientific, Waltham, MA, USA) in HPLC-buffer containing 50 mM KH<sub>2</sub>PO<sub>4</sub>/K<sub>2</sub>HPO<sub>4</sub> pH 6.0, 10 mM tetrabutylammonium bromide and 10-15% acetonitrile. Subsequently, samples were analyzed using the HPLC integrator (Chromeleon 7.2, Thermo Scientific, Waltham, MA, USA). Initial rates of GDP production were plotted against the GTP concentration using GraFit5 (v.5.0.13, Erithacus Software). The number of experiments is indicated in the graph and data point is the average (±s.e.m.) of indicated repetitions. The Michaelis-Menten equation was fitted to determine K<sub>M</sub> (±s.e.) and k<sub>cat</sub> (±s.e.). Excel sheets used for the calculation of means are provided. No values are reported if the HPLC separation failed (e.g. unstable baseline).</p> <p><strong>Charcoal GTP hydrolysis assay. </strong>The [γ-32P]GTP charcoal assay was performed as previously described (Bollag and McCormick, 1995). Briefly, 0.1 µM full-length LRRK2 or 0.5 µM 6xHIS-MBP-RocCOR was incubated with different GTP concentrations, ranging from 75 µM to 8 mM, in the presence of [γ-<sup>32</sup>P] GTP in GTPase assay buffer (30 mM Tris pH 8, 150 mM NaCl, 10 mM MgCl<sub>2</sub>, 5% (v/v) Glycerol and 3 mM DTT). Samples were taken at different time-points and immediately quenched with 5% activated charcoal in 20 mM phosphoric acid. All non-hydrolyzed GTP and proteins were stripped by the activated charcoal and sedimented by centrifugation. The radioactivity of the isolated inorganic phosphates was then measured by scintillation counting. The initial rates of γ-phosphate release and the Michaelis-Menten kinetics were calculated as described above.</p> <p><strong>(2) Profile plots (Raw data) obtained for the Mass photometry analysis for T1343A vs WT LRRK2.</strong></p> <p>MP was performed as described in (Guaitoli et al., 2023).<strong> </strong>Briefly, the dimer ratio of LRRK2 was determined on a Refeyn Two MP instrument (Refeyn). Prior to the experiment, a standard curve relating particle contrasts to molecular weight was established using a Native molecular weight standard (Invitrogen, 1:200 dilution in HEPES-based elution buffer: 50 mM HEPES [pH 8.0], 150 mM NaCl supplemented with 200 µM desthiobiotin). Prior to mass photometry, the proteins, either WT or T1343A LRRK2, were incubated with 0.5 mM ATP or buffer (control) for 30 min at 30 ℃. The LRRK2 protein was diluted to 2x of the final concentration (end concentrations: 75 nM and 100 nM) in elution buffer. The optical setup was focused in 10 μl elution buffer before adding 10 µl of the adjusted protein sample. Depending on the obtained count numbers, acquisition times were chosen between 20 s to 1 min. The dimer ratio in each measurement was normalize according to the equation. The measurement was perfomed in triplicates.</p> <p><strong>(3) AlphaFold3 model of LRRK2-pT1343 either bound to GDP/Mg or GTP/Mg.</strong></p> <p>Using AlphaFold3 (Abramson et al., 2024), we modeled and compared the GDP vs the GTP-state of phospho-T1343 LRRK2. Interestingly, the AlphaFold3 model suggests, that the phosphate group of the pT1343 residue is orientated inwards thereby substituting the gamma phosphate of the GTP in the GDP-bound state of LRRK2. This finding is in well agreement with MD simulations published recently (Stormer et al., 2023).</p> <p><strong>(4) Western blot RAW files for the cell-based phospho Rab asssay (RAW data for Figure 6 supplement 2/ Supplemental Figure 4 in the preprint version, Gilsbach et al, 2024)</strong></p> <p>Cell-based LRRK2 activity assays were performed as previously described (Singh et al., 2022). Briefly,<strong> </strong>HEK293T cells were cultured in DMEM (supplemented with 10% Fetal Bovine Serum and 0.5% Pen/Strep). For the assay, the cells were seeded onto six-well plates and transfected at a confluency of 50-70% with SF-tagged LRRK2 variants using PEI-based lipofection. After 48 hours cells were lysed in lysis buffer [30 mM Tris-HCl (pH7.4), 150 mM NaCl, 1% NonidentP-40 substitute, complete protease inhibitor cocktail, PhosStop phosphatase inhibitors (Roche)]. Lysates were cleared by centrifugation at 10,000 x g and adjusted to a protein concentration of 1 µg/µl in 1x Laemmli Buffer. Samples were subsequently subjected to SDS PAGE and Western Blot analysis to determine LRRK2 pS935 and Rab10 T73 phosphorylation levels, as described below. Total LRRK2 and Rab10 levels were determined as a reference for normalization. For Western blot analysis, protein samples were separated by SDS–PAGE using NuPAGE 10% Bis-Tris gels (Invitrogen) and transferred onto PVDF membranes (Thermo Fisher). To allow simultaneous probing for LRRK2 on the one hand and Rab10 on the other hand, membranes were cut horizontally at the 140 kDa MW marker band. After blocking non-specific binding sites with 5% non-fat dry milk in TBST (1 h, RT) (25 mM Tris, pH 7.4, 150 mM NaCl, 0.1% Tween-20), membranes were incubated overnight at 4°C with primary antibodies at dilutions specified below. Phospho-specific antibodies were diluted in TBST/ 5% BSA (Roth GmbH). Non-phospho-specific antibodies were diluted in TBST/ 5% non-fat dry milk powder (BioRad). Phospho-Rab10 levels were determined by the site-specific rabbit monoclonal antibody anti-pRAB10(pT73) (Abcam, ab230261) and LRRK2 pS935 was determined by the site-specific rabbit monoclonal antibody UDD2 (Abcam, ab133450), both at a dilution of 1:2,000. Total LRRK2 levels were determined by the in-house rat monoclonal antibody anti-pan-LRRK2 (clone 24D8; 1:10,000) (Carrion et al., 2017). Total Rab10 levels were determined by the rabbit monoclonal antibody anti-RAB10/ERP13424 (Abcam, ab181367) at a dilution of 1:5,000. For detection, goat anti-rat IgG or anti-rabbit IgG HRP-coupled secondary antibodies (Jackson ImmunoResearch) were used at a dilution of 1:15,000 in TBST/ 5% non-fat dry milk powder. Antibody–antigen complexes were visualized using the ECL plus chemiluminescence detection system (GE Healthcare) using the Stella imaging system (Raytest) for detection and quantification.</p> <p><strong>Figure 6 Source Data 1:</strong> <span>Images generated by the Stella system are shown which were used for quantification. The annotation file equals Figure6-figure supplement 2 (Gilsbach et al., eLife 2024, doi:10.7554/eLife.91083). The lines corresponding to </span>LRRK2 pS935, total LRRK2, Rab10 pT73 and total Rab10 were <span>used for the quantification shown in Figure 6.</span></p>
PBMC CITE-seq, 10x Multiome, and TEA-seq multiomic datasets from Swanson, et al. eLife (2021).
<p>Assembled multiomic datasets from Swanson, et al. <em>Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq</em>. eLife 2021;10:e63632 DOI: <a href="https://doi.org/10.7554/eLife.63632">10.7554/eLife.63632</a> </p> <p>Datasets were assembled from the GEO repository: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE158013">GSE158013</a> </p> <p>Data are provided as SeuratObjects stored in separate .rds files using the saveRDS() function in R.</p> <p>MuData files for use with Python tools (muon, scanpy, scvitools, et al.) will be added soon.</p> <p>Contact Lucas Graybuck (lucasg at alleninstitute dot org) if there are problems with these datasets.</p>
AOSLO Single Cell Blood Flow - Raw Data (eLife paper: Joseph et al. 2019)
<p>Raw AOSLO videos of eLife paper Joseph et al. 2019 - 'Imaging single-cell blood flow in the smallest to largest vessels in the living retina'. <a href="https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.7554_eLife.45077&d=DwMFaQ&c=kbmfwr1Yojg42sGEpaQh5ofMHBeTl9EI2eaqQZhHbOU&r=EdsTL7DuEvOHun7eVBmBd9sxUuPhDEmdFDf0tlkKUO4&m=56MyNrE_d-v6PSU7Go9NePVOrIpAvWHBaC8wVvgs3_k&s=G7SkWT-fSV9d3SJmpWEUUGp2a6mGotbG-uAwNZftpIo&e=">https://doi.org/10.7554/eLife.45077</a> . For additional data or questions, please contact author Aby Joseph (aby.joseph@rochester.edu, dreamworks1991@gmail.com)</p>
Sprint for your eLife! How the eLife Innovation Sprint Helps Drive Forward Open Science Projects
<p><strong>Episode Summary</strong></p> <p>In this episode we cover the eLife Innovation Sprint 2020, that was held online on September 2nd and 3rd, 2020. The sprint facilitates collaboration between people who are working on tools, services, and other projects that enhance open science and research.</p> <p>We talk to the organiser Dr Emmy Tsang, who at the time was the Innovation Community Manager for eLife but is now the Community Engagement Manager for TU Delft. As well as participants from two of the projects that took part: Dr Cássio Amorim, creator of SciGen.Report, 'a platform to easily share and view any information that researchers may have on the reproducibility of papers'. And Esha Datta and Daniel Nüst, who who both joined the sprint to work on the Expanding Open Grants project, developed by Dr Hao Ye, which increases accessibility to examples of grants and proposals. </p> <p><strong>Episode Links: </strong></p> <p><a href="https://sprint.elifesciences.org/">eLife Sprint</a></p> <ul> <li>Emmy Tsang: @emmy_ft </li> <li>@eLifeInnovation</li> </ul> <p><a href="https://scigen.report/">SciGen.Report</a></p> <ul> <li>@ScigenReport </li> </ul> <p><a href="https://www.ogrants.org/">Expanding Open Grants</a></p> <ul> <li>@nordholmen</li> <li>The new GH org for the sprint with latest developments:<br> <a href="https://github.com/expanding-open-grants/">https://github.com/expanding-open-grants/</a></li> <li>The wrap up slides are at:<br> <a href="https://docs.google.com/presentation/d/1PlW7Iq5xOKu1kbo_CLvnYXBJ3ldPdd4ulggZJl4F2-Y/edit#slide=id.g96e132450a_0_112">https://docs.google.com/presentation/d/1PlW7Iq5xOKu1kbo_CLvnYXBJ3ldPdd4ulggZJl4F2-Y/edit#slide=id.g96e132450a_0_112</a> </li> </ul>
Dataset related to the publication: eLife-VOR-RA-2023-92344R1
<p><span>The data presented in this repository is the normalized gene expression (<a href="../api/records/10963444/draft/files/dataset1_TBM_mortality_gene_expression.txt/content" target="_blank" rel="noopener">dataset1_TBM_mortality_gene_expression.txt</a>) and the qPCR validation (<a href="../api/records/10963444/draft/files/dataset2_TBM_mortality_qPCR_Validation.txt/content" target="_blank" rel="noopener">dataset2_TBM_mortality_qPCR_Validation.txt</a>) data sets used in our work “<em><span>Whole blood transcriptional profiles and the pathogenesis of tuberculous meningitis”</span></em>, available at <a href="https://doi.org/10.7554/eLife.92344.1">https://doi.org/10.7554/eLife.92344.1</a>. The normalized gene expression data set (#1) were generated from RNA-seq which was done at Ramaciotti Centre for Genomics used </span><span>TruSeq Stranded Total RNA Ribo-zero Globin kit and NovaSeq 6000 sequencing system. </span><span> We used an in-house pipline described in our manuscripts to generate raw gene count expression which was then normalized by Deseq2 package and batch corrected by combat function in SVA package. The qPCR validation data set (#2) was generated by microfluidic RT-qPCR using Biomark™ 48.48 Complete Bundle with Delta Gene™ Assays and BioMark™ HD system.</span></p>
data and model associated with "Generalization of learned responses in the mormyrid electrosensory lobe" published in eLife
<p>Data and code for model of negative image formation associated with "Generalization of learned responses in the mormyrid electrosensory lobe" published in eLife.</p>
MiSeq data from Henssen et al. Elife 2015 DOI: 10.7554/eLife.10565
<p>This data represents the output (FASTQ format) of sequencing FLEA-PCR products, as described in Henssen et al. "Genomic DNA transposition induced by human PGBD5" DOI: <a href="https://doi.org/10.7554/eLife.10565">10.7554/eLife.10565</a> </p> <p>All scripts that were used to analyse this data can be found <a href="https://zenodo.org/record/22206#.Xmd-IqhKiUk">https://zenodo.org/record/22206#.Xmd-IqhKiUk</a></p> <p>The annotation of the samples can be found in the attached excel file Summary_MiSeq.xlsx </p> <p>Any questions regarding this dataset should be addressed to kentsisresearchgroup@gmail.com or henssenlab@gmail.com </p>
Data for: Plastic vasomotion entrainment in eLife
<p>The presence of global synchronization of vasomotion induced by oscillating visual stimuli was identified in the mouse brain. Endogenous autofluorescence was used and the vessel "shadow" was quantified to evaluate the magnitude of the frequency-locked vasomotion. This method allows vasomotion to be easily quantified in non-transgenic wild-type mice using either the wide-field macro-zoom microscopy or the deep-brain fiber photometry methods. Vertical stripes horizontally oscillating at a low temporal frequency (0.25 Hz) were presented to the awake mouse and oscillatory vasomotion locked to the temporal frequency of the visual stimulation was induced not only in the primary visual cortex but across a wide surface area of the cortex and the cerebellum. The visually induced vasomotion adapted to a wide range of stimulation parameters. Repeated trials of the visual stimulus presentations resulted in the entrainment of the amplitude of the vasomotion. Horizontally oscillating visual stimulus is known to induce horizontal optokinetic response (HOKR). The amplitude of the eye movement is known to increase with repeated training sessions and the flocculus region of the cerebellum is known to be essential for this learning to occur. Here, we show a strong correlation between the average HOKR performance gain and the vasomotion entrainment magnitude in the cerebellar flocculus. Therefore, the plasticity of vasomotion and neuronal circuits appeared to occur in parallel. Efficient energy delivery by the entrained vasomotion may contribute to meeting the energy demand for increased coordinated neuronal activity and the subsequent neuronal circuit reorganization.</p>
Data and code for Wosniack et al. , eLife, 2022
<p>This is a (pre-processed) dataset associated to the following publication :<br> </p> <blockquote> <p>Wosniack et al. eLife 2022;11:e75826. DOI:<a href="https://doi.org/10.7554/eLife.75826"> https://doi.org/10.7554/eLife.75826</a></p> </blockquote> <p>The associated code is currently hosted on gitHub:<br> <a href="https://github.com/comp-neural-circuits/adaptation-of-drosophila-larva-foraging">https://github.com/comp-neural-circuits/adaptation-of-drosophila-larva-foraging</a></p> <p><strong>Original author : </strong>Marina E Wosniack<br> <strong>Current repository curator : </strong>Dylan Festa</p> <p> </p>
Data for: Plastic vasomotion entrainment in eLife
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NMR data in 'Ca2+-dependent release of Synaptotagmin-1 from the SNARE complex on phosphatidylinositol 4,5-bisphosphate-containing membranes' by Voleti, Jaczynska and Rizo, eLife 2020
<p>The Ca<sup>2+</sup> sensor synaptotagmin-1 and the SNARE complex cooperate to trigger neurotransmitter release. Structural studies elucidated three distinct synaptotagmin-1-SNARE complex binding modes involving 'polybasic', 'primary' and 'tripartite' interfaces of synaptotagmin-1. We investigated these interactions using NMR and fluorescence spectroscopy. Synaptotagmin-1 binds to the SNARE complex through the polybasic and primary interfaces in solution. Ca<sup>2+</sup>-free synaptotagmin-1 binds to SNARE complexes anchored on PIP<sub>2</sub>-containing nanodiscs. R398Q/R399Q and E295A/Y338W mutations at the primary interface, which strongly impair neurotransmitter release, disrupt and enhance synaptotagmin-1-SNARE complex binding, respectively. Ca<sup>2+</sup> induces tight binding of synaptotagmin-1 to PIP<sub>2</sub>-containing nanodiscs, releasing synaptotagmin-1-SNARE interactions. Specific effects of mutations in the polybasic region on Ca<sup>2+</sup>-dependent synaptotagmin-1-PIP<sub>2</sub>-membrane interactions correlate with their effects on release. Our data suggest that synaptotagmin-1 binds to the SNARE complex through the primary interface and that Ca<sup>2+</sup> releases this interaction, inducing PIP<sub>2</sub>/membrane binding and allowing cooperation between synaptotagmin-1 and the SNAREs in membrane fusion to trigger release.</p>
"interactive" version of data associated with the eLife paper "Integrative genomic analysis of the human immune response to influenza vaccination"
This archive contains an "interactive" version of data associated with the eLife paper "Integrative genomic analysis of the human immune response to influenza vaccination" by Luis M Franco, Kristine L Bucasas, Janet M Wells, Diane Niño, Xueqing Wang, Gladys E Zapata, Nancy Arden, Alexander Renwick, Peng Yu, John M Quarles, Molly S Bray, Robert B Couch, John W Belmont, Chad A Shaw http://dx.doi.org/10.7554/eLife.00299 (doi:10.7554/eLife.00299) Installation: (1) Download the rar file, link available from the paper. (2) Unrar the file: Mac OS X: Use UnRarX - http://www.unrarx.com Linux : unrar command - http://en.wikipedia.org/wiki/Unrar (3) Open the file "vaxgenomics.htm" in your favorite browser use File>Open : Once opened in your browser, you should see a "Circos"-style circular plot of the data, and links to the tables and figures used in this application. Each table includes a link-out from GeneID to the Entrez entry for that Gene as well as additional links to the data. The link to Entrez requires access to NCBI, so will only work if you have Internet availability for this to work. All other links point to content contained within the application/archive. (4) Figures: High-resolution copies of the figures included in the paper. Fig 1: eQTL profile of flu vaccination response. Markers associated with cis gene expression identified in the discovery cohort were replicated in a vaidation cohort and -log10 p-values for both data sets are shown in the genome wide circularized graphic. Fig 2: Effect of the treatment on eQTL assocation. We observed that the pattern of association between gene expression and SNP changed over time after vaccination. Panel A of this figure shows this phenomena for a single gene NECAB2. Panel B shows the aggregate character of this phenomena across large numbers of markers. The change in R2 compared to the initial time point is depicted; this change in R squared appears to correspond to an increase the magnitude of the slope (additive association with genotype). Fig 3: Pathways and processes identified as enriched in our candidates. Both Ingenuity IPA Analysis and GO and KEGG pathway databases were used. Heavily implicated immunologic response classes are repsresented. Fig 4: Human immune response as measured by our Antibody Response scores are correlated with gene expression changes, and these patterns are recapituoated in discovery and validation (male/female) cohorts. Fig 5: Immune cellular context of the genes identifeies in our eQTL and immune response analysis. A striking number of our validated genes occuue in the antigen processing and presentatio pnthway. Fig 6: Q-Q plot depicting that the strength of association between genotype and phenotype (titer response) is stronger for markers that have a SNP association with expression and where expression is associated with titer response than would be expected for random SNPs. Fig 7: Causal and Reactive Model Analyses. Three-way association between genotype, expression and trait. Our data are more consistent with a causal relationship compared to reactive, but the results are not definitive. We explored the sample size necessary to investigate this in the supplement. Fig 8: Diagram demonstrating the experimental design of this study. Fig 9: Population structure analysis performed on our cohort using the genome wide SNP data confirms the European ancestry and ethnic homogeoneity of our ty sample Fig 10: Schematic of the eQTL analysis. The time course of gene expression change is integrated with a single model considering effects of Day, Genotype, Day-Genotype interaction and random effects for each individual to account for the longitudinal nature of the design. email: cashaw@bcm.edu with questions or comments.
NMR data in 'Ca2+-dependent release of Synaptotagmin-1 from the SNARE complex on phosphatidylinositol 4,5-bisphosphate-containing membranes' by Voleti, Jaczynska and Rizo, eLife 2020
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eLife_2024_Fudan_B.F.
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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.
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International Brain Laboratory public data
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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.