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zenodo48/100

Supplementary data to accompany Information flow, cell types and stereotypy in a full olfactory connectome

Supplemental file 1 Layers assigned by the probabilistic graph traversal model. bodyId refers to neurons’ unique ID in ne- uPrint. layer mean contains the mean layer after 10,000 iterations of the main model (Figure 2). layer - olf mean and layer th mean contain the mean layers from running the traversal model with ORNs and THN/HRNs, respectively (Figure S2). S1 hemibrain neuron layers.csv Supplemental file 2 Sensory meta-information related to each glomerulus. Columns: glomerulus (canonical name for one of the 51 olfactory + 7 thermo/hygrosensory antennal lobe glomeruli), laterality (whether the glomerulus receives bilateral or only unilateral innervation from ALRNs), expected cit (a citation that describes the expected number of RNs in this glomerulus), expected RN female 1h (number of expected RNs in one hemi- sphere), expected RN female SD (standard deviation in the expected number of RNs), missing (qualitative assessment of glomeruli truncation), RN frag (if the RNs in that glomerulus are fragmented), receptor (the OR or IR expressed by cognate ALRNs (Bates et al., 2020; Task et al., 2020)), odour scenes (the general ‘odour scene(s)’ which this glomerulus may help signal (Mansourian and Stensmyr, 2015; Bates et al., 2020)), key ligand(the ligand that excites the cognate ALLRN or receptor the most, based on pooled data from multiple studies (Mu ̈nch and Galizia, 2016)), valence (the presumed valence of this odour chan- nel (Badel et al., 2016)). Exists as hemibrain glomeruli summary in our R package hemibrainr. S2 hemibrain olfactory information.csv Supplemental file 3 File listing all identified antennal lobe receptor neurons (ALRNs) in the hemibrain, including information shown in neuPrint. See above for column explanations. Exists as rn.info in our R package hemibrainr. S3 hemibrain ALRN meta.csv Supplemental file 4 All the hemibrain neurons we have classed as antennal lobe local neurons (ALLNs). See above for column explanations. Exists as alln.info in our R package hemibrainr. S4 hemibrain ALLN meta.csv Supplemental file 5 All the hemibrain neurons we have classed as antennal lobe projection neurons (ALPNs). See above for column explanations. In addition, across dataset cluster refers to the clustering with left and right FAFB PNs; is canonical indicates whether that ALPN is one of the well studied “canonical” uPNs. Exists as pn.info in our R package hemibrainr. 40 S5 hemibrain ALPN meta.csv Supplemental file 6 All the hemibrain neurons we have classed as third-order olfactory neurons (TOONs) including lateral horn neurons (LHNs), as well as wedge projection neurons (WEDPNs), lateral horn centrifugal neurons (LHCENT) and other projection neuron classes (Figure 1). See above for column explanations. Exists as ton.info in our R package hemibrainr. S6 hemibrain TOON meta.csv Supplemental file 7 All the hemibrain neurons we have classed as neurons that descend to the ventral nervous system (DNs). See above for column explanations. Exists as dn.info in our R package hemibrainr. S8 hemibrain DN meta.csv Supplemental file 8 The root point in hemibrain voxel space, for each hemibrain neuron. This is either the location of the soma, or the tip of a severed cell body fibre tract, where possible. Exists as hemibrain somas in our R package hemibrainr. S8 hemibrain root points.csv Supplemental file 9 The start points for different neuron compartments. Nodes downstream of this position in the 3D structure of the neuron indicated with bodyid, belong to the compartment type designated by Label. A product of running flow centrality on hemibrain neurons, exists as hemibrain splitpoints in our R package hemi- brainr. S9 hemibrain compartment startpoints.csv Supplemental file 10 3D triangle mesh for the hemibrain surface as a .obj file. This mesh was generated by first merging individual ROI meshes from neuPrint and then filling the gaps in between in a semi-manual process. It also exists as hemibrain.surf in our R package hemibrainr. S10 hemibrain raw.obj Supplemental file 11 3D meshes of 51 olfactory + 7 thermo/hygrosensory antennal lobe glomeruli for the hemibrain volume, generated from ALRN presynapses. 41 Note that hemibrain coordinate system has the anterior-posterior axis aligned with the Y axis (rather than the Z axis, which is more commonly observed). S11 hemibrain AL glomeruli meshes RN-based.zip Supplemental file 12 3D meshes of 51 olfactory + 7 thermo/hygrosensory antennal lobe glomeruli for the hemibrain volume, generated from ALPN presynapses. Note that hemibrain coordinate system has the anterior-posterior axis aligned with the Y axis (rather than the Z axis, which is more commonly observed). These meshes are also available as hemibrain al.surf in our R package hemibrainr. S12 hemibrain AL glomeruli meshes PN-based.zip

opencc-by-4.0Dec 2020View details →

zenodo48/100

Data for Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex

Data for the paper: Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex, Nature 640, 2025 In brief, this data archive includes information about the skeleton morphology and synaptic features of neurons whose cell bodies fell within a 100 micron by 100 micron column spanning all layers of mouse visual cortex. See <a href="https://www.microns-explorer.org/cortical-mm3">MICrONs-Explorer</a> for a full description of the broader volume and how it was collected. The data here include both data tables of cell locations, neuronal features, synapse lists, and more, as well as files containing morphological descriptions of all neurons used for the analysis in the initial version of the preprint. See the README.md file for more complete information about the individual files. Note: Data has been updated with post-publication files.

opencc-by-3.0-usFeb 2023View details →

zenodo48/100

Supporting data for: Type 1 diabetes risk genes mediate pancreatic beta cell survival in response to proinflammatory cytokines

SUMMARY OF THE STUDY We combined functional genomics and human genetics to investigate processes that affect type 1 diabetes (T1D) risk by mediating beta-cell survival in response to proinflammatory cytokines. We mapped 38,931 cytokine-responsive candidate cis-regulatory elements (cCREs) in beta-cells using ATAC-seq and snATAC-seq and linked them to target genes using co-accessibility and HiChIP. Using a genome-wide CRISPR screen in EndoC-βH1 cells we identified 867 genes affecting cytokine-induced survival, and genes promoting survival and up-regulated in cytokines were enriched at T1D risk loci. Using SNP-SELEX, we identified 2,229 variants in cytokine-responsive cCREs altering transcription factor (TF) binding, and variants altering binding of TFs regulating stress, inflammation and apoptosis were enriched for T1D risk.  At the 16p13 locus, a fine-mapped T1D variant altering TF binding in a cytokine-induced cCRE interacted with SOCS1, which promoted survival in cytokine exposure. Our findings reveal processes and genes acting in beta-cells during inflammation that modulate T1D risk. DESCRIPTION OF FILES: <ul> <li>Supplementary Data 1. List of islet cCREs annotated with cell type and cytokine response  - also in GSE205853</li> <li>Supplementary Data 2. Coaccessible sites in untreated beta cells and promoter annotations - also in GSE205853</li> <li>Supplementary Data 3. Coaccessible sites in cytokine-treated beta cells and promoter annotations - also in GSE205853</li> <li>Supplementary Data 4. Coaccessible sites in cytokine treated and untreated beta cells and promoter annotations - also in GSE205853</li> <li>Supplementary Data 5. Chromatin interactions in EndoC-BH1 cells - also in GSE205853</li> <li>Supplementary Data 6. Variants selected for SNP-SELEX assay </li> <li>Supplementary Data 7. Variants with TF binding and allelic binding results from SNP-SELEX</li> <li>Supplementary Data 8. snATAC-seq barcodes and metadata - also in GSE205853</li> <li>Supplementary Data 9. CRISPR-KO screen results - also in GSE205853</li> <li>Supplementary Data 10. Bulk ATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 11. Bulk RNA-seq count matrix - also in GSE205853</li> <li>Supplementary Data 12. Alpha cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 13. Acinar cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 14. Beta cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 15. Stellate cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 16. Endothelial cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 17. Delta cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 18. Luciferase assay rs10483809</li> <li>Supplementary Data 19. SOCS1 knockdown qPCR results</li> <li>Supplementary Data 20. SOCS1 knockdown Apotracker (flow-cytometry)results</li> </ul> Raw data deposited at GEO, accessions GSE205853 and GSE118725. Please refer to publication and GEO for details on methods.

opencc-by-4.0Dec 2021View details →

zenodo44/100

Data set for "Cell-type-specific nicotinic input disinhibits mouse barrel cortex during active sensing"

Data set for: Gasselin C, Hohl B, Vernet A, Crochet C, Petersen CCH (2021) Cell-type-specific nicotinic input disinhibits mouse barrel cortex during active sensing. Neuron doi: 10.1016/j.neuron.2020.12.018 There are 2 files in this upload: 1. The file named "2021_Gasselin_Neuron.pdf" is the Open Access pdf of the online publication in Neuron. 2. The file named "Gasselin_data_code.zip" (~9 GB) is a zipped version of a folder "Gasselin_data_code" (~13 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. To access the data and the code, first unzip the file. Then add the folder with subfolders to the Matlab path and run the different codes. The current folder must be the main folder (‘Gasselin_data_code’). Each code computes and plots the results used in the corresponding figure. Figures and Tables are saved in the subfolder ‘Figures’. The subfolder ‘Functions’ contains functions called by the main codes. The main folder contains the following codes: Gasselin_Figure1: computes and plots the results for the panels D, E and F of figure 1. Gasselin_Figure2: computes and plots the results for the panels B and C of figure 2. Gasselin_Figure3: computes and plots the results for the panels B, C and D of figure 3. Gasselin_Figure4: computes and plots the results for the panels A, B and C of figure 4. Gasselin_FigureS1: computes and plots the results for the panels A, B and C of figure S1. Gasselin_FigureS2: computes and plots the results for the panels A and B of figure S2.   The subfolder ‘Data’ contains the data structures used for the different figures: data_figure1.mat data_figure2.mat data_figure3.mat data_figure4_MECA.mat data_figure4_Activation.mat data_figure4_Inactivation.mat data_figureS2_Activation.mat data_figureS2_Inactivation.mat data_Axon.mat   The data structures contain the following fields: Mouse_Name : name of the mouse. Mouse_DateOfBirth: date of birth of the mouse (YMD). Mouse_Sex: sex of the mouse (F or M). Mouse_Genotype: genotype of the mouse. Mouse_Drug: experimental condition of the recording (control = ‘No Drug’; blockade of glutamatergic transmission = ‘CNQX_DAPV’; blockade of glutamatergic transmission and nicotinic receptors = ‘CNQX_DAPV_MECA’; blockade of nicotinic receptors only = ‘MECA’). Mouse_Virus: virus injected if any. Cell_Counter; cell recorded in a given mouse. Cell_Type: type of the recorded cell based on 2P imaging. (EXC, VIP, PV, SST, 5HT3aR_non_VIP). Cell_Depth: depth of the recorded cell relative to pia (µm). Cell_TargetedBrainArea: cortical area targeted (C2 column of the barrel cortex = C2). Cell_Fluorescence: expression of the genetically encoded fluorophore (FALSE or TRUE). A neuron recorded in a VIP_IRES_Cre x LSL_tdTomato (cf Mouse_Genotype) with Cell_Fluorescence=TRUE is considered as a VIP neuron (cf Cell_Type). Sweep_Counter: number of the sweep recorded for a given neuron (data were acquired across successive continuous sweeps of 30-60 s). Sweep_Type: experimental condition during that sweep (Only spontaneous whisking onset = ‘Onset’; Whisking onset and whisker stimulus = ‘Onset_Whisker_Stim’ ; Optogenetic stimulation = ‘Opto_Stim’;  Optogenetic activation = ‘Opto_Activation’; Optogenetic inactivation = ‘Opto_Inactivation’;  ). Sweep_Start_Time: time at the beginning of the sweep recording (YMDHms). Sweep_WhiskerAngle: C2 whisker angular position extracted from simultaneous high-speed video filming (deg). Sweep_WhiskerAngle_SamplingRate: sampling rate of the whisker angle trace. Sweep_WhiskingOnset_Time: time of identified whisking onset - excluding any whisker stimulus shortly before or after (s). Sweep_MembranePotential: membrane potential recording (mV) after cutting of the APs. Sweep_MembranePotential_SamplingRate: sampling rate of the membrane potential signal (pt.s-1). Sweep_CurrentInjected: current injected into the cell (pA). Sweep_CurrentInjected_SamplingRate: sampling rate of current signal (pt.s-1). Sweep_WhiskerStim_Name: whisker to which the magnetic stimulus was applied to (C2 or B2&C2). Sweep_WhiskerStim_Time: onset times of the whisker stimulus (s). Sweep_OptoStim_Power: light power applied for optogenetic manipulations (% of the max power). Sweep_OptoStim_Time: onset times of the light pulses for optogenetic manipulations (s). Cell_ID: unique cell identifier (= Mouse_Name+Cell_Counter). SpikeThreshold: spike threshold used to detect APs (mV). Trial_WhiskingOnset: data structure containing the cut signals used to compute averaged responses around whisking onset times. Trial_WhiskerStim: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times. Trial_WhiskerStim_QuietTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without whisker movements. Trial_WhiskerStim_WhiskingTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with whisker movements. Trial_Opto: data structure containing the cut signals used to compute averaged responses around optogenetic stimulus onset times. Trial_OptoAndWhisker_QuietTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with optogenetic manipulation and no whisker movements. Trial_OptoAndWhisker_WhiskingTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with optogenetic manipulation and with whisker movements. Trial_OnlyWhisker_QuietTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without optogenetic manipulation and without whisker movements. Trial_OnlyWhisker_WhiskingTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without optogenetic manipulation and with whisker movements. Trial_OnlyOpto: data structure containing the cut signals used to compute averaged responses around optogenetic stimulus onset times in trials without whisker stimulus.

opencc-by-4.0Jan 2021View details →

zenodo44/100

Type III interferons may suppress viral infections by triggering cell death -- Imaging Dataset

This dataset accompanies the article "Type III interferons may suppress viral infections by triggering cell death". Earlier version is available as a preprint, <a href="https://doi.org/10.1101/2024.09.09.612051" target="_blank" rel="noopener">https://doi.org/10.1101/2024.09.09.612051</a>. The updated dataset includes quantifications for Figure 7C and Figure 7D.

opencc-by-4.0Sep 2024View details →

zenodo44/100

scGPT: End-to-End Protocol for Fine-tuned Retina Cell Type Annotation

<h1>Abstract</h1> Single-cell research faces challenges in accurately annotating cell types at high resolution, especially when dealing with large-scale datasets and rare cell populations. To address this, foundation models like scGPT offer flexible, scalable solutions by leveraging transformer-based architectures. This protocol provides a comprehensive guide to fine-tuning scGPT for cell-type classification in single-cell RNA sequencing (scRNA-seq) data. We demonstrate how to fine-tune scGPT on a custom retina dataset, highlighting the model’s efficiency in handling complex data and improving annotation accuracy achieving 99.5% F1-score. This protocol automates key steps, including data preprocessing, model fine-tuning, and evaluation. This protocol enables researchers to efficiently deploy scGPT for their own datasets. The provided tools, including a command-line script and Jupyter Notebook, simplify the customization and exploration of the model, proposing an accessible workflow for users with minimal Python and Linux knowledge. The protocol offers an off-the-shell solution of high-precision cell-type annotation using scGPT for researchers with intermediate bioinformatics.

opencc-by-4.0Sep 2024View details →

zenodo44/100

Catalog of PAM and MBON cell types

A catalog of some of the published anatomical findings on DAN PAM and MBON cell types in the mushroom body of Drosophila melanogaster. Major source is the major table in Aso et al. 2014 (https://doi.org/10.7554/eLife.04577). Also includes results from other papers and combines into a single spreadsheet.

opencc-by-4.0Oct 2022View details →

zenodo44/100

Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data.

Bulk ATAC-seq data of tumour samples result in an averaged signal across different cell-types (cancer, stromal, vascular and immune cells). We propose a deconvolution framework called EPIC-ATAC (<a href="https://doi.org/10.7554/eLife.94833.1">https://doi.org/10.7554/eLife.94833.1</a>), which relies on newly identified cell-type specific ATAC-Seq marker peaks and reference profiles for all major cancer-relevant cell-types to predict the proportions of each cell-type. To evaluate EPIC-ATAC, we generated a bulk ATAC-Seq dataset from peripheral blood mononuclear cells (PBMCs) samples, from which the number of cells in each cell-type has been estimated using flow cytometry, as ground truth for cell proportions. The data provided in this Zenodo deposit correspond to: - The raw counts matrix for each peak called in this ATAC-Seq dataset: PBMC_counts.txt - The normalized (TPM-like) counts matrix for each peak called in this ATAC-Seq dataset: PBMC_counts_norm.txt - The cell fractions of each cell type in each sample: PBMC_cell_fractions.txt - The peaks called in each sample using MACS2 (*narrow.peaks): *_normalized.narrowPeak - Bed files listing ATAC-Seq fragments for each sample: *.bed We also evaluated EPIC-ATAC on multiple pseudobulks generated from single-cell ATAC-Seq data. We provide rds files containing the pseudobulks data used in our work for the evaluation of EPIC-ATAC. The rds files are located in the zip file "pseudobulks.zip". The file "additional_data.zip" contains additional files used to generate the reference profiles in EPIC-ATAC and to reproduce the main analyses performed in the manuscript: <a href="https://doi.org/10.7554/eLife.94833.1">https://doi.org/10.7554/eLife.94833.1</a>. These files are required to run the code available on the following GitHub repository: GfellerLab/EPIC-ATAC_manuscript.

opencc-by-4.0Oct 2023View details →

zenodo44/100

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

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

opencc-by-4.0Jul 2018View details →

zenodo44/100

Data and Code for "Cell Type-specific Genome Scans of DNA Methylation Diversity Indicate an Important Role for Transposable Elements"

This is a release of the gitlab repository "meta-methylome" (https://gitlab.com/okartal/meta-methylome.git) that, in addition to the code, also contains the resulting genomic data. Extract the directory on the command line using <pre><code class="language-bash">$ tar -xhzvf meta-methylome.tar.gz</code></pre> to preserve the symbolic links.

opencc-by-4.0Oct 2019View details →

zenodo44/100

Characterizing cell-type spatial relationships across length scales in spatially resolved omics data: data repository

<h1>CRAWDAD</h1> Spatially resolved omics (SRO) technologies enable the identification of cell types while preserving their organization within tissues. Application of such technologies offers the opportunity to delineate cell-type spatial relationships, particularly across different length scales, and enhance our understanding of tissue organization and function. To quantify such multi-scale cell-type spatial relationships, we develop CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, as an open-source R package with source code and additional documentation at https://jef.works/CRAWDAD/. During CRAWDAD's development, we generated simulated datasets and new cell-type annotations for human spleen data, provided here. The external datasets such as the mouse cerebellum, mouse embryo, mouse brain, and human breast cancer data used in the paper can be found in their original publication. See more information in CRAWDAD's data availability statement. <h2>Simulated Datasets</h2> <ul> <li>sim.csv: the simulated data. Used in Figure 1 b-g, Supplementary Figure 1 a-c, and Supplementary Figure 9 a-b.</li> <li>ext_sim.csv: the extended simulated data. Used in Supplementary Figure 1 d-f.</li> <li>null_sim_visualization.csv: the null simulated data. Used to generate the plots Supplementary Figure 2 a-d.</li> <li>null_sim_1.csv - null_sim_10.csv: the 10 null simulated datasets. Used to quantitatively compare CRAWDAD, Squidpy’s co-occurrence implementation, and Ripley’s K Cross.</li> </ul> <h2>HuBMAP Datasets</h2> <ul> <li>pkhl.csv: annotated cell types and positions of sample HBM389.PKHL.936 from donor HBM966.VNKN.965. Used in Figure 5 a-h, Supplementary Figure 5 a, Supplementary Figure 7 a-c, and Supplementary Figure 8 c. doi:10.35079/HBM389.PKHL.936</li> <li>xxcd.csv: annotated cell types and positions of sample HBM772.XXCD.697 from donor HBM966.VNKN.965. Used in Figure 5 d-h, Supplementary Figure 5 a-c, and Supplementary Figure 7 a-c. doi:10.35079/HBM772.XXCD.697</li> <li>fsld.csv: annotated cell types and positions of sample HBM342.FSLD.938 from donor HBM245.ZWNT.288. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM342.FSLD.938</li> <li>pbvn.csv: annotated cell types and positions of sample HBM825.PBVN.284 from donor HBM245.ZWNT.288. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM825.PBVN.284</li> <li>ksfb.csv: annotated cell types and positions of sample HBM556.KSFB.592 from donor HBM298.KGNJ.374. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM556.KSFB.592</li> <li>ngpl.csv: annotated cell types and positions of sample HBM568.NGPL.345 from donor HBM298.KGNJ.374. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM568.NGPL.345</li> </ul> <h2>External Datasets</h2> <ul> <li>Mouse cerebellum: Used in Figure 2 a-e, Supplementary Figure 3 a-b, Supplementary Figure 4 a-d, and Supplementary Figure 8 a.</li> <li>Mouse embryo: Used in Figure 2 f-j, Supplementary Figure 3 c-d, Supplementary Figure 4 e-h, and Supplementary Figure 8 b.</li> <li>Human breast cancer: Used in Figure 3 a-c.</li> <li>Mouse brains: Used in Figure 4 a-e.</li> </ul>

opengpl-3.0-or-laterOct 2024View details →

zenodo44/100

Data set for "Cell type-specific membrane potential changes in dorsolateral striatum accompanying reward-based sensorimotor learning"

Data set for: Sippy T, Chaimowitz C, Crochet S, Petersen CCH (2021) Cell type-specific membrane potential changes in dorsolateral striatum accompanying reward-based sensorimotor learning. FUNCTION 2: zqab049. https://doi.org/10.1093/function/zqab049 There are 2 files in this upload: 1. The file named "2021_Sippy_FUNCTION.pdf" is the Open Access pdf of the online publication in FUNCTION. 2. The file named "Sippy_data_code.zip" (~5 GB) is a zipped version of a folder ‘Sippy_data_code’, which contains the data analyzed in the study along with the Matlab codes used to generate the published figures. To access the data and the codes, first unzip the file, add the folder with subfolders to the Matlab path and run the different codes. The current folder must be the main folder (‘Sippy_data_code’). You first need to run ‘AnalyzeDataStructure.m’ and afterwards you can run the other codes. Each code computes and plots the results used in the corresponding figure. Figures are saved in the subfolder ‘Figures’. The subfolder ‘Data’ contains the data structure ‘Data.mat’ to be analyzed, as well as a Matlab file called ‘p_value_colormap.mat’ used to plot the p value color bars in some figures. The subfolder ‘Functions’ contains functions called by the main codes. The subfolder ‘Codes’ contains the following codes: ‘AnalyzeDataStructure.m’: computes the results and saves them as a new data structure called ‘Analyzed_Data’, in the subfolder ‘Results’. ‘Figure_1.m’: computes and plots the results for the panels D, E and F of Figure 1. ‘Figure_2.m’: computes and plots the results for the panels D-G and I-K of Figure 2. ‘Figure_3.m’: computes and plots the results for the panels A-F of Figure 3. ‘SuppFigure_2.m’: computes and plots the results for the panels B, D and F of Supplementary Figure 2. ‘SuppFigure_3.m’: computes and plots the results for the panels A-D of Supplementary Figure 3. ‘SuppFigure_4.m': computes and plots the results for the panels A-C of Supplementary Figure 4.   The data structures contain the following fields: ‘Mouse_Name’: name of the mouse. ‘Mouse_RecordingDate’: date of recording (YMD). ‘Mouse_DateOfBirth’: date of birth of the mouse (YMD). ‘Mouse_Sex’: sex of the mouse (F or M). ‘Mouse_Genotype’: genotype of the mouse (strain of the two parents): A2A-Cre = Adora2a-Cre mice; D1-Cre = Drd1a-Cre mice; TdTomato = Lox-Stop-Lox-tdTomato mice; D1TdTomato = Drd1a-tdTomato mice; D2GFP = Drd2-GFP mice. ‘Mouse_Level’: Training level (NAÏVE or EXPERT). ‘Cell_Counter’: cell recorded in a given mouse. ‘Cell_Type’: type of the recorded cell (dSPN, iSPN or TAN). ‘Cell_TargetedBrainArea’: Brain area targeted (DLS). ‘Cell_Recovered’: Indicate cells that have been labelled and anatomically recovered (TRUE). ‘Cell_Coordinates’: Cell coordinates (in mm) relative to bregma (Lateral, AP, Ventro-dorsal) ‘Cell_Fluorescence’: expression of the genetically encoded fluorophore (FALSE or TRUE) and fluorophore (TdTomato or GFP). A neuron recorded in a Drd1a-tdTomato x Drd2-GFP (cf Mouse_Genotype) with Cell_Fluorescence= {TRUE, TdTomato} is considered as a dSPN (cf Cell_Type). ‘Sweep_Counter’: number of the sweep recorded for a given neuron (data were acquired across successive continuous sweeps of 30-300 s). ‘Sweep_Type’: experimental condition during that sweep (characterization = electrophysiological identification of the neurons; behavior = behavioral task). ‘Sweep_MembranePotential’: membrane potential recording (mV) after cutting of the APs. ‘Sweep_CurrentInjected’: current injected into the cell (pA). ‘Sweep_PiezoLick’: voltage signal from the piezo sensor attached to the water spout used to detect licking in behavior sweeps. ‘Sweep_Trial’: voltage command triggering the onset of each trial (both Catch and Stimulus trials) in behavior sweeps. ‘Sweep_WhiskerStim’: voltage command triggering the onset of each whisker stimulus in behavior sweeps. ‘Sweep_Valve’: voltage command triggering the opening of the valve delivering the reward in Hit trials. ‘Sweep_SamplingRate’: sampling rate (sample.s-1) of the recorded signals for each sweep. ‘Sweep_TimeStamp’: time at the beginning of the recorded sweep (H/min/s). ‘Sweep_Reward’: voltage command indicating reward availability during the response window following whisker stimulus in behavior sweeps. ‘Sweep_APThresh’: Threshold (V) used to detect action potentials (AP) during current injection.

opencc-by-4.0Oct 2021View details →

zenodo44/100

TF-Marker: A comprehensive manually curated database for transcription factors and related markers in specific cell and tissue types in human.

Here, we developed the TF-Marker database (TF-Marker, http://bio.liclab.net/TF-Marker/) which is committed to a comprehensive manual curation of TFs and related markers with experimental evidence in specific cell and tissue types in human. Currently, through reviewing 2,091 published literature, we have manually classified TFs and related markers into five types according to their functions: 1) TF: TFs, which regulate the expression of markers; 2) T Marker: markers, which are regulated by TFs (TF and T Marker pairs can identify cell types more specifically); 3) I Marker: markers, which influence the activity of TFs (I Markers can also influence the development of specific cells and tissues); 4) TFMarker: TFs, which play roles as markers (TFMarkers are cell/tissue-specific TFs used as cell markers in biology experiments); and 5) TF Pmarker: TFs, which play roles as potential markers. By curating thousands of published literature, 5,905 entries including 1,316 TFs, 1,092 T Markers, 473 I Markers, 1,600 TFMarkers and 1,424 TF Pmarkers, were annotated in 383 cell types and 95 tissue types in human. Moreover, TF-Marker divided markers into disease markers and tissue/cell-specific markers. TF-Marker is an elaborate database, which provides TFs and related markers supported by experimental evidence. We believe TF-Marker will provide strong support for research into cell/tissue-specific TFs and related markers.

opencc-by-4.0Oct 2021View details →

zenodo40/100

Bulk RNA-Seq PBMC data of SLE patients and healthy volunteers/ profiling of 29 individual immune cell types as well as PBMCs of healthy donors

This Zenodo project contains processed gene expression data from two publicly available data sets. It includes the gene expression data of peripheral blood mononuclear cells (PBMCs) of systemic lupus erythematosus (SLE) patients as well as healthy volunteers (GSE122459). The project also comprises the bulk RNA-Seq profiling of 29 immune cell types as well as PBMCs of healthy individuals (GSE107011). In both cases, the raw RNA-Seq data was downloaded, aligned and processed. The gene expression data is available in form of a count matrix (GSE107011) or count matrix and transcript-per-million (TPM) values (GSE122459). For the latter, an annotation file is attached. Further details are provided in the information file.

opencc-by-4.0Dec 2019View details →

zenodo40/100

Data set for "Pathway-, layer- and cell-type-specific thalamic input to mouse barrel cortex"

Data set for: Sermet BS, Truschow P, Feyerabend M, Mayrhofer JM, Oram TB, Yizhar O, Staiger JF, Petersen CCH (2019) Pathway-, layer- and cell-type-specific thalamic input to mouse barrel cortex. eLife 8: e52665. https://doi.org/10.7554/eLife.52665 There are 2 files in this upload: 1. The file named "2019_Sermet_eLife.pdf" is the Open Access pdf file of the manuscript published in eLife. 2. The file named "Sermet_data_code.zip" (~5 GB) is a zipped version of a folder "Sermet_data_code" (~5 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. When unzipped, the folder contains 8 Matlab '.m' files with analysis code and one '.mat' data file. In order to run the analysis of the data set, you need to execute 'PopPlot.m'.

opencc-by-4.0Dec 2019View details →

zenodo40/100

A novel phosphoproteomic landscape evoked in response to type I interferon in the brain and in glial cells

Type I interferons (IFN-I) are key responders to central nervous system infection and injury. They mediate their effects primarily via transcriptional regulation of several hundred interferon-regulated genes. Using a mouse model for IFN-I-induced neurodegeneration, we identified widespread protein phosphorylation as a new mechanism by which IFN-I mediate their effects. Protein phosphorylation aligned with the clinical hallmarks and pathological outcome, including impaired development, motor dysfunction and seizures. In vitro experiments revealed extensive and rapid IFN-I-induced protein phosphorylation in microglia and astrocytes, the brain’s primary IFN-I-responding cells. Response to acute IFN-I stimulation was independent of gene expression and mediated by a small number of kinase families. The changes in the phosphoproteome affected a diverse range of cellular processes and functional analysis suggested that this response induced an immediate reactive state and prepared cells for subsequent transcriptional responses. Our studies reveal a hitherto unappreciated role for changes in the protein phosphorylation landscape in cellular responses to IFN-I and thus provide insights for novel diagnostic and therapeutic strategies for neurological diseases caused by IFN-I.

opencc-by-4.0Dec 2020View details →

zenodo40/100

A novel phosphoproteomic landscape evoked in response to type I interferon in the brain and in glial cells

Type I interferons (IFN-I) are key responders to central nervous system infection and injury. They mediate their effects primarily via transcriptional regulation of several hundred interferon-regulated genes. Using a mouse model for IFN-I-induced neurodegeneration, we identified widespread protein phosphorylation as a new mechanism by which IFN-I mediate their effects. Protein phosphorylation aligned with the clinical hallmarks and pathological outcome, including impaired development, motor dysfunction and seizures. In vitro experiments revealed extensive and rapid IFN-I-induced protein phosphorylation in microglia and astrocytes, the brain’s primary IFN-I-responding cells. Response to acute IFN-I stimulation was independent of gene expression and mediated by a small number of kinase families. The changes in the phosphoproteome affected a diverse range of cellular processes and functional analysis suggested that this response induced an immediate reactive state and prepared cells for subsequent transcriptional responses. Our studies reveal a hitherto unappreciated role for changes in the protein phosphorylation landscape in cellular responses to IFN-I and thus provide insights for novel diagnostic and therapeutic strategies for neurological diseases caused by IFN-I.

opencc-by-4.0Dec 2020View details →

zenodo40/100

Processed data to accompany "Clonally heritable gene expression imparts a layer of diversity within cell types"

This is the processed data underlying the paper "Clonally heritable gene expression imparts a layer of diversity within cell types" by Mold, Weissman, et al.  Data has been gone through preprocessing steps, using the Python Notebooks found at <a href="https://github.com/MartyWeissman/ClonalOmics/tree/main/Data">https://github.com/MartyWeissman/ClonalOmics/tree/main/Data</a>.   Smaller files are provided in .csv (comma-separated-value) format and larger files such as expression matrices are provided in .loom format (<a href="https://anndata.readthedocs.io/en/latest/">using the AnnData package</a>).

opencc-by-4.0Dec 2023View details →

dryad40/100

Raw differential gene expression data, data S1, from: Molecular cascades and cell type-specific signatures in ASD revealed by single cell genomics

Genomic profiling in post-mortem brain from autistic individuals has consistently revealed convergent molecular changes. What drives these changes and how they relate to genetic susceptibility in this complex condition is not understood. We performed deep single nuclear RNA sequencing (snRNAseq) to examine cell composition and transcriptomics, identifying dysregulation of cell type-specific gene regulatory networks (GRNs) in autism, which we corroborated using snATAC-seq and spatial transcriptomics. Transcriptomic changes were primarily cell type-specific, involving multiple cell types, most prominently interhemispheric and callosal-projecting neurons, interneurons within superficial laminae, and distinct glial reactive states involving oligodendrocytes, microglia, and astrocytes. Autism-associated GRN drivers and their targets were enriched in rare and common genetic risk variants, connecting autism genetic susceptibility and cellular and circuit alterations in the human brain. This data is the raw differential gene expression comparing ASD versus CTL subjects for each cell cluster.

opencc-zeroJan 2024View details →

zenodo40/100

Multi-cell type deconvolution using a probabilistic model for single-molecule DNA methylation haplotypes

Files required to run deconvolution with CelFIE-ISH and Epistate, in U250 regions from Loyfer et al. 2023, in both "pat" and "epiread" formats.

opencc-by-4.0Mar 2024View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record