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Zip archive containing datasets described in the manuscript entitled "Distribution-agnostic Deep Learning Enables Accurate Single‐Cell Data Recovery and Transcriptional Regulation Interpretation"
<p>The datasets used in the manuscript entitled "Distribution-agnostic Deep Learning Enables Accurate Single‐Cell Data Recovery and Transcriptional Regulation Interpretation". These datasets encompass all the experiments conducted in the manuscript, including simulation experiments, downsampling experiments, clustering, differential expression analysis, enrichment analysis, trajectory inference, batch correction, and clinical case discovery.</p> <p>The open-source software is available at https://github.com/XuYuanchi/Bis.</p>
CMS High Granularity Calorimeter Trigger Cell Simulated Dataset (Part 1)
<p>The dataset consists of simulated events of electron-positron pairs (<em>e</em><sup>+</sup><em>e</em><sup>−</sup>) with flat transverse momentum <em>p</em><sub>T</sub> distribution <em>p</em><sub>T</sub> ∈ [1,200] GeV, with Phase 2 conditions, 200 pileup, V11 geometry, HLT TDR Summer20 campaign The <a href="https://cmsweb.cern.ch/das/request?input=dataset%3D%2FDoubleElectron_FlatPt-1To100%2FPhase2HLTTDRSummer20ReRECOMiniAOD-PU200_111X_mcRun4_realistic_T15_v1-v2%2FGEN-SIM-DIGI-RAW-MINIAOD&instance=prod/global">original dataset (CMS-internal)</a>.</p> <p>This derived dataset in ROOT format contains generator-level particle and simulated detector information. More information about how the dataset is derived is available at this <a href="https://twiki.cern.ch/twiki/bin/viewauth/CMS/HGCALTriggerPrimitivesSimulation">TWiki (CMS-internal)</a>.</p> <p>A description of each variable is below.</p> <table> <thead> <tr> <th>Variable</th> <th>Description</th> <th>Type</th> </tr> </thead> <tbody> <tr> <td><code>run</code></td> <td>Run number</td> <td><code>int</code></td> </tr> <tr> <td><code>event</code></td> <td>Event number</td> <td><code>int</code></td> </tr> <tr> <td><code>lumi</code></td> <td>Luminosity section</td> <td><code>int</code></td> </tr> <tr> <td><code>gen_n</code></td> <td>Number of primary generated particles</td> <td><code>int</code></td> </tr> <tr> <td><code>gen_PUNumInt</code></td> <td>Number of pileup interactions</td> <td><code>int</code></td> </tr> <tr> <td><code>gen_TrueNumInt</code></td> <td>Number of true interactions</td> <td><code>float</code></td> </tr> <tr> <td><code>vtx_x</code></td> <td>Simulated primary vertex <em>x</em> position in cm</td> <td><code>float</code></td> </tr> <tr> <td><code>vtx_y</code></td> <td>Simulated primary vertex <em>y</em> position in cm</td> <td><code>float</code></td> </tr> <tr> <td><code>vtx_z</code></td> <td>Simulated primary vertex <em>z</em> position in cm</td> <td><code>float</code></td> </tr> <tr> <td><code>gen_eta</code></td> <td>Primary generated particle pseudorapidity <em>η</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>gen_phi</code></td> <td>Primary generated particle azimuthal angle <em>ϕ</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>gen_pt</code></td> <td>Primary generated particle transverse momentum <em>p</em><sub>T</sub> in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>gen_energy</code></td> <td>Primary generated particle energy in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>gen_charge</code></td> <td>Initial generated particle charge</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>gen_pdgid</code></td> <td>Primary generated particle PDG ID</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>gen_status</code></td> <td>Primary generated particle generator status</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>gen_daughters</code></td> <td>Primary generated particle daughters (empty)</td> <td><code>vector<vector<int>></code></td> </tr> <tr> <td><code>genpart_eta</code></td> <td>Primary and secondary generated particle pseudorapidity <em>η</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_phi</code></td> <td>Primary and secondary generated particle azimuthal angle <em>ϕ</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_pt</code></td> <td>Primary and secondary generated particle transverse momentum <em>p</em><sub>T</sub> in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_energy</code></td> <td>Primary and secondary generated particle energy in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_dvx</code></td> <td>Primary and secondary generated particle decay vertex <em>x</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_dvy</code></td> <td>Primary and secondary generated particle decay vertex <em>y</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_dvz</code></td> <td>Primary and secondary generated particle decay vertex <em>z</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_ovy</code></td> <td>Primary and secondary generated particle original vertex <em>y</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_ovz</code></td> <td>Primary and secondary generated particle original vertex <em>z</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_mother</code></td> <td>Primary and secondary generated particle parent particle index (-1 indicates no parent)</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>genpart_exphi</code></td> <td>Primary and secondary generated particle azimuthal angle <em>ϕ</em> extrapolated to the corresponding HGCAL coordinate</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_exeta</code></td> <td>Primary and secondary generated particle pseudorapidity <em>η</em> extrapolated to the corresponding HGCAL coordinate</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_exx</code></td> <td>Primary and secondary generated particle decay vertex <em>x</em> extrapolated to the corresponding HGCAL coordinate</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_exy</code></td> <td>Primary and secondary generated particle decay vertex <em>y</em> extrapolated to the corresponding HGCAL coordinate</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_fbrem</code></td> <td>Primary and secondary generated particle decay vertex <em>z</em> extrapolated to the corresponding HGCAL coordinate</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_pid</code></td> <td>Primary and secondary generated particle PDG ID</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>genpart_gen</code></td> <td>Index of associated primary generated particle</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>genpart_reachedEE</code></td> <td>Primary and secondary generated particle flag: <code>2</code> indicates that the particle reached the HGCAL, <code>1</code> indicates the particle reached the barrel calorimeter, and <code>0</code> indicates other cases</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>genpart_fromBeamPipe</code></td> <td>Deprecated variable, always true</td> <td><code>vector<bool></code></td> </tr> <tr> <td><code>genpart_posx</code></td> <td>Primary and secondary generated particle position <em>x</em> coordinate in cm</td> <td><code>vector<vector<float>></code></td> </tr> <tr> <td><code>genpart_posy</code></td> <td>Primary and secondary generated particle position <em>y</em> coordinate in cm</td> <td><code>vector<vector<float>></code></td> </tr> <tr> <td><code>genpart_posz</code></td> <td>Primary and secondary generated particle position <em>z</em> coordinate in cm</td> <td><code>vector<vector<float>></code></td> </tr> <tr> <td><code>ts_n</code></td> <td>Number of trigger sums</td> <td><code>int</code></td> </tr> <tr> <td><code>ts_id</code></td> <td>Trigger sum ID</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>ts_subdet</code></td> <td>Trigger sum subdetector</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>ts_zside</code></td> <td>Trigger sum endcap (plus or minus endcap)</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>ts_layer</code></td> <td>Trigger sum layer ID</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>ts_wafer</code></td> <td>Trigger sum wafer ID</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>ts_wafertype</code></td> <td>Trigger sum wafer type: 0 indicates fine divisions of wafer with 120 <em>μ</em>m thick silicon, 1 indicates coarse divisions of wafer with 200 <em>μ</em>m thick silicon, and 2 indicates coarse divisions of wafer with 300 <em>μ</em>m thick silicon</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>ts_data</code></td> <td>Trigger sum ADC value</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>ts_pt</code></td> <td>Trigger sum transverse momentum in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_mipPt</code></td> <td>Trigger sum energy in units of transverse MIP</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_energy</code></td> <td>Trigger sum energy in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_eta</code></td> <td>Trigger sum pseudorapidity <em>η</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_phi</code></td> <td>Trigger sum azimuthal angle <em>ϕ</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_x</code></td> <td>Trigger sum <em>x</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_y</code></td> <td>Trigger sum <em>y</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_z</code></td> <td>Trigger sum <em>z</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_n</code></td> <td>Number of trigger cells</td> <td><code>int</code></td> </tr> <tr> <td><code>tc_id</code></td> <td>Trigger cell unique ID</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_subdet</code></td> <td>Trigger cell subdetector ID (EE, EH silicon, or EH scintillator)</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_zside</code></td> <td>Trigger cell endcap (plus or minus endcap)</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_layer</code></td> <td>Trigger cell layer number</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_waferu</code></td> <td>Trigger cell wafer <em>u</em> coordinate; <em>u</em>-axis points along − <em>x</em>-axis</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_waferv</code></td> <td>Trigger cell wafer <em>v</em> coordinate; <em>v</em>-axis points at 60 degrees with respect to <em>x</em>-axis</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_wafertype</code></td> <td>Trigger cell wafer type: <code>0</code> indicates fine divisions of wafer with 120 <em>μ</em>m thick silicon, <code>1</code> indicates coarse divisions of wafer with 200 <em>μ</em>m thick silicon, and <code>2</code> indicates coarse divisions of wafer with 300 <em>μ</em>m thick silicon)</td> <td> </td> </tr> <tr> <td><code>tc_cellu</code></td> <td>Trigger cell <em>u</em> coordinate within wafer; <em>u</em>-axis points along − <em>x</em>-axis</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_cellv</code></td> <td>Trigger cell <em>v</em> coordinate within wafer; <em>v</em>-axis points at 60 degrees with respect to <em>x</em>-axis</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_data</code></td> <td>Trigger cell ADC data at 21-bit precision after decoding from 7-bit encoding</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_uncompressedCharge</code></td> <td>Trigger cell ADC data at full precision before compression</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_compressedCharge</code></td> <td>Trigger cell ADC data compressed into 7-bit encoding</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_pt</code></td> <td>Trigger cell transverse momentum <em>p</em><sub>T</sub> in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_mipPt</code></td> <td>Trigger cell energy in units of transverse MIPs</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_energy</code></td> <td>Trigger cell energy in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_simenergy</code></td> <td>Trigger cell energy from simulated particles in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_eta</code></td> <td>Trigger cell pseudorapidity <em>η</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_phi</code></td> <td>Trigger cell azimuthal angle <em>ϕ</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_x</code></td> <td>Trigger cell <em>x</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_y</code></td> <td>Trigger cell <em>y</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_z</code></td> <td>Trigger cell <em>z</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_cluster_id</code></td> <td>ID of the 2D cluster in which the trigger cell is clustered</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_multicluster_id</code></td> <td>ID of the 3D cluster in which the trigger cell is clustered</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_multicluster_pt</code></td> <td>Transverse momentum <em>p</em><sub>T</sub> in GeV of the 3D cluster in which the trigger cell is clustered</td> <td><code>vector<float></code></td> </tr> </tbody> </table>
Deep Learning-based 3D single-cell imaging analysis pipeline for quantifying cell-cell interaction dynamics in the tumor microenvironment
<p>These are 3D live-cell imaging datasets of gastric tumor organoids in co-culture with primary human Natural Killer (NK) cells. The datasets were analyzed by a new, deep learning-based 3D image analysis software tool, SiQ-3D, which we developed and presented in the paper titled "Deep Learning-based 3D single-cell imaging analysis pipeline for quantifying cell-cell interaction dynamics in the tumor microenvironment". Interested users can download the SiQ-3D software code from GitHub (https://github.com/simonlbd1/SiQ-3D) or Code Ocean (https://codeocean.com/capsule/6676007/tree/v2), analyze the 3D image datasets locally, and cross-check the results with the SiQ-3D quantified results that we provided here.</p>
Fragment-sequencing unveils local tissue microenvironments at single-cell resolution
<p>This dataset contains the Resolve dataset that was used in the publication. Other data set can be found in GEO with the accession number GSE216189. The code that was used during the analysis can be found on our GitHub repository: https://github.com/Moors-Code/Fragment-sequencing</p><p> </p><p> </p>
Data from: Single-cell analysis identifies conserved features of immune dysfunction in simulated microgravity and spaceflight
<p>3-dimensional super-resolution microscopy volumes of human PBMCs recorded on a Zeiss LSM980 Airyscan2 laser scanning confocal microscope.</p> <p>Sample preparation and image capture:</p> <p>Live PBMCs were stained with 60 nM MitoTracker Red-CMX-Ros (ThermoFisher, Waltham, MA) either in 6-well plates or in the microgravity chambers for the last 2 hr of the microgravity simulation. At the end of the microgravity simulation cells were immediately fixed by 1:1 mixing the cell suspensions with 2× concentrated fixative (10% Sucrose (w/v) 120 mM KCl, 1% (w/v) glutaraldehyde, 8% (w/v) PFA pH 7.4) and incubated for 15 minutes at room temperature followed by 15 minutes on ice. Fixed cells were washed and stored in PBS until further staining for up to a week at 4 °C. 1 million fixed cells were resuspended in 1 mL of permeabilization solution (0.1% TritonX-100 in PBS) for 5 minutes. After twice washing in PBS, pellets were resuspended in 0.5 mL 1% BSA PBS containing Phalloidin-iFluor-488 (cat# ab176753, Abcam plc., Cambridge, UK) at the manufacturer’s recommended dilution, and were incubated for 90 minutes with gentle agitation. After washing in PBS, cells were stained with Hoechst 33342 (1 µg/mL in PBS) for 10 minutes. The fixed-stained cells were immobilized at 3 × 10<sup>5</sup> cells per well density in glass-bottom 96-well microplates (Greiner Bio-One, Monroe, NC), which were pre-coated with polyethyleneimine (1:15,000 (w/v)) for 16 hours in a 37 °C incubator, and washed twice with PBS. Microplates with the cell suspensions were centrifuged in a swing plate rotor centrifuge (Eppendorf 5810 R) at 400 × <em>g</em> and for 10 min and then fixed on the surface by adding an equal volume of 8% (w/v) PFA for 5 min. Finally, the fixative was replaced with 100 µL of antifade reagent (Vector Prolong Gold (ThermoFisher)). Samples were imaged immediately after this procedure on a Zeiss LSM980 Airyscan2 laser scanning confocal microscope (Carl Zeiss Microscopy, White Plains, NY). Single PBMCs were manually selected for recording based on low-resolution preview scans showing only nuclei. All singlet cells were selected in a small neighborhood to avoid biases. In each microscopy session, 24-40 cells were selected for recording in one well for each condition. This was performed in an interleaved manner, capturing 6-8 cells at a time, and then moving to the next well and then repeating this multiple times using the Experiment Designer module for automation. Super-resolution volumes of (358 × 358 × 70 pixels, 0.035 × 0.035 × 0.13 µm/voxel resolution) were recorded in the above-determined positions using Definite Focus autofocusing. A Plan-Apochromat 63 × 1.40 Oil lens, Airyscan2 SR (super-resolution) mode with optimal sampling and frame switching between 3 fluorescence channels to minimize spectral cross-bleed were used. MitoTracker Red, iFluor488, and Hoechs33342 were excited with 561, 488, and 405 nm solid-state lasers, respectively, using the optimal emission filter for each channel. 3D Airyscan2 processing was performed with standard filtering settings.</p> <p>File naming:</p> <p>Four zip files were deposited named as <Donor#id>.zip, where id goes from 1 to 4.</p> <p>Each zip file contains the following Zeiss Microscopy format image files: <Condition>_<Donor#id>_<Stain#batch>_<Cell>.czi</p> <p><Condition>:</p> <ul> <li>1G – Control culturing in 6-well plates for 25h</li> <li>uG – simulated microgravity culturing for 25h in NASA Rotating Wall Vessels</li> <li>1G+TLR – as above, with TLR 7/8 agonist (1 μM R848)</li> <li>uG+TLR– as above, with TLR 7/8 agonist (1 μM R848)</li> <li>1G+CyD– as above, with cytochalasin D</li> <li>uG+CyD– as above, with cytochalasin D</li> <li>1G+Q – as above, with quercetin 50µM</li> <li>uG+Q – as above, with quercetin 50µM</li> </ul> <p><Donor#id>: 1-4 indicates biological replicates</p> <p><Stain#batch>: 1-2 indicates experimental replicates of phalloidin staining and imaging session</p> <p><Cell>: arbitrary number to distinguish images within the same condition/donor/stain set.</p> <p>See image analysis pipelines used with these data at: https://github.com/gerencserlab/Superresolution-actin-and-mitochondria-analysis</p>
Artificial cell synthesis using biocatalytic polymerisation-induced self-assembly
<p>These are the raw data underlying the pre-print Synthesis of artificial cells via biocatalytic polymerisation-induced self-assembly <a href="https://doi.org/10.26434/chemrxiv-2023-c3nhg">10.26434/chemrxiv-2023-c3nhg</a> , to be published in Nature Chemistry (with the updated title Artificial cell synthesis using biocatalytic polymerisation-induced self-assembly)</p>
Data used in paper "Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON"
<p>This directory contains the data resources of the following paper:</p> <p>"Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON"</p>
Single-cell and spatially resolved transcriptomic data of mouse regenerative livers under normal and fibrotic conditions
<p>A single-cell spatial-temporal transcriptomic atlas of liver regeneration under normal and fibrotic condition, including a total of 30 mouse liver samples obtained from 15 normal and 15 fibrotic mice at timepoints Day 0, 1, 2, 3, and 7 after a partial hepatectomy (PHx) procedure with three replicates for each time point, followed by the scRNA-seq and SRT sequencing for each sample using the Stereo-seq platform. </p>
Supplement: In situ spatial reconstruction of distinct normal and pathological cell populations within the human adrenal gland
<p>Supplemental legends, figure, and table for In situ spatial reconstruction of distinct normal and pathological cell populations within the human adrenal gland.</p>
Low-loss contacts on textured substrates for inverted perovskite solar cells
<p>Inverted perovskite solar cells (PSCs) promise enhanced operating stability compared to their normal-structure counterparts. To improve efficiency further, it is crucial to combine effective light management with low interfacial losses. Here we develop a conformal self-assembled monolayer as the hole-selective contact on light-managing textured substrates. Molecular dynamics simulations indicate cluster formation during phosphonic acid adsorption leads to incomplete SAM coverage. We devise a co-adsorbent strategy that disassembles high-order clusters, thus homogenizing the distribution of phosphonic acid molecules, thereby minimizing interfacial recombination and improving electronic structures. We report a lab-measured power-conversion efficiency (PCE) of 25.3% and a certified quasi-steady-state PCE of 24.8% for inverted PSCs, with a photocurrent approaching 95% of the Shockley-Queisser maximum. An encapsulated device having a PCE of 24.6% at room temperature retains 95% of its peak performance when stressed at 65°C and 50% relative humidity following > 1000 hours of maximum power point tracking under 1-sun illumination. </p>
Structural Adaptation of Fungal Cell Wall in Hypersaline Environment
<p>The unprocessed ssNMR data files generated in this study "Structural Adaptation of Fungal Cell Wall in Hypersaline Environment" have been deposited in Zenodo. Here we employ solid-state NMR spectroscopy to compare the cell wall architecture of Aspergillus sydowii across salinity gradients. </p>
Epigenetic deregulation of IFN and WNT pathways in AT2 cells impairs alveolar regeneration (in COPD)
<h2>Data-sets</h2><p>RNAseq_CountTable.tsv: Raw count table from RNA-seq experiments</p><p>WGBS_MethylationTable.tsv: Total read coverage and number of methylated sites at CpG positions (reference genome: hg19) </p><h2>Abstract</h2><p>Chronic lung diseases, including chronic obstructive pulmonary disease (COPD), affect over 500 million people and are a leading cause of death worldwide. A common feature of both chronic and acute lung diseases is altered respiratory barrier integrity and impaired lung regeneration. We hypothesized that alveolar type 2 (AT2) cells, as alveolar epithelial progenitors, will carry molecular alterations that compromise alveolar regeneration in COPD. Sorted AT2 cells from ex-smokers with and without COPD at different disease stages were subjected to RNA sequencing and whole-genome bisulfite sequencing to generate unbiased transcriptome and DNA methylation maps of alveolar progenitors in the lung. Our analysis revealed genome-wide epigenetic changes in AT2 cells during COPD that were associated with global gene expression changes. Integrative data analysis uncovered a strong anti-correlation between gene expression and promoter methylation, suggesting that dysregulation of COPD-associated pathways in AT2 cells may be regulated by DNA methylation. Interferon (IFN) signaling was the top-upregulated pathway associated with the concomitant loss of promoter DNA methylation. Epigenetic regulation of the IFN pathway was validated in both global and targeted DNA demethylation assays in A549 cells. Notably, targeted DNA demethylation of IRF9 triggered upregulation of IFN signaling, mimicking the effects observed in COPD AT2 cells in the profiling data. Our findings suggest that COPD-triggered epigenetic alterations in AT2 cells impair internal regeneration programs in human lung parenchyma.</p>
Functional anaysis of miR-143-3p/KSR2 interaction and oncogenic function in JURKAT and ALL-SIL T-cell acute lymphoblastic leukemia cell lines
<p>1. FCS files from GFP competition assay performed in ALL-SIL and JURKAT cell lines upon transduction with hsa-mir-143 expression vector (pCDH miR-143-3p) or empty vector (pCDH EV) as control.</p><p>2. Uncropped chemiluminescent immunoblot in JURKAT and ALL-SIL cell lines transduced with hsa-mir-143 expression vector (pCDH miR-143-3p) or empty vector (pCDH EV) as control. Upper band is KSR2 protein (~100 kDa) and lower band is loading control GAPDH protein (~37 kDa). Order of samples on the membrane: JURKAT pCDH miR-143-3p replicate 1, pCDH EV replicate 1, pCDH EV replicate 2, pCDH miR-143-3p replicate 2, pCDH EV replicate 3, pCDH miR-143-3p replicate 3, ALL-SIL pCDH miR-143-3p replicate 1, pCDH miR-143-3p replicate 2, pCDH EV replicate 1, pCDH miR-143-3p replicate 3, pCDH EV replicate 2, pCDH EV replicate 3.</p><p>3. RT-qPCR amplification data for relative quantification of <i>KSR2 </i>expression in reference to <i>ACTB </i>and <i>GAPDH </i>in JURKAT and ALL-SIL cell lines expressing deadCas9-KRAB system for transcriptional repression, upon transduction with sgRNA targeting <i>KSR2 </i>transcription start site vector (<i>KSR2 </i>sgRNA1 and <i>KSR2 </i>sgRNA2) or non-targeting sgRNA vector (NT) as control.</p><p>4. FCS files from GFP competition assay performed in ALL-SIL and JURKAT cell lines expressing deadCas9-KRAB system for transcriptional repression, upon transduction with sgRNA targeting <i>KSR2 </i>transcription start site vector (<i>KSR2 </i>sgRNA1 and <i>KSR2 </i>sgRNA2) or non-targeting sgRNA vector (NT) as control.</p>
Mime-seq 2.0: a method to sequence microRNAs from specific mouse cell types
<p>These files contain the raw sequencing data (demultiplexed, unaligned BAM files or FASTQ files) collected as part of following manuscript: "Mime-seq 2.0: a method to sequence microRNAs from specific mouse cell types.". Included are oxidized and unoxidized small RNA sequencing libraries collected from RKO cells (human cell line, timecourse for methyltransferase expression), M. musculus B cells (splenic, CD43 depleted; methyltransferase under Cd79a-Cre driver), M. Musculus Plasma cells (sorted, methyltransferase under Bhlha15-Cre driver). </p><p> </p>
Neuenhäuser Friedhof in Celle
overview old cemetary in the City of Celle in northern Germany. Unique for the "Celler Adelsfeld" wich will be one of our next projects. Source: Objaverse 1.0 / Sketchfab
Cultivation of Chlamydomonas cells
Open the record for dataset details and reuse information.
Pregnancy-Responsive Pools of Adult Neural Stem Cells For Transient Neurogenesis in Mothers
<p>These are the RDS objects of the resulting files from our analyses, converted to Spatial Experiment object. These files can be loaded into spatialLIBD directly to allow interactive exploration of the data locally.</p> <p>In the new version of the dataset, we included the high resolution .tiff images of all of the samples. Furthermore, we included the low resolution (lowres) output from the spaceranger for the researchers who want to use these files. Higher resolution images (hires) were previously shared in the GEO submission alongside the raw sequencing data.</p>
Window Study of Nivolumab With or Without Ipilimumab in Squamous Cell Carcinoma of the Oral Cavity
ClinicalTrials.gov study NCT02919683. IPD Sharing: NO. Countries: 1. Publications: 2.
Dose-Escalation Trial of Carfilzomib With and Without Romidepsin in Cutaneous T-Cell Lymphoma
ClinicalTrials.gov study NCT01738594. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Re-irradiation With Fractionated Stereotactic Radiosurgery Plus Cetuximab in Patients With Recurrent Squamous Cell Carcinoma of the Head and Neck
ClinicalTrials.gov study NCT01104922. IPD Sharing: NO. Countries: 1. Publications: 1.
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.