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111,247 results for “cells”
PANDA cell-type-specific networks for S858R and WT mouse kidney
<p>The below files are from the data/results directory of this associated project and include the following:</p> <ul> <li><strong>PANDA networks</strong> : cell-type-specific TF-gene regulatory networks constructed using multi-omic inputs (snRNA-seq, TF-motif, and PPI) for all cell types in S858R and WT kidney tissues (n = 34). All the files included for PANDA networks indicate the condition (heterozygous or control), tissue (kidney) followed by _PANDA.Rdata. (example: pericytes_heterozygouskidneyexpression_PANDA.Rdata)</li> </ul>
Unit-cell-thick zeolitic imidazolate framework films for membrane application
<p>raw data for the manuscript with title of Unit-cell-thick zeolitic imidazolate framework films for membrane application</p>
DINO4cells example single cells
<p>This dataset contains 15k single cells taken from the Human Protein Project, as well as a csv file with their metadata.</p>
Accompanying data for: Local negative feedback of Rac activity at the leading edge underlies a pilot pseudopod-like program for amoeboid cell guidance
<p>This is the data underlying all of the figures in: Local negative feedback of Rac activity at the leading edge underlies a pilot pseudopod-like program for amoeboid cell guidance by Jason P. Town and Orion D. Weiner</p> <p>Paper Abstract: To migrate efficiently, neutrophils must polarize their cytoskeletal regulators along a single axis of motion. This polarization process is thought to be mediated through local positive feedback that amplifies leading edge signals and global negative feedback that enables sites of positive feedback to compete for dominance. Though this two-component model efficiently establishes cell polarity, it has potential limitations, including a tendency to “lock” onto a particular direction, limiting the ability of cells to reorient. We use spatially-defined optogenetic control of a leading edge organizer (PI3K) to probe how neutrophil-like HL-60 cells balance “decisiveness” needed to polarize in a single direction with the flexibility needed to respond to new cues. Underlying this balancing act is a local Rac inhibition process that destabilizes the leading edge to promote exploration. We show that this local inhibition enables cells to process input signal dynamics, linking front stability and orientation to local temporal increases in input signals. </p> <p>All data can be processed using standard scientific python packages using the Jupyter Notebooks located <a href="https://zenodo.org/badge/latestdoi/672328942">here</a>. To run the notebooks and reproduce the figures in the paper, download all of the zipped data files, unzip them, and arrange them into the following directory structure:</p> <ul> <li>projectfolder <ul> <li>data <ul> <li>fig03data</li> <li>fig04data</li> <li>fig05data</li> <li>figS01data</li> <li>figS08data</li> <li>figS10data</li> <li>simpleStimdata</li> </ul> </li> <li>figure01.ipynb</li> <li>figure02.ipynb</li> <li>...</li> </ul> </li> </ul> <p>Microscopy data was collected using an automated, Pycromanager-based pipeline. The most recent version of the pipeline can be found <a href="https://zenodo.org/badge/latestdoi/583790048">here</a>. </p> <p>This zipped folders contains microscopy data (images & metadata) in several directories:</p> <ul> <li>"simpleStimData" contains data used in several figures in the paper. It shows the dynamic behaviors of neutrophils as they are subjected to local optogenetic activation of optoPI3K. <ul> <li>File folders are not entirely standardized, but typically contain <ul> <li>The data of the experiment (YYMMDD format)</li> <li>The cell line used and the constructs expressed (35 is the optogenetic anchor, 52 is the optogenetic cargo, 97 is the PH-Akt-Halo PIP<sub>3</sub> reporter, and mcPak is the Pak-PBD-mCherry Rac reporter)</li> <li>A generic phrase 'Assays', 'VariousFrontStimulations', 'simpleStimScreen' that has no experimental meaning</li> <li>An indicator of the pre-programmed assay "type" - either 'none, 'front', 'side', 'back', 'global', 'lateral45', 'lateral90', '0Rad', '0.2 Rad', '0.39Rad', '0.79Rad' or '1.57Rad'</li> <li>The intensity of blue LED light used in the experiment ('50000'). This did not turn out to be very important for the paper in this particular dataset, but cells do not display an all-or-none response to optogenetic PI3K acitvation.</li> <li>And finally a replicate number for that assay that day (i.e. '000', '001', ...)</li> </ul> </li> <li>Within each file folder, there is a collection of images (Note that each image has a lot of extra information in the associated metadata) <ul> <li>Channel 0 is the PHAkt PIP<sub>3</sub> reporter fluorescence data</li> <li>Channel 1 is the Pak-PBD Rac reporter fluorescence data</li> <li>Channel 2 is a reflected blue light dataset. A small degree of blue light reflected off the bottoms of coverslips and could be collected on the camera</li> <li>Channel 3 is a binary segmentation channel generated on-the-fly during acquisition by our automated microscopy software</li> <li>Channel 4 is another binary segmentation channel, but this one is a prediction of the real image of the optogenetic stimulus based on a calibrated affine transformation of the projected image onto the microscopy field of view. </li> <li>if present, Channels 5 and 6 are control images taken at the end of the experiment to verify the presence of optogenetic constructs 35 (405 channel) and 52 (488 channel)</li> </ul> </li> <li>There is a small amount of data here that was not directly used in the paper. For example, the 'frontToGlobal' experiments did not produce interesting results, but actually led us to develop the method shown in Figure 5 in our paper where we did something closer to a 'sideToGlobal' assay (See Fig 5 in the paper). </li> </ul> </li> <li>"fig03Data" contains data used to generate Fig 3 and Fig S7B in the paper. This data is structured identically to the data in simpleStimData described above</li> <li>"fig04Data" contains data used to generate Fig 4 and Fig S9D in the paper. Some of this is from older software, so the metadata (i.e. LED intensities) are stored in a separate file called intensities.txt. This text file contains time stamps and LED intensity data that can be matched with the temporal metadata from the microscopy images. </li> <li>"fig05Data" contains data used to generate Fig 5 and Fig S11. These are structured identically to the simpleStimData structure described above. (Though these files are stored in more organized subdirectories compared to simpleStimData). </li> <li>"figS01Data" contains the microscopy data used to generate Fig S1</li> <li>"figS08Data" contains data used to generate Fig S8 in the paper. This is from older microscopy automation software, so the metadata (i.e. LED intensities) are stored in a separate file called metadata.txt. This text file contains time stamps and LED intensity changes that can be matched with the temporal metadata from the microscopy images. </li> <li>"figS10Data" contains data used to generate Fig S10 in the paper. This is a combination of data structures similar to that described above in "fig04Data" for the step inputs and "simpleStimData" for the turning assays.</li> </ul> <p> </p> <p> </p>
Data from: Desmoplastic stroma restricts T cell extravasation and mediates immune exclusion and immunosuppression in solid tumors
<p>In this study, we used our modified <span>precision-cut tumor slice assay of tumors treated with CAR T cells <em>in vivo </em></span><span>prior to harvest and harvested at different time-points post-treatment, which can better reflect the behavior of CAR T cells in the TME </span><em>in vivo</em><span>.</span></p>
An Interpretable Framework to Characterize Compound Treatments on Filamentous Fungi using Cell Painting and Deep Metric Learning
<p>This deposit contains:</p> <ul> <li>{train,test,val}.csv: Meta-data files</li> <li>images.tar.gz: Archive of images</li> <li>checkpoint.pth.tar: Model weights</li> <li>code.tar.gz: Code necessary to reproduce our experiments.</li> </ul>
Data and results for "Germline-encoded specificities and the predictability of the B cell response"
<p>Data and results for Vieira et al., <a href="https://www.biorxiv.org/content/10.1101/2022.09.16.508315v4">Germline-encoded specificities and the predictability of the B cell response</a></p> <p>Code for the analyses is available at <a href="https://github.com/cobeylab/v_gene_selection/">https://github.com/cobeylab/v_gene_selection/</a></p> <p> </p>
Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios
<p>All total 152 datasets are collected in the benchmarking study.</p> <p>Every dataset contains two parts: the gene expression matrix (or well-established model by dynwrap for trajectory) and the data information including the data id, repository, accession number, URL, technology platform, species, organ (source), cell number, gene number, data type, ERCC spike-in, dilution factor, volume, group condition, treatment, batch information and cluster labels.</p> <p>There are 23 datasets (data79-data101) for evaluating the simulation ability for cell trajectories which are derived from another Zenodo repository (https://zenodo.org/record/1443566).</p>
BSSE_QGF_61757 L010-L016 (Single-cell mRNA profiling reveals the hierarchical response of miRNA targets to miRNA induction)
<p>Fastq files from sequencing of sample BSSE_QGF_61757 L010 to L016</p> <p>Publication: https://doi.org/10.15252/msb.20188266</p>
BSSE_QGF_61757 L001-L009 (Single-cell mRNA profiling reveals the hierarchical response of miRNA targets to miRNA induction)
<p>Fastq files from sequencing of sample BSSE_QGF_61757 L001 to L009</p> <p>Publication: https://doi.org/10.15252/msb.20188266</p>
BSSE_QGF_61756 (Single-cell mRNA profiling reveals the hierarchical response of miRNA targets to miRNA induction)
<p>Fastq files from sequencing of sample BSSE_QGF_61756</p> <p>Publication: https://doi.org/10.15252/msb.20188266</p>
BSSE_QGF_61759 (Single-cell mRNA profiling reveals the hierarchical response of miRNA targets to miRNA induction)
<p>Fastq files from sequencing of sample BSSE_QGF_61759</p> <p>Publication: https://doi.org/10.15252/msb.20188266</p>
BSSE_QGF_61758 (Single-cell mRNA profiling reveals the hierarchical response of miRNA targets to miRNA induction)
<p>Fastq files from sequencing of sample BSSE_QGF_61758</p> <p>Publication: https://doi.org/10.15252/msb.20188266</p>
Single Cell RNA sequencing data of ADT treated Prostate cancer patients
<p>The data was generated from a study that was conducted according to guidelines approved by the Review Board at the University of Texas Southwestern Medical Center. We procured patient biopsy samples from two distinct studies. The first is titled "Tissue Collection and Results Gathering for Radiotherapy Patients & Healthy Individuals" (STU 072010-098), and the second is a Phase I Clinical Study on Stereotactic Ablative Radiotherapy (SABR) for Pelvic and Prostate Areas in High-Risk Prostate Cancer Patients (STU062014-027). The single-cell RNA sequencing (scRNA-seq) took place in Dr. Douglas Strand's laboratory, adhering to the method outlined in Henry et al<sup>1</sup>. We used a 1-hour treatment with 5mg/ml of collagenase type I, 10mM of ROCK inhibitor, and 1mg of DNase. Barcode labeling for 3' GEX was done using a 10X machine, and the sequencing process utilized an Illumina NextSeq 500 device.</p> <p> </p> <p>1. Henry, G. H., Malewska, A., Joseph, D. B., Malladi, V. S., Lee, J., Torrealba, J., ... & Strand, D. W. (2018). A cellular anatomy of the normal adult human prostate and prostatic urethra. <em>Cell reports</em>, <em>25</em>(12), 3530-3542.</p>
Data for "Derivative voltammetry: a simple tool to probe reaction selectivity in photoelectrochemical cells"
<p>Data archive for "Derivative voltammetry: a simple tool to probe reaction selectivity in photoelectrochemical cells" </p> <p><a href="https://doi.org/10.1039/D2SE00692H">https://doi.org/10.1039/D2SE00692H</a></p>
Transcriptomic profiles of MCF7-derived tamoxifen resistant cell lines
<p>We report mRNA profiles of human breast cancer cell lines, MCF7 parental, and MCF7-derived tamoxifen-resistant cell lines MCF7-TR1 and MCF7-TR2.</p> <p>These cells have been previously described in </p> <p>Ines Barone, Lauren Brusco, Guowei Gu, Jennifer Selever, Amanda Beyer, Kyle R. Covington, Anna Tsimelzon, Tao Wang, Susan G. Hilsenbeck, Gary C. Chamness, Sebastiano Andò, and Suzanne A.W. Fuqua. Loss of Rho GDIα and Resistance to Tamoxifen via Effects on Estrogen Receptor α. J Natl Cancer Inst. 2011 Apr 6; 103(7): 538–552. PMID: 21447808</p>
Data from: The CST complex facilitates cell survival under oxidative genotoxic stress
<p><span>Genomic DNA is constantly exposed to a variety of genotoxic stresses, and it is crucial for organisms to be equipped with mechanisms for repairing the damaged genome. Previously, it was demonstrated that the mammalian CST (CTC1-STN1-TEN1) complex, which was originally identified as a single-stranded DNA-binding trimeric protein complex essential for telomere maintenance, is required for survival in response to hydroxyurea (HU), which induces DNA replication fork stalling. It is still unclear, however, how the CST complex is involved in the repair of diverse types of DNA damage induced by oxidizing agents such as H<sub>2</sub>O<sub>2</sub>.</span></p> <p><em><span>STN1</span></em><span> knockdown (KD) sensitized HeLa cells to high doses of H<sub>2</sub>O<sub>2</sub>. While H<sub>2</sub>O<sub>2</sub>-induced DNA strand breaks throughout the cell cycle, <em>STN1</em> KD cells were as resistant as control cells to H<sub>2</sub>O<sub>2</sub> treatment when challenged in the G1 phase of the cell cycle, but they were sensitive when exposed to H<sub>2</sub>O<sub>2</sub> in S/G2/M phase. <em>STN1</em> KD cells showed a failure of DNA synthesis and RAD51 foci formation upon H<sub>2</sub>O<sub>2</sub> treatment. Chemical inhibition of RAD51 in sh<em>STN1</em> cells did not exacerbate the sensitivity to H<sub>2</sub>O<sub>2</sub>, implying that the CST complex and RAD51 act in the same pathway. Collectively, our results suggest that the CST complex is required for maintaining genomic stability in response to oxidative DNA damage, possibly through RAD51-dependent DNA repair/protection mechanisms.</span></p>
MITF/miR-579-3p novel regulatory axis controls BRAF-mutated melanoma decision between cell senescence or progression upon exposure to MAPK inhibitors.
<p><strong>Abstract</strong></p> <p>Therapy of metastatic melanoma has improved dramatically over the last years thanks to the development of targeted therapies (MAPK inhibitors) and immunotherapies. However, drug resistance continues to be a major limitation to the efficacy of these therapies. Our research group has provided robust evidence as to the involvement of a set of microRNAs in the development of resistance to target therapy in BRAF-mutated melanoma cell lines. Among them, a pivotal role is played by miR-579-3p, whose role as oncosuppressor was discovered by our group some years ago. Here we show that miR-579-3p and the microphthalmia-associated transcription factor (MITF) influence reciprocally their expression through positive feedback regulatory loops. In particular we show that miR-579-3p is specifically deregulated in BRAF-mutant melanomas and that its expression levels mirror those of MITF. Luciferase and ChIP studies show that MITF is a positive regulator of miR-579-3p, which is located in the intron 11 of the human gene ZFR (Zink-finger recombinase) and is co-transcribed with its host gene. Moreover, miR-579-3p, by targeting BRAF is able to stabilize MITF protein thus inducing its own transcription. As a consequence, upon exposure to MAPK inhibitors or, alternatively upon miR-579-3p transfection, the activation of this newly uncovered miR-579-3p/MITF axis induces a potent block of proliferation and senescence of BRAF-mutant melanoma cells. Moreover, the long term development of resistance to MAPKi is able to select cells characterized by the loss of both miR-579-3p and MITF and the same down-regulation is also present in patients relapsing after targeted therapies treatments. Altogether these findings suggest that miR-579-3p/MITF interplay potentially governs the balance between proliferation, senescence and resistance to target therapies in BRAF-mutant melanomas.</p> <p><strong>KEYWORDS: </strong></p> <p><strong>Acknowledgements</strong></p> <p>This study was supported by Italian Association for Cancer Research (AIRC) grants IG19865 to G. Ciliberto and IG24451 to R. Mancini and by the Lazio Innova grants 2018 n. 85-2017-13750 and 2020 n. A0375-2020-36657 to R. Mancini and by Italian Minister of Health grant PRIN 2017 (Prot. 2017HWTP2K) to G. Ciliberto and R. Mancini, by Italian Minister of Health through “Ricerca Corrente” grant L2/1 to P.A. Ascierto. The study was also supported by Lega Italiana per la Lotta ai Tumori (LILT) grant n. 2021U0001637 to L. Fattore. We thank all patients who donated samples for this research.</p>
FOG2 bioChIP-seq in HL1 cells
<p>Chromatin Immunoprecipitation of FOG2 protein over expressed in HL-1 cells. Eland output files that were aligned to mm9 genome used to call peaks with macs2. Output of macs2 was converted to mouse genome mm10.</p>
Mapped data: Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency
<p>This record contains mapped sequencing data for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen <em>et al</em>. It contains single-cell RNA-seq (scRNA) and single-cell ATAC-seq (scATAC) data from a time course of human dermal fibroblasts induced with Yamanaka factors OSKM using a Sendai virus based delivery system. The scRNA and scATAC data is performed at days 0, 2, 4, 6, 8, 10, 12, 14 and the final iPSCs. The experiment was re-performed and single-nucleus multiome (ATAC+RNA) was collected on days 1 and 2. </p> <p>The data is as follows:</p> <p><strong>scATAC</strong>: We used Chromap (commit <a href="https://github.com/haowenz/chromap/tree/6e97125b9">https://github.com/haowenz/chromap/tree/6e97125b9</a>, <a href="https://doi.org/10.1038/s41467-021-26865-w">https://doi.org/10.1038/s41467-021-26865-w</a>) to perform barcode correction, alignment and filtering for each of our samples. The corresponding fragment files (tab separated file containing mapped fragments with columns: chr, start, end, barcode, number of reads) and their tabix indices are available for each sample.</p> <p><strong>scRNA</strong>: We used cellranger v6.0.2 for read mapping and quantification to obtain the counts matrix. We used the GRCh38 2020-A reference. For each sample, the raw and filtered counts matrices are provided. E.g. `D0/raw_feature_bc_matrix.h5` contains an HDF5 object containing gene counts for each barcode and associated metadata for the Day 0 sample. Similarly, the files in `D0/raw_feature_bc_matrix/` contain the same gene x barcode matrix, with the counts matrix in Matrix Market format (`matrix.mtx.gz`), and gene (`features.tsv.gz`) and barcode names (`barcodes.tsv.gz`). </p> <p><strong>multiome</strong>: The ATAC and RNA components are separately processed using the same tools as mentioned above for scATAC and scRNA. Outputs are in the `snATAC` and `snRNA` subdirectories respectively. In addition, the `ATAC.RNA.bc.map.tsv` file contains a map to link snATAC barcodes to snRNA barcodes. </p>
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.