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1,322 results for “Cells maps”
Single-Cell Mapping Reveals Several Immune Subsets Associated with Liver Metastasis of Pancreatic Ductal Adenocarcinoma
<p>Identifying a metastasis-correlated immune cell composition within the tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) will help to develop promising and innovative therapeutic strategies. Twenty-six samples from 11 patients (including 11 primary tumor tissues, 10 blood, and 5 lymph nodes) with different stages were used to develop a multiscale immune profile. High-dimensional single-cell analysis with mass cytometry was performed to search for metastasis-correlated immune changes in the microenvironment.</p> <p>The details about the files uploaded are as follows:</p> <p>1. panelA_Blood.zip includes 10 .fcs files from blood samples in Panel A;</p> <p>2. panelA_LN.zip includes 5 .fcs files from lymph node samples in Panel A;</p> <p>3. panelA_Tumor.zip includes 11 .fcs files from tumor tissue samples in Panel A;</p> <p>4. panelB_Tumor.zip includes 11 .fcs files from tumor tissue samples in Panel B;</p> <p>5. panel_metadata.xlsx describes marker used in Panel A and B;</p> <p>6. sample_metadata.xlsx describes detailed sample information.</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>
Enhanced Mapping of Small Molecule Binding Sites in Cells
<p>Selected docking poses for Wozniak et al manuscript. File name include the structure id (PDBID or Alphafold model) and the probe id, separated by an underscore (i.e., 6tjk_4.pdb, or AF-P10620-F1-model_6.pdb).</p>
Lung-MAP: Biomarker-Targeted Second-Line Therapy in Treating Patients With Recurrent Stage IV Squamous Cell Lung Cancer
ClinicalTrials.gov study NCT02154490. IPD Sharing: Not stated. Countries: 2. Publications: 2.
Lung-MAP: Nivolumab With or Without Ipilimumab as Second-Line Therapy in Treating Patients With Recurrent Stage IV Squamous Cell Lung Cancer and No Matching Biomarkers
ClinicalTrials.gov study NCT02785952. IPD Sharing: Not stated. Countries: 2. Publications: 2.
High-resolution mapping of the period landscape reveals polymorphism in cell cycle frequency tuning
Open the record for dataset details and reuse information.
A look beyond topography: transient phenomena of Escherichia coli cell division captured with high-speed in-line force mapping
Open the record for dataset details and reuse information.
ZipSeq : barcoding for real-time mapping of single cell transcriptomes
Open the record for dataset details and reuse information.
SCRINSHOT, a spatial method for single-cell resolution mapping of cell states in tissue sections
<p>The submitted dataset, correspond to the RAW (*.czi format) and analysis files of the manuscript "SCRINSHOT, a spatial method for single-cell resolution mapping of cell states in tissue sections", which has been processed for revision (2020-01-31) by PLOS Biology.</p> <p>The files are in *.zip format and the naming follows thenumbering of the manuscript figures, which will be found in bioRxiv.org (<strong>ID#: BIORXIV/2020/938571</strong>). Each *.zip files contains a document with the description of all the provided files.</p>
Cell mechanics measurements in force mapping AFMBioMed Summer school 2020
<p>A 15 min video tutorial to perform AFM cell mechanics measurements in force mapping mode.</p> <p>The video presents:</p> <ul> <li>Preparation of the sample</li> <li>Cantilever coating and calibration </li> <li>Laser alignment and approach</li> <li>How to perform force curves and force maps on living adherent cells</li> </ul>
Fibronectin-Based Nanomechanical Biosensors to Map 3D Surface Strains in Live Cells and Tissue (Raw Data)
<p>This is the raw microscope imaging data for the manuscript titled "Fibronectin-Based Nanomechanical Biosensors to Map 3D Surface Strains in Live Cells and Tissue."</p>
Data from: Early treatment response in non-small cell lung cancer patients using diffusion-weighted imaging and functional diffusion maps - a feasibility study
Objective: The aim of this study was to prospectively evaluate the feasibility of monitoring treatment response to chemotherapy in patients with non-small cell lung carcinoma using functional diffusion maps (fDMs). Materials and Methods: This study was approved by the Cantonal Research Ethics Committee and informed written consent was obtained from all patients. Nine patients (mean age = 66 years; range = 53–76 years, 5 females, 4 males) with overall 13 lesions were included. Imaging was performed within two weeks before initiation of chemotherapy and at one, two, and six weeks after initiation of chemotherapy. Imaging included a respiratory-triggered diffusion-weighted sequence including three b-factors (100, 600, and 800 s/mm2). Treatment response was defined by change in tumor diameter on computed tomography (CT) after two cycles of chemotherapy. Changes in the apparent diffusion coefficient (ADC) on a per-lesion basis and the percentages of voxel with significantly increased or decreased ADCs on fDMs were analyzed using repeated measures analysis of variance (ANOVA). Changes in tumor size were used as covariate to examine the ability of ADCs and fDM parameters to predict treatment response. Results: Repeated measures ANOVA revealed that the percentage of voxels with increased ADCs on fDMs (p = 0.002) as well as the mean ADC increase (p = 0.011) were significantly higher in good responders with a large reduction in tumor size on CT. Conclusion: Our results indicate that the percentage of voxels with significantly increased ADCs on fDMs seems to be a promising biomarker for early prediction of treatment response in patients with non-small cell lung carcinoma. Contrary to averaged values, this approach allows the spatial heterogeneity of treatment response to be resolved.
The model for new data mapping to human endoderm-derived organoids cell atlas (HEOCA)
<p><strong>The model for new data mapping to human endoderm-derived organoids cell atlas (HEOCA).</strong></p>
Data for MAPS: Pathologist-level Cell Type Annotation from Tissue Images through Machine Learning
<p>Extracted datasets and processed images used to generate figures in manuscript <i>"MAPS: Pathologist-level Cell Type Annotation from Tissue Images through Machine Learning".</i></p>
HAPPY: a deep learning pipeline for mapping cell-to-tissue graphs across placenta histology whole slide images
<p>These two zipped folders contain all data necessary to train, validate and reproduce results from the paper.</p> <p>Unzipping the files will create 6 folders. Data from folders with the same name across both zips should be combined into one folder. The 'annotations' folder contains all ground truth annotations for training all three deep learning models. The 'datasets' folder contains images for training the nuclei localisation and cell classification models. The 'embeddings' folder contains cell embedding vectors and nuclei coordinates from two slides used to create nodes to train the graph tissue classification model. The 'graph_splits' folder contains regions defining the validation and test splits for the graph model. The 'slides' folder contains a sample region of a whole slide image as a .tiff file for running the inference demo. The 'trained_models' folder contains trained weights for each of the three models.</p> <p>Further instructions for dataset use and creation of custom datasets are available in the GitHub readme: https://github.com/Nellaker-group/happy.</p>
A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure.
<p>https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84133</p>
Goal-Directed-Sensitive Firing Rate Maps of Hippocampal Pyramidal Cell
<p>The Video #1 shows a pyramidal cell that exhibited the goal-directed-sensitive firing rate map while leaving and moving toward the reward location (water plate).</p>
Mapping of phosphorylation modifications of insect cell derived huntingtin (2017/10/04)
<p>Huntingtin structure-function open lab notebook project</p>
Mapping of phosphorylation modifications of insect cell derived huntingtin (2017/10/05)
<p>Huntingtin structure-function open lab notebook project</p>
Mapping of phosphorylation modifications of insect cell derived huntingtin (2017/11/06)
<p>Huntingtin structure function open lab notebook</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.