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11,105
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11,105 results for “single cells”
Phertilizer: growing a clonal tree from single-cell DNA sequencing data of tumors
<p>The is the supplementary data repository for the simulation input data for Phertilizer: growing a clonal tree from single-cell DNA sequencing data of tumors.</p>
Data from: Single cell transcriptomics shows dose-dependent disruption of hepatic zonation by TCDD in mice
<p>2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) dose-dependently induces the development of hepatic fat accumulation and inflammation with fibrosis in mice initially in the portal region. Conversely, differential gene and protein expression is first detected in the central region. To further investigate cell-specific and spatially resolved dose-dependent changes in gene expression elicited by TCDD, single-nuclei RNA sequencing and spatial transcriptomics were used for livers of male mice gavaged with TCDD every 4 days for 28 days. The proportion of 11 cell (sub)types across 131,613 nuclei dose-dependently changed with 68% of all portal and central hepatocyte nuclei in control mice being overtaken by macrophages following TCDD treatment. We identified 368 (portal fibroblasts) to 1,339 (macrophages) differentially expressed genes. Spatial analyses revealed initial loss of portal identity that eventually spanned the entire liver lobule with increasing dose. Induction of R-spondin 3 (<em>Rspo3</em>) and pericentral <em>Apc</em>, suggested dysregulation of the Wnt/β-catenin signaling cascade in zonally resolved steatosis. Collectively, the integrated results suggest disruption of zonation contributes to the pattern of TCDD-elicited NAFLD pathologies.</p>
Figure 3: The microscopy images fo neuronal cells generated by SWCNT (a) and MWCNT (b)-COMPARATIVE STUDY OF SINGLE- AND MULTI-WALL CARBON NANOTUBES WITH APPLICATION IN CEREBRAL ANEURYSM
<p>Carbon nanotubes (CNTs) are nanometer-scale cylindrical graphitic struc-<br> tures that exhibit extraordinary physical properties as determined by their<br> structure [6]. Developing neural implants and the process of neuron regener-<br> ation are extremely di±cult. Nerve cells require the right environment and<br> the right growth factors at the right time to grow and proliferate. The elec-<br> trical conductive properties of these nanotubes o®er the possibility of using<br> it as a replacement to transmit and receive signals. The resulting 'hair like'<br> conductive wires that incorporate the properties of electrodes, permeable mi-<br> cro°uidic conduits and the porosity of the CNTs was found to promote cell<br> growth, migration and proliferation. The bridging consists either of an axon<br> or bundles of axons and dendrites. In some cases the bridge is covered with<br> clusters of cells [7]. These bridges form very e±ciently over quartz surfaces<br> which are apparently very poor surfaces for cell attachment. Fig. 2 shows the<br> evolution of a network generated by SWCNT and MWCNT. The data show<br> that cells ¯rst aggregate at the NT islands. As they complete this step axons<br> and dendrites begin to form and to build connections.<br> Also, has been observed for MWCNT higher connections than for SWCNT,<br> Figure 3.</p>
Figure 2: The microscopy images fo neuronal cells control (a) generated by MWCNT (b) and SWCNT (c)-COMPARATIVE STUDY OF SINGLE- AND MULTI-WALL CARBON NANOTUBES WITH APPLICATION IN CEREBRAL ANEURYSM
<p>Fig. 2 shows the evolution of a network generated by SWCNT and MWCNT. The data show<br> that cells ¯rst aggregate at the NT islands. As they complete this step axons and dendrites begin to form and to build connections.</p>
single cell RNA seq data of 3 healthy sample from frontal lobe and temporal lobe
<p><span>Frontotemporal lobe abnormalities are linked to neuropsychiatric disorders and cognition, but the role of cellular heterogeneity between temporal lobe (TL) and frontal lobe (FL) in the vulnerability to genetic risk factors remains to be elucidated. We provided single-nucleus transcriptome analysis in “fresh” human FL and TL which are integrated with genetic susceptibility, gene dysregulation in neuropsychiatric disease, and psychoactive drug response data. We show how intrinsic differences between TL and FL contribute to the vulnerability of specific cell types to both genetic risk factors and psychoactive drugs. Neuronal populations, specifically PVALB-neurons, were most highly vulnerable to genetic risk factors for psychiatric disease. These psychiatric disease-associated genes were mostly upregulated in the TL, and dysregulated in the brain of patients with obsessive-compulsive disorder, bipolar disorder and schizophrenia. these data provide prefound insight into brain frontotemporal lobe.</span></p>
Single-cell profiling reveals immune-based mechanisms underlying tumor radiosensitization by a novel Mn porphyrin clinical candidate, MnTnBuOE-2-PyP5+ (BMX-001)
<p>Manganese porphyrins reportedly exhibit synergic effects when combined with irradiation. However, an in-depth understanding of intratumoral heterogeneity and immune pathways, as affected by Mn porphyrins, remains limited. Here, we explored the mechanisms underlying immunomodulation of a clinical candidate, MnTnBuOE-2-PyP<sup>5+</sup> (BMX-001, MnBuOE), using single-cell analysis in murine carcinoma<em> </em>model. Mice bearing 4T1 tumors were divided into 4 groups: control, MnBuOE, radiotherapy (RT), combined MnBuOE, and radiotherapy (MnBuOE/RT). In epithelial cells, epithelial-mesenchymal transition, TNF-α signaling via NF-кB, angiogenesis, and hypoxia-related genes were significantly downregulated in the MnBuOE/RT compared to the RT. All subtypes of cancer-associated fibroblasts (CAFs) were reduced in MnBuOE and MnBuOE/RT. Inhibitory receptor-ligand interactions, in which epithelial cells and CAFs interacted with CD8+ T cells, were significantly lower in the MnBuOE/RT than in the RT. Trajectory analysis showed that DC maturation-associated markers were increased in MnBuOE/RT. M1 macrophages were significantly increased in the MnBuOE/RT compared to the RT, whereas myeloid-derived suppressor cells were decreased. CellChat analysis showed that the number of cell-cell communications was the lowest in the MnBuOE/RT. Our study is the first to provide evidence for the combined radiotherapy with a novel Mn porphyrin clinical candidate, BMX-001 from the perspective of each cell type within the tumor microenvironment.</p>
Optimized summary-statistic-based single-cell meta-analysis. Input files
<p>This dataset contains information about the input files used in the Optimized summary-statistic-based single-cell meta-analysis research project. </p> <p> </p>
Ultra-sensitive and multiplexed tracking of single cells using whole-body PET/CT
<p><em>In vivo </em>molecular imaging tools are crucially important for elucidating how cells move through complex biological systems, however, achieving single-cell sensitivity over the entire body remains challenging. Here, we report a highly sensitive and multiplexed approach for tracking upwards of 20 single cells simultaneously in the same subject using positron emission tomography (PET). The method relies on a statistical tracking algorithm (PEPT-EM) to achieve a sensitivity of 4 Bq/cell, and a streamlined workflow to reliably label single cells with over 50 Bq/cell of <sup>18</sup>F-fluorodeoxyglucose (FDG). To demonstrate the potential of the method, we tracked the fate of over 70 melanoma cells after intracardiac injection and found they primarily arrested in the small capillaries of the pulmonary, musculoskeletal, and digestive organ systems. This study bolsters the evolving potential of PET in offering unmatched insights into the earliest phases of cell trafficking in physiological and pathological processes and in cell-based therapies.</p>
Data from: Dissecting gene activation and chromatin remodeling dynamics in single human cells undergoing reprogramming
<p>During cell fate transitions, cells remodel their transcriptome, chromatin, and epigenome; however, it has been difficult to determine the temporal dynamics and cause-effect relationship between these changes at the single-cell level. Here, we employ the heterokaryon-mediated reprogramming system as a single-cell model to dissect key temporal events during early stages of pluripotency conversion using super-resolution imaging. We reveal that, following heterokaryon formation, the somatic nucleus undergoes global chromatin decompaction and removal of repressive histone modifications H3K9me3 and H3K27me3 without acquisition of active modifications H3K4me3 and H3K9ac. The pluripotency gene OCT4 (POU5F1) shows nascent and mature RNA transcription within the first 24 h after cell fusion without requiring an initial open chromatin configuration at its locus. NANOG, conversely, has significant nascent RNA transcription only at 48 h after cell fusion but, strikingly, exhibits genomic reopening early on. These findings suggest that the temporal relationship between chromatin compaction and gene activation during cellular reprogramming is gene context dependent. </p>
Single-cell and single-nucleus RNA-sequencing from paired normal-adenocarcinoma lung samples provides both common and discordant biological insights
<p>The datasets generated by <em>Cellranger </em>for all 24 samples (.h5 format).<br><br></p>
FedscGen: privacy-aware federated batch effect correction of single-cell RNA sequencing data -- Preprocessed datasets
<div> <div> <div> <div> <p>This dataset accompanies the publication "FedscGen: Privacy-Aware Federated Batch Effect Correction of Single-Cell RNA Sequencing Data" and includes eight single-cell RNA sequencing (scRNA-seq) datasets used to benchmark the FedscGen and scGen methods. The datasets are provided in <code>.h5ad</code> format and include comprehensive metadata necessary for replication and further analysis.</p> <h3>Datasets</h3> <p>We analyze various datasets to compare FedscGen against scGen (centralized) in terms of batch correction. For simplicity, we refer to the dataset by abbreviations:</p> <ol> <li> <p><strong>Cell Line (CL)</strong>:</p> <ul> <li>Derived from the 293t_jurkat experiment with three batches: Zheng et al., 2017.</li> </ul> </li> <li> <p><strong>Human Dendritic Cells (HDC)</strong>:</p> <ul> <li>scRNA-seq data of human dendritic cells across two batches: Villani et al., 2017.</li> </ul> </li> <li> <p><strong>Human Pancreas (HP)</strong>:</p> <ul> <li>Consolidated data from five sources with 14,767 cells each: Baron et al., 2016; Muraro et al., 2016; Segerstolpe et al., 2016; Wang et al., 2016; Xin et al., 2016.</li> </ul> </li> <li> <p><strong>Mouse Brain (MB)</strong>:</p> <ul> <li>Merged datasets with 691,600 and 141,606 cells: Saunders et al., 2018; Rosenberg et al., 2018.</li> </ul> </li> <li> <p><strong>Mouse Cell Atlas (MCA)</strong>:</p> <ul> <li>Data focusing on 11 cell types from various organs: Han et al., 2018; The Tabula Muris Consortium, 2018.</li> </ul> </li> <li> <p><strong>Mouse Hematopoietic Stem and Progenitor Cells (MHSPC)</strong>:</p> <ul> <li>Data from SMART-seq2 and MARS-seq protocols: Nestorowa et al., 2016; Paul et al., 2015.</li> </ul> </li> <li> <p><strong>Mouse Retina (MR)</strong>:</p> <ul> <li>Data from two unassociated laboratories with 26,830 and 44,808 cells: Macosko et al., 2015; Shekhar et al., 2016.</li> </ul> </li> <li> <p><strong>PBMC (human Peripheral Blood Mononuclear Cell)</strong>:</p> <ul> <li>scRNA-seq data with two batches: Zheng et al., 2017.</li> </ul> </li> </ol> <p><strong>Usage Notes</strong>: Each dataset is provided in <code>.h5ad</code> format, compatible with common single-cell analysis tools such as Scanpy. Detailed metadata is included within each file.</p> <p><strong>Keywords</strong>: Single-cell RNA sequencing, scRNA-seq, Batch effect correction, Privacy-aware, Federated learning, scGen, FedscGen, Clinical multi-center studies, Genomics, Bioinformatics</p> <p><strong>Contact</strong>: For questions or further information, please contact Mohammad Bakhtiari at <a href="mailto:mohammad.bakhtiari@uni-hamburg.de.">mohammad.bakhtiari@uni-hamburg.de.</a></p> <p><strong>License</strong>: Creative Commons Attribution 4.0 International (CC BY 4.0)</p> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
SI Figure 4: SEM images of either unwashed (left) or washed (right) E. antarcticus nematodes. A. Unwashed head region with arrows pointing to attached material and possible fungal hyphae. B. Washed head region with arrows pointing to the remaining attached material. C. Unwashed annules with arrows pointing to commonly attached foreign material. D. Washed annules with arrows pointing to remaining attached material. E. Unwashed somatic pore with arrows pointing to the common organic material. F. Washed vulva with an arrow pointing to remaining attached organic material. G. Unwashed cuticle with arrows showing a possible biofilm. H. Washed cuticle showing single attached cells indicated with arrows. I. Unwashed cuticle showing an off-axis line of attached material. J. Washed cuticle showing a similar off-axis line of material (as indicated with arrow) but reduced in quantity compared to the unwashed. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 4: SEM images of either unwashed (left) or washed (right) E. antarcticus nematodes. A. Unwashed head region with arrows pointing to attached material and possible fungal hyphae. B. Washed head region with arrows pointing to the remaining attached material. C. Unwashed annules with arrows pointing to commonly attached foreign material. D. Washed annules with arrows pointing to remaining attached material. E. Unwashed somatic pore with arrows pointing to the common organic material. F. Washed vulva with an arrow pointing to remaining attached organic material. G. Unwashed cuticle with arrows showing a possible biofilm. H. Washed cuticle showing single attached cells indicated with arrows. I. Unwashed cuticle showing an off-axis line of attached material. J. Washed cuticle showing a similar off-axis line of material (as indicated with arrow) but reduced in quantity compared to the unwashed.
Single cell T-cell Atlas (V3)
<p>The attached datasets comprised of the merging of 21 high quality single cell T cell based dataset that had both the TCR-seq and GEx. The object contains ~1.3 paired TCR-seq with GEx in the Seurat Object (<a target="_blank" rel="noopener noreferrer">supercluster_added_ID-240531.rds</a>).</p> <p>We also included the original identifiers in the <a target="_blank" rel="noopener noreferrer">Sup_Update_labels.csv</a> a. See our <a href="https://stegor.readthedocs.io/en/latest/" target="_blank" rel="noopener">https://stegor.readthedocs.io/en/latest/</a> for how we processed the 12 datasets (V2) and decided on the current 47 T cell annotation models using scGate (TcellFunction). Additionally, based on collaborator recommendataion, we have also now included a simpler T cell annotion model in STEGO.R process (Tsimplefunctions). </p> <p>This is the accompanying data set for the paper entitled ‘T cell receptor-centric approach to streamline multimodal single-cell data analysis.’, which is currently available as a preprint (<a href="https://www.biorxiv.org/content/10.1101/2023.09.27.559702v2" target="_blank" rel="noopener">https://www.biorxiv.org/content/10.1101/2023.09.27.559702v2</a>). Details on the origin of the datasets, and processing steps can be found there.</p> <p>The purpose of this atlas both the full dataset and down sampling version is to aid in improving the interpretability of other T cell based datasets. This can be done by adding in the down sampled object that contains up to 500 cells per annotation model. This dataset aims to improve the capacity to identify TCR-specific signature by ensuring a well covered background, which will improve the robustness of the FindMarker Function in Seurat package.</p> <p> </p>
Single-cell proteo-transcriptomic profiling reveals altered characteristics of stem and progenitor cells in patients receiving cytoreductive hydroxyurea in early-phase chronic myeloid leukemia
<p>This repository contains CITE-seq data generated from CML stem and progenitor cells before and after hydroxyurea treatment using the BD Rhapsody Single-Cell Analysis System. </p> <p><strong><br>File descriptions:</strong></p> <p>1. RSEC-adjusted UMI count files generated using the BD Rhapsody Targeted Analysis Pipeline (v. 1.10.1):</p> <ul> <li>CartridgeS1_RSEC_MolsPerCell.csv</li> </ul> <p>2. RSEC-adjusted UMI counts for cells remaining after cell quality filtering using SeqGeq software (genes expressed vs library size):</p> <ul> <li>CartridgeS1_RSEC_MolsPerCell_postQC.csv</li> </ul> <p>3. Sample tag (sample of origin) calls for each putative cell, outputted by the BD Rhapsody Targeted Analysis Pipeline (v. 1.10.1). </p> <ul> <li>CartridgeS1_Sample_Tag_Calls.csv</li> </ul> <p> </p>
FIB-SEM tomography datasets of entire single-cell human monocyte derived macrophages
<p>FIB-SEM tomography was used to determine the absolute number of internalized gold nanoparticles in GM-CSF and M-CSF human monocyte-derived macrophages at the single-cell level. All experiments were performed using a Thermo Scientific Scios 2 Dual Beam microscope (Thermo Fisher Scientific, Waltham, MA, USA). A selected cell was protected with a platinum (Pt) layer of 1 µm thickness (30 kV and current of 1 nA). Then, a trench of 5 µm in depth was milled on the front face and each side of the cell of interest using the ion beam at 7 nA current. Finally, the front face of the volume of interest was polished with an ion beam current of 1 nA until the beginning of the cell was visible to optimize the focus and contrast. FIB-SEM tomography of the whole cell was performed using the FEI Slice and View software (Thermo Fisher Scientific, Waltham, MA, USA, version 4.1). The electron beam acceleration voltage was set to 5 kV, the current to 0.4 nA, the resolution to 1536 × 1024 pixels, and the dwell time to 30 µs. Images were acquired in immersion mode with the backscattered electron detector, yielding clear signals from AuNPs due to the detector's sensitivity to backscattered electrons, which correlates with the atomic number of Au (Z = 79). The ion beam operating with a current of 1 nA current at 30 kV was used to slice through the cell of interest at an interval of 18 nm and a depth of 5 µm. The datasets available on the repository have been aligned and cropped.</p>
Individual allotype responses to HEK-293T-based cell lines ex-pressing single MHC class I chain-related gene B alleles
<p><span>Figure S1.</span><span> Individual allotype responses between 64 sera collected from kidney transplant patients and 5 single </span><span>MICB</span><span> allele-expressing cell lines established in </span><span>HLA</span><span> class I, </span><span>MICA,</span><span> and </span><span>MICB</span><span>-null HEK-293T cells. </span><span>HLA</span><span> class I, </span><span>MICA,</span><span> and </span><span>MICB</span><span> genes were removed using CRISPR/Cas9 in previous studies [20, 21]. Some of the 64 sera showed responses to single </span><span>MICA</span><span> allele-expressing cell lines [21]. However, none of the 64 sera showed individual allotype responses in this study.</span></p>
A FRET based biosensor for measuring Gα13 activation in single cells
<p>The figures and raw data that are presented in the paper "A FRET based biosensor for measuring Gα13 activation in single cells"</p>
Noninvasive detection of macrophage activation with single-cell resolution through machine learning
<p>Data related to the article "Noninvasive detection of macrophage activation with<br> single-cell resolution through machine learning".</p> <p>The package contains 2 folders:<br> - RawData: This package contains raw data and examples of processing to extract the<br> variables employed to train and assess the models.<br> - Variables: This package contains the extracted data from the various experiments<br> showed in the article.</p>
Data for: "A high-throughput microscopy method for single-cell analysis of event-time correlations in nanoparticle-induced cell death"
<p>Data related to the publication Murschhauser <em>et al.</em>: <a href="https://doi.org/10.1038/s42003-019-0282-0">A high-throughput microscopy method for single-cell analysis of event-time correlations in nanoparticle-induced cell death</a>. It contains fluorescence time traces of single cells marked with cell-event markers and observed by time-lapse microscopy. The cells were treated with nanoparticles at different doses (NP25 and NP100), with staurosporine (sts) or were left untreated for control (ctrl). See the above-mentioned publication for more details.</p> <p>The format of the data is described below.</p> <p>The file <code>Data_A549.zip</code> contains data measured with A549 cells, and the file <code>Data_Huh7.zip</code> contains data measured with Huh7 cells. Both files have the same structure. Each file contains the directories <code>Raw</code> and <code>Fitted</code> as well as a checksum file. The <code>Raw</code> directory contains single-cell fluorescence time courses as obtained by time-lapse microscopy. The <code>Fitted</code> directory contains the results of fitting model functions as well as properties of identified events, such as event times. The checksum file contains SHA256 checksums of all files within these directories and can be used to check file integrity.</p> <p>Both directories contain measurement directories. Each measurement directory contains the data corresponding to one experiment. The name of the measurement directory is the measurement identifier. Each measurement directory contains condition directories. Each condition directory contains data corresponding to one condition measured in the measurement and is named after the condition. Each condition directory contains marker directories. They are named after the fluorescence markers measured and contain files with single-cell data corresponding to the respective markers.</p> <p>The names of those files consist of multiple parts separated by underscores. The first two parts identify a position of the microscope. Since pairs of markers were measured, each position is present in two marker directories. The third part is the measurement identifier. The other parts will be described below.</p> <p>The <code>Raw</code> directory contains only CSV files with the raw fluorescence time courses. The filenames contain no other parts and have the suffix “.txt”. The first row of each CSV file is the time (in units of 10 minutes), and the other rows are the fluorescence time courses of the cells observed at the corresponding position (in arbitrary units). Each file in the <code>Raw</code> directory corresponds to a group of files in the <code>Fitted</code> directory.</p> <p>The <code>Fitted</code> directory contains three types of CSV files. Their names have “ALL” as fourth part, a session identifier as sixth part and the suffix “.csv”. The fifth part indicates the type of file and is one of the following:</p> <ul> <li>“PARAMS” indicates the estimated values for the model parameters. Each row stands for one cell and each column for a parameter of the model function fitted to the data. The model functions are published with the <a href="https://doi.org/10.5281/zenodo.1418465">fitting software</a>.</li> <li>“SIMULATED” indicates the fitted traces. The traces are calculated using the model functions and the estimated parameters. The format is the same as for the raw traces, but the time is in units of hours and has a higher resolution.</li> <li>“STATE” indicates additional information extracted from the fitted traces. Each row stands for a cell and each column for a property. The first column is the number of the cell. The second column is the event time found (in hours); non-finite values indicate that no event time was found. The third and fourth columns contain the absolute and relative amplitude of the trace, respectively. The fifth column is the logarithmic likelihood of the best fit. The sixth column indicates an algorithm used for postprocessing, and the seventh column indicates the trace slope at the event. See the fitting software for details.</li> </ul> <p> </p>
Single-cell -omics datasets containing a trajectory
<p>Contains datasets to compare trajectory inference methods. See <a href="https://benchmark.dynverse.org">https://benchmark.dynverse.org</a></p> <p>These files can be opened in R using `readRDS` .</p> <p>Each file is a list containing at least:</p> <ul> <li>expression and counts: filtered log<sub>2</sub> normalised expression files and raw counts</li> <li>milestone_network, milestone_percentages and divergence_regions: the trajectory model</li> </ul>
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.