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273 results for “single-cell resolution”
Cell metadata for "The emergent landscape of the mouse gut endoderm at single-cell resolution"
<p>Cell metadata for the data published in "The emergent landscape of the mouse gut endoderm at single-cell resolution"</p> <p> </p>
Development of Multiomics in situ Pairwise Sequencing (MiP-Seq) for Single-cell Resolution Multidimensional Spatial Omics
<p>The original data used in the article: Development of Multiomics in situ Pairwise Sequencing (MiP-Seq) for Single-cell Resolution Multidimensional Spatial Omics</p> <p>Delineating the spatial multiomics landscape will pave the way to understanding the molecular basis of physiology and pathology. However, current spatial omics technology development is still in its infancy. Here, we developed a high-throughput targeted in situ sequencing strategy, multiomics in situ pairwise sequencing (MiP-Seq), to efficiently decipher multiplexed DNAs, RNAs, proteins, and small biomolecules at subcellular resolution. MiP-Seq simultaneously sequenced the dual barcode base of padlock probes, dramatically increasing the detection capacity to 10N by N rounds of sequencing. We delineated spatial gene profiles in the hypothalamus using MiP-Seq. Moreover, MiP-Seq was unitized to detect tumor gene mutations and allele-specific expression of parental genes and to differentiate sites with and without the m6A RNA modification at specific sites. MiP-Seq was combined with in vivo Ca2+ imaging and Raman imaging to obtain a spatial multiomics atlas correlated to neuronal activity and cellular biochemical fingerprints. Importantly, we proposed a “signal dilution strategy” to resolve the crowded signals that challenge the applicability of in situ sequencing. Together, our method improves spatial multiomics and precision diagnostics, and facilitates analyzing cell function in connection with gene profiles.</p>
Vectra Polatis image of human colorectal cancer (CRC1) from: A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at single-cell resolution.
<p>Two 4 µm thick serial sections were cut from CRC1 FFPE block using a microtome. The first slide was dewaxed and rehydrated before carrying out HIER with Antigen Retrieval Reagent-Basic (R&D Systems). The tissue was then blocked and incubated with the anti-CD3 antibody (Dako, Supplementary Table 2) followed by horseradish peroxidase (HRP) conjugated anti-rabbit antibody (Dako) and stained with 3,3' diaminobenzidine (DAB) substrate (Abcam) and haematoxylin. Areas with CD3<sup>+</sup> infiltration in the proximity of the tumour invasive margin were identified by a clinical pathologist (M. R-J.)</p> <p>The second slide was stained with a panel of six antibodies (CD8, PD1, Ki67, PDL1, CD68, GzB, Supplementary Table 2), Opal fluorophores and 4’,6-diamidino-2-phenylindole (DAPI) on a Ventana Discovery Ultra automated staining platform (Roche). Expected expression and cellular localisation of each marker as well as fluorophore brightness were used to minimise fluorescence spillage upon antibody-Opal pairing. Following a one-hour incubation at a 60°C, the slide was subjected to an automated staining protocol on an autostainer. The protocol involved deparaffinisation (EZ-Prep solution, Roche), HIER (DISC. CC1 solution, Roche) and seven sequential rounds of: one hour incubation with the primary antibody, 12 minutes incubation with the HRP-conjugated secondary antibody (DISC. Omnimap anti-Ms HRP RUO or DISC. Omnimap anti-Rb HRP RUO, Roche) and 16 minute incubation with the Opal reactive fluorophore (Akoya Biosciences). For the last round of staining, the slide was incubated with Opal TSA-DIG reagent (Akoya Biosciences) for 12 minutes followed by Opal 780 reactive fluorophore for our hour (Akoya Biosciences). A denaturation step (100°C for 8 minutes) was introduced between each staining round in order to remove the primary and secondary antibodies from the previous cycle without disrupting the fluorescent signal. The slide was counterstained with DAPI (Akoya Biosciences) and coverslipped using ProLong Gold antifade mounting media (Thermo Fisher Scientific). The Vectra Polaris automated quantitative pathology imaging system (Akoya Biosciences) was used to scan the labelled slide. Six fields of view, within the area selected by the pathologist, were scanned at 20x and 40x magnification using appropriate exposure times and loaded into inForm{Kramer, 2018 #23} for spectral unmixing and autofluorescence isolation using the spectral libraries. After spectral unmixing and merging of six 20x fields of view for a total of >5mm<sup>2</sup> ROI (Table 2), one single-tiff image was extracted for each marker and its intensity was rescaled from 0 to 1 with custom R scripts.</p>
Automatic monitoring of neural activity with single-cell resolution in behaving Hydra
<p>The ability to record every spike from every neuron in a behaving animal is one of the holy grails of neuroscience. Here, we report coming one step closer towards this goal with the development of an end-to-end pipeline that automatically tracks and extracts calcium signals from individual neurons in the cnidarian <em>Hydra vulgaris</em>. We imaged dually labeled (nuclear tdTomato and cytoplasmic GCaMP7s) transgenic <em>Hydra </em>and developed an open-source Python platform (TraSE-IN) for the Tracking and Spike Estimation of Individual Neurons in the animal during behavior. The TraSE-IN platform comprises a series of modules that segments and tracks each nucleus over time and extracts the corresponding calcium activity in the GCaMP channel. Another series of signal processing modules allows robust prediction of individual spikes from each neuron's calcium signal. This complete pipeline will facilitate the automatic generation and analysis of large-scale datasets of single-cell resolution neural activity in <em>Hydra</em>, and potentially other model organisms, paving the way towards deciphering the neural code of an entire animal.</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>
Imaging Mass Cytometry Images (APP1) from: A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at single-cell resolution.
<p>Four µm-thick sections were cut from the APP1 FFPE block with a microtome and used for staining with a panel of 26 antibodies targeting the main immune, stromal and epithelial cell populations of the gastrointestinal tract (Supplementary Table 2). The optimal dilution of each antibody in the panel was identified by staining and ablating FFPE appendix sections. The resulting images were reviewed by a mucosal immunologist (J.S.) and the dilution giving the best signal to background ratio was selected for each antibody (Supplementary Table 2). To perform the staining for IMC, slides were dewaxed after a one-hour incubation at 60°C, rehydrated and heat-induced antigen retrieval was performed with a pressure cooker in Antigen Retrieval Reagent-Basic (R&D Systems). Slides were incubated in a 10% BSA (Sigma), 0.1% Tween (Sigma), and 2% Kiovig (Shire Pharmaceuticals) Superblock Blocking Buffer (Thermo Fisher) blocking solution at room temperature for two hours. Each antibody was added to a primary antibody mix at the selected concentration in blocking solution and incubated overnight at 4°C. After two washes in PBS and PBS-0.1% Tween, the slides were treated with the DNA intercalator Cell-ID™ Intercalator-Ir (Fluidigm) (containing the two iridium isotopes 191Ir and 193Ir) 1.25 mM in a PBS solution. After a 30-minute incubation, the slides were washed once in PBS and once in MilliQ water and air-dried. The stained slides were then loaded in the Hyperion Imaging System (Fluidigm) imaging module to obtain light-contrast high resolution images of approximately four mm<sup>2</sup>. These images were used to select the ROI in each slide. For APP1, a one mm<sup>2</sup> ROI containing a lymphoid follicle in its whole depth alongside a portion of lamina propria and of epithelium was selected. ROIs were ablated at a o µm/pixel resolution and 200 Hz frequency.</p>
Dual-modality imaging of immunofluorescence and imaging mass cytometry for high-resolution whole slide imaging with accurate single-cell segmentation
<p>Imaging mass cytometry (IMC) is a powerful multiplexed tissue imaging technology that allows simultaneous detection of more than 30 makers on a single slide. It has been increasingly used for single-cell based spatial phenotyping in a wide range of samples. However, it only acquires a small, rectangle field of view (FOV) with a low image resolution that hinders downstream analysis. Here, we reported a highly practical dual-modality imaging method that combines high-resolution immunofluorescence (IF) and high-dementional IMC on the same tissue slide. Our computational pipeline uses the whole slide image (WSI) of IF as spatial reference, integrates small FOV IMC into a WSI of IMC. The high-resolution IF images enable accurate single-cell segmentation to extract robust high-dimensional IMC features for downstream analysis. We applied this method in esophageal adenocarcinoma of different stages, identified the single-cell pathology landscape via reconstruction of WSI IMC images and demonstrated the advantage of the dual-modality imaging strategy.</p>
Source data for paper "Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data"
<p>Sample-paired scRNA-seq and scATAC-seq data collected from human peripheral blood mononuclear cells with <em>Staphylococcus aureus</em><em> </em>infection. ScATAC-seq data collected from human peripheral blood mononuclear cells with <em>COVID-19</em> infection. </p>
Gene expression dataset of the Spatially Resolved Single-cell Translatomics at Molecular Resolution
<p>Here are the gene expression datasets of RIBOmap included in "<strong>Spatially Resolved Single-cell Translatomics at Molecular Resolution</strong>" from Zeng et al. Please refer to the README file for more detailed information. </p> <p> </p> <p><strong>Abstract</strong></p> <p>The precise control of mRNA translation is a crucial step in post-transcriptional gene regulation of cellular physiology. However, it remains a major challenge to systematically study mRNA translation at the transcriptomic scale with spatial and single-cell resolution. Here, we report the development of RIBOmap, a three-dimensional (3D) in situ profiling method to detect mRNA translation of thousands of genes simultaneously in intact cells and tissues. By applying RIBOmap to 981 genes in HeLa cells, we revealed a remarkable dependency of translation on cell-cycle stages and subcellular localization. Furthermore, we profiled single-cell translatomes of 5,413 genes in adult mouse brain tissues yielding a spatial cell atlas of 119,173 cells. The pairwise spatial mapping of single-cell translatome and transcriptome in two adjacent mouse brain slices revealed cell-type and brain-region-dependent translational regulation and suggested a translation remodeling during oligodendrocyte lineage maturation. The spatial translatome profiling detected widespread patterns of localized translation in neuronal and glial cells in intact brain tissue networks. Together, RIBOmap presents the first spatially resolved single-cell translatomics technology, accelerating our understanding of protein synthesis in the context of subcellular architecture, cell types, and tissue anatomy.</p>
Automatic monitoring of neural activity with single-cell resolution in behaving Hydra
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SCRINSHOT, a spatial method for single-cell resolution mapping of cell states in tissue sections (Revision data)
<p>The submitted dataset, correspond to the RAW (*.czi format) and analysis files of the <strong>revisions</strong> of 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>
ChromoPhyloGen: characterizing copy number alteration patterns in heterogeneous tumor cell populations at Single-Cell Resolution
<p>The human liver cancer cell line Huh7 was obtained from the American Type Culture Collection (ATCC). Huh7 cells were cultivated in Dulbecco's Modified Eagle Medium (DMEM, Gibco, C11995), supplemented with 1% penicillin/streptomycin (Gibco, 15140122), and 10% fetal bovine serum (FBS, Excell, FSP500). Huh7 cell line was maintained under a 95% O2 and 5% CO2 humidified atmosphere in an incubator at 37˚C. </p> <p>The scDNA-seq library was performed using the Chromium Single cell DNA Library & Gel Bead kit (10x Genomics, PN1000040) in combination with the Chromium instrument. The samples were processed on Chromium Single cell Chip C and D (10x Genomics, 1000022 and 1000042, respectively) according to the manufacturer's user guide and subsequently run on a thermocycler. The barcoded libraries were sequenced using the Novaseq 6000 300 cycle high-output flow cells.</p> <p>The scRNA-seq library was generated using the 10x Genomics Chromium Single Cell 3' & Gel Bead Kit v3 (10x Genomics, PN100075) in combination with the Chromium instrument. The samples were processed on Chromium Single cell Chip B (10x Genomics,1000154) according to the manufacturer's protocol and subsequently run on a thermocycler. The 3' gene expression libraries were sequenced using the Novaseq 6000 300 cycle high-output flow cells.</p> <p> </p>
Data from: Effect of stretching on inflammation in a subcutaneous carrageenan mouse model analyzed at single-cell resolution
<p>Understanding the factors that influence the biological response to inflammation is crucial, due to its involvement in physiological and pathological processes, including tissue repair/healing, cancer, infections, and autoimmune diseases. We have previously demonstrated that in vivo stretching can reduce inflammation and increase local pro-resolving lipid mediators in rats, suggesting a direct mechanical effect on inflammation resolution. Here, we aimed to explore further the effects of stretching at the cellular/molecular level in a mouse subcutaneous carrageenan-inflammation model. Stretching for 10 minutes twice a day reduced inflammation, increased the production of pro-resolving mediator pathway intermediate 17-HDHA at 48h post carrageenan injection, and decreased both pro-resolving and pro-inflammatory mediators (e.g., PGE<sub>2</sub> and PGD<sub>2</sub>) at 96h. ScRNAseq analysis of inflammatory lesions at 96h showed that stretching increased the expression of both pro-inflammatory (<em>Nos2</em>) and pro-resolution (<em>Arg1</em>) genes in M1 and M2 macrophages at 96 hours. An intercellular communication analysis predicted specific ligand-receptor interactions orchestrated by neutrophils and M2a macrophages, suggesting a continuous neutrophil presence recruiting immune cells such as activated macrophages to contain the antigen while promoting resolution and preserving tissue homeostasis.</p>
Data for the paper titled 'Dynamic Molecular Atlas for Cardiac Fibrosis at Single-Cell and Spatial Resolution: CD248 in Orchestrating Fibroblast-Immune Interaction'
<p>The deposited data were employed to generate the figures concerning single-cell RNA (scRNA) and spatial transcriptomic analyses in the paper titled 'Dynamic Molecular Atlas for Cardiac Fibrosis at Single-Cell and Spatial Resolution: CD248 in Orchestrating Fibroblast-Immune Interaction'.</p>
SIRV: Spatial inference of RNA velocity at the single-cell resolution
<p>Spatial transcriptomics and scRNA-seq datasets used for integration and prediction of un/spliced expression for spatially measured genes using SIRV, used to infer the RNA velocity in the spatial context</p>
Identifying cell states in single-cell RNA-seq data at statistically maximal resolution
<p>In this repository we provide the datasets for the results of the Cellstates method as shown in the article: “Identifying cell states in single-cell RNA-seq data at statistically maximal resolution” by Pascal Grobecker, Thomas Sakoparnig, and Erik van Nimwegen.</p> <p>A preprint is available under the following DOI: https://doi.org/10.1101/2023.10.31.564980</p> <p>There is a README file that describes the contents and formats of each of the tab-separated values files. There is one subdirectory containing files with Cellstates' results on the dataset of Zeisel et al. (DOI: 10.1016/j.cell.2018.06.021) which is accompanied by another README file describing the formats and contents of these files. </p>
Preprocessed dataset of the Spatially Resolved Single-cell Translatomics at Molecular Resolution
<p>Here are the pre-processed image datasets of RIBOmap included in "<strong>Spatially Resolved Single-cell Translatomics at Molecular Resolution</strong>" from Zeng et al. Please refer to the README file for more detailed information. </p> <p> </p> <p><strong>Abstract</strong></p> <p>The precise control of mRNA translation is a crucial step in post-transcriptional gene regulation of cellular physiology. However, it remains a major challenge to systematically study mRNA translation at the transcriptomic scale with spatial and single-cell resolution. Here, we report the development of RIBOmap, a three-dimensional (3D) in situ profiling method to detect mRNA translation of thousands of genes simultaneously in intact cells and tissues. By applying RIBOmap to 981 genes in HeLa cells, we revealed a remarkable dependency of translation on cell-cycle stages and subcellular localization. Furthermore, we profiled single-cell translatomes of 5,413 genes in adult mouse brain tissues yielding a spatial cell atlas of 119,173 cells. The pairwise spatial mapping of single-cell translatome and transcriptome in two adjacent mouse brain slices revealed cell-type and brain-region-dependent translational regulation and suggested a translation remodeling during oligodendrocyte lineage maturation. The spatial translatome profiling detected widespread patterns of localized translation in neuronal and glial cells in intact brain tissue networks. Together, RIBOmap presents the first spatially resolved single-cell translatomics technology, accelerating our understanding of protein synthesis in the context of subcellular architecture, cell types, and tissue anatomy.</p>
In silico spatial transcriptomic editing at single-cell resolution
<p>The data for training the GAN (Inversion) model and reproduce the results reported in the paper </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: Effect of stretching on inflammation in a subcutaneous carrageenan mouse model analyzed at single-cell resolution
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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.