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111,247 results for “cells”
Epithelial cell chirality emerges through the dynamic concentric pattern of actomyosin cytoskeleton
<p>Supporting Information for</p> <p>Epithelial cell chirality emerges through the dynamic concentric pattern of actomyosin cytoskeleton</p> <p>Takaki Yamamoto, Tomoki Ishibashi, Yuko Mimori-Kiyosue, Sylvain Hiver, Naoko Tokushige, Mitsusuke Tarama, Masatoshi Takeichi, Tatsuo Shibata</p> <p>Tatsuo Shibata<br>Email: tatsuo.shibata@riken.jp</p> <p>Numerical data used for Figs. 1B, and C: </p> <p>Fig1B.csv,<br>Fig1C.csv</p> <p>Numerical data used for Fig. 1 figure supplement 1:</p> <p>Fig1S1_Collagen.csv,<br>Fig1S1_Fibronectin.csv,<br>Fig1S1_Non-coated.csv,<br>Fig1S1_PLL.csv</p> <p>Numerical data used for Figs. 2B, C and D: </p> <p>Fig2B.csv,<br>Fig2C_Blebbistatin.csv,<br>Fig2C_CK666.csv,<br>Fig2C_DMSO.csv,<br>Fig2C_Nocodazole.csv,<br>Fig2C_SMIFH2.csv,<br>Fig2D_DMSO.csv,<br>Fig2D_SMIFH2.csv</p> <p>Numerical data used for Fig. 2 figure supplement 2 A, D:</p> <p>Fig2S2A.csv,<br>Fig2S2D_NC.csv,<br>Fig2S2D_DAAM1_siRNA.csv,<br>Fig2S2D_DIAPH2_siRNA.csv</p> <p>Numerical data used for Fig. 2 figure supplement 3:</p> <p>Fig2S3A_NC.csv,<br>Fig2S3A_Myo2A_siRNA.csv,<br>Fig2S3A_Myo2B_siRNA.csv,<br>Fig2S3A_Myo2A_B_siRNA.csv,<br>Fig2S3B.csv</p> <p>Numerical data used for Fig. 2 figure supplement 4:</p> <p>Fig2S4A_NC.csv,<br>Fig2S4A_VCL_siRNA.csv,<br>Fig2S4B.csv</p> <p>Note: Fig2S2D_NC.csv, Fig2S3A_NC.csv and Fig2S4A_NC.csv represent data from the same control experiment.</p> <p>Numerical data used for Figs. 6C and D:<br>Fig6C.xls,<br>Fig6D_1.xls,<br>Fig6D_2.xls</p> <p>Numerical data used for Figs. 6 figure supplement 1:<br>Fig6S1I.xls,<br>Fig6S1J.xls</p> <p>Note: these data were also used in Figure 7 figure supplement 2A-H.</p> <p>Numerical data used for Figs. 8D and E:<br>Fig8DE.csv: </p> <p>FreeFEM++ script to perform the numerical computation used in Fig. 7 and matlab script for Fig 7 BCDFGH, and Fig. 7 figure supplement 2I: </p> <p>caco2ActiveChiralModel.edp,<br>PlotSimulationResults.m</p>
Comparison of Fixed Single Cell RNA-seq Methods to Enable Transcriptome Profiling of Neutrophils in Clinical Samples
<p>Monitoring neutrophil gene expression is a powerful tool for understanding disease mechanisms, developing new diagnostics, therapies and optimizing clinical trials. Neutrophils are sensitive to the processing, storage and transportation steps that are involved in clinical sample analysis. This study is the first to evaluate the capabilities of technologies from 10X Genomics, PARSE Biosciences, and HIVE (Honeycomb Biotechnologies) to generate high-quality RNA data from human blood-derived neutrophils. Our comparative analysis shows that all methods produced high quality data, importantly capturing the transcriptomes of neutrophils. 10X FLEX cell populations in particular showed a close concordance with the flow cytometry data. Here, we establish a reliable single-cell RNA sequencing workflow for neutrophils in clinical trials: we offer guidelines on sample collection to preserve RNA quality and demonstrate how each method performs in capturing sensitive cell populations in clinical practice.</p> <p><strong>This dataset includes the FACS, 10X 3', Parse, 10X Flex, and Hive data and analysis.</strong></p>
Cross-disease integration of single-cell RNA sequencing data from lung myeloid cells reveals TAM signature in in vitro model
<p>Single cells from a 3D human cell-based model comprising tumor cell line-derived spheroids, cancer-associated fibroblasts and primary monocytes were dissociated and analyzed using scRNAseq. 4 monocyte donors were used in the 3D model, and 3 monocyte donors were used for 2D differentiation of macrophages.</p>
Datasets associated with the manuscript "Differential detection workflows for multi-sample single-cell RNA-seq data"
<p>In this Zenodo repository, we share the data that is required to reproduce all the analyses from our publication "Differential detection workflows for multi-sample single-cell RNA-seq data".</p> <p>This repository includes all* input data, intermediate results and final outputs that are represented in our manuscript. For a more elaborate description of the data, we refer to the companion GitHub. https://github.com/statOmics/DD_benchmarks for the benchmarks and https://github.com/statOmics/DD_cases for the case studies, respectively.</p>
Single-cell RNAseq of Day 49 midbrain organoids from healthy and alpha-synuclein triplication iPSC lines
<p>Unbiased single-cell RNAseq of Day 49 midbrain dopaminergic organoids (10x 3' v3) of the Patikas et al. publication.</p> <p> </p> <p>The mono-unt-celltypes.h5ad refers to the object shown at Fig 2A and contains 3 cell lines:</p> <ol> <li>KOLF2 ( Control cell line)</li> <li>SNCA-3x alpha Synuclein triplication Parkinson's Disease patient-derived iPSC line</li> <li>SNCA-corr (SNCA-3x isogenic control with the triplication mutation corrected)</li> </ol> <p>The all-celltypes.h5ad refers to the object shown at Fig 4 onwards and contains the 3 cell lines that are included in model-dataset.h5ad and 7 other single-cell RNAseq samples:</p> <ol> <li>SNCA-3x+KOLF2 (chimera organoid condition of SNCA-3x and KOLF2 of iPSCs grown together in a midbrain organoid)</li> <li>SNCA-corr+KOLF2 (chimera organoid condition of SNCA-corr and KOLF2 of iPSCs grown together in a midbrain organoid)</li> <li>5 paired rotenone conditions. For each condition (KOLF2, SNCA-3x, SNCA-corr, SNCA-3x+KOLF2, SNCA-corr+KOLF2) a paired condition with acute 24h rotenone treatment in an antioxidant-free medium.</li> </ol>
Multiplexed single-cell characterization of alternative polyadenylation regulators (HEK293FT & K562 Perturb-seq data)
<p>This site provides access to datasets from the CPA-Perturb-seq <a href="https://www.biorxiv.org/content/10.1101/2023.02.09.527751v1">manuscript</a> Kowalski*, Wessels*, Linder* et al., including processed Perturb-seq datasets from HEK293FT and K562. We release these data as Seurat objects, where each object contains single-cell quantifications of gene expression (RNA assay), and in addition, quantifications of polyA site usage (polyA site assay). To explore these data, please install the <a href="https://github.com/satijalab/PASTA">PASTA</a> (PolyA Site analysis using relative Transcript Abundance) package, which provides infrastructure and analytical tools to explore alternative polyadenylation at single-cell resolution. For each dataset, we also include a fragment file which enables visualization of read coverage plots across groups of cells. </p> <p>The files include:</p> <p>1. CPA_K562.Rds : Seurat object containing the K562 CPA-Perturb-seq dataset </p> <p>2. CPA_K562_fragments.tsv.gz : Fragment file for the K562 dataset </p> <p>3. CPA_K562_fragments.tsv.gz.tbi : Fragment file index for the K562 dataset </p> <p> </p> <p>R code below:</p> <pre><code>library(PASTA) k562 <- readRDS("CPA_K562.Rds") # Add fragments for plotting Fragments(k562) <- CreateFragmentObject(path = "download/CPA_K562_blocks.tsv.gz", cells = Cells(k562)) # visualize polyA site usage PolyACoveragePlot(k562, region ="chr7-26212195-26213351")</code></pre>
Joint profiling of cell morphology and gene expression during in vitro neurodevelopment
<p>Cell Painting processed data for "Joint profiling of cell morphology and gene expression during in vitro neurodevelopment".</p>
Source Data and Scripts - Reconstitution of Human Brain Cell Diversity in Organoids via Four Protocols
<p>Source data and scripts associated with the manuscript "<em>Reconstitution of Human Brain Cell Diversity in Organoids via Four Protocols</em>" (Naas et al. 2024, <em>bioRxiv</em>, <a href="https://doi.org/10.1101/2024.11.15.623576" rel="nofollow">DOI: 10.1101/2024.11.15.623576</a>). Corresponding scripts to reproduce all figures and tables presented in the manuscript are also available on <a href="https://github.com/jn-goe/brain_organoids_four_protocols">https://github.com/jn-goe/brain_organoids_four_protocols</a>.</p> <div> <div> <p>The therein introduced NEST-Score is available as R package on <a href="https://github.com/jn-goe/NESTScore">https://github.com/jn-goe/NESTScore</a>.</p> <p>The interactive Shiny App data explorer is available on <a href="https://vienna-brain-organoid-explorer.vbc.ac.at/">https://vienna-brain-organoid-explorer.vbc.ac.at</a>.</p> </div> </div>
Oscillatory dynamics of mRNA metabolism and chromatin accessibility in mESCs during the cell cycle
<p>Processed single-cell RNAseq and multiome sequencing--single-nucleus RNAseq and ATACseq for mESCs</p> <table> <tbody> <tr> <td><strong>Filename</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>scrna_deepdycle.h5ad</td> <td>single-cell RNAseq data, has information about unsplcied and spliced levels and the inferred cell cycle phase</td> </tr> <tr> <td>snrna_deepdycle_rep1.h5ad</td> <td>single-nulceus RNAseq data (replicate 1), has information about unsplcied and spliced levels and the inferred cell cycle phase</td> </tr> <tr> <td>snrna_deepdycle_rep2.h5ad</td> <td>single-nulceus RNAseq data (replicate 2), has information about unsplcied and spliced levels and the inferred cell cycle phase</td> </tr> <tr> <td>body_coverage_lif1.tsv.gz</td> <td>single nucleus ATACseq data (replicate 1), counts table of ATACseq peaks mapped to the genes</td> </tr> <tr> <td>body_coverage_lif2.tsv.gz</td> <td>single nucleus ATACseq data (replicate 2), counts table of ATACseq peaks mapped to the genes</td> </tr> <tr> <td>model_predictions.zip</td> <td>gene-wise predictions for gene expression--unspliced and spliced, and mRNA metabolism rates--synthesis, splicing, export, and degradation rates for single-cell and single-nucleus data</td> </tr> </tbody> </table>
Pan-cancer analyses refine the single-cell portrait of tumor-infiltrating dendritic cells
<p>This is the dataset for "Pan-cancer Analyses Refine the Single-Cell Portrait of Tumor-Infiltrating Dendritic Cells".</p> <p>File "panDC_all_h5ad.gz" contains processed expression .h5ad data.</p> <p>File "panDC_metadata.csv" contains the meta data for this study.</p>
Data sets for Shiburah et al., "The absence of the Leishmania major telomerase TERT component links telomeres and cell homeostasis with infectivity"
<p>These files correspond to the figures and information contained in Shiburah et al., "The absence of the <em>Leishmania major</em> telomerase TERT component links telomeres and cell homeostasis with infectivity"</p>
Single Cell RNA Transcriptomics of Mantle Cell Lymphoma Reveals the Presence of Treatment-Resistant Subclones at the Time of Diagnosis - Supplementary Data
<p><strong>Supplementary Table 4.</strong> Results of optical genomic mapping for patient P009. The table lists genomic aberrations shared for diagnosis and relapse and those exclusive at both timepoints.</p> <p><strong>Supplementary Table 5.</strong> <strong>Sheet A:</strong> Results of differential gene expression analysis between relapse and diagnosis MCL cells of four patients (P009, P022, P027 and P087) performed using Wilcoxon rank-sum test. Only genes with adjusted p-value < 0.01, absolute log2 fold change > 1, and expression in at least 25% of cells in the upregulated group are included. <strong>Sheet B: </strong>Results of GO biological process enrichment analysis performed using clusterProfiler on upregulated and downregulated genes from each patient comparison. Multiple testing correction was performed using the Benjamini-Hochberg method. Only terms with q-value < 0.01 are shown.</p> <p><strong>Supplementary Table 6.</strong> Proportions of cells at four cell cycle phases (G0, G1, S, and G2M) at diagnosis (DG) and their changes after relapse (REL) in four aggressive MCL patients. Statistical analysis was performed using chi-square test.</p> <p><strong>Supplementary Table 7. </strong>Characterization of lymphoma sub-clones detected using CNV inference. <strong>Sheet A: </strong>Cell counts and proportions (prop) relative to total MCL cells in each subclone at diagnosis (DG) and relapse (REL). prop_FC – proportion fold change at relapse compared to diagnosis (calculated as prop_REL/prop_DG for expanding clones, or prop_DG/prop_REL for contracting clones). Subclone labels indicate putative treatment sensitivity: "Sens" denotes subclones that contracted or disappeared at relapse (suggesting chemotherapy sensitivity), while "Res" denotes subclones that expanded at relapse (suggesting chemotherapy resistance). <strong>Sheet B:</strong> CNV profiles of MCL subclones identified through inferCNV analysis. Each row specifies chromosomal coordinates (chr, start, end), copy number state (cn), cytogenetic location (cytobands), and genes within altered regions. Copy number states: cn=1 (deletion), cn=2 (normal), cn=3+ (amplification).</p> <p><strong>Supplementary Table 8.</strong> Differential gene expression analysis between resistant and sensitive MCL subclones. <strong>Sheet A:</strong> Summary of pairwise subclone comparisons. For each comparison between resistant (Res) and sensitive (Sens) subclones, the table reports counts of total, upregulated and downregulated differentially expressed genes and their ratio, enriched Gene Ontology (GO) terms and Hallmark gene sets in each direction. <strong>Sheet B:</strong> Complete list of differentially expressed genes from all pairwise subclone comparisons. Each row represents a single gene with statistical metrics including p-value, adjusted p-value, average log2 fold change (avg_log2FC), expression percentages in each subclone (pct.1, pct.2), regulatory direction (up/down), and gene description. <strong>Sheet C:</strong> Gene Ontology (GO) enrichment analysis results for differentially expressed genes. Only significantly enriched (q-value < 0.05) biological processes are shown with enrichment statistics including gene ratio, background ratio, fold enrichment, z-score, adjusted p-values, and lists of genes contributing to each enriched term. <strong>Sheet D: </strong>Hallmark gene set enrichment analysis (GSEA) results identifying coordinated expression programs differentially active between resistant and sensitive subclones. Results include normalized enrichment scores (NES), statistical significance, leading edge metrics, and core enrichment genes for each pathway.</p> <p><strong>Supplementary Table 9.</strong> Somatic variants in P069 samples. The variants were identified using a consensus approach integrating three variant callers (Mutect2, VarScan2, and Strelka2) and filtered using SomaticSeq with a PASS threshold score of 0.5. The table includes sample identifiers, genomic coordinates (hg38), variant allele frequencies (VAF) and genotypes (GT) for matched normal and tumor samples, sequencing depth (DP), and comprehensive functional annotations generated by Funcotator using GENCODE and HGNC databases. Key annotation fields include gene symbols, variant classifications, transcript information, protein changes, and gene ontology details. Only variants passing consensus filtering criteria with VAF ≥1% for heterozygous and ≥85% for homozygous calls are included.</p> <p><strong>Supplementary Data Object 1. </strong>SingleCellExperiment object with 57751 cells post-QC and 36601 genes. Data columns include sample, patient, timepoint, compartment, cell type annotation (cell_type_manual), cell cycle phase (tricyclePhase), tumor subclones (subclone_label).</p>
Neuroblastoma cell lines display heterogeneity in differentiation responses
<p>Underlying and extended data for the manuscript "Neuroblastoma cell lines display heterogeneity in differentiation responses", Ferguson et al (2024). Includes Underlying data for Figures 2A-C and Figures 3A-D, and Extended data - Table S1: qRT-PCR primer sequences, Table S2 - List of genes in clusters identified in [7] (Table S2 added after version 1).</p>
Datasets, models and demos associated to "Celldetective: an AI-enhanced image analysis tool for unraveling dynamic cell interactions"
<p>This repository contains datasets, models and demos associated to <a href="https://github.com/remyeltorro/celldetective">Celldetective</a>, a software for single-cell analysis from multimodal time lapse microscopy images. </p> <h1>Demos</h1> <h2>Cell-cell interaction assay: ADCC</h2> <p>We imaged a co-culture of MCF-7 breast cancer cells (targets) and human primary NK cells (effectors), interacting in the presence of bispecific antibodies, to measure antibody dependent cellular cytotoxicity (ADCC). The nuclei of all cells are marked with the Hoechst nuclear stain, the dead nuclei with the propidium iodide nuclear stain, the cytoplasm of the NK cells with CFSE. The system in epifluorescence and brightfield at either 20 or 40X magnification. We provide a single position demo for the ADCC assay, as "demo_adcc.zip". After unzipping, the demo_adcc folder can be loaded in Celldetective for testing. </p> <h2>Cell-surface interaction assay: RICM</h2> <p>We imaged human primary NK cells engaging in spreading with a surface coated with a bispecific antibody similar to the one used in the ADCC assay (replacing the target cells with a flat surface). The system is imaged using the RICM technique. Images are normalized using a median estimate of the background, pooled from all the positions in a well and dividing the images by this estimate. Here, we provide a single position demo for the cell-surface interactiona assay imaged in RICM, as "demo_ricm.zip". As above, after unzipping, the experiment can be tested and processed in Celldetective.</p> <h1>Datasets</h1> <h2>Image annotations for segmentation</h2> <h3>Cell-cell interaction assay: ADCC</h3> <p>We generated two sets of annotations from images of a co-culture of MCF-7 breast cancer cells and human primary NK cells, interacting in the presence of bispecific antibodies, to measure antibody dependent cellular cytotoxicity (ADCC). Since there are two separate cell populations of interest, the targets (MCF-7) and effectors (NK cells), we curated two datasets. Each sample in a dataset consists of a multichannel image (up to five channels in the context of ADCC, among brightfield , Hoechst nuclear stain, PI nuclear stain, CFSE, LAMP1), the associated instance segmentation annotation for the population of interest and a json file summarizing the content of each channel and the spatial calibration of the image. These sample data are generated directly in Celldetective, using a custom napari plugin.</p> <ul> <li>db_mcf7_nuclei_w_lymphocytes: MCF-7 cell nuclei are annotated specifically on images where primary NK cells (or rarely primary T cells), and RBCs co-exist. The annotation exploits up to four channels simultaneously.</li> <li>db_primary_NK_w_mcf7: human primary NK cells, with annotated cytoplasm (mostly from CFSE) but exploiting brightfield and Hoechst to segment out of focus or poorly labelled cells.</li> </ul> <p>These datasets are used to train several segmentation models to segment on one hand the MCF-7 nuclei and on the other hand the primary NK cells.</p> <h3>Cell-surface interaction assay: RICM</h3> <ul> <li>db_spreading_lymphocytes: we provide a dataset of primary NK cells (and occasionnaly mice T cells) imaged in RICM (with sometimes paired brightfield images). Cells are detected as soon as they start forming interferences on the image (hovering behavior). A pre-annotation was performed using a threshold based segmentation on the RICM modality. Manuel separation of cell-cell contacts and removal of false positive objects was performed by an expert annotator (using brightfield when available). RBCs are ignored in the annotations. </li> </ul> <h2>Single-cell signal annotations for classification and regression</h2> <h3>Cell-cell interaction assay: ADCC</h3> <p>We generated several signal classification/regression datasets with Celldetective to characterize the ADCC assay. Briefly, for a given event cells can be classified as "the event occured during the observation", "no event occured during the observation", "the event already occured prior to observation". If the event occurred during the observation, we can estimate when (the regression). Each single-cell is a dictionary with a collection of signals. The attribute "class" sets the class and "t0" the time of event (default is -1 for absence of event). </p> <ul> <li>db-si-NucPI: classification and regression of single-cells with respect to lysis events characterized by a strong PI increase upon lysis (also associated with decreasing nuclear area and sometimes a decreasing Hoechst)</li> <li>db-si-NucCondensation: classification and regression of single-cells with respect to nucleus shrinking events characterized by a decreasing nuclear area (UPDATE on 23/01/2024)</li> </ul> <h1>Models</h1> <h2>Segmentation models</h2> <h3>Generalist models</h3> <p>We integrated in Celldetective select published models for cellular segmentation from StarDist and Cellpose. We wraped the models with an input configuration to help Celldetective handle the normalization, rescaling and channel selection upon inference. </p> <ul> <li>Cellpose [1,2]: <em>cyto3</em>, <em>livecell</em>, <em>tissuenet</em>, <em>nuclei</em></li> <li>StarDist [3]: <em>versatile_fluo</em>, <em>versatile_he</em></li> </ul> <p>If you use any of these models your research, don't forget to cite the StarDist or Cellpose papers accordingly!</p> <h3>ADCC models</h3> <ul> <li>MCF-7 (in the presence of lymphocytes): <em>mcf7_nuc_multimodal, mcf7_nuc_stardist_transfer</em></li> <li>primary NKs (in the presence of MCF-7): <em>primNK_multimodal</em>, <em>primNK_SD</em>, <em>primNK_cfse</em></li> </ul> <h3>Spreading-assay models</h3> <ul> <li>Lymphocytes: <em>lymphocytes_ricm</em></li> </ul> <h2>Signal analysis models</h2> <p>We developed Deep Learning models that classify and regress the time of events from single-cell signals, applied to the ADCC assay.</p> <ul> <li> lysis detection: <em>lysis_H_PI</em>, <em>lysis_PI_area</em><em>. </em>Detect lysis events characterized at least by an increase of PI from one or more measurements (respectively PI+Hoechst and PI+nucleus area, trained on db-si-NucPI)</li> <li>nucleus shrinking detection:<em> NucCond</em>. Detect nucleus shrinking events from nuclear area signal (db-si-NucCondensation)</li> </ul> <h1>References</h1> <ol> <li>Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18, 100–106 (2021).</li> <li>Pachitariu, M. & Stringer, C. Cellpose 2.0: how to train your own model. Nat Methods 19, 1634–1641 (2022).</li> <li>Schmidt, U., Weigert, M., Broaddus, C. & Myers, G. Cell Detection with Star-Convex Polygons. in Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 (eds. Frangi, A. F., Schnabel, J. A., Davatzikos, C., Alberola-López, C. & Fichtinger, G.) 265–273 (Springer International Publishing, Cham, 2018). doi:10.1007/978-3-030-00934-2_30.</li> </ol> <p> </p> <p> </p>
A dataset for testing a single-cell Hi-C softwares
Open the record for dataset details and reuse information.
Dataset related to article "Neutrophils mediate protection in colitis and carcinogenesis by controlling bacterial invasion and IL-22 production by gdT cells"
<p>This record contains raw data related to article "<strong>Neutrophils mediate protection in colitis and carcinogenesis by controlling bacterial invasion and IL-22 production by gd T cells"</strong></p><p>Abstract</p><p>Neutrophils are the most abundant leukocytes in human blood and play a primary role in resistance against invading microorganisms and in the acute inflammatory response. However, their role in colitis and colitis-associated colorectal cancer is still under debate. Therefore, this study aims to dissect the role of neutrophils in these pathological contexts by using a rigorous genetic approach. Neutrophil-deficient mice (Csf3r-/- mice) were challenged with classic models of colitis and colitis-associated colorectal cancer and the role of neutrophils was assessed by histological, cellular and molecular analyses coupled with adoptive cell transfer and correlative analyses using human datasets. Csf3r-/- mice showed increased susceptibility to colitis and colitis-associated colorectal cancer compared to control Csf3r+/+ mice and adoptive transfer of neutrophils in Csf3r-/- mice reverted the phenotype. In colitis, Csf3r-/- mice showed increased bacterial invasion and reduced number of healing ulcers in the colon, indicating compromised regenerative capacity of epithelial cells. Neutrophils were essential for T cell polarization and IL-22 production. In patients with ulcerative colitis, the expression of CSF3R was positively correlated with IL-22 and IL-23 expression. Moreover, gene signatures associated with epithelial cell development, proliferation and antimicrobial response were enriched in CSF3Rhigh patients. Our data support a model where neutrophils mediate protection against intestinal inflammation and colitis-associated colorectal cancer by controlling the intestinal microbiota and driving the activation of an IL-22-dependent tissue repair pathway.</p><p> </p>
Enabling Long-term Cycling Stability of Na3V2(PO4)3/C vs. Hard Carbon Full-cells
<p>Battery cycling data to research paper: </p><p>https://doi.org/10.26434/chemrxiv-2023-jf6mq </p><p>for questions on the data files, please contact pirmin[dot]stueble[at]kit.edu or anna[dot]smith[at]kit.edu</p>
Identification of structural and regulatory cell-shape determinants in Haloferax volcanii
<p>Archaea are key players in various processes, yet little is known about their cell biology, including cell-shape determination. <em>Haloferax volcanii</em> is a haloarchaeon that can form both rod- and disk-shaped cells, depending on growth conditions. In this study, we used proteomics, genetics, and live-cell imaging to identify mutants that are defective in rod or disk formation. Diverse proteins were implicated to be important for cell-shape determination, including predicted transporters, transducers, signaling molecules, and transcriptional regulators. We identified and phenotypically characterized two deletion strains, strains missing rod-determining factor A (RdfA) and disk-determining factor A (DdfA), which are required for rod and disk formation, respectively. We also identified an actin homolog, volactin, which we characterized with live-cell imaging and determined to be dynamic and important for disk-shape determination.<br><br>This Zenodo project includes the CellProfiler pipeline (.cpproj file) used for analyzing and quantifying cell-shape images. Additionally, the cell-shape images taken during the proteomics sample collection (comparing H53, ∆<em>rdfA</em>, and ∆<em>ddfA </em>at early- and late-log growth phases) are provided. This project also includes whole genome sequencing Illumina files for H53, ∆<em>rdfA</em>, ∆<em>sph3</em>, ∆<em>ddfA</em>, JK3, and JK5.<br><br>These data accompany:<br>Schiller, H., Hong, Y., Kouassi, J., Rados, T., Kwak, J., DiLucido, A., Safer, D., Marchfelder, A., Pfeiffer, F., Bisson, A., Schulze, S., and Pohlschroder, M. 2024. Identification of structural and regulatory cell-shape determinants in Haloferax volcanii. Nat Commun 15(1):1414. https://doi.org/10.1038/s41467-024-45196-0.</p>
Gigantic animal cells suggest organellar scaling mechanisms across a 50-fold range in cell volume
<p>Across the tree of life, cell size varies by orders of magnitude, and organelles scale to maintain cell function. Depending on their shape, organelles can scale by increasing volume, length, or number. Scaling may also reflect demands placed on organelles by increased cell size. The 8,653 species of amphibians exhibit diverse cell sizes, providing a powerful system to investigate organellar scaling. Using transmission electron microscopy and stereology, we analyzed three frog and salamander species whose enterocyte cell volumes range from 228 to 10,593 μm3. We show that the nucleus increases in radius while the mitochondria increase in total network length; the endoplasmic reticulum and Golgi apparatus, with their complex shapes, are intermediate. Notably, all four organelles increase in volume proportionate to cell volume. This pattern suggests that protein concentrations are the same across amphibian species that differ 50-fold in cell size, and that organellar building blocks are incorporated into more or larger organelles following the same "rules" across cell sizes, despite variation in metabolic and transport demands. This conclusion contradicts results from experimental cell size increases, which produce severe proteome dilution. We hypothesize that salamanders have evolved the biosynthetic capacity to maintain a functional proteome despite a huge cell volume. </p>
Intracellular delivery of nitric oxide enhances the therapeutic efficacy of mesenchymal stem cells for myocardial infarction
<p>Cell therapy by autologous mesenchymal stem cells (MSCs) is a clinically acceptable strategy for treating various diseases. Unfortunately, the therapeutic efficacy is largely affected by the low quality of MSCs collected from patients. Herein, we showed that the gene expression of MSCs from patients with diabetes was differentially regulated compared to that of MSCs from healthy controls. Then, MSCs were genetically engineered to catalyse an NO prodrug to release NO intracellularly. Compared to extracellular NO conversion, intracellular NO delivery effectively prolonged survival and enhanced the paracrine function of MSCs, as demonstrated by in vitro and in vivo assays. The enhanced therapeutic efficacy of engineered MSCs combined with intracellular NO delivery was further confirmed in mouse and rat models of myocardial infarction, and a clinically relevant cell administration paradigm through secondary thoracotomy has been attempted.</p>
ScienceDex guides
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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.