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111,247 results for “cells”
Cell growth dataset for "Suppression of bacterial cell death underlies the antagonistic interaction between ciprofloxacin and tetracycline in Escherichia coli"
<div>This dataset contains growth data of <em>E. coli</em> cells measured by optical density at a wavelenght of 600 nm (OD600) to determine the conditions where the combination of ciprofloxacin (CIP) and tetracycline (TET) is antagonistic (or suppressive), using a medium that supports fast, intermediate and slow growth (M9 based medium supplemented with: glucose and amino acid, glucose, and glycerol, respectively).</div> <div>Two types of assays were performed: a checkerboard assay with a 2-dimension gradient of antibiotic concentrations shown in "Fig-S1-Growth_rates-Checkerboard_assay.zip", and bulk growth rates assay shown in "Fig-S2-Bulk_doubling_rates-Bioreactor.zip". </div> <div>These experiments are presented in the supplementary figures of the following manuscript published as preprint in bioRxiv: https://doi.org/10.1101/2024.04.18.590101.</div> <div> </div> <p><strong>Fig-S1-Growth_rates-Checkerboard_assay.zip:</strong></p> <div>- growth-OD600-Blank_correct.xlsx: contains the blank corrected (subtracted by initial OD of sterile medium) OD600 data for each well measured in the checkerboard assay. The negative control is named as "Blank B" in the spreasheet and the antibiotic concentrations for each well is defined in column C (Content).</div> <div>- growth_data.xlsx: contains the processed data from "Growth-OD600-Blank_correct.xlsx":growth curves were smoothed with a 5-window moving median and outliers corrected using the filloutliers function in MATLAB. Outliers that were missed were manually corrected.</div> <div>- time_h.xlsx: contains the time in hours used by the matlab function to build the dose response figure S1B.</div> <div>- analysis_code_checkerboard_assay.m: matlab function used to build the dose response figure S1B.</div> <p> </p> <p><strong>Fig-S2-Bulk_doubling_rates-Bioreactor.zip:</strong></p> <p>- "bulk_doubling rates_gly/glu/gluaa.csv": contains the calculated doubling rates for each of three experimental replicates (rows) and for each antibiotic treatment (columns: Ctrl, CIP, TET, CIP-TET) as shown in Figure S2. These values were used to calculated the Bliss independence (for further details see https://gitlab.com/MEKlab/single-cell-suppression-2024/figure plotting.ipynb<br> - Folders containing OD600 measurements and calculated growth rates for each growth medium, which are further separated into folders for each experimental replicate. Each replicate folder is labelled as the date the experiment was performed (denoted as "*" hereon). Each contains the following: raw optical density data (*.txt files), summary of experiment and results (*.docx file), the function compute_growth_rates.m, and the script Growth_curves_*.m.</p> <div>For more information on methods, strains used and table header descriptions, please refer to the README.txt. </div>
CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images
<p>This is the dataset from the preprint "Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images" by Raufeisen et al. (2024). It contains 6,683 annotations (3,991 nuclei and 2,607 whole cells) of head and neck squamous cell carcinoma cells in hematoxylin and eosin stained histological images. The annotations are in COCO format and distributed over 83 PNG images. Cyto R-CNN was trained on this dataset and compared with other state-of-the-art methods. The CytoNuke dataset is released under the CC BY 4.0 license.</p> <p>The histological images are from the CPTAC dataset:<br>National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC). (2018). The Clinical Proteomic Tumor Analysis Consortium Head and Neck Squamous Cell Carcinoma Collection (CPTAC-HNSCC) (Version 15) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2018.UW45NH81</p> <p>Funding: Behrus Puladi was funded by the Medical Faculty of RWTH Aachen University as part of the Clinician Scientist Program. We acknowledge FWF enFaced 2.0 [KLI 1044, https://enfaced2.ikim.nrw/] and KITE (Plattform für KI-Translation Essen) from the REACT-EU initiative [https://kite.ikim.nrw/, EFRE-0801977]. Fabian Hörst, Jianning Li, Jens Kleesiek and Jan Egger received funding from the Cancer Research Center Cologne Essen (CCCE).</p>
Inferring and perturbing cell fate regulomes in human cerebral organoids
<p>Supplementary data for the manuscript: Inferring and perturbing cell fate regulomes in human cerebral organoids.</p> <p>Note: Due to the size limit of the repository, this update doesn't include all the relevant data. The following data are available via the same repository but different versions:</p> <ul> <li>Processed data in Seurat objects: <a href="https://doi.org/10.5281/zenodo.7687749" target="_blank" rel="noopener">Version 3</a> - seurat_objects.tar.gz</li> <li>scATAC-seq fragment files by Cell Ranger: <a href="https://zenodo.org/records/13254037" target="_blank" rel="noopener">Version 4</a></li> </ul>
Discovery of microsized RNA-derived RNAs and polypeptides in human cells
<p>This repository comprises the predicted sequences for msdRs and msdPs, main source/raw data, and scripts for msdR/msdP prediction. Refer to the AAA_README.pdf for details.</p>
Distinct Stromal Cell Populations Define the B-cell Acute Lymphoblastic Leukemia Microenvironment
<p>Processed single-cell RNA-seq from the study </p> <ul> <li>10X Genomics CellRanger output (barcodes.tsv, genes.tsv, matrix.mtx) for each each sample</li> <li>Metadata</li> <li>Seurat object of the integrated scRNAseq dataset</li> <li>Xenium object of the spatial transcriptomic data</li> </ul> <p>Distinct Stromal Cell Populations Define the B-cell Acute Lymphoblastic Leukemia Microenvironment</p> <p>Mauricio N. Ferrao Blanco<sup>1</sup>, Bexultan Kazybay<sup>1</sup>, Mirjam Belderbos<sup>1</sup>, Olaf Heidenreich<sup>1</sup>, Hermann Josef Vormoor<sup>1,2</sup></p> <p><sup>1 </sup>Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands</p> <p><sup>2 </sup>University Medical Center Utrecht, Utrecht, the Netherlands</p> <p><strong>Abstract</strong></p> <p>The bone marrow microenvironment plays a critical role in B-cell acute lymphoblastic leukemia (B-ALL) progression, yet its cellular heterogeneity remains poorly understood. Using single-cell RNA sequencing on patient-derived of bone marrow aspirates from pediatric B-ALL patients, we identified two distinct mesenchymal stromal cell (MSC) populations: early mesenchymal progenitors and adipogenic progenitors. Spatial transcriptomic analysis further revealed the localization of these cell types and identified a third stromal population, osteogenic-lineage cells, exclusively present in the bone biopsy. Functional <em>ex vivo</em> assays using sorted stromal populations derived from B-ALL patient bone marrow aspirates demonstrated that both early mesenchymal and adipogenic progenitors secrete key niche-supportive factors, including CXCL12 and Osteopontin, and support leukemic cell survival and chemoresistance. Transcriptomic profiling revealed that B-ALL cells interact differently with stromal subtypes. Notably, adipogenic progenitors, but not early mesenchymal progenitors, provide support to leukemic cells through interleukin-7 and VCAM1 signaling. Stromal cells from B-ALL patients exhibited an enhanced adipogenic differentiation capacity compared to healthy controls. Moreover, co-culture experiments showed that B-ALL cells induce adipogenic differentiation in healthy MSCs through a cell contact-dependent mechanism. Adipogenic progenitors were also enriched in relapse samples, implicating them in disease progression. These findings highlight the complexity of the B-ALL microenvironment and identify different specialized stromal niches with which the leukemic cells can engage.</p> <p> </p> <p> </p> <p> </p>
Single-cell RNA sequencing identifies shared differentiation paths of mouse thymic innate T cells
<p>scRNA sequencing datasets used in the paper titled 'Single-cell RNA sequencing identifies shared differentiation paths of mouse thymic innate T cells' published in Nature Communications<br> <br> https://www.nature.com/articles/s41467-020-18155-8</p>
Cryo-4D-STEM datasets on cells and cellular organelles for demonstrating a dose-Efficient cryo-EM technique: tilt-Corrected Scanning Transmission Electron Microscopy
<p>This upload contains three 4D-STEM datasets in .raw format for demonstrating a dose-efficient cryo-EM technique for thick samples: tilt-corrected Scanning Transmission Electron Microscopy (tcBF-STEM). The dataset dimension is 128130256*256. Data were acquired on vitrified intact E.coli cells and isolated human cell organelles. This upload also contains the EFTEM images in .mrc acqired in the same ROI as the 4D-STEM dataset. </p> <p>It also contains analysis of the manuscript's Fig 3 and Ext. data fig 8. </p>
Underlying and Extended Data for Refined and benchmarked homemade media for cost-effective, weekend-free human pluripotent stem cell culture
<p>Extended Data and raw data for the manuscript "Refined home-brew media for cost-effective, weekend-free hiPSC culture and genetic engineering"</p> <p> </p> <p dir="ltr">Extended Data 1.zip - protocol for preparation of the supplement for hE8 and B8+ media</p> <p dir="ltr">Extended Data 2.zip - Gene counts, supporting files, and output results of bulk analyses</p> <p dir="ltr">Extended Data 3.zip - Images of iPS cells adapted to cE8, hE8 and B8+ taken 24, 48 and 72 hours after passage. Contains raw .tiff files for each image and a .pdf with a compiled figure</p> <p dir="ltr">Extended Data 4.zip - Results of miloR analysis on the differences in the distribution of cells adapted to cE8, hE8 and B8+ to assigned monocle clusters </p> <p dir="ltr">Manuscript Data.zip – Raw data underlying the Figures 1, 2, 4, 5, 6 and 7.</p> <p>Bulk_RNA_seq_archive – archived source code used for generating results in Figure 3</p>
Phylogenomic analysis of cell-surface receptors and downstream signaling components in the plant lineage
<p>Here we identified cell-surface receptors and downstream signaling components from the genomes of 350 plant species. </p><p>Zip file contains:</p><p>Folder 'seqeunces for downstream signaling components' - FASTA and TREE files of the identified downstream signaling components.</p><p>Folder 'sequences for cell-surface receptors' - FASTA and TREE files of the identified cell-surface receptors.</p><p>Folder 'Specific analysis' - Contains specific analysis for the identified cell-surface receptors.</p><p>Subfolder 'ID analysis' - Contains information on ID clusters and motifs analysis in IDs.</p><p>Subfolder 'LRR motif gap analysis' - Contains information on small (10-29 aa) and large (30-90) gaps between LRR motifs in RLPs and RLKs.</p><p>Subfolder 'LRR-RLK & LRR-RLP phylogenetic analysis' - Contains FASTA and TREE files of the specific domain/regions (C3, C3-F, eJM-TM-cJM, and all) in LRR-RLPs and LRR-RLKs. This subfolder also contains the specific amino acid, charge and motif analysis in this region (see C3F-end features.xlsx).</p><p>Protein counts per species file - Contains the total number of each protein family/subfamily in each of the 350 species.</p><p>simpleToFullNames (translator file)- Translator file for the original ID of each gene. </p>
Divergent molecular networks program functionally distinct CD8+ skin-resident memory T cells
<p>Skin-resident CD8+ T cells comprise distinct IFN-γ- (TRM1) and IL-17-producing (TRM17) subsets that differentially contribute to immune responses. However, whether these populations employ common mechanisms to establish tissue residence is unknown. Here, we show that TRM1 and TRM17 cells navigate divergent trajectories to acquire tissue residency in skin. While TRM1 cells depend on a T-bet-Hobit-IL-15 axis, TRM17 cells develop independently of these factors. Instead, c-Maf commands a tissue-resident program in TRM17 cells parallel to that induced by Hobit in TRM1 cells, with an ICOS-c-Maf-IL-7 axis pivotal to TRM17 cell commitment. Accordingly, targeting this pathway enables ablation of skin TRM17 cells without compromising their TRM1 counterparts. Thus, skin-resident T cells rely on distinct molecular circuitries, which can be exploited to strategically modulate local immunity.</p>
Single-cell datasets for cell cycle plasticity underlies fractional resistance to palbociclib in ER+/HER2- breast tumor cells
<p>There are 7 files uploaded in the data.</p><p>tumor_preprocessed.h5ad: Full primary tumor dataset post-feature selection and standardization across three treatment conditions (0, 10, and 100 nM palbociclib). AnnData object format. 14 cell cycle features, phase labels and other cell metadata, and two PHATE dimensions for manifold visualization.</p><p>T47D_preprocssed.h5ad: Full dataset of main text T47D dataset post-feature selection and standardization across three treatment conditions (0, 10, and 100 nM palbociclib). AnnData object format. 14 cell cycle features, phase labels and other cell metadata, and two PHATE dimensions for manifold visualization.</p><p>sketched_integrated.h5ad: After downsample 6,000 (2,000 per condition) from T47D_preprocessed and tumor_preprocessed, we integrate the two datasets into one joint latent space using TRANSACT. Now included in the data are the consensus component columns ('0',..,'13'). AnnData object.</p><p>sketched_integrated_df.csv: sketched_integrated.h5ad in .csv format.</p><p>T47D_replicate_preprocessed: Replicate experimental dataset of T47D for supplementary analysis post-feature selection and standardization across three treatment conditions (0, 10, and 100 nM palbociclib). 15 cell cycle features (same 14 but with CDK6).</p><p>sketched_rep.h5ad: Representative downsample of the T47D_replicate_preprocessed. Selecting 6,000 cells (2,000 for each of the three treatment conditions) using kernel herding sketching. AnnData object.</p><p>sketched_rep_df.csv: Same data as sketched_rep.h5ad in csv format.</p><p>T47D_triplicate_preprocessed.h5ad: T47D biological replicate sample collected in triplicate form (three wells for 0, 10, and 100 nM of palbociclib). Wells were joined and the data were sketched down to 20,000 per condition.</p><p>T47D_triplicate_preprocessed.h5ad: T47D triplicate in .csv form.</p><p>tumor_2_preprocessed.h5ad: An additional tumor sample from a new patient with the same treatment conditions of palbociclib. Sketched down to 2,000 cells per condition.</p><p>tumor_2_preprocessed.csv: The additional tumor sample in .csv form.</p><p> </p><p>Further description of sketched_integrated: This is the joint dataset between the T47D and primary tumor, after subsampling using kernel herding sketching. This is a dataset consisting of T47D and primary tumor cells resected from a consented patient. The samples were imaged using iterative indirect immunofluorescent imaging (4i) to get proteomic measurements on a single-cell level. The T47D and tumor samples were gathered, cultured, and imaged separately. Each sample was treated with three conditions of CDK4/6 inhibitor palbociclib (control, 10 nM, and 100 nM). Then, we used kernel sketching to representatively downsample each dataset, selecting 2,000 from each of the three treatment conditions (6,000 cells from each of the two sources). We used an integration method called TRANSACT to integrate the two datasets into one shared, latent space. The dataset here is consisting of these 12,000 cells. The columns ('0','1',...'13') are the principal vectors of the joint latent space. After that, there are the columns of the standardized proteomic measurements of different cell cycle effectors, and biological annotations of interest. The standardization is done for each data source separately. Well refers to the treatment condition. 'prb_ratio' is a marker of if a cell is still proliferating or arrested, found by selecting the upper modality of pRB/RB values. 'phase' are cell cycle phase labels found by unsupervised clustering done on a handful of known cell cycle markers.</p>
CD45+ cells from human bladder cancer specimens
<h3><span>Background</span></h3> <p><span>NK cells are important innate defenders against tumours and have unique abilities to recognize and eliminate cancer cells. Responses to targeted antibody therapeutics are typically limited in bladder tumours, and the functional and immunosuppressive phenotypes of NK cells in this disease are largely unknown. </span></p> <h3><span>Methods</span></h3> <p><span>Single cell RNA sequencing (scRNAseq) and high-dimensional flow cytometry were used to investigate the phenotype of intratumoural NK cells compared to circulating in patients with bladder cancer.</span></p> <h3><span>Findings</span></h3> <p><span>NK cells residing within bladder tumours had reduced expression of Fc</span><span>γ</span><span>RIIIa/CD16, the critical receptor for NK-cell-mediated ADCC, on both a transcriptional and protein level. Transcriptional signatures of </span><span>TGF-β-signalling, a pleiotropic cytokine with known immunosuppressive effects on NK cells, were upregulated in tumour NK cells compared to the blood. In concert, a high TGF-β signature expression also correlated with worse survival and CD16 downregulation. We directly validated this TGF-β mediated CD16 downregulation on NK cells in vitro and it was accompanied by a transition to ILC1-like NK cells. We also uncovered a high proportion of tumour infiltrating-Treg cells, and in vivo studies show that NK cells delivered in the presence of accompanying immune cells have a greater reduction of CD16 compared to NK cells delivered alone. </span></p> <h3><span>Interpretation</span></h3> <p><span>Thus, this study highlights how TGF-β rich bladder cancers could inhibit NK cell ADCC by downregulating CD16, whereby Treg cells could be a major limiter of NK cell effector functions in these tumours.</span></p>
distinct mesenchymal cell states mediate prostate cancer progression
<p>These are the count matrices (in .h5ad format) used to produce the results and figures in the paper entitled "distinct mesenchymal cell states mediate prostate cancer progression". </p><ul><li>adata_mouse.h5ad: the normalized count matrix and cell annotations for the mesenchymal prostate data from all mouse models.</li><li>adata_mouse_myo.h5ad: the normalized count matrix for the myofibroblasts and pericytes (both are c0).</li><li>scenic_mesenchyme_auc.csv: the auc matrix of the regulons activity in the different cells and clusters in the mouse mesenchymal data.</li><li>scenic_mesenchyme_binary.csv: the binary matrix of regulons activity in the mouse mesenchymal data.</li><li>adata_human.h5ad: the normalized count matrix and cell annotations for the mesenchymal human primary prostate cancer data.</li><li>adata_human_boneMet_stroma.h5ad: the normalized count matrix and cell annotation for the bone metastasis data derived from Kfoury et al.</li><li>visium_PRN.h5ad: the normalized count matrix and cell annotation for the Visium spatial transcriptomics data of the PRN mouse model.</li><li>visium_WT.h5ad: the normalized count matrix and cell annotation for the Visium spatial transcriptomics data of the WT (PRN-WT) mouse model.</li><li>natHistory_expression: the normalized expression matrix for the natural history cohort.</li><li>natHistory_label: the metastasis indicators for the natural history cohort.</li></ul>
Metabolomics Analysis for the Identification of Biomarkers in Small Cell Lung Cancer
<p>Small cell lung cancer (SCLC), a highly aggressive malignancy with a poor prognosis is usually detected at the extensive stage of the disease. The demand for early diagnostic methods and reliable biomarkers is increasing, although a number of tumor markers such as NSE and NCAM have already been utilized in clinics. Here, we conducted untargeted metabolomics in 54 plasma samples from 34 patients with SCLC and 20 healthy controls.</p>
3D cell tracking dataset of bacterial biofilm deformation and recovery under shear flow
<p>This MAT file includes dataset in the scientific article "<i>In vivo</i> microrheology reveals elastic and plastic responses inside three-dimensional bacterial biofilms" by the following authors: Takuya Ohmura, Dominic Skinner, Konstantin Neuhaus, Gary Choi, Jörn Dunkel, Knut Drescher. This MAT file can be conveniently opened with Matlab. </p><p>When you open this file with Matlab, you will find 4 variables stored in the file "Data_v3_loop2_newRxy_bidx1_274.mat"</p><p><strong>Variable 1: name_parameter</strong></p><p>Names of 31 parameters for columns in 3 variables: 'deformation_all', 'recovery_all' , 'plasticity_all'. The parameters have cell displacements, orientations, coordinates, biofilm indexes and experimental conditions. When the parameters have units, they are shown in the names. </p><ul><li>'x_Frame1[um]'</li><li>'y_Frame1[um]'</li><li>'z_Frame1[um]'</li><li>'Normalized_x_Frame1'</li><li>'Normalized_y_Frame1'</li><li>'Normalized_z_Frame1'</li><li>'LocalDensity_Frame1(VolumeFractionAround30px)'</li><li>'LocalCellNumberDensity_Frame1(VolumeFractionAround30px)'</li><li>'NematicOrderParameter_Frame1'</li><li>'AlignmentFlow_Frame1[rad]'</li><li>'AlignmentRadial_Frame1[rad]'</li><li>'AlignmentZaxis_Frame1[rad]'</li><li>'d_x[um]'</li><li>'d_y[um]'</li><li>'d_z[um]'</li><li>'Normalized_d_x'</li><li>'Normalized_d_y'</li><li>'Normalized_d_z'</li><li>'d_LocalDensity'</li><li>'d_LocalNumberDensity[um^-3]'</li><li>'d_NematicOrderParameter'</li><li>'d_AlignmentFlow[rad]'</li><li>'d_AlignmentRadial[rad]'</li><li>'d_AlignmentZaxis[rad]'</li><li>'CrossProduct[um^2]'</li><li>'BiofilmIndexNumber'</li><li>'Biofilm_width[um]'</li><li>'Biofilm_height[um]'</li><li>'Biofilm_volume[um^3]'</li><li>'FlowRate[ul/min]'</li><li>'Duration[min]'</li></ul><p><strong>Variable 2: deformation_all</strong></p><p>The rows indicate 704198 single-cell trackings in deformations of 274 bacterial biofilms. Each of the 274 bacterial biofilms has a different 'BiofilmIndexNumber'. The columns indicate 31 parameters which names are shown in 'name_parameter'.</p><p><strong>Variable 3: recovery_all</strong></p><p>The rows indicate 685991 single-cell trackings in recoveries of 274 bacterial biofilms. Each of the 274 bacterial biofilms has a different 'BiofilmIndexNumber'. The columns indicate 31 parameters which names are shown in 'name_parameter'.</p><p><strong>Variable 4: plasticity_all</strong></p><p>The rows indicate 665749 single-cell trackings in plasticities of 274 bacterial biofilms. Each of the 274 bacterial biofilms has a different 'BiofilmIndexNumber'. The columns indicate 31 parameters which names are shown in 'name_parameter'.</p><p> </p><p>To plot the cell tracked data in the figures of the article, use our MATLAB code uploaded in our GitHub (https://github.com/knutdrescher/biofilm-rheology).</p>
ThermoCyte: an inexpensive open-source temperature control system for in vitro live cell imaging
<p>Live-cell imaging is a common technique in microscopy to investigate dynamic cellular behaviour and permits the accurate and relevant analysis of a wide range of cellular and tissue parameters, such as motility, cell division, wound healing responses, and calcium (Ca2+) signalling in cell lines, primary cell cultures, and ex vivo preparations. Furthermore, this can take place under many experimental conditions, making live-cell imaging indispensable for biological research. Systems which maintain cells at physiological conditions outside of a CO<sub>2</sub> incubator are often bulky, expensive, and use proprietary components. Here we present an inexpensive, open-source temperature control system for in vitro live cell imaging. Our system 'ThermoCyte', which is constructed from standard electronic components, enables precise tuning, control, and logging of a temperature 'set point' for imaging cells at physiological temperature. We achieved stable thermal dynamics, with reliable temperature cycling and a standard deviation of 0.42°C over 1 hour. Furthermore, the device is modular in nature, and is adaptable to the researcher's specific needs. This represents simple, inexpensive, and reliable tool for laboratories to carry out custom live-cell imaging protocols, on a standard lab bench, at physiological temperature.</p>
Data from : Metabolic footprint of Vero E6 cells highlights the key metabolic routes associated with SARS-CoV-2 infection and response to drug combinations
<p>This dataset contains representative 1D 1H NMR spectra and data used in the manuscript " Metabolic footprint of Vero E6 cells highlights the key metabolic routes associated with SARS-CoV-2 infection and response to drug combinations " . </p><p> </p><p>The present study used Nuclear Magnetic Resonance-based metabolic footprinting to characterize the secreted cellular metabolite levels (exometabolomes) of Vero E6 cells in response to SARS-CoV-2 infection and to two candidate drugs (Remdesivir, RDV and Azithromycin, AZI). </p><p> </p><p><strong>Supplementary File 1.zip = </strong>Representative 1D 1H NMR profiles of examined VE6 esometabolomes, </p><p> </p><p><strong>Supplementary File 2.xlsx</strong> = Average Mean ± Standard Deviations of NMR relative quantified data (integrals, a.u.) from examined VE6 esometabolomes. </p><p> </p><p><strong>Supplementary File 3.csv = </strong>p–values and associated False Discover Rate (FDR) derived from univariate ANOVA with Fischer's LDS post-hoc test comparisons carried out on NMR relative quantified data.</p><p> </p><p><strong>List of Supplementary Files derived from Metabolite Set Enrichment Analysis (MSEA) : </strong></p><p> </p><p><strong>Supplementary File 4.csv </strong>= Tabular Results from MSEA performed on VE6+ VE6- comparison.</p><p><strong>Supplementary File 5.csv </strong>= Tabular Results from MSEA performed on VE6+ RDV vs. VE6+ comparison.</p><p><strong>Supplementary File 6.csv </strong>= Tabular Results from MSEA performed on VE6+ AZI vs. VE6+ comparison.</p><p><strong>Supplementary File 7.csv</strong> = Tabular Results from MSEA performed on VE6+ R+A vs. VE6+ comparison.</p><p> </p>
Single-cell mouse and PC9 data for "TP53 loss with whole genome doubling mediates heterogeneous intra-patient therapy response through Chromosomal Instability"
<p>This repository includes the processed data (including copy number profiles and related analysis) for the E/EP mouse tumors and for the PC9 resistance cell lines for all the analyses of the manuscript "TP53 loss with whole genome doubling mediates heterogeneous intra-patient therapy response through Chromosomal Instability".</p><p>The code for the related analyses is available in GitHub at https://github.com/zaccaria-lab/TP53loss_WGD</p>
Supplementary Data for Stabilizing the Proteomes of Acute Myeloid Leukemia Cells: Implications for Cancer Proteomics
<p>Supplementary data for: <br>Stabilizing the Proteomes of Acute Myeloid Leukemia Cells: Implications for Cancer Proteomics<br>Authors: Robert Sprung, Qiang Zhang, Michael H. Kramer, Matthew C. Christopher, Petra Erdmann-Gilmore, Yiling Mi, James P. Malone, Timothy J. Ley, and R. Reid Townsend.</p><p>Table S1 - AML Case descriptors and LC-MS data files<br>Table S2 - All Peptides by Case -LFQ<br>Table S3 - Identification of tryptic and non-tryptic peptides from five AML cases with high and low expression of ELANE<br>Table S4 - Number of proteins identified by LFQ proteomics with a minimum of 2 tryptic peptides<br>Table S5 - DFP Adduct Database Search Tryptic Peptides<br>Table S6 - Protein quantification from TMT 11-plex tryptic peptides with and without DFP<br>Table S7 - Tryptic peptides used for protein quantification from TMT 11-plex with and without DFP<br>Table S8 - Changes in TMT relative abund. with DFP treatment<br>Table S9 - Protein quantification from LFQ tryptic peptides with and without DFP<br>Table S10 - Proteins with significant change in abundance with DFP treatment using Label-Free Quantitation</p>
SURFBIO Training: "Analytical methods for the study of microbial cell-Surface and Surface-colloid interactions" (2021)
<p>2021. SURFBIO project training within WP1.</p><p>Content:</p><ul><li><strong>Webinar on Vertical scanning interferometry: a microscopic technique to analyze surface reactivity, </strong>by Dr. Cornelius Fischer (HZDR, Germany) </li><li><strong>Webinar on An introduction to radiolabelling as a versatile tool in colloid tracing, </strong>by Stefan Schymura (HZDR, Germany).</li><li><strong>Webinar on Development and construction of biocarriers and aggregates for potential industrial applications</strong>, by Dr. Andre Skirtach and Dr. Bogdan Parakhonskiy (GHENT University). </li><li><strong>Materials and fluidic design to study artificially functionalized microorganisms</strong>, by Dr. Andre Skirtach and Dr. Bogdan Parakhonskiy (GHENT University). </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.