Find research datasets worth reusing

opencc-by-4.0Jul 2021View details →

Associated data for D-SCRIPT, Cell Systems

Trained models, processed sequence and PPI training data, and new predictions described in "D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions", by Sledzieski, Singh, Cowen, and Berger

opencc-by-4.0May 2021View details →

Resolving Organoid Brain Region Identities by Mapping Single-Cell Genomic Data to Reference Atlases

Data underlying the figures in the publication “Resolving organoid brain region identities by mapping single-cell genomic data to reference atlases”, published in Cell Stem Cell, 2021, 28, 1148–1159. <a href="https://www.sciencedirect.com/science/article/pii/S1934590921000655">https://www.sciencedirect.com/science/article/pii/S1934590921000655</a> Table of contents: 1. patscreen_srt.rds; Numerical data for Figure 7: RNA-seq data of a patterning screen in organoids with an array of small molecules. The dataset is in the rds data format, which can be opened in the R programming language using the function `readRDS()`. Once opened, the dataset is a Seurat object (https://satijalab.org/seurat/) and contains both the transcript counts and the metadata for all samples in the screen. The raw data used in figure 7 was also deposited in ArrayExpress (<a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-10037/">https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-10037/</a>)

opencc-by-4.0Dec 2020View details →

Pandemic, epidemic, endemic: B cell repertoire analysis reveals unique anti-viral responses to SARS-CoV-2, Ebola and Respiratory Syncytial Virus

VDJ gene usage, and associated amino acid sequences and properties, from healthy controls as well as patients with COVID-19, RSV or Ebola and Yellow fever vaccine recipients.

opencc-zeroJul 2021View details →

Cell type labels for all clustering and normalization combinations compared for CODEX multiplexed imaging

We performed CODEX (co-detection by indexing) multiplexed imaging on four sections of the human colon (ascending, transverse, descending, and sigmoid) using a panel of 47 oligonucleotide-barcoded antibodies. Subsequently images underwent standard CODEX image processing (tile stitching, drift compensation, cycle concatenation, background subtraction, deconvolution, and determination of best focal plane), and single cell segmentation. Output of this process was a dataframe of nearly 130,000 cells with fluorescence values quantified from each marker. We used this dataframe as input to 1 of the 5 normalization techniques of which we compared z, double-log(z), min/max, and arcsinh normalizations to the original unmodified dataset. We used these normalized dataframes as inputs for 4 unsupervised clustering algorithms: k-means, leiden, X-shift euclidian, and X-shift angular. From the clustering outputs, we then labeled the clusters that resulted for cells observed in the data producing 20 unique cell type labels. We also labeled cell types by hiearchical hand-gating data within cellengine (cellengine.com). We also created another gold standard for comparison by overclustering unormalized data with X-shift angular clustering. Finally, we created one last label as the major cell type call from each cell from all 21 cell type labels in the dataset. Consequently the dataset has individual cells segmented out in each row. Then there are columns for the X, Y position in pixels in the overall montage image of the dataset. There are also columns to indicate which region the data came from (4 total). The rest are labels generated by all the clustering and normalization techniques used in the manuscript and what were compared to each other. These also were the data that were used for neighborhood analysis for the last figure of the manuscript. These are provided at all four levels of cell type level granularity (from 7 cell types to 35 cell types).

opencc-by-4.0May 2021View details →

Annotated quantitative phase microscopy cell dataset of various adherent cell lines for segmentation purposes

This dataset contains microscopic images of multiple cell lines captured by quantitative phase microscopy (QPI) without use of any fluorescent labeling and a manually annotated ground truth for subsequent use in segmentation algorithms. Dataset also includes images reconstructed according to the methods described below in order to ease further segmentation.  Our data consist of quantitative phase microscopy images: <ul> <li>244 labelled images of PC-3 (7,907 cells), 205 labelled PNT1A (9,288 cells), 25 labelled images of G361, 25 labelled images of A2050 and 25 labelled images of HOB cells , in the paper designated as "labelled", and</li> <li>1,819 unlabelled images with a mixture of 22Rv1, A2058, A2780, DU145, Fadu, G361, HOB and LNCaP used for pretraining, in the paper designated as "unlabelled".</li> </ul> <ul> <li>See Vicar et al. XXXX 2021  DOI XXX (TBA after publishing)</li> <li>Code using this dataset is available at <a href="https://github.com/tomasvicar/Deep-QPI-Cell-Segmentation">github.com/tomasvicar/Deep-QPI-Cell-Segmentation</a></li> </ul> Materials and methods  A set of adherent cell lines of various origins, tumorigenic potential, and morphology were used in this paper (PC-3, PNT1A, 22Rv1, DU145, LNCaP, A2058, A2780, Fadu, G361, HOB). PC-3, PNT1A, 22Rv1, DU145, LNCaP, A2780, and G361 cell lines were cultured in RPMI-1640 medium, A2058, FaDu, and HOB cell lines were cultured in DMEM-F12 medium, all supplemented with antibiotics (penicillin 100 U/ml and streptomycin 0.1 mg/ml), and with 10% fetal bovine serum (FBS). Prior to microscopy acquisition, the cells were maintained at 37 °C in a humidified (60%) incubator with 5%  CO\textsubscript{2} (Sanyo, Japan). For acquisition purposes, the cells were cultivated in the Flow chamber µ-Slide I Luer Family (Ibidi, Martinsried, Germany). To maintain standard cultivation conditions during time-lapse experiments, cells were placed in the gas chamber H201 - for Mad City Labs Z100/Z500 piezo Z-stage (Okolab, Ottaviano NA, Italy). For the acquisition of QPI, a coherence-controlled holographic  microscope (Telight,  Q-Phase) was used. Objective Nikon Plan 10×/0.3 was used for hologram acquisition with a CCD camera (XIMEA MR4021MC). Holographic data were numerically reconstructed with the Fourier transform method (described in Slaby, 2013 and phase unwrapping was used on the phase image. QPI datasets used in this paper were acquired during various experimental setups and treatments. In most cases, experiments were conducted with the time-lapse acquisition. The final dataset contains images acquired at least three hours apart. Folder structure and file and filename description labelled (labelled): cells with segmentation labels, e.g. 00001_PC3_img.tif - 32bit tiff image (in pg/um2 values) 00001_PC3_mask.png - 8bit image with mask with unique grayscale value corresponding to single cell in FOV. unlabelled (unlabelled): 11 varying cell lines, total 1819 FOVs,  32bit tiff image (in pg/um2 values)

opencc-by-4.0Jul 2021View details →

Impact of RbF and NaF Postdeposition Treatments on Charge Carrier Transport and Recombination in Ga-Graded Cu(In,Ga)Se2 Solar Cells

Excel file with data to all figures published in our article found at <a href="https://doi.org/10.1002/adfm.202103663">https://doi.org/10.1002/adfm.202103663  </a>(Advanced Functional Materials)

High-resolution mapping of the period landscape reveals polymorphism in cell cycle frequency tuning

Biological oscillators adapt to environmental changes with widely tunable frequencies, a property theoretical studies attributed to positive feedbacks. However, no experiments have tested this theory. Here, we created synthetic cells to independently tune the frequency and feedback strength of a cell-cycle oscillator, enabling continuous mapping of period landscape in response to network perturbations. We found that although inhibiting positive feedback of cyclin-dependent kinase (Cdk1) reduces the tunability, the reduction is not as significant as theoretically predicted, and the Cdk1-counteracting phosphatase, PP2A, provides additional machinery to ensure frequency regulation. Additionally, cells exhibit polymorphic responses to PP2A inhibition, showing a monomodal distribution of oscillatory cells at low or high PP2A inhibition or a bimodal distribution at both low and high inhibitions. We explained the polymorphism by a model of two interlinked bistable switches of Cdk1 and PP2A where cell-cycle oscillations exhibit two modes in the presence or absence of PP2A bistability.

T cells migration followed with TrackMate

T cells migrating on ICAM-1 were automatically tracked using StarDist directly implemented within TrackMate. Raw image courtesy of Nathan H. Roy, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA.

STonKGs Cell Line Model

The fine-tuned model trained on cell line annotations.

Data from: Identifying extracellular vesicles from single cells

Extracellular vesicles (EVs) are constantly secreted from both eukaryotic and prokaryotic cells. EVs, including those referred to as exosomes, may have an impact on cell signaling and an incidence in diseased cells. In this manuscript, a platform to capture, quantify, and phenotypically classify the EVs secreted from single cells is introduced. Microfluidic chambers of about 300 pL are employed to trap and isolate individual cells. The EVs secreted within these chambers are then captured by surface-immobilized monoclonal antibodies (mAbs), irrespectively of their intracellular origin. Immunostaining against both plasma-membrane and cytosolic proteins was combined with highly sensitive, multi-color total internal reflection fluorescence microscopy (TIRFM) to characterize the immobilized vesicles. The data analysis of high-resolution images allowed the assignment of each detected EV to one of 15 unique populations, and demonstrated the presence of highly-heterogeneous phenotypes even at the single-cell level. The analysis also revealed that each mAb isolates phenotypically-different EVs, and more vesicles were effectively immobilized when CD63 was targeted instead of CD81. Finally, we demonstrate how an heterogeneous suppression in the secreted vesicles is obtained when the enzyme neutral sphingomyelinase is inhibited.

Segmenting cells in a spheroid in 3D using 2D StarDist within TrackMate

3D image of cells in a spheroid, imaged on a confocal microscope, used in a tutorial to demonstrate how to hack TrackMate to segment cells in 3D using the 2D segmentation algorithms it ships. Image by Guillaume Jacquemet. For more details see https://imagej.net/plugins/trackmate/trackmate-stardist#generation-of-3d-labels-by-tracking-2d-labels-using-trackmate

High-speed video microscopy analysis of cilia before and after airway cell culture

High-speed video microscopy analysis (HSVA) is a diagnostic tool used within the UK Primary Ciliary Dyskinesia (PCD) Service to access airway ciliary function on nasal brushing biopsies.  The Southampton PCD group is based at the University of Southampton, Faculty of Medicine and the University Hospital Southampton NHS Foundation Trust and is led by Professor Jane Lucas.  We also use air-liquid interface (ALI) culture to differentiate airway epithelial cells to regrow healthy cilia to repeat standard PCD tests (including HSVA, immunofluoresence labelling of cilia proteins, transmission electron microscopy and functional genomics) and provide PCD research samples, which also allow us to develop new diagnostic approaches.  ALI-culture can restore normal ciliary movement when secondary damage (due to infection or poor cell health) temporarily causes of abnormal cilia movement or a lack of cilia. ALI-culture can also re-confirm when ciliary defects and abnormal ciliary function are permanent and cause by inherited PCD (a ciliopathy).

Tracking Data II/II of the publication "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"

DATA belonging to the paper "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction" Katharina Löffler, Tim Scherr, Ralf Mikut doi: https://doi.org/10.1101/2021.03.16.435631 ----------------------------- To investigate the influence of different segmentation errors on the tracking performance we simulate errorneous segmentation data: - under-segmentation (referred to as "merge" in the folders), over-segmentation("split"), False Negatives ("remove"), combination of the aforementioned errors ("mixed") - percentages: 1,2,5,10,20 of errorneous masks per dataset - runs: 5 randomly initialized runs per combination - datasets: Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ each with two image sequences ---> in total 4 (error types) * 5 (percentage) * 5 (runs) * 2 (data sets) * 2 (image sequences) = 400 datasets The datasets can be recreated by running our code https://git.scc.kit.edu/KIT-Sch-GE/2021-cell-tracking ---------------------------- RESULTS We evuated the four tracking algorithms KIT-Sch-GE(1), KTH-SE, MU-Lux-CZ and our proposed algorithm on the aforementioned datasets and compare their performance using the CTC metrics DET, SEG and TRA. This repository contains all metrics as xls files and all tracking results as image sequences. PLEASE NOTE: this repository contains only the folder compare_postprocessing_synth_bm All other datasets and files are provided in 10.5281/zenodo.5227595 due to size restrictions. xls files ----------- compare_all_trackers_on_synt_bm.csv Comparing the tracking algorithms MU-Lux-CZ, KTH-SE, KIT-Sch-GE(1) and the proposed tracking algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets). Reported scores are DET, SEG and TRA from the Cell Tracking Challenge (Fig8 and Fig9 and Supplementary Figures 3 and 4 are created from this data) compare_postprocessing_on_synth_bm.csv Comparing the different post-processing strategies of the proposed tracking algorithm algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets). Reported scores are DET, SEG and TRA from the Cell Tracking Challenge (Fig7 and Fig8 and Supplementary Figures 1 and 2 are created from this data)   folders (decompressed approximately 90GB of data!) ----------- tracking_data     compare_all_synth_bm     Contains all tracking results for each tracking algorithm on the synthetically degraded datasets ()     compare_all_synth_bm_no_error     Contains the tracking results for each tracking algorithm provided with the perfect ground truth segmentation data     compare_postprocessing_synth_bm [will be stored in 10.5281/zenodo.5227610 due to size restrictions]     Contains all tracking resuls for each postprocessing configuration of the proposed cell tracking algorithm     the leaf folders are names run_xPOSTPROCESSING where x is the run number and POSTPROCESSING the postprocessing key     Postprocessing keys: ("no untangle" or "no masks" is indicated by an overline in the paper)     all ("untangle + masks" in the paper)     nd ("no untangle + masks")     nd_ns-l ("no untangle + no masks")     ns-l ("untangle + no masks")

Tracking with TrackMate using mask images of cell migration

Tutorial dataset used to show how to use mask images for tracking with TrackMate. Two movies are provided, one small and one large to play with. For more information, check here: https://imagej.net/plugins/trackmate/trackmate-mask-detector

Discovery proteomics by mass spectrometry comparing secreted proteins from young and old mouse lung mesenchymal stromal cells

Lung mesenchymal stromal cells (L-MSCs) were isolated from the young ( 3 months) and old (22-24 months) mice (N = 4 each) following collagenase digestion of the lungs and anchorage-dependent growth. Culture media was collected from young and old L-MSCs (10^6 cells) grown in culture for 24 hrs in serum-free condition. Culture media was then centrifuged to remove any cellular debris. Five (5) ml of supernatants were concentrated using 3kDa Molecular weight cut-off filters, and 10 micrograms of proteins were run as multiple MW 1D PAGE fractions using a standard GeLC approach with a nano-HPLC in-line with a Velos Pro Orbitrap MS. Individual data files were searched using Sequest, the results were then combined for each sample, followed by grouping, filtering, and quantifying by normalized spectral counts.

Improving the efficiency of single cell genome sequencing based on overlapping pooling strategy

Single cell genome sequencing has become a useful tool in medicine and biology studies. However, an independent library is required for each cell in single cell genome sequencing, so that the cost grows in step with the number of cells. In this study, we report a study on efficient single-cell copy number variation (CNV) analysis based on overlapping pooling strategy together with branch and bound (B&B) algorithm. Single cells are overlapped pooled before sequencing, and later are assorted into specific types by estimating their CNV patterns by B&B algorithm. Instead of constructing libraries for each cell, a library is required only for each pool. As long as the number of pools is smaller than the cells, fewer libraries are needed, and a lower cost is spent. Through computer simulations, we overlapping pooled 80 cells into 40 and 27 pools and classified them into cell types based on CNV pattern. The results showed that 84% cells in 40 pools and 76.5% cells in 27 pools were correctly classified on average, while only half or one-third of the sequencing libraries are required. Combining with traditional approaches, our method is expected to significantly improve the efficiency of single cell genome sequencing.

opencc-by-4.0Sep 2021View details →

Single-cell profiling of human bone marrow progenitors reveals mechanisms of failing erythropoiesis in Diamond-Blackfan anemia

Ribosome dysfunction underlies the pathogenesis of many cancers and heritable ribosomopathies. Here we investigate how mutations in either ribosomal protein large (RPL) or ribosomal protein small (RPS) subunit genes selectively affect erythroid progenitor development and clinical phenotypes in Diamond-Blackfan anemia (DBA), a rare ribosomopathy with limited therapeutic options. Using single-cell assays of patient-derived bone marrow, we delineated two distinct cellular trajectories segregating with ribosomal protein genotypes: almost complete loss of erythroid specification were observed in RPS-DBA. In contrast, we observed relative preservation of qualitatively abnormal erythroid progenitors and precursors in RPL-DBA. Although both DBA genotypes exhibited a pro-inflammatory bone marrow milieu, RPS-DBA was characterized by erythroid differentiation arrest, whereas RPL-DBA was characterized by preserved GATA1 expression and activity. Compensatory stress erythropoiesis in RPL-DBA exhibited disordered differentiation underpinned by an altered glucocorticoid molecular signature, including reduced ZFP36L2 expression, leading to milder anemia and improved corticosteroid response. This integrative analysis approach identified distinct pathways of erythroid failure and defined genotype-phenotype correlations in DBA. These findings may help facilitate therapeutic target discovery.

Input Data for "Assembly and Analysis of Cell-Scale Membrane Envelopes"

Input structures for a manuscript, along with selected output data and structures. This directory structure contains a cut-down copy of the directories used to generate the simulation data and the analysis. In order to make this fit into the 50GB Zenodo limit, it was constructed with the following tar command: `tar -zcvf protocellmodeling.tar.gz --exclude="*BAK" --exclude="*#" --exclude="*xtc" --exclude="*gro" --exclude="*trr" --exclude="*js" --exclude="*[0-9].out" --exclude="*old" --exclude="*dcd" --exclude="*tmp" --exclude="*xst" --exclude="*edr" --exclude="*state_prev.cpt" --exclude="*.o[0-9]*" cgDracula`, which intentionally excludes large files. The full 4.8TB dataset that includes trajectories is available upon request. The data is split into multiple subdirectories and largely undocumented, however here are the highlights: <ul> <li>The Analysis subdirectory is where the analysis in the paper lives. All other directories are related to building or running systems.</li> <li>getsources.py in the main directory is the script that downloads the initial structure from MemProtMD.</li> <li>transform.py builds the initial protein models from MemProtMD.</li> <li>vesiclebuilder.py builds the lipid ball.</li> <li>protpatchplacer.py sets up the ultra-coarse grained simulation, which is in the supercg directory.</li> <li>movepatches.py takes the results from the ultra-coarse grained simulation, and builds the protein ball.</li> <li>gendx.tcl generates the density maps from the protein ball.</li> <li>This is used in lipids/picklipids.py, which cuts out the pieces of the lipid that need to be removed.</li> <li>The water is added to the system with addwater/quicksolvate.sh</li> <li>The system is ionized by ionize.py</li> <li>And a topology is written by writetop.py</li> </ul>

opencc-by-nd-4.0Jun 2010View details →

FIGURE 3 in Fine Structure Of The Male Genital Systems, Spermatophores And Unusual Sperm Cells Of Saxidromidae (Acari, Actinotrichida)

FIGURE 3: TEM micrographs of vas deferens and ejaculatory duct of Bovidromus roussouwi. (a) – Composed figure showing complex structure of ejaculatory duct in cross section. Note large synspermium in dorsal chamber lined by peculiar cuticle. Arrow points to eugenital (primary) genital opening. Scale bar: 20 µm. (b) – Vas deferens. Note flat epithelium. Scale bar: 20 µm. (c) – Detail of epithelium of vas deferens with irregularly shaped microvilli. Cells contain many mitochondria and are underlain by a muscular layer. Scale bar: 2 µm. (d) – Dorsal chamber of ejaculatory duct with cuticular fringes and secretion. Scale bar: 10 µm. (e) – The ejaculatory duct is surrounded by a thick muscular layer. Scale bar: 10 µm. (f) – Eugenital opening (arrow) and accessory gland. Scale bar: 20 µm.