Find research datasets worth reusing

openSep 2024View details →

Interferon-induced activation of dendritic cells and monocytes by yellow fever vaccination correlates with early antibody responses

Bulk RNA-seq analysis of sorted subpopulations isolated from PBMC of yellow fever vaccinees from before and 3, 7, 14 and 28 days after vaccination and single cell RNA-seq analysis of sorted DC and monocytes fractions isolated from PBMC of of yellow fever vaccinees from before and 3 and 7 days after vaccination.

opencc-by-4.0Aug 2024View details →

A pro-inflammatory stem cell niche drives myelofibrosis through a targetable galectin-1 axis

Myeloproliferative neoplasms are stem cell-driven cancers associated with a large burden of morbidity and mortality. The majority of <a>patients p</a>resent with early-stage disease, but a substantial proportion progress to myelofibrosis and/or secondary leukemia, advanced cancers with a poor prognosis and high symptom burden. Currently, it remains difficult to predict progression, and therapies that reliably prevent or reverse fibrosis are lacking. A major bottleneck to the discovery of disease-modifying therapies has been an incomplete understanding of the interplay between perturbed cellular and molecular states. Several cell types have individually been implicated, but a comprehensive analysis of myelofibrotic bone marrow is lacking. We therefore mapped the crosstalk between bone marrow cell types in myelofibrotic bone marrow. We found that inflammation and fibrosis are orchestrated by a ‘quartet’ of immune and stromal cell lineages – with basophils and mast cells creating a TNF signaling hub, communicating with megakaryocytes, mesenchymal stromal cells and pro-inflammatory fibroblasts. We identified the b-galactoside binding protein galectin-1 as a striking biomarker of progression to myelofibrosis and poor survival in multiple patient cohorts, and as a promising therapeutic target, with reduced myeloproliferation and fibrosis <a>in vitro</a> and in vivo and improved survival following galectin-1 inhibition. In human bone marrow organoids, TNF increased galectin-1 expression, suggesting a feedback loop wherein the pro-inflammatory MPN clone creates a self-reinforcing niche, fueling progression to advanced disease. This study provides a valuable resource for studying hematopoietic cell-niche interactions, with broad relevance for cancer-associated inflammation and disorders of tissue fibrosis.

opencc-by-4.0Sep 2024View details →

Processed data for "Characterising the evolutionary dynamics of cancer proliferation in single-cell clones with SPRINTER"

This dataset contains the processed data for the figures and analyses performed in the publication "Characterising the evolutionary dynamics of cancer proliferation in single-cell clones with SPRINTER" from Lucas O., Ward S., Zaidi R., Bunkum A., ..., Zaccaria S. Nature genetics, in press, 2024. The processed data are separated into three respective folders: <ul> <li>GT contains all the data related to the analysis of the generated ground truth datasets;</li> <li>NSCLC contains all the data related to the analysis of the NSCLC dataset;</li> <li>TNBC_HGSC contains all the data related to the analysis of the TNBC and HGSC datasets. </li> </ul>

Nonlinear methods for dimensionality reduction and clustering of bacterial single-cell sequencing data - intermediate data and figures (MSc thesis)

Data, intermediate results and figures for analyses of my master's thesis in biostatistics at LMU Munich. I took a look on how to use Nonlinear Matrix Decomposition (NMD) (<a href="https://doi.org/10.1137/21M1405769">Saul, L., 2022</a>) in the context of bacterial scRNA-seq analysis (Heumos, L., et. al. 2023), replacing Principal Component Analysis in the optimized workflow, as outlined in Ostner, J. (2024). My thesis was structured along the following objectives: <ul> <li>implement the algorithms from <a href="https://arxiv.org/abs/2305.08687">Seraghiti, G., et. al. (2023)</a> in the Python module <a href="https://github.com/flatironinstitute/nomad/">nomad</a> in cooperation with <a href="https://www.simonsfoundation.org/flatiron/" rel="nofollow">Flatiron Institute</a></li> <li>code for the simulation study of the algorithms in <a href="https://arxiv.org/abs/2305.08687">Seraghiti, G., et. al. (2023)</a> with varying sparsity can be found in <code>/simulation</code></li> <li>apply NMD in the context of the BacSC workflow (<a href="https://www.biorxiv.org/content/10.1101/2024.06.22.600071v1">Ostner, J., et. al. (2024)</a>) on raw and normalized counts (found in <code>/application/analysis</code>), also for manually set number of latent dimensions</li> <li>explore NMD's potential for imputation of <a href="https://www.nature.com/articles/s41467-021-27729-z" rel="nofollow">sampling zeros</a> (check <code>/application/NMD_zero_imputation /</code>)</li> <li>potential of Poisson-Hurdle model-based clustering (<a href="https://academic.oup.com/bioinformatics/article/39/1/btac782/6873739">Qiao, Z., et. al. (2023)</a>) for scRNA-seq (<code>/application/poisson_hurdle</code>).</li> </ul>

Spatially resolved single-cell atlas unveils a distinct cellular signature of fatal lung COVID-19 in a Malawian population

This record contains all the fully processed single cell RNA sequencing objects included in the paper Nyirenda et al (2024) 'Spatially resolved single-cell atlas unveils a distinct cellular signature of fatal lung COVID-19 in a Malawian population'.  <h3>Abstract</h3> Postmortem single-cell studies have transformed understanding of lower respiratory tract diseases (LRTD) including Covid19, but there is minimal data from African settings where HIV, malaria and other environmental exposures may affect disease pathobiology and treatment targets. We used histology and high-dimensional imaging to characterise fatal lung disease in Malawian adults with (n=9) and without (n=7) Covid19, and generated single-cell transcriptomics data from lung, blood and nasal cells. Data integration with other  cohorts showed a conserved Covid19 histopathological signature, driven by contrasting immune and inflammatory mechanisms:  in USA, European and Asian cohorts by type I/III interferon responses, particularly in blood-derived monocytes, in the Malawi cohort, by response to interferon-gamma (IFN-γ) in lung-resident macrophages. HIV status had minimal impact on histology or immunopathology. Our study provides a data resource and highlights the importance of studying the cellular mechanisms of disease in underrepresented populations, indicating shared and distinct targets for treatment. <h3>Figure 3</h3> COSMIC_Lung_Atlas.h5ad COSMIC_Lung_Stromal_Atlas.h5ad COSMIC_Lung_Immune_Atlas.h5ad <h3>Figure 4</h3> COSMIC_HCLA_Integration.rds <h3>Figure 5</h3> COSMIC_Nasal_Atlas.h5ad COSMIC_Blood_Atlas.h5ad <h3>Total Post-mortem COVID-19 Lung Autopsy Papers Integration</h3> This data object was generated whereby all publicly available fatal COVID-19 lung single cell cohorts were integrated together. We include this analysis to show that the observed interferon responses detailed in the paper are preserved in not only the HLCA, but also existing post-mortem lung cohorts. The codebase for this additional analysis can be found here: <a href="https://github.com/olympiahardy/Malawi_Integration">https://github.com/olympiahardy/Malawi_Integration</a>  COSMIC_Delorey_Melms_Integration.rds     <h3>Imaging Mass Cytometry Analysis</h3> This Zenodo record can be accessed using the DOI here: <a href="10.5281/zenodo.13899297">10.5281/zenodo.13899297</a> . The GitHub repository can be accessed here: <a href="https://github.com/joaolsf/Spatial_Single_Cell_Lung_Atlas_Malawi_COVID">https://github.com/joaolsf/Spatial_Single_Cell_Lung_Atlas_Malawi_COVID</a>

Data used in analyses of Danaher et al. 2024 "Childhood-onset lupus nephritis is characterized by complex interactions between kidney stroma and infiltrating immune cells"

CosMx 1000-plex data and R code from childhood-onset lupus nephris samples, generated for the article Danaher et al. 2024 "Childhood-onset lupus nephritis is characterized by complex interactions between kidney stroma and infiltrating immune cells".

Decoding NY-ESO-1 TCR T Cells: Transcriptomic Insights Reveal Dual Mechanisms of Tumor Targeting in a Melanoma Murine Xenograft Model

Single-cell RNA-seq data of NY-ESO-1-specific TCR T-cells generated with the BD Rhapsody™ system. Biogroup information: Control (n = 4), PB (murine peripheral blood, n = 4). Cell preparation: NY-ESO-1-specific TCR T-cells were obtained via a retroviral transduction of an anti-NY-ESO-1-TCR construct, murine peripheral blood T-cells were enriched using anti-CD3 magnetic separation via MojoSortTM Human CD3 Selection Kit. Single-cell analysis system: BD Rhapsody™ Library strategy: 3' mRNA sequencing Library preparation protocol: BD Rhapsody™ Targeted mRNA and Sample Tag Library Preparation mRNA panel: BD Rhapsody™ Immune Response Panel HS BD Pipeline version: 1.11L

opencc-zeroMar 2024View details →

Linked collectors and determiners for: Downward flux of phytoplankton cells on the West Greenland continental shelf, July 2021.

Natural history specimen data linked to collectors and determiners held within, "Downward flux of phytoplankton cells on the West Greenland continental shelf, July 2021". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/e053797e-1548-4498-af63-1f7fc1791d1d">https://bionomia.net/dataset/e053797e-1548-4498-af63-1f7fc1791d1d</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/e053797e-1548-4498-af63-1f7fc1791d1d">https://gbif.org/dataset/e053797e-1548-4498-af63-1f7fc1791d1d</a>. Formatted as a Frictionless Data package.

opencc-zeroMar 2024View details →

Linked collectors and determiners for: Downward flux of phytoplankton cells in East Greenland fjords, August 2022.

Natural history specimen data linked to collectors and determiners held within, "Downward flux of phytoplankton cells in East Greenland fjords, August 2022". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/61137d30-c560-4309-b278-b2e5aebce92f">https://bionomia.net/dataset/61137d30-c560-4309-b278-b2e5aebce92f</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/61137d30-c560-4309-b278-b2e5aebce92f">https://gbif.org/dataset/61137d30-c560-4309-b278-b2e5aebce92f</a>. Formatted as a Frictionless Data package.

2D trajectories of human pluripotent stem cell colonies

This dataset is a part of the following manuscript submitted for publication in Life (MDPI): "Human Pluripotent Stem Cell Colony Migration is Related to Culture Environment and Morphological Phenotype" by Vitaly V. Gursky, Alina S. Chabina, Olga A. Krasnova, Anastasiya A. Kovaleva, Daria V. Kriger, Michail S. Zadorsky, Konstantin N. Kozlov, and Irina E. Neganova A trajectory in the csv file is an array of the 2D coordinates {{x1,y1}, {x2,y2}, ...} (in µm; centered at {x,y}={0,0}), which represent the migration history of the center of one cell colony. Trajectories were obtained from the time-lapse bright-field images, using the Manual Tracking tool in ImageJ (Fiji). The data were collected for colonies from three human pluripotent stem cell lines (human induced pluripotent stem cell line AD3, patient specific human induced pluripotent stem cell line HPCASRi002-A (CaSR), and human embryonic stem cell line H9), grown under two culture conditions (media/matrix = mTESR1/MG or E8/GT). The colonies were phenotyped according to their morphological characteristics associated with the pluripotency status ("good" and "bad" phenotype); cells with the "bad" phenotype showed signs of incipient differentiation. The time interval between adjacent coordinates is 15 min. The data were obtained under the financial supprot of the Russian Science Foundation, grant number 21-75-20132.

opencc-by-4.0Aug 2024View details →

uncropped western blots for analysis of RPN13 ubiquitylation and NRF1 activation by protein aggregates, as well as source data for qPCR plots and flow cytometry gating and FCS files for agDD-GFP in HeLa or HEK cells

This entry contains uncropped blots for Fig 4D and Fig S4C, Fig. 5B, Fig S5 and Fig S6, and the raw FCS files for Flow Cytometry data in doi.org/10.1101/2024.08.30.610524.

A Data-Driven Epigenetic Characterization of Morning Fatigue Severity in Oncology Patients Receiving Chemotherapy: Associations with Epigenetic Age Acceleration, Blood Cell Types, and Expression-Associated Methylation

This dataset contains supplementary materials including the eCpG mapping analysis results and annotation. The manuscript has been accepted for publication at Cancer Medicine. Please cite both the paper as well as the DOI of this dataset if you make use of the data.

opencc-by-4.0Jul 2024View details →

Hydroxyurea inhibits proliferation and stimulates apoptosis through inducible nitric oxide synthase in erythroid cells.

<a name="_Hlk164856369"></a>Hydroxyurea (HU) arrests cells in S-phase through the inhibition of ribonucleotide reductase and DNA synthesis, which is contributed significantly by the release of nitric oxide (NO). We investigated the mediation of inducible NO synthase (NOS2) in HU cytostatic effects, <a name="_Hlk165036602"></a>using in vitro shRNA-induced knock-down of NOS2 or specific NOS2 inhibitor (1400W), and supported by Nos2 knockout mice (Nos2-/-) to observe ex vivo murine erythroid progenitors (mERP).<a name="_Hlk165036694"></a>As a long-term effect, HU increased NOS2 expression in human erythroleukemic HEL92.1.7 cells (via NFκB signaling) and mERP. As a short-term result, HU increased the activity of purified and HEL92.1.7 cell-derived NOS2, as confirmed by NO / citrulline production and in silico molecular docking via hydrogen bonds. Molecular dynamic simulation showed a slight decrease in NOS2 receptor rigidity upon HU binding, confirming the stability of HU at the active site of the NOS2 receptor. Both 1400W and shRNA prevented the in vitro reduction of proliferation and induction of apoptosis of HEL92.1.7 cells by HU. Thus, shRNA blocked preferentially early apoptosis and only shRNA overcame HU-induced S-phase arrest of HEL92.1.7 cells. Both 1400W and Nos2 knockout prevented the in vivo decrease in proliferation and induction of apoptosis of mERP by HU from wild-type and Nos2-/- mice, respectively. Therefore, HU increased NOS2 activity and expression via NFκB signaling. This study demonstrated the NOS2 dependance in the HU inhibition of proliferation and stimulation of apoptosis of erythroid cells.

10X Single Cell RNA-Seq On WT and Mutation mouse models

Four four-week-old mice, consisting of wild type, Pten mutation, MAP3K3 mutation, and MAP3K3+Pten mutation, were sampled at 4 weeks of age. Endothelial cells were enriched using CD31 magnetic beads, followed by 10X single-cell RNA sequencing.

Code and data of "Uncovering disease-related multicellular pathway modules on large-scale single-cell transcriptomes with scPAFA"

Code and data to reproduce the analyses and figures presented in "Uncovering disease-related multicellular pathway modules on large-scale single-cell transcriptomes with scPAFA"

EV based miR-6772-5p secreted by breast cancer cells in response to radiation exposure is involved in development of radioresistance

This dataset serves as a supplementary material for the research article EV-based miR-6772-5p secreted by breast cancer cells in response to radiation exposure is involved in development of radioresistance by Tynjälä et al. For further details, please refer to our publication for a detailed description of the methods used to generate this dataset. In summary, we isolated and sequenced miRNAs from extracellular vesicles (EVs) isolated from irradiated and non-irradiated MCF7 cells. Sequencing libraries were prepared with QIAseq miRNA Library Kit (Qiagen, Germany) and sequenced with Illumina NextSeq 500 platform (Illumina). In-house bioinformatics workflow was used to process the sequencing data. Unique molecular identifier (UMI) sequences were first extracted and added to FASTQ header with UMI-tools while discarding the 3’ adapter and primer sequences. Reads shorter than 16 bp were discarded with cutadapt. Provided sequencing data is in FASTQ format and trimmed from adapter and UMI sequences. We have also provided miRNA counts and results of following differential gene expression analysis in tab-delimited format.

opencc-by-4.0Jun 2021View details →

Polysome profile glucose beta-cells

Polysome profile analysis of human pancreatic beta-cells exposure to glucose A human pancreatic beta cell line (betaH2) was exposed to glucose after starvation. Total RNA was collected for a bulk RNA-seq analysis. Three biological repeats were produced for each of the the low and high glucose conditions resulting in to 6 sequence (fastq) files. Three different mRNA-ribosomal fractions were selected from a sucrose gradient corresponding to single ribosomes (monosomes), light ribosomes (2-4) and heavy ribosomes (5 and more). Three biological repeats were produced for each fraction and for the two conditions (low and high glucose) resulting to 18 different (fastq) sequenced files. In total the study consists of 24 submitted fastq files, 6 corresponding to total RNA from two conditions (three replicates) and 18 corresponding to polysome profile, 3 fractions times two conditions times 3 replicates. All 24 *.fastq.gz files are available in the reads_PolysomeProfile_RNAseq_BetaCells.tar archive. In addition to the raw reads we provide the processed data: The raw counts table ( countsTOTALS_CodingGenes.tsv) and the Transcripts Per Million table (TPMs) (polysomeProfile_TPM_proteinCoding.csv) files of the data produced from the polysome profile and RNA-seq experiments described in the experimental design that can be found in: https://github.com/parisepigenetics/Translatome_Bcells_glucose

opencc-by-4.0Sep 2020View details →

Raw data for UV-Selective Optically Transparent Zn(O,S)-Based Solar Cells

In the following the raw data lying the foundation of the paper “UV-Selective Optically Transparent Zn(O,S)-Based Solar Cells” (Lopez-Garcia et al.) published in Solar Rapid Research Letters Vol. 4, 2000470, 2020, are described. They were obtained under the funding provided by the European Union H2020 Framework Programme under Grant Agreement no. 826002 (Tech4Win) and by the Basque Country PI2018-08 (PISCES). UV–vis measurements were acquired with a dual-beam spectrophotometer setup (Perkin Elmer Lambda L35) in transmittance mode (light source and detector normal to sample’s surface (i.e., 0o)) scanning from 300 to 800 nm. Raman spectroscopy was performed with a FHR640 Horiba Jobin–Yvon spectrometer coupled to a Raman probe developed at Institut de Recerca en Energia de Catalunya (IREC) and a cryogenically cooled charge coupled device detector. Measurements were carried out in backscattering configuration and with a 325 nm UV laser as the excitation wavelength. An excitation power density of about 25W/cm2 was used to inhibit thermal effects on the samples. The Raman shift was calibrated using a Si monocrystal reference and adjusting the Raman shift for the main Si band at 520 cm-1. FE-SEM images were acquired with a ZEISS Auriga Series system. The images were acquired at 5 kV, aperture of 20 μm, and working distance of around 4mm with the InLens detector. J–V measurements under illumination were carried out using a homemade setup consisting on a 150W xenon broadband arc Lamp (Thorlabs SLS401) calibrated using a NREL-certified Si reference solar cell (Abet Technologies, Model 15150). Electrical measurements were carried out with a source-measure unit (Keithley 2400) in four-wire sense mode, controlled by the software Tracer (ReRa solutions) using a IEEE 488 GPIB Instrument Control Device (National Instruments GPIB-USB-HS). EQE curves were obtained using a spectral response system (Bentham PVE300) calibrated with a Si photodiode.

opencc-by-4.0Jun 2021View details →

In vitro antiproliferative data of metallodrugs in skin cancer cell lines

Compiled in vitro antitumoral potency (IC50, GI50 and/or TGI values) of metallodrugs in skin cancer cell lines. This information is complemented by structural parameters of each compound testted, such as molecular weight with and without the counter-ion, charge, oxidation state of the metal center and ligand type. Information on the antiproliferative assay is also given, such as type of cell line and incubation time. Additional details are provided when the compounds have been assayed upon light activation (PDT),  as part of the NCI-60 panel and if in vivo data is also available.