Find research datasets worth reusing

opencc-by-4.0Apr 2023View details →

Ergolide Mediates Anti-Cancer Effects on Metastatic Uveal Melanoma Cells and Modulates their Cellular and Extracellular Vesicle proteomes

Underlying dataset and extended dataset of the results described in the article "Ergolide Mediates Anti-Cancer Effects on Metastatic Uveal Melanoma Cells and Modulates their Cellular and Extracellular Vesicle proteomes".

opencc-by-4.0Sep 2022View details →

Single-molecule DNA methylation patterns of full-length human-specific LINE-1 (L1HS) retrotransposons in a panel of cell lines.

We used bs-ATLAS-seq to comprehensively map the genomic location and assess the DNA methylation status of full-length human-specific LINE-1 elements (L1HS). The approach capture region 1-210 of L1HS elements, which corresponds to the most 5' end of its promoter sequence. This was performed in a panel of 12 human primary or transformed cell lines (BJ, IMR90, MRC5, H1, K562, HCT116, HeLa S3, HepG2, MCF7, HEK-293, HEK-293T, 2102Ep), many being shared with the encode project. These datasets provide a visualization for DNA methylation patterns at the single molecule level for each L1HS loci.

opencc-by-4.0Nov 2022View details →

Spatial immunophenotyping of the tumor microenvironment in non-small cell lung cancer

A dataset with spatial immune cell information on a lung cancer cohort from Uppsala University Hospital, Sweden, with anonymized clinical data. For more information please refer to the 'readme' file and the original study (https://doi.org/10.1016/j.ejca.2023.02.012).

opencc-by-4.0Apr 2023View details →

Data to reproduce analysis in "Systematic analysis of transcriptional and epigenetic effects of genetic variation in Kupffer cells enables discrimination of cell intrinsic and environment-dependent mechanisms"

Here you can find the datasets necessary to reproduce all analyses described in the Glass lab paper by <a href="https://www.biorxiv.org/content/10.1101/2022.09.22.509046v1">Bennett et al</a>. The python and R code for reproducing analysis and figures can be found on our linked <a href="https://github.com/HunterBennett/KupfferCell_NaturalGeneticVariation">github repository.</a> Briefly, this paper explores the effect of natural genetic variation in vivo, using Kupffer cells as a model cell type. We collect and analyze transcriptional and epigenetic data (ATAC-seq, H3K27Ac ChIP-seq) to identify putative trans regulators driving differential gene expression across inbred strains of mice. Additionally, we provide evidence that trans effects control a majority of strain differential genes at homeostasis while cis effects dominate the transcriptional response to an external signal (lipopolysaccharide). References: Hunter Bennett, Ty D. Troutman, Enchen Zhou, Nathanael J. Spann, Verena M. Link, Jason S. Seidman, Christian K. Nickl, Yohei Abe, Mashito Sakai, Martina P. Pasillas, Justin M. Marlman, Carlos Guzman, Mojgan Hosseini, Bernd Schnabl, Christopher K. Glass bioRxiv 2022.09.22.509046; doi: <a href="https://doi.org/10.1101/2022.09.22.509046">https://doi.org/10.1101/2022.09.22.509046</a>

opencc-by-4.0Jun 2023View details →

Systematic investigation of mitochondrial transfer between cancer and T cells at singlecell level

The benmark datasets about MT transfer, including mtSNV profile, coverage information, cell information and expression information.

TCGA Head & Neck Squamous Cell Carcinoma (HNSC) Gene Expression

Abstract: The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset contains information about HNSC, a type of cancer that originates in the squamous cells lining the mucosal surfaces of the head and neck region, including the oral cavity, throat, and larynx.  The gene expression profile was measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform by the University of North Carolina TCGA genome characterization center. The Sample IDs serve as unique identifiers for each sample. Inspiration: This dataset was uploaded to UBRITE for GTKB project.  Instruction: The log2(x+1) normalization was removed, and z-normalization was performed on the dataset using a Python script. Acknowledgments: Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8 The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764 U-BRITE last update: 07/13/2023

TCGA Kidney Renal Clear Cell Carcinoma (KIRC) Gene Expression

Abstract: The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset contains information about KIRC, the most common and aggressive subtype of kidney cancer, originating from the cells lining the tubules of the kidney and characterized by its clear appearance under the microscope. The gene expression profile was measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform by the University of North Carolina TCGA genome characterization center. The Sample IDs serve as unique identifiers for each sample. Inspiration: This dataset was uploaded to UBRITE for GTKB project.  Instruction: The log2(x+1) normalization was removed, and z-normalization was performed on the dataset using a Python script. Acknowledgments: Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8 The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764 U-BRITE last update: 07/13/2023

TCGA Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC) Gene Expression

Abstract: The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset contains information about CESC, a type of cancer that affects the cells lining the cervix and can have squamous cell or adenocarcinoma histological subtypes. The gene expression profile was measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform by the University of North Carolina TCGA genome characterization center. The Sample IDs serve as unique identifiers for each sample. Inspiration: This dataset was uploaded to UBRITE for GTKB project.  Instruction: The log2(x+1) normalization was removed, and z-normalization was performed on the dataset using a Python script. Acknowledgments: Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8 The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764 U-BRITE last update: 07/13/2023

TCGA Head & Neck Squamous Cell Carcinoma (HNSC) Clinical Data

Abstract: The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset includes curated survival data from the Pan-cancer Atlas paper titled <a href="http://www.cell.com/cell/fulltext/S0092-8674(18)30229-0">"An Integrated TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) to drive high quality survival outcome analytics"</a>. The paper highlights four types of carefully curated survival endpoints, and <a href="http://www.cell.com/action/showFullTableImage?isHtml=true&tableId=tbl3&pii=S0092867418302290">recommends the use of the endpoints of OS, PFI, DFI, and DSS for each TCGA cancer type</a>. The dataset also includes phenotypic information about HNSC. The Sample IDs are unique identifiers, which can be paired with the gene expression dataset.  Inspiration: This dataset was uploaded to UBRITE for GTKB project.  Instruction: The survival and phenotype data were merged into one file. Empty columns were removed. Columns with the same value for every sample were also removed.  Acknowledgments: Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8 Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11. <a href="https://doi.org/10.1016/j.cell.2018.02.052">https://doi.org/10.1016/j.cell.2018.02.052</a> The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764 U-BRITE last update: 07/13/2023

opencc-by-4.0Oct 2023View details →

KSR inhibitor APS-2-79 sensitivity test in JURKAT and ALL-SIL T-cell acute lymphoblastic leukemia cell lines measured by Cell Counting Kit 8

APS-2-79 compound was purchased from MedChemExpress (Monmoutyh Junction, NJ, USA). The 20 mg/ml stock solution was prepared in DMSO. To calculate the IC50, JURKAT and ALL-SIL cells were cultured for 72h with a range of APS-2-79 concentrations (5-15 µM) added as equal volumes. Cells treated with 0.5% DMSO (vehicle) were used as negative control. Cells treated with 10% DMSO were used as positive control. The viability of cells was measured using Cell Counting Kit 8 (Sigma Aldrich) and GloMax Microplate Reader system (Promega) with 450 nm wavelength and 600 nm as reference wavelength. The relevant reads are made from following wells: 2A-2D (15 µM APS-2-79), 3A-3D (12.5 µM APS-2-79), 4A-4D (10 µM APS-2-79), 5A-5D (7.5 µM APS-2-79), 6A-6D (5 µM APS-2-79), 7A-7D (vehicle), 8A-8D (positive control).

opencc-by-4.0May 2019View details →

Cell metadata for "The emergent landscape of the mouse gut endoderm at single-cell resolution"

Cell metadata for the data published in "The emergent landscape of the mouse gut endoderm at single-cell resolution"

opencc-by-4.0Dec 2019View details →

Tumor growth kinetics of human LM2-4LUC+ triple negative breast carcinoma cells

Cell culture and data set Tumor growth data used in this study were obtained from experiments involving the use of a LM2-4LUC+ cells (or LM2-4), a metastatic variant of the  human triple-negative breast carcinoma MDA-MB-231 cells. Animal studies were performed as described previously under Roswell Park Comprehensive Cancer Center (RPCCC) Institutional Animal Care and Use Committee (IACUC) protocol number 1227M [1-7]. Tumor growth data were pooled from eight separate experiments conducted with a total of 581 observations, and represent control (vehicle-treated) animals from published studies [1-7]. Vehicle formulation was carboxymethylcellulose sodium (USP, 0.5% w/v), NaCl (USP, 1.8% w/v), Tween-80 (NF, 0.4% w/v), benzyl alcohol (NF, 0.9% w/v), and reverse osmosis deionized water (added to final volume) and adjusted to pH 6 (see [3]) and was given at 10ml/kg/day for 7-14 days prior after tumor implantation and before tumor resection [1-7]. <ul> </ul> Tumor injections LM2-4LUC+ cells were orthotopically implanted (106 cells per injection) into the right inguinal mammary fat pads of 6- to 8-week-old female severe combined immunodeficient (SCID) mice. Tumor measurements Tumor size was measured regularly with calipers to a maximum volume of  2 cm3, calculated by the formula  \(V = \frac{\pi}{6} w^2 L\) (ellipsoid) where L is the largest and w is the smallest tumor diameter. Please cite: Vaghi C, Rodallec A, Fanciullino R, Ciccolini J, Mochel JP, et al. (2020) Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors, PLoS Comput Biol, 16, p. e1007178. <a href="https://doi.org/10.1371/journal.pcbi.1007178">https://doi.org/10.1371/journal.pcbi.1007178</a>   In the file, the columns correspond to: <ul> <li>ID: identifier of the animal</li> <li>Time: day of the tumor measurement after implantation</li> <li>Observation: tumor measurement (in mm3)</li> </ul>   References [1] Benzekry, S., Lamont, C., Beheshti, A., Tracz, A., Ebos, J. M. L., Hlatky, L., & Hahnfeldt, P. (2014). Classical mathematical models for description and prediction of experimental tumor growth. PLoS Comput Biol, 10(8), e1003800. http://doi.org/10.1371/journal.pcbi.1003800 [2] Benzekry S, Tracz A, Mastri M, Corbelli R, Barbolosi D, Ebos JML. (2016) Modeling Spontaneous Metastasis Following Surgery: An In Vivo-In Silico Approach. Cancer Res.;76(3):535–547. doi:10.1158/0008-5472.CAN-15-1389. [3] Ebos JML, Lee CR, Bogdanovic E, Alami J, Van Slyke P, Francia G, et al. (2008) Vascular Endothelial Growth Factor-Mediated Decrease in Plasma Soluble Vascular Endothelial Growth Factor Receptor-2 Levels as a Surrogate Biomarker for Tumor Growth. Cancer Res.;68(2):521–529. doi:10.1158/0008-5472.CAN-07-3217. [4] Ebos JML, Mastri M, Lee CR, Tracz A, Hudson JM, Attwood K, et al. (2014) Neoadjuvant antiangiogenic therapy reveals contrasts in primary and metastatic tumor efficacy. EMBO Mol Med;6:1561–76. https://doi.org/10.15252/emmm.201403989 [5] Ebos JML, Lee CR, Cruz-Munoz W, Bjarnason GA, Christensen JG, Kerbel RS. (2009) Accelerated metastasis after short-term treatment with a potent inhibitor of tumor angiogenesis. Cancer Cell;15:232–9. https://doi.org/10.1016/j.ccr.2009.01.021 [6] Mastri M, Tracz A, Lee CR, Dolan M, Attwood K, Christensen JG, et al. (2018) A Transient Pseudosenescent Secretome Promotes Tumor Growth after Antiangiogenic Therapy Withdrawal. Cell Rep.; 25 (13):3706–20 e8. Epub 2018/12/28. https://doi.org/10.1016/j.celrep.2018.12.017 [7] Vaghi C, Rodallec A, Fanciullino R, Ciccolini J, Mochel JP, et al. (2020) Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors, PLoS Comput Biol, 16, p. e1007178. <a href="https://doi.org/10.1371/journal.pcbi.1007178">https://doi.org/10.1371/journal.pcbi.1007178</a>

opencc-by-4.0Jan 2020View details →

Predicting gene expression using morphological cell responses to nanotopography

This dataset contains the raw files, results files and R workspace files (.RData) associated with the paper: Predicting gene expression using morphological cell responses to nanotopography Please note that this dataset is separated according to the Figure presented in the published and peer-reviewed version of the manuscript. Particular folders contain its own README file to facilitate reproduction/replication of results and figures.

opencc-by-4.0Mar 2020View details →

Detection of HER2+ Breast Cancer Cells using Bioinspired DNA-Based Signal Amplification

Circulating tumor cells (CTC) are promising biomarkers for metastatic cancer detection and monitoring progression. However, CTC detection remains challenging due to their low frequency and heterogeneity. Herein, we report a bioinspired approach to detect individual cancer cells, based on a signal amplification cascade using a programmable DNA hybridization chain reaction (HCR) circuits. We applied this approach to detect HER2+ cancer cells using the anti-HER2 antibody (trastuzumab) coupled to initiator DNA eliciting a HCR cascade that leads to a fluorescent signal at the cell surface. At 4°C, this HCR detection scheme resulted in highly efficient, specific and sensitive signal amplification of the DNA hairpins specifically on the membrane of the HER2+ cells in a background of HER2- cells and peripheral blood leukocytes, which remained almost non-fluorescent. The results indicate that this system offers a new strategy that may be further developed toward an in vitro diagnostic platform for the sensitive and efficient detection of CTC.

opencc-by-4.0Jan 2015View details →

Logical model for Model checking to assess T-helper cell plasticity

Logical modeling has proven suitable for the dynamical analysis of large signaling and transcriptional regulatory networks. In this context, signaling input components are generally meant to convey external stimuli, or environmental cues. In response to such external signals, cells acquire specific gene expression patterns modeled in terms of attractors (e.g. stable states). The capacity for cells to alter or reprogram their differentiated states upon changes in environmental conditions is referred to as cell plasticity. In <a href="https://dx.doi.org/10.3389/fbioe.2014.00086">[1]</a>, it is presented an extended version of a published logical model of T-helper cell differentiation and plasticity, which accounts for novel cellular subtypes. The model encompasses 20 signaling pathways, a dozen of transcription factors, and about 30 cytokines, amounting to 101 components in total. Computational methods recently developed to efficiently analyze large models <a href="http://ginsim.org/node/185#ref1">[1]</a> are first used to study static properties of the model (i.e. stables states). Symbolic model checking is then applied to get further insights into reachability properties between Th canonical subtypes upon changes of specific prototypic environmental cues. The model reproduces novel reported Th subtypes (Tfh, Th9, Th22) and predicts additional Th hybrid subtypes in term of stables states. Using the model checker NuSMV-ARCTL, an abstract view of the dynamics, called reprograming graph, is produced providing a global and synthetic view of Th plasticity. The model is consistent with experimental data showing the polarization of naïve Th cells into the canonical Th subtypes. The model further predicts substancial plasticity of Th subtypes depending on the signalling environment.

opencc-by-4.0Nov 2015View details →

Logical model for Molecular Pathways Enabling Tumour Cell Invasion and Migration

Understanding the etiology of metastasis is very important in clinical perspective, since it is estimated that metastasis accounts for 90% of cancer patient mortality. Metastasis results from a sequence of multiple steps including invasion and migration. The early stages of metastasis are tightly controlled in normal cells and can be drastically affected by malignant mutations; therefore, they might constitute the principal determinants of the overall metastatic rate even if the later stages take long to occur. To elucidate the role of individual mutations or their combinations affecting the metastatic development, a logical model has been constructed that recapitulates published experimental results of known gene perturbations on local invasion and migration processes, and predict the effect of not yet experimentally assessed mutations. The model has been validated using experimental data on transcriptome dynamics following TGF-β-dependent induction of Epithelial to Mesenchymal Transition in lung cancer cell lines. A method to associate gene expression profiles with different stable state solutions of the logical model has been developed for that purpose. In addition, we have systematically predicted alleviating (masking) and synergistic pairwise genetic interactions between the genes composing the model with respect to the probability of acquiring the metastatic phenotype. We focused on several unexpected synergistic genetic interactions leading to theoretically very high metastasis probability. Among them, the synergistic combination of Notch overexpression and p53 deletion shows one of the strongest effects, which is in agreement with a recent published experiment in a mouse model of gut cancer. The mathematical model can recapitulate experimental mutations in both cell line and mouse models. Furthermore, the model predicts new gene perturbations that affect the early steps of metastasis underlying potential intervention points for innovative therapeutic strategies in oncology.   Included files: <ul> <li>Master Model: the model includes detailed regulation of the major players involved in the crosstalks between Notch and p53 pathways</li> <li>Modular Model: the model is a reduction of the master model. To reduce the master model, we lumped together some entities that belonged to a module.</li> </ul>

opencc-by-4.0Mar 2020View details →

Fastq data for Vero E6 cell infeceted with SARS CoV 2

Vero cells infected with SARS-CoV 2 and the transcriptome sequenced by dRNAseq on nanopore.

opencc-by-4.0Apr 2020View details →

Highly multiplexed histology reveals phenotypic and spatial characteristics of human Innate Lymphoid Cells in chronic inflammation - MELC tonsil data-set

53 marker MELC Run in human tonsil. Each image depicts the same field of view, sequentially stained with the depicted fluorescence-labelled antibodies. Images contain 2048 x 2048 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have not been normalized and intensities have not been adjusted.

opencc-by-4.0Apr 2020View details →

Operation of a multijunction solar cell

Este vídeo ilustra el proceso de absorción de fotones en una célula solar multiunión. En particular, el caso que se ilustra es el de una célula formada por tres uniones cada una de ellas representada por un gap diferente. La luz del Sol está compuesta por fotones de diferentes energías representados en el vídeo por diferentes colores. En la animación observaremos la acción de fotones de 3 colores: azul (con una longitud de onda de unos 380-475 nm), verde (~500-550 nm) y rojo (~645-700 nm). En la representación de los diagramas de bandas correspondientes a cada célula vemos que el fotón más energético (de color azul) es absorbido en la primera célula. Esto es debido a que su energía es igual o mayor al valor de energía del gap de semiconductor correspondiente a la primera célula.  Vemos, sin embargo, que los menos energéticos (verde y rojo) la atraviesan sin verse afectados. Esto pasará de nuevo en la siguiente capa, absorbiéndose el verde en esta ocasión. Por último, el rojo será absorbido en la capa final. Cuando un fotón es absorbido, su energía se emplea en excitar un electrón (de color amarillo en la animación) de la banda de valencia a la banda de conducción. Deja atrás una vacante o hueco (de color negro). Es decir, se genera un par electrón-hueco. (Este vídeo es parte de un trabajo de la asignatura de Materiales Funcionales II del Grado de Materiales de la Universidad Politécnica de Madrid del curso 2019-2020 y forma parte de un proyecto para la experimentación en la diseminación en acceso abierto de la docencia en energía solar fotovoltaica del proyecto GRECO).     This video illustrates the photon absorption process in a multi-junction solar cell. In particular, the case illustrated is that of a cell formed by three junctions, each one represented by a different gap. Sunlight is made up of photons of different energies represented in the video by different colors. In the animation we observe the action of  three coloured photons: blue (with a wavelength of about 380-475 nm), green (~ 500-550 nm) and red (~ 645-700 nm). In the representation of the band diagrams corresponding to each cell we see that the most energetic photon (blue in color) is absorbed in the first cell. This is because its energy is equal to or greater than the energy value of the semiconductor gap corresponding to the first cell. We see, however, that the less energetic ones (green and red) pass through it unaffected. This will happen again in the next layer, absorbing the green this time. Finally, the red will be absorbed in the final layer. When a photon is absorbed, its energy is used to excite an electron (yellow in animation) from the valence band to the conduction band and leaving behind a vacancy or hole (black). That is, an electron-hole pair is generated. (This video is part of a work of the Functional Materials II subject of the Materials Degree of the Universidad Politécnica de Madrid for the 2019-2020 academic year and is part of a project for experimentation in the open access dissemination of energy teaching photovoltaic solar from the GRECO project).