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1,982 results for “HepG2”
Synthetic datasets reflecting the shRNA-seq knockdown ENCODE data for HepG2 and K562 with coresponding GRN
<p>Synthetic data correspond to the ENCODE data for cell lines HepG2 (https://www.encodeproject.org/biosamples/ENCBS282XVK/) and K562 (https://www.encodeproject.org/biosamples/ENCBS023XVB/). The data and networks were generated using GeneSPIDER (publicly available at https://bitbucket.org/sonnhammergrni/genespider/).</p> <p> </p> <p><strong>Table.1 </strong>Description of the files</p> <table> <tbody> <tr> <td>data_HepG2like_SNR_L=0.0054699_diff=1.6188e-05.txt</td> <td>Synthetic gene expression knockdown (shRNA-seq) data immitating the ENCODE data for HepG2 cell line. Data size: 232 RBPs vs 464 experiments (2 replicates). SNR_L is the value of signal to noise ratio. Difference (diff) value tells the difference between replicate correlation coefficients of real and synthetic ENCODE data. Columns represent experiments, rows represent genes.</td> </tr> <tr> <td>data_K562like_SNR_L=0.0028692_diff=0.00017339.txt</td> <td>Synthetic gene expression knockdown (shRNA-seq) data immitating the ENCODE data for K562 cell line. Data size: 232 RBPs vs 464 experiments (2 replicates). SNR_L is the value of signal to noise ratio. Difference (diff) value tells the difference between replicate correlation coefficients of real and synthetic ENCODE data. Columns represent experiments, rows represent genes.</td> </tr> <tr> <td>network_HEPG2like_sparsity4.txt</td> <td>Synthetic scale-free gene regulatory network compatibile with data_HepG2like_SNR_L=0.0054699_diff=1.6188e-05.txt. Sparsity (average node degree) is 4 including selfloops. Direction should be read from columns to rows.</td> </tr> <tr> <td>network_K562like_sparsity4.txt</td> <td>Synthetic scale-free gene regulatory network compatibile with data_K562like_SNR_L=0.0028692_diff=0.00017339.txt. Sparsity (average node degree) is 4 including selfloops. Direction should be read from columns to rows.</td> </tr> <tr> <td>perturbations_HepG2&K562_2replicates.txt</td> <td>Perturbation matrix including information about knockeddown RBPs. Data size: 232 RBPs vs 464 experiments (2 replicates).</td> </tr> </tbody> </table> <p> </p> <p>Created by Garbulowski et al. (2024) as a part of the work entitled "Comprehensive analysis of the RBP regulome reveals functional modules and drug candidates in liver cancer"</p>
RNA-seq data of HepG2 cultured in 2D or as organoids
<p>Cancer cells cultured as organoids could better represent cancer cells grown in vivo and were shown to harbor increased stemness compared with cancer cells in 2D culture. Hep G2 cell line was used as parental cell line to establish a liver cancer organoid line. Hep G2 2D culture and organoid culture were subjected to bulk RNA-seq. </p>
Differential interference contrast (DIC) image of unstained living HepG2 human liver cancer cells
<p><strong>Introduction</strong></p> <p>This dataset is associated with our submission to Computers in Biology and Medicine, titled "Accurate Detection and Instance Segmentation of Unstained Living Adherent Cells in Differential Interference Contrast Images". The submission number for this manuscript is CIBM-D-23-09623R1.</p> <p><strong>Authors</strong>: Fei Pan, Yutong Wu, Kangning Cui, Shuxun Chen, Yanfang Li, Yaofang Liu, Adnan Shakoor, Han Zhao, Beijia Lu, Shaohua Zhi, Raymond Hon-Fu Chan, Dong Sun</p> <p><strong>Dataset Description</strong></p> <p>Our dataset comprises 520 differential interference contrast (DIC) images of 12,198 unstained HepG2 human liver cancer cells, each with a corresponding fluorescence image stained with calcein acetoxymethyl (AM), ensuring high-quality ground-truth annotations. Unique in addressing the multi-state nature of adherent cells commonly seen in wet labs, it includes both healthy and unhealthy cells in a single image, providing a valuable resource for studying multi-state cell detection and instance segmentation.<br>Citation</p> <p>We kindly request that researchers who use this dataset cite both our paper and this dataset. This will help acknowledge the work and facilitate further advancements in the field.</p> <p><br><strong>Please cite as follows:</strong></p> <p><strong>Paper:</strong><br>Pan, F., Wu, Y., Cui, K., Chen, S., Li, Y., Liu, Y., Shakoor, A., Zhao, H., Lu, B., Zhi, S., Chan, R. H.-F., & Sun, D. "Accurate detection and instance segmentation of unstained living adherent cells in differential interference contrast images,” <em>Computers in Biology and Medicine</em>, vol. 182, p. 109151, Nov. 2024, doi: 10/g5p9d8.</p> <p><strong>Dataset:</strong><br>Pan, F., Chen, S., Li, Y., Shakoor, A., Zhao, H., & Sun, D. (2024). Differential interference contrast (DIC) image of unstained living HepG2 human liver cancer cells. Zenodo. </p> <p>Thank you for your interest and support in our work. We look forward to seeing the innovative research that this dataset will enable.</p> <p> </p> <p> </p>
Wound Healing and Fluorescence Analyzes of Theranekron D6 AppliedAML-12 and HepG2 cells
<p>This file includes original jpeg files of wound closure area calculation of AML-12 and HepG2 cells and fluorescence microscopy photos of TD6 applied AML-12 and HepG2 cells uniquely.</p>
Red rice bran polyphenols suppress invasive properties of HepG2 cells, possibly through Wnt/β-catenin-mediated EMT reversal.
<p>Data collection in its raw form is performed in a specific sequence dictated by the manuscript.</p>
Annotated dataset of HepG2 spheroids (YOLO format)
<p>Dataset of birghtfield images of HepG2 spheroids in SP5D culture plates (Kugelmeiers) after 5 days of culture using a custom made inverted microscope with 4X magnification. The dataset consists of 1,395 images with a total of 19,129 labels. Three classes were defined: 0 for 'None', 1 for 'Healthy', 2 for 'Unhealthy'. The labelling was partially manual and partially automated: some spheroids were exposed to a toxic compound and automatically labelled as 'Unhealthy'. The resulting dataset includes 12,599 spheroids labelled as 'Healthy' and 6,530 as 'Unhealthy'. The dataset was partitioned into three sets for the training of a YOLO network: 70% images for training, 15% for testing and 15% for validation. More details will be available in the corresponding publication.</p>
Dataset to "Collagen I Increases Palmitate-Induced Lipotoxicity in HepG2 Cells via Integrin-Mediated Death"
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Raw data for: Artificial cells eavesdropping on HepG2 cells
<p><span>Cellular communication between artificial and natural cells are explored by fabricating hydrogel based artificial cells (ACs) that can eavesdrop on liver cells with regards to their cytochrome P450 (CYP450) activity. ACs are capable of eavesdropping on HepG2 cells by releasing D-cysteine and the D-cysteine release from ACs is quantified by colorimetric and luminometric detection. The CYP450 activity of HepG2 cells is evaluated by the production of 2-cyano-6-hydroxybenzothiazole (CHB), which react with D-cysteine to produce D-luciferin. Thereby, the capability of ACs to eavesdrop on HepG2 cells is measured by detecting D-luciferin in the presence of luciferase. The eavesdropping experiment is performed in different experimental set ups, but is centered around the production of D-luciferin due to the production of CHB by HepG2 cells and D-cysteine release from ACs. In addition, bright field microscopy and confocal laser scanning microscopy was performed to visualize the ACs and aggregates of ACs and HepG2 cells. </span></p> <p><span>Our findings thereby present means of designing communication between AC and mammalian cells. Further, the ACs provides an easily implemented approach to monitor a core hepatic function when co-cultured with hepatocytes.</span></p>
Fig. 10. Compound 4 induced HepG2 cells apoptosis. HepG2 in Artemidubolides A T, cytotoxic unreported guaiane-type sesquiterpenoid dimers against three hepatoma cell lines from Artemisia dubia
Fig. 10. Compound 4 induced HepG2 cells apoptosis. HepG2 cells were treated with different concentrations (0.0, 3.5, 7.0, and 10.5 μM) of 4 for 48 h. (A) and (B) Flow cytometric analysis and cell apoptosis quantification of HepG2 cells. (C) and (D) The apoptosis-related protein levels in hepatoma cells treated with compound 4 for 48 h. Western blot and statistical results of Bax, BCL-2, PARP-1, and cleaved-PARP-1. The results were normalized to the β-actin loading control. *P <0.05, **P <0.01, and ***P <0.001, n = 3.
Fig. 6 in Germacranolide- and guaianolide-type sesquiterpenoids from Achillea alpina L. reduce insulin resistance in palmitic acid-treated HepG2 cells via inhibition of the NLRP3 inflammasome pathway
Fig. 6. The inhibitory effects of 4 on the production of IL-1β and ROS in HepG2-IR cells. The expression levels of IL-1β were measured by ELISA analysis (A), Fluorescence intensity of ROS was recorded by a multimode microplate reader (B). ##P <0.01 vs CON group, *P <0.05, **P <0.01 vs PA treatment group.
Fig. 5 in Germacranolide- and guaianolide-type sesquiterpenoids from Achillea alpina L. reduce insulin resistance in palmitic acid-treated HepG2 cells via inhibition of the NLRP3 inflammasome pathway
Fig. 5. The glucose consumption assay of compounds 4–6, 10 and 11 in different concentrations. The concentrations of these compounds were from 1.56 to 50 μM, ROSI was the positive control. *p <0.05, **p <0.01, compared to the PA treatment group. ##p <0.01, compared to the control (CON) group.
Fig. 7 in Germacranolide- and guaianolide-type sesquiterpenoids from Achillea alpina L. reduce insulin resistance in palmitic acid-treated HepG2 cells via inhibition of the NLRP3 inflammasome pathway
Fig. 7. Inhibitory effects of 4 on the expression of proteins related to NLRP3 inflammasome in PA-induced HepG2-IR cells. Representative protein bands of Western blot (A and B). Densitometric analysis of the Western blot analysis results (C). Glibenclamide (1 μM, Gliben) was a positive control. ##P <0.01 vs CON group, *P <0.05, **P <0.01 vs PA treatment group.
Raw data for: Artificial cells eavesdropping on HepG2 cells
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Data from: Identification of the additional mitochondrial liabilities of 2-hydroxyflutamide when compared to its parent compound, flutamide in HepG2 cells
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Antioxidant and anti-inflammatory function of walnut green husk aqueous extract (WNGH-AE) on human hepatocellular carcinoma cells (HepG2) treated with t-BHP
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HepG2 and K562 cCRE syntenic ages
<p>### DESCRIPTION ###</p> <p>HepG2 and K562 ENCODE 3 cCRE pELS, dELS, pELS + CTCF, dELS +CTCF.</p> <p>### METHOD ###</p> <p>To assign sequence ages enhancer cCRE .bed coordinates were intersected with hg38 100-way multiZ alignment syntenic blocks from UCSC genome browser.</p> <p> </p> <p>### Column names ###</p> <p>[#chr start_syn end_syn enh_id #chr_enh start_enh end_enh segment_number simple_0_complex_1 core_0_derived_1 mrca_age]</p> <p>### explanation of column names ###</p> <p>[#chr start_syn end_syn]: syntenic block .bed coordinates in hg38</p> <p>[ enh_id #chr_enh start_enh end_enh]: cCRE .bed coordinates in hg38</p> <p>[segment_number]: We count the number of syntenic block segments overlapping a single enhancer. </p> <p>[simple_0_complex_1]: Some syntenic blocks overlap multiple syntenic blocks, which we term "complex". Others overlap a single syntenic block, which we term "simple". Complex elements are annotated with 1 and simple elements are annotated with 0. </p> <p>[core_0_derived_1]: In complex enhancer sequences (i.e. enhancer sequences with multiple sequence ages). We refer to the oldest sequence in an enhancer is the "core" (annotated as 1) and the younger sequence age(s) as "derived" (annotated as 0). </p> <p>[mrca_age]: For each syntenic segment, we annotate the most recent common ancestor (MRCA) age. This corresponds to the estimated branch length between humans and the related MRCA. </p> <p>Taxon and summarized taxon ("Taxon2") for each MRCA age is annotated in the hg38_syn_taxon.bed file. </p>
Fig. 3 in Germacranolide- and guaianolide-type sesquiterpenoids from Achillea alpina L. reduce insulin resistance in palmitic acid-treated HepG2 cells via inhibition of the NLRP3 inflammasome pathway
Fig. 3. Experimental and calculated ECD spectra of 1–8.
Fig. 2. 1H–1H in Germacranolide- and guaianolide-type sesquiterpenoids from Achillea alpina L. reduce insulin resistance in palmitic acid-treated HepG2 cells via inhibition of the NLRP3 inflammasome pathway
Fig. 2. 1H–1H COSY, key HMBC and ROESY correlations of compounds 1 and 6–8.
Fig. 1 in Germacranolide- and guaianolide-type sesquiterpenoids from Achillea alpina L. reduce insulin resistance in palmitic acid-treated HepG2 cells via inhibition of the NLRP3 inflammasome pathway
Fig. 1. Structures of compounds 1–20 isolated from A. alpina.
Control ChIP-seq from HepG2 (ENCSR254UDV)
GEO Series GSE136482. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
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OpenNeuro
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