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Fig. 7 in Cytotoxicity of methanolic extract of Swertia petiolata against gastric cancer cell line SNU-5 is via induction of apoptosis ⁎
Fig. 7. Structures of the compounds tentatively identified from the methanolic extract of S. petiolata, (i) Chlorogenic acid (ii) p-Coumaric acid (iii) Ursolic acid (iv) Myrecetin 3-O- rahamnoside (v) Quercetin 3-arabinoside (vi) Kaempherol 3-O-glucoside (vii) Naringenin (viii) Genistein (ix) Isorhamnetin (x) Swerchirin.
Fig. 7 in Ethanolic extract of Mimosa caesalpiniifolia leaves: Chemical characterization and cytotoxic effect on human breast cancer MCF-7 cell line
Fig. 7. Agarose gel electrophoresis of MCF-7 cell genomic DNA. 1, ladder marker; 2, negative control; 3, cyclophosphamide (CP)-treated; 4–7, ethanolic extracts of Mimosa caesalpiniifolia leaves (EEM) in different concentrations (5.0; 20.0; 160.0 and 320.0 μg/mL). The results are representative of three independent experiments carried out in the same conditions. DNA ladder formation indicates apoptosis as seen in lanes 3–7.
Fig. 6. MCF-7 in Ethanolic extract of Mimosa caesalpiniifolia leaves: Chemical characterization and cytotoxic effect on human breast cancer MCF-7 cell line
Fig. 6. MCF-7 cell death (%) after treatment with cyclophosphamide (CP, 550 μg/mL) and different concentrations (5.0–320.0 μg/mL) of the ethanolic extract of Mimosa caesalpiniifolia leaves (EEM) for 24 h, compared to the negative control cells (NC), estimated by the Fast green color dye exclusion. Results are expressed as mean ± SEM of three independent experiments. Different letters indicate significant differences (p <0.01) by the Tukey test. Inset: appearance of cells after fast green-hematoxylin–eosin staining; 1, living cell; 2, green dead cell. EEM treatment kills MCF-7 cells in a concentrationdependent manner.
Fig. 3 in Ethanolic extract of Mimosa caesalpiniifolia leaves: Chemical characterization and cytotoxic effect on human breast cancer MCF-7 cell line
Fig. 3. Morphology of MCF-7 cells stained with hematoxylin–eosin, after 24 and 48 h incubation. NC, negative control cells; CP, cells treated with 550 μg/mL cyclophosphamide; 5, 80 and 320, cells treated, respectively, with 5.0, 80.0 and 320.0 μg/mL ethanolic extract of Mimosa caenalpiniifolia leaves. All the pictures are typical of three independent experiments, each carried out under identical conditions. Bar = 5 μm. Arrow—nucleolus; R—rounding; CC—chromatin condensation.
Fig. 2. MCF-7 in Ethanolic extract of Mimosa caesalpiniifolia leaves: Chemical characterization and cytotoxic effect on human breast cancer MCF-7 cell line
Fig. 2. MCF-7 cell protein content decreasing (%), estimated by the sulforhodamine B assay, after treatment with cyclophosphamide (CP, 550 μg/mL) and different concentrations of the ethanolic extract of Mimosa caesalpiniifolia leaves (EEM 5.0 - 320.0 μg/mL) for 24 and 48 h. The results are expressed as mean ± SEM of three independent experiments. Different letters indicate significant differences (p <0.001) by the Tukey test. Protein content was calculated relative to the negative control and 320.0 μg/mL EEM produced the maximum effect.
Fig. 1 in Ethanolic extract of Mimosa caesalpiniifolia leaves: Chemical characterization and cytotoxic effect on human breast cancer MCF-7 cell line
Fig. 1. HPLC-DAD-ESI-MS analysis of the ethanolic extract of Mimosa caesalpiniifolia leaves. (A) UV 360 nm; (B) ESI-MS, base peak chromatogram, negative ion mode, m/z 100–1500. No additional peaks were detected when the UV trace was recorded at wavelengths down to 360 nm. Peaks assigned as m/z: 288.97, 318.00, and 576.77 were identified, respectively, as catechin, 2,3 dihydroquercetagetin, and procyanidin B2 [(epi)catechin–(epi)catechin)] (see structures).
Fig. 4. MCF-7 in Ethanolic extract of Mimosa caesalpiniifolia leaves: Chemical characterization and cytotoxic effect on human breast cancer MCF-7 cell line
Fig. 4. MCF-7 cell diameter (μm) after treatment with cyclophosphamide (CP, 550 μg/mL) and different concentrations (5.0–320.0 μg/mL) of the ethanolic extract of Mimosa caesalpiniifolia leaves (EEM) for 24 or 48 h, compared to the negative control cells (NC). Results are expressed as mean ± SEM of three independent experiments. Different letters indicate significant differences (p <0.01) by the Tukey test. Note cell-diameter reduction after treatment, in comparison to the negative control cells, thereby indicating EEM cytotoxicity.
Immunotherapy-mediated thyroid dysfunction: genetic risk and impact on outcomes with PD-1 blockade in non-small cell lung cancer
<p>Polygenic risk score weights derived using LDpred for hypothyroidism and thyroid medication use.</p>
Synergistic antitumor interaction of valproic acid and simvastatin and docetaxel in Prostate cancer cells
<p>Synergistic antitumor interaction of valproic acid, simvastatin and docetaxel in prostate cancer cells on cell migration, cell cycle perturbation, apoptosis, 3D cell culture models and stem marker reduction in<em> in vivo</em> model.</p>
Hydroxymethylation profile of cell free DNA is a biomarker for early colorectal cancer
<p>The files in this data release represent processed data from the FORESEE study conducted by Cambridge Epigenetix Ltd, and reported in the preprint manuscript: "Hydroxymethylation profile of cell free DNA is a biomarker for early colorectal cancer" (<a href="https://www.researchsquare.com/article/rs-667874/v1">Walker et al. 2021</a>). </p> <p> </p> <p>As described in the manuscript, classifiers were trained and validated on genomic features extracted from sequencing datasets across cases and controls. Several classes of genomic features were constructed for training and validation data sets which are described below:</p> <p> </p> <p><strong>CRC_enhancer_znorm_training_matrix_v1.csv<br> CRC_enhancer_znorm_validation_matrix_v1.csv</strong></p> <p>Columns contain sample names, rows contain genomic features. </p> <p><em>Description of feature generation process. </em></p> <p>To calculate 5hmC levels at gene enhancers, we first calculated read counts using Bam readcounts v0.01. RPKM were calculated over candidate gene-enhancers downloaded from GeneCards v4.4. 5hmC enrichment was computed as the log2 ratio between the hydroxymethylome library RPKM and the input library RPKM after the inclusion of pseudocounts. Feature scaled (z-score normalization) 5hmC levels of enhancers quantile-normalized over samples.</p> <p><br> <strong>CRC_cegxdelfi_znorm_training_matrix_v1.tsv<br> CRC_cegxdelfi_znorm_validation_matrix_v1.tsv</strong></p> <p>Columns contain sample names, rows contain genomic features. <br> </p> <p><em>Description of feature generation process. </em><br> We divided the genome into 100KB bins and quantified cfDNA fragment sizes per bin. We removed blacklisted regions, genomic gaps (UCSC table) and non-standard chromosomes a priori. We excluded outlier bins in fragment size, only retaining fragments between 100nt to 220nt length. Finally, we split the genome into 100KB bins (in total 26170 non-overlapping genomic regions) and calculated the following characteristics of fragment size distribution per genomic bin: number of short fragments (100-150nt), number of long fragments (151-220nt), ratio between short and long fragments and the total number of fragments. This approach generates 26170 features per metric and per sample. The last step is the averaging of the 100 KB bins into larger non-overlapping genomic regions of 5 MB (in total 512 bins).</p> <p><br> <strong>CRC_cegxnps_znorm_training_matrix_v1.tsv</strong></p> <p><strong>CRC_cegxnps_validation_matrix_v1.tsv</strong></p> <p> Columns contain sample names, rows contain genomic features. <br> <em>Description of feature generation process. </em><br> Further detail in the manuscript: <a href="http://www.researchsquare.com/article/rs-667874/v1">Walker et al. 2021</a></p> <p><br> <strong>FORESEE_sample_description.tsv</strong></p> <p>This file holds sample data for colorectal cancer and control samples described in <a href="http://www.researchsquare.com/article/rs-667874/v1">Walker et al. 2021</a></p> <ul> <li>The sample_name column links to the column names in the *_matrix.tsv files</li> <li>The columns denoted raw_file1 and raw_file2 link the sample metadata with the enhancer readcount files contained in the gh_readcount_training.tar and gh_readcount_validation.tar.</li> </ul> <p>The columns in the table are briefly described below:</p> <p><em>sample_name</em>:<em> </em>Sample identifier<br> <em>Title</em>: Composed of the the disease name, gender and sample_name<br> <em>Source_name</em>: Tissue source<br> <em>Organism</em>: Contains the term: “Homo sapiens”<br> <em>Characteristics_indication</em>: Disease indication <br> <em>Characteristics_stage</em>: Cancer stage where appropriate. Indicated by roman numerals (I,II,III,IV)<br> <em>Characteristics_gender</em>: Described as “Female” or “Male”<br> <em>Characteristics_ethnicity</em>: Ethnicity description<br> <em>Characteristics_age_at_collection</em>: Age value in years<br> <em>Molecule</em>: Contains the value “cell free DNA”<br> <em>Description</em>: Contains value: “Training sample” or “Validation sample”</p> <p><em>Processed_data_file</em>: Contains the term: “CRC_enhancer_training_matrix” or “CRC_enhancer_validation_matrix”. <br> <em>raw_file1</em>: Refers to the readcount file from the 5hmC capture library<br> <em>raw_file2</em>: Refers to the readcount file from the Input control (shallow sequenced) library</p> <p> </p> <p><strong>gh_readcount_training.tar<br> gh_readcount_validation.tar</strong></p> <p>These tar files include the raw read counts computed across enhancer regions for case and control data and are referenced in the FORESEE_sample_description.tsv file.</p> <p> </p> <p><strong>Manuscript Abstract</strong></p> <p>Our classifier discriminated CRC samples from controls with an area under the receiver operating characteristic curve (AUC) of 90% (sensitivity was 55% at 95% specificity). Performance was similar for early stage 1 (AUC 89%) and late stage 4 CRC (AUC 94%). Performance was independent of the proportion of tumor-DNA in the cell free DNA. </p> <p>We expanded the classifier to include information about cell free DNA fragment size and abundance across the genome. Overall performance was similar (AUC 91%), with gains in sensitivity (63% at 95% specificity). </p> <p>The 5-hydroxymethylcytosine signal allows detection of CRC, even in cell free DNA samples with undetectable tumor DNA. Including 5-hydroxymethylcytosine in multi-analyte screening, will improve sensitivity for early-stage cancer. </p>
Single-nucleus Transcriptomics of IDH1- and TP53-mutant Glioma Stem Cells Displays Diversified Commitment on Highly Invasive Cancer Progenitors
<p><strong>Fig. S1</strong>. <strong>Marker genes for Seurat clusters.</strong> (<strong>A</strong>) distribution of marker genes for cluster 0 on the 2D-UMAP space. (<strong>B</strong>) distribution of marker genes for cluster 1 on the 2D-UMAP space. (<strong>C</strong>) distribution of marker genes for cluster 2 on the 2D-UMAP space. (<strong>D</strong>) distribution of marker genes for cluster 3 on the 2D-UMAP space. (<strong>E</strong>) distribution of marker genes for cluster 4 on the 2D-UMAP space. (<strong>F</strong>) distribution of marker genes for cluster 5 on the 2D-UMAP space. (<strong>G</strong>) Stuck violin plot of marker gene expression for Seurat clusters (bottom panel) and their annotation (right side panel). The violin shape displays the number of the cells expressing a gene, the continuous color panel defines median expression value of a gene from the absence of expression (white) to high expression (dark blue).</p> <p><strong>Fig. S2</strong>. <strong>Expression of genes marking cell malignization.</strong> (<strong>A</strong>) expression of collagens in Surat clusters (bottom panel) (<strong>B</strong>) expression of genes linked to Migration and ECM in Surat clusters (bottom panel) (<strong>C</strong>) expression of genes classified as Proto-oncogenes in Surat clusters (bottom panel). The violin shape displays the number of the cells expressing a gene, the violin color defines the Seurat cluster. Gene expression displayed in log-transformed normalized expression values.</p> <p><strong>Fig. S3</strong>. <strong>Expression of genes involved in proliferation and survival of cancer cells.</strong> (<strong>A</strong>) Genes involved in Wnt-pathway in Surat clusters (bottom panel). (<strong>B</strong>) Genes involved in Akt-pathway in Surat clusters (bottom panel). (<strong>C</strong>) Genes inducing resistance to cancer therapeutics in Surat clusters (bottom panel). The violin shape displays the number of the cells expressing a gene, the violin color defines the Seurat cluster. Gene expression displayed in log-transformed normalized expression values.\</p> <p><strong>Fig. S4</strong>. <strong>Expression of genes marking CSC profile.</strong> (<strong>A</strong>) Ion channel genes in Surat clusters (bottom panel). (<strong>B</strong>) Antioncogenes in Surat clusters (bottom panel). <strong>C</strong>. Stem-cell genes in Surat clusters (bottom panel). (<strong>D</strong>) Antiapoptotic genes in Surat clusters (bottom panel). The violin shape displays the number of the cells expressing a gene, the violin color defines the Seurat cluster. Gene expression displayed in log-transformed normalized expression values.</p> <p><strong>Fig. S5</strong>. <strong>Genes differentially expressed between UMAP clusters</strong>. (<strong>A</strong>) Heatmap for wt-GSCs. (<strong>B</strong>) Heatmap for mt-GSCs. Upper colour panel in the heatmap designates Seurat clusters. Gene expression is indicated by continuous colour panel starting from the most downregulated (blue) to the most upregulated (red).</p> <p><strong>Fig. S6</strong>. <strong>Marker genes defying cell annotations</strong>. (<strong>A</strong>) Stack violin plot displays marker gene expression in wt-GSC clusters. (<strong>B</strong>) Stack violin plot displays marker gene expression in mt-GSC clusters. Genes grouped by cell annotations (side description) and UMAP clusters (down column bar). The violin shape displays the number of the cells expressing a gene, the continuous color panel defines median expression value of a gene from the absence of expression (white) to high expression (dark blue).</p> <p><strong>Fig. S7</strong>. <strong>Differentially expressed proliferation and adhesion pathways comparing mutant samples to wild type.</strong> (<strong>A</strong>) ERBB signalling pathway. (<strong>B</strong>) Wnt signalling pathway. (<strong>C</strong>) Genes linked to Focal adhesion. (<strong>D</strong>) Genes classified as Cell adhesion molecules. Red rectangles display upregulated genes (proteins), green rectangles define downregulated genes (proteins). Pictures obtained by KEGG pathview.</p> <p><strong>Table S1. Glioma genotyping primers</strong></p> <p><strong>Table S2. Smart-seq2 Primers</strong></p>
Deep learning to estimate durable clinical benefit and prognosis from patients with non-small cell lung cancer treated with PD-1/PD-L1 blockade
<p>Different biomarkers based on genomics variants have been used to predict the response of patients treated with PD-1/programmed death receptor 1 ligand (PD-L1) blockade. We aimed to use deep-learning algorithm to estimate clinical benefit in patients with non-small-cell lung cancer (NSCLC) before immunotherapy. Peripheral blood samples or tumor tissues of 915 patients from three independent centers were profiled by whole-exome sequencing or next-generation sequencing. Based on convolutional neural network (CNN) and three conventional machine learning (cML) methods, we used multi-panels to train the models for predicting the durable clinical benefit (DCB) and combined them to develop a nomogram model for predicting prognosis. In the three cohorts, the CNN achieved the highest area under the curve of predicting DCB among cML, PD-L1 expression, and tumor mutational burden (area under the curve [AUC] = 0.965, 95% confidence interval [CI]: 0.949–0.978, <em>P</em> < 0.001; AUC =0.965, 95% CI: 0.940–0.989, <em>P</em> < 0.001; AUC = 0.959, 95% CI: 0.942–0.976, <em>P</em> < 0.001, respectively). Patients with CNN-high had longer progression-free survival (PFS) and overall survival (OS) than patients with CNN-low in the three cohorts. Subgroup analysis confirmed the efficient predictive ability of CNN. Combining three cML methods (CNN, SVM, and RF) yielded a robust comprehensive nomogram for predicting PFS and OS in the three cohorts (each <em>P</em> < 0.001). The proposed deep-learning method based on mutational genes revealed the potential value of clinical benefit prediction in patients with NSCLC and provides novel insights for combined machine learning in PD-1/PD-L1 blockade.</p>
Bioenergetics in human tongue pre-cancerous dysplastic oral keratinocytes and squamous cancer cells
<p>In an endeavor to understand the metabolic phenotype behind oral squamous cell carcinomas, we characterized the bioenergetic profile of a human tongue derived cancer cell line (SCC-4 cells) and compared this profile to a pre-cancerous dysplastic oral keratinocyte (DOK) cell line also derived from human tongue. The human SCC-4 cancer cells had greater mitochondrial abundance but lower mitochondrial oxygen consumption rates than DOK cells. The lower oxygen consumption rate in SCC-4 cells can be partially explained by lower NADH-related enzymatic activity and lower mitochondrial complex 1 activity when compared to pre-cancerous DOK cells. In addition, SCC-4 cells have greater extracellular acidification rate (an index of glycolytic flux) when compared to DOK cells. In addition, treatment with recombinant human IL-6 (rhIL-6), known to drive<em> anoikis</em> resistance in SCC-4 cells but not DOK cells, impairs oxygen consumption in SCC-4 but not DOK cells, without affecting mitochondrial abundance. We conclude that SCC-4 cells have a less oxidative phenotype compared to DOK cells and that IL-6 attenuates mitochondrial function in SCC-4 cells while increasing glycolytic flux.</p>
Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-αSMA (smooth muscle cells / cancer associated firbroblasts)
<p><strong>LICENSE</strong></p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>)</p> <p>For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp).</p> <p>A Tar.gz file contains the following files:</p> <p>- HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png</p> <p>- Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png</p> <p>Each image file is 984x984 px.</p> <p>posX and posY are the leftmost position in WSI coordinate.</p> <p>Mask files store binary segmentation mask (background : 0, target : 1)</p> <p> </p> <p>A csv file contains the following information:</p> <p>antigen : Antibodies for this antigen were used to create the segmentation mask.</p> <p>filename: filename of image or mask file.</p> <p>train_val_test : train, validation, or test sample in the paper.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset for your research, please cite our paper.</p> <p>Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.</p>
Multiplex imaging of breast cancer lymph node metastases identifies prognostic single-cell populations independent of clinical classifiers
<p>This repository contains the continuation of dataset 10.5281/zenodo.7494413 and 10.5281/zenodo.7494509.</p> <p>The file tiff_stacks_masks.zip contains the IMC image stacks and single-cell masks as tiff files.</p> <p>The IHC_TMAs.zip contains the scans of the IHC stains of ZTMA25 and the QuPATH projects used to extract the single-cell data (incl. the single-cell measurements as csv files).</p>
Phototoxicity and Cell Passage Affect Intracellular Reactive Oxygen Species Levels and Sensitivity Towards Non-Thermal Plasma Treatment in Fluorescently-Labeled Cancer Cells
<p>Raw data for the manuscript "Phototoxicity and Cell Passage Affect Intracellular Reactive Oxygen Species Levels and Sensitivity Towards Non-Thermal Plasma Treatment in Fluorescently-Labeled Cancer Cells".</p>
Mechanical manipulation of cancer cell tumorigenicity via heat shock protein signaling
<p><span>Biophysical cues of rigid tumor matrix play a critical role in cancer cell malignancy. Herein, we report that stiffly confined cancer cells exhibit robust growth of spheroids in the stiff hydrogel that exerts substantial confining stress on the cells. The stressed condition activated Hsp (heat shock protein)-STAT3 signaling via the TRPV4-PI3K/Akt axis, thereby upregulating the expression of the stemness-related markers in cancer cells, whereas these signaling activities were suppressed in cancer cells cultured in softer hydrogels or stiff hydrogels with stress relief or Hsp70 knockdown/inhibition. This mechanopriming based on 3D culture enhanced cancer cell tumorigenicity and metastasis in animal models upon transplantation, and pharmaceutically inhibiting Hsp70 improved the anticancer efficacy of chemotherapy. Mechanistically, our study reveals the crucial role of Hsp70 in regulating cancer cell malignancy under mechanically stressed conditions and its impacts on cancer prognosis-related molecular pathways for cancer treatments. </span></p>
Profiling single cancer cell metabolism via high-content SRS imaging with chemical sparsity
<p>Raw data for Profiling single cancer cell metabolism via high-content SRS imaging with chemical sparsity </p>
Fig. 5 in Hyperelodiones A-C, monoterpenoid polyprenylated acylphoroglucinols from Hypericum elodeoides, induce cancer cells apoptosis by targeting RXRα
Fig. 5. The interaction between RXRα-LBD and compounds. After treated with compounds 1–3, the binding affinity of compound toward RXRα-LBD was measured via fluorescence quenching technology at 298 K.
Fig. 7 in Hyperelodiones A-C, monoterpenoid polyprenylated acylphoroglucinols from Hypericum elodeoides, induce cancer cells apoptosis by targeting RXRα
Fig. 7. Cytotoxic effects of compounds 1–3. HeLa and MCF-7 cells were exposed to various concentrations of compounds 1–3 (0, 3.125, 6.25, 12.5, 25, 50 μΜ). After 48 h, cytotoxic activities of compounds against cancer cells were measured by MTT assay. The values are the mean ± SD for four independent replicates.
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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.