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303 results for “HUVEC”
Long read proteogenomics to characterize protein isoform diversity in human umbilical vein endothelial cells (HUVECs)
<p>Endothelial cells (ECs) comprise the lumenal lining of all blood vessels and are critical for the functioning of the cardiovascular system and their phenotypes can be modulated by protein isoforms. To characterize the isoform landscape within EC, we applied a long read proteogenomics approach to analyze human umbilical vein endothelial cells (HUVECs). Transcripts delineated from PacBio sequencing serve as the basis for a sample-specific protein database used for downstream MS analysis to infer protein isoform expression. We detected 53,836 transcript isoforms from 10,426 genes, with 22,195 of those transcripts being novel. Furthermore, the predominant isoform in HUVECs does not correspond with the accepted “reference isoform” 25% of the time, with vascular pathway-related genes among this group. We found 2,597 protein isoforms supported through unique peptides, with an additional 2,280 isoforms nominated upon incorporation of long-read transcript evidence. We characterized a novel alternative acceptor for endothelial-related gene <em>CDH5</em>, suggesting potential changes in its associated signaling pathways. Finally, we identified novel protein isoforms arising from a diversity of splicing mechanisms supported by uniquely mapped novel peptides. Our results represent a high resolution atlas of known and novel isoforms of potential relevance to endothelial phenotypes and function.</p>
BrdU assay HUVEC
<p><span><span>Bromodeoxyuridine (BrdU) incorporation assay</span></span> <span><span>was used as a measure of DNA synthesis rate reflecting the rate of proliferation after application of different photobiomodulation conditions. </span></span></p>
StarDist_HUVEC_nuclei_dataset
<p>This repository contains a StarDist deep learning model and its training and validation datasets for segmenting endothelial nuclei while ignoring cancer cells. The cancer cells were perfused over an endothelial cell monolayer. The initial dataset consisted of 17 images, where cancer cell nuclei were manually removed after segmentation with the StarDist Versatile Nuclei model. This dataset was augmented to 68 paired images using computational techniques like rotation and flipping. The model was trained for 200 epochs, achieving an average F1 Score of 0.976, demonstrating high accuracy in segmenting endothelial nuclei while excluding cancer cells.</p> <h3>Specifications</h3> <ul> <li> <p>Model: StarDist for segmenting endothelial nuclei while ignoring cancer cells</p> </li> <li> <p>Training Dataset:</p> </li> <ul> <li> <p>Number of Original Images: 17 paired predictions of nuclei and label images</p> </li> <li> <p>Augmented Dataset: Expanded to 68 paired images using rotation and flipping</p> </li> <li> <p>Source Image Generation: Generated using a pix2pix model trained to predict nuclei from brightfield images of cancer cells on top of an endothelium (DOI: 10.5281/zenodo.10617532)</p> </li> <li> <p>Target Image Generation: Masks obtained via manual segmentation</p> </li> <li> <p>File Format: TIFF (.tif)</p> </li> <ul> <li> <p>Brightfield Images: 8-bit</p> </li> <li> <p>Masks: 8-bit</p> </li> </ul> <li> <p>Image Size: 1024 x 1022 pixels (uncalibrated)</p> </li> </ul> <li> <p>Training Parameters:</p> </li> <ul> <li> <p>Epochs: 200</p> </li> <li> <p>Patch Size: 1024 x 1024 pixels</p> </li> <li> <p>Batch Size: 2</p> </li> </ul> <li> <p>Performance:</p> </li> <ul> <li> <p>Average F1 Score: 0.976</p> </li> <li> <p>Average IoU: 0.927</p> </li> </ul> <li> <p>Model Training: Conducted using ZeroCostDL4Mic (<a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki</a>)</p> </li> </ul> <div> <h3><strong>Reference</strong></h3> <div><strong>Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers</strong></div> </div> <div>Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet</div> <div>bioRxiv 2024.09.30.615654; doi: <a href="https://www.biorxiv.org/content/10.1101/2024.09.30.615654v1">https://doi.org/10.1101/2024.09.30.615654</a></div>
pix2pix_HUVEC_nuclei_cancer_cells_dataset
<p>This repository contains a Pix2Pix deep learning model to generate synthetic nuclear staining from brightfield images. The model was trained on 258 paired brightfield and fluorescent microscopy images of circulating cancer cells perfused over an endothelial cell monolayer. To improve performance, the dataset was augmented computationally by a factor of 8. The model was trained over 400 epochs using a patch size of 512x512, a batch size of 1, and a vanilla GAN loss function. The final model was selected based on its performance metrics and visual fidelity when compared to ground truth images, achieving an average SSIM score of 0.755 and an LPIPS score of 0.120.</p> <h3>Specifications</h3> <ul> <li> <p>Model: Pix2Pix for generating synthetic nuclear staining from brightfield images</p> </li> <li> <p>Training Dataset:</p> </li> <ul> <li> <p>Cancer Cells: 258 paired brightfield and fluorescent microscopy images</p> </li> <li> <p>Microscope: Nikon Eclipse Ti2-E, brightfield/fluorescence microscope with a 20x objective</p> </li> <li> <p>Data Type: Brightfield and fluorescent microscopy images</p> </li> <li> <p>File Format: TIFF (.tif), 16-bit</p> </li> <li> <p>Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)</p> </li> </ul> <li> <p>Training Parameters:</p> </li> <ul> <li> <p>Epochs: 400</p> </li> <li> <p>Patch Size: 512 x 512 pixels</p> </li> <li> <p>Batch Size: 1</p> </li> <li> <p>Loss Function: Vanilla GAN loss function</p> </li> </ul> <li> <p>Model Performance:</p> </li> <ul> <li> <p>Circulating Cancer Cells:</p> </li> <ul> <li> <p>SSIM Score: 0.755</p> </li> <li> <p>LPIPS Score: 0.120</p> </li> </ul> </ul> <li> <p>Model Selection: Models were selected based on quality metric scores and visual inspection compared to ground truth images.</p> </li> <li> <p>Model Training: Conducted using ZeroCostDL4Mic (<a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/pix2pix">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki</a>)</p> </li> </ul> <div> <h3>Reference</h3> <div><strong>Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers</strong></div> </div> <div>Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet</div> <div>bioRxiv 2024.09.30.615654; doi: <a href="https://www.biorxiv.org/content/10.1101/2024.09.30.615654v1">https://doi.org/10.1101/2024.09.30.615654</a></div> <p> </p>
pix2pix_HUVEC_nuclei_immuno_cells_dataset
<p>This repository contains a Pix2Pix deep learning model designed to generate synthetic nuclear staining from brightfield images of circulating immune cells. The model was trained on a dataset of 226 paired brightfield and fluorescent microscopy images, which were augmented computationally by a factor of 8 to enhance model performance. The model was trained over 400 epochs using a patch size of 512x512, a batch size of 1, and a vanilla GAN loss function. The final model was selected based on quality metric scores and visual comparison to ground truth images, achieving an average SSIM score of 0.756 and an LPIPS score of 0.130.</p> <h3>Specifications</h3> <ul> <li> <p>Model: Pix2Pix for generating synthetic nuclear staining from brightfield images of circulating immune cells</p> </li> <li> <p>Training Dataset:</p> </li> <ul> <li> <p>Immune Cells: 226 paired brightfield and fluorescent microscopy images</p> </li> <li> <p>Microscope: Nikon Eclipse Ti2-E, brightfield/fluorescence microscope with a 20x objective</p> </li> <li> <p>Data Type: Brightfield and fluorescent microscopy images</p> </li> <li> <p>File Format: TIFF (.tif), 16-bit</p> </li> <li> <p>Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)</p> </li> </ul> <li> <p>Training Parameters:</p> </li> <ul> <li> <p>Epochs: 400</p> </li> <li> <p>Patch Size: 512 x 512 pixels</p> </li> <li> <p>Batch Size: 1</p> </li> <li> <p>Loss Function: Vanilla GAN loss function</p> </li> </ul> <li> <p>Model Performance:</p> </li> <ul> <li> <p>Immune Cells:</p> </li> <ul> <li> <p>SSIM Score: 0.756</p> </li> <li> <p>LPIPS Score: 0.130</p> </li> </ul> </ul> <li> <p>Model Selection: Models were chosen based on quality metric scores and visual inspection compared to ground truth images.</p> </li> <li> <p>Model Training: Conducted using ZeroCostDL4Mic (<a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/pix2pix">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki</a>)</p> </li> </ul> <div> <h3>Reference</h3> <div><strong>Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers</strong></div> </div> <div>Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet</div> <div>bioRxiv 2024.09.30.615654; doi: <a href="https://www.biorxiv.org/content/10.1101/2024.09.30.615654v1">https://doi.org/10.1101/2024.09.30.615654</a></div>
pix2pix_HUVEC_juctions_dataset
<p>This repository contains a Pix2Pix deep learning model to generate synthetic PECAM-1 staining from brightfield images. The model was trained on an initial dataset of 484 paired brightfield and fluorescent microscopy images, which was computationally augmented by a factor of 6 to enhance model performance. The training was conducted over 245 epochs with a patch size of 512x512, a batch size of 1, and a vanilla GAN loss function. The final model was selected based on quality metric scores and visual comparison to ground truth images, achieving an average SSIM score of 0.273 and an LPIPS score of 0.360 on the test dataset.</p> <h3>Specifications</h3> <ul> <li> <p>Model: Pix2Pix for generating synthetic PECAM staining from brightfield images</p> </li> <li> <p>Training Dataset:</p> </li> <ul> <li> <p>Original Dataset: 484 paired brightfield and fluorescent microscopy images</p> </li> <li> <p>Augmented Dataset: Expanded to 2,904 paired images through computational augmentation</p> </li> <li> <p>Microscope: Nikon Eclipse Ti2-E, brightfield/fluorescence microscope with a 20x objective</p> </li> <li> <p>Data Type: Brightfield and fluorescent microscopy images</p> </li> <li> <p>File Format: TIFF (.tif), 16-bit</p> </li> <li> <p>Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)</p> </li> </ul> <li> <p>Training Parameters:</p> </li> <ul> <li> <p>Epochs: 245</p> </li> <li> <p>Patch Size: 512 x 512 pixels</p> </li> <li> <p>Batch Size: 1</p> </li> <li> <p>Loss Function: Vanilla GAN loss function</p> </li> </ul> <li> <p>Model Performance:</p> </li> <ul> <li> <p>SSIM Score: 0.273</p> </li> <li> <p>LPIPS Score: 0.360</p> </li> </ul> <li> <p>Model Selection: The best model was chosen based on quality metric scores and visual inspection compared to ground truth images.</p> </li> <li> <p>Model Training: Conducted using ZeroCostDL4Mic (<a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/pix2pix">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki</a>)</p> </li> </ul> <div> <h3>Reference</h3> <div><strong>Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers</strong></div> </div> <div>Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet</div> <div>bioRxiv 2024.09.30.615654; doi: <a href="https://www.biorxiv.org/content/10.1101/2024.09.30.615654v1">https://doi.org/10.1101/2024.09.30.615654</a></div>
HUVEC CD44 siRNA perfusion tracking dataset
<p>This dataset contains tracking results of AsPC1 and MiaPaca cells perfused on CD44 siRNA-silenced endothelial monolayers under physiological flow speeds.</p> <p>Videos were recorded using a Nikon Eclipse Ti2-E microscope and 20x objective.</p> <p>Perfused cells from the generated videos were segmented using custom-trained Stardist models. Tracking was performed using TrackMate, and tracking results were analyzed using a custom CellTracksColab notebook. </p> <p>The dataset here contains the CSV files generated by TrackMate (Track and Spots information), the tracking data stored in the CellTracksColab format (Analysis.zip), and the analysis output used in the paper (Analysis.zip). </p> <h3>Specifications</h3> <ul> <li> <p>Sample information</p> </li> <ul> <li> <p>AsPC1 and MiaPaca cells perfused on HUVEC cells under physiological flow speeds: 400 µm/s (p1), 200 µm/s (p2), 100 µm/s (p3) and 400 µm/s (p4). </p> </li> <li> <p>CD44 siRNA silencing of the HUVEC monolayer</p> </li> </ul> <li> <p>Imaging specs</p> </li> <ul> <li> <p>Microscope: Nikon Eclipse Ti2-E, 20x objective</p> </li> <li> <p>Data Type: Brightfield microscopy images (16-bit)</p> </li> <li> <p>Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)</p> </li> <li> <p>Recording speed 25 frames/s</p> </li> </ul> <li> <p>DL models:</p> </li> <ul> <li> <p>Cancer cells: <a href="https://doi.org/10.5281/zenodo.10572122">https://doi.org/10.5281/zenodo.10572122</a> </p> </li> <li> <p>Neutrophils: <a href="https://doi.org/10.5281/zenodo.10572231">https://doi.org/10.5281/zenodo.10572231</a></p> </li> <li> <p>Mononucleated cells: <a href="https://doi.org/10.5281/zenodo.10572200">https://doi.org/10.5281/zenodo.10572200</a></p> </li> <li> <p>Model Training and predictions: Conducted using ZeroCostDL4Mic (<a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki</a>)</p> </li> </ul> <li> <p>Tracking parameters (TrackMate):</p> </li> <ul> <li> <p>Detection: label detector</p> </li> <li> <p>Tracking: Simple LAP detector: Linking max distance: 20 px; Gap-closing max distance: 20 px; Gap-closing max frame gap: 4. </p> </li> <li> <p>Track filtering: min number of spots in the tracks 11.79 </p> </li> </ul> <li> <p>Tracking analysis</p> </li> <ul> <li> <p>Tracks were analyzed using a customized CellTracksColab notebook (<a href="https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab">https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab</a>)</p> </li> </ul> </ul> <h3>Contents of the repository</h3> <ul> <li> <p>Analysis.zip</p> </li> <li> <p>As_HUsi1.zip dataset</p> </li> <li> <p>As_HUsi2.zip dataset</p> </li> <li> <p>As_HUsi3.zip dataset</p> </li> <li> <p>As_HUsiCtrl.zip dataset</p> </li> <li> <p>Mia_HUsi1.zip dataset</p> </li> <li> <p>Mia_HUsi2.zip dataset</p> </li> <li> <p>Mia_HUsi3.zip dataset</p> </li> <li> <p>Mia_HUsiCtrl.zip dataset</p> </li> </ul> <div> <h3>Reference</h3> <div><strong>Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers</strong></div> </div> <div>Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet</div> <div>bioRxiv 2024.09.30.615654; doi: <a href="https://www.biorxiv.org/content/10.1101/2024.09.30.615654v1">https://doi.org/10.1101/2024.09.30.615654</a></div>
Supplementary Video 4 Matrigel HUVEC with pericytes
<p>Supplementary Video 4 Matrigel HUVEC with pericytes.</p> <p>Mixed endothelial (green)/pericyte (red) cultures plated on Matrigel.</p>
HUVEC monolayer under periodic and step pressure stimulation
<p>Recorded images at 0.5 and 1 frame per second. HUVEC monolayer under periodic pressure stimulation.</p>
Supplementary Video 2 HUVEC with pericytes
<p>Supplementary Video 2 HUVEC with pericytes</p>
Supplementary Video 1 HUVEC only
<p>Supplementary Video 1 HUVEC only</p>
Supplementary Video 3 Matrigel HUVEC only
<p>Supplementary Video 3. HUVEC monocultures (green) plated on Matrigel.</p>
Expression data from SPHINX (SPaceflight of Huvec: an INtegrated eXperiment)
Changes in the physical environment modulate cell responses and may lead to the impairment or even failure of tissue function as a result of mechanotransduction processes. It has been suggested that this situation occurs in some age-related diseases and some pathological conditions observed in space such as cardiovascular deconditioning bone loss muscle atrophy and impaired immune responses. All of these are associated with endothelial dysfunction but the precise mechanism is still unclear. We used the microarray approach to obtain insights into the mechanism responsible for endothelial dysfunction by taking advantage of the challenging environment of gravitational unloading onboard the International Space Station. The effects of gravitational unloading on HUVEC gene expression were investigated by means of cDNA microarray analyses of six randomly chosen samples (three for each of the two conditions of spaceflight and 1g) using Affymetrix Gene Human 1.0 ST Arrays
Effect of microgravity on HUVECs (Human Umbilical vein Endothelial cells) cells and its transcriptome analysis.
Adaptation of humans in low gravity conditions is a matter of utmost importance when efforts are on to a gigantic leap in human space expeditions for tourism and formation of space colonies. In this connection cardiovascular adaptation in low gravity is a critical component of human space exploration. Deep high-throughput sequencing approach allowed us to analyze the miRNA and mRNA expression profiles in human umbilical cord vein endothelial cells (HUVEC) cultured under gravity (G) and stimulated microgravity (MG) achieved with a clinostat. The present study identified totally 1870 miRNAs differentially expressed in HUVEC under MG condition when compared to the cells subjected to unitary G conditions. The functional association of identified miRNAs targeting specific mRNAs revealed that miRNAs hsa-mir-496 hsa-mir-151a hsa-miR-296-3p hsa-mir-148a hsa-miR-365b-5p hsa-miR-3687 hsa-mir-454 hsa-miR-155-5p and hsa-miR-145-5p differentially regulated the genes involved in cell adhesion angiogenesis cell cycle JAK-STAT signaling MAPK signaling nitric oxide signaling VEGF signaling and wound healing pathways. Further the q-PCR based experimental studies of upregulated and downregulated miRNA and mRNAs demonstrate that the above reported miRNAs influence the cell proliferation and vascular functions of the HUVEC in MG conditions effectively. Consensus on the interactome results indicates restricted fluctuations in the transcriptome of the HUVEC exposed to short-term MG that could lead to higher levels of endothelial functions like angiogenesis and vascular patterning.
Transcriptome sequencing of HUVECs
GEO Series GSE155290. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Cellular scRNA-seq of human umbilical cord mesenchymal stem cell (HUCMSC) and human umbilical vein endothelial cells (HUVEC)
GEO Series GSE199071. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Expression data from control and COUP-TFII siRNA treated HUVEC cells
GEO Series GSE33301. Homo sapiens. 6 samples. Type: Expression profiling by array.
Gene signature of young and replicative senescent human umbilical vein endothelial cells (HUVECs)
GEO Series GSE37091. Homo sapiens. 5 samples. Type: Expression profiling by array.
RNA Sequencing Facilitates Quantitative Analysis of VEGF treated HUVECs Transcriptomesand with or without Verteporfin (VP) pre-treatment
GEO Series GSE181880. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
RNA sequencing of spheroids from HUVECs for differences in Cis- and Trans-signaling of IL-11
GEO Series GSE234142. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
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