DOI: 10.6084/m9.figshare.12674465
title: LipidDroplets-siRNA-plate02

description: COLLECTION TITLE:<br>* 2020_RegressionPlane_LipidDroplets_siRNA<br><br>ARTICLE (when using these files, please, cite the following article):<br>* A. Szkalisity, F. Piccinini, A. Beleon, T. Balassa, I.G. Varga, E. Migh, C. Molnar, L. Paavolainen, S. Timonen, I. Banerjee, E. Ikonen, Y. Yamauchi, I. Ando, J. Peltonen, V. Pietiäinen, V. Honti, P. Horvath, Regression plane concept for analysing continuous cellular processes with machine learning. Nature Communications, 2021<br><br>KEYWORDS:<br>* High-Throughput Screening, Fluorescence Microscopy, Lipid Droplets, Machine Learning, Cardiovascular Diseases<br><br>CATEGORIES:<br>* Bioinformatics, Biological Techniques<br><br>BIOLOGICAL APPLICATION:<br>* In the Lipid Droplet (LD) study, hepatocytes (Huh-7) were transfected with 1-7 siRNAs (10 nM) /gene for 72 h to silence the expression of specific genes and examine the effect of their silencing on lipid droplets.<br>* The list of the target genes and control siRNAs is provided in the plate layout file: a two-column csv file containing the information (i.e. Well_ID, gene_ID) of the siRNA treatments used in the different wells.<br>* The Huh-7 cells were seeded into a 384-well plate (750 cells/well) and after 72 h of siRNA transfection, they were fixed with 4 % PFA and stained for LDs with LipidTox Green (HCS LipidTox Green Neutral Lipid Stain Invitrogen) and for nuclei with DAPI (Sigma-Aldrich).<br>* In the majority of the conditions (siRNAs + controls) over 2200 cells were analysed all-together in 2 plates.<br><br>IMAGES:<br>* 9 images/channel/well were acquired with an automated epifluorescence ScanR microscope (Olympus) with a 150W Mercury-Xenon mixed gas arc burner, a 20x long working distance objective (UIS2) and a digital monochrome CCD camera (Hamamatsu), for a total of 3956‬ images (2 plates, 1978 images in each) and 232084 cells (note that lines A, B, N, O, P and columns 1, 2, 23, 24 of the 384-well plate have been not imaged).<br>* Original image size is 1344x1024 pixels and 16 bit per channel.<br>* In this dataset the images are already prepared for being directly analysed in Advanced Cell Classifier (ACC) hence transformed into 8-bit per channel RGB PNG format.<br>* The original images are available upon request.<br><br>FORMAT OF THE FILES:<br>* The files in this collection follow the Advanced Cell Classifier (ACC) standard.<br><br>HOW TO OPEN THE DATASET IN ACC:<br>* Download a plate (LipidDroplets_siRNA_plate01 or LipidDroplets_siRNA_plate02) into a folder (this parent folder is termed ACC Project Folder)<br>* Download the corresponding trained project file from LipidDroplets-ACC_TrainedProjectFiles (LipidDroplets-siRNA-plate01__TrainingData.mat or LipidDroplets-siRNA-plate02__TrainingData.mat).<br>* Launch ACC (freely available for download at cellclassifier.org)<br>* Click 'Open project' in the toolbar and select the downloaded trained project file. When you are asked for a new datapath specify your ACC project folder (the folder containing the folder named LipidDroplets_siRNA_plate01 or LipidDroplets_siRNA_plate02).<br><br>IMAGING INFO:<br>* These images were generated by Vilja Pietiainen and Sanna Timonen at Institute for Molecular Medicine Finland (FIMM), Helsinki, Finland.<br>* Image features were extracted by Peter Horvath at FIMM.<br>* Please contact Vilja Pietiainen (vilja.pietiainen@helsinki.fi) or Peter Horvath (peter.horvath@brc.hu) for more information.<br><br>MAIN CONTACTS:<br>* Vilja Pietiainen, Institute for Molecular Medicine Finland-FIMM, Helsinki, Finland. Email: vilja.pietiainen@helsinki.fi<br>* Peter Horvath, Biological Research Centre (BRC), Szeged, Hungary. Email: horvath.peter@brc.hu<br><br>COPYRIGHT:<br>* Copyright (c) 2020, Vilja Pietiäinen, Sanna Timonen, Peter Horvath<br>* Institute for Molecular Medicine Finland-FIMM, Helsinki, Finland<br>* Biological Research Centre (BRC), Szeged, Hungary<br>* All rights reserved.<br>* Redistribution and use of the material, with or without modification, is provided for academic research purpose only.<br>* This material is free; you can redistribute it and/or modify it under the terms of the GNU General Public License version 3 (or higher) as published by the Free Software Foundation.<br>* This material is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.<br>* See the GNU General Public License for more details.<br>