Drosophila_plate02_control

COLLECTION TITLE: * 2020_ACC_RP_DrosophilaBloodCells ARTICLE (when using these files, please, cite the following article): * 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 KEYWORDS: * High-Throughput Screening, Fluorescence Microscopy, Drosophila Blood Cells, Machine Learning, Differentiation, Hematopoiesis CATEGORIES: * Bioinformatics, Biological Techniques BIOLOGICAL APPLICATION: * In the blood cell differentiation study, the immune response of Drosophila melanogaster larvae - the differentiation of lamellocytes - were induced by wounding with an insect pin. * The larvae were bled 12 hours after the wounding, and hemocytes were cultured in a well of an 8 well µ-slide (Ibidi, Cat:80826) in Schneider’s medium (Lonza, Cat: 04-351 Q) supplemented with 10% FBS (Gibco, Cat: 10270), 0,01 mg/ml gentamicin (Sigma, Cat: G3632), 0,065 mg/ml penicillin (Sigma, Cat: P7794) and 0,1 mg/ml streptamicin (Sigma, Cat: S6501) at 25 °C. * The transdifferentiation of plasmatocytes into lamellocytes was monitored by cell type specific transgenes (eaterGFP for plasmatocytes, and MSNF9MOmCherry for lamellocytes as described in Anderl et al., Transdifferentiation and proliferation in two distinct hemocyte lineages in Drosophila melanogaster larvae after wasp infection. PLoS Pathogens, 12, 7, e1005746 2016). IMAGES: * 15-frame RGB image sequence/field (141 fields), representing the brightfield, mCherry, EGFP channels, with 2 hours gap between subsequent frames. * Images were acquired with an high-content screening microscope (Operetta, Perkin Elmer) with a 60x high-numeric-aperture objective and a digital high resolution 14-bit CCD camera, for a total of 4230 images (2 plates, 2115 images in each). * The image size is 1360x1024 pixels and 8-bit per channel RGB TIFF format. * In this dataset the images are already prepared for being directly analysed in Advanced Cell Classifier (ACC). FORMAT OF THE FILES: * The files in this collection follow the Advanced Cell Classifier (ACC) standard. HOW TO OPEN THE DATASET IN ACC: * Download a plate ("plate01_induced" or "plate02_control") into a folder (this parent folder is termed ACC Project Folder) * Download the corresponding trained project file ("Test_ACC_Project.mat"). * Launch ACC (freely available for download at cellclassifier.org) * 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 ("plate01_induced" or "plate02_control"). IMAGING INFO: * These images were generated by Viktor Honti, Istvan Gergely Varga, and Istvan Ando at the Biological Research Centre (BRC), Szeged, Hungary. * Image features were extracted by Peter Horvath at BRC. * Please contact Viktor Honti ([e-mail]) or Peter Horvath ([e-mail]) for more information. MAIN CONTACTS: * Viktor Honti, Biological Research Centre (BRC), Szeged, Hungary. Email: [e-mail] * Peter Horvath, Biological Research Centre (BRC), Szeged, Hungary. Email: [e-mail] COPYRIGHT: * Copyright (c) 2020, Viktor Honti, Istvan Gergely Varga, Istvan Ando, Peter Horvath * Biological Research Centre (BRC), Szeged, Hungary * All rights reserved. * * Redistribution and use of the material, with or without modification, is provided for academic research purpose only. * * 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. * This material is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. * See the GNU General Public License for more details.

数据集标题：2020_ACC_RP_果蝇血细胞 相关数据集（使用本数据集文件时，请引用下述文献）：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. 《基于机器学习分析连续细胞过程的回归平面概念》，《自然-通讯》，2021年

关键词：高通量筛选、荧光显微镜成像、果蝇血细胞、机器学习、细胞分化、血细胞生成

分类：生物信息学、生物学实验技术

生物学应用：
本数据集用于血细胞分化研究：通过昆虫针穿刺损伤诱导黑腹果蝇幼虫的免疫应答，即片状血细胞（lamellocytes）的分化。
损伤12小时后采集果蝇幼虫的血液，将血细胞接种于8孔µ-slide（Ibidi，货号：80826）的单孔中，使用Schneider培养基（Lonza，货号：04-351 Q）进行培养，培养基添加10%胎牛血清（FBS，Gibco，货号：10270）、0.01 mg/ml庆大霉素（Sigma，货号：G3632）、0.065 mg/ml青霉素（Sigma，货号：P7794）及0.1 mg/ml链霉素（Sigma，货号：S6501），培养温度维持在25℃。
通过细胞类型特异性转基因标记监测浆细胞（plasmatocytes）向片状血细胞的转分化：其中eaterGFP标记浆细胞，MSNF9MOmCherry标记片状血细胞，具体方法参见Anderl等人发表于《PLOS Pathogens》2016年的研究：《黑腹果蝇幼虫被寄生蜂感染后两类血细胞谱系的转分化与增殖》，2016年，第12卷第7期，e1005746。

图像信息：
每个视野包含15帧RGB图像序列（共141个视野），分别对应明场、mCherry及增强绿色荧光蛋白（EGFP）通道，相邻帧的采集间隔为2小时。
图像采集使用高内涵筛选显微镜（Operetta，珀金埃尔默），搭配60倍高数值孔径物镜及高分辨率14位CCD相机，共生成4230张图像（2块检测板，每块含2115张图像）。
图像分辨率为1360×1024像素，单通道为8位，存储格式为RGB TIFF。
本数据集内的图像已预处理完成，可直接用于高级细胞分类器（Advanced Cell Classifier，ACC）的分析。

文件格式：
本数据集内的文件均符合高级细胞分类器（ACC）的标准格式。

在ACC中打开本数据集的方法：
1. 将某一块检测板文件（"plate01_induced"或"plate02_control"）下载至某个文件夹（该父文件夹称为ACC项目文件夹）；
2. 下载对应的已训练项目文件（"Test_ACC_Project.mat"）；
3. 启动ACC（可从cellclassifier.org免费下载）；
4. 在工具栏中点击“打开项目”，选择已下载的已训练项目文件；当系统提示指定新的数据路径时，选择你的ACC项目文件夹（"plate01_induced"或"plate02_control"）。

成像相关信息：
本数据集的图像由匈牙利塞格德生物研究中心（Biological Research Centre, BRC）的Viktor Honti、Istvan Gergely Varga及Istvan Ando完成；图像特征提取由BRC的Peter Horvath完成。如需更多信息，请联系Viktor Honti（邮箱：[e-mail]）或Peter Horvath（邮箱：[e-mail]）。

主要联系人：
1. Viktor Honti，匈牙利塞格德生物研究中心（BRC），邮箱：[e-mail]；
2. Peter Horvath，匈牙利塞格德生物研究中心（BRC），邮箱：[e-mail]。

版权声明：
©2020 Viktor Honti、Istvan Gergely Varga、Istvan Ando、Peter Horvath，匈牙利塞格德生物研究中心（BRC），保留所有权利。
本材料的再分发与使用（无论是否经过修改）仅可用于学术研究目的。
本材料为免费开源资源，您可根据自由软件基金会发布的GNU通用公共许可证第3版（或更高版本）的条款对其进行再分发与修改。本材料按“原样”提供，不附带任何形式的明示或默示担保，包括但不限于适销性或特定用途适用性的担保。如需更多细节，请参阅GNU通用公共许可证。