Image analysis workflows to reveal the spatial organization of cell nuclei and chromosomes.

Ricardo S Randall, Claire Jourdain, Anna Nowicka, Kateřina Kaduchová, Michaela Kubová, Mohammad A Ayoub, Veit Schubert, Christophe Tatout, Isabelle Colas, Kalyanikrishna, Sophie Desset, Sarah Mermet, Aurélia Boulaflous-Stevens, Ivona Kubalová, Terezie Mandáková, Stefan Heckmann, Martin A Lysak, Martina Panatta, Raffaella Santoro, Daniel Schubert, Ales Pecinka, Devin Routh, Célia Baroux
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引用次数: 5

Abstract

Nucleus, chromatin, and chromosome organization studies heavily rely on fluorescence microscopy imaging to elucidate the distribution and abundance of structural and regulatory components. Three-dimensional (3D) image stacks are a source of quantitative data on signal intensity level and distribution and on the type and shape of distribution patterns in space. Their analysis can lead to novel insights that are otherwise missed in qualitative-only analyses. Quantitative image analysis requires specific software and workflows for image rendering, processing, segmentation, setting measurement points and reference frames and exporting target data before further numerical processing and plotting. These tasks often call for the development of customized computational scripts and require an expertise that is not broadly available to the community of experimental biologists. Yet, the increasing accessibility of high- and super-resolution imaging methods fuels the demand for user-friendly image analysis workflows. Here, we provide a compendium of strategies developed by participants of a training school from the COST action INDEPTH to analyze the spatial distribution of nuclear and chromosomal signals from 3D image stacks, acquired by diffraction-limited confocal microscopy and super-resolution microscopy methods (SIM and STED). While the examples make use of one specific commercial software package, the workflows can easily be adapted to concurrent commercial and open-source software. The aim is to encourage biologists lacking custom-script-based expertise to venture into quantitative image analysis and to better exploit the discovery potential of their images.Abbreviations: 3D FISH: three-dimensional fluorescence in situ hybridization; 3D: three-dimensional; ASY1: ASYNAPTIC 1; CC: chromocenters; CO: Crossover; DAPI: 4',6-diamidino-2-phenylindole; DMC1: DNA MEIOTIC RECOMBINASE 1; DSB: Double-Strand Break; FISH: fluorescence in situ hybridization; GFP: GREEN FLUORESCENT PROTEIN; HEI10: HUMAN ENHANCER OF INVASION 10; NCO: Non-Crossover; NE: Nuclear Envelope; Oligo-FISH: oligonucleotide fluorescence in situ hybridization; RNPII: RNA Polymerase II; SC: Synaptonemal Complex; SIM: structured illumination microscopy; ZMM (ZIP: MSH4: MSH5 and MER3 proteins); ZYP1: ZIPPER-LIKE PROTEIN 1.

Abstract Image

Abstract Image

Abstract Image

图像分析工作流揭示细胞核和染色体的空间组织。
细胞核、染色质和染色体组织的研究在很大程度上依赖于荧光显微镜成像来阐明结构和调控成分的分布和丰度。三维(3D)图像堆栈是信号强度水平和分布以及空间分布模式的类型和形状的定量数据来源。他们的分析可以导致新的见解,否则在定性分析中错过。定量图像分析需要特定的软件和工作流程来进行图像渲染、处理、分割、设置测量点和参考帧以及导出目标数据,然后再进行进一步的数值处理和绘图。这些任务通常需要开发定制的计算脚本,并且需要实验生物学家社区无法广泛获得的专业知识。然而,高分辨率和超分辨率成像方法的日益普及推动了对用户友好的图像分析工作流程的需求。在这里,我们提供了一个由成本行动INDEPTH培训学校的参与者开发的策略纲要,用于分析通过衍射限制共聚焦显微镜和超分辨率显微镜方法(SIM和STED)获得的3D图像堆栈中的核和染色体信号的空间分布。虽然示例使用了一个特定的商业软件包,但工作流可以很容易地适应并发的商业和开源软件。其目的是鼓励缺乏基于定制脚本的专业知识的生物学家冒险进行定量图像分析,并更好地利用其图像的发现潜力。缩写:3D FISH:三维荧光原位杂交;3 d:三维;asyn1: asynaptic 1;答:染色中心;公司:交叉;6-diamidino-2-phenylindole DAPI: 4;Dmc1: DNA减数分裂重组酶1;DSB:双链断裂;FISH:荧光原位杂交;Gfp:绿色荧光蛋白;he10:人类入侵增强因子;甲:Non-Crossover;NE:核包膜;Oligo-FISH:寡核苷酸荧光原位杂交;RNPII: RNA聚合酶;SC:突触复合体;SIM:结构照明显微镜;ZMM (ZIP: MSH4: MSH5和MER3蛋白);Zyp1:类似拉链的蛋白质
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