With the combination of different open-source projects a new, increasingly important requirement of image analysis software is emerging: interoperability. In fact, it is usually necessary to use a combination of different programs in order to solve all the aspects of the image analysis task in hand. There is so much freely available software that it is practically impossible for anyone without expert knowledge to know which to use in any one case. This makes it very difficult to choose a suitable application for a particular image analysis problem. However, open-source tools are often designed to solve highly specific problems and can therefore satisfy only certain aspects of the requirements made of them.Įven applications developed for universal use have frequently been created within the framework of specific projects and are found wanting when required to perform other image analysis tasks. Besides being able to process a gigantic amount of heterogeneous image data, it must be easy to use. Modern software for processing and analyzing biological image data has to meet a wide range of specifications. The BioFormats library for example reads approximately 125 image formats of various microscope manufacturers including Zeiss LSM, Metamorph Stack, Leica LCS LEI or DICOM. However, open-source applications are now getting off the ground, partly because freely available program libraries are making it possible to convert almost any image format into standard formats. So far, proprietary file formats of image analysis software supplied with microscopes have made it difficult to use open-source platforms. The last few years have seen a remarkable number of new platforms and software packages for processing biological image data. Automatic image analysis enables huge amounts of data to be processed within a short time and ensures reproducible results. All rights reserved.There are many different applications for imaging techniques in life sciences: they quantify and localize signaling proteins, measure dynamic changes of entire cell structures, track cancer cell growth in time-lapse recordings, or distinguish the phases of the cell life cycle. The efficacy of the macro toolset is demonstrated using a sample set of images from SH-SY5Y, C2C12, and mouse embryo fibroblast (MEF) cell cultures treated under different conditions and exhibiting hyperfused, fused, and fragmented mitochondrial network morphologies.įission Fusion Image analysis Mitochondria Mitochondrial dynamics Mitochondrial network.Ĭopyright © 2017 Elsevier GmbH. The tool incorporates optional preprocessing steps to enhance the quality of images before converting the images to binary and producing a morphological skeleton for calculating nine parameters to quantitatively capture the morphology of the mitochondrial network.
Here, we describe the Mitochondrial Network Analysis (MiNA) toolset, a relatively simple pair of macros making use of existing ImageJ plug-ins, allowing for semi-automated analysis of mitochondrial networks in cultured mammalian cells. Over the past decade, the significance of the mitochondrial network has been increasingly appreciated, motivating the development of various approaches to analyze it. Mitochondria exist in a dynamic cycle of fusion and fission whose balance directly influences the morphology of the 'mitochondrial network', a term that encompasses the branched, reticular structure of fused mitochondria as well as the separate, punctate individual organelles within a eukaryotic cell.
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