Department of Hand, Plastic and Aesthetic Surgery, University of Munich, Pettenkoferstr. 8a, 80336, Munich, Germany.
Department of Hand, Plastic and Aesthetic Surgery, University of Munich, Pettenkoferstr. 8a, 80336, Munich, Germany.
Acta Histochem. 2020 May;122(4):151537. doi: 10.1016/j.acthis.2020.151537. Epub 2020 Mar 18.
The understanding of fat tissue plays an eminent role in plastic surgery as well as in metabolic research. Histopathological analysis of tissue samples provides insight in free fat graft survival and culture experiments help to better understand fat tissue derived stem cells (ASCs). To facilitate such experiments, modern image-based histology could provide an automatized approach to a large amount of data to gain not only qualitative but also quantitative data. This study was designed to critically evaluate image-based analysis of fat tissue samples in cell culture or in tissue probes and to identify critical parameters to avoid bias in further studies. In the first part of the study, ASCs were harvested and differentiated into adipocytes in cell culture. Histology was performed with the fluorescent dye BODIPY and the obtained digital images were analyzed using Image J software. In the second part of the study, digitalized histology of a previous in vivo study was subjected to automatized fat vacuole quantification using Image J. Both approaches were critically reviewed, and different software parameter settings were tested. Results showed that automatized digital image analysis allows the quantification of fat tissue probes with enough precision giving significant results. But the testing of different software parameters revealed a significant influence of parameters themselves on calculated results. Therefore, we recommend the use of image-based analysis to quantify fat tissue probes to improve the comparability of studies. But we also emphasize to calibrate software using internal controls in every single experimental approach.
脂肪组织的理解在整形外科学以及代谢研究中都起着重要的作用。组织样本的组织病理学分析提供了游离脂肪移植物存活的深入了解，而培养实验有助于更好地了解脂肪组织来源的干细胞（ASCs）。为了促进这些实验，现代基于图像的组织学可以为大量数据提供一种自动化的方法，不仅可以获得定性数据，还可以获得定量数据。本研究旨在批判性地评估细胞培养或组织探针中脂肪组织样本的基于图像的分析，并确定避免进一步研究中出现偏差的关键参数。在研究的第一部分中，从 ASCs 中分离出来并在细胞培养中分化为脂肪细胞。使用荧光染料 BODIPY 进行组织学检查，并使用 Image J 软件分析获得的数字图像。在研究的第二部分中，使用 Image J 对以前的体内研究的数字组织学进行自动脂肪空泡定量。对这两种方法进行了批判性评价，并测试了不同的软件参数设置。结果表明，自动数字图像分析允许对脂肪组织探针进行足够精确的定量，从而得出有意义的结果。但是，测试不同的软件参数表明，参数本身对计算结果有显著影响。因此，我们建议使用基于图像的分析来定量脂肪组织探针，以提高研究的可比性。但是，我们也强调在每个单独的实验方法中使用内部对照来校准软件。
