Automated Identification of Subcellular Organelles by Coherent Anti-Stokes Raman Scattering

作者：El Mashtoly Samir F; Niedieker Daniel; Petersen Dennis; Krauss Sascha D; Freier Erik; Maghnouj Abdelouahid; Mosig Axel; Hahn Stephan; Koetting Carsten; Gerwertt Klaus^*

来源：Biophysical Journal, 2014, 106(9): 1910-1920.

DOI：10.1016/j.bpj.2014.03.025

Coherent anti-Stokes Raman scattering (CARS) is an emerging tool for label-free characterization of living cells. Here, unsupervised multivariate analysis of CARS datasets was used to visualize the subcellular compartments. In addition, a supervised learning algorithm based on the "random forest" ensemble learning method as a classifier, was trained with CARS spectra using immunofluorescence images as a reference. The supervised classifier was then used, to our knowledge for the first time, to automatically identify lipid droplets, nucleus, nucleoli, and endoplasmic reticulum in datasets that are not used for training. These four subcellular components were simultaneously and label-free monitored instead of using several fluorescent labels. These results open new avenues for label-free time-resolved investigation of subcellular components in different cells, especially cancer cells.

• 出版日期2014-5-6