Journal
BIOPHYSICAL JOURNAL
Volume 106, Issue 9, Pages 1910-1920Publisher
CELL PRESS
DOI: 10.1016/j.bpj.2014.03.025
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Funding
- Protein Research Unit Ruhr within Europe, Ministry of Innovation, Science and Research of North-Rhine Westphalia, Germany
- Center for Vibrational Microscopy, European Regional Development Fund, European Union and North-Rhine Westphalia, Germany
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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.
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