Image-based Cell Phenotyping Using Deep Learning

Abstract

The ability to phenotype cells is fundamentally important in biological research and medicine. Current methods rely primarily on fluorescence labeling of specific markers. However, there are many situations where this approach is unavailable or undesirable. Machine learning has been used for image cytometry but has been limited by cell agglomeration and it is unclear if this approach can reliably phenotype cells indistinguishable to the human eye. Here, we show disaggregated single cells can be phenotyped with a high degree of accuracy using low-resolution bright-field and non-specific fluorescence images of the nucleus, cytoplasm, and cytoskeleton. Specifically, we trained a convolutional neural network using automatically segmented images of cells from eight standard cancer cell-lines. These cells could be identified with an average classification accuracy of 94.6%, tested using separately acquired images. Our results demonstrate the potential to develop an "electronic eye" to phenotype cells directly from microscopy images indistinguishable to the human eye.Image Based Cell Phenotyping Using Deep-Learning

Image-based phenotyping of disaggregated cells using deep learning

“…Custom software and the NIS JOB protocol have been made available at https://github. com/SamBerryman/Image-based-Cell-Phenotyping-Using-Deep-Learning 31 .…”

UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues

“…In fluorescence microscopy, nuclei are most commonly stained using intercalating dyes such as DAPI or Hoechst 33342 (Al-Kofahi et al, 2010;Schmidt et al, 2018;Berryman et al, 2019;Vuola, Akram and Kannala, 2019), SiR-DNA (Yang et al, 2020), TO-PRO (Byun et al, 2006), and hematoxylin (Al-Kofahi et al, 2010;Qi et al, 2012;Xu, Lu and Mandal, 2014;Chen et al, 2016;Xu et al, 2017;Vu et al, 2019;Lee and Jeong, 2020;Shahzad M et al, 2020). When expression of recombinant proteins is feasible (e.g., in cell lines), cells expressing fluorescent protein fusions to histones (Challen and Goodell, 2008) or spindle components is an effective means to label nuclei (Wen et al, 2018;Wang et al, 2019); this has also been done in genetically engineered mouse models (Tumbar et al, 2004) but is not relevant to analysis of human tissues.…”