Journal: Nature Communications
Article Title: Deep learning-based predictive identification of neural stem cell differentiation
doi: 10.1038/s41467-021-22758-0
Figure Lengend Snippet: a NSCs were induced to differentiate into neurons/astrocytes/oligodendrocytes, and the cells were stained with NeuN (red)/GFAP (green)/Olig2 (yellow), collected and subjected to image flow cytometry. b Brightfield and darkfield (labelled) single-cell images were used as training data for the screening system; a schematic of the convolutional neural network (CNN) is presented. c Various inducers in different forms that act on different pathways were used to guide NSCs to differentiate into neurons, and brightfield (unlabelled) single-cell image patches were obtained by flow cytometry. These independent test sets were evaluated with the deep network model to show its generalizability.
Article Snippet: The cells were fixed in cold 80% methanol for 5 min and then disrupted using 0.1% Triton X-100 on ice for 20 min. Later, the cells were incubated with anti-GFAP (Bioss, catalogue no. bs-0199R-AF488, 1:200), anti-NeuN (Bioss, catalogue no. bs-10394R-APC, 1:200) and anti-Olig2 (Bioss, catalogue no. bs-11194R-PE, 1:200) antibodies for 2 h on ice.
Techniques: Staining, Flow Cytometry