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. 2019 Sep 17;9:13467. doi: 10.1038/s41598-019-50010-9

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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ConvNet accurately achieves mixed-cell population identification. (a) Mixed-cell experiment preparation and application workflow. Two types of cell were passaged and mixed together before being incubated overnight. The mixed-cell population was then identified through the same protocol as illustrated in Fig. 1e for cell type classification. Data from different days (uncontrolled densities) and controlled densities were pooled together for ConvNet training. (b) Representative brightfield images and fluorescent images of mixed-cell population of HEK-293A and HT1080.Three representative pairs are shown, one frame with similar number of HEK-293A and HT1080 cells, one HEK-293A-rich frame, and one HT1080-rich frame. (c) Accuracy of individual frames, identifying cell types from mixed-cell population (from one biological repeat). (d) Accuracy of 3 biological repeats of mixed-cell population classification.