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Label-free quantitative phase imaging (QPI) of highly scattering samples like embryos, spheroids etc., have been studied recently using gradient light interference microscopy (GLIM). [1-3] While GLIM images are quantitative, they lack the specificity brought on by fluorescence staining. However, fluorescence staining has its own drawbacks ranging from phototoxicity to extensive sample preparation and small window of observation owing to the photobleaching effects. [4] To introduce specificity in the quantitative phase images from GLIM, we use machine learning in a technique called phase imaging with computational specificity (PICS) [5]. Combining GLIM with a confocal microscopy setup and PICS gave rise to a new imaging capability, referred to as artificial confocal microscopy (ACM). [6] In ACM, the input images are obtained by transmission GLIM imaging, which is label-free. Through deep learning and training on fluorescence confocal data as ground truth, we obtain ACM images from GLIM data, but with axial resolution and chemical specificity associated with confocal. Thus, ACM is an ideal, nondestructive tool for characterizing live embryos.
In this study, we used ACM to image 99 blastocyst stage mouse embryos. These embryos were stained with 7-AAD for nuclear detection. We trained a deep neural network (U-Net with efficient-net B0 encoder) on pairs of GLIM image (phase) and nucleus fluorescence images. Our model predictions can highlight the positions of the nuclei inside the cells, with SSIM and Pearson correlation coefficient values of 0.833 and 0.924, respectively. This 3D structure is then analyzed to determine the “structure function” associated with the nuclear position in 3D. The “form function” is given by the average shape of the nuclei. [7] We use biophysical markers, as well as form and structure functions derived from the 3D structure to grade the embryos and determine their viability.
Funding:
NSF: 1353368; 1652150;
References
[1] M. E. Kandel et al., "Epi-illumination gradient light interference microscopy for imaging opaque structures," Nature communications, vol. 10, no. 1, pp. 1-9, 2019.
[2] M. J. Fanous, Y. Li, M. E. Kandel, A. A. Abdeen, K. A. Kilian, and G. Popescu, "Effects of substrate patterning on cellular spheroid growth and dynamics measured by gradient light interference microscopy (GLIM)," Journal of biophotonics, vol. 12, no. 12, p. e201900178, 2019.
[3] T. H. Nguyen, M. E. Kandel, M. Rubessa, M. B. Wheeler, and G. Popescu, "Gradient light interference microscopy for 3D imaging of unlabeled specimens," Nat Commun, vol. 8, no. 1, p. 210, Aug 08 2017, doi: 10.1038/s41467-017-00190-7.
[4] R. A. Hoebe, C. H. Van Oven, T. W. Gadella, Jr., P. B. Dhonukshe, C. J. Van Noorden, and E. M. Manders, "Controlled light-exposure microscopy reduces photobleaching and phototoxicity in fluorescence live-cell imaging," Nat Biotechnol, vol. 25, no. 2, pp. 249-53, Feb 2007, doi: 10.1038/nbt1278.
[5] M. E. Kandel et al., "PICS: Phase Imaging with Computational Specificity," arXiv preprint arXiv:2002.08361, 2020.
[6] X. Chen et al., "Artificial confocal microscopy for deep label-free imaging," arXiv preprint arXiv:2110.14823, 2021.
[7] G. Popescu, Quantitative phase imaging of cells and tissues. McGraw-Hill Education, 2011.
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