High-volume, label-free imaging for quantifying single-cell dynamics in induced pluripotent stem cell colonies
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Abstract
To facilitate the characterization of unlabeled induced pluripotent stem cells (iPSCs) during culture and expansion, we developed an AI pipeline for nuclear segmentation and mitosis detection from phase contrast images of individual cells within iPSC colonies. The analysis uses a 2D convolutional neural network (U-Net) plus a 3D U-Net applied on time lapse images to detect and segment nuclei, mitotic events, and daughter nuclei to enable tracking of large numbers of individual cells over long times in culture. The analysis uses fluorescence data to train models for segmenting nuclei in phase contrast images. The use of classical image processing routines to segment fluorescent nuclei precludes the need for manual annotation. We optimize and evaluate the accuracy of automated annotation to assure the reliability of the training. The model is generalizable in that it performs well on different datasets with an average F1 score of 0.94, on cells at different densities, and on cells from different pluripotent cell lines. The method allows us to assess, in a non-invasive manner, rates of mitosis and cell division which serve as indicators of cell state and cell health. We assess these parameters in up to hundreds of thousands of cells in culture for more than 36 hours, at different locations in the colonies, and as a function of excitation light exposure.
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When calculating doubling times based on mitotic events in the remaining cells that were470not undergoing apoptosis (Figure 6D), the doubling times are similar to those for unexposed cells
Again, it's great to see something like this quantified so carefully!
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Higher intensities of excitation light exposure led to significant cell death that was apparent by manual447inspection of images, and by the reduced relative cell numbers as shown by the green lines
It seems surprising that there is such a big difference from 1x to 1.4x. Is this by design? (was the 1x intensity chosen from prior experience or experiments to be as high as possible without inhibiting cell division?)
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s shown in Figure 6A,438exposure of cells to the minimal intensity of fluorescence excitation light (56 mJ/cm2 referred to as 1x)
It's super helpful that an absolute measure of intensity is provided here, but it would be great to also include the wavelength (or range of wavelengths) of the excitation light.
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Individual cells in the center of the colony tend to move less than cells near the
Is it possible to correct this for the fact that, as the colony itself expands, cells near the edge necessarily must move more than cells in the center (which will not move at all, if the colony as a whole is stationary)?
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Average mitotic rates do not appear to depend on431distance from the colony edge (Figure 5D) and do not correlate with the increased cell motion
It's great to see a subtle detail like this quantified so carefully! Is this consistent with prior work (if there is any)?
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The233manual data was paired to the 3D U-Net inferenced results using a linear sum assignment routine with234the cost function being proportional to the distance between mitosis events in space with an empirically235determined spatial cutoff of 15 pixels and a time cutoff of 6 frames.
This is a bit hard to understand. How is distance in time measured? (i.e. the difference between the time of mitosis onset in the manual annotations and the segmentation results)
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are 1 to 2 to 20 to 20.
how were these weights chosen? And is it correct to think of these weights as a kind of correction for the class imbalance between non-mitotic and mitotic nuclei?
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in each of the 5202frames before division, and as class 3 (one or two daughter cells) in each of the 3 frames after division.
how were these numbers of frames chosen?
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The binary masks are created by151inferencing with 3 instances of the same model and thresholded by 2 (as explained in more detail in152Supplemental Figure 1B and 1C.
This is a little confusing, especially the "thresholded by 2" part, and I didn't find the caption in Supp Fig 1 to be that much clearer. It would help to explain the origin of the variability in the predictions (in other words, what is an "instance of the same model"?)
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We trained a 2D U-Net to segment single-cell nuclei from phase contrast images starting with a pre-136trained U-Net (14) as our initial network
It would be great to mention what kind of images the pre-trained model trained on. Do you have a sense for how important it is that pre-training be done on similar images? (and what kinds of similarity are most important: cell type, imaging modality, magnification, etc)
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