  Fluorescence microscopy plays a vital role in understanding the subcellular
structures of living cells. However, it requires considerable effort in sample
preparation related to chemical fixation, staining, cost, and time. To reduce
those factors, we present a virtual fluorescence staining method based on deep
neural networks (VirFluoNet) to transform fluorescence images of molecular
labels into other molecular fluorescence labels in the same field-of-view. To
achieve this goal, we develop and train a conditional generative adversarial
network (cGAN) to perform digital fluorescence imaging demonstrated on human
osteosarcoma U2OS cell fluorescence images captured under Cell Painting
staining protocol. A detailed comparative analysis is also conducted on the
performance of the cGAN network between predicting fluorescence channels based
on phase contrast or based on another fluorescence channel using human breast
cancer MDA-MB-231 cell line as a test case. In addition, we implement a deep
learning model to perform autofocusing on another human U2OS fluorescence
dataset as a preprocessing step to defocus an out-focus channel in U2OS
dataset. A quantitative index of image prediction error is introduced based on
signal pixel-wise spatial and intensity differences with ground truth to
evaluate the performance of prediction to high-complex and throughput
fluorescence. This index provides a rational way to perform image segmentation
on error signals and to understand the likelihood of mis-interpreting biology
from the predicted image. In total, these findings contribute to the utility of
deep learning image regression for fluorescence microscopy datasets of
biological cells, balanced against savings of cost, time, and experimental
effort. Furthermore, the approach introduced here holds promise for modeling
the internal relationships between organelles and biomolecules within living
cells.
