  Gram staining has been one of the most frequently used staining protocols in
microbiology for over a century, utilized across various fields, including
diagnostics, food safety, and environmental monitoring. Its manual procedures
make it vulnerable to staining errors and artifacts due to, e.g., operator
inexperience and chemical variations. Here, we introduce virtual Gram staining
of label-free bacteria using a trained deep neural network that digitally
transforms darkfield images of unstained bacteria into their Gram-stained
equivalents matching brightfield image contrast. After a one-time training
effort, the virtual Gram staining model processes an axial stack of darkfield
microscopy images of label-free bacteria (never seen before) to rapidly
generate Gram staining, bypassing several chemical steps involved in the
conventional staining process. We demonstrated the success of the virtual Gram
staining workflow on label-free bacteria samples containing Escherichia coli
and Listeria innocua by quantifying the staining accuracy of the virtual Gram
staining model and comparing the chromatic and morphological features of the
virtually stained bacteria against their chemically stained counterparts. This
virtual bacteria staining framework effectively bypasses the traditional Gram
staining protocol and its challenges, including stain standardization, operator
errors, and sensitivity to chemical variations.
