Holotomography-driven learning for in-silico staining of single cells in flow cytometry avoiding co-registration

Abstract Virtual staining is the current state-of-the-art computational technique to cleverly enhance intracellular specificity in unstained biological samples by using convolutional neural networks (CNNs) trained on co-registered pairs of unstained/stained images. While effective, this approach suffers from unpredictable biases inherent to fluorescence microscopy and encounters challenges when applied to flow cytometry data as it would require accurate co-registration on a huge number of images. Here, we present a novel method that exploits for the first time a Holotomography-driven learning to completely eliminate the need for co-registration. We demonstrate that training a CNN on a stain-free dataset of 3D refractive index tomograms of flowing cells elegantly unlocks stain-free intracellular specificity in quantitative phase imaging flow cytometry. This breakthrough, by circumventing the critical co-registration bottleneck, opens unprecedented perspectives for label-free, high-throughput imaging flow cytometry, offering a powerful new paradigm for advanced 2D and 3D single-cell analysis. Competing Interest Statement The authors have declared no competing interest. Footnotes ↵** lisa.miccio{at}isasi.cnr.it https://github.com/danpir94/Holotomography_driven_learning_for_in_silico_staining Data availability The dataset of 2D QPMs employed to train and test the Holotomography-driven CNN is publicly available in the GitHub repository https://github.com/danpir94/Holotomography_driven_learning_for_in_silico_staining.