{"paper_id":"17ec7ff0-8b4b-4d24-9e2e-3539a898e656","body_text":"Abstract\nVirtual 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.\nCompeting Interest Statement\nThe authors have declared no competing interest.\nFootnotes\n↵** lisa.miccio{at}isasi.cnr.it\nhttps://github.com/danpir94/Holotomography_driven_learning_for_in_silico_staining\nData availability\nThe 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.","source_license":"CC-BY-4.0","license_restricted":false}