The Light My Cells France-Bioimaging challenge aims to contribute to the development of new image-to-image 'deep-label' methods in the fields of biology and microscopy. The main task is to predict the best focus image of multiple fluorescently labelled organelles from label-free transmitted-light images. In order to make them usable, the aim of this challenge is to produce new open source methods which can handle a large acquisition variability: Z-focus, multiple channels, acquisition sites, input-modalities (Bright Field, Phase Contrast & Differential Interference Contrast or DIC), instruments, magnifications, cells and markers. The high variability of the database is possible thanks to the structuring role of the France-Bioimaging national infrastructure, which federates 23 imaging acquisition sites distributed all over France. Biomedical point of view : In order to obtain fluorescence microscopy images, it is necessary to perform a manual biochemical labelling treatment - time-consuming and costly - over cells with specific fluorescent probes and dyes. But, the cells studied may themselves be perturbed by the fluorescence microscopy process, both by exposure to excitation light (phototoxicity) and by the probes themselves. As phototoxicity increases with light exposure, it impairs long term imaging. Similarly, fluorophore dimming through photobleaching limits the signal-to-noise ratio of the images. Furthermore, adding markers is an invasive method. The fluorophore might hinder its target's molecular interactions and protein overexpression increases its concentration in the cytoplasm, disrupting regulation processes. Worse, the fluorophores themselves can be cytotoxic. As fluorescence microscopy induces temporal and functional perturbations, it is thus crucial for live microscopy to limit the number of fluorescent probes used in an experiment. On the contrary, label-free transmitted light microscopy such as bright field, phase contrast and DIC is non-invasive, phototoxicity is sharply reduced, and the signal quality is conserved throughout the acquisition. The biological aim of this challenge is to recover fluorescence images in silico from bright field images. Technical point of view: We want to give a boost for multi-output deep learning methods based on a single input, when the training database is made up of images that do not always include all the required channels and have a high degree of variability (e.g. magnification, depth of focus, numerical aperture). This leads participants to develop in particular new architectures and loss functions dedicated for sparse output. The purpose is to offer a tool for biologists that can be robust on any acquisition protocol and effective for the whole community, irrespective of the size of the images, cell line, acquisition site, modality or instrument. In order to assess the generalisability of the methods developed, we will exclude one complete acquisition site from the training database and leave it for the final evaluation. For the "Light my cells" challenge, we want to evaluate the ability of the methods to predict the best Z-focus plane for any organelle even in bad acquisition conditions. To achieve this goal, participants will have the possibility to perform data augmentation provided by the acquisitions. It consists in large Z-stacks images of transmitted light microscopy containing a majority of out focus planes. We defined metrics for each of the 4 organelles and for each (5) deviations of the focus plane to measure the ability to perform the task. We will evaluate each participant on this 4x5 metrics matrix, and the winners will be the ones with the best average of all the metrics. Moreover, participants will get an additional bonus for : code quality and accessibility, lightweight deep learning model, short time of training and prediction, and evaluation of the carbon footprint. Among the current state-of-the-art approaches for image-to-image tasks in bio-imaging are "DeepHCS: Bright-field to fluorescence microscopy image conversion using multi-task learning with adversarial losses for label-free high-content screening" (2021) and "Label-free prediction of cell painting from bright field images" (2022), both of which focus their methodologies solely on the use of the bright-field imaging modality, while "In Silico Labelling: Predicting Fluorescent Labels in Unlabeled Images" (2018) uses the same three modalities as our approach. While "DeepHCS" (2021) and "In Silico Labelling" (2018) use a wide range of metrics to assess image quality, "Label-free prediction of cell painting" (2022) uses a more restricted set of metrics. However, these previous works present a very low diversity of applications and do not provide an easily accessible database. In addition, "DeepHCS" (2021) faces limitations due to the fixed sizes and specific dyes of its database,"In Silico Labelling" (2018) uses fixed formats that are not typical of those used in microscopy and similarly, the authors of "Label-free prediction of cell painting" (2022) admit limitations in the size and diversity of their database. Nevertheless, a more extensive and publicly accessible JUMP-CP database exists for cell painting, which can be used for pretraining the 'Light My Cells' challenge. Yet to the best of our knowledge, the desired methods for the 'Light my cells' challenge have no open source equivalent, and aspires to be rooted with an open database and open algorithms. Impact : We want to contribute to open science by making available training and testing databases with high variability, even in the case of different acquisitions. In order to make the models accessible for any biologist, we will integrate the best open source methods into open-science image processing and analysis software (e.g. BioImage Model Zoo, Napari). The "Light My Cells" challenge will become a reference for 'deep-label' methods for fluo-free-labelling bio-imaging. This is the first France-Bioimaging challenge, which will be followed by several others each year.