T cell migration and effector function differences in familial adenomatous polyposis patients with APC gene mutations.

Familial adenomatous polyposis (FAP) is an inherited disease characterized by the development of large number of colorectal adenomas with high risk of evolving into colorectal tumors. Mutations of the Adenomatous polyposis coli (APC) gene is often at the origin of this disease, as well as of a high percentage of spontaneous colorectal tumors. APC is therefore considered a tumor suppressor gene. While the role of APC in intestinal epithelium homeostasis is well characterized, its importance in immune responses remains ill defined. Our recent work indicates that the APC protein is involved in various phases of both CD4 and CD8 T cells responses. This prompted us to investigate an array of immune cell features in FAP subjects carrying APC mutations. A group of 12 FAP subjects and age and sex-matched healthy controls were studied. We characterized the immune cell repertoire in peripheral blood and the capacity of immune cells to respond ex vivo to different stimuli either in whole blood or in purified T cells. A variety of experimental approaches were used, including, pultiparamater flow cytometry, NanosString gene expression profiling, Multiplex and regular ELISA, confocal microscopy and computer-based image analyis methods. We found that the percentage of several T and natural killer (NK) cell populations, the expression of several genes induced upon innate or adaptive immune stimulation and the production of several cytokines and chemokines was different. Moreover, the capacity of T cells to migrate in response to chemokine was consistently altered. Finally, immunological synapses between FAP cytotoxic T cells and tumor target cells were more poorly structured. Our findings of this pilot study suggest that mild but multiple immune cell dysfunctions, together with intestinal epithelial dysplasia in FAP subjects, may facilitate the long-term polyposis and colorectal tumor development. Although at an initial discovery phase due to the limited sample size of this rare disease cohort, our findings open new perspectives to consider immune cell abnormalities into polyposis pathology.

Democratising deep learning for microscopy with ZeroCostDL4Mic.

Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier that novice users often find difficult to overcome. Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google Colab. ZeroCostDL4Mic allows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation (using U-Net and StarDist), object detection (using YOLOv2), denoising (using CARE and Noise2Void), super-resolution microscopy (using Deep-STORM), and image-to-image translation (using Label-free prediction - fnet, pix2pix and CycleGAN). Importantly, we provide suitable quantitative tools for each network to evaluate model performance, allowing model optimisation. We demonstrate the application of the platform to study multiple biological processes.