Inflammatory Type 2 cDCs Acquire Features of cDC1s and Macrophages to Orchestrate Immunity to Respiratory Virus Infection.

The phenotypic and functional dichotomy between IRF8+ type 1 and IRF4+ type 2 conventional dendritic cells (cDC1s and cDC2s, respectively) is well accepted; it is unknown how robust this dichotomy is under inflammatory conditions, when additionally monocyte-derived cells (MCs) become competent antigen-presenting cells (APCs). Using single-cell technologies in models of respiratory viral infection, we found that lung cDC2s acquired expression of the Fc receptor CD64 shared with MCs and of IRF8 shared with cDC1s. These inflammatory cDC2s (inf-cDC2s) were superior in inducing CD4+ T helper (Th) cell polarization while simultaneously presenting antigen to CD8+ T cells. When carefully separated from inf-cDC2s, MCs lacked APC function. Inf-cDC2s matured in response to cell-intrinsic Toll-like receptor and type 1 interferon receptor signaling, upregulated an IRF8-dependent maturation module, and acquired antigens via convalescent serum and Fc receptors. Because hybrid inf-cDC2s are easily confused with monocyte-derived cells, their existence could explain why APC functions have been attributed to MCs.

An end-to-end pipeline based on open source deep learning tools for reliable analysis of complex 3D images of ovaries.

Computational analysis of bio-images by deep learning (DL) algorithms has made exceptional progress in recent years and has become much more accessible to non-specialists with the development of ready-to-use tools. The study of oogenesis mechanisms and female reproductive success has also recently benefited from the development of efficient protocols for three-dimensional (3D) imaging of ovaries. Such datasets have a great potential for generating new quantitative data but are, however, complex to analyze due to the lack of efficient workflows for 3D image analysis. Here, we have integrated two existing open-source DL tools, Noise2Void and Cellpose, into an analysis pipeline dedicated to 3D follicular content analysis, which is available on Fiji. Our pipeline was developed on larvae and adult medaka ovaries but was also successfully applied to different types of ovaries (trout, zebrafish and mouse). Image enhancement, Cellpose segmentation and post-processing of labels enabled automatic and accurate quantification of these 3D images, which exhibited irregular fluorescent staining, low autofluorescence signal or heterogeneous follicles sizes. In the future, this pipeline will be useful for extensive cellular phenotyping in fish or mammals for developmental or toxicology studies.

C-ECi: a CUBIC-ECi combined clearing method for three-dimensional follicular content analysis in the fish ovary†.

Deciphering mechanisms of oocyte development in the fish ovary still remain challenging, and a comprehensive overview of this process at the level of the organ is still needed. The recent development of optical tissue clearing methods has tremendously boosted the three-dimensional (3D) imaging of large size biological samples that are naturally opaque. However, no attempt of clearing on fish ovary that accumulates extremely high concentration of lipids within oocytes has been reported to date. To face with this ovarian-specific challenge, we combined two existing clearing methods, the nontoxic solvent-based ethyl cinnamate (ECi) method for efficient clearing and the Clear Unobstructed Brain Imaging Cocktails and Computational (CUBIC) method to enhance lipid removal and reduce nonspecific staining. The methyl green fluorescent dye was used to stain nuclei and delineate the follicular structures that include oocytes. Using this procedure (named CUBIC-ECi [C-ECi]), ovaries of both medaka and trout could be imaged in 3D and follicles analyzed. To our knowledge, this is the first procedure elaborated for clearing and imaging fish ovary in 3D. The C-ECi method thus provides an interesting tool for getting precise quantitative data on follicular content in fish ovary and promises to be useful for further developmental and morphological studies.