FiCRoN, a deep learning-based algorithm for the automatic determination of intracellular parasite burden from fluorescence microscopy images.

Protozoan parasites are responsible for dramatic, neglected diseases. The automatic determination of intracellular parasite burden from fluorescence microscopy images is a challenging problem. Recent advances in deep learning are transforming this process, however, high-performance algorithms have not been developed. The limitations in image acquisition, especially for intracellular parasites, make this process complex. For this reason, traditional image-processing methods are not easily transferred between different datasets and segmentation-based strategies do not have a high performance. Here, we propose a novel method FiCRoN, based on fully convolutional regression networks (FCRNs), as a promising new tool for estimating intracellular parasite burden. This estimation requires three values, intracellular parasites, infected cells and uninfected cells. FiCRoN solves this problem as multi-task learning: counting by regression at two scales, a smaller one for intracellular parasites and a larger one for host cells. It does not use segmentation or detection, resulting in a higher generalization of counting tasks and, therefore, a decrease in error propagation. Linear regression reveals an excellent correlation coefficient between manual and automatic methods. FiCRoN is an innovative freedom-respecting image analysis software based on deep learning, designed to provide a fast and accurate quantification of parasite burden, also potentially useful as a single-cell counter.

Extracellular vesicles from pristane-treated CD38-deficient mice express an anti-inflammatory neutrophil protein signature, which reflects the mild lupus severity elicited in these mice.

In CD38-deficient ( Cd38-/- ) mice intraperitoneal injection of pristane induces a lupus-like disease, which is milder than that induced in WT mice, showing significant differences in the inflammatory and autoimmune processes triggered by pristane. Extracellular vesicles (EV) are present in all body fluids. Shed by cells, their molecular make-up reflects that of their cell of origin and/or tissue pathological situation. The aim of this study was to analyze the protein composition, protein abundance, and functional clustering of EV released by peritoneal exudate cells (PECs) in the pristane experimental lupus model, to identify predictive or diagnostic biomarkers that might discriminate the autoimmune process in lupus from inflammatory reactions and/or normal physiological processes. In this study, thanks to an extensive proteomic analysis and powerful bioinformatics software, distinct EV subtypes were identified in the peritoneal exudates of pristane-treated mice: 1) small EV enriched in the tetraspanin CD63 and CD9, which are likely of exosomal origin; 2) small EV enriched in CD47 and CD9, which are also enriched in plasma-membrane, membrane-associated proteins, with an ectosomal origin; 3) small EV enriched in keratins, ECM proteins, complement/coagulation proteins, fibrin clot formation proteins, and endopetidase inhibitor proteins. This enrichment may have an inflammation-mediated mesothelial-to-mesenchymal transition origin, representing a protein corona on the surface of peritoneal exudate EV; 4) HDL-enriched lipoprotein particles. Quantitative proteomic analysis allowed us to identify an anti-inflammatory, Annexin A1-enriched pro-resolving, neutrophil protein signature, which was more prominent in EV from pristane-treated Cd38-/- mice, and quantitative differences in the protein cargo of the ECM-enriched EV from Cd38-/- vs WT mice. These differences are likely to be related with the distinct inflammatory outcome shown by Cd38-/- vs WT mice in response to pristane treatment. Our results demonstrate the power of a hypothesis-free and data-driven approach to transform the heterogeneity of the peritoneal exudate EV from pristane-treated mice in valuable information about the relative proportion of different EV in a given sample and to identify potential protein markers specific for the different small EV subtypes, in particular those proteins defining EV involved in the resolution phase of chronic inflammation.

CD38 Deficiency Ameliorates Chronic Graft-Versus-Host Disease Murine Lupus via a B-Cell-Dependent Mechanism.

The absence of the mouse cell surface receptor CD38 in Cd38-/- mice suggests that this receptor acts as a positive regulator of inflammatory and autoimmune responses. Here, we report that, in the context of the chronic graft-versus-host disease (cGVHD) lupus inducible model, the transfer of B6.C-H2bm12/KhEg(bm12) spleen cells into co-isogenic Cd38-/- B6 mice causes milder lupus-like autoimmunity with lower levels of anti-ssDNA autoantibodies than the transfer of bm12 spleen cells into WT B6 mice. In addition, significantly lower percentages of Tfh cells, as well as GC B cells, plasma cells, and T-bet+CD11chi B cells, were observed in Cd38-/- mice than in WT mice, while the expansion of Treg cells and Tfr cells was normal, suggesting that the ability of Cd38-/- B cells to respond to allogeneic help from bm12 CD4+ T cells is greatly diminished. The frequencies of T-bet+CD11chi B cells, which are considered the precursors of the autoantibody-secreting cells, correlate with anti-ssDNA autoantibody serum levels, IL-27, and sCD40L. Proteomics profiling of the spleens from WT cGVHD mice reflects a STAT1-driven type I IFN signature, which is absent in Cd38-/- cGVHD mice. Kidney, spleen, and liver inflammation was mild and resolved faster in Cd38-/- cGVHD mice than in WT cGVHD mice. We conclude that CD38 in B cells functions as a modulator receptor that controls autoimmune responses.