Krautter, F. | Search bioRxiv

bioRxiv2023

SHORT-TERM CALORIC RESTRICTION IN MICE PROMOTES RESOLUTION OF ATHEROSCLEROSIS, WHILE WEIGHT REGAIN ACCELERATES ITS PROGRESSION

ABSTRACTWhile weight loss is highly recommended for those with obesity, >60% will regain their lost weight. This weight cycling is associated with elevated risk of cardiovascular disease, relative to never having lost weight. How weight loss/regain directly influence atherosclerotic inflammation is unknown. Thus, we studied short-term caloric restriction (stCR) in obese hypercholesterolemic mice, without confounding effects from changes in diet composition. Weight loss was found to promote atherosclerosis resolution independent of plasma cholesterol. From single-cell RNA-sequencing and subsequent mechanistic studies, this can be partly attributed to a unique subset of macrophages accumulating with stCR in epididymal adipose tissue (eWAT) and atherosclerotic plaques. These macrophages, distinguished by high expression of Fcgr4, help to clear necrotic cores in atherosclerotic plaques. Conversely, weight regain (WR) following stCR accelerated atherosclerosis progression with disappearance of Fcgr4+ macrophages from eWAT and plaques. Furthermore, WR caused reprogramming of immune progenitors, sustaining hyper-inflammatory responsiveness. In summary, we have developed a model to investigate the inflammatory effects of weight cycling on atherosclerosis and the interplay between adipose tissue, bone marrow, and plaques. The findings suggest potential approaches to promote atherosclerotic plaque resolution in obesity and weight cycling through induction of Fcgr4+ macrophages and inhibition of immune progenitor reprogramming. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=146 SRC="FIGDIR/small/539777v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@c6e857org.highwire.dtl.DTLVardef@bfd443org.highwire.dtl.DTLVardef@88973org.highwire.dtl.DTLVardef@78198f_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2020

CASTLE: Cell Adhesion with Supervised Training and Learning Environment

Different types of microscopy are used to uncover signatures of cell adhesion and mechanics. Automating the identification and analysis often involve sacrificial routines of cell manipulation such as in vitro staining. Phase-contrast microscopy (PCM) is rarely used in automation due to the difficulties with poor quality images. However, it is the least intrusive method to provide insights into the dynamics of cells, where other types of microscopy are too destructive to monitor. In this study, we propose an efficient workflow to automate cell counting and morphology in PCM images. We introduce Cell Adhesion with Supervised Training and Learning Environment (CASTLE), available as a series of additional plugins to ImageJ. CASTLE combines effective techniques for phase-contrast image processing with statistical analysis and machine learning algorithms to interpret the results. The proposed workflow was validated by comparing the results to a manual count and manual segmentation of cells in images investigating different adherent cell types, including monocytes, neutrophils and platelets. In addition, the effect of different molecules on cell adhesion was characterised using CASTLE. For example, we demonstate that Galectin-9 leads to differences in adhesion of leukocytes. CASTLE also provides information using machine learning techniques, namely principal component analysis and k-means clustering, to distinguish morphology currently inaccessible with manual methods. All scripts and documentation is open-source and available at the corresponding GitLab project.

immunology↗