Cholesterol homeostasis regulated by ABCA1 is critical for retinal ganglion cell survival.

Genome-wide association studies have suggested a link between primary open-angle glaucoma and the function of ABCA1. ABCA1 is a key regulator of cholesterol efflux and the biogenesis of high-density lipoprotein (HDL) particles. Here, we showed that the POAG risk allele near ABCA1 attenuated ABCA1 expression in cultured cells. Consistently, POAG patients exhibited lower ABCA1 expression, reduced HDL, and higher cholesterol in white blood cells. Ablation of Abca1 in mice failed to form HDL, leading to elevated cholesterol levels in the retina. Counting retinal ganglion cells (RGCs) by using an artificial intelligence (AI) program revealed that Abca1-deficient mice progressively lost RGCs with age. Single-cell RNA sequencing (scRNA-seq) revealed aberrant oxidative phosphorylation in the Abca1-/- retina, as well as activation of the mTORC1 signaling pathway and suppression of autophagy. Treatment of Abca1-/- mice using atorvastatin reduced the cholesterol level in the retina, thereby improving metabolism and protecting RGCs from death. Collectively, we show that lower ABCA1 expression and lower HDL are risk factors for POAG. Accumulated cholesterol in the Abca1-/- retina causes profound aberrant metabolism, leading to a POAG-like phenotype that can be prevented by atorvastatin. Our findings establish statin use as a preventive treatment for POAG associated with lower ABCA1 expression.

Automatic counting of retinal ganglion cells in the entire mouse retina based on improved YOLOv5.

Glaucoma is characterized by the progressive loss of retinal ganglion cells (RGCs), although the pathogenic mechanism remains largely unknown. To study the mechanism and assess RGC degradation, mouse models are often used to simulate human glaucoma and specific markers are used to label and quantify RGCs. However, manually counting RGCs is time-consuming and prone to distortion due to subjective bias. Furthermore, semi-automated counting methods can produce significant differences due to different parameters, thereby failing objective evaluation. Here, to improve counting accuracy and efficiency, we developed an automated algorithm based on the improved YOLOv5 model, which uses five channels instead of one, with a squeeze-and-excitation block added. The complete number of RGCs in an intact mouse retina was obtained by dividing the retina into small overlapping areas and counting, and then merging the divided areas using a non-maximum suppression algorithm. The automated quantification results showed very strong correlation (mean Pearson correlation coefficient of 0.993) with manual counting. Importantly, the model achieved an average precision of 0.981. Furthermore, the graphics processing unit (GPU) calculation time for each retina was less than 1 min. The developed software has been uploaded online as a free and convenient tool for studies using mouse models of glaucoma, which should help elucidate disease pathogenesis and potential therapeutics.

Morphological components detection for super-depth-of-field bio-micrograph based on deep learning.

Accompanied with the clinical routine examination demand increase sharply, the efficiency and accuracy are the first priority. However, automatic classification and localization of cells in microscopic images in super depth of Field (SDoF) system remains great challenges. In this paper, we advance an object detection algorithm for cells in the SDoF micrograph based on Retinanet model. Compared with the current mainstream algorithm, the mean average precision (mAP) index is significantly improved. In the experiment of leucorrhea samples and fecal samples, mAP indexes are 83.1% and 88.1%, respectively, with an average increase of 10%. The object detection model proposed in this paper can be applied to feces and leucorrhea detection equipment, and significantly improve the detection efficiency and accuracy.