Genome-wide polygenic risk score for rheumatoid arthritis prediction in postmenopausal women.

BACKGROUND: Rheumatoid arthritis (RA), a common autoimmune disease, exhibits a vital genetic component. Polygenic risk scores (PRS) derived from genome-wide association studies (GWAS) offer potential utility in predicting disease susceptibility. The present study aimed to develop and validate a PRS for predicting RA risk in postmenopausal women. METHODS: The study developed a novel PRS using 225,000 genetic variants from a GWAS dataset. The PRS was developed in a cohort of 8967 postmenopausal women and validated in an independent cohort of 6269 postmenopausal women. Among the development cohort, approximately 70% were Hispanic and approximately 30% were African American. The testing cohort comprised approximately 50% Hispanic and 50% Caucasian individuals. Stratification according to PRS quintiles revealed a pronounced gradient in RA prevalence and odds ratios. RESULTS: High PRS was significantly associated with increased RA risk in individuals aged 60-70 years, ≥ 70 years, and overweight and obese participants. Furthermore, at age 65 years, individuals in the bottom 5% of the PRS distribution have an absolute risk of RA at 30.6% (95% confidence interval = 18.5%-42.6%). The risk increased to 53.8% (95% confidence interval = 42.8%-64.9%) for those in the top 5% of the PRS distribution. CONCLUSIONS: The PRS developed in the present study is significantly associated with RA risk, showing the potential for early screening of RA in postmenopausal women. This work demonstrates the feasibility of personalized medicine in identifying high-risk individuals for RA, indicating the need for further studies to test the utility of PRS in other populations.

Tensile force field plays a crucial role in local invasion of tumor cells through a mechano-chemical coupling mechanism.

Cancer metastasis starts from early local invasion, during which tumor cells detach from the primary tumor, penetrate the extracellular matrix (ECM), and then invade neighboring tissues. However, the cellular mechanics in the detaching and penetrating processes have not been fully understood, and the underlying mechanisms that influence cell polarization and migration in the 3D matrix during tumor invasion remain largely unknown. In this study, we employed a dual tumor-spheroid model to investigate the cellular mechanisms of the tumor invasion. Our results revealed that the tensional force field developed by the active contraction of cells and tissues played a pivotal role in tumor invasion, acting as the driving force for remodeling the collagen fibers during the invasion process. The remodeled collagen fibers promoted cell polarization and migration because of the stiffening of the fiber matrix. The aligned fibers facilitated tumor cell invasion and directed migration from one spheroid to the other. Inhibiting/shielding the cellular contractility abolished matrix remodeling and re-alignment and significantly decreased tumor cell invasion. By developing a coarse-grained cell model that considers the mutual interaction between cells and fibers, we predicted the tensional force field in the fiber network and the associated cell polarization and cell-matrix interaction during cell invasion, which revealed a mechano-chemical coupling mechanism at the cellular level of the tumor invasion process. Our study highlights the roles of cellular mechanics at the early stage of tumor metastasis and may provide new therapeutic strategies for cancer therapy.

Semi-Supervised Instance Segmentation in Whole Slide Images via Dense Spatial Variability Enhancing.

Current whole slide image (WSI) segmentation aims at extracting tumor regions from the background. Unlike this, segmenting distinct tumor areas (instances) within a WSI driven by limited annotated data remains under-explored. In this paper, we formally propose semisupervised instance segmentation (Semi-IS) in WSIs. We address a key challenge: learning intra-class similarity and inter-class dissimilarity driven by unlabeled data. Specifically, we generally perceive the patch as composed of tokens (together), not the patch alone. We employ contrastive learning to develop a segmentation framework. In the SemiIS, we find that the boundaries of segmented instances are usually disturbed by noise. We jointly eliminate and preserve noise features to address this problem. We conduct extensive experiments to evaluate the effectiveness and generalizability of Semi-IS, including histopathology and cellular pathology. The results show that in clinical multi instance segmentation tasks, Semi-IS achieves almost fullsupervised state-of-the-art results with only 30% annotated data. Semi-IS can improve segmentation accuracy by about 2% on public cell pathology datasets.

Optogenetic therapeutic strategies for diabetes mellitus.

Diabetes mellitus (DM) is a common chronic disease affecting humans globally. It is characterized by abnormally elevated blood glucose levels due to the failure of insulin production or reduction of insulin sensitivity and functionality. Insulin and glucagon-like peptide (GLP)-1 replenishment or improvement of insulin resistance are the two major strategies to treat diabetes. Recently, optogenetics that uses genetically encoded light-sensitive proteins to precisely control cell functions has been regarded as a novel therapeutic strategy for diabetes. Here, we summarize the latest development of optogenetics and its integration with synthetic biology approaches to produce light-responsive cells for insulin/GLP-1 production, amelioration of insulin resistance and neuromodulation of insulin secretion. In addition, we introduce the development of cell encapsulation and delivery methods and smart bioelectronic devices for the in vivo application of optogenetics-based cell therapy in diabetes. The remaining challenges for optogenetics-based cell therapy in the clinical translational study are also discussed.

Optogenetic Strategies for Optimizing the Performance of Phospholipids Biosensors.

High-performance biosensors play a crucial role in elucidating the intricate spatiotemporal regulatory roles and dynamics of membrane phospholipids. However, enhancing the sensitivity and imaging performance remains a significant challenge. Here, optogenetic-based strategies are presented to optimize phospholipid biosensors. These strategies involves presequestering unbound biosensors in the cell nucleus and regulating their cytosolic levels with blue light to minimize background signal interference in phospholipid detection, particularly under conditions of high expression levels of biosensor. Furthermore, optically controlled phase separation and the SunTag system are employed to generate punctate probes for substrate detection, thereby amplifying biosensor signals and enhancing visualization of the detection process. These improved phospholipid biosensors hold great potential for enhancing the understanding of the spatiotemporal dynamics and regulatory roles of membrane lipids in live cells and the methodological insights in this study might be valuable for developing other high-performance biosensors.

Deep learning-enabled fast DNA-PAINT imaging in cells.

DNA-based point accumulation in nanoscale topography (DNA-PAINT) is a well-established technique for single-molecule localization microscopy (SMLM), enabling resolution of up to a few nanometers. Traditionally, DNA-PAINT involves the utilization of tens of thousands of single-molecule fluorescent images to generate a single super-resolution image. This process can be time-consuming, which makes it unfeasible for many researchers. Here, we propose a simplified DNA-PAINT labeling method and a deep learning-enabled fast DNA-PAINT imaging strategy for subcellular structures, such as microtubules. By employing our method, super-resolution reconstruction can be achieved with only one-tenth of the raw data previously needed, along with the option of acquiring the widefield image. As a result, DNA-PAINT imaging is significantly accelerated, making it more accessible to a wider range of biological researchers.