Biology of the Cell最新文献

Organoids represent a significant advancement in disease modeling, demonstrated by their capacity to mimic the physiological/pathological structure and functional characteristics of the native tissue. Recently CRISPR/Cas9 technology has emerged as a powerful tool in combination with organoids for the development of novel therapies in preclinical settings. This review explores the current literature on applications of pooled CRISPR screening in organoids and the emerging role of these models in understanding cancer. We highlight the evolution of genome-wide CRISPR gRNA library screens in organoids, noting their increasing adoption in the field over the past decade. Noteworthy studies utilizing these screens to investigate oncogenic vulnerabilities and developmental pathways in various organoid systems are discussed. Despite the promise organoids hold, challenges such as standardization, reproducibility, and the complexity of data interpretation remain. The review also addresses the ideas of assessing tumor organoids (tumoroids) against established cancer hallmarks and the potential of studying intercellular cooperation within these models. Ultimately, we propose that organoids, particularly when personalized for patient-specific applications, could revolutionize drug screening and therapeutic approaches, minimizing the reliance on traditional animal models and enhancing the precision of clinical interventions.

Mechanobiology studies how mechanical forces influence biological processes at different scales, both in homeostasis and in pathology. Organoids, 3D structures derived from stem cells, are particularly relevant tools for modeling tissues and organs in vitro. They currently constitute one of the most suitable models for mechanobiology studies. This review provides an overview of existing or applicable approaches to organoids for mechanical studies. We first present the different types of culture supports, including hydrogels and organ-on-chip. We then discuss advanced imaging techniques, particularly suitable for studying the physical properties of cells, allowing the visualization of mechanical forces and cellular responses. We also describe the approaches and tools available to observe the organoids by microscopy. Finally, we present analytical methods, including computational models and biophysical measurement approaches, which facilitate the quantification of mechanical interactions. This review aims to provide the most comprehensive overview possible of the methods, instrumentations, and tools available to conduct a mechanobiological study on organoids.

Collagen is a key protein in mammals that maintains structural integrity within tissues. A failure in fibrillar collagen reorganization can induce cancer or fibrosis formation, such as in spinal cord injury (SCI), where the healing process after the initial trauma leads to the formation of scar tissue, which includes fibrosis. As there is no current treatment targeting the fibrotic process directly, a better understanding of collagen properties can thus help to apprehend malignant states.

Characterization of collagen fibers has been widely explored on second-harmonic generation (SHG) images, due to the label-free nature of the SHG imaging technique. It has been performed with various fibers extraction methods such as curvelet transform (CT) implemented in the open-source software CurveAlign. However, when it comes to investigating undergrowth collagen fibers (collagen fibers that are still under reorganization) as observed in SCI, the CT method becomes complex to tune for nonadvanced users in order to properly segment the fibers. To improve collagen detection in the case of undergrowth fibers, we propose a methodology based on the fingerprint enhancement (FP-E) algorithm that requires fewer user input parameters and is less time-consuming. Our method was extensively tested on SHG data from injured spinal cord samples.

We obtained metrics that depicted changes in collagen organization over time, particularly a significant increase in fiber density, demonstrating the FP-E algorithm was properly adapted to address the evolution of collagen properties after SCI. Besides the simpler tuning of the method compared to commonly used software, the combination with further characterization of the extracted fibers could lead to consider fibrillar collagen as a biomarker in diseases where fibers are under development. The FP-E algorithm is provided in the article.