Dissecting the mechanisms of MASLD fibrosis in the era of single-cell and spatial omics.

Fabio Colella,Neil C Henderson,Prakash Ramachandran
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Abstract

Metabolic dysfunction-associated steatotic liver disease (MASLD), now the most common cause of chronic liver disease, is estimated to affect around 30% of the global population. In MASLD, chronic liver injury can result in scarring or fibrosis, with the degree of fibrosis being the best-known predictor of adverse clinical outcomes. Hence, there is huge interest in developing new therapies to inhibit or reverse fibrosis in MASLD. However, this has been challenging to achieve, as the biology of fibrosis and candidate antifibrotic therapeutic targets have remained poorly described in patient samples. In recent years, the advent of single-cell and spatial omics approaches that can be applied to human samples have started to transform our understanding of fibrosis biology in MASLD. In this Review, we describe these technological advances and discuss the new insights such studies have provided, focusing on the role of epithelial cell plasticity, mesenchymal cell activation, scar-associated macrophage accumulation, and inflammatory cell stimulation as regulators of liver fibrosis. We also consider how omics techniques can enhance our understanding of evolving concepts in the field, such as hot versus cold fibrosis and the mechanisms of liver fibrosis regression. Finally, we touch on future developments and how they are likely to inform a more mechanistic understanding about how fibrosis might differ between patients and how this could influence optimal therapeutic approaches.
在单细胞和空间组学时代剖析MASLD纤维化的机制。
代谢功能障碍相关脂肪变性肝病(MASLD)是目前慢性肝病最常见的病因,估计影响全球约30%的人口。在MASLD中,慢性肝损伤可导致瘢痕形成或纤维化,纤维化程度是最著名的不良临床结果预测指标。因此,人们对开发新的疗法来抑制或逆转MASLD中的纤维化有着巨大的兴趣。然而,由于纤维化生物学和候选抗纤维化治疗靶点在患者样本中仍然描述不清,因此实现这一目标一直具有挑战性。近年来,可以应用于人类样本的单细胞和空间组学方法的出现已经开始改变我们对MASLD纤维化生物学的理解。在这篇综述中,我们描述了这些技术进展,并讨论了这些研究提供的新见解,重点关注上皮细胞可塑性、间充质细胞活化、疤痕相关巨噬细胞积累和炎症细胞刺激作为肝纤维化调节因子的作用。我们还考虑组学技术如何增强我们对该领域不断发展的概念的理解,例如热与冷纤维化以及肝纤维化回归的机制。最后,我们谈到了未来的发展,以及它们如何可能为更机械地理解纤维化在患者之间的差异以及这如何影响最佳治疗方法提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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