IgA nephropathy (IgAN) is the most common glomerulonephritis. It is characterized by the deposition of immune complexes containing immunoglobulin A (IgA) in the kidney’s glomeruli, triggering an inflammatory process. In many patients, the disease has a progressive course, eventually leading to end-stage kidney disease. The current understanding of IgAN’s pathophysiology is incomplete, with the involvement of several potential players, including the mucosal immune system, the complement system, and the microbiome. Dissecting this complex pathophysiology requires an integrated analysis across molecular, cellular, and organ scales. Such data can be obtained by employing emerging technologies, including single-cell sequencing, next-generation sequencing, proteomics, and complex imaging approaches. These techniques generate complex “big data,” requiring advanced computational methods for their analyses and interpretation. Here, we introduce such methods, focusing on the broad areas of bioinformatics and artificial intelligence and discuss how they can advance our understanding of IgAN and ultimately improve patient care. The close integration of advanced experimental and computational technologies with medical and clinical expertise is essential to improve our understanding of human diseases. We argue that IgAN is a paradigmatic disease to demonstrate the value of such a multidisciplinary approach.

IgA 肾病 (IgAN) 是最常见的肾小球肾炎。它的特征是含有免疫球蛋白 A (IgA) 的免疫复合物在肾脏的肾小球中沉积，从而引发炎症过程。在许多患者中，该疾病具有进行性病程，最终导致终末期肾病。目前对 IgAN 的病理生理学的理解是不完整的，涉及到几个潜在的参与者，包括黏膜免疫系统、补体系统和微生物组。剖析这种复杂的病理生理学需要跨分子、细胞和器官尺度的综合分析。这些数据可以通过采用新兴技术获得，包括单细胞测序、下一代测序、蛋白质组学和复杂的成像方法。这些技术生成复杂的“大数据，” 需要先进的计算方法来进行分析和解释。在这里，我们将介绍这些方法，重点关注生物信息学和人工智能的广泛领域，并讨论它们如何促进我们对 IgAN 的理解并最终改善患者护理。先进的实验和计算技术与医学和临床专业知识的紧密结合对于提高我们对人类疾病的理解至关重要。我们认为 IgAN 是一种典型疾病，可以证明这种多学科方法的价值。先进的实验和计算技术与医学和临床专业知识的紧密结合对于提高我们对人类疾病的理解至关重要。我们认为 IgAN 是一种典型疾病，可以证明这种多学科方法的价值。先进的实验和计算技术与医学和临床专业知识的紧密结合对于提高我们对人类疾病的理解至关重要。我们认为 IgAN 是一种典型疾病，可以证明这种多学科方法的价值。