3D quantification of NASH-related liver fibrosis using open-top light-sheet microscopy

摘要 在美国，非酒精性脂肪性肝病(NAFLD)的患病率估计为25%。 它是慢性肝病最常见的原因。非酒精性脂肪性肝炎 进行性NAFLD，影响1-3%的美国人口，预计到 2030年。目前，非酒精性脂肪性肝病尚无有效的药物治疗方法，但有多种治疗方法。 有希望的候选药物正在临床试验中进行评估。但是，的主端点 这些组织学纤维化试验有缺陷，包括抽样误差和使用 主观五类量表量化一个连续变量。我们的团队率先使用了 3D开顶式光片(OTLS)显微镜技术，使快速、高通量成像成为可能 大量临床样本。结合尖端的机器学习技术，我们 假设3D OTLS显微镜可以提供更准确和一致的评估 来自NASH患者的肝活检中的纤维化。我们将通过开发一个 多重染色协议和机器学习分析流水线，将于20日试行 存档的FFPE肝活检。我们的化验结果将与目前使用的 利用SING存档促进进一步研究的主要和次要结果措施 具有响应性和结果数据的临床试验样本。

Abstract Non-alcoholic fatty liver disease (NAFLD) prevalence is estimated at >25% in the U.S., making it the most common cause of chronic liver disease. Non-alcoholic steatohepatitis (NASH), the progressive form of NAFLD, affects 1-3% of the U.S. population and is expected to double by 2030. Currently, there is no effective pharmacotherapy for NAFLD, but there are numerous promising drug candidates being evaluated in clinical trials. However, the primary endpoint for these trials, histologic fibrosis, has shortcomings including sampling error and use of a subjective five category scale to quantify a continuous variable. Our team has pioneered the use of 3D open-top light-sheet (OTLS) microscopy, which enables rapid, high-throughput imaging of large clinical samples. In combination with cutting-edge machine learning techniques, we hypothesize that 3D OTLS microscopy can provide more accurate and consistent assessment of fibrosis in liver biopsies from NASH patients. We will test this hypothesis by developing a multiplex staining protocol and machine learning analysis pipeline, which will be piloted on 20 archived FFPE liver biopsies. Results from our assay will be correlated with currently used primary and secondary outcome measures to motivate further studies with sing archived samples from clinical trials with responsivity and outcomes data.