PO.CL12.04 · 临床研究
利用无标记成像和自动化分析流程对患者来源癌症类器官进行快速评估
Rapid assessment of patient derived cancer organoids using label-free imaging and an automated analysis pipeline
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景：肿瘤异质性是有效癌症治疗中的一项重大挑战，尤其是在结直肠癌（CRC）中，它限制了治疗的疗效并驱动耐药。患者来源癌症类器官（PDCO）已成为强有力的临床前模型，能够忠实地重现原发肿瘤的基因组、形态学和代谢特征。然而，目前快速且可重复地评估PDCO的方法有限。无标记成像方法是测量类器官水平异质性并快速筛选PDCO药物反应的有前景的工具。然而，对宽场光学氧化还原图像进行人工分析对于大规模药物筛选而言效率低下且费力。在此，我们开发了一套用于PDCO分割、单个PDCO追踪以及自体荧光图像背景校正的自动化流程。
方法：宽场光学氧化还原成像（WF ORI）通过测量代谢辅酶NAD(P)H和FAD的自体荧光强度，在无标记或无需额外试剂的情况下提供类器官水平的治疗反应测量，其光学氧化还原比定义为[NAD(P)H/NAD(P)H+FAD]的荧光强度，用于测量多个CRC PDCO细胞系的氧化还原状态。前沿分析工具的开发将ORI测量隔离到PDCO外缘32μm的区域，有助于最大化利用WF ORI测量CRC PDCO治疗反应的灵敏度和可重复性。该自动化流程包括使用微调后的Cellpose模型进行分割、通过自定义python代码随时间自动追踪单个PDCO以及背景校正。使用Glass's delta（GΔ）来测量PDCO治疗效应量。
结果：前沿分析提高了对治疗后PDCO氧化还原变化的灵敏度（GΔ = 1.462 对比 GΔ = 1.233）。与人工掩膜相比，自动化分割实现了平均Dice分数≥0.8，表明高度可重复性。此外，与人工追踪相比，自动化PDCO追踪的准确率在召回率和Jaccard指数两项指标上均超过94%。重要的是，该自动化流程能够随时间分辨单个PDCO的反应，其对药物治疗的灵敏度与人工方法相当，而处理时间比人工流程快127倍以上。
结论：总体而言，我们证明了将PDCO与易获取的成像和分析技术相结合，能够对肿瘤异质性和治疗反应进行高通量的详细评估。
查看英文原文 English abstract
Background: Tumor heterogeneity presents a major challenge in effective cancer treatment, particularly in colorectal cancer (CRC), by limiting the efficacy of therapies and driving resistance. Patient-derived cancer organoids (PDCOs) have emerged as powerful preclinical models that faithfully recapitulate the genomic, morphological, and metabolic profiles of primary tumors. However, current methods for rapidly and reproducibly assessing PDCOs are limited. Label-free imaging methods are a promising tool to measure organoid level heterogeneity and rapidly screen drug response in PDCOs. However, manual analysis of wide-field optical redox images is inefficient and laborious for large-scale drug screens. Here, we developed an automated pipeline for PDCO segmentation, single-PDCO tracking, and background correction in autofluorescence images.
Methods: Wide field optical redox imaging (WF ORI) provided organoid-level measurements of treatment response without labels or additional reagents by measuring the autofluorescence intensity of the metabolic co-enzymes NAD(P)H and FAD, and the optical redox ratio, defined as the fluorescence intensity of [NAD(P)H/NAD(P)H+FAD], was used to measure the oxidation-reduction state of multiple CRC PDCO lines. Development of leading-edge analysis tools, isolating the ORI measurement to a 32μm region at the outer edge of the PDCOs, helped to maximize the sensitivity and reproducibility of treatment response measurements using WF ORI in CRC PDCOs. The automated pipeline includes segmentation using a fine-tuned Cellpose model, automated single-PDCO tracking over time via custom python code, and background correction. Glass's delta (G∆) is used to measure the PDCO treatment effect size.
Results: Leading-edge analysis improves sensitivity to redox changes in treated PDCOs (G∆ = 1.462 vs G∆ = 1.233). Automated segmentation, when compared to manual masks, achieved mean Dice scores ≥0.8, indicating high reproducibility. Additionally, automated PDCO tracking accuracy exceeded 94% by two metrics, recall and Jaccard index, when compared to manual tracking. Importantly, the automated pipeline resolves single-PDCO responses over time with comparable sensitivity to drug treatment with over 127× faster processing time compared to the manual process.
Conclusion: Overall, we demonstrate that combining PDCOs with accessible imaging and analysis techniques enables high-throughput detailed evaluation of tumor heterogeneity and therapeutic response.
利益披露 Disclosure
A. A. Gillette, None..
A. Hsu, None.