Wu Henry H L, Lang Yandong, Handley Shannon, Knab Aline, Agha Adnan, Tian Yuan, Bhargava Akanksha, Goldys Ewa M, Pollock Carol A, Saad Sonia
Renal Research Laboratory, Kolling Institute of Medical Research, Royal North Shore Hospital & The University of Sydney, Sydney, Australia.
ARC Centre of Excellence for Nanoscale Biophotonics, School of Biomedical Engineering, The University of New South Wales, Sydney, Australia.
Kidney360. 2025 Jun 20. doi: 10.34067/KID.0000000879.
Complications relating to delayed or deteriorating graft function following kidney transplantation are common. There is no validated method apart from transplant kidney biopsy which can accurately identify between the histopathological causes of graft dysfunction. Considering an unmet critical need for a non-invasive approach to reliably diagnose kidney transplant complications, this work proposes a novel methodology based on the assessment of exfoliated proximal tubule cells (PTCs) extracted from urine of kidney transplant recipients by using their multispectral autofluorescence features.
Three groups of 10 patients who have undergone clinically indicated transplant kidney biopsy and was subsequently diagnosed with either acute tubular necrosis (ATN), graft rejection or non-rejection associated interstitial fibrosis and tubular atrophy (IFTA) took part in this study. Exfoliated PTCs from urine collected prior to transplant biopsy were extracted using a validated immunomagnetic separation method based on anti-CD13 and anti-SGLT2 antibodies. Imaging was performed on a custom-made multispectral autofluorescence microscopy and camera system. Multispectral autofluorescence images of PTCs were quantitatively analysed by using optimised small sets of features to prevent overfitting. Binary classification was carried out by a random forest classifier, and the AutoGluon machine learning software. Results were validated by 5-fold cross validation.
For random forest classification, features were selected using entropy-based feature selection, resulting in AUC values of 0.92 (ATN versus graft rejection), 0.86 (ATN versus IFTA) and 0.62 (graft rejection versus IFTA) respectively. The AutoGluon classifier optimisation for the same features resulted in AUC values of 0.95 (ATN versus graft rejection), 0.92 (ATN versus IFTA) and 0.91 (graft rejection vs IFTA).
Our results demonstrate a proof-of-concept that measurement of autofluorescent features from urinary exfoliated PTCs multispectral autofluorescence could differentiate between patient groups with ATN, graft rejection and IFTA in kidney transplant recipients to an excellent degree of accuracy using AutoGluon classifier optimisation.
肾移植后与移植肾功能延迟或恶化相关的并发症很常见。除了移植肾活检外,没有经过验证的方法能够准确区分移植肾功能障碍的组织病理学原因。鉴于可靠诊断肾移植并发症的非侵入性方法存在迫切未满足的需求,本研究提出了一种基于评估从肾移植受者尿液中提取的脱落近端肾小管细胞(PTC)多光谱自发荧光特征的新方法。
三组各10名接受了临床指示的移植肾活检并随后被诊断为急性肾小管坏死(ATN)、移植排斥或与非排斥相关的间质纤维化和肾小管萎缩(IFTA)的患者参与了本研究。在移植活检前收集的尿液中,使用基于抗CD13和抗SGLT2抗体的经过验证的免疫磁珠分离方法提取脱落的PTC。成像在定制的多光谱自发荧光显微镜和相机系统上进行。使用优化的少量特征对PTC的多光谱自发荧光图像进行定量分析,以防止过拟合。通过随机森林分类器和AutoGluon机器学习软件进行二元分类。结果通过五折交叉验证进行验证。
对于随机森林分类,使用基于熵的特征选择来选择特征,结果ATN与移植排斥的AUC值为0.92,ATN与IFTA的AUC值为0.86,移植排斥与IFTA的AUC值为0.62。对相同特征进行AutoGluon分类器优化后,ATN与移植排斥的AUC值为0.95,ATN与IFTA的AUC值为0.92,移植排斥与IFTA的AUC值为0.91。
我们的结果证明了一个概念验证,即使用AutoGluon分类器优化,通过测量尿液脱落PTC的多光谱自发荧光特征,能够在肾移植受者中以极高的准确度区分ATN、移植排斥和IFTA患者组。