Noninvasive Assessment of Urinary Exfoliated Proximal Tubule Cell Multispectral Autofluorescence May Differentiate between Causes of Kidney Transplant Dysfunction.
- Publisher:
- Wolters Kluwer
- Publication Type:
- Journal Article
- Citation:
- Kidney360, 2025, 6, (11), pp. 1853-1862
- Issue Date:
- 2025-11-01
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KEY POINTS: There is an unmet critical need currently for a noninvasive approach to accurately diagnose the cause of kidney transplant complications. Cell multispectral autofluorescence signals have demonstrated to be highly biologically informative, reflecting cell and tissue metabolic status. Urinary exfoliated proximal tubule cell multispectral autofluorescence could potentially be used to differentiate between causes of transplant graft dysfunction. BACKGROUND: 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 histopathologic causes of graft dysfunction. Considering an unmet critical need for a noninvasive 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. METHODS: Three groups of ten 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 before transplant biopsy were extracted using a validated immunomagnetic separation method based on anti-CD13 and anti-sodium-glucose co-transport 2 antibodies. Imaging was performed on a custom-made multispectral autofluorescence microscopy and camera system. Multispectral autofluorescence images of PTCs were quantitatively analyzed by using optimized small sets of features to prevent overfitting. Binary classification was performed by a random forest classifier and the AutoGluon machine learning software. The results were validated by five-fold cross-validation. RESULTS: For random forest classification, features were selected using entropy-based feature selection, resulting in area under the curve 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 area under the curve values of 0.95 (ATN versus graft rejection), 0.92 (ATN versus IFTA), and 0.91 (graft rejection versus IFTA). CONCLUSIONS: 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.
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