Biomarkers of disease progression and treatment response are urgently needed for patients with lymphangioleiomyomatosis (LAM). Activity-based nanosensors, an emerging biosensor class, detect dysregulated proteases in vivo and release a reporter to provide a urinary readout of disease. Because proteases are dysregulated in LAM and may directly contribute to lung function decline, activity-based nanosensors may enable quantitative, real-time monitoring of LAM progression and treatment response. We aimed to assess the diagnostic utility of activity-based nanosensors in a pre-clinical model of pulmonary LAM.

Tsc2-null cells were injected intravenously into female nude mice to establish a mouse model of pulmonary LAM. A library of 14 activity-based nanosensors, designed to detect proteases across multiple catalytic classes, was administered into the lungs of LAM mice and healthy controls, urine was collected, and mass spectrometry was performed to measure nanosensor cleavage products. Mice were then treated with rapamycin and monitored with activity-based nanosensors. Machine learning was performed to distinguish diseased from healthy and treated from untreated mice.

Multiple activity-based nanosensors (PP03 (cleaved by metallo, aspartic and cysteine proteases), p_adjusted<0.0001; PP10 (cleaved by serine, aspartic and cysteine proteases), p_adjusted=0.017)) were differentially cleaved in diseased and healthy lungs, enabling strong classification with a machine learning model (area under the curve (AUC) 0.95 from healthy). Within 2 days after rapamycin initiation, we observed normalisation of PP03 and PP10 cleavage, and machine learning enabled accurate classification of treatment response (AUC 0.94 from untreated).

Activity-based nanosensors enable noninvasive, real-time monitoring of disease burden and treatment response in a pre-clinical model of LAM.

淋巴管平滑肌瘤病 (LAM) 患者迫切需要疾病进展和治疗反应的生物标志物。基于活性的纳米传感器是一种新兴的生物传感器，可检测体内失调的蛋白酶并释放报告基因以提供疾病的尿液读数。由于 LAM 中的蛋白酶失调，可能直接导致肺功能下降，因此基于活性的纳米传感器可以实现对 LAM 进展和治疗反应的定量、实时监测。我们的目的是评估基于活性的纳米传感器在肺 LAM 临床前模型中的诊断效用。

将Tsc2缺失细胞静脉注射至雌性裸鼠体内，建立肺LAM小鼠模型。将 14 个基于活性的纳米传感器库（旨在检测多种催化类别的蛋白酶）注入 LAM 小鼠和健康对照组的肺部，收集尿液，并进行质谱分析以测量纳米传感器裂解产物。然后用雷帕霉素治疗小鼠并用基于活性的纳米传感器进行监测。进行机器学习来区分患病小鼠与健康小鼠以及接受治疗的小鼠与未经治疗的小鼠。

多种基于活性的纳米传感器（PP03（由金属、天冬氨酸和半胱氨酸蛋白酶切割），p_调整<0.0001；PP10（由丝氨酸、天冬氨酸和半胱氨酸蛋白酶切割），p_调整=0.017））在患病和健康肺部中的切割存在差异，通过机器学习模型实现强分类（健康曲线下面积 (AUC) 0.95）。开始使用雷帕霉素后 2 天内，我们观察到 PP03 和 PP10 裂解正常化，并且机器学习能够准确分类治疗反应（未治疗的 AUC 0.94）。

基于活动的纳米传感器能够在 LAM 临床前模型中对疾病负担和治疗反应进行无创实时监测。