A Sensor Array Composed of Organelle-Targeting Fluorescent Probes and Polydopamine Particles for Deep Learning-Assisted Identification and Ablation of Drug-Resistant Lung Tumors

Lung cancer, a leading cause of global cancer-related mortality, predominantly features nonsmall cell lung cancer (NSCLC), constituting 80% of all lung malignancies. Despite chemotherapy being the primary NSCLC treatment, the emergence of drug resistance poses a significant challenge. Identifying drug-resistant cells and characterizing the resistance type is crucial for guiding clinical interventions in NSCLC. The homogeneity of drug-sensitive/resistant cancer cells presents a challenge in their identification as well as in distinguishing tumor slices. Organelles, pivotal for cellular function, exhibit notable variations in the microenvironment among diverse cell types. In this work, three organelle-targeting nanoparticles, composed of fluorescent probes and polydopamine particles, collectively formed PPTA-SA (an organelle-targeting sensor array) for imaging NSCLC cells and tumor slices. With a deep learning network, PPTA-SA could be used for identification of drug-resistant lung cells and tumors. The achieved identification accuracy for drug-resistant NSCLC cells and NSCLC tumor slices was more than 99%. Moreover, the multiorganelle targeting photothermal therapy demonstrated superior tumor ablation effects compared to conventional single-organelle targeting photothermal therapy. The combination of fluorescent probes and polydopamine not only served as a valuable tool for drug-sensitive/resistant NSCLC identification but also facilitated photothermal therapy with enhanced effects.