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Autophagy is a cellular process with important functions that drive neurodegenerative diseases and cancers. Lysosomal hyperacidification is a hallmark of autophagy. Lysosomal pH is currently measured by fluorescent probes in cell culture, but existing methods do not allow for quantitative, transient or in vivo measurements. In the present study, we developed near-infrared optical nanosensors using organic color centers (covalent sp3 defects on carbon nanotubes) to measure autophagy-mediated endolysosomal hyperacidification in live cells and in vivo. The nanosensors localize to the lysosomes, where the emission band shifts in response to local pH, enabling spatial, dynamic and quantitative mapping of subtle changes in lysosomal pH. Using the sensor, we observed cellular and intratumoral hyperacidification on administration of mTORC1 and V-ATPase modulators, revealing that lysosomal acidification mirrors the dynamics of S6K dephosphorylation and LC3B lipidation while diverging from p62 degradation. This sensor enables the transient and in vivo monitoring of the autophagy–lysosomal pathway. 

Abstract Chirality purification of single-walled carbon nanotubes (SWCNTs) is desirable for applications in many fields, but general utility is currently hampered by low throughput. We discovered a method to obtain single-chirality SWCNT enrichment by the aqueous two-phase extraction (ATPE) method in a single step. To achieve appropriate resolution, a biphasic system of non-ionic tri-block copolymer surfactant is varied with an ionic surfactant. A nearly-monochiral fraction of SWCNTs can then be harvested from the top phase. We also found, via high-throughput, near-infrared excitation-emission photoluminescence spectroscopy, that the parameter space of ATPE can be mapped to probe the mechanics of the separation process. Finally, we found that optimized conditions can be used for sorting of SWCNTs wrapped with ssDNA as well. Elimination of the need for surfactant exchange and simplicity of the separation process make the approach promising for high-yield generation of purified single-chirality SWCNT preparations. 

Conventional molecular recognition elements, such as antibodies, present issues for developing biomolecular assays for use in certain technologies, such as implantable devices. Additionally, antibody development and use, especially for highly multiplexed applications, can be slow and costly. We developed a perception-based platform based on an optical nanosensor array that leverages machine learning algorithms to detect multiple protein biomarkers in biofluids. We demonstrated this platform in gynecologic cancers, often diagnosed at advanced stages, leading to low survival rates. We investigated the detection of protein biomarkers in uterine lavage samples, which are enriched with certain cancer markers compared to blood. We found that the method enables the simultaneous detection of multiple biomarkers in patient samples, with F1-scores of ~0.95 in uterine lavage samples from patients with cancer. This work demonstrates the potential of perception-based systems for the development of multiplexed sensors of disease biomarkers without the need for specific molecular recognition elements.