δ-Tocopherol Effect on Endocytosis and Its Combination with Enzyme Replacement Therapy for Lysosomal Disorders: A New Type of Drug Interaction?

Induction of lysosomal exocytosis alleviates lysosomal storage of undigested metabolites in cell models of lysosomal disorders (LDs). However, whether this strategy affects other vesicular compartments, e.g., those involved in endocytosis, is unknown. This is important both to predict side effects and to use this strategy in combination with therapies that require endocytosis for intracellular delivery, such as lysosomal enzyme replacement therapy (ERT). We investigated this using δ-tocopherol as a model previously shown to induce lysosomal exocytosis and cell models of type A Niemann-Pick disease, a LD characterized by acid sphingomyelinase (ASM) deficiency and sphingomyelin storage. δ-Tocopherol and derivative CF3-T reduced net accumulation of fluid phase, ligands, and polymer particles via phagocytic, caveolae-, clathrin-, and cell adhesion molecule (CAM)-mediated pathways, yet the latter route was less affected due to receptor overexpression. In agreement, δ-tocopherol lowered uptake of recombinant ASM by deficient cells (known to occur via the clathrin pathway) and via targeting intercellular adhesion molecule-1 (associated to the CAM pathway). However, the net enzyme activity delivered and lysosomal storage attenuation were greater via the latter route. Data suggest stimulation of exocytosis by tocopherols is not specific of lysosomes and affects endocytic cargo. However, this effect was transient and became unnoticeable several hours after tocopherol removal. Therefore, induction of exocytosis in combination with therapies requiring endocytic uptake, such as ERT, may represent a new type of drug interaction, yet this strategy could be valuable if properly timed for minimal interference.

Label-free, non-invasive, and repeatable cell viability bioassay using dynamic full-field optical coherence microscopy and supervised machine learning.

We present a novel method that can assay cellular viability in real-time using supervised machine learning and intracellular dynamic activity data that is acquired in a label-free, non-invasive, and non-destructive manner. Cell viability can be an indicator for cytology, treatment, and diagnosis of diseases. We applied four supervised machine learning models on the observed data and compared the results with a trypan blue assay. The cell death assay performance by the four supervised models had a balanced accuracy of 93.92 ± 0.86%. Unlike staining techniques, where criteria for determining viability of cells is unclear, cell viability assessment using machine learning could be clearly quantified.