Inflammation up-regulates cochlear expression of TRPV1 to potentiate drug-induced hearing loss.

Aminoglycoside antibiotics are essential for treating life-threatening bacterial infections, despite the risk of lifelong hearing loss. Infections induce inflammation and up-regulate expression of candidate aminoglycoside-permeant cation channels, including transient receptor potential vanilloid-1 (TRPV1). Heterologous expression of TRPV1 facilitated cellular uptake of (fluorescently tagged) gentamicin that was enhanced by agonists, and diminished by antagonists, of TRPV1. Cochlear TRPV1 was immunolocalized near the apical membranes of sensory hair cells, adjacent supporting cells, and marginal cells in the stria vascularis. Exposure to immunostimulatory lipopolysaccharides, to simulate of bacterial infections, increased cochlear expression of TRPV1 and hair cell uptake of gentamicin. Lipopolysaccharide exposure exacerbated aminoglycoside-induced auditory threshold shifts and loss of cochlear hair cells in wild-type, but not in heterozygous Trpv1+/- or Trpv1 knockout, mice. Thus, TRPV1 facilitates cochlear uptake of aminoglycosides, and bacteriogenic stimulation upregulates TRPV1 expression to exacerbate cochleotoxicity. Furthermore, loss-of-function polymorphisms in Trpv1 can protect against immunogenic exacerbation of aminoglycoside-induced cochleotoxicity.

Simplified, automated methods for assessing pixel intensities of fluorescently-tagged drugs in cells.

Assessing the cytoplasmic uptake of fluorescently-tagged drugs in heterogeneous cell types currently involves time-consuming manual segmentation of confocal microscopy images. We developed a set of methods that incorporate map algebra techniques to facilitate and expedite image segmentation, particularly of the parenchyma of intermediate cells in the stria vascularis of the inner ear. Map algebra is used to apply a convolution kernel to pixel neighborhoods to create a masking image to select pixels in the original image for further operations. Here, we describe the utility of integrated intensity-based, percentile-based, and local autocorrelation-based methods to automate segmentation of images into putative morphological regions for pixel intensity analysis. Integrated intensity-based methods are variants of watershed segmentation tools that determine morphological boundaries from rates of change in integrated pixel intensity. Percentile- and local autocorrelation-based methods evolved out of the process of developing map algebra- and integrated intensity-based tools. We identified several simplifications that are surprisingly effective for image segmentation and pixel intensity analysis. These methods were empirically validated on three levels: first, the algorithms were developed based on iterations of inspected results; second, algorithms were tested for various types of robustness; and third, developed algorithms were validated against results from manually-segmented images. We conclude the key to automated segmentation is supervision of output data.