Quantitative measurement of intraorganelle pH in the endosomal-lysosomal pathway in neurons by using ratiometric imaging with pyranine.

Organelle acidification is an essential element of the endosomal-lysosomal pathway, but our understanding of the mechanisms underlying progression through this pathway has been hindered by the absence of adequate methods for quantifying intraorganelle pH. To address this problem in neurons, we developed a direct quantitative method for accurately determining the pH of endocytic organelles in live cells. In this report, we demonstrate that the ratiometric fluorescent pH indicator 8-hydroxypyrene-1,3,6-trisulfonic acid (HPTS) is the most advantageous available probe for such pH measurements. To measure intraorganelle pH, cells were labeled by endocytic uptake of HPTS, the ratio of fluorescence emission intensities at excitation wavelengths of 450 nm and 405 nm (F450/405) was calculated for each organelle, and ratios were converted to pH values by using standard curves for F450/405 vs. pH. Proper calibration is critical for accurate measurement of pH values: standard curves generated in vitro yielded artifactually low organelle pH values. Calibration was unaffected by the use of culture medium buffered with various buffers or different cell types. By using this technique, we show that both acidic and neutral endocytically derived organelles exist in the axons of sympathetic neurons in different steady-state proportions than in the cell body. Furthermore, we demonstrate that these axonal organelles have a bimodal pH distribution, indicating a rapid acidification step in their maturation that reduces the average pH of a fraction of the organelles by 2 pH units while leaving few organelles of intermediate pH at steady state. Finally, we demonstrate a spatial gradient or organelle pH along axons, with the relative frequency of acidic organelles increasing with proximity to the cell body.

Neural-network-based classification of cognitively normal, demented, Alzheimer disease and vascular dementia from single photon emission with computed tomography image data from brain.

Single photon emission with computed tomography (SPECT) hexamethylphenylethyleneamineoxime technetium-99 images were analyzed by an optimal interpolative neural network (OINN) algorithm to determine whether the network could discriminate among clinically diagnosed groups of elderly normal, Alzheimer disease (AD), and vascular dementia (VD) subjects. After initial image preprocessing and registration, image features were obtained that were representative of the mean regional tissue uptake. These features were extracted from a given image by averaging the intensities over various regions defined by suitable masks. After training, the network classified independent trials of patients whose clinical diagnoses conformed to published criteria for probable AD or probable/possible VD. For the SPECT data used in the current tests, the OINN agreement was 80 and 86% for probable AD and probable/possible VD, respectively. These results suggest that artificial neural network methods offer potential in diagnoses from brain images and possibly in other areas of scientific research where complex patterns of data may have scientifically meaningful groupings that are not easily identifiable by the researcher.