Obesity and diabetes often lead to peripheral neuropathy. Damage and axonal die-back of the peripheral nervous system constitutes peripheral neuropathy. By 2030, half of the US adult population is projected to be obese, and type 2 diabetes mellitus is most commonly caused by obesity. As incidences of obesity and diabetes increase, the adverse effects of neuropathy will also increase. Neuropathy, previously thought to only affect skin layers of distal extremities, has recently been discovered in subcutaneous adipose tissue depots. Obese adipose tissue is fibrotic, resulting in excess collagen deposition. Collagen organizes the peripheral nervous system, but its interaction with adipose nerves has not been thoroughly investigated. Using 2-photon microscopy combined with second harmonic generation microscopy, we examined the spatial relationship between collagen and nerve in the adipose microenvironment to gain a better understanding of neuropathy pathways and mechanisms. Pearson’s Correlation Coefficient analysis suggests that an obese diet leads to greater colocalization between nerve and collagen in adipose tissue than a lean diet. These findings motivate further investigation as the Pearson Correlation Coefficient is restrictively optimized for structures that are overlapped, whereas nerves may simply be wrapped with or tightly associated with collagen. Here we present an adaptation of the multiscale 2D Wavelet Transform Modulus Maxima method to reveal different anisotropic signatures across adiposeresiding nerve and collagen fibers in tissues from mice fed obese and lean diets, respectively. Based on these promising preliminary results, additional development of multiscale wavelet-based techniques will offer insight into neuropathy through thorough investigation of nerve and collagen spatial relationships.
The extraction of fluorophore lifetimes in a biological sample provides useful information about the probe environment that is not readily available from fluorescence intensity alone. Cell membrane potential, pH, concentration of oxygen ([O2]), calcium ([Ca2+]), NADH and other ions and metabolites are all regularly measured by lifetime-based techniques. These measurements provide invaluable knowledge about cell homeostasis, metabolism and communication with the cell environment. Fluorescence lifetime imaging microscopy (FLIM) produces spatial maps with time-correlated singlephoton counting (TCSPC) histograms collected and analyzed at each pixel, but traditional TCSPC analysis is often hampered by the low number of photons that can reasonably be collected while maintaining high spatial resolution. More important, traditional analysis fails to employ the spatial linkages within the image. Here, we present a different approach, where we work under the assumption that mixtures of a global set of lifetimes (often only 2 or 3) can describe the entire image. We determine these lifetime components by globally fitting precise decays aggregated over large spatial regions of interest, and then we perform a pixel-by-pixel calculation of decay amplitudes (via simple linear algebra applied to coarser time-windows). This yields accurate amplitude images (Decay Associate Images, DAI) that contain stoichiometric information about the underlying mixtures while retaining single pixel resolution. We collected FLIM data of dye mixtures and bacteria expressing fluorescent proteins with a two-photon microscope system equipped with a commercial single-photon counting card, and we used these data to benchmark the gDAI program.