Towards multimodal nonlinear optical tomography - experimental methodology

Imaging living organisms with molecular selectivity typically requires the introduction of specific labels. Many applications in biology and medicine, however, would significantly benefit from a noninvasive imaging technique that circumvents such exogenous probes. In vivo microscopy based on vibrational spectroscopic contrast offers a unique approach for visualizing tissue architecture with molecular specificity. We have developed a sensitive technique for vibrational imaging of tissues by combining coherent anti-Stokes Raman scattering (CARS) with video-rate microscopy. Backscattering of the intense forward-propagating CARS radiation in tissue gives rise to a strong epi-CARS signal that makes in vivo imaging possible. This substantially large signal allows for real-time monitoring of dynamic processes, such as the diffusion of chemical compounds, in tissues. By tuning into the CH(2) stretching vibrational band, we demonstrate CARS imaging and spectroscopy of lipid-rich tissue structures in the skin of a live mouse, including sebaceous glands, corneocytes, and adipocytes, with unprecedented contrast at subcellular resolution.

Coherent anti-Stokes Raman scattering (CARS) microscopy is a label-free imaging technique that is capable of real-time, nonperturbative examination of living cells and organisms based on molecular vibrational spectroscopy. Recent advances in detection schemes, understanding of contrast mechanisms, and developments of laser sources have enabled superb sensitivity and high time resolution. Emerging applications, such as metabolite and drug imaging and tumor identification, raise many exciting new possibilities for biology and medicine.

Many biological pathways were first uncovered by identifying mutants with visible phenotypes and by scoring every sample in a screen via tedious and subjective visual inspection. Now, automated image analysis can effectively score many phenotypes. In practical application, customizing an image-analysis algorithm or finding a sufficient number of example cells to train a machine learning algorithm can be infeasible, particularly when positive control samples are not available and the phenotype of interest is rare. Here we present a supervised machine learning approach that uses iterative feedback to readily score multiple subtle and complex morphological phenotypes in high-throughput, image-based screens. First, automated cytological profiling extracts hundreds of numerical descriptors for every cell in every image. Next, the researcher generates a rule (i.e., classifier) to recognize cells with a phenotype of interest during a short, interactive training session using iterative feedback. Finally, all of the cells in the experiment are automatically classified and each sample is scored based on the presence of cells displaying the phenotype. By using this approach, we successfully scored images in RNA interference screens in 2 organisms for the prevalence of 15 diverse cellular morphologies, some of which were previously intractable.