Increasing sophistication in the design and interrogation of biological models, and the advent of novel fluorescent probes have led to new demands on molecular imaging systems to deliver enhanced sensitivity, reliable quantitation, and the ability to resolve multiple simultaneous signals. Sensitivity is limited, especially in the visible spectral range, by the presence of ubiquitous autofluorescence signals (mostly arising from the skin and gut), which need to be separated from those of targeted fluorophores. Fluorescence-based imaging is also affected by absorbing and scattering properties of tissues in the visible and to a lesser extent in the near-infrared (NIR). However, the small size of typical animal models (usually mice) often permit the detection of enough light arising even from relatively deep locations to allow capture of signals with acceptable signal-to-noise. Multispectral imaging, through its ability to separate autofluorescence from the label fluorescence, can increase sensitivity by as much as 300-fold compared to conventional approaches, and concomitantly improves quantitative accuracy. In the NIR region, autofluorescence, while still significant, poses less of a problem. However, the task of disentangling signals from multiple fluorophores remains. Multispectral imaging allows the separation of five or more fluorophores, with each signal quantitated and visualized separately. Preclinical small-animal imaging is often accompanied by microscopic analysis, both before and after the in vivo phase. This can involve tissue culture manipulations and/or histological examination of fixed or frozen tissue. Due to the same advantages already discussed with respect to sensitivity, quantitation and multiplexing, microscopy-based multispectral techniques form an excellent complement to in vivo imaging.
Multispectral imaging is the underlying enabling technology used in the Maestro™ and Nuance™ product lines from CRi. The software algorithms built into these products provide the end-user with the ability to resolve one or more fluorescent signals in a biological sample that are spectrally or spatially overlapping as separate component images (spectral species) of the whole. Each of the signals can be separated as a component image of the whole from across the entire visible spectrum of the whole image, quantify the contribution of each signal and store that information in memory on the computer as an "image stack" or "image cube." For the final display the separate component images are assigned a different color for each spectral species and the algorithm provides the final unmixed composite with pseudo-color representation that is differentiated from the original RGB image. In this way, multispectral imaging technology enables the end-user to study the activity of multiple proteins in a signaling pathway or parallel pathways at the same time.
These protocols describe methods to track multiple markers at the whole animal level by fluorescent-based in vivo imaging in Maestro and then confirm the analysis in vitro using Nuance 2 for imaging resolution of multiple signaling pathway-related proteins in cancer cells in tissue sections, on a cell-by-cell basis, even if the proteins are co-localized.
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