Semi-Supervised Nonnegative Matrix Factorization of Wide-Field Fluorescence Microscopic Images for Tissue Diagnosis

Abstract This study tests the use of a constrained nonnegative matrix factorization (NMF) algorithm to explore the comparatively new field of chemometric microscopy to support tissue diagnosis. The algorithm can extract the spectral signature and the absolute concentration map of endogenous fluorophores from wide-field microscopic images. The resultant data distinguished normal and fibrous calvarial tissues, based on the changes in their spectral signatures. The absolute concentration map of endogenous fluorophores, nicotinamide adenine dinucleotide (NADH), flavin adenine dinucleotide (FAD), and lipofuscin were derived from microscopic images and compared with the fluorescence from pure fluorophores. While the absolute concentration of NADH increased, the same of FAD and lipofuscin decreased from a normal to fibrous calvarial condition. An increase in the optical redox ratio, possibly due to the metabolic changes during the development of fibrosis, was observed. Differentiating tissue types using the absolute concentration map was found to be considerably more precise than that achievable with relative concentration. The quantification of fluorophores with reference to the absolute concentration map can eliminate uncertainties due to system responses or measurement details, thereby generating more biologically apposite data. Wide-field microscopy augmented with a constrained NMF algorithm could emerge as an advanced diagnostic tool, potentially heralding the emergence of chemometric microscopy.