Rapid, high-resolution, non-destructive assessments of metabolic and morphological homogeneity uniquely identify high-grade cervical precancerous lesions.

Purpose: Two-photon microscopy (2PM) is an emerging clinical imaging modality with the potential to non-invasively assess tissue metabolism and morphology in high-resolution. This study aimed to assess the translational potential of 2PM for improved detection of high-grade cervical precancerous lesions. Experimental Design: 2P images attributed to reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and oxidized flavoproteins (FP) were acquired from the full epithelial thickness of freshly excised human cervical tissue biopsies (N = 62). Fifteen biopsies harbored high-grade squamous intraepithelial lesions (HSILs), 14 biopsies harbored low-grade SILs (LSILs), and 33 biopsies were benign. Quadratic discriminant analysis (QDA) leveraged morphological and metabolic functional metrics extracted from these images to predict the presence of HSILs. We performed gene set enrichment analysis (GSEA) using datasets available on the Gene Expression Omnibus (GEO) to validate the presence of metabolic reprogramming in HSILs. Results: Integrating metabolic and morphological 2P-derived metrics from finely sampled, full-thickness epithelia achieved a high 90.8 ± 6.1% sensitivity and 72.3 ± 11.3% specificity of HSIL detection. Notably, sensitivity (91.4 ± 12.0%) and specificity (77.5 ± 12.6%) were maintained when utilizing metrics from only two images at 12- and 72-µm from the tissue surface. Upregulation of glycolysis, fatty acid metabolism, and oxidative phosphorylation in HSIL tissues validated the metabolic reprogramming captured by 2P biomarkers. Conclusion: Label-free 2P images from as few as two epithelial depths enable rapid and robust HSIL detection through the quantitative characterization of metabolic and morphological reprogramming, underscoring the potential of this tool for clinical evaluation of cervical precancers.

Label-free, High-Resolution Optical Metabolic Imaging of Human Cervical Precancers Reveals Potential for Intraepithelial Neoplasia Diagnosis.

While metabolic changes are considered a cancer hallmark, their assessment has not been incorporated in the detection of early or precancers, when treatment is most effective. Here, we demonstrate that metabolic changes are detected in freshly excised human cervical precancerous tissues using label-free, non-destructive imaging of the entire epithelium. The images rely on two-photon excited fluorescence from two metabolic co-enzymes, NAD(P)H and FAD, and have micron-level resolution, enabling sensitive assessments of the redox ratio and mitochondrial fragmentation, which yield metrics of metabolic function and heterogeneity. Simultaneous characterization of morphological features, such as the depth-dependent variation of the nuclear:cytoplasmic ratio, is demonstrated. Multi-parametric analysis combining several metabolic metrics with morphological ones enhances significantly the diagnostic accuracy of identifying high-grade squamous intraepithelial lesions. Our results motivate the translation of such functional metabolic imaging to in vivo studies, which may enable improved identification of cervical lesions, and other precancers, at the bedside.

Noninvasive assessment of mitochondrial organization in three-dimensional tissues reveals changes associated with cancer development.

Mitochondrial organization is often altered to accommodate cellular bioenergetic and biosynthetic demands. Changes in metabolism are a hallmark of a number of diseases, including cancer; however, the interdependence between mitochondrial metabolic function and organization is not well understood. Here, we present a noninvasive, automated and quantitative method to assess mitochondrial organization in three-dimensional (3D) tissues using exclusively endogenous two-photon excited fluorescence (TPEF) and show that mitochondrial organization reflects alterations in metabolic activities. Specifically, we examine the organization of mitochondria within live, engineered epithelial tissue equivalents that mimic normal and precancerous human squamous epithelial tissues. We identify unique patterns of mitochondrial organization in the different tissue models we examine, and we attribute these to differences in the metabolic profiles of these tissues. We find that mitochondria are clustered in tissues with high levels of glycolysis and are more highly networked in tissues where oxidative phosphorylation is more dominant. The most highly networked organization is observed within cells with high levels of glutamine consumption. Furthermore, we demonstrate that mitochondrial organization provides complementary information to traditional morphological hallmarks of cancer development, including variations in nuclear size. Finally, we present evidence that this automated quantitative analysis of endogenous TPEF images can identify differences in the mitochondrial organization of freshly excised normal and pre-cancerous human cervical tissue specimens. Thus, this method could be a promising new modality to assess the role of mitochondrial organization in the metabolic activity of 3D tissues and could be further developed to serve as an early cancer clinical diagnostic biomarker.