Quantitative Spatial Analysis of Metabolic Heterogeneity Across in vivo and in vitro Tumor Models.

Tiffany M Heaster,Melissa C Skala,Bennett A Landman

doi:10.3389/fonc.2019.01144

Abstract

Metabolic preferences of tumor cells vary within a single tumor, contributing to tumor heterogeneity, drug resistance, and patient relapse. However, the relationship between tumor treatment response and metabolically distinct tumor cell populations is not well-understood. Here, a quantitative approach was developed to characterize spatial patterns of metabolic heterogeneity in tumor cell populations within in vivo xenografts and 3D in vitro cultures (i.e., organoids) of head and neck cancer. Label-free images of cell metabolism were acquired using two-photon fluorescence lifetime microscopy of the metabolic co-enzymes NAD(P)H and FAD. Previous studies have shown that NAD(P)H mean fluorescence lifetimes can identify metabolically distinct cells with varying drug response. Thus, density-based clustering of the NAD(P)H mean fluorescence lifetime was used to identify metabolic sub-populations of cells, then assessed in control, cetuximab-, cisplatin-, and combination-treated xenografts 13 days post-treatment and organoids 24 h post-treatment. Proximity analysis of these metabolically distinct cells was designed to quantify differences in spatial patterns between treatment groups and between xenografts and organoids. Multivariate spatial autocorrelation and principal components analyses of all autofluorescence intensity and lifetime variables were developed to further improve separation between cell sub-populations. Spatial principal components analysis and Z-score calculations of autofluorescence and spatial distribution variables also visualized differences between models. This analysis captures spatial distributions of tumor cell sub-populations influenced by treatment conditions and model-specific environments. Overall, this novel spatial analysis could provide new insights into tumor growth, treatment resistance, and more effective drug treatments across a range of microscopic imaging modalities (e.g., immunofluorescence, imaging mass spectrometry).

Highlights

Cancer cells within a single tumor have heterogeneous function and phenotype [1], resulting in unpredictable progression and treatment response [2]
Tools to characterize intra-tumor heterogeneity at the cellular level and its effect on disease progression have been limited to destructive methods that require tumor dissociation, and removal from the host
Previous studies have established that Optical metabolic imaging (OMI) can resolve phenotypic differences within 2D and 3D in vitro tumor models, and in vivo mouse tumors [23, 24, 27, 30, 33, 36, 59]

Summary

Introduction

Cancer cells within a single tumor have heterogeneous function and phenotype [1], resulting in unpredictable progression and treatment response [2]. 3D organotypic cultures (i.e., organoids) are a popular emerging model system because organoids offer increased throughput compared to in vivo models, while maintaining key features of the original tumor, including drug response [4] Both models enable microscopic imaging of tumor cell function and metabolic activity. Quantifying spatial functional heterogeneity within in vivo mouse models and in vitro tumor organoids could establish a link between global tumor drug response and tumor cell heterogeneity, while highlighting differences between in vivo and in vitro 3D model systems This link between cell-level behavior and overall tumor response would provide fundamental insight toward developing new treatments that target multiple cell subpopulations, and comparisons between 3D cell culture and in vivo systems could inform on the best use of each model system

Methods

Results

Conclusion