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Abstract Breast cancer metastasis occurs via blood and lymphatic vessels. Breast cancer cells ‘educate’ lymphatic endothelial cells (LECs) to support tumor vascularization and growth. However, despite known metabolic alterations in breast cancer, it remains unclear how lymphatic endothelial cell metabolism is altered in the tumor microenvironment and its effect in lymphangiogenic signaling in LECs. We analyzed metabolites inside LECs in co-culture with MCF-7, MDA-MB-231, and SK-BR-3 breast cancer cell lines using $$^1\hbox {H}$$ 1 H nuclear magnetic resonance (NMR) metabolomics, Seahorse, and the spatial distribution of metabolic co-enzymes using optical redox ratio imaging to describe breast cancer-LEC metabolic crosstalk. LECs co-cultured with breast cancer cells exhibited cell-line dependent altered metabolic profiles, including significant changes in lactate concentration in breast cancer co-culture. Cell metabolic phenotype analysis using Seahorse showed LECs in co-culture exhibited reduced mitochondrial respiration, increased reliance on glycolysis and reduced metabolic flexibility. Optical redox ratio measurements revealed reduced NAD(P)H levels in LECs potentially due to increased NAD(P)H utilization to maintain redox homeostasis. $$^{13}\hbox {C}$$ 13 C -labeled glucose experiments did not reveal lactate shuttling into LECs from breast cancer cells, yet showed other $$^{13}\hbox {C}$$ 13 C signals in LECs suggesting internalized metabolites and metabolic exchange between the two cell types. We also determined that breast cancer co-culture stimulated lymphangiogenic signaling in LECs, yet activation was not stimulated by lactate alone. Increased lymphangiogenic signaling suggests paracrine signaling between LECs and breast cancer cells which could have a pro-metastatic role. 

High-throughput drug screening of patient-derived organoids offers an attractive platform to determine cancer treatment efficacy. Here, selective plane illumination microscopy (SPIM) was used to determine treatment response in organoids with endogenous fluorescence from the metabolic coenzymes NAD(P)H and FAD. Rapid 3-D autofluorescence imaging of colorectal cancer organoids was achieved. A quantitative image analysis approach was developed to segment each organoid and quantify changes in endogenous fluorescence caused by treatment. Quantitative analysis of SPIM volumes confirmed the sensitivity of patient-derived organoids to standard therapies. This proof-of-principle study demonstrates that SPIM is a powerful tool for high-throughput screening of organoid treatment response.

 

Spectral imaging approaches provide new possibilities for measuring and discriminating fluorescent molecules in living cells and tissues. These approaches often employ tunable filters and robust image processing algorithms to identify many fluorescent labels in a single image set. Here, we present results from a novel spectral imaging technology that scans the fluorescence excitation spectrum, demonstrating that excitation‐scanning hyperspectral image data can discriminate among tissue types and estimate the molecular composition of tissues. This approach allows fast, accurate quantification of many fluorescent species from multivariate image data without the need of exogenous labels or dyes. We evaluated the ability of the excitation‐scanning approach to identify endogenous fluorescence signatures in multiple unlabeled tissue types. Signatures were screened using multi‐pass principal component analysis. Endmember extraction techniques revealed conserved autofluorescent signatures across multiple tissue types. We further examined the ability to detect known molecular signatures by constructing spectral libraries of common endogenous fluorophores and applying multiple spectral analysis techniques on test images from lung, liver and kidney. Spectral deconvolution revealed structure‐specific morphologic contrast generated from pure molecule signatures. These results demonstrate that excitation‐scanning spectral imaging, coupled with spectral imaging processing techniques, provides an approach for discriminating among tissue types and assessing the molecular composition of tissues. Additionally, excitation scanning offers the ability to rapidly screen molecular markers across a range of tissues without using fluorescent labels. This approach lays the groundwork for translation of excitation‐scanning technologies to clinical imaging platforms.