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Spatially resolved measurement of dynamic glucose uptake in live ex vivo tissues.

ABSTRACT: Highly proliferative cells depend heavily on glycolysis as a source of energy and biological precursor molecules, and glucose uptake is a useful readout of this aspect of metabolic activity. Glucose uptake is commonly quantified by using flow cytometry for cell cultures and positron emission tomography for organs in vivo. However, methods to detect spatiotemporally resolved glucose uptake in intact tissues are far more limited, particularly those that can quantify changes in uptake over time in specific tissue regions and cell types. Using lymph node metabolism as a case study, we developed an optimized method to detect dynamic and spatially resolved glucose uptake in living tissue by combining ex vivo tissue slice culture with a fluorescent glucose analogue. Live slices of murine lymph node were treated with the glucose analogue 2-[N-(7-nitrobenz-2-oxa-1,3-dia-xol-4-yl)amino]-2-deoxyglucose (2-NBDG). Incubation parameters were optimized to differentiate glucose uptake in activated versus naïve lymphocytes. Regional glucose uptake could be imaged at both the tissue level, by widefield microscopy, and at the cellular level, by confocal microscopy. Furthermore, the glucose assay was readily multiplexed with live immunofluorescence labelling to generate maps of 2-NBDG uptake across tissue regions, revealing highest uptake in T cell-dense regions. The signal was predominantly intracellular and localized to lymphocytes rather than stromal cells. Finally, we demonstrated that the assay was repeatable in the same slices, and imaged the dynamic distribution of glucose uptake in response to ex vivo T cell stimulation for the first time. We anticipate that this method will serve as a broadly applicable, user-friendly platform to quantify dynamic metabolic activities in complex tissue microenvironments.

SUBMITTER: Dunn AF

PROVIDER: S-EPMC7701360 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundTumor heterogeneity can manifest itself by sub-populations of cells having distinct phenotypic profiles expressed as diverse molecular, morphological and spatial distributions. This inherent heterogeneity poses challenges in terms of diagnosis, prognosis and efficient treatment. Consequently, tools and techniques are being developed to properly characterize and quantify tumor heterogeneity. Multiplexed immunofluorescence (MxIF) is one such technology that offers molecular insight into both inter-individual and intratumor heterogeneity. It enables the quantification of both the concentration and spatial distribution of 60+ proteins across a tissue section. Upon bioimage processing, protein expression data can be generated for each cell from a tissue field of view.ResultsThe Multi-Omics Heterogeneity Analysis (MOHA) tool was developed to compute tissue heterogeneity metrics from MxIF spatially resolved tissue imaging data. This technique computes the molecular state of each cell in a sample based on a pathway or gene set. Spatial states are then computed based on the spatial arrangements of the cells as distinguished by their respective molecular states. MOHA computes tissue heterogeneity metrics from the distributions of these molecular and spatially defined states. A colorectal cancer cohort of approximately 700 subjects with MxIF data is presented to demonstrate the MOHA methodology. Within this dataset, statistically significant correlations were found between the intratumor AKT pathway state diversity and cancer stage and histological tumor grade. Furthermore, intratumor spatial diversity metrics were found to correlate with cancer recurrence.ConclusionsMOHA provides a simple and robust approach to characterize molecular and spatial heterogeneity of tissues. Research projects that generate spatially resolved tissue imaging data can take full advantage of this useful technique. The MOHA algorithm is implemented as a freely available R script (see supplementary information).

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Spatially resolved measurement of dynamic glucose uptake in live ex vivo tissues.

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