A deeper understanding of intestinal organoid metabolism revealed by combining fluorescence lifetime imaging microscopy (FLIM) and extracellular flux analyses

Irina A. Okkelman, Nuno Neto, Dmitri B. Papkovsky, Michael Monaghan, Ruslan I. Dmitriev

Preprint posted on September 16, 2019

Article now published in Redox Biology at

Shedding light on unique metabolic properties of the Lgr5+ stem cell niche by O2 phosphorescence & NAD(P)H fluorescence imaging of intestinal organoids.

Selected by Jessica Xie

Categories: cell biology


To say that organoid culture technologies have revolutionized human biology and medicine in recent years is nothing short of an understatement. The discovery and development of methods to generate 3D organoids for virtually all tissues have enabled the study of self-organized complex systems at a depth previously painfully limited by accessibility of human samples. Consequently, organoids have seen use in a wide range of applications—from developmental biology to disease modeling, drug screening, personalized medicine, and even regenerative medicine.

Several challenges remain, however. For all their advantages, the very heterogeneity that distinguishes and defines organoids also renders it difficult to perform assays previously developed for homogeneous 2D cultures. Intestinal organoids, this study’s chosen model system, are especially notable among the many organoid models for their dramatic 3D spatial heterogeneity—far more than being cellular aggregations, they display clear apicobasal polarity with crypt domains containing LGR5stem cells, and villus domains containing differentiated enterocytes and enteroendocrine cells.

Current methods to study metabolism generally fall into either of two categories: 1) bulk methods that take extracellular measurements (most commonly of oxygen and pH) of media containing a whole organoid, or 2) disruptive methods following flow sorting of dissociated organoids. Here, the authors pioneer a third approach: microscopy-based techniques to measure oxygen (O2) and NADH/NADPH in intact intestinal organoids while preserving spatial information.

Key findings

Figure 2B from Okkelman et al. shows higher O2 levels in GFP+ regions relative to GFP, as determined by O2-PLIM and GFP fluorescence imaging. Made available under a CC-BY-NC-ND 4.0 International license.


To measure oxygen levels, they use a small molecule—Pt-Glc—whose phosphorescence is quenched by oxygen. After mouse intestinal organoids are stained with the probe, oxygen levels can be determined by phosphorescence lifetime imaging microscopy (PLIM). The authors also measure fluorescence along with phosphorescence in LGR5-GFP organoids, where GFP marks the stem cell niche and non-fluorescent regions contain more differentiated cells. Using this setup, they find higher oxygenation in GFP+ stem cell-containing regions compared to GFP differentiated regions across various culture conditions of metabolic stress (namely low glucose and pyruvate withdrawal).

Next, the authors use two-photon excitation microscopy to measure NAD(P)H autofluorescence in stem cell-containing and differentiated regions of LGR5-GFP organoids. Analysis of the fluorescence decay curves provides information on properties of NADH/NADPH: the proportion of enzyme-bound (vs free) NAD(P)H, and mean fluorescence lifetime of bound NAD(P)H. Here, the authors report a decrease in NAD(P)H lifetime in GFP+ regions compared to GFP regions, as well as some differences in the proportion of bound NAD(P)H in GFP+ and GFP regions across various media conditions.

As with every nascent technology, however, there were several limitations and complications: firstly, NADPH and NADH must often—as in this case—be measured together due to their very similar fluorescence properties. Secondly, only protein-bound NAD(P)H could be accurately measured, as the picosecond-range fluorescence lifetime of free NAD(P)H was too short. Additionally, autofluorescence was seen in the intestinal organoid lumen. Finally, the biological significance of changes in these properties was not entirely straightforward to determine.

Nonetheless, the authors provide evidence to indicate distinct metabolic dynamics in stem and differentiated cells within intestinal organoids, demonstrating the utility of these techniques for studying metabolism within specific regions of a complex organoid system.

Questions for the authors

  • What other complementary techniques can be used to cross-validate oxygen and NAD(P)H measurements?
  • Which other pathways or molecules might be most interesting to look at, alongside NAD(P)H & O2?
  • You saw clear differences in Olevels in GFP+ vs GFP regions, but nothing as obvious for NAD(P)H—I’m curious if you would attribute that to limited sensitivity of the technique, or do you think that biological differences aren’t as large?
  • Similarly, in Figure 5 you show that organoids with more distinct LGR5-GFP domains have larger differences in O2 level than more ‘mosaic’ organoids. Do you think this is due to imaging resolution/sensitivity, or biological differences (perhaps organoids with more distinct GFP domains had higher quality differentiation than organoids that became more mixed)?


Posted on: 30th October 2019


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Author's response

The author team shared

Thank you for choosing to highlight our preprint! We are glad that you asked the questions – my Team works on phosphorescence quenching-aided oxygen imaging of 3D tissue models since ~ 2013 and we find intestinal organoids as the most exciting model to work with. It’s also very encouraging to see the growing interest in our work and multi-parameter FLIM of 3D tissue models in general. Indeed, there are a good number of complementary techniques or biomarkers that we could and intend to implement in the future – for example, a FLIM-based labeling of proliferating cells (this is like EdU but enables real-time monitoring of live tissues, together with a labeling mitochondrial polarization ( We also feel that the performance of the described methods would be significantly improved by more efficient segmentation of different cell type populations, e.g. by using other than ‘Lgr5-GFP’ transgenic models. Measuring FAD, cross-validating oxygenation in Matrigel matrix, correlating these data with lipid metabolism and luminal composition could be also very interesting. However, even the described ‘simplified O2+NAD(P)H’ approach was able to detect differences in metabolism between stem and non-stem cells. We hope that this study will stimulate the progress towards improved ‘3D-FLIM’, bringing more measured parameters on board and enable detecting physiological and meaningful differences in individual cells metabolism more reliably.

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