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Single-cell metabolic imaging reveals a SLC2A3-dependent glycolytic burst in motile endothelial cells

Nature Metabolism volume 3, pages 714–727 (2021)Cite this article

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Abstract

Single-cell motility is spatially heterogeneous and driven by metabolic energy. Directly linking cell motility to cell metabolism is technically challenging but biologically important. Here, we use single-cell metabolic imaging to measure glycolysis in individual endothelial cells with genetically encoded biosensors capable of deciphering metabolic heterogeneity at subcellular resolution. We show that cellular glycolysis fuels endothelial activation, migration and contraction and that sites of high lactate production colocalize with active cytoskeletal remodelling within an endothelial cell. Mechanistically, RhoA induces endothelial glycolysis for the phosphorylation of cofilin and myosin light chain in order to reorganize the cytoskeleton and thus control cell motility; RhoA activation triggers a glycolytic burst through the translocation of the glucose transporter SLC2A3/GLUT3 to fuel the cellular contractile machinery, as demonstrated across multiple endothelial cell types. Our data indicate that Rho-GTPase signalling coordinates energy metabolism with cytoskeleton remodelling to regulate endothelial cell motility.

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Fig. 1: Single-cell LPR distinguishes metabolically heterogeneous subpopulations.
Fig. 2: Endothelial LPR in response to shear stress is heterogeneous.
Fig. 3: Single-cell metabolism links migratory cell phenotypes to glycolysis states.
Fig. 4: Single-cell imaging of lactate production reveals RhoA drives endothelial cell glycolysis, which is necessary for contractions.
Fig. 5: RhoA induces glucose transporter SLC2A3 translocation which drives subcellular glycolysis in thrombin-stimulated contractile endothelial cells.
Fig. 6: Subcellular distribution of LPR colocalizes with actin remodelling.

Data availability

Gene Expression Omnibus datasets (GEOD) were downloaded from EndoDB (https://endotheliomics.shinyapps.io/endodb/): E-GEOD-15760, E-GEOD-20741, E-MTAB-5921, E-GEOD-40999, E-GEOD-16067, E-GEOD-11870, E-GEOD-1576, E-GEOD-47067. Original data that support our findings in this study are available from the corresponding author upon reasonable request or at https://doi.org/10.5281/zenodo.4638059. Source data are provided with this paper.

Code availability

The code generated during this study is available at https://github.com/wulab-code/laconic without restriction.

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Acknowledgements

This work was supported by NIH grants R00AI106941 (J.H.), R21AI120010 (J.H.), R01HL138223 (Y.F.) and R01HL136765 (Y.F.), NIH New Innovator Award DP2AI144245 (J.H.), T32EB009412 (D.L.H.), T32HL007381 (D.L.H.) and F32HL134288 (D.W.) and NIH Pathway to Independence Award K99HL145113 (D.W.) and R00HL145113 (D.W.). NVIDIA GPU Grant (D.W.), CSCTR Early Career Development Award (D.W.) and NSF Career Award 1653782 (J.H.) also supported this work.

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Author notes

  1. These authors contributed equally: David Wu, Devin L. Harrison.

Authors and Affiliations

  1. Department of Medicine, Biological Sciences Division, The University of Chicago, Chicago, IL, USA

    David Wu, Devin L. Harrison, Tzu-Pin Shentu, Angelo Meliton, Ru-Ting Huang, Zhengjie Zhou, Gökhan M. Mutlu & Yun Fang

  2. Graduate Program in Biophysical Sciences, The University of Chicago, Chicago, IL, USA

    Devin L. Harrison, Jun Huang & Yun Fang

  3. Pritzker School of Molecular Engineering, The University of Chicago, Chicago, IL, USA

    Devin L. Harrison & Jun Huang

  4. Research Computing Center, The University of Chicago, Chicago, IL, USA

    Teodora Szasz

  5. Division of Cardiology, Department of Internal Medicine and Cardiovascular Center, National Taiwan University Hospital, Taipei, Taiwan

    Chih-Fan Yeh

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Contributions

D.W. and D.L.H. planned and executed experiments, analysed data and interpreted results. T.S. analysed data. C.-F.Y., A.M., T.-P.S., R.-T.H. and Z.Z. performed experiments. J.H. and Y.F. planned experiments and interpreted results. G.M.M. helped to design experiments. D.W., D.L.H., J.H. and Y.H. wrote and edited the manuscript.

Corresponding authors

Correspondence to Jun Huang or Yun Fang.

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The authors declare no competing interests.

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Peer review information Nature Metabolism thanks Ali Ertürk, Marc Tramier and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Christoph Schmitt.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Single-cell metabolic assay utilizes deep learning-enabled segmentation.

a, mTFP/Venus (or 1/FRET) change is linearly correlated over 6 orders of magnitude of log lactate (n = 287 cells; black line is semilog fit with R2 = 0.96, gray dotted lines are 95% confidence interval); error bars are SEM. Standard deviation of regression coefficients for R2 for (a) is 0.049. b, Deep learning architecture for semantic segmentation, modified from Ronneberger et al7. The U-Net consisted of a contracting path (encoder) and an expansive path (decoder). The contracting path consists of layers composed of two 3 × 3 convolutions, each followed by a rectified linear unit (ReLU) activation function. Each layer is followed by a 2 × 2 max pooling operation with a stride of two- which will double the number of feature channels being used. The convolutions start with 64 feature channels and 128 × 128 images and continue until they reach 512 channels and 16 × 16 images. In the expansive path, 2 × 2 up-convolutions (up sampling) of stride 2 to decrease by half the feature channels and increase the size of the image. The layers composed of 3 × 3 convolutions and ReLU are concatenated with the pair of layers in the contracting side, in order to reinclude the localization information. In the final layer, a 1 × 1 convolution layer is used to map the resulting 64-component feature channels vector to the 4 segmentation channels for background, boundary, cytoplasm, and nuclei. c, Ground Truth Fluorescence vs. Predicted Fluorescence, as evaluated with deep-learning-enabled semantic segmentation. (n ~ 20,000 cells) Ground truth fluorescence was defined as signal inside the cell boundary. C.I., confidence interval. d, Frequency distribution of fluorescence slopes. Data from (c) were randomly sampled 100 times and fitted to a line. This process was repeated 10,000 times and the distribution of fitted slopes was plotted (open black circles). The red line is a Gaussian fit with mean 0.9 and standard deviation of 0.05. Thus, the error in estimating slopes of fluorescence is approximately 0.05/0.9, or approximately 5%.

Extended Data Fig. 2 Single-cell laconic accurately mimics bioenergetics assays.

a, Extracellular flux assay of HAECs treated with glucose then pCMBA (n = 5) using Seahorse; biological replicates, error bars are s.e.m., P < 0.0001 for both comparisons. b, A single-cell glycolytic stress test using lactate FRET sensor. Cells were first starved in zero glucose buffer for 1 hour. Glucose addition increased intracellular lactate, which was further increased by adding oligomycin and glucose. Addition of 2-deoxyglucose (2-DG) blocked all glycolysis, causing intracellular lactate to drop. c, Overnight treatment of endothelial cells with 500 μM DMOG increases ECAR measured by extracellular flux relative to DMSO-treated cells using Seahorse. (n = 11 combined biological and technical replicates for each condition; error bars are s.e.m., P < 0.0001). Statistical significance determined by one-way ANOVA followed by Bonferroni test (a) or by two-sided Welch’s t-test (c). ***P <= 0.0005.

Extended Data Fig. 3 RhoA activity induces contraction using glycolysis.

a, LPR of HAECs transfected with either RhoA-T19N-eBFP2 (dominant negative (DN) form, cyan) or RhoA-Q63L-mScarlet-i (constitutively active (CA) form, red), mixed, then deconvoluted by colour (n = 174 “cyan” and 52 “red”, respectively). Error bars are s.e.m., P = 0.0193. b, TEER measurement of HAECs after thrombin treatment, either pre-treated with Y27632 or 2DG compared to control (no Y27632 or 2DG). Statistical significance determined by two-sided Welch’s t-test. *P <= 0.05.

Extended Data Fig. 4 SLC2A3 but not SLC2A1 knockdown mitigates thrombin-induced phosphorylation of CFL and MLC and glycolysis.

a, Western blot of SLC2A1, SLC2A3, phospho-cofilin and phospho-MLC in the presence or absence of siRNA targeted towards SLC2A1 and thrombin treatment (n = 3 biological replicates). Loading control for each protein is ACTB unless specifically denoted in parentheticals. Phospho-proteins are additionally normalized to total. Original blot in Source Data c. b, Heatmap of glucose transporter expression relative to SLC2A1 in HAECs captured by qRT-PCR (n = 3 biological replicates) c-d, Western blot of SLC2A1, SLC2A3, phospho-cofilin and phospho-MLC in the presence or absence of two distinct siRNA (separate from the one used in Fig. 5a) (#1 in c and #2 in d) targeted towards SLC2A3 and thrombin treatment (n = 3 biological replicates. For c, P = 0.0190 for pCFL and 0.0490 for ppMLC. For d, P = 0.0272 for ppMLC.). Loading control for each protein is ACTB unless specifically denoted in parentheticals. Phospho-proteins are additionally normalized to total. Original blot in Source Data d-e. e, Single-cell LPR of HAECs treated with thrombin in the presence of siSLC2A1 or siSLC2A3 (n = 277 for thrombin vs. control, n = 281 for thrombin vs. thrombin + siSLC2A1, and n = 278 for thrombin vs. thrombin + siSLC2A3, P < 0.0001, P = 0.0384, P < 0.0001, respectively.). f-g, Glucose (f) and ATP levels (g) in single-cells in the presence or absence of SLC2A3 knockdown and thrombin treatment (f: n = 248, 248, 108, 190 from left to right, P < 0.0001 for all comparisons; g: 190, 598, 150, 287 from left to right, P < 0.0001 for all comparisons). h, Gap index of cells treated with or without thrombin in the presence or absence of SLC2A3 knockdown (n= 12, 16, 8, 32 from left to right). i-j, Representative images of HAECs treated with siSLC2A3 or control with varying concentrations of glucose (i) or with oligomycin (j) in the presence of thrombin. Red color is CDH5. Gap size index and gap index quantification of images (i, n = 10, 10, 7, 8 from left to right, p = 0.022 for gap size index and P = 0.0019; j, n = 6 for gap size index, P = 0.0013 for siSLC2A3 vs control and P = 0.040 for siSLC2A3 vs. siSLC2A3 + oligomycin, and n = 10, 11, 7 from left to right for gap index, P < 0.0001). White stars highlight gaps in the monolayer. Scale bars = 250 μm. Statistical significance determined by multiple unpaired two-tailed t-tests (a, c, d), one-way ANOVA followed by Bonferroni test (e-g, i) or by two-sided Welch’s t-test (h, j). All error bars are s.e.m. *P <= 0.05; **P <= 0.005; ***P <= 0.0005.

Source data

Extended Data Fig. 5 SLC2A3 inhibition abrogates thrombin-induced phosphorylation of CFL and MLC and contraction across multiple endothelial cell types.

a-b, Western blot analysis of glucose transporter expression, phosphorylated CFL, and phosphorylated MLC, in human umbilical vein endothelial cells (HUVEC, a, n = 3; biological replicates, P = 0.0257 for pCFL and 0.0414 or ppMLC) and human microvascular endothelial cells (HMVEC, b, n = 3 for SLC2A1, SLC2A3, n = 4 for CFL and n = 5 for MLC; biological replicates, P < 0.001 for pCFL and P= 0.0014 for ppMLC). Loading control for each protein is ACTB unless specifically denoted in parentheticals. Original blot in Source Data f-g. c, Representative images and cell size of contractile HUVECs subjected to thrombin stimulation in the absence or presence of SLC2A3 knockdown. (n = 1375, 1279, 760, 1162 from left to right, P < 0.0001) d, Representative images, gap size index, and gap index of contractile HMVECs subjected to thrombin stimulation in the absence or presence of SLC2A3 knockdown. Representative images are phalloidin. (n = 4 biological replicates, P = 0.0171 for gap size index and P = 0.0262 for gap index) Statistical significance determined by two-sided Welch’s t-test. All error bars are s.e.m. n.s., not significant. *P <= 0.05; **P <= 0.005; ***P <= 0.0005.

Source data

Extended Data Fig. 6 In vivo overexpression of SLC2A3 increased vascular leak.

a, GEO datasets of primary endothelial mouse cells comparing Slc2a1 and Slc2a3 from different organs. Fold change indicates that data are normalized to Slc2a1 except for E-GEOD-47067 which was normalized to average of Slc2a1 in all tissues. Only controls of experiments in the GEO datasets were used in these analyses. (n = 3 independent biological replicates for each condition for E-GEOD-15760; n = 6 independent biological replicates for each condition for E-GEOD-20741; n = 3 independent biological replicates for each condition for E-MTAB-5921; n = 3 independent biological replicates for each condition for E-GEOD-40999; n = 3 independent biological replicates for each condition for E-GEOD-11870; n = 3 independent biological replicates for each condition for E-GEOD-1576; n = 3 independent biological replicates for condition in each organ for E-GEOD-47067) b, Immunofluorescence of an arterial section demonstrating enhanced mScarlet-i fluorescence (red) colocalized with ve-cadh (green) which marks the endothelium next to the lumen (yellow, merge) (Scale bar, 500 μm, representative of n = 4 mice). c, SLC2A3 expressing plasmid uses an endothelial specific CDH5 promoter. SLC2A3 is detected by qRT-PCR of the intima of the mouse aorta, and far less than in the media and adventitia. (n = 4 independent biological replicates, P = 0.0030) d, Evans blue (OD 620 nm) quantification of mouse aortas, normalized by weight. (n = 8 independent biological replicates for each condition, P = 0.0050). Scale bar is 1 mm. Statistical significance was determined by two-sided Welch’s t-test. All error bars are s.e.m. **P <= 0.005.

Extended Data Fig. 7 Subcellular RhoA activity, actin turnover, and LPR distribution in single motile endothelial cells.

a, Montage of actin timelapse in a migratory endothelial cell; maximum projection of change in actin structure (Lifeact difference image, obtained by sequential subtraction of preceding 10 images, taken 1 minute apart, followed by maximum intensity projection of the absolute value of the differences), per pixel LPR map (LPR), Lifeact difference and LPR overlap (“difference+LPR”, merge), scale bar = 20 µm. White arrows in montage indicate cell spreading. Green outline shows enhanced LPR near the regions of cell spreading. Representative of n = 14 cells, 3 separate experiments. b, Pearson’s correlation coefficient (PCC) and Manders’ overlap coefficient (MOC) of Lifeact difference and LPR, compared to random. The random PCC/MOC is calculated by randomizing the LPR image and computing with the Lifeact difference image. Fold change of experimental PCC/MOC over randomized is shown. (n = 14). Error bars are s.e.m, P = 0.014 for PCC and P = 0.0489 for MOC. c-l, RhoA activity measured by RhoA-FLARE in contractile endothelial cells. Representative data of total n = 9 cells in 3 replicates. (Scale bar = 16 μm in c, g, k. Scale bar = 7 μm in d, f, h, l). d,f, regions of contractile cell from (c) showing RhoA membrane activity. Circle in (f) notes moving RhoA activity. e, kymograph showing contractile edge from (d) and increasing RhoA activity over time. h, region of contractile cell from (g) showing increased RhoA membrane activity. i-j, kymograph showing non-contractile edge (i) without RhoA increase and contractile edge (j) from (g) and increasing RhoA activity over time. l, region of contractile cell from (k) showing RhoA membrane activity. Dotted ellipses notes highlight increasing RhoA activity along with active contraction. Statistical significance determined by two-sided Welch’s t-test. *P <= 0.05.

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Source Data Fig. 4

Unmodified western blots with markers for Fig. 4f

Source Data Fig. 5

Unmodified western blots with markers for Fig. 5a

Source Data Extended Data Fig. 4

Unmodified western blots with markers for Extended Data Fig. 4a–c

Source Data Extended Data Fig. 5

Unmodified western blots with markers for Extended Data Fig. 5a,b

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Wu, D., Harrison, D.L., Szasz, T. et al. Single-cell metabolic imaging reveals a SLC2A3-dependent glycolytic burst in motile endothelial cells. Nat Metab 3, 714–727 (2021). https://doi.org/10.1038/s42255-021-00390-y

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