Development of a single-cell metabolic imaging method
To study motile phenotypes of individual endothelial cells, we first devised a single-cell metabolism quantification method to monitor the lactate concentration and glycolytic rate of individual cells expressing a genetically encoded biosensor. Here we used Laconic, a Förster resonance energy transfer (FRET)-based lactate biosensor comprising of an mTFP/Venus FRET pair and a bacterial transcription factor LldR11. LldR binds lactate causing the FRET pair to change their intramolecular distance that leads to increased mTFP (acceptor)/Venus (donor) (or 1/FRET) signal. In this way, lactate concentrations are positively proportional to 1/FRET signals (Extended Data Fig. 1a). Laconic-expressing human endothelial cells were imaged, segmented using a semantic segmentation pipeline, and assembled into fluorescence traces under biochemical and/or biomechanical stimulations (Fig. 1a). This allowed us to monitor the terminal metabolite in glycolysis, lactate, in endothelial cells, thus directly revealing endothelial glycolytic reprogramming for energy production. A deep learning approach with U-nets10 using custom code in MATLAB rapidly and reliably performed pixel-level classification of images and segmentation of single cells with Boundary F1 score12 for identifying the cytoplasm > 90%, and class-specific cytoplasm accuracy, precision, recall, and Dice score > 80%, (Extended Data Fig. 1b, Extended Data Table 1). Single-cell fluorescence following segmentation was highly correlated with ground-truth fluorescence (Extended Data Fig. 1c).
To reliably measure and calibrate physiological levels of intracellular lactate and glycolysis using laconic, we controlled intracellular lactate concentrations by permeabilizing human aortic endothelial cells (HAECs) and subjecting them to different concentrations of lactate. As shown in Extended Data Fig. 1a, the 1/FRET change is linear to 6 orders of magnitude of log lactate concentrations (R2 = 0.96). However, lactate export is a dynamic process in live cells that is regulated by the concentration gradient between the extra- and intra-cellular compartments13. Thus, in order to measure the glycolytic rate of the cell, lactate export was blocked through inhibition of monocarboxylate transporter 1 (MCT1), the major lactate transporter in endothelial cells14 (Fig. 1a). Upon MCT1 blockade by p-chloromercuribenzoic acid (pCMBA), we observed a continuous increase of lactate in individual cells, the slope of which we defined as the lactate production rate (LPR) (Fig. 1b), which reflects the overall glycolytic rate in a single endothelial cell. Furthermore, the single-cell LPR increased linearly, in agreement with bulk extracellular acidification measurements, as a function of extracellular glucose concentration (Fig. 1c). Employing a bootstrap algorithm simulating LPR, by drawing from ground truth fluorescence values and fitting slopes to the deep learning-measured fluorescence (Extended Data Fig. 1c), we estimated the absolute quantification error of LPR to be 5% (see Methods, Extended Data Fig. 1d).
Consistently, the steady-state intracellular concentration of lactate measured by single-cell FRET was within the range estimated by bulk mass spectrometry (~0-1.6 mM) (Fig. 1d). Corresponding to single-cell LPR, blockade of MCT1 with pCMBA13 also reduced extracellular acidification in a bulk bioenergetics flux assay (Extended Data Fig. 2a). Additionally, we were able to increase intracellular lactate at the single-cell level with glucose or oligomycin stimulation, and inhibit lactate production by 2-deoxyglucose, like those used in an extracellular flux glycolysis stress test at the bulk level (Extended Data Fig. 2b).
To test the LPR’s ability to distinguish heterogeneous glycolytic rates in cell populations, which is critical for differentiating subpopulations, we mimicked hypoxic conditions in endothelial cells by treating with dimethyloxaloylglycine (DMOG). DMOG inhibits prolyl hydroxylase, thereby promoting the stabilization of transcription factor hypoxia inducible factor-1a (HIF-1a)2. We found that the single-cell LPR was elevated in DMOG-treated cells (Fig. 1e), in agreement with an increase in ECAR measured in bulk extracellular flux (Extended Data Fig. 2c). We next forced metabolic heterogeneity by mixing vehicle (DMSO)- or DMOG-treated cells that were previously labeled with either eBFP2 or mCherry fluorescent proteins, respectively (Fig. 1f). By assessing the LPR of each cell and then de-mixing by color (Fig. 1g), our method distinguished authentic metabolic subpopulations.
In addition to lactate, we further employed other genetically encoded FRET-based sensors to measure key glycolytic metabolites including pyruvate15, glucose16, and ATP17 to cross-validate these findings with DMOG. Pyruvate production, glucose uptake, and ATP levels were all increased in DMOG-treated cells relative to DMSO control (Fig. 1h-j). Notably, pyruvate and glucose have alternative fates and ATP is not exclusively produced by glycolysis, whereas lactate production is a definitive surrogate for glycolysis. The results not only validated our primary results using the laconic sensor, but also laid a solid foundation for the general application of our single-cell metabolism method across different metabolites.
Measuring glycolytic rates of single endothelial cells in response to flow
We then assessed single-cell glycolytic heterogeneity in a spatial context by exploiting the hemodynamic flow-dependent activation of endothelial cells, which are generally subject to different shear stresses depending on the local flow conditions in the vasculature18. Disturbed flow-induced cell activation leads to atherosclerosis at arterial branch points18, driven by low shear stress-induced endothelial glycolysis2,3, and abrogated by high shear stress which is known to repress glycolysis4 through reducing the expression of a key glycolytic enzyme, PFKFB3, in a transcription factor KLF2-dependent manner4. However, the ability to study flow-induced metabolic changes has so far been limited to extracellular flux without spatial resolution2.
Using a cone and plate device to generate spatially heterogeneous shear forces over microscopic distances (Fig. 2a), we focused on an area at the flow boundary (indicated by endothelial elongation and alignment, Fig. 2b) to determine force-dependent metabolic rates. As shown in Fig. 2c, cells subjected to high shear stress had reduced LPR compared to those exposed to low shear stress, consistent with our previously published bulk data as measured by ECAR2. We next linked metabolism to a morphological phenotype (flow-induced endothelial cell elongation) at the single-cell level. By analyzing endothelial cells exposed to shear stress, we found a direct association between force-induced cell morphology and glycolytic rate. We plotted the LPR and shear stress as a function of cell length and found a negative correlation with LPR but a positive correlation with shear stress (Fig. 2d). At 20× magnification, cell length is strongly negatively correlated with LPR (Fig. 2e, f). This result was partially caused by the presence of external forces (shear stress), which could change metabolic rates independent of cell morphology, though shorter cells within the high shear stress region exhibit higher LPR (Fig. 2e).
Glycolysis fuels the migration of single endothelial cells
Endothelial cells migrate during angiogenesis or wound closure. This process requires energy, involves dramatic changes in cell shape, and is dependent on RhoA/ROCK-1 remodeling of the actin cytoskeleton5. In typical wound assays, single-cell migrations are heterogeneous where some cells are motile while others remain stationary19. Previously, it has been shown that endothelial cell migration requires glucose, whereas endothelial cell growth requires glutamine usage in oxidative phosphorylation1,20. However, it is not known whether individual migrating cells are more glycolytic than stationary cells, which could elucidate whether metabolism is a driver of heterogeneous cell migration in a disrupted cell monolayer. We first demonstrated that glucose is necessary for migration whereas mitochondrial metabolism is dispensable for wound closure (treatment with Antimycin A, Fig. 3a). The results indicated that cell migration is driven by glycolysis. Consistently, HAECs treated with DMOG, which increases glycolytic capacity in a HIF-1a-dependent manner21, enhanced migration and are able to close wounds faster than control (Fig. 3b).
However, it remains unknown whether individual cells that migrated further were more glycolytic, as activation of HIF-1a by DMOG could turn on thousands of genes2. In order to elucidate whether cell glycolysis fuels individual cell motility, we applied single-cell metabolic assays to analyze migrating endothelial cells in a wound assay. By tracking individual cell migrations (Fig. 3c) over the course of 12 hours, we confirmed that cells in the more stationary “tissue” migrated less than cells in the more migratory “wound”, overall (Fig. 3d). We found that migratory cells, cells that traveled more than one cell length, were more glycolytic compared to stationary cells (Fig. 3e). We further found that cell migration distance is positively correlated with cell glycolysis (Fig. 3f), regardless of the final cell positions in the tissue or wound regions. These results suggest that the migration of endothelial cells is fueled by glycolysis, which is moreover proportional to work performed.
Single-cell intracellular lactate production reveals that RhoA activation increases endothelial glycolysis
Having shown that glycolysis fuels cell migration, we next quantified the glycolytic rate during cell contraction, a key motile endothelial process that occurs over a short timespan (minutes) and is thus independent of transcriptional control. Because GTPase RhoA activation is central to induce the contractile apparatus of endothelial cells within minutes6, we hypothesized that controlled RhoA agonism would induce a glycolytic burst to power cell contractions. RhoA activation or inactivation in HAECs was achieved by overexpressing constitutively active RhoA (RhoA-Q63L) or dominant negative RhoA (RhoA-T19N). As shown in Figure 4a, RhoA-Q63L (CA) increased single cell LPR compared to RhoA-T19N (DN). To remove possible batch effects, we next conjugated fluorescent protein eBFB2 to RhoA-T19N and fluorescent protein mScarlet-i to RhoA-Q63L, followed by cell mixing and single-cell LPR measurements. Deconvolution by color showed significantly increased glycolysis in RhoA-activated (mScarlet-i positive) HAECs when compared to RhoA-inactivated (eBFP2 positive) cells (Fig. 4b). Switching fluorescent labeling of RhoA-Q63L and RhoA-T19N had no effect on the results (Extended Data Fig. 3a). We next treated HAECs with lysophosphatidic acid (LPA), another well-known RhoA agonist22, which significantly increased single-cell LPR compared to untreated control (Fig. 4c). Furthermore, treatment with thrombin, a serine protease that rapidly stimulates RhoA and subsequently RhoA kinase-1 (ROCK-1)23,24 to produce contractions and barrier breakdown, significantly increased LPR in 7 minutes, which was largely abrogated by the treatment of a specific ROCK-1 inhibitor, Y27632 (Fig. 4d). Consistently, glucose uptake and ATP levels were also elevated shortly after thrombin treatment (Fig. 4e). Thus, activation of GTPase RhoA causatively drives intracellular lactate production and glycolysis in single endothelial cells.
RhoA promotes endothelial glycolysis to induce phosphorylation of cofilin and myosin light chain
Because the activity of RhoA-depedent kinases requires ATP, we hypothesized that the downstream functions of RhoA/ROCK-1 are dependent on glycolysis. RhoA/ROCK-1 activation causes rapid phosphorylation of cofilin (CFL) which enacts reorganization of the actin cytoskeleton from cortical actin to stress fibers25,26. RhoA/ROCK-1 activity also causes phosphorylation of myosin light chain (MLC) which actively pulls on stress fibers, causing cell contractions and intercellular gaps in endothelial monolayers (failure of the endothelial paracellular barrier) as well as the reduction in monolayer impedance27-29. Phosphorylation of CFL and MLC occurs in 2-30 minutes with relaxation of the cell to its basal state after 1 hour as the stimulus is depleted26,30. To test our hypothesis, we used hexokinase inhibitor 2-deoxyglucose (2-DG) to block glycolysis. Indeed, 2DG significantly reduced basal CFL and MLC phosphorylation in HAECs and moreover, completely blocked thrombin-induced phosphorylation of CFL and MLC (Fig. 4f), demonstrating that RhoA/ROCK1 downstream effects are dependent on active glycolysis. Consistently, the degree of contraction, or change in monolayer impedance, was dependent on the concentration of glucose in the media, as measured by transendothelial electrical resistance (TEER) (Fig. 4g). Concordantly, depleting endothelial glycolysis with 2-DG also prevented thrombin-induced barrier failure by TEER (Fig. 4h). Furthermore, inhibition of ROCK-1 with Y27632 followed by thrombin treatment phenocopied pre-treatment with 2DG (Extended Data Fig. 3b). These data collectively demonstrated that RhoA/ROCK1-dependent glycolysis is required for the contractile machinery of endothelial cells.
RhoA simulates endothelial contraction by triggering a glycolytic burst via glucose transporter SLC2A3/GLUT3
We next determined the molecular mechanisms by which RhoA induces glycolysis to stimulate endothelial contraction. Since the increase in glycolysis occurs over a short timespan (measured at 7 minutes) following RhoA activation (Fig. 4d) and availability of glucose determines the degree of downstream phosphorylation and contraction (Fig. 4f, g), we hypothesized that glucose uptake plays a critical role in RhoA-induced glycolysis. Previous studies have shown that SLC2A1/GLUT1 is a major glucose transporter in endothelial cells. SLC2A1 contributes to the basal endothelial glycolytic capacity in the blood brain barrier31 and pulmonary vasculature32, as well as to increased glycolysis stimulated by chronic (hours) exposure to hypoxia and disturbed flow in large arteries2. We therefore first tested the role of SLC2A1. Unexpectedly, small interfering RNA (siRNA)-mediated SLC2A1 knockdown did not reduce thrombin-induced phosphorylation of CFL and MLC in HAECs (Extended Data Fig. 4a). At the same time, qRT-PCR analysis identified that SLC2A3 is the most abundant glucose transporter transcript in HAECs (Extended Data Fig. 4b). We therefore tested the role of SLC2A3 in RhoA-induced glycolysis. Our data showed that SLC2A3 knockdown by siRNAs reduced basal phosphorylation of CFL and MLC and significantly abrogated thrombin-induced phosphorylation of CFL and MLC with three separate siRNAs (Fig. 5a, Extended Data Fig. 4c,d).
Although SLC2A3 knockdown had no effect on basal endothelial glycolysis, it largely abolished the thrombin-induced glycolytic burst, as detected by single-cell LPR (Fig. 5b). In direct comparison, SLC2A1 knockdown led to a measurable decrease in LPR after thrombin stimulation but this decrease was far less than that of SLC2A3 knockdown (Extended Data Fig. 4e). Consistently, the thrombin-induced increase in glucose and ATP levels were abrogated by SLC2A3 knockdown (Extended Data Fig. 4f, g). Because the glycolytic burst could not be attributed to SLC2A3 transcriptional regulation given the acute response (measured within 7 minutes), we hypothesized that SLC2A3 is regulated post-transcriptionally by RhoA/ROCK-1. To test this hypothesis, we performed immunofluorescence imaging of SLC2A3 in thrombin-treated HAECs. Using total internal reflection fluorescence (TIRF) microscopy, which selectively illuminates membrane fluorophores close to the cover slip (within ~100 nm)33,34, we detected significantly increased intensity of SLC2A3 in granules near the plasma membrane of single HAECs following thrombin stimulation; such accumulation of SLC2A3 were abrogated by ROCK inhibitor Y27632, further suggesting the involvement of Rho/ROCK signaling pathway (Fig. 5c-d). In agreement with the SLC2A3-dependent glycolytic burst and phosphorylation of CFL and MLC, thrombin-induced endothelial contraction was significantly abrogated by SLC2A3 knockdown, demonstrated by increased monolayer impedance by TEER (Fig. 5e) and reduced intercellular gap size (gap size index, Fig. 5f), although the number of gaps was unchanged (gap index, Extended Data Fig. 4h)35. Furthermore, increasing the amount of extracellular glucose increased gap size and gap number, which was blunted by SLC2A3 knockdown (Extended Data Fig. 4i). Contraction (gap size index), but not gap number, was partially rescued under SLC2A3 knockdown by treatment with oligomycin to divert all glucose to be utilized via glycolysis (Extended Data Fig. 4j). Thrombin-induced phosphorylation of CFL and MLC were also significantly abrogated by SLC2A3 knockdown in human umbilical endothelial cells (HUVECs) (Extended Data Fig. 5a) and human lung microvascular cells (HMVECs) (Extended Data Fig. 5b). Thrombin-induced HUVEC contractions (as measured by cell size, Extended Data Fig. 5c) and HMVEC contractions (as measured by intercellular gaps, Extended Data Fig. 5d) were also significantly reduced by SLC2A3, indicating this pathway is conserved across multiple endothelial cell types.
The functional role of SLC2A3 in endothelial glycolysis and contraction is largely unknown. In order to explore the in vivo relevance of SLC2A3 in barrier regulation, we needed to first ascertain the expression profile of Slc2a1 and Slc2a3 in primary endothelial from different organs in mice. Gene Expression Omnibus datasets (GEOD) were downloaded from EndoDB, a database of curated endothelial transcriptomics datasets36. As shown in Extended Figure 6a, Slc2a3 has expression levels similar to Slc2a1 across multiple organs (normalized to Slc2a1). We next overexpressed SLC2A3 under the endothelial-specific CDH5/VE-cadherin promoter and evaluated vascular leak in mouse aortas. Mouse aortas, because to their intrinsic curvature, exhibit RhoA activation due to hemodynamic shear forces37. We employed a delivery system which integrates polyethylenimine (PEI) nanoparticles and plasmids with the endothelial-specific CDH5/VE-cadherin promoter. Intravenous injection of CDH5-driven, mScarlet-i-expressing plasmids resulted in mScarlet-i fluorescence in arterial endothelium, highlighted by co-localization with ve-cadh (which markes endothelium) adjacent to the lumen, but not the underlying media and adventitia (yellow signal in merge panel, Extended Fig. 6b), demonstrating endothelial-specific overexpression in mice. The same approach using CDH5-driven, SLC2A3-expresssing plasmids led to SLC2A3 overexpression in aortic endothelium but not in the media and adventitia in mice, measured by quantitative real-time PCR (Extended Data Fig. 6c). To demonstrate functional relevance of SLC2A3, mice were injected with PEI nanoparticles containing CDH5-SLC2A3 or CHD5-Ctrl plasmids 24 hours prior to sacrifice. 1 hour prior to sacrifice, Evans blue dye was injected retro-orbitally. Evans blue dye binds to albumin and transits the endothelial barrier in a paracellular fashion under conditions of diminished tight and adherent junctions and quantitatively measures the leakiness of mouse aortas38,39. Endothelial overexpression of SLC2A3 results in leakiness of aorta as quantified by Evans blue spectroscopy (OD 620), normalized to aorta weight after dye extraction (Extended Data Fig. 6d). This experiment suggests that SLC2A3 regulates the endothelial barrier in vivo. Collectively, these results demonstrated the importance of SLC2A3 on fueling RhoA/ROCK-1 downstream phosphorylation targets and its importance in both in vitro and in vivo barrier function.
Subcellular glycolysis is correlated with actin cytoskeletal remodeling
After finding RhoA activation induces endothelial glycolysis and contraction within minutes at the single-cell scale (Fig. 4a-d), we sought to evaluate how glycolysis relates to morphological changes within individual endothelial cells with subcellular resolution. Reasoning that actin remodeling is dependent on high-energy phosphates, we hypothesized that localized changes in actin is correlated with glycolytic activity at the subcellular spatial scale. To test this hypothesis, we again stimulated actin remodeling with a RhoA agonist thrombin. By co-expressing laconic and Lifeact-RFP40 (an F-actin label) in endothelial cells, we measured LPR and actin remodeling in single endothelial cells with subcellular resolution at 40× magnification (Fig. 6a). Lifeact signal increases and shimmers at the edges of thrombin-induced contracting cells, indicating active actin remodeling. In order to visualize actin remodeling, we computed the maximum intensity projection of the difference in Lifeact signals in the 7 minutes following thrombin addition prior to performing the LPR assay (Fig. 6a, difference image). The LPR image was captured by performing pixel-wise calculation of the slope of the FRET signal after addition of pCMBA (Fig. 6a, LPR). As shown in the merged image, the LPR signal was spatially correlated with the Lifeact difference image (Fig. 6a, “LPR+difference” and “LPR+difference+nucleus”). The Lifeact difference and LPR images together demonstrated enhancement of signals on the contracting edge and center of the cell. To quantify the correlation, we calculated the degree of overlap using Manders’ overlap coefficient (MOC) and Pearson’s correlation coefficient (PCC). Thrombin-stimulated cells showed much higher levels of correlation between subcellular LPR and Lifeact than those of non-stimulated cells (Fig. 6b). This correlation was diminished by inhibition of ROCK (Y-27632), actin polymerization (Latrunculin B), and myosin II (para-amino-Blebbistatin) (Fig. 6b). Moreover, SLC2A3 knockdown reduced the correlation between Lifeact difference and LPR after thrombin treatment, indicating RhoA-dependent SLC2A3 activity is necessary for the co-occurrence of subcellular glycolysis and actin remodeling (Fig. 6c).
Similarly, we assessed subcellular glycolysis using Laconic in migrating endothelial cells. Here, we computed the Lifeact difference image in the 10 minutes prior to performing the LPR assay (Extended Data Fig. 7a, difference image). In migrating HAECs, we found that LPR and Lifeact difference were significantly correlated, compared to control (performed by randomizing the LPR images 10,000 times and computing the MOC or PCC) (Extended Data Fig. 7b). These results indicate spatial cytoskeletal remodeling and glycolysis are correlated in multiple types of endothelial motilities.
To determine whether subcellular LPR was spatially related to RhoA, we next evaluated RhoA activity using a FRET-based biosensor RhoA-Flare41 in single contractile endothelial cells. We found enhanced RhoA activity and increased LPR co-located in similar areas following thrombin stimulation: the center of the cells (Fig 6d) as well as along the edges of the contractile aspects of the cell (Fig. 6d-f, and Extended Data Fig. 7c-l). In contrast, where the cell edge did not move, there was no FRET (RhoA activity) (Extended Data Fig. 7i). Edges with persistent contraction consistently demonstrated high RhoA-Flare FRET (Extended Data Fig. 7j). The similar distribution of RhoA activity and LPR was further confirmed by comparing stationary edges vs contractile edges across multiple cells, averaged together (Fig. 6g). This data revealed that the non-uniform localized distribution of metabolism correlates with cytoskeleton remodeling at the subcellular level. These results further showed co-localization between LPR and RhoA activities, suggesting that an endothelial cell generates energy using glycolysis to drive actin remodeling through GTPase RhoA.
Altogether, our single-cell metabolic assays revealed molecular mechanisms that RhoA/ROCK-1 activates SLC2A3 translocation to trigger a glycolytic burst to power the endothelial contractile apparatus across multiple endothelial types (Fig. 6h). Furthermore, subcellular analysis of LPR revealed the non-uniform, localized distribution of glycolysis at site of actin turnover and RhoA activity, indicating RhoA signaling coordinates energy production through glycolysis with energy consumption through cytoskeletal remodeling to control cell motility.