Abstract
A two-color vibrational imaging technique for simultaneously mapping glucose uptake and incorporation activity inside single living cell is reported. Heterogeneous patterns of glucose metabolism are directly visualized from the ratiometric two-color images for various cell types, cells undergoing epithelia-to-mesenchymal transitions and live mouse brain tissues. The two-color imaging of glucose metabolism here demonstrates the development of multi-functional vibrational probes for multicolor imaging of cellular metabolism.
Graphical abstract

A two-color vibrational imaging technique for simultaneously mapping glucose uptake and incorporation activity inside living cells is reported.
Glucose plays a variety of important roles in both anabolic and catabolic metabolism of cells, such as providing building blocks for cellular mass synthesis, generating ATP, and regulating intracellular redox potential. Glucose metabolism is also closely linked to metabolic transformation under various pathologic conditions. For instance, the reprogrammed glucose metabolism with enhanced aerobic glycolysis has been considered as one of cancer hallmarks.1, 2
Given the importance of glucose metabolism, multiple techniques have been developed. Positron emission tomography (PET) with radioactive glucose analogues is commonly applied in clinical diagnostics by measuring glucose uptake activity in tumors, especially with 18F-deoxyglucose (FDG).3 Magnetic resonance spectroscopy (MRS) can follow glucose metabolism in vivo through hyperpolarized glucose.4, 5 But both PET and MRS have limited spatial resolution for studying glucose metabolism at the cellular level. Mass spectrometry imaging (MSI) can visualize cellular glucose metabolism via glucose tracers,6, 7 but is a destructive approach not applicable in living system. Fluorescence microscopy has also been applied to image glucose uptake activity with cellular resolution, using fluorescent glucose analogues.8 However, all these techniques rely on a single probe to measure either glucose uptake or incorporation activity. Thus, they only provide partial information of glucose metabolism in cells.
To reveal more comprehensive information of glucose metabolism, herein we report a two-color vibrational imaging approach based on both alkyne and deuterium labeled glucose analogues to simultaneously visualize glucose uptake and incorporation activity in the same living cell. We show that two-color ratiometric imaging of both glucose incorporation and uptake offers direct information on glucose utilization (i.e., the efficiency of glucose incorporation into biomass) between various cell types, before and after epithelia-to-mesenchymal transition (EMT), and different regions of live brain tissue.
Stimulated Raman scattering (SRS) microscopy, as an emerging nonlinear optical imaging technique, is highly sensitive and specific in detecting chemical bonds,9, 10 with expanding application in biology.11–13 In particular, Raman-active vibrational probes coupled with SRS microscopy provide a powerful platform for bioorthogonal chemical imaging of many small biomolecules in living systems.14, 15 Recently, two glucose analogues, 3-O-propargyl-D-glucose (3-OPG)16 and D7-glucose17, have been separately developed to study glucose uptake activity and incorporation in lipogenesis with SRS, respectively. However, direct combination of these two probes is difficult due to the significant spectral overlap of Raman peaks between 3-OPG (2129 cm−1) and D7-glucose (broad peak between 2060 cm−1 and 2250 cm−1).
Enlightened by the isotope-editing strategies in vibrational spectroscopy,18–20 we expect that 13C isotopic editing of the alkyne tag in 3-OPG can generate a spectrally new probe with little crosstalk with D7-glucose. Thus, we rationally designed and synthesized 13C-labeled 3-O-propargyl-D-glucose (3-OPG-13C3, 1, Figure 1a). In order to successfully install the 13C-labeled alkyne moiety, we used propargyl tosylate-13C3, which is prepared from commercial available propargyl alcohol-13C3 to undergo the nucleophilic substitution. Deprotection under acidic condition provided the desired 3-OPG-13C3 1 in an overall 33% yield.
Fig 1.
Raman probes for studying the glucose metabolism. a) Synthetic route of 3-OPG-13C3 and structure of D7-glucose. Stars indicate 13C labeling. b) Spontaneous Raman spectra of 1.3 M D7-glucose in PBS (dark cyan), PC-3 cells cultured with 25 mM D7-glucose for 48 hours (green), 50 mM 3-OPG in PBS (orange), 50 mM 3-OPG-13C3 in PBS (black), PC-3 cells cultured with 25 mM 3-OPG-13C3 for 2 hours (bule), PC-3 cells cultured with 25 mM D7-glucose for 48 hours and then with 25 mM 3-OPG-13C3 for 2 hours (red). c) Two-color SRS imageing of PC-3 cells cultured with 25 mM D7-glucose for 48 hours and then with 25 mM 3-OPG-13C3 for 2 hours, indicating glucose incorporation (2133 cm−1, cyan hot), glucose uptake (2053 cm−1, red hot) and off-resonance (2000 cm−1). Scale bar: 20 µm.
We first measured the spontaneous Raman spectra of newly synthesized 3-OPG-13C3 in PBS (Figure 1b, black), which shows a sharp peak at 2053 cm−1 in the cell Raman-silent window, indicating the Raman mode of 13C≡13C stretching. The 13C isotopic strategy dramatically shifted the vibrational frequency by 76 cm−1, from 2129 cm−1 to 2053 cm−1.19, 20 Spontaneous Raman spectrum and SRS images of non-treated PC-3 cells confirmed the negligible cellular background in the silent region (Figure S1). We also measured the uptake of 3-OPG-13C3 in PC-3 cells by culturing with 25 mM 3-OPG-13C3 for 2 hours. Indeed, isotopic alkyne peak (2053 cm−1) shows up in live cells (Figure 1b, blue). We then tested two-color labeling in PC-3 cells with 25 mM D7-glucose for 48 hours and 25 mM 3-OPG-13C3 for 2 hours. These cells show two distinct peaks at 2053 cm−1 and 2133 cm−1 (Figure 1b, red), indicating active glucose uptake and incorporation metabolism, respectively. The two Raman peaks are well resolved with <10% cross-talk (Figure S2). By targeting at both peaks, we can successfully visualize both glucose uptake and incorporation activity inside the same living cell by SRS imaging at 2053 cm−1 (Figure 1c, middle panel) and 2133 cm−1 (Figure 1c, left panel). SRS image at off-resonance frequency (2000 cm−1) shows negligible background. Thus, the newly synthesized 3-OPG-13C3 enables the realization of simultaneous two-color SRS imaging of glucose metabolism in single living cell.
We then demonstrated two-color imaging of glucose uptake and incorporation activity in multiple cell lines, including human glioblastoma cells U-87 MG, human prostate cancer cells PC-3, monkey kidney cells COS-7 and human prostate normal cells RWPE-1. For each cell type, both glucose incorporation and uptake activity are imaged in the same set of living cells (Figure 2a–d), with 3-OPG-13C3 evenly distributed throughout the cells and D7-glucose incorporated into intracellular structures. We also calculated the ratiometric images (C-D/13C≡13C) for each cell to quantitatively visualize the efficiency of glucose incorporation into biomass at the single cell level (Figure 2a–d, right panel). Based on the quantitative analysis (Figure 2e), the prostate normal cells RWPE-1 exhibit the highest ratio (2.89±0.01). In contrast, the glioblastoma cancer cells U-87 MG show the lowest ratio (1.21±0.05). Thus, normal cells have higher anabolic activity for biomass synthesis, and cancer cells have more catabolic activity for fast energy production when using glucose. The ratiometric images based on the two-color SRS technique can directly reveal the inherent difference of glucose utilization and metabolic pathway between normal and cancer cells.
Fig 2.
Two-color glucose metabolism imaging of different cell lines. a) U87 MG cells. b) PC-3 cells. c) COS-7 cells. d) RWPE-1 cells. SRS images of glucose incorporation (C-D, cyan hot) and glucose uptake (13C≡13C, red hot), and ratiometric images (C-D/13C≡13C, royal). f) Quantitative analysis of the ratio of C-D/13C≡13C for different cell types. Data are shown as mean + standard deviation (SD; n ≥ 3 replicates for each group). Scale bar: 20 µm.
We further showed that two-color imaging of glucose metabolism could be applied to the co-culture of cancer cells PC-3 and normal cells RWPE-1 of the same prostate tissue origin (Figure 3). In the co-culture system, the profile of a specified region in the ratiometric image shows significant higher ratio of glucose incorporation into biomass in RWPE-1 than PC-3. The lower glucose incorporation activity in the cancerous PC-3 might be attributed to the Warburg effect, which has upregulated aerobic glycolysis and lactate secretion rather than anabolic biomass synthesis.21
Fig 3.
Two-color imaging of glucose metabolism in co-cultured prostate cells. a) SRS images of the co-culture PC-3/RWPE-1. SRS images of glucose incorporation (C-D, cyan hot) and glucose uptake (13C≡13C, red hot), and ratiometric image (C-D/13C≡13C, royal). b) Plot profile of dashed region (average in the vertical direction) in the ratiometric image. Scale bar: 20 µm.
D-7 glucose has been shown to be incorporated into cellular lipids.17 We verified that de novo lipogenesis is indeed the major metabolic pathway of D-7 glucose17 using an inhibitor of fatty acid synthase, TVB-3166,22 in prostate normal cells RWPE-1 (Figure S3) and cancer cells PC-3 (Figure S4). 10 µM TVB-3166 could dramatically decrease the glucose incorporation after 24 hours treatment without influence on glucose uptake activity in both cell lines, resulting in significant reduction in the C-D/13C≡13C ratio by ~ 63% (RWPE-1) and 27% (PC-3) (Figure S3 and S4).
Epithelial-to-mesenchymal transition (EMT) process, encompassing the conversion of epithelial cells to invasive mesenchymal cells, plays an important role in cancer metastasis.23 We next applied two-color SRS imaging of both glucose uptake and incorporation activity in detecting the altered glucose metabolism during EMT (Figure 4). The EMT in MCF-7 cells is confirmed by immunofluorescence (Figure S5).24 The SRS images show that glucose incorporation into the cellular lipids is dramatically decreased after EMT, while the glucose uptake activity is not significantly changed (Figure 4c). Quantitative analysis of the ratiometric images reveals 30% reduction in C-D/13C≡13C ratio during EMT. Compared to the epithelial state, this suggests that cells in the mesenchymal state utilize less glucose for biomass synthesis and possibly more for catabolic metabolism and energy production to support high-energy requirement of cell mobility during EMT.25 The demonstration here suggests that the ratiometric two-color SRS imaging of glucose metabolism might provide a sensitive tool for detecting circulating tumor cells and their metabolism during cancer metastasis at single-cell level.
Fig 4.
Two-color glucose metabolism imaging of MCF-7 cells with epithelia-to-mesenchymal transition (EMT). a) Epithelial MCF-7 cells. b) Mesenchymal MCF-7 cells. c) Quantitative analysis of C-D signal, 13C≡13C signal and the ratio of C-D/13C≡13C for epithelia and mesenchymal cells. Data are shown as mean + standard deviation (SD; n ≥ 3 replicates for each group). p values of Student’s t-test: p < 0.0001; p > 0.2; p < 0.001 (from left to right). Scale bar: 20 µm.
Last, we demonstrated two-color SRS imaging of glucose metabolism in ex vivo live mouse brain tissue with both D7-glucose and 3-OPG-13C3 probes. The choroid plexus (CP) is responsible for production of cerebrospinal fluids (CSF), which is of great importance for the development and maintenance of central nervous system.26 The distinct activity of glucose metabolism in CP is directly visualized by SRS for the first time. Label-free SRS images show the distributions of lipids (CH2, green), protein (CH3, blue) and their merged pattern (Figure 5a). CP is identified by its unique morphology and its high protein but low lipid content (white-solid line). Pronounced signals are present in both the glucose incorporation channel (C-D, cyan hot) and glucose uptake (13C≡13C, red hot) channel (Figure 5b). The ratiometric image (Figure 5b, right panel) exhibits elevated glucose incorporation in the CP region when compared with the nearby brain tissue (yellow-solid line). Additionally, in the magnified images, the CP epithelial cells (Figure 5c, arrow-headed) clearly show the polarization with diminished activity on the apical side (facing the brain ventricle) and elevated activity on the basolateral side (facing central stroma). Importantly, such asymmetry is required for the sorting and trafficking of the CSF component.27 Therefore, two-color SRS imaging of both glucose uptake and incorporation activity is applicable in live brain tissues with subcellular resolution.
Fig 5.
Two-color glucose metabolism imaging of choroid plexus (CP) in ex vivo live mouse brain tissue. a) SRS images of lipid (CH2, green), protein (CH3, blue) and merged one. b) SRS images of glucose incorporation (C-D, cyan hot) and glucose uptake (13C≡13C, red hot), and ratiometric image (C-D/13C≡13C, royal). CP shown as the white-solid line region, nearby brain tissue shown as the yellow-solid line region. c) SRS images of 2× zoom in of the CP region (CP epithelial cells, arrow-headed). Scale bar: 20 µm.
In summary, we have presented a two-color SRS imaging approach to study both the glucose uptake and incorporation activity in living cells and mouse brain tissues with high specificity, resolution and biocompatibility. With 13C-isotope editing strategy, the new 3-OPG-13C3 probe not only preserves the characteristics of the original 3-OPG probe for visualizing glucose uptake activity, but also provides a new Raman frequency to be spectrally resolved from the other glucose probe, D7-glucose for incorporation activity. Combination of these two vibrational probes thus provides a complete set to simultaneously study glucose uptake and incorporation activity with SRS microscopy. We have also shown that the two-color ratiometric images could be used as a quantitative visualization of glucose utilization and the contribution of biomass incorporation in living systems with subcellular resolution. We further anticipate this technique can serve as a useful tool in cancer diagnostics and therapeutic development by selectively targeting the reprogrammed glucose metabolism2, 28.
Supplementary Material
Acknowledgments
W.M. acknowledges support from an NIH Director’s New Innovator Award (Grand 1DP2EB016573), NIH R01 (Grand EB020892), the US Army Research Office (W911NF-12-1-0594), the Alfred P. Sloan Foundation, and the Camille and Henry Dreyfus Foundation.
Footnotes
Electronic Supplementary Information (ESI) available: [details of any supplementary information available should be included here]. See DOI: 10.1039/x0xx00000x
Notes and references
- 1.Ward PS, Thompson CB. Cancer Cell. 2012;21:297–308. doi: 10.1016/j.ccr.2012.02.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Hay N. Nat Rev Cancer. 2016;16:635–649. doi: 10.1038/nrc.2016.77. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Ben-Haim S, Ell P. J Nucl Med. 2009;50:88–99. doi: 10.2967/jnumed.108.054205. [DOI] [PubMed] [Google Scholar]
- 4.Rodrigues TB, Serrao EM, Kennedy BW, Hu DE, Kettunen MI, Brindle KM. Nat Med. 2014;20:93–97. doi: 10.1038/nm.3416. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Lumata L, Yang C, Ragavan M, Carpenter N, DeBerardinis RJ, Merritt ME. Methods Enzymol. 2015;561:73–106. doi: 10.1016/bs.mie.2015.04.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Wang J, Qiu S, Chen S, Xiong C, Liu H, Wang J, Zhang N, Hou J, He Q, Nie Z. Anal Chem. 2015;87:422–430. doi: 10.1021/ac504294s. [DOI] [PubMed] [Google Scholar]
- 7.Hensley CT, Faubert B, Yuan Q, Lev-Cohain N, Jin E, Kim J, Jiang L, Ko B, Skelton R, Loudat L, Wodzak M, Klimko C, McMillan E, Butt Y, Ni M, Oliver D, Torrealba J, Malloy CR, Kernstine K, Lenkinski RE, DeBerardinis RJ. Cell. 2016;164:681–694. doi: 10.1016/j.cell.2015.12.034. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Yamada K, Saito M, Matsuoka H, Inagaki N. Nat Protoc. 2007;2:753–762. doi: 10.1038/nprot.2007.76. [DOI] [PubMed] [Google Scholar]
- 9.Freudiger CW, Min W, Saar BG, Lu S, Holtom GR, He C, Tsai JC, Kang JX, Xie XS. Science. 2008;322:1857–1861. doi: 10.1126/science.1165758. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Cheng JX, Xie XS. Science. 2015;350:aaa8870. doi: 10.1126/science.aaa8870. [DOI] [PubMed] [Google Scholar]
- 11.Min W, Freudiger CW, Lu S, Xie XS. Annu Rev Phys Chem. 2011;62:507–530. doi: 10.1146/annurev.physchem.012809.103512. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Liao CS, Cheng JX. Annu Rev Anal Chem (Palo Alto Calif) 2016;9:69–93. doi: 10.1146/annurev-anchem-071015-041627. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Prince RC, Frontiera RR, Potma EO. Chem Rev. 2016 doi: 10.1021/acs.chemrev.6b00545. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Wei L, Hu F, Chen Z, Shen Y, Zhang L, Min W. Acc Chem Res. 2016;49:1494–1502. doi: 10.1021/acs.accounts.6b00210. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Zhao Z, Shen Y, Hu F, Min W. Analyst. 2017 doi: 10.1039/c7an01001j. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Hu F, Chen Z, Zhang L, Shen Y, Wei L, Min W. Angew Chem Int Ed Engl. 2015;54:9821–9825. doi: 10.1002/anie.201502543. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Li J, Cheng JX. Sci Rep. 2014;4:6807. doi: 10.1038/srep06807. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Liu Z, Li X, Tabakman SM, Jiang K, Fan S, Dai H. J Am Chem Soc. 2008;130:13540–13541. doi: 10.1021/ja806242t. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Chen Z, Paley DW, Wei L, Weisman AL, Friesner RA, Nuckolls C, Min W. J Am Chem Soc. 2014;136:8027–8033. doi: 10.1021/ja502706q. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Wei L, Chen Z, Shi L, Long R, Anzalone AV, Zhang L, Hu F, Yuste R, Cornish VW, Min W. Nature. 2017;544:465–470. doi: 10.1038/nature22051. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Chen YJ, Huang X, Mahieu NG, Cho K, Schaefer J, Patti GJ. Biochemistry. 2014;53:4755–4757. doi: 10.1021/bi500763u. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Ventura R, Mordec K, Waszczuk J, Wang Z, Lai J, Fridlib M, Buckley D, Kemble G, Heuer TS. EBioMedicine. 2015;2:808–824. doi: 10.1016/j.ebiom.2015.06.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Huang R, Zong X. Crit Rev Oncol Hematol. 2017;115:13–22. doi: 10.1016/j.critrevonc.2017.04.005. [DOI] [PubMed] [Google Scholar]
- 24.Zhang L, Min W. J Biomed Opt. 2017;22:1–7. doi: 10.1117/1.JBO.22.10.106010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Jiang L, Deberardinis R, Boothman DA. Mol Cell Oncol. 2015;2:e981994. doi: 10.4161/23723556.2014.981994. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Lun MP, Monuki ES, Lehtinen MK. Nat Rev Neurosci. 2015;16:445–457. doi: 10.1038/nrn3921. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Christensen IB, Gyldenholm T, Damkier HH, Praetorius J. Front Physiol. 2013;4:344. doi: 10.3389/fphys.2013.00344. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Elf SE, Chen J. Cancer. 2014;120:774–780. doi: 10.1002/cncr.28501. [DOI] [PMC free article] [PubMed] [Google Scholar]
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