Mid-infrared metabolic imaging with vibrational probes


Understanding metabolism is indispensable in unraveling the mechanistic basis of many physiological and pathological processes. However, in situ metabolic imaging tools are still lacking. Here we introduce a framework for mid-infrared (MIR) metabolic imaging by coupling the emerging high-information-throughput MIR microscopy with specifically designed IR-active vibrational probes. We present three categories of small vibrational tags including azide bond, 13C-edited carbonyl bond and deuterium-labeled probes to interrogate various metabolic activities in cells, small organisms and mice. Two MIR imaging platforms are implemented including broadband Fourier transform infrared microscopy and discrete frequency infrared microscopy with a newly incorporated spectral region (2,000–2,300 cm−1). Our technique is uniquely suited to metabolic imaging with high information throughput. In particular, we performed single-cell metabolic profiling including heterogeneity characterization, and large-area metabolic imaging at tissue or organ level with rich spectral information.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Design of MIR probes for metabolic imaging in cells, small organisms and mice.
Fig. 2: MIR metabolic imaging with high-information throughput.
Fig. 3: Single-cell metabolic profiling with FTIR and DFIR imaging.
Fig. 4: Application of MIR metabolic imaging on brain development and tumor progression.

Data availability

The source data for Figs. 3 and 4 are available online as Source Data. Other data that support the findings of this study are available from the corresponding author upon reasonable request.


  1. 1.

    Kandel, E. R. The molecular biology of memory storage: a dialogue between genes and synapses. Science 294, 1030 (2001).

    CAS  PubMed  Google Scholar 

  2. 2.

    Roche, F. K., Marsick, B. M. & Letourneau, P. C. Protein synthesis in distal axons is not required for growth cone responses to guidance cues. J. Neurosci. 29, 638 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Hinz, F. I., Dieterich, D. C., Tirrell, D. A. & Schuman, E. M. Noncanonical amino acid labeling in vivo to visualize and affinity purify newly synthesized proteins in larval zebrafish. ACS Chem. Neurosci. 3, 40–49 (2012).

    CAS  PubMed  Google Scholar 

  4. 4.

    Zilberter, Y. & Zilberter, M. The vicious circle of hypometabolism in neurodegenerative diseases: ways and mechanisms of metabolic correction. J. Neurosci. Res. 95, 2217–2235 (2017).

    CAS  PubMed  Google Scholar 

  5. 5.

    Hebert, S. L. & Nair, K. S. Protein and energy metabolism in type 1 diabetes. Clin. Nutr. 29, 13–17 (2010).

    CAS  PubMed  Google Scholar 

  6. 6.

    Pavlova, N. N. & Thompson, C. B. The emerging hallmarks of cancer metabolism. Cell Metab. 23, 27–47 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Kim, M. M., Parolia, A., Dunphy, M. P. & Venneti, S. Non-invasive metabolic imaging of brain tumours in the era of precision medicine. Nat. Rev. Clin. Oncol. 13, 725–739 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Steinhauser, M. L. et al. Multi-isotope imaging mass spectrometry quantifies stem cell division and metabolism. Nature 481, 516–519 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Palonpon, A. F., Sodeoka, M. & Fujita, K. Molecular imaging of live cells by Raman microscopy. Curr. Opin. Chem. Biol. 17, 708–715 (2013).

    CAS  PubMed  Google Scholar 

  10. 10.

    Wei, L. et al. Live-cell bioorthogonal chemical imaging: stimulated Raman scattering microscopy of vibrational probes. Acc. Chem. Res. 49, 1494–1502 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Shen, Y., Hu, F. & Min, W. Raman imaging of small biomolecules. Annu. Rev. Biophysics. 48, 347–369 (2019).

    CAS  Google Scholar 

  12. 12.

    Hu, F., Shi, L. & Min, W. Biological imaging of chemical bonds by stimulated Raman scattering microscopy. Nat. Methods 16, 830–842 (2019).

    CAS  PubMed  Google Scholar 

  13. 13.

    Lee, H. J. et al. Assessing cholesterol storage in live cells and C. elegans by stimulated Raman scattering imaging of phenyl-diyne cholesterol. Sci. Rep. 5, 7930 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Schiessl, K. T. et al. Phenazine production promotes antibiotic tolerance and metabolic heterogeneity in Pseudomonas aeruginosa biofilms. Nat. Commun. 10, 762 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Shi, L. et al. Optical imaging of metabolic dynamics in animals. Nat. Commun. 9, 2995 (2018).

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    Zhang, L. et al. Spectral tracing of deuterium for imaging glucose metabolism. Nat. Biomed. Eng. 3, 402–413 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Lin-Vien, D., Colthup, N. B., Fateley, W. G. & Grasselli, J. G. The Handbook of Infrared and Raman Characteristic Frequencies of Organic Molecules (Academic Press, 1991).

  18. 18.

    Ma, J., Pazos, I. M., Zhang, W., Culik, R. M. & Gai, F. Site-specific infrared probes of proteins. Annu. Rev. Phys. Chem. 66, 357–377 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Suydam, I. T. & Boxer, S. G. Vibrational Stark effects calibrate the sensitivity of vibrational probes for electric fields in proteins. Biochemistry 42, 12050–12055 (2003).

    CAS  PubMed  Google Scholar 

  20. 20.

    Pilling, M. & Gardner, P. Fundamental developments in infrared spectroscopic imaging for biomedical applications. Chem. Soc. Rev. 45, 1935–1957 (2016).

    CAS  PubMed  Google Scholar 

  21. 21.

    Yeh, K., Kenkel, S., Liu, J.-N. & Bhargava, R. Fast infrared chemical imaging with a quantum cascade laser. Anal. Chem. 87, 485–493 (2015).

    CAS  PubMed  Google Scholar 

  22. 22.

    Haase, K., Kröger-Lui, N., Pucci, A., Schönhals, A. & Petrich, W. Real-time mid-infrared imaging of living microorganisms. J. Biophotonics. 9, 61–66 (2016).

    CAS  PubMed  Google Scholar 

  23. 23.

    Gazi, E. et al. A FTIR microspectroscopic study of the uptake and metabolism of isotopically labelled fatty acids by metastatic prostate cancer. Vib. Spectrosc. 50, 99–105 (2009).

    CAS  Google Scholar 

  24. 24.

    Bai, Y., Zhang, D., Li, C., Liu, C. & Cheng, J.-X. Bond-selective imaging of cells by mid-infrared photothermal microscopy in high wavenumber region. J. Phys. Chem. B. 121, 10249–10255 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Clède, S. et al. Synchrotron radiation FTIR detection of a metal-carbonyl tamoxifen analog. Correlation with luminescence microscopy to study its subcellular distribution. Biotechnol. Adv. 31, 393–395 (2013).

    PubMed  Google Scholar 

  26. 26.

    McCoy, S. & Caughey, W. S. Infrared studies of azido, cyano, and other derivatives of metmyoglobin, methemoglobin, and hemins. Biochemistry 9, 2387–2393 (1970).

    CAS  PubMed  Google Scholar 

  27. 27.

    Błasiak, B., Londergan, C. H., Webb, L. J. & Cho, M. Vibrational probes: from small molecule solvatochromism theory and experiments to applications in complex systems. Acc. Chem. Res. 50, 968–976 (2017).

    PubMed  Google Scholar 

  28. 28.

    Prescher, J. A. & Bertozzi, C. R. Chemistry in living systems. Nat. Chem. Biol. 1, 13–21 (2005).

    CAS  PubMed  Google Scholar 

  29. 29.

    Pazos, I. M., Ghosh, A., Tucker, M. J. & Gai, F. Ester carbonyl vibration as a sensitive probe of protein local electric field. Angew. Chem. Int. Ed. 53, 6080–6084 (2014).

    CAS  Google Scholar 

  30. 30.

    Muhamadali, H., Chisanga, M., Subaihi, A. & Goodacre, R. Combining Raman and FT-IR spectroscopy with quantitative isotopic labeling for differentiation of E. coli cells at community and single cell levels. Anal. Chem. 87, 4578–4586 (2015).

    CAS  PubMed  Google Scholar 

  31. 31.

    Cheng, J.-X. & Xie, X. S. Vibrational spectroscopic imaging of living systems: an emerging platform for biology and medicine. Science 350, aaa8870 (2015).

    PubMed  Google Scholar 

  32. 32.

    Wrobel, T. P. & Bhargava, R. Infrared spectroscopic imaging advances as an analytical technology for biomedical sciences. Anal. Chem. 90, 1444–1463 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Yeh, K., Lee, D. & Bhargava, R. Multicolor discrete frequency infrared spectroscopic imaging. Anal. Chem. 91, 2177–2185 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Hosios, AaronM. et al. Amino acids rather than glucose account for the majority of cell mass in proliferating mammalian cells. Dev. Cell. 36, 540–549 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Peng, S. K., Ho, K. J. & Taylor, C. B. Biologic effects of prolonged exposure to deuterium oxide. A behavioral, metabolic, and morphologic study. Arch. Pathol. 94, 81–89 (1972).

    CAS  PubMed  Google Scholar 

  36. 36.

    Hodel, A., Gebbers, J. O., Cottier, H. & Laissue, J. A. Effects of prolonged moderate body deuteration on proliferative activity in major cell renewal systems in mice. Life Sci. 30, 1987–1996 (1982).

    CAS  PubMed  Google Scholar 

  37. 37.

    Ran, S., Berisha, S., Mankar, R., Shih, W.-C. & Mayerich, D. Mitigating fringing in discrete frequency infrared imaging using time-delayed integration. Biomed. Opt. Express 9, 832–843 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Mittal, S. et al. Simultaneous cancer and tumor microenvironment subtyping using confocal infrared microscopy for all-digital molecular histopathology. Proc. Natl Acad. Sci. USA 115, E5651 (2018).

    CAS  PubMed  Google Scholar 

  39. 39.

    Liu, Y.-L. et al. Assessing metastatic potential of breast cancer cells based on EGFR dynamics. Sci. Rep. 9, 3395 (2019).

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Knobloch, M. et al. Metabolic control of adult neural stem cell activity by Fasn-dependent lipogenesis. Nature 493, 226–230 (2013).

    CAS  PubMed  Google Scholar 

  41. 41.

    Snaidero, N. & Simons, M. Myelination at a glance. J. Cell Sci. 127, 2999 (2014).

    CAS  PubMed  Google Scholar 

  42. 42.

    Ji, M. et al. Rapid, label-free detection of brain tumors with stimulated Raman scattering microscopy. Sci. Transl. Med. 5, 201ra119 (2013).

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Röhrig, F. & Schulze, A. The multifaceted roles of fatty acid synthesis in cancer. Nat. Rev. Cancer 16, 732–749 (2016).

    PubMed  Google Scholar 

  44. 44.

    Clemens, M. J. Targets and mechanisms for the regulation of translation in malignant transformation. Oncogene 23, 3180–3188 (2004).

    CAS  PubMed  Google Scholar 

  45. 45.

    Zhang, D. et al. Depth-resolved mid-infrared photothermal imaging of living cells and organisms with submicrometer spatial resolution. Sci. Adv. 2, e1600521 (2016).

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Shi, J. et al. High-resolution, high-contrast mid-infrared imaging of fresh biological samples with ultraviolet-localized photoacoustic microscopy. Nat. Photonics 13, 609–615 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Dazzi, A. & Prater, C. B. AFM-IR: technology and applications in nanoscale infrared spectroscopy and chemical imaging. Chem. Rev. 117, 5146–5173 (2017).

    CAS  PubMed  Google Scholar 

  48. 48.

    Andrew Chan, K. L. & Kazarian, S. G. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) imaging of tissues and live cells. Chem. Soc. Rev. 45, 1850–1864 (2016).

    CAS  PubMed  Google Scholar 

  49. 49.

    Pleitez, M. A. et al. Label-free metabolic imaging by mid-infrared optoacoustic microscopy in living cells. Nat. Biotechnol. 38, 293–296 (2020).

    CAS  PubMed  Google Scholar 

  50. 50.

    Pupeza, I. et al. Field-resolved infrared spectroscopy of biological systems. Nature 577, 52–59 (2020).

    CAS  PubMed  Google Scholar 

  51. 51.

    Wei, L. et al. Imaging complex protein metabolism in live organisms by stimulated Raman scattering microscopy with isotope labeling. ACS Chem. Biol. 10, 901–908 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Baker, M. J. et al. Using Fourier transform IR spectroscopy to analyze biological materials. Nat. Protoc. 9, 1771–1791 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).

    PubMed  PubMed Central  Google Scholar 

Download references


We thank K.A. McHose and L. Tisinger from Agilent Inc. for technical supports on FTIR microscope and Lu Wei for helpful discussion. W.M. acknowledges support from NIH R01 (GM128214 and GM128214-02S1) and R01 (EB029523). L.E.P.D. was supported by NIH/NIAID grant no. R01 AI103369.

Author information




Lixue Shi performed spectroscopy and microscopy studies and analyzed the data. X.L. performed single-cell study and data analysis. Lingyan Shi contributed to mice study and data analysis. H.T.S. and J.R. contributed to DFIR microscope custom design. L.J.K., C.R.E. and L.E.P.D. contributed to biofilm study. C.Z. contributed to C. elegans study. Lixue Shi and W.M. conceived the concept and wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Wei Min.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Rita Strack was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 Azide probes.

A single azide band at 2098 cm−1 can be consistently detected on azido-palmitic acid labeled (a) and AHA labeled (b) but not on unlabeled control inside Raw264.7 cells. c, High-definition imaging on single azido-palmitic acid labeled Raw264.7 cells. Pixel size = 0.66 μm. The diffraction-limited resolution is about ~3 μm for the azide band. d, Imaging on C. elegans labeled with azido-palmitic acid. Pixel size = 3.3 μm. Experiments in a-c were repeated five times independently with similar results. Experiments in d were repeated three times independently with similar results. Scale bars, 5 μm in c, 100 μm in d.

Extended Data Fig. 2 13C-substituted precursors and carbon-deuterium probes.

a, Spectral identification of 13C and 12C carbonyl bonds (both amide I and ester carbonyl) in bacteria grown in M9 minimum medium with 13C6/12C6-glucose as the only carbon source. Experiments were repeated three times independently with similar results. b, The ratio maps as A1616 (absorbance at 1616 cm−1) over A1651 increase after culturing MDA-MB-468 with 13C-AA medium. Experiments were repeated three times independently with similar results. c, Single-pixel FTIR spectrum of differentiated adipocyte labeled with 13C6-glucose for 4 days. The squared region was enlarged as in Fig. 1i. d, Imaging on de novo lipogenesis in differentiated 3T3-L1 adipocytes with 13C6-glucose. Experiments in c and d were repeated three times with similar results. e, Imaging on C. elegans labeled with d7-glucose. Experiments were repeated three times with similar results. Pixel size = 3.3 μm for b-e. f, Single-pixel FTIR spectrum in cerebellum of pup (P21) mice labeled with 2% d7-glucose for 30 days (E11 to P21). The squared region (2050-2250 cm−1) was enlarged in the right. Experiments were repeated on three tissue slices with similar results. Scale bars: 10 μm in d, 40 μm in e.

Extended Data Fig. 3 Imaging speed comparison between broadband FTIR and single-frequency SRS.

a, Broadband FTIR microscope acquires a hyperspectral data cubic in a single scan. C-D image was generated as the transmittance difference between on-resonant (2140 cm-1) and off-resonant (2000 cm-1) with 3.3-μm pixel size. Noise was estimated as the spectral noise in the spectral region of 2100-2200 cm-1. 128 co-scans background was utilized. With 8 co-scans of signal measurement, total acquisition time is 80 min for 3.3-μm pixel size (25× objective), 30 min for 5.5-μm pixel size (15× objective) and 3 min for 20.2−μm pixel size (4× objective). b, Narrowband picosecond excitation was utilized in SRS microscope. C-D image was generated as the intensity difference between on-resonant (2135 cm-1) and off-resonant (2370 cm-1). 100-mW pump and 150-mW Stokes were utilized with 20-μs pixel dwell time and 20-μs lock-in time constant. Noise was estimated by the s.d. among pixels with only pump laser under the same laser power and lock-in time constant. Total acquisition time for a single frequency is about 20 min for 2-μm pixel size, and 80 min for 1-μm pixel size, respectively. Experiments in a and b were not repeated. Scale bars, 1 mm.

Extended Data Fig. 4 Spectral unmixing for D2O labeled tissues from various organs.

a, Spectral characterization on CD vibrations in lipid and protein with solution standards. The isolated D-labeled lipids spectrum agrees with the CD peak in 12-d1-PA (left) and D-labeled protein spectrum matches with the C(α)D peak from d4-alanine (right). Experiments were repeated two times independently with similar results. b, Retrieval of CDP and CDL signals for different organs of D2O labeled mouse. The linear combinational algorithms for different organs are presented below each spectrum. Raw spectra were truncated to spectral region of 2080-2220 cm-1 followed by polynomial baseline correction and normalization. Black solid lines are spectra for untreated tissues, red dashed lines are spectra for lipid components (protein were digested with Proteinase K) and blue dashed lines are spectra for protein components (lipids were removed by methanol wash). Experiments were repeated three times independently with similar results.

Extended Data Fig. 5 Multiplexed imaging of macromolecules synthesis activities in D2O labeled mice.

CDP and CDL are D2O-dereived protein synthesis signal and de novo lipid synthesis signal after linear unmixing (with the algorithms in Extended Data Fig. 4), respectively. Experiments were repeated on three tissue slices for each organ with similar results. Scale bars, 1 mm for brain, cerebellum and kidney; 500 μm for olfactory bulb, intestine and liver.

Extended Data Fig. 6 MIR imaging of brain metabolic activities during development.

a, b, CDL, CDP, Amide I and CHL FTIR images (a) and generated maps of protein synthesis and de novo lipid synthesis activities (b) on brains of adult and young mouse. cc: corpus callosum; fi: fimbria; sm: stria medullaris; int: internal capsule; mtt: mammilothalmic tract; HPF: hippocampus; DG-sg: granule cell layer of dentate gyrus. c, d, FTIR images (c) and generated maps of protein synthesis and de novo lipid synthesis activities (d) on olfactory bulb of adult and young mouse. P35 is 35 days postnatal mice given with 25% D2O in drinking water for P1-P35 duration and adult mice were also labeled with 25% D2O in drinking water for 35 days. e, f, FTIR images (e) and generated maps of protein synthesis and de novo lipid synthesis activities (f) on cerebellum of adult and young mouse. Experiments in a-f were repeated on three tissue slices each with similar results. (g) Immunofluorescence imaging of myelin basic protein (MBP) and NeuN (neuronal nuclei) on adult and young mouse. NeuN is mainly restricted to granular layer of cerebellum. Experiments were not repeated. Scale bars, 1 mm in a, b and e-g, 500 μm in c, d.

Extended Data Fig. 7 MIR metabolic imaging on tumor progression with D2O labeling.

Left column is the early stage and right column is the late stage. Early stage: label from day 0-15 post xenograft; Late stage: label from day 11-26 post xenograft. Experiments were repeated on three tissue slices each with similar results. Scale bars: 1 mm.

Supplementary information

Source data

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Shi, L., Liu, X., Shi, L. et al. Mid-infrared metabolic imaging with vibrational probes. Nat Methods 17, 844–851 (2020). https://doi.org/10.1038/s41592-020-0883-z

Download citation