Special Issue on Single Cell Analysis (Part II) — Guest Editors: Shuhua Yue, Xuantao Su, Minbiao Ji, Fu Wang and Xunbin Wei – Review ArticlesOpen Access

Coherent Raman scattering imaging of lipid metabolism in cancer

Shuo Zhang

Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, P. R. China

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Yexuan He

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, and

Shuhua Yue

https://orcid.org/0000-0003-1830-008X

MoE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, P. R. China

E-mail Address: [email protected]

Corresponding author.

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https://doi.org/10.1142/S1793545822300154Cited by:2 (Source: Crossref)

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Abstract

Cancer cells dysregulate lipid metabolism to accelerate energy production and biomolecule synthesis for rapid growth. Lipid metabolism is highly dynamic and intrinsically heterogeneous at the single cell level. Although fluorescence microscopy has been commonly used for cancer research, bulky fluorescent probes can hardly label small lipid molecules without perturbing their biological activities. Such a challenge can be overcome by coherent Raman scattering (CRS) microscopy, which is capable of chemically selective, highly sensitive, submicron resolution and high-speed imaging of lipid molecules in single live cells without any labeling. Recently developed hyperspectral and multiplex CRS microscopy enables quantitative mapping of various lipid metabolites in situ. Further incorporation of CRS microscopy with Raman tags greatly increases molecular selectivity based on the distinct Raman peaks well separated from the endogenous cellular background. Owing to these unique advantages, CRS microscopy sheds new insights into the role of lipid metabolism in cancer development and progression. This review focuses on the latest applications of CRS microscopy in the study of lipid metabolism in cancer.

Keywords:

References

1. W. Otto, “The metabolism of carcinoma cells,” J. Cancer Res. 9, 148–163 (1925). Crossref, Google Scholar
2. W. H. Koppenol, P. L. Bounds, C. V. Dang, “Otto Warburg’s contributions to current concepts of cancer metabolism,” Nat. Rev. Cancer 11, 325–337 (2011). Crossref, Web of Science, Google Scholar
3. R. Munir, J. Lisec, J. V. Swinnen, N. Zaidi, “Lipid metabolism in cancer cells under metabolic stress,” Br. J. Cancer 120, 1090–1098 (2019). Crossref, Web of Science, Google Scholar
4. R. J. DeBerardinis, N. S. Chandel, “Fundamentals of cancer metabolism,” Sci. Adv. 2, e1600200 (2016). Crossref, Web of Science, Google Scholar
5. T. Mashima, H. Seimiya, T. Tsuruo, “De novo fatty-acid synthesis and related pathways as molecular targets for cancer therapy,” Br. J. Cancer 100, 1369–1372 (2009). Crossref, Web of Science, Google Scholar
6. P. J. Mullen, R. Yu, J. Longo, M. C. Archer, L. Z. Penn, “The interplay between cell signalling and the mevalonate pathway in cancer,” Nat. Rev. Cancer 16, 718–731 (2016). Crossref, Web of Science, Google Scholar
7. D. Ackerman, S. Tumanov, B. Qiu, E. Michalopoulou, M. Spata, A. Azzam, H. Xie, M. C. Simon, J. J. Kamphorst, “Triglycerides promote lipid homeostasis during hypoxic stress by balancing fatty acid saturation,” Cell Rep. 24, 2596–2605.e5 (2018). Crossref, Web of Science, Google Scholar
8. M. T. Snaebjornsson, S. Janaki-Raman, A. Schulze, “Greasing the wheels of the cancer machine: The role of lipid metabolism in cancer,” Cell Metab. 31, 62–76 (2020). Crossref, Web of Science, Google Scholar
9. K. Bensaad, E. Favaro, C. A. Lewis, B. Peck, S. Lord, J. M. Collins, K. E. Pinnick, S. Wigfield, F. M. Buffa, J. L. Li, Q. Zhang, M. J. O. Wakelam, F. Karpe, A. Schulze, A. L. Harris, “Fatty acid uptake and lipid storage induced by HIF-1 $α$ contribute to cell growth and survival after hypoxia-reoxygenation,” Cell Rep. 9, 349–365 (2014). Crossref, Web of Science, Google Scholar
10. J. Long, C. J. Zhang, N. Zhu, K. Du, Y. F. Yin, X. Tan, D. F. Liao, L. Qin, “Lipid metabolism and carcinogenesis, cancer development,” Am. J. Cancer Res. 8, 778–791 (2018). Web of Science, Google Scholar
11. T. C. Walther, R. V. Farese Jr., “Lipid droplets and cellular lipid metabolism,” Annu. Rev. Biochem. 81, 687–714 (2012). Crossref, Web of Science, Google Scholar
12. Beloribi-Djefaflia S., Vasseur S., Guillaumond F., “Lipid metabolic reprogramming in cancer cells,” Oncogenesis 5, e189 (2016). Crossref, Web of Science, Google Scholar
13. Luo X., Cheng C., Tan Z., Li N., Tang M., Yang L., Cao Y., “Emerging roles of lipid metabolism in cancer metastasis,” Mol. Cancer 16, 76 (2017). Crossref, Web of Science, Google Scholar
14. Hakumäki J. M., R. A. Kauppinen, “1H NMR visible lipids in the life and death of cells,” Trends Biochem. Sci. 25, 357–362 (2000). Crossref, Web of Science, Google Scholar
15. Nabi M. M., Mamun M. A., Islam A., Hasan M. M., Waliullah A. S. M., Tamannaa Z., Sato T., Kahyo T., Setou M., “Mass spectrometry in the lipid study of cancer,” Expert Rev. Proteomics 18, 201–219 (2021). Crossref, Web of Science, Google Scholar
16. Kristensen V. N., Lingjaerde O. C., Russnes H. G., Vollan H. K., Frigessi A., Borresen-Dale A. L., “Principles and methods of integrative genomic analyses in cancer,” Nat. Rev. Cancer 14, 299–313 (2014). Crossref, Web of Science, Google Scholar
17. Holzlechner M., Eugenin E., Prideaux B., “Mass spectrometry imaging to detect lipid biomarkers and disease signatures in cancer,” Cancer Rep. (Hoboken) 2, e1229 (2019). Crossref, Google Scholar
18. Chen C., Zhao Z., Qian N., Wei S., Hu F., Min W., “Multiplexed live-cell profiling with Raman probes,” Nat. Commun. 12, 3405 (2021). Crossref, Web of Science, Google Scholar
19. Cheng J. X. and Xie X. S., “Vibrational spectroscopic imaging of living systems: An emerging platform for biology and medicine,” Science 350, aaa8870 (2015). Crossref, Web of Science, Google Scholar
20. Yue S., Cheng J. X., “Deciphering single cell metabolism by coherent Raman scattering microscopy,” Curr. Opin. Chem. Biol. 33, 46–57 (2016). Crossref, Web of Science, Google Scholar
21. Cui S., Zhang S., Yue S., “Raman spectroscopy and imaging for cancer diagnosis,” J. Healthc. Eng. 2018, 8619342 (2018). Crossref, Web of Science, Google Scholar
22. Huang K. C., Li J., Zhang C., Tan Y., Cheng J. X., “Multiplex stimulated Raman scattering imaging cytometry reveals lipid-rich protrusions in cancer cells under stress condition,” iScience 23, 100953 (2020). Crossref, Web of Science, Google Scholar
23. Shen Y., Hu F., Min W., “Raman imaging of small biomolecules,” Annu. Rev. Biophys. 48, 347–369 (2019). Crossref, Web of Science, Google Scholar
24. Lee H. J., Cheng J. X., “Imaging chemistry inside living cells by stimulated Raman scattering microscopy,” Methods 128, 119–128 (2017). Crossref, Web of Science, Google Scholar
25. Shi L., Fung A. A., Zhou A., “Advances in stimulated Raman scattering imaging for tissues and animals,” Quant Imaging Med. Surg. 11, 1078–1101 (2021). Crossref, Web of Science, Google Scholar
26. C. W. Freudiger, W. Min, B. G. Saar, S. Lu, G. R. Holtom, C. He, J. C. Tsai, J. X. Kang, X. Sunney Xie, “Label-free biomedical imaging with high sensitivity by stimulated Raman scattering microscopy,” Science 322, 1857–1861 (2008). Crossref, Web of Science, Google Scholar
27. Zhang D., Slipchenko M. N., Leaird D. E., Weiner A. M., Cheng J. X., “Spectrally modulated stimulated Raman scattering imaging with an angle-to-wavelength pulse shaper,” Opt. Express 21, 13864–13874 (2013). Crossref, Web of Science, Google Scholar
28. I. W. Schie, C. Krafft, J. Popp, “Applications of coherent Raman scattering microscopies to clinical and biological studies,” Analyst 140, 3897–3909 (2015). Crossref, Web of Science, Google Scholar
29. R. Li, P. Wang, L. Lan, F. P. Lloyd Jr., C. J. Goergen, S. Chen, J. X. Cheng, “Assessing breast tumor margin by multispectral photoacoustic tomography,” Biomed. Opt. Express 6, 1273–1281 (2015). Crossref, Web of Science, Google Scholar
30. Fu D., Holtom G., Freudiger C., Zhang X., Xie X. S., “Hyperspectral imaging with stimulated Raman scattering by chirped femtosecond lasers,” J. Phys. Chem. B 117, 4634–4640 (2013). Crossref, Web of Science, Google Scholar
31. Cheng J. X., “Coherent anti-stokes Raman scattering microscopy,” Appl. Spectrosc. 61, 197–208 (2007). Crossref, Web of Science, Google Scholar
32. Ji M., Orringer D. A., Freudiger C. W., Ramkissoon S., Liu X., Lau D., Golby A. J., Norton I., Hayashi M., Agar N. Y., Young G. S., Spino C., Santagata S., Camelo-Piragua S., Ligon K. L., Sagher O., Sunney Xie X., “Rapid, label-free detection of brain tumors with stimulated Raman scattering microscopy,” Sci. Transl. Med. 5, 201ra119 (2013). Crossref, Web of Science, Google Scholar
33. Zhao Z., Shen Y., Hu F., Min W., “Applications of vibrational tags in biological imaging by Raman microscopy,” Analyst 142, 4018–4029 (2017). Crossref, Web of Science, Google Scholar
34. Hu F., Shi L., Min W., “Biological imaging of chemical bonds by stimulated Raman scattering microscopy,” Nat. Methods 16, 830–842 (2019). Crossref, Web of Science, Google Scholar
35. Li J., Cheng J. X., “Direct visualization of de novo lipogenesis in single living cells,” Sci. Rep. 4, 6807 (2014). Crossref, Web of Science, Google Scholar
36. Hong S., Chen T., Zhu Y., Li A., Huang Y., Chen X., “Live-cell stimulated Raman scattering imaging of alkyne-tagged biomolecules,” Angew. Chem. Int. Ed. Engl. 53, 5827–5831 (2014). Crossref, Web of Science, Google Scholar
37. Matthaus C., Krafft C., Dietzek B., Brehm B. R., Lorkowski S., Popp J., “Noninvasive imaging of intracellular lipid metabolism in macrophages by Raman microscopy in combination with stable isotopic labeling,” Anal. Chem. 84, 8549–8556 (2012). Crossref, Web of Science, Google Scholar
38. Lee D., Du J., Yu R., Su Y., Heath J. R., Wei L., “Visualizing subcellular enrichment of glycogen in live cancer cells by stimulated Raman scattering,” Anal. Chem. 92, 13182–13191 (2020). Crossref, Web of Science, Google Scholar
39. Alfonso-Garcia A., Pfisterer S. G., Riezman H., Ikonen E., Potma E. O., “D38-cholesterol as a Raman active probe for imaging intracellular cholesterol storage,” J. Biomed. Opt. 21, 61003 (2016). Crossref, Web of Science, Google Scholar
40. Wei L., Hu F., Shen Y., Chen Z., Yu Y., Lin C. C., Wang M. C., Min W., “Live-cell imaging of alkyne-tagged small biomolecules by stimulated Raman scattering,” Nat. Methods 11, 410–412 (2014). Crossref, Web of Science, Google Scholar
41. Fu D., “Quantitative chemical imaging with stimulated Raman scattering microscopy,” Curr. Opin. Chem. Biol. 39, 24–31 (2017). Crossref, Web of Science, Google Scholar
42. Koundouros N., Poulogiannis G., “Reprogramming of fatty acid metabolism in cancer,” Br. J. Cancer 122, 4–22 (2020). Crossref, Web of Science, Google Scholar
43. Santos C. R., Schulze A., “Lipid metabolism in cancer,” FEBS J. 279, 2610–2623 (2012). Crossref, Web of Science, Google Scholar
44. E. Currie, A. Schulze, R. Zechner, T. C. Walther, R. V. Farese Jr., “Cellular fatty acid metabolism and cancer,” Cell Metab. 18, 153–161 (2013). Crossref, Web of Science, Google Scholar
45. Huang B., Song B. L., Xu C., “Cholesterol metabolism in cancer: Mechanisms and therapeutic opportunities,” Nat. Metab. 2, 132–141 (2020). Crossref, Google Scholar
46. Cruz, A. L. S., Barreto, E. A., Fazolini, N. P. B., Viola, J. P. B., Bozza, P. T., “Lipid droplets: Platforms with multiple functions in cancer hallmarks,” Cell Death Dis. 11, 105 (2020). Crossref, Web of Science, Google Scholar
47. Nan X., Cheng J. X., Xie X. S., “Vibrational imaging of lipid droplets in live fibroblast cells with coherent anti-Stokes Raman scattering microscopy,” J. Lipid Res. 44, 2202–2208 (2003). Crossref, Web of Science, Google Scholar
48. Chang T. Y., Chang C. C., Ohgami N., Yamauchi Y., “Cholesterol sensing, trafficking, and esterification,” Annu. Rev. Cell Dev. Biol. 22, 129–157 (2006). Crossref, Web of Science, Google Scholar
49. Kuzu O. F., Noory M. A., Robertson G. P., “The role of cholesterol in cancer,” Cancer Res. 76, 2063–2070 (2016). Crossref, Web of Science, Google Scholar
50. Garcia-Bermudez J., Baudrier L., Bayraktar E. C., Shen Y., La K., Guarecuco R., Yucel B., Fiore D., Tavora B., Freinkman E., Chan S. H., Lewis C., Min W., Inghirami G., Sabatini D. M., Birsoy K., “Squalene accumulation in cholesterol auxotrophic lymphomas prevents oxidative cell death,” Nature 567, 118–122 (2019). Crossref, Web of Science, Google Scholar
51. Zhu Y., Chen C. Y., Li J., Cheng J. X., Jang M., Kee- Hong K., “In vitro exploration of ACAT contributions to lipid droplet formation during adipogenesis,” J. Lipid Res. 59, 820–829 (2018). Crossref, Web of Science, Google Scholar
52. Yang W., Bai Y., Xiong Y., Zhang J., Chen S., Zheng X., Meng X., Li L., Wang J., Xu C., Yan C., Wang L., Chang C. C., Chang T. Y., Zhang T., Zhou P., Song B. L., Liu W., Sun S. C., Liu X., Li B. L., Xu C., “Potentiating the antitumour response of CD8(+) T cells by modulating cholesterol metabolism,” Nature 531, 651–655 (2016). Crossref, Web of Science, Google Scholar
53. Li J., Gu D., Lee S. S., Song B., Bandyopadhyay S., Chen S., Konieczny S. F., Ratliff T. L., Liu X., Xie J., Cheng J. X., “Abrogating cholesterol esterification suppresses growth and metastasis of pancreatic cancer,” Oncogene 35, 6378–6388 (2016). Crossref, Web of Science, Google Scholar
54. Bemlih S., Poirier M. D., El Andaloussi A., “Acyl-coenzyme A: Cholesterol acyltransferase inhibitor Avasimibe affect survival and proliferation of glioma tumor cell lines,” Cancer Biol. Ther. 9, 1025–1032 (2010). Crossref, Web of Science, Google Scholar
55. Lee S. S., Li J., Tai J. N., Ratliff T. L., Park K., Cheng J. X., “Avasimibe encapsulated in human serum albumin blocks cholesterol esterification for selective cancer treatment,” ACS Nano 9, 2420–2432 (2015). Crossref, Web of Science, Google Scholar
56. Geng F., Cheng X., Wu X., Yoo J. Y., Cheng C., Guo J. Y., Mo X., Ru P., Hurwitz B., Kim S. H., Otero J., Puduvalli V., Lefai E., Ma J., Nakano I., Horbinski C., Kaur B., Chakravarti A., Guo D., “Inhibition of SOAT1 suppresses glioblastoma growth via blocking SREBP-1-mediated lipogenesis,” Clin. Cancer Res. 22, 5337–5348 (2016). Crossref, Web of Science, Google Scholar
57. Lee H. J., Li J., Vickman R. E., Li J., Liu R., Durkes A. C., Elzey B. D., Yue S., Liu X., Ratliff T. L., Cheng J. X., “Cholesterol esterification inhibition suppresses prostate cancer metastasis by impairing the Wnt/beta-catenin pathway,” Mol. Cancer Res. 16, 974–985 (2018). Crossref, Web of Science, Google Scholar
58. Yue S., Li J., Lee S. Y., Lee H. J., Shao T., Song B., Cheng L., Masterson T. A., Liu X., Ratliff T. L., Cheng J. X., “Cholesteryl ester accumulation induced by PTEN loss and PI3K/AKT activation underlies human prostate cancer aggressiveness,” Cell Metab. 19, 393–406 (2014). Crossref, Web of Science, Google Scholar
59. Lee H. J., Chen Z., Collard M., Chen F., Chen J. G., Wu M., Alani R. M., Cheng J.-X., “Multimodal metabolic imaging reveals pigment reduction and lipid accumulation in metastatic melanoma,” BME Front. 2021, 1–17 (2021). Crossref, Google Scholar
60. Li J., Qu X., Tian J., Zhang J. T., Cheng J. X., “Cholesterol esterification inhibition and gemcitabine synergistically suppress pancreatic ductal adenocarcinoma proliferation,” PLoS One 13, e0193318 (2018). Web of Science, Google Scholar
61. Bandyopadhyay S., Li J., Traer E., Tyner J. W., Zhou A., Oh S. T., Cheng J. X., “Cholesterol esterification inhibition and imatinib treatment synergistically inhibit growth of BCR-ABL mutation-independent resistant chronic myelogenous leukemia,” PLoS One 12, e0179558 (2017). Crossref, Web of Science, Google Scholar
62. Yan S., Cui S., Ke K., Zhao B., Liu X., Yue S., Wang P., “Hyperspectral stimulated Raman scattering microscopy unravels aberrant accumulation of saturated fat in human liver cancer,” Anal. Chem. 90, 6362–6366 (2018). Crossref, Web of Science, Google Scholar
63. Li J., Condello S., Thomes-Pepin J., Ma X., Xia Y., Hurley T. D., Matei D., Cheng J. X., “Lipid desaturation is a metabolic marker and therapeutic target of ovarian cancer stem cells,” Cell Stem Cell 20, 303–314.e5 (2017). Crossref, Web of Science, Google Scholar
64. Drutis D. M., Hancewicz T. M., Pashkovski E., Feng L., Mihalov D., Holtom G., Ananthapadmanabhan K. P., Xie X. S., Misra M., “Three-dimensional chemical imaging of skin using stimulated Raman scattering microscopy,” J. Biomed. Opt. 19, 111604 (2014). Crossref, Web of Science, Google Scholar
65. Zhang L., Shi L., Shen Y., Miao Y., Wei M., Qian N., Liu Y., Min W., “Spectral tracing of deuterium for imaging glucose metabolism,” Nat. Biomed. Eng. 3, 402–413 (2019). Crossref, Web of Science, Google Scholar
66. Wei M., Shi L., Shen Y., Zhao Z., Guzman A., Kaufman L. J., Wei L., Min W., “Volumetric chemical imaging by clearing-enhanced stimulated Raman scattering microscopy,” Proc. Natl. Acad. Sci. USA 116, 6608–6617 (2019). Crossref, Web of Science, Google Scholar
67. Du J., Su Y., Qian C., Yuan D., Miao K., Lee D., Ng A. H. C., Wijker R. S., Ribas A., Levine R. D., Heath J. R., Wei L., “Raman-guided subcellular pharmaco-metabolomics for metastatic melanoma cells,” Nat. Commun. 11, 4830 (2020). Crossref, Web of Science, Google Scholar
68. Shi L., Zheng C., Shen Y., Chen Z., Silveira E. S., Zhang L., Wei M., Liu C., de Sena-Tomas C., Targoff K., Min W., “Optical imaging of metabolic dynamics in animals,” Nat. Commun. 9, 2995 (2018). Crossref, Web of Science, Google Scholar
69. Greenlee J. D., Subramanian T., Liu K., King M. R., “Rafting down the metastatic cascade: The role of lipid rafts in cancer metastasis, cell death, and clinical outcomes,” Cancer Res. 81, 5–17 (2021). Crossref, Web of Science, Google Scholar
70. Szlasa W., Zendran I., Zalesinska A., Tarek M., Kulbacka J., “Lipid composition of the cancer cell membrane,” J. Bioenerg. Biomembr. 52, 321–342 (2020). Crossref, Web of Science, Google Scholar
71. Goossens P., Rodriguez-Vita J., Etzerodt A., Masse M., Rastoin O., Gouirand V., Ulas T., Papantonopoulou O., Van Eck M., Auphan-Anezin N., Bebien M., Verthuy C., Vu Manh T. P., Turner M., Dalod M., Schultze J. L., Lawrence T., “Membrane cholesterol efflux drives tumor-associated macrophage reprogramming and tumor progression,” Cell Metab. 29, 1376–1389.e4 (2019). Crossref, Web of Science, Google Scholar
72. Yue S., Cardenas-Mora J. M., Chaboub L. S., Lelievre S. A., Cheng J. X., “Label-free analysis of breast tissue polarity by Raman imaging of lipid phase,” Biophys. J. 102, 1215–1223 (2012). Crossref, Web of Science, Google Scholar
73. Le T. T., Huff T. B., Cheng J. X., “Coherent anti-Stokes Raman scattering imaging of lipids in cancer metastasis,” BMC Cancer 9, 42 (2009). Crossref, Web of Science, Google Scholar
74. Shen Y., Zhao Z., Zhang L., Shi L., Shahriar S., Chan R. B., Di Paolo G., Min W., “Metabolic activity induces membrane phase separation in endoplasmic reticulum,” Proc. Natl. Acad. Sci. USA 114, 13394–13399 (2017). Crossref, Web of Science, Google Scholar
75. Chen T., Yavuz A., Wang M. C., “Dissecting lipid droplet biology with coherent Raman scattering microscopy,” J. Cell Sci. 135(5), jcs252353 (2022). Crossref, Web of Science, Google Scholar
76. N. Qian and W. Min, Supermultiplexed vibrational imaging: From probe development to biomedical applications, in Stimulated Raman Scattering Microscopy: Techniques and Applications, J.-X. ChengW. MinY. OzekiD. Polli., Chap. 21, pp. 311–328, Elsevier (2022). Crossref, Google Scholar
77. Jang H., Li Y., Fung A. A., Bagheri P., Hoang K., Skowronska-Krawczyk D., Chen X., Wu J. Y., Bintu B., Shi L., “Super-resolution stimulated Raman Scattering microscopy with A-PoD,” bioRxiv (2022), https://doi.org/10.1101/2022.06.04.494813. Google Scholar
78. Zhang W., Li Y., Fung A. A., Li Z., Jang H., Zha H., Chen X., Gao F., Wu J. Y., Sheng H., Yao J., Skowronska-Krawczyk D., Jain S., Shi L., “Multi-molecular hyperspectral PRM-SRS imaging,” bioRxiv (2022), https://doi.org/10.1101/2022.07.25.501472. Google Scholar
79. Fung A. A., Hoang K., Zha H., Chen D., Zhang W., Shi L., “Imaging sub-cellular methionine and insulin interplay in triple negative breast cancer lipid droplet metabolism,” Front. Oncol. 12, 858017 (2022). Crossref, Web of Science, Google Scholar

Vol. 16, No. 03

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History

Received 30 July 2022

Revised 22 September 2022

Accepted 28 September 2022

Published: 24 November 2022

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This is an Open Access article. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC-BY) License. Further distribution of this work is permitted, provided the original work is properly cited.

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Coherent Raman scattering imaging of lipid metabolism in cancer

Abstract

References

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Coherent Raman scattering imaging of lipid metabolism in cancer

Abstract

References

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