1932

Abstract

Over the last half century, the autofluorescence of the metabolic cofactors NADH (reduced nicotinamide adenine dinucleotide) and FAD (flavin adenine dinucleotide) has been quantified in a variety of cell types and disease states. With the spread of nonlinear optical microscopy techniques in biomedical research, NADH and FAD imaging has offered an attractive solution to noninvasively monitor cell and tissue status and elucidate dynamic changes in cell or tissue metabolism. Various tools and methods to measure the temporal, spectral, and spatial properties of NADH and FAD autofluorescence have been developed. Specifically, an optical redox ratio of cofactor fluorescence intensities and NADH fluorescence lifetime parameters have been used in numerous applications, but significant work remains to mature this technology for understanding dynamic changes in metabolism. This article describes the current understanding of our optical sensitivity to different metabolic pathways and highlights current challenges in the field. Recent progress in addressing these challenges and acquiring more quantitative information in faster and more metabolically relevant formats is also discussed.

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2023-06-08
2024-12-14
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Literature Cited

  1. 1.
    McNally LA, Altamimi TR, Fulghum K, Hill BG. 2021. Considerations for using isolated cell systems to understand cardiac metabolism and biology. J. Mol. Cell. Cardiol. 153:26–41
    [Google Scholar]
  2. 2.
    Agrawal RR, Tamucci KA, Pera M, Larrea D. 2020. Assessing mitochondrial respiratory bioenergetics in whole cells and isolated organelles by microplate respirometry. Methods Cell Biol. 155:157–80
    [Google Scholar]
  3. 3.
    Divakaruni AS, Rogers GW, Murphy AN. 2014. Measuring mitochondrial function in permeabilized cells using the Seahorse XF Analyzer or a Clark-type oxygen electrode. Curr. Protoc. Toxicol. 60:25.2.1–16
    [Google Scholar]
  4. 4.
    TeSlaa T, Teitell MA. 2014. Techniques to monitor glycolysis. Methods Enzymol. 542:91–114
    [Google Scholar]
  5. 5.
    Rahmim A, Zaidi H. 2008. PET versus SPECT: strengths, limitations and challenges. Nucl. Med. Commun. 29:193–207
    [Google Scholar]
  6. 6.
    Mariani G, Bruselli L, Kuwert T, Kim EE, Flotats A et al. 2010. A review on the clinical uses of SPECT/CT. Eur. J. Nucl. Med. Mol. Imaging 37:1959–85
    [Google Scholar]
  7. 7.
    O'Connor JPB, Robinson SP, Waterton JC. 2019. Imaging tumour hypoxia with oxygen-enhanced MRI and BOLD MRI. Br. J. Radiol. 92:20180642
    [Google Scholar]
  8. 8.
    Fischbach F, Bruhn H. 2008. Assessment of in vivo 1H magnetic resonance spectroscopy in the liver: a review. Liver Int. 28:297–307
    [Google Scholar]
  9. 9.
    Soares DP, Law M. 2009. Magnetic resonance spectroscopy of the brain: review of metabolites and clinical applications. Clin. Radiol. 64:12–21
    [Google Scholar]
  10. 10.
    Wilson RH, Nadeau KP, Jaworski FB, Tromberg BJ, Durkin AJ. 2015. Review of short-wave infrared spectroscopy and imaging methods for biological tissue characterization. J. Biomed. Opt. 20:030901
    [Google Scholar]
  11. 11.
    Karlas A, Pleitez MA, Aguirre J, Ntziachristos V. 2021. Optoacoustic imaging in endocrinology and metabolism. Nat. Rev. Endocrinol. 17:323–35
    [Google Scholar]
  12. 12.
    Srinivasan S, Pogue BW, Jiang S, Dehghani H, Kogel C et al. 2003. Interpreting hemoglobin and water concentration, oxygen saturation, and scattering measured in vivo by near-infrared breast tomography. PNAS 100:12349–54
    [Google Scholar]
  13. 13.
    Hielscher AH, Bluestone AY, Abdoulaev GS, Klose AD, Lasker J et al. 2002. Near-infrared diffuse optical tomography. Dis. Markers 18:313–37
    [Google Scholar]
  14. 14.
    Zonios G, Bykowski J, Kollias N. 2001. Skin melanin, hemoglobin, and light scattering properties can be quantitatively assessed in vivo using diffuse reflectance spectroscopy. J. Investig. Dermatol. 117:1452–57
    [Google Scholar]
  15. 15.
    Faber DJ, Mik EG, Aalders MC, van Leeuwen TG. 2003. Light absorption of (oxy-)hemoglobin assessed by spectroscopic optical coherence tomography. Opt. Lett. 28:1436–38
    [Google Scholar]
  16. 16.
    Robles FE, Chowdhury S, Wax A. 2010. Assessing hemoglobin concentration using spectroscopic optical coherence tomography for feasibility of tissue diagnostics. Biomed. Opt. Express 1:310–17
    [Google Scholar]
  17. 17.
    Ehrenberg B, Montana V, Wei MD, Wuskell JP, Loew LM. 1988. Membrane potential can be determined in individual cells from the Nernstian distribution of cationic dyes. Biophys. J. 53:785–94
    [Google Scholar]
  18. 18.
    Yamamoto N, Ueda-Wakagi M, Sato T, Kawasaki K, Sawada K et al. 2015. Measurement of glucose uptake in cultured cells. Curr. Protoc. Pharmacol. 71:12.14.1–26
    [Google Scholar]
  19. 19.
    Ihnat MA, Thorpe JE, Kamat CD, Szabo C, Green DE et al. 2007. Reactive oxygen species mediate a cellular ‘memory’ of high glucose stress signalling. Diabetologia 50:1523–31
    [Google Scholar]
  20. 20.
    Kauffman ME, Kauffman MK, Traore K, Zhu H, Trush MA et al. 2016. MitoSOX-based flow cytometry for detecting mitochondrial ROS. React. Oxyg. Species 2:361–70
    [Google Scholar]
  21. 21.
    Kaur A, Kolanowski JL, New EJ. 2016. Reversible fluorescent probes for biological redox states. Angew. Chem. Int. Ed. Engl. 55:1602–13
    [Google Scholar]
  22. 22.
    Cameron WD, Bui CV, Hutchinson A, Loppnau P, Graslund S, Rocheleau JV. 2016. Apollo-NADP+: a spectrally tunable family of genetically encoded sensors for NADP+. Nat. Methods 13:352–58
    [Google Scholar]
  23. 23.
    Hung YP, Albeck JG, Tantama M, Yellen G. 2011. Imaging cytosolic NADH-NAD+ redox state with a genetically encoded fluorescent biosensor. Cell Metab. 14:545–54
    [Google Scholar]
  24. 24.
    Cheng JX, Xie XS. 2015. Vibrational spectroscopic imaging of living systems: an emerging platform for biology and medicine. Science 350:aaa8870
    [Google Scholar]
  25. 25.
    Takei Y, Hirai R, Fukuda A, Miyazaki S, Shimada R et al. 2021. Visualization of intracellular lipid metabolism in brown adipocytes by time-lapse ultra-multiplex CARS microspectroscopy with an onstage incubator. J. Chem. Phys. 155:125102
    [Google Scholar]
  26. 26.
    Folick A, Min W, Wang MC. 2011. Label-free imaging of lipid dynamics using Coherent Anti-stokes Raman Scattering (CARS) and Stimulated Raman Scattering (SRS) microscopy. Curr. Opin. Genet. Dev. 21:585–90
    [Google Scholar]
  27. 27.
    Chance B, Schoener B, Oshino R, Itshak F, Nakase Y. 1979. Oxidation-reduction ratio studies of mitochondria in freeze-trapped samples. NADH and flavoprotein fluorescence signals. J. Biol. Chem. 254:4764–71
    [Google Scholar]
  28. 28.
    Chance B, Thorell B. 1959. Localization and kinetics of reduced pyridine nucleotide in living cells by microfluorometry. J. Biol. Chem. 234:3044–50
    [Google Scholar]
  29. 29.
    Mayevsky A, Chance B. 2007. Oxidation-reduction states of NADH in vivo: from animals to clinical use. Mitochondrion 7:330–39
    [Google Scholar]
  30. 30.
    Kolenc OI, Quinn KP. 2018. Evaluating cell metabolism through autofluorescence imaging of NAD(P)H and FAD. Antioxid. Redox. Signal. 30:875–89
    [Google Scholar]
  31. 31.
    Georgakoudi I, Quinn KP. 2012. Optical imaging using endogenous contrast to assess metabolic state. Annu. Rev. Biomed. Eng. 14:351–67
    [Google Scholar]
  32. 32.
    Mayevsky A, Rogatsky GG. 2007. Mitochondrial function in vivo evaluated by NADH fluorescence: from animal models to human studies. Am. J. Physiol. Cell. Physiol. 292:C615–40
    [Google Scholar]
  33. 33.
    Xu HN, Li LZ. 2014. Quantitative redox imaging biomarkers for studying tissue metabolic state and its heterogeneity. J. Innov. Opt. Health Sci. 7:1430002
    [Google Scholar]
  34. 34.
    Xiao W, Wang RS, Handy DE, Loscalzo J. 2018. NAD(H) and NADP(H) redox couples and cellular energy metabolism. Antioxid. Redox. Signal. 28:251–72
    [Google Scholar]
  35. 35.
    Ying W. 2008. NAD+/NADH and NADP+/NADPH in cellular functions and cell death: regulation and biological consequences. Antioxid. Redox. Signal. 10:179–206
    [Google Scholar]
  36. 36.
    Blacker TS, Duchen MR. 2016. Investigating mitochondrial redox state using NADH and NADPH autofluorescence. Free Radic. Biol. Med. 100:53–65
    [Google Scholar]
  37. 37.
    Tilokani L, Nagashima S, Paupe V, Prudent J. 2018. Mitochondrial dynamics: overview of molecular mechanisms. Essays Biochem. 62:341–60
    [Google Scholar]
  38. 38.
    Benard G, Bellance N, James D, Parrone P, Fernandez H et al. 2007. Mitochondrial bioenergetics and structural network organization. J. Cell Sci. 120:838–48
    [Google Scholar]
  39. 39.
    Mitra K, Wunder C, Roysam B, Lin G, Lippincott-Schwartz J. 2009. A hyperfused mitochondrial state achieved at G1-S regulates cyclin E buildup and entry into S phase. PNAS 106:11960–65
    [Google Scholar]
  40. 40.
    Weir HJ, Yao P, Huynh FK, Escoubas CC, Goncalves RL et al. 2017. Dietary restriction and AMPK increase lifespan via mitochondrial network and peroxisome remodeling. Cell Metab. 26:884–96.e5
    [Google Scholar]
  41. 41.
    Yerevanian A, Murphy LM, Emans S, Zhou Y, Ahsan FM et al. 2022. Riboflavin depletion promotes longevity and metabolic hormesis in Caenorhabditis elegans. Aging Cell 21:11e13718
    [Google Scholar]
  42. 42.
    Bhat AH, Dar KB, Anees S, Zargar MA, Masood A et al. 2015. Oxidative stress, mitochondrial dysfunction and neurodegenerative diseases; a mechanistic insight. Biomed. Pharmacother. 74:101–10
    [Google Scholar]
  43. 43.
    Huang S, Heikal AA, Webb WW. 2002. Two-photon fluorescence spectroscopy and microscopy of NAD(P)H and flavoprotein. Biophys. J. 82:2811–25
    [Google Scholar]
  44. 44.
    Blinova K, Carroll S, Bose S, Smirnov AV, Harvey JJ et al. 2005. Distribution of mitochondrial NADH fluorescence lifetimes: steady-state kinetics of matrix NADH interactions. Biochemistry 44:2585–94
    [Google Scholar]
  45. 45.
    Blinova K, Levine RL, Boja ES, Griffiths GL, Shi ZD et al. 2008. Mitochondrial NADH fluorescence is enhanced by complex I binding. Biochemistry 47:9636–45
    [Google Scholar]
  46. 46.
    Cao S, Li H, Liu Y, Wang M, Zhang M et al. 2020. Dehydrogenase binding sites abolish the “dark” fraction of NADH: implication for metabolic sensing via FLIM. J. Phys. Chem. B 124:6721–27
    [Google Scholar]
  47. 47.
    Kunz WS, Kuznetsov AV, Winkler K, Gellerich FN, Neuhof S, Neumann HW. 1994. Measurement of fluorescence changes of NAD(P)H and of fluorescent flavoproteins in saponin-skinned human skeletal muscle fibers. Anal. Biochem. 216:322–27
    [Google Scholar]
  48. 48.
    Lakowicz JR, Szmacinski H, Nowaczyk K, Johnson ML. 1992. Fluorescence lifetime imaging of free and protein-bound NADH. PNAS 89:1271–75
    [Google Scholar]
  49. 49.
    Patterson GH, Knobel SM, Arkhammar P, Thastrup O, Piston DW. 2000. Separation of the glucose-stimulated cytoplasmic and mitochondrial NAD(P)H responses in pancreatic islet beta cells. PNAS 97:5203–7
    [Google Scholar]
  50. 50.
    Rice WL, Kaplan DL, Georgakoudi I. 2010. Two-photon microscopy for non-invasive, quantitative monitoring of stem cell differentiation. PLOS ONE 5:e10075
    [Google Scholar]
  51. 51.
    Yu Q, Heikal AA. 2009. Two-photon autofluorescence dynamics imaging reveals sensitivity of intracellular NADH concentration and conformation to cell physiology at the single-cell level. J. Photochem. Photobiol. B 95:46–57
    [Google Scholar]
  52. 52.
    Gafni A, Brand L. 1976. Fluorescence decay studies of reduced nicotinamide adenine dinucleotide in solution and bound to liver alcohol dehydrogenase. Biochemistry 15:3165–71
    [Google Scholar]
  53. 53.
    Rocheleau JV, Head WS, Piston DW. 2004. Quantitative NAD(P)H/flavoprotein autofluorescence imaging reveals metabolic mechanisms of pancreatic islet pyruvate response. J. Biol. Chem. 279:31780–87
    [Google Scholar]
  54. 54.
    Blacker TS, Mann ZF, Gale JE, Ziegler M, Bain AJ et al. 2014. Separating NADH and NADPH fluorescence in live cells and tissues using FLIM. Nat. Commun. 5:3936
    [Google Scholar]
  55. 55.
    Quinn KP, Sridharan GV, Hayden RS, Kaplan DL, Lee K, Georgakoudi I. 2013. Quantitative metabolic imaging using endogenous fluorescence to detect stem cell differentiation. Sci. Rep. 3:3432
    [Google Scholar]
  56. 56.
    Varone A, Xylas J, Quinn KP, Pouli D, Sridharan G et al. 2014. Endogenous two-photon fluorescence imaging elucidates metabolic changes related to enhanced glycolysis and glutamine consumption in precancerous epithelial tissues. Cancer Res. 74:3067–75
    [Google Scholar]
  57. 57.
    Kunz WS, Kunz W. 1985. Contribution of different enzymes to flavoprotein fluorescence of isolated rat liver mitochondria. Biochim. Biophys. Acta 841:237–46
    [Google Scholar]
  58. 58.
    Kunz WS, Gellerich FN. 1993. Quantification of the content of fluorescent flavoproteins in mitochondria from liver, kidney cortex, skeletal muscle, and brain. Biochem. Med. Metab. Biol. 50:103–10
    [Google Scholar]
  59. 59.
    Chorvat D Jr., Kirchnerova J, Cagalinec M, Smolka J, Mateasik A, Chorvatova A. 2005. Spectral unmixing of flavin autofluorescence components in cardiac myocytes. Biophys. J. 89:L55–57
    [Google Scholar]
  60. 60.
    van den Berg PAW, Feenstra KA, Mark AE, Berendsen HJC, Visser AJWG. 2002. Dynamic conformations of flavin adenine dinucleotide: simulated molecular dynamic of the flavin cofactor related to the time-resolved fluorescence characteristics. J. Phys. Chem. B 106:8858–69
    [Google Scholar]
  61. 61.
    Chorvat D Jr., Chorvatova A. 2006. Spectrally resolved time-correlated single photon counting: a novel approach for characterization of endogenous fluorescence in isolated cardiac myocytes. Eur. Biophys. J. 36:73–83
    [Google Scholar]
  62. 62.
    Heikal AA. 2010. Intracellular coenzymes as natural biomarkers for metabolic activities and mitochondrial anomalies. Biomark. Med. 4:241–63
    [Google Scholar]
  63. 63.
    Pouli D, Balu M, Alonzo CA, Liu Z, Quinn KP et al. 2016. Imaging mitochondrial dynamics in human skin reveals depth-dependent hypoxia and malignant potential for diagnosis. Sci. Transl. Med. 8:367ra169
    [Google Scholar]
  64. 64.
    Xylas J, Quinn KP, Hunter M, Georgakoudi I. 2012. Improved Fourier-based characterization of intracellular fractal features. Opt. Express 20:23442–55
    [Google Scholar]
  65. 65.
    Xylas J, Varone A, Quinn KP, Pouli D, McLaughlin-Drubin ME et al. 2015. Noninvasive assessment of mitochondrial organization in three-dimensional tissues reveals changes associated with cancer development. Int. J. Cancer 136:322–32
    [Google Scholar]
  66. 66.
    Shiu J, Zhang L, Lentsch G, Flesher JL, Jin S et al. 2022. Multimodal analyses of vitiligo skin identify tissue characteristics of stable disease. JCI Insight 7:e154585
    [Google Scholar]
  67. 67.
    Pouli D, Thieu HT, Genega EM, Baecher-Lind L, House M et al. 2020. Label-free, high-resolution optical metabolic imaging of human cervical precancers reveals potential for intraepithelial neoplasia diagnosis. Cell Rep. Med. 1:100017
    [Google Scholar]
  68. 68.
    Vargas I, Alhallak K, Kolenc OI, Jenkins SV, Griffin RJ et al. 2018. Rapid quantification of mitochondrial fractal dimension in individual cells. Biomed. Opt. Express 9:5269–79
    [Google Scholar]
  69. 69.
    Tandon I, Kolenc OI, Cross D, Vargas I, Johns S et al. 2020. Label-free metabolic biomarkers for assessing valve interstitial cell calcific progression. Sci. Rep. 10:10317
    [Google Scholar]
  70. 70.
    Lochocki B, Boon BDC, Verheul SR, Zada L, Hoozemans JJM et al. 2021. Multimodal, label-free fluorescence and Raman imaging of amyloid deposits in snap-frozen Alzheimer's disease human brain tissue. Commun. Biol 4:474
    [Google Scholar]
  71. 71.
    Vanek M, Mravec F, Szotkowski M, Byrtusova D, Haronikova A et al. 2018. Fluorescence lifetime imaging of red yeast Cystofilobasidium capitatum during growth. EuroBiotech J. 2:114–20
    [Google Scholar]
  72. 72.
    Bonet ML, Ribot J, Galmes S, Serra F, Palou A. 2020. Carotenoids and carotenoid conversion products in adipose tissue biology and obesity: pre-clinical and human studies. Biochim. Biophys. Acta Mol. Cell Biol. Lipids 1865:158676
    [Google Scholar]
  73. 73.
    Palczewska G, Boguslawski J, Stremplewski P, Kornaszewski L, Zhang J et al. 2020. Noninvasive two-photon optical biopsy of retinal fluorophores. PNAS 117:22532–43
    [Google Scholar]
  74. 74.
    Singh AK, Das J. 1998. Liposome encapsulated vitamin A compounds exhibit greater stability and diminished toxicity. Biophys. Chem. 73:155–62
    [Google Scholar]
  75. 75.
    Dysli C, Wolf S, Berezin MY, Sauer L, Hammer M, Zinkernagel MS. 2017. Fluorescence lifetime imaging ophthalmoscopy. Prog. Retin. Eye Res. 60:120–43
    [Google Scholar]
  76. 76.
    Ablonczy Z, Higbee D, Anderson DM, Dahrouj M, Grey AC et al. 2013. Lack of correlation between the spatial distribution of A2E and lipofuscin fluorescence in the human retinal pigment epithelium. Investig. Ophthalmol. Vis. Sci. 54:5535–42
    [Google Scholar]
  77. 77.
    Brunk UT, Terman A. 2002. Lipofuscin: mechanisms of age-related accumulation and influence on cell function. Free Radic. Biol. Med. 33:611–19
    [Google Scholar]
  78. 78.
    Ng KP, Gugiu B, Renganathan K, Davies MW, Gu X et al. 2008. Retinal pigment epithelium lipofuscin proteomics. Mol. Cell. Proteomics 7:1397–405
    [Google Scholar]
  79. 79.
    Yin DZ, Brunk UT. 1991. Microfluorometric and fluorometric lipofuscin spectral discrepancies: a concentration-dependent metachromatic effect?. Mech. Ageing Dev. 59:95–109
    [Google Scholar]
  80. 80.
    Breunig HG, Studier H, Konig K. 2010. Multiphoton excitation characteristics of cellular fluorophores of human skin in vivo. Opt. Express 18:7857–71
    [Google Scholar]
  81. 81.
    Teuchner K, Ehlert J, Freyer W, Leupold D, Almeyer P et al. 2000. Fluorescence studies of melanin by stepwise two-photon femtosecond laser excitation. J. Fluoresc. 10:275–81
    [Google Scholar]
  82. 82.
    Krasieva TB, Stringari C, Liu F, Sun CH, Kong Y et al. 2013. Two-photon excited fluorescence lifetime imaging and spectroscopy of melanins in vitro and in vivo. J. Biomed. Opt. 18:31107
    [Google Scholar]
  83. 83.
    Dimitrow E, Riemann I, Ehlers A, Koehler MJ, Norgauer J et al. 2009. Spectral fluorescence lifetime detection and selective melanin imaging by multiphoton laser tomography for melanoma diagnosis. Exp. Dermatol. 18:509–15
    [Google Scholar]
  84. 84.
    Dancik Y, Favre A, Loy CJ, Zvyagin AV, Roberts MS. 2013. Use of multiphoton tomography and fluorescence lifetime imaging to investigate skin pigmentation in vivo. J. Biomed. Opt. 18:26022
    [Google Scholar]
  85. 85.
    Pena A, Strupler M, Boulesteix T, Schanne-Klein M. 2005. Spectroscopic analysis of keratin endogenous signal for skin multiphoton microscopy. Opt. Express 13:6268–74
    [Google Scholar]
  86. 86.
    Palero JA, de Bruijn HS, van der Ploeg van den Heuvel A, Sterenborg HJ, Gerritsen HC. 2007. Spectrally resolved multiphoton imaging of in vivo and excised mouse skin tissues. Biophys. J. 93:992–1007
    [Google Scholar]
  87. 87.
    Ehlers A, Riemann I, Stark M, Konig K. 2007. Multiphoton fluorescence lifetime imaging of human hair. Microsc. Res. Tech. 70:154–61
    [Google Scholar]
  88. 88.
    Hanahan D, Weinberg RA. 2011. Hallmarks of cancer: the next generation. Cell 144:646–74
    [Google Scholar]
  89. 89.
    Levitt JM, McLaughlin-Drubin ME, Munger K, Georgakoudi I. 2011. Automated biochemical, morphological, and organizational assessment of precancerous changes from endogenous two-photon fluorescence images. PLOS ONE 6:e24765
    [Google Scholar]
  90. 90.
    Shiino A, Haida M, Beauvoit B, Chance B. 1999. Three-dimensional redox image of the normal gerbil brain. Neuroscience 91:41581–85
    [Google Scholar]
  91. 91.
    Chang T, Zimmerley MS, Quinn KP, Lamarre-Jouenne I, Kaplan DL et al. 2013. Non-invasive monitoring of cell metabolism and lipid production in 3D engineered human adipose tissues using label-free multiphoton microscopy. Biomaterials 34:8607–16
    [Google Scholar]
  92. 92.
    Quinn KP, Bellas E, Fourligas N, Lee K, Kaplan DL, Georgakoudi I. 2012. Characterization of metabolic changes associated with the functional development of 3D engineered tissues by non-invasive, dynamic measurement of individual cell redox ratios. Biomaterials 33:5341–48
    [Google Scholar]
  93. 93.
    Liu Z, Pouli D, Alonzo CA, Varone A, Karaliota S et al. 2018. Mapping metabolic changes by noninvasive, multiparametric, high-resolution imaging using endogenous contrast. Sci. Adv. 4:eaap9302
    [Google Scholar]
  94. 94.
    Datta R, Heaster TM, Sharick JT, Gillette AA, Skala MC. 2020. Fluorescence lifetime imaging microscopy: fundamentals and advances in instrumentation, analysis, and applications. J. Biomed. Opt. 25:071203
    [Google Scholar]
  95. 95.
    Skala MC, Riching KM, Gendron-Fitzpatrick A, Eickhoff J, Eliceiri KW et al. 2007. In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia. PNAS 104:19494–99
    [Google Scholar]
  96. 96.
    Walsh AJ, Cook RS, Sanders ME, Aurisicchio L, Ciliberto G et al. 2014. Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer. Cancer Res. 74:5184–94
    [Google Scholar]
  97. 97.
    Kolenc OI, Quinn KP. 2019. Evaluating cell metabolism through autofluorescence imaging of NAD(P)H and FAD. Antioxid. Redox. Signal. 30:875–89
    [Google Scholar]
  98. 98.
    Zhou H, Nguyen L, Arnesano C, Ando Y, Raval M et al. 2020. Non-invasive optical biomarkers distinguish and track the metabolic status of single hematopoietic stem cells. iScience 23:100831
    [Google Scholar]
  99. 99.
    Schaefer PM, Hilpert D, Niederschweiberer M, Neuhauser L, Kalinina S et al. 2017. Mitochondrial matrix pH as a decisive factor in neurometabolic imaging. Neurophotonics 4:045004
    [Google Scholar]
  100. 100.
    Schaefer PM, Kalinina S, Rueck A, von Arnim CAF, von Einem B. 2019. NADH autofluorescence—a marker on its way to boost bioenergetic research. Cytometry A 95:34–46
    [Google Scholar]
  101. 101.
    Vogel A, Noack J, Huttman G, Paltauf G. 2005. Mechanisms of femtosecond laser nanosurgery of cells and tissues. Appl. Phys. B 81:1015–47
    [Google Scholar]
  102. 102.
    Nadiarnykh O, Thomas G, Van Voskuilen J, Sterenborg HJ, Gerritsena HC. 2012. Carcinogenic damage to deoxyribonucleic acid is induced by near-infrared laser pulses in multiphoton microscopy via combination of two- and three-photon absorption. J. Biomed. Opt. 17:116024
    [Google Scholar]
  103. 103.
    Masters BR, So PT, Buehler C, Barry N, Sutin JD et al. 2004. Mitigating thermal mechanical damage potential during two-photon dermal imaging. J. Biomed. Opt. 9:1265–70
    [Google Scholar]
  104. 104.
    König K, So PT, Mantulin WW, Gratton E. 1997. Cellular response to near-infrared femtosecond laser pulses in two-photon microscopes. Opt. Lett. 22:135–36
    [Google Scholar]
  105. 105.
    Konig K, So PTC, Mantulin WW, Tromberg BJ, Gratton E. 1996. Two-photon excited lifetime imaging of autofluorescence in cells during UVA and NIR photostress. J. Microsc. 183:197–204
    [Google Scholar]
  106. 106.
    Tirlapur UK, König K, Peuckert C, Krieg R, Halbhuber KJ. 2001. Femtosecond near-infrared laser pulses elicit generation of reactive oxygen species in mammalian cells leading to apoptosis-like death. Exp. Cell. Res. 263:88–97
    [Google Scholar]
  107. 107.
    Galli R, Uckermann O, Andresen EF, Geiger KD, Koch E et al. 2014. Intrinsic indicator of photodamage during label-free multiphoton microscopy of cells and tissues. PLOS ONE 9:e110295
    [Google Scholar]
  108. 108.
    Klinger A, Krapf L, Orzekowsky-Schroeder R, Koop N, Vogel A, Hüttmann G. 2015. Intravital autofluorescence 2-photon microscopy of murine intestinal mucosa with ultra-broadband femtosecond laser pulse excitation: image quality, photodamage, and inflammation. J. Biomed. Opt. 20:116001
    [Google Scholar]
  109. 109.
    Tiede LM, Nichols MG. 2006. Photobleaching of reduced nicotinamide adenine dinucleotide and the development of highly fluorescent lesions in rat basophilic leukemia cells during multiphoton microscopy. Photochem. Photobiol. 82:656–64
    [Google Scholar]
  110. 110.
    Talone B, Bazzarelli M, Schirato A, Dello Vicario F, Viola D et al. 2021. Phototoxicity induced in living HeLa cells by focused femtosecond laser pulses: a data-driven approach. Biomed. Opt. Express 12:7886–905
    [Google Scholar]
  111. 111.
    Zipfel WR, Williams RM, Christie R, Nikitin AY, Hyman BT, Webb WW. 2003. Live tissue intrinsic emission microscopy using multiphoton-excited native fluorescence and second harmonic generation. PNAS 100:7075–80
    [Google Scholar]
  112. 112.
    Cannon TM, Shah AT, Walsh AJ, Skala MC. 2015. High-throughput measurements of the optical redox ratio using a commercial microplate reader. J. Biomed. Opt. 20:010503
    [Google Scholar]
  113. 113.
    Jones JD, Ramser HE, Woessner AE, Quinn KP. 2018. In vivo multiphoton microscopy detects longitudinal metabolic changes associated with delayed skin wound healing. Commun. Biol. 1:198
    [Google Scholar]
  114. 114.
    Xu HN, Nioka S, Glickson JD, Chance B, Li LZ. 2010. Quantitative mitochondrial redox imaging of breast cancer metastatic potential. J. Biomed. Opt. 15:036010
    [Google Scholar]
  115. 115.
    Ostrander JH, McMahon CM, Lem S, Millon SR, Brown JQ et al. 2010. Optical redox ratio differentiates breast cancer cell lines based on estrogen receptor status. Cancer Res. 70:4759–66
    [Google Scholar]
  116. 116.
    Alonzo CA, Karaliota S, Pouli D, Liu Z, Karalis KP, Georgakoudi I. 2016. Two-photon excited fluorescence of intrinsic fluorophores enables label-free assessment of adipose tissue function. Sci. Rep. 6:31012
    [Google Scholar]
  117. 117.
    Theodossiou A, Hu L, Wang N, Nguyen U, Walsh AJ. 2021. Autofluorescence imaging to evaluate cellular metabolism. J. Vis. Exp. 177:e63282
    [Google Scholar]
  118. 118.
    Walsh AJ, Sharick JT, Skala MC, Beier HT. 2016. Temporal binning of time-correlated single photon counting data improves exponential decay fits and imaging speed. Biomed. Opt. Express 7:1385–99
    [Google Scholar]
  119. 119.
    Alhallak K, Rebello LG, Muldoon TJ, Quinn KP, Rajaram N. 2016. Optical redox ratio identifies metastatic potential-dependent changes in breast cancer cell metabolism. Biomed. Opt. Express 7:4364–74
    [Google Scholar]
  120. 120.
    Chance B, Estabrook RW, Ghosh A. 1964. Damped sinusoidal oscillations of cytoplasmic reduced pyridine nucleotide in yeast cells. PNAS 51:1244–51
    [Google Scholar]
  121. 121.
    Jones JD, Ramser HE, Woessner AE, Veves A, Quinn KP. 2020. Quantifying age-related changes in skin wound metabolism using in vivo multiphoton microscopy. Adv. Wound Care 9:90–102
    [Google Scholar]
  122. 122.
    Campbell JM, Mahbub SB, Bertoldo MJ, Habibalahi A, Goss DM et al. 2022. Multispectral autofluorescence characteristics of reproductive aging in old and young mouse oocytes. Biogerontology 23:237–49
    [Google Scholar]
  123. 123.
    Hou J, Williams J, Botvinick EL, Potma EO, Tromberg BJ. 2018. Visualization of breast cancer metabolism using multimodal nonlinear optical microscopy of cellular lipids and redox state. Cancer Res. 78:2503–12
    [Google Scholar]
  124. 124.
    Tu H, Liu Y, Marjanovic M, Chaney EJ, You S et al. 2017. Concurrence of extracellular vesicle enrichment and metabolic switch visualized label-free in the tumor microenvironment. Sci. Adv. 3:e1600675
    [Google Scholar]
  125. 125.
    Tu H, Liu Y, Turchinovich D, Marjanovic M, Lyngso J et al. 2016. Stain-free histopathology by programmable supercontinuum pulses. Nat. Photonics 10:534–40
    [Google Scholar]
  126. 126.
    Kasischke KA, Vishwasrao HD, Fisher PJ, Zipfel WR, Webb WW. 2004. Neural activity triggers neuronal oxidative metabolism followed by astrocytic glycolysis. Science 305:99–103
    [Google Scholar]
  127. 127.
    Kasischke KA, Lambert EM, Panepento B, Sun A, Gelbard HA et al. 2011. Two-photon NADH imaging exposes boundaries of oxygen diffusion in cortical vascular supply regions. J. Cereb. Blood Flow Metab. 31:68–81
    [Google Scholar]
  128. 128.
    Hariri LP, Liebmann ER, Marion SL, Hoyer PB, Davis JR et al. 2010. Simultaneous optical coherence tomography and laser induced fluorescence imaging in rat model of ovarian carcinogenesis. Cancer Biol. Ther. 10:438–47
    [Google Scholar]
  129. 129.
    Park J, Jo JA, Shrestha S, Pande P, Wan Q, Applegate BE. 2010. A dual-modality optical coherence tomography and fluorescence lifetime imaging microscopy system for simultaneous morphological and biochemical tissue characterization. Biomed. Opt. Express 1:186–200
    [Google Scholar]
  130. 130.
    Pande P, Shrestha S, Park J, Gimenez-Conti I, Brandon J et al. 2016. Automated analysis of multimodal fluorescence lifetime imaging and optical coherence tomography data for the diagnosis of oral cancer in the hamster cheek pouch model. Biomed. Opt. Express 7:2000–15
    [Google Scholar]
  131. 131.
    Li J, Bower AJ, Arp Z, Olson EJ, Holland C et al. 2018. Investigating the healing mechanisms of an angiogenesis-promoting topical treatment for diabetic wounds using multimodal microscopy. J. Biophotonics 11:e201700195
    [Google Scholar]
  132. 132.
    Muller MG, Valdez TA, Georgakoudi I, Backman V, Fuentes C et al. 2003. Spectroscopic detection and evaluation of morphologic and biochemical changes in early human oral carcinoma. Cancer 97:1681–92
    [Google Scholar]
  133. 133.
    Croce AC, Bottiroli G. 2017. Autofluorescence spectroscopy for monitoring metabolism in animal cells and tissues. Methods Mol. Biol. 1560:15–43
    [Google Scholar]
  134. 134.
    Georgakoudi I, Jacobson BC, Muller MG, Sheets EE, Badizadegan K et al. 2002. NAD(P)H and collagen as in vivo quantitative fluorescent biomarkers of epithelial precancerous changes. Cancer Res. 62:682–87
    [Google Scholar]
  135. 135.
    Cheng LC, Chang CY, Lin CY, Cho KC, Yen WC et al. 2012. Spatiotemporal focusing-based widefield multiphoton microscopy for fast optical sectioning. Opt. Express 20:8939–48
    [Google Scholar]
  136. 136.
    Hoover EE, Squier JA. 2013. Advances in multiphoton microscopy technology. Nat. Photonics 7:93–101
    [Google Scholar]
  137. 137.
    Fittinghoff D, Wiseman P, Squier J. 2000. Widefield multiphoton and temporally decorrelated multifocal multiphoton microscopy. Opt. Express 7:273–79
    [Google Scholar]
  138. 138.
    Bewersdorf J, Pick R, Hell SW. 1998. Multifocal multiphoton microscopy. Opt. Lett. 23:655–57
    [Google Scholar]
  139. 139.
    Tal E, Oron D, Silberberg Y. 2005. Improved depth resolution in video-rate line-scanning multiphoton microscopy using temporal focusing. Opt. Lett. 30:1686–88
    [Google Scholar]
  140. 140.
    Xue Y, Berry KP, Boivin JR, Wadduwage D, Nedivi E, So PTC. 2018. Scattering reduction by structured light illumination in line-scanning temporal focusing microscopy. Biomed. Opt. Express 9:5654–66
    [Google Scholar]
  141. 141.
    Digman MA, Caiolfa VR, Zamai M, Gratton E. 2008. The phasor approach to fluorescence lifetime imaging analysis. Biophys. J. 94:L14–16
    [Google Scholar]
  142. 142.
    Ranjit S, Malacrida L, Jameson DM, Gratton E. 2018. Fit-free analysis of fluorescence lifetime imaging data using the phasor approach. Nat. Protoc. 13:1979–2004
    [Google Scholar]
  143. 143.
    Stringari C, Sierra R, Donovan PJ, Gratton E. 2012. Label-free separation of human embryonic stem cells and their differentiating progenies by phasor fluorescence lifetime microscopy. J. Biomed. Opt. 17:046012
    [Google Scholar]
  144. 144.
    Stuntz E, Gong Y, Sood D, Liaudanskaya V, Pouli D et al. 2017. Endogenous two-photon excited fluorescence imaging characterizes neuron and astrocyte metabolic responses to manganese toxicity. Sci. Rep. 7:1041
    [Google Scholar]
  145. 145.
    Ma N, Digman MA, Malacrida L, Gratton E. 2016. Measurements of absolute concentrations of NADH in cells using the phasor FLIM method. Biomed. Opt. Express 7:2441–52
    [Google Scholar]
  146. 146.
    Stringari C, Cinquin A, Cinquin O, Digman MA, Donovan PJ, Gratton E. 2011. Phasor approach to fluorescence lifetime microscopy distinguishes different metabolic states of germ cells in a live tissue. PNAS 108:13582–87
    [Google Scholar]
  147. 147.
    Dvornikov A, Gratton E. 2018. Hyperspectral imaging in highly scattering media by the spectral phasor approach using two filters. Biomed. Opt. Express 9:3503–11
    [Google Scholar]
  148. 148.
    Torrado B, Dvornikov A, Gratton E. 2021. Method of transmission filters to measure emission spectra in strongly scattering media. Biomed. Opt. Express 12:3760–74
    [Google Scholar]
  149. 149.
    Hedde PN, Cinco R, Malacrida L, Kamaid A, Gratton E. 2021. Phasor-based hyperspectral snapshot microscopy allows fast imaging of live, three-dimensional tissues for biomedical applications. Commun. Biol. 4:721
    [Google Scholar]
  150. 150.
    Tian L, Hunt B, Bell MAL, Yi J, Smith JT et al. 2021. Deep learning in biomedical optics. Lasers Surg. Med. 53:748–75
    [Google Scholar]
  151. 151.
    Shen B, Liu S, Li Y, Pan Y, Lu Y et al. 2022. Deep learning autofluorescence-harmonic microscopy. Light Sci. Appl. 11:76
    [Google Scholar]
  152. 152.
    Jones JD, Rodriguez MR, Quinn KP. 2021. Automated extraction of skin wound healing biomarkers from in vivo label-free multiphoton microscopy using convolutional neural networks. Lasers Surg. Med. 53:1086–95
    [Google Scholar]
  153. 153.
    Smith JT, Yao R, Sinsuebphon N, Rudkouskaya A, Un N et al. 2019. Fast fit-free analysis of fluorescence lifetime imaging via deep learning. PNAS 116:24019–30
    [Google Scholar]
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