Fluorescence lifetime imaging microscopy in immuno-oncology: tracking tumor heterogeneity, cell death, and immune response dynamics

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Abstract

Fluorescence lifetime imaging microscopy (FLIM) has developed significantly over the past two decades and is now a powerful tool in biomedical research. Recent advances in fluorescent probes have greatly expanded the range of its potential applications. As fluorescence lifetime is highly sensitive to microenvironmental and molecular changes, FLIM is a promising technique for detecting pathological conditions, including cancer, and monitoring the efficacy of antineoplastic therapies. This technology allows for the observation of tumor structure and the real-time monitoring of dynamic processes, enabling researchers to probe living cancer cells and their microenvironment with remarkable precision. FLIM is especially valuable for developing and evaluating immunotherapeutic strategies. In solid tumor therapy; in particular, it is crucial to assess how treatment affects tumor metabolism and heterogeneity, cell death mechanisms, and immune response dynamics.

This review provides a comprehensive analysis of current research supporting the feasibility of FLIM as a key research technique to advance cancer immunotherapy.

About the authors

Alina R. Khuzina

Sirius University of Science and Technology

Email: huzinaar@gmail.com
ORCID iD: 0009-0007-9873-7471
Russian Federation, Sirius

Victoria D. Turubanova

Sirius University of Science and Technology; National Research Lobachevsky State University of Nizhny Novgorod

Author for correspondence.
Email: turubanova@neuro.nnov.ru
ORCID iD: 0000-0002-4648-0738
SPIN-code: 8262-6560

Cand. Sci. (Biology)

Russian Federation, Sirius; Nizhny Novgorod

References

  1. Ouyang Y, Liu Y, Wang ZM, et al. FLIM as a promising tool for cancer diagnosis and treatment monitoring. Nanomicro Lett. 2021;13(1):133. doi: 10.1007/s40820-021-00653-z EDN: UCHANB
  2. Datta R, Heaster TM, Sharick JT, et al. Fluorescence lifetime imaging microscopy: fundamentals and advances in instrumentation, analysis, and applications. J Biomed Opt. 2020;25(7):1–43. doi: 10.1117/1.JBO.25.7.071203 EDN: SETCAC
  3. Shirshin EA, Shirmanova MV, Gayer AV, et al. Label-free sensing of cells with fluorescence lifetime imaging: The quest for metabolic heterogeneity. Proc Natl Acad Sci U S A. 2022;119(9):e2118241119. doi: 10.1073/pnas.2118241119 EDN: DPJSCE
  4. Skala MC, Riching KM, Gendron-Fitzpatrick A, et al. In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia. Proc Natl Acad Sci U S A. 2007;104(49):19494–19499. doi: 10.1073/pnas.0708425104
  5. Blacker TS, Duchen MR, Bain AJ. NAD(P)H binding configurations revealed by time-resolved fluorescence and two-photon absorption. Biophys J. 2023;122(7):1240–1253. doi: 10.1016/j.bpj.2023.02.014 EDN: SFDCEO
  6. Huang S, Heikal AA, Webb WW. Two-photon fluorescence spectroscopy and microscopy of NAD(P)H and flavoprotein. Biophys J. 2002;82(5):2811–2825. doi: 10.1016/S0006-3495(02)75621-X
  7. Komarova AD, Sinyushkina SD, Shchechkin ID, et al. Insights into metabolic heterogeneity of colorectal cancer gained from fluorescence lifetime imaging. Elife. 2024;13. doi: 10.7554/eLife.94438 EDN: VKZMQV
  8. Zhang J, Wallrabe H, Siller K, et al. Measuring metabolic changes in cancer cells using two-photon fluorescence lifetime imaging microscopy and machine-learning analysis. J Biophotonics. 2025;18(1):e202400426. doi: 10.1002/jbio.202400426 EDN: ZFNBTW
  9. Hao L, Li ZW, Zhang DY, et al. Monitoring mitochondrial viscosity with anticancer phosphorescent Ir(iii) complexes via two-photon lifetime imaging. Chem Sci. 2018;10(5):1285–1293. doi: 10.1039/c8sc04242j
  10. Rahim MK, Zhao J, Patel HV, et al. Phasor analysis of fluorescence lifetime enables quantitative multiplexed molecular imaging of three probes Anal Chem. 2022;94(41):14185–14194. doi: 10.1021/acs.analchem.2c02149 EDN: MPDSXC
  11. Goryashchenko AS, Pakhomov AA, Ryabova AV, et al. FLIM-based intracellular and extracellular ph measurements using genetically encoded ph sensor. Biosensors (Basel). 2021;11(9):340. doi: 10.3390/bios11090340 EDN: DDUOOI
  12. Zlobovskaya OA, Sergeeva TF, Shirmanova MV, et al. Genetically encoded far-red fluorescent sensors for Caspase-3 activity. 2016;60(2):62–68. doi: 10.2144/000114377 EDN: WRSBPB
  13. Sagar MAK, Cheng KP, Ouellette JN, et al. Machine learning methods for fluorescence lifetime imaging (FLIM) based label-free detection of microglia. Front Neurosci. 2020;14:931. doi: 10.3389/fnins.2020.00931 EDN: LASDPQ
  14. Shchechkin ID. Recent trends of fluorescence lifetime imaging microscopy analysis using machine learning. Journal of Biomedical Photonics & Engineering. 2025;11(1):010201. doi: 10.18287/JBPE25.11.010201 EDN: UHYICS
  15. Hanahan D. Hallmarks of cancer: new dimensions. Cancer Discov. 2022;12(1):31–46. doi: 10.1158/2159-8290.CD-21-1059 EDN: LPSAUZ
  16. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–674. doi: 10.1016/j.cell.2011.02.013 EDN: OAGZMH
  17. Warburg O. On the origin of cancer cells. Science. 1956;123(3191):309–314. doi: 10.1126/science.123.3191.309 EDN: ICRUGV
  18. Eggert D, Gaertner D, Rühm A, et al. Differentiation of tumors of the upper respiratory tract using optical metabolic imaging. Lasers Surg Med. 2025;57(2):147–153. doi: 10.1002/lsm.23870 EDN: PTJNSI
  19. Lukina M, Yashin K, Kiseleva EE, et al. Label-free macroscopic fluorescence lifetime imaging of brain tumors. Front Oncol. 2021;11:666059. doi: 10.3389/fonc.2021.666059 EDN: CWIFPJ
  20. Vander Heiden MG, DeBerardinis RJ. Understanding the intersections between metabolism and cancer biology. Cell. 2017;168(4):657–669. doi: 10.1016/j.cell.2016.12.039
  21. Jia D, Park JH, Jung KH, et al. Elucidating the metabolic plasticity of cancer: mitochondrial reprogramming and hybrid metabolic states. Cells. 2018;7(3):21. doi: 10.3390/cells7030021
  22. Pickett MR, Chen YI, Kamra M, et al. Assessing the impact of extracellular matrix fiber orientation on breast cancer cellular metabolism. Cancer Cell Int. 2024;24(1):199. doi: 10.1186/s12935-024-03385-3 EDN: GMKPME
  23. Karrobi K, Tank A, Fuzail MA, et al. Fluorescence lifetime imaging microscopy (flim) reveals spatial-metabolic changes in 3D breast cancer spheroids. Sci Rep. 2023;13(1):3624. doi: 10.1038/s41598-023-30403-7 EDN: PCWEGO
  24. Gershanov S, Michowiz S, Toledano H, et al. Fluorescence lifetime imaging microscopy, a novel diagnostic tool for metastatic cell detection in the cerebrospinal fluid of children with medulloblastoma. Sci Rep. 2017;7(1):3648. doi: 10.1038/s41598-017-03892-6 EDN: YICPNG
  25. Yahav G, Hirshberg A, Salomon O, et al. Fluorescence lifetime imaging of DAPI-stained nuclei as a novel diagnostic tool for the detection and classification of B-cell chronic lymphocytic leukemia. Cytometry A. 2016;89(7):644–652. doi: 10.1002/cyto.a.22890
  26. Zahavi T, Yahav G, Shimshon Y, et al. Utilizing fluorescent life time imaging microscopy technology for identify carriers of BRCA2 mutation. Biochem Biophys Res Commun. 2016;480(1):36–41. doi: 10.1016/j.bbrc.2016.10.013
  27. Dong L, Neuzil J. Targeting mitochondria as an anticancer strategy. Cancer Commun (Lond). 2019;39(1):63. doi: 10.1186/s40880-019-0412-6 EDN: WZMBNV
  28. Morelli M, Lessi F, Barachini S, et al. Metabolic-imaging of human glioblastoma live tumors: A new precision-medicine approach to predict tumor treatment response early. Front Oncol. 2022;12:969812. doi: 10.3389/fonc.2022.969812 EDN: CXKNSF
  29. Yuzhakova DV, Sachkova DA, Shirmanova MV, et al. Measurement of patient-derived glioblastoma cell response to temozolomide using fluorescence lifetime imaging of NAD(P)H. Pharmaceuticals (Basel). 2023;16(6):796. doi: 10.3390/ph16060796 EDN: HBEKML
  30. Lukina MM, Dudenkova VV, Ignatova NI, et al. Metabolic cofactors NAD(P)H and FAD as potential indicators of cancer cell response to chemotherapy with paclitaxel. Biochim Biophys Acta Gen Subj. 2018;1862(8):1693–1700. doi: 10.1016/j.bbagen.2018.04.021 EDN: XXFNIL
  31. Druzhkova I, Nikonova E, Ignatova N, et al. Effect of collagen matrix on doxorubicin distribution and cancer cells’ response to treatment in 3D tumor model. Cancers (Basel). 2022;14(22):5487. doi: 10.3390/cancers14225487 EDN: VQRNCH
  32. Alam SR, Wallrabe H, Svindrych Z, et al. Investigation of mitochondrial metabolic response to doxorubicin in prostate cancer cells: An NADH, FAD and tryptophan FLIM assay. Sci Rep. 2017;7(1):10451. doi: 10.1038/s41598-017-10856-3 EDN: YHLKTA
  33. Shakirova JR, Baigildin VA, Solomatina AI, et al. Intracellular PH sensor based on heteroleptic bis-cyclometalated Iridium(III) complex embedded into block-copolymer nanospecies: application in phosphorescence lifetime imaging microscopy. Advanced Functional Materials. 2023;33. doi: 10.1002/adfm.202212390 EDN: DSBSUW
  34. Druzhkova I, Komarova A, Nikonova E, et al. Monitoring the intracellular pH and metabolic state of cancer cells in response to chemotherapy using a combination of phosphorescence lifetime imaging microscopy and fluorescence lifetime imaging microscopy. International Journal of Molecular Sciences. 2023;25:49. doi: 10.3390/ijms25010049 EDN: RPOFMC
  35. González Rubio S, Montero Pastor N, García C, et al. Enhanced cytotoxic activity of mitochondrial mechanical effectors in human lung carcinoma H520 cells: pharmaceutical implications for cancer therapy. Front Oncol. 2018;8:514. doi: 10.3389/fonc.2018.00514 EDN: GOSVQC
  36. Gillette AA, Babiarz CP, VanDommelen AR, et al. Autofluorescence imaging of treatment response in neuroendocrine tumor organoids. Cancers (Basel). 2021;13(8):1873. doi: 10.3390/cancers13081873 EDN: DICIVR
  37. Walsh AJ, Cook RS, Sanders ME, et al. Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer. Cancer Res. 2014;74(18):5184–5194. doi: 10.1158/0008-5472.CAN-14-0663
  38. Walsh AJ, Castellanos JA, Nagathihalli NS, et al. Optical imaging of drug-induced metabolism changes in murine and human pancreatic cancer organoids reveals heterogeneous drug response. Pancreas. 2016;45(6):863–869. doi: 10.1097/MPA.0000000000000543
  39. Sharick JT, Walsh CM, Sprackling CM, et al. Metabolic heterogeneity in patient tumor-derived organoids by primary site and drug treatment. Front Oncol. 2020;10:553. doi: 10.3389/fonc.2020.00553 EDN: MWLSCQ
  40. Pasch CA, Favreau PF, Yueh AE, et al. Patient-derived cancer organoid cultures to predict sensitivity to chemotherapy and radiation. Clin Cancer Res. 2019;25(17):5376–5387. doi: 10.1158/1078-0432.CCR-18-3590
  41. Moloudi K, Abrahamse H, George BP. Photodynamic therapy induced cell cycle arrest and cancer cell synchronization: review. Front Oncol. 2023;13:1225694. doi: 10.3389/fonc.2023.1225694 EDN: LKGHPR
  42. Zhou R, Zeng X, Zhao H, et al. Combating the hypoxia limit of photodynamic therapy through reversing the survival-related pathways of cancer cells. Coordination Chemistry Reviews. 2022;452:214306. doi: 10.1016/j.ccr.2021.214306 EDN: HWZEWP
  43. Dos Santos DNS, Naskar N, Delgado-Pinar E, et al. Bromine indirubin FLIM/PLIM sensors to measure oxygen in normoxic and hypoxic PDT conditions. Photodiagnosis Photodyn Ther. 2024;45:103964. doi: 10.1016/j.pdpdt.2024.103964 EDN: MTVTGA
  44. Kalinina S, Breymayer J, Reeß K, et al. Correlation of intracellular oxygen and cell metabolism by simultaneous PLIM of phosphorescent TLD1433 and FLIM of NAD(P)H. J Biophotonics. 2018;11(10):e201800085. doi: 10.1002/jbio.201800085 EDN: QOOHKH
  45. Bassler MC, Hiller J, Wackenhut F, et al. Fluorescence lifetime imaging unravels the pathway of glioma cell death upon hypericin-induced photodynamic therapy. RSC Chem Biol. doi: 10.1039/d4cb00107a EDN: APXDGY
  46. Shimolina LE, Khlynova AE, Elagin VV, et al. Unraveling microviscosity changes induced in cancer cells by photodynamic therapy with targeted genetically encoded photosensitizer. Biomedicines. 2024;12(11):2550. doi: 10.3390/biomedicines12112550 EDN: LGXQBZ
  47. Chen B, Pogue BW, Hoopes PJ, Hasan T. Vascular and cellular targeting for photodynamic therapy. Crit Rev Eukaryot Gene Expr. 2006;16(4):279–305. doi: 10.1615/critreveukargeneexpr.v16.i4.10 EDN: MESOQP
  48. Domka W, Bartusik-Aebisher D, Mytych W, et al. The use of photodynamic therapy for head, neck, and brain diseases. Int J Mol Sci. 2023;24(14):11867. doi: 10.3390/ijms241411867 EDN: YVZDBT
  49. Shirmanova MV, Lukina MM, Sirotkina MA, et al. Effects of photodynamic therapy on tumor metabolism and oxygenation revealed by fluorescence and phosphorescence lifetime imaging. Int J Mol Sci. 2024;25(3):1703. doi: 10.3390/ijms25031703 EDN: OWBHSK
  50. Belashov AV, Zhikhoreva AA, Salova AV, et al. PDT-induced variations of radachlorin fluorescence lifetime in living cells in vitro. Photonics. 2023;10(11):1262. doi: 10.3390/photonics10111262 EDN: JATPOE
  51. Shah AT, Demory Beckler M, Walsh AJ, et al. Optical metabolic imaging of treatment response in human head and neck squamous cell carcinoma. PLoS One. 2014;9:e90746. doi: 10.1371/journal.pone.0090746
  52. Shah AT, Diggins KE, Walsh AJ, et al. In vivo autofluorescence imaging of tumor heterogeneity in response to treatment. Neoplasia. 2015;17(12):862–870. doi: 10.1016/j.neo.2015.11.006
  53. Qi X, Li Q, Che X, et al. Application of regulatory cell death in cancer: based on targeted therapy and immunotherapy. Front Immunol. 2022;13:837293. doi: 10.3389/fimmu.2022.837293 EDN: HYYEFX
  54. Liu J, Hong M, Li Y, et al. Programmed cell death tunes tumor immunity. Front Immunol. 2022;13:847345. doi: 10.3389/fimmu.2022.847345 EDN: VYRPKM
  55. Yu L, Huang K, Liao Y, et al. Targeting novel regulated cell death: Ferroptosis, pyroptosis and necroptosis in anti-PD-1/PD-L1 cancer immunotherapy. Cell Prolif. 2024;57(8):e13644. doi: 10.1111/cpr.13644 EDN: CMXQBA
  56. Shirmanova MV, Gavrina AI, Kovaleva TF, et al. Insight into redox regulation of apoptosis in cancer cells with multiparametric live-cell microscopy. Sci Rep. 2022;12(1):4476. doi: 10.1038/s41598-022-08509-1 EDN: WRZYJF
  57. Bower AJ, Sorrells JE, Li J, et al. Tracking metabolic dynamics of apoptosis with high-speed two-photon fluorescence lifetime imaging microscopy. Biomed Opt Express. 2019;10(12):6408–6421. doi: 10.1364/BOE.10.006408
  58. Pan W, Qu J, Chen T, et al. FLIM and emission spectral analysis of caspase-3 activation inside single living cell during anticancer drug-induced cell death. Eur Biophys J. 2009;38(4):447–456. doi: 10.1007/s00249-008-0390-0 EDN: LVTYAN
  59. Xiao A, Gibbons AE, Luker KE, Luker GD. Fluorescence lifetime imaging of apoptosis. Tomography. 2015;1(2):115–124. doi: 10.18383/j.tom.2015.00163
  60. Liu Q, Osterlund EJ, Chi X, et al. Bim escapes displacement by BH3-mimetic anti-cancer drugs by double-bolt locking both Bcl-XL and Bcl-2. Elife. 2019;8. doi: 10.7554/eLife.37689
  61. Buytaert E, Callewaert G, Vandenheede JR, Agostinis P. Deficiency in apoptotic effectors BAX and BAK reveals an autophagic cell death pathway initiated by photodamage to the endoplasmic reticulum. Autophagy. 2006;2(3):238–240. doi: 10.4161/auto.2730
  62. Kessel D, Vicente MG, Reiners JJ Jr. Initiation of apoptosis and autophagy by photodynamic therapy. Lasers Surg Med. 2006;38(5):482–488. doi: 10.1002/lsm.20334
  63. Ciccarone F, Di Leo L, Lazzarino G, et al. Aconitase 2 inhibits the proliferation of MCF-7 cells promoting mitochondrial oxidative metabolism and ROS/FoxO1-mediated autophagic response. Br J Cancer. 2020;122(2):182–193. doi: 10.1038/s41416-019-0641-0 EDN: UKYBPZ
  64. Li M, Chen Y, He W, Guo Z. Fluorescence and lifetime imaging of endoplasmic reticulum polarity change during ferroptosis. Chemistry. 2024;30(35):e202401285. doi: 10.1002/chem.202401285 EDN: ROUPXM
  65. Liang X, Zhao Y, Yan J, et al. Mechanosensitive fluorescence lifetime probes for investigating the dynamic mechanism of ferroptosis. Proc Natl Acad Sci U S A. 2024;121(41):e2316450121. doi: 10.1073/pnas.2316450121 EDN: NLGMPU
  66. Wang Y, Gao W, Shi X, et al. Chemotherapy drugs induce pyroptosis through caspase-3 cleavage of a gasdermin. Nature. 2017;547(7661):99–103. doi: 10.1038/nature22393 EDN: TOILOK
  67. Wei X, Xie F, Zhou X, et al. Role of pyroptosis in inflammation and cancer. Cell Mol Immunol. 2022;19(9):971–992. doi: 10.1038/s41423-022-00905-x EDN: MWJHZD
  68. Jiang S, Qin K, Sun L. Time-lapse live-cell imaging of pyroptosis by confocal microscopy. STAR Protoc. 2023;4(4):102708. doi: 10.1016/j.xpro.2023.102708 EDN: HEQZMD
  69. Huang P, Chen G, Jin W, et al. Molecular mechanisms of parthanatos and its role in diverse diseases. Int J Mol Sci. 2022;23(13):7292. doi: 10.3390/ijms23137292 EDN: NKGKXH
  70. Brosey CA, Ho C, Long WZ, et al. Defining NADH-driven allostery regulating apoptosis-inducing factor. Structure. 2016;24(12):2067–2079. doi: 10.1016/j.str.2016.09.012 EDN: FECQWH
  71. van der Windt GJ, Pearce EL. Metabolic switching and fuel choice during T-cell differentiation and memory development. Immunol Rev. 2012;249(1):27–42. doi: 10.1111/j.1600-065X.2012.01150.x EDN: ROLHEV
  72. Patel M, Manzella-Lapeira J, Akkaya M. Analysis of mitochondrial performance in lymphocytes using fluorescent lifetime imaging microscopy. Methods Mol Biol. 2022;2497:269–280. doi: 10.1007/978-1-0716-2309-1_17
  73. Floudas A, Neto N, Orr C, et al. Loss of balance between protective and pro-inflammatory synovial tissue T-cell polyfunctionality predates clinical onset of rheumatoid arthritis. Ann Rheum Dis. 2022;81(2):193–205. doi: 10.1136/annrheumdis-2021-220458 EDN: TAPESF
  74. Walsh AJ, Mueller KP, Tweed K, et al. Classification of T-cell activation via autofluorescence lifetime imaging. Nat Biomed Eng. 2021;5(1):77–88. doi: 10.1038/s41551-020-0592-z EDN: AUGTEY
  75. Paillon N, Ung TPL, Dogniaux S, et al. Label-free single-cell live imaging reveals fast metabolic switch in T lymphocytes. Mol Biol Cell. 2024;35(1):ar11. doi: 10.1091/mbc.E23-01-0009 EDN: JKVLRT
  76. Izosimova AV, Shirmanova MV, Shcheslavskiy VI, et al. FLIM of NAD(P)H in lymphatic nodes resolves T-cell immune response to the tumor. Int J Mol Sci. 2022;23(24):15829. doi: 10.3390/ijms232415829 EDN: DYPCGJ
  77. Pavillon N, Hobro AJ, Akira S, Smith NI. Noninvasive detection of macrophage activation with single-cell resolution through machine learning. Proc Natl Acad Sci U S A. 2018;115(12):E2676–E2685. doi: 10.1073/pnas.1711872115
  78. Alfonso-García A, Smith TD, Datta R, et al. Label-free identification of macrophage phenotype by fluorescence lifetime imaging microscopy. J Biomed Opt. 2016;21(4):46005. doi: 10.1117/1.JBO.21.4.046005
  79. Sheikh E, Agrawal K, Roy S, et al. Multimodal imaging of pancreatic cancer microenvironment in response to an antiglycolytic drug. Adv Healthc Mater. 2023;12(31):e2301815. doi: 10.1002/adhm.202301815 EDN: TVSMNE
  80. Szulczewski JM, Inman DR, Entenberg D, et al. In vivo visualization of stromal macrophages via label-free FLIM-based metabolite imaging. Sci Rep. 2016;6:25086. doi: 10.1038/srep25086
  81. Kanno H, Hiramatsu K, Mikami H, et al. High-throughput fluorescence lifetime imaging flow cytometry. Nat Commun. 2024;15(1):7376. doi: 10.1038/s41467-024-51125-y EDN: HSBJAN
  82. Wang S, Luo C, Guo J, et al. Enhancing therapeutic response and overcoming resistance to checkpoint inhibitors in ovarian cancer through cell cycle regulation. Int J Mol Sci. 2024;25(18):10018. doi: 10.3390/ijms251810018 EDN: VNWUDJ
  83. Yang M, Mahanty A, Jin C, et al. Label-free metabolic imaging for sensitive and robust monitoring of anti-CD47 immunotherapy response in triple-negative breast cancer. J Immunother Cancer. 2022;10(9):e005199. doi: 10.1136/jitc-2022-005199 EDN: TWCNKE
  84. Izosimova AV, Mozherov AM, Shirmanova MV, et al. Fluorescence lifetime imaging of NAD(P)H T cells autofluorescence in the lymphatic nodes to assess the effectiveness of anti-CTLA-4 immunotherapy. Modern technologies in medicine. 2023;15(30):5–15. doi: 10.17691/stm2023.15.3.01 EDN: UFWPEQ
  85. Yuzhakova DV, Sachkova DA, Izosimova AV, et al. Fluorescence lifetime imaging of NAD(P)H in patients’ lymphocytes: evaluation of efficacy of immunotherapy. Cells. 2025;14(2):97. doi: 10.3390/cells14020097 EDN: QIXLYU

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