Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Transmembrane water-efflux rate measured by magnetic resonance imaging as a biomarker of the expression of aquaporin-4 in gliomas

Nature Biomedical Engineering volume 7, pages 236–252 (2023)Cite this article

Subjects

Abstract

The water-selective channel protein aquaporin-4 (AQP4) contributes to the migration and proliferation of gliomas, and to their resistance to therapy. Here we show, in glioma cell cultures, in subcutaneous and orthotopic gliomas in rats, and in glioma tumours in patients, that transmembrane water-efflux rate is a sensitive biomarker of AQP4 expression and can be measured via conventional dynamic-contrast-enhanced magnetic resonance imaging. Water-efflux rates correlated with stages of glioma proliferation as well as with changes in the heterogeneity of intra-tumoural and inter-tumoural AQP4 in rodent and human gliomas following treatment with temozolomide and with the AQP4 inhibitor TGN020. Regions with low water-efflux rates contained higher fractions of stem-like slow-cycling cells and therapy-resistant cells, suggesting that maps of water-efflux rates could be used to identify gliomas that are resistant to therapies.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: AQP4 signal-amplification and detection strategy in MRI.
Fig. 2: AQP4-regulated kio in glioma cells (C6 and U87MG).
Fig. 3: Biomarker kio can precisely detect the dynamic expression of AQP4 in the C6 cell line during TMZ treatment.
Fig. 4: Biomarker kio accurately and precisely follows the dynamic regulation of AQP4 in the U87MG cell line during proliferation cycles.
Fig. 5: kio map obtained from water-exchange DCE-MRI can precisely and accurately reveal intra-tumoural AQP4 heterogeneity in vivo.
Fig. 6: Effect of pharmacological inhibition of AQP4 on kio in the subcutaneous rat glioma model.
Fig. 7: kio map obtained from water-exchange DCE-MRI reveals intra-tumoural AQP4 distribution in human glioma.
Fig. 8: Low kio (AQP4) reflects treatment-resistance glioma phenotypes.

Similar content being viewed by others

Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. The raw and analysed datasets generated during the study are too large to be publicly shared, yet they are available for research purposes from the corresponding authors on reasonable request. The raw patient data are available from the authors, subject to approval from the IRB of the Shandong Provincial Hospital affiliated to Shandong First Medical University. Source data are provided with this paper.

References

  1. Omuro, A. & DeAngelis, L. M. Glioblastoma and other malignant gliomas. JAMA 310, 1842–1850 (2013).

    CAS  PubMed  Google Scholar 

  2. Milano, M. T. et al. Patterns and timing of recurrence after temozolomide-based chemoradiation for glioblastoma. Int. J. Radiat. Oncol. Biol. Phys. 78, 1147–1155 (2010).

    CAS  PubMed  Google Scholar 

  3. Sun, D.-P., Lee, Y.-W., Chen, J.-T., Lin, Y.-W. & Chen, R.-M. The bradykinin–BDKRB1 axis regulates aquaporin 4 gene expression and consequential migration and invasion of malignant glioblastoma cells via a Ca2+–MEK1–ERK1/2–NF-κB mechanism. Cancers 12, 667 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Nico, B. et al. Aquaporin-4 contributes to the resolution of peritumoural brain oedema in human glioblastoma multiforme after combined chemotherapy and radiotherapy. Eur. J. Cancer 45, 3315–3325 (2009).

    CAS  PubMed  Google Scholar 

  5. Papadopoulos, M. C. & Verkman, A. S. Aquaporin water channels in the nervous system. Nat. Rev. Neurosci. 14, 265–277 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Maugeri, R. et al. Aquaporins and brain tumors. Int. J. Mol. Sci. 17, 1029 (2016).

    PubMed  PubMed Central  Google Scholar 

  7. Warth, A., Mittelbronn, M. & Wolburg, H. Redistribution of the water channel protein aquaporin-4 and the K+ channel protein Kir4.1 differs in low- and high-grade human brain tumors. Acta Neuropathol. 109, 418–426 (2005).

    CAS  PubMed  Google Scholar 

  8. Montgomery, M. K. et al. Glioma-induced alterations in neuronal activity and neurovascular coupling during disease progression. Cell Rep. 31, 107500 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Noell, S. et al. Dynamics of expression patterns of AQP4, dystroglycan, agrin and matrix metalloproteinases in human glioblastoma. Cell Tissue Res. 347, 429–441 (2012).

    CAS  PubMed  Google Scholar 

  10. Tome-Garcia, J. et al. Analysis of chromatin accessibility uncovers TEAD1 as a regulator of migration in human glioblastoma. Nat. Commun. 9, 1–13 (2018).

    CAS  Google Scholar 

  11. Lan, Y. L. et al. Gamabufotalin induces a negative feedback loop connecting ATP1A3 expression and the AQP4 pathway to promote temozolomide sensitivity in glioblastoma cells by targeting the amino acid Thr794. Cell Prolif. 53, 1–21 (2020).

    Google Scholar 

  12. Ruggieri, S. et al. Dp71 expression in human glioblastoma. Int. J. Mol. Sci. 20, 7–10 (2019).

    Google Scholar 

  13. Ruiz-Ontañon, P. et al. Cellular plasticity confers migratory and invasive advantages to a population of glioblastoma-initiating cells that infiltrate peritumoral tissue. Stem Cells 31, 1075–1085 (2013).

    PubMed  Google Scholar 

  14. Kitchen, P. et al. Targeting aquaporin-4 subcellular localization to treat central nervous system edema. Cell 181, 784–799 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Fukuda, A. M. et al. Posttraumatic reduction of edema with aquaporin-4 RNA interference improves acute and chronic functional recovery. J. Cereb. Blood Flow. Metab. 33, 1621–1632 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Hoang-Minh, L. B. et al. Infiltrative and drug‐resistant slow‐cycling cells support metabolic heterogeneity in glioblastoma. EMBO J. 37, 1–21 (2018).

    Google Scholar 

  17. Suvà, M. L. & Tirosh, I. The glioma stem cell model in the era of single-cell genomics. Cancer Cell 37, 630–636 (2020).

    PubMed  Google Scholar 

  18. Klughammer, J. et al. The DNA methylation landscape of glioblastoma disease progression shows extensive heterogeneity in time and space. Nat. Med. 24, 1611–1624 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Blocher, J. et al. Aquaporins AQP1 and AQP4 in the cerebrospinal fluid of bacterial meningitis patients. Neurosci. Lett. 504, 23–27 (2011).

    CAS  PubMed  Google Scholar 

  20. Castañeyra-Ruiz, L. et al. Aquaporin-4 expression in the cerebrospinal fluid in congenital human hydrocephalus. Fluids Barriers CNS 10, 18 (2013).

    PubMed  PubMed Central  Google Scholar 

  21. Mukherjee, A., Wu, D., Davis, H. C. & Shapiro, M. G. Non-invasive imaging using reporter genes altering cellular water permeability. Nat. Commun. 7, 13891 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Okuchi, S. et al. Diagnostic accuracy of dynamic contrast-enhanced perfusion MRI in stratifying gliomas: a systematic review and meta-analysis. Cancer Med. 8, 5564–5573 (2019).

    PubMed  PubMed Central  Google Scholar 

  23. Bai, R. et al. Shutter-speed DCE-MRI analyses of human glioblastoma multiforme (GBM) data. J. Magn. Reson. Imaging 52, 850–863 (2020).

    PubMed  Google Scholar 

  24. Li, X., Mangia, S., Lee, J.-H., Bai, R. & Springer, C. S. J. NMR shutter-speed elucidates apparent population inversion of 1H2O signals due to active transmembrane water cycling. Magn. Reson. Med. 82, 411–424 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Yang, B. & Verkman, A. S. Water and glycerol permeabilities of aquaporins 1–5 and MIP determined quantitatively by expression of epitope-tagged constructs in Xenopus oocytes. J. Biol. Chem. 272, 16140–16146 (1997).

    CAS  PubMed  Google Scholar 

  26. Huber, V. J., Tsujita, M. & Nakada, T. Identification of aquaporin 4 inhibitors using in vitro and in silico methods. Bioorg. Med. Chem. 17, 411–417 (2009).

    CAS  PubMed  Google Scholar 

  27. Chen, Y. et al. Down-regulation of AQP4 expression via p38 MAPK signaling in temozolomide-induced glioma cells growth inhibition and invasion impairment. J. Cell. Biochem. 118, 4905–4913 (2017).

    CAS  PubMed  Google Scholar 

  28. Jovanović, M. et al. Novel TrxR1 inhibitors show potential for glioma treatment by suppressing the invasion and sensitizing glioma cells to chemotherapy. Front. Mol. Biosci. 7, 1–17 (2020).

    Google Scholar 

  29. Ding, T. et al. Knockdown a water channel protein, aquaporin-4, induced glioblastoma cell apoptosis. PLoS ONE 8, 1–9 (2013).

    Google Scholar 

  30. Mou, K. et al. AQP-4 in peritumoral edematous tissue is correlated with the degree of glioma and with expression of VEGF and HIF-α. J. Neurooncol. 100, 375–383 (2010).

    CAS  PubMed  Google Scholar 

  31. Kong, H. et al. AQP4 knockout impairs proliferation, migration and neuronal differentiation of adult neural stem cells. J. Cell Sci. 121, 4029–4036 (2008).

    CAS  PubMed  Google Scholar 

  32. Küppers, E. et al. AQP4 expression in striatal primary cultures is regulated by dopamine—implications for proliferation of astrocytes. Eur. J. Neurosci. 28, 2173–2182 (2008).

    PubMed  Google Scholar 

  33. Suzuki, Y. et al. Aquaporin-4 positron emission tomography imaging of the human brain: first report. J. Neuroimaging 23, 219–223 (2013).

    PubMed  Google Scholar 

  34. Harrison, I. F. et al. Impaired glymphatic function and clearance of tau in an Alzheimer’s disease model. Brain 143, 2576–2593 (2020).

    PubMed  PubMed Central  Google Scholar 

  35. Bai, R., Springer, C. S., Plenz, D. & Basser, P. J. Fast, Na+/K+ pump driven, steady-state transcytolemmal water exchange in neuronal tissue: a study of rat brain cortical cultures. Magn. Reson. Med. 79, 3207–3217 (2018).

    CAS  PubMed  Google Scholar 

  36. Gates, E. D. H. et al. Guiding the first biopsy in glioma patients using estimated Ki-67 maps derived from MRI: conventional versus advanced imaging. Neuro. Oncol. 21, 527–536 (2019).

    PubMed  PubMed Central  Google Scholar 

  37. Kowalski-Chauvel, A. et al. Alpha-6 integrin promotes radioresistance of glioblastoma by modulating DNA damage response and the transcription factor Zeb1. Cell Death Dis. 9, 872 (2018).

    PubMed  PubMed Central  Google Scholar 

  38. Kawanishi, M. et al. Effect of delayed mild brain hypothermia on edema formation after intracerebral hemorrhage in rats. J. Stroke Cerebrovasc. Dis. 17, 187–195 (2008).

    PubMed  Google Scholar 

  39. Salman, M. M. et al. Hypothermia increases aquaporin 4 (AQP4) plasma membrane abundance in human primary cortical astrocytes via a calcium/transient receptor potential vanilloid 4 (TRPV4)- and calmodulin-mediated mechanism. Eur. J. Neurosci. 46, 2542–2547 (2017).

    PubMed  PubMed Central  Google Scholar 

  40. Springer, C. S. Using 1H2O MR to measure and map sodium pump activity in vivo. J. Magn. Reson. 291, 110–126 (2018).

    CAS  PubMed  Google Scholar 

  41. Kanai, R. et al. Oncolytic virus-mediated manipulation of DNA damage responses: synergy with chemotherapy in killing glioblastoma stem cells. J. Natl Cancer Inst. 104, 42–55 (2012).

    CAS  PubMed  Google Scholar 

  42. Visvader, J. E. & Lindeman, G. J. Cancer stem cells in solid tumours: accumulating evidence and unresolved questions. Nat. Rev. Cancer 8, 755–768 (2008).

    CAS  PubMed  Google Scholar 

  43. Seo, Y. S. et al. Radiation-induced changes in tumor vessels and microenvironment contribute to therapeutic resistance in glioblastoma. Front. Oncol. 9, 1259 (2019).

    PubMed  PubMed Central  Google Scholar 

  44. Jablonski, E. M., Webb, A. N., McConnell, N. A., Riley, M. C. & Hughes, F. M. Plasma membrane aquaporin activity can affect the rate of apoptosis but is inhibited after apoptotic volume decrease. Am. J. Physiol. Cell Physiol. 286, C975–C985 (2004).

    CAS  PubMed  Google Scholar 

  45. Soeda, A. et al. The evidence of glioblastoma heterogeneity. Sci. Rep. 5, 7979 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Sottoriva, A. et al. Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc. Natl Acad. Sci. USA 110, 4009–4014 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Gallaher, J. A. et al. From cells to tissue: how cell scale heterogeneity impacts glioblastoma growth and treatment response. PLoS Comput. Biol. 16, 1–27 (2020).

    Google Scholar 

  48. Warth, A., Kröger, S. & Wolburg, H. Redistribution of aquaporin-4 in human glioblastoma correlates with loss of agrin immunoreactivity from brain capillary basal laminae. Acta Neuropathol. 107, 311–318 (2004).

    CAS  PubMed  Google Scholar 

  49. Vodnala, S. K. et al. T cell stemness and dysfunction in tumors are triggered by a common mechanism. Science 363, eaau0135 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Papadopoulos, M. C. & Saadoun, S. Key roles of aquaporins in tumor biology. Biochim. Biophys. Acta Biomembr. 1848, 2576–2583 (2015).

    CAS  Google Scholar 

  51. Smith, A. J., Jin, B. J., Ratelade, J. & Verkman, A. S. Aggregation state determines the localization and function of M1- and M23-aquaporin-4 in astrocytes. J. Cell Biol. 204, 559–573 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Peng, Y., Wu, W., Shang, Z., Li, W. & Chen, S. Inhibition of lncRNA LINC00461/miR-216a/aquaporin 4 pathway suppresses cell proliferation, migration, invasion, and chemoresistance in glioma. Open Life Sci. 15, 532–543 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Badaut, J. et al. Brain water mobility decreases after astrocytic aquaporin-4 inhibition using RNA interference. J. Cereb. Blood Flow. Metab. 31, 819–831 (2011).

    CAS  PubMed  Google Scholar 

  54. Jullienne, A. et al. Modulating the water channel AQP4 alters miRNA expression, astrocyte connectivity and water diffusion in the rodent brain. Sci. Rep. 8, 1–12 (2018).

    CAS  Google Scholar 

  55. Bai, R. et al. Feasibility of filter-exchange imaging (FEXI) in measuring different exchange processes in human brain. NeuroImage 219, 117039 (2020).

    CAS  PubMed  Google Scholar 

  56. Le Bihan, D. & Iima, M. Diffusion magnetic resonance imaging: what water tells us about biological tissues. PLoS Biol. 13, 1–13 (2015).

    Google Scholar 

  57. Bai, R., Stewart, C. V., Plenz, D. & Basser, P. J. Assessing the sensitivity of diffusion MRI to detect neuronal activity directly. Proc. Natl Acad. Sci. USA 113, E1728–E1737 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Gupta, R. K. et al. Relationships between choline magnetic resonance spectroscopy, apparent diffusion coefficient and quantitative histopathology in human glioma. J. Neurooncol. 50, 215–226 (2000).

    CAS  PubMed  Google Scholar 

  59. Smith, A. J. & Verkman, A. S. The ‘glymphatic’ mechanism for solute clearance in Alzheimer’s disease: game changer or unproven speculation? FASEB J. 32, 543–551 (2018).

    CAS  PubMed  Google Scholar 

  60. Callaghan, P. T. & Furó, I. Diffusion–diffusion correlation and exchange as a signature for local order and dynamics. J. Chem. Phys. 120, 4032–4038 (2004).

    CAS  PubMed  Google Scholar 

  61. Lasič, S., Nilsson, M., Lätt, J., Ståhlberg, F. & Topgaard, D. Apparent exchange rate mapping with diffusion MRI. Magn. Reson. Med. 66, 356–365 (2011).

    PubMed  Google Scholar 

  62. Schilling, F. et al. MRI measurements of reporter-mediated increases in transmembrane water exchange enable detection of a gene reporter. Nat. Biotechnol. 35, 75–80 (2017).

    CAS  PubMed  Google Scholar 

  63. Nedjati-Gilani, G. L. et al. Machine learning based compartment models with permeability for white matter microstructure imaging. NeuroImage 150, 119–135 (2017).

    PubMed  Google Scholar 

  64. Hill, I. et al. Machine learning based white matter models with permeability: an experimental study in cuprizone treated in-vivo mouse model of axonal demyelination. NeuroImage 224, 117425 (2021).

    CAS  PubMed  Google Scholar 

  65. Bai, R., Springer, C. S., Plenz, D. & Basser, P. J. Brain active transmembrane water cycling measured by MR is associated with neuronal activity. Magn. Reson. Med. 81, 1280–1295 (2019).

    PubMed  Google Scholar 

  66. Dickie, B. R., Parker, G. J. M. & Parkes, L. M. Measuring water exchange across the blood-brain barrier using MRI. Prog. Nucl. Magn. Reson. Spectrosc. 116, 19–39 (2020).

    CAS  PubMed  Google Scholar 

  67. Ohene, Y. et al. Non-invasive MRI of brain clearance pathways using multiple echo time arterial spin labelling: an aquaporin-4 study. NeuroImage 188, 515–523 (2019).

    PubMed  Google Scholar 

  68. Ran, F. A. et al. Genome engineering using the CRISPR–Cas9 system. Nat. Protoc. 8, 2281–2308 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Atkins, R. J. et al. Cell quiescence correlates with enhanced glioblastoma cell invasion and cytotoxic resistance. Exp. Cell. Res. 374, 353–364 (2019).

    CAS  PubMed  Google Scholar 

  70. Deleyrolle, L. P. et al. Evidence for label-retaining tumour-initiating cells in human glioblastoma. Brain 134, 1331–1343 (2011).

    PubMed  PubMed Central  Google Scholar 

  71. Filby, A., Begum, J., Jalal, M. & Day, W. Appraising the suitability of succinimidyl and lipophilic fluorescent dyes to track proliferation in non-quiescent cells by dye dilution. Methods 82, 29–37 (2015).

    CAS  PubMed  Google Scholar 

  72. Landini, G., Martinelli, G. & Piccinini, F. Colour deconvolution: stain unmixing in histological imaging. Bioinformatics 37, 1485–1487 (2021).

    CAS  PubMed  Google Scholar 

  73. Tanner, V. A., Ploug, T. & Tao-Cheng, J. H. Subcellular localization of SV2 and other secretory vesicle components in PC12 cells by an efficient method of preembedding EM immunocytochemistry for cell cultures. J. Histochem. Cytochem. 44, 1481–1488 (1996).

    CAS  PubMed  Google Scholar 

  74. Du, J., Tao-Cheng, J. H., Zerfas, P. & McBain, C. J. The K+ channel, Kv2.1, is apposed to astrocytic processes and is associated with inhibitory postsynaptic membranes in hippocampal and cortical principal neurons and inhibitory interneurons. Neuroscience 84, 37–48 (1998).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The study was supported by the National Key Research and Development Program of China (grant 2022ZD0206000, to R.B.), the National Natural Science Foundation of China (NSFC) (grants 82172050, 81873894, and 82222032 to R.B.; grant 81641176, to Y.C.L.; grant 82202114, to W.B.), the Natural Science Foundation of Zhejiang Province, China (grant LR20H180001, to R.B.), the Taishan Scholars Program (no. tsqn20161070, to Y-C.L.) and the Natural Science Foundation of Shandong Province (grant ZR2019HM067, to Y-C.L.). We appreciate discussions and constructive comments from P. J. Basser at the National Institutes of Health, J. Polimeni at Harvard Medical School and Massachusetts General Hospital, and Z. Chen at the Department of Mathematics, Shandong University. We also thank the support from the MOE Frontier Science Center for Brain Science & Brain-Machine Integration, Zhejiang University.

Author information

Author notes

  1. These authors contributed equally:Yinhang Jia, Shangchen Xu, Guangxu Han.

Authors and Affiliations

  1. Department of Physical Medicine and Rehabilitation of the Affiliated Sir Run Run Shaw Hospital AND Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University School of Medicine, Hangzhou, China

    Yinhang Jia & Ruiliang Bai

  2. Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China

    Yinhang Jia, Guangxu Han, Zejun Wang & Ruiliang Bai

  3. Department of Neurosurgery, Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China

    Yinhang Jia, Shangchen Xu, Chuanjin Lan, Peng Zhao, Meng Gao & Yingchao Liu

  4. Department of Radiology, Qilu Hospital of Shandong University, Jinan, China

    Bao Wang

  5. Department of Radiology, Provincial Hospital Affiliated to Shandong University, Jinan, China

    Yi Zhang

  6. Zhejiang University School of Medicine, Hangzhou, China

    Wenhong Jiang, Biying Qiu & Rui Liu

  7. MR Collaboration, Siemens Healthcare, Shanghai, China

    Yi-Cheng Hsu & Yi Sun

  8. MOE Frontier Science Center for Brain Science and Brain-machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, China

    Chong Liu & Ruiliang Bai

  9. Shandong National Center for Applied Mathematics, Shandong University, Jinan, China

    Yingchao Liu

Authors
  1. Yinhang Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shangchen Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guangxu Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zejun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chuanjin Lan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Peng Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Meng Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Wenhong Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Biying Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Rui Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Yi-Cheng Hsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Yi Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Chong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Yingchao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Ruiliang Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

R.B., Y-C.L, Y.J., S.X. and G.H. designed the research study and analysed and interpreted the data. Y.J., G.H. and S.X. performed most experiments and analysed the data. Y.J., G.H., W.J., B.Q., R.L. and C.L. performed the cell-culture experiments. Y.J., G.H. and Z.W performed the animal experiments. S.X., Y.Z., P.Z., M.G., Y-C.L. and B.W. performed the human test. Y.-C.H. and Y.S. provided MRI-sequence support. Y.J., S.X. and C-J.L. performed the histology, IHC and flow cytometry. Y.J., S.X., G.H., B.W., Z.W., C-J.L., P.Z., M.G., C.L., Y-C.L. and R.B. critically read the manuscript. Y.J., G.H., Y-C.L., R.B. and S.X. contributed to manuscript writing, and Y.J. and R.B. wrote the manuscript.

Corresponding authors

Correspondence to Yingchao Liu or Ruiliang Bai.

Ethics declarations

Competing interests

R.B., Y.J. and G.H. have filed a patent application that describe aspects of this technology (2022100372579, China, 2022). The other authors declare no competing interests.

Peer review

Peer review information

Nature Biomedical Engineering thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 DCE-MRI measurements of cell cultures in vitro.

a: the customized chamber to collect cells and perform MRI measurements inside the bench-top MRI system. The cell collection in the MR tube and setup for MRI experiments followed the order from a-1 to a-4. b: An example of the IR-TSE raw images of U87MG at [CA] = 5 mM (upper) and 0 mM (lower). The ROI layer of cell pellet is illustrated with red rectangle. c: The longitudinal relaxation rate constants (R1 ≡ 1/T1) as a function of CA concentration in PBS at room temperature (23.5 ± 1 °C), n = (3, 3, 3, 3, 3, 3), the data is shown as mean (dots) +/- SEM. d, e: an example of the normalized IR-TSE signal (Supplementary Methods Section 1, equation (1)) of the U87MG cells (ROI-averaged) at CA concentration [CA] = 5 mM (d) and 0 mM (e) in which the blue circles and continuous red curves are the normalized IR-TSE data and the model fitting results with SS model, respectively.

Source data

Extended data Fig. 2 Typical AQP4 expression and distribution in C6 cell line following TMZ treatment.

a, Typical confocal microscopy images of AQP4 (red) and DAPI (blue) in C6 cell lines. Scale bar, 25 µm. b, c, Fluorescence colocalization analysis between nucleus and AQP4 by line profiles (the dotted, white lines in a) of staining intensity for AQP4 (red line) and nucleus (blue line).

Source data

Extended Data Fig. 3 Changes of AQP4, kio and other characters upon TMZ treatment on C6 cell line.

a-f: The changes in a, kio, n = (4, 6, 4, 6), p = 0.0099, p = 0.0006. b, AQP4 (rfu) / DAPI (rfu), n = (2, 5, 3, 3), p = 0.0328, p < 0.0001 c, migration length (normalized by the control groups), n = (5, 4, 8, 8), p = 0.0836, p = 0.0001, d, Ki-67 (rfu) / DAPI (rfu), n = (3, 6, 3, 6), p = 0.1041, p = 0.0133 e, cell proliferation speed, n = (6, 15, 3, 34, 13), p = 0.0505, p = 0.0001, and f, the SCCs fraction (OG+ cells/ total cells), n = (4, 3, 3, 3), p < 0.0001. Here, control group represents C6 cell lines incubated with DMSO only. In e, the results from AQP4 KO group is also shown. The data is shown as mean ± SEM. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, ns, Non-significant, From a to f, two-sided unpaired t-test between 3days and 7days in control, p = (0.3612, 0.7030, 0.9999, 0.3611, 0.7981, 0.1001). Here, the data points overlay the corresponding box.

Source data

Extended Data Fig. 4 Correlations between kio and proliferation speed (and SCCs) in U87MG cell line experiencing proliferation cycles.

a: The dynamic changes in cell counting (black, dashed circle), proliferation rate (black dots), and kio (red triangles) as a function of cell culture time. The data is shown as mean (dots) ± SEM (error bar), n = (9, 9, 8, 7, 11, 16, 7). Here the three proliferation phases were defined as (I) lag phase (0hr-48hr), (II) logarithmic growth phase (72hr-96hr), and (III) stationary and decline phase (from 120 hr). b: Linear correlation was observed between kio and proliferation rate. n = (9, 8, 7, 11, 16). c: Typical results of cell tracing with OG at different culture time. Scale bar: 50 µm. d: Statistics of OG+ fractions as a function of culture time (mean + /-SEM). From up to down p < 0.0001, p < 0.0001, p = 0.0016, p = 0.0005, p = 0.0004, ** p < 0.01, *** p < 0.001, **** p < 0.0001, n = (4, 4, 4, 5). Here, the data points overlay the corresponding box, two-sided unpaired t-test. e, Linear correlation was also observed between kio and OG+ fraction (that is, SCC fraction) In (b, e), areas between the two dotted lines reflect the 95% confidence interval in linear regression, the data is shown as mean (dots) ± SEM.

Source data

Extended Data Fig. 5 Optimization of DCE-MRI for precise kio measurements in vivo.

a: The implementation of MGE sequence in Water-exchange DCE-MRI eliminates potential T2* artifacts caused by the contrast agent at 7 T by fitting the MGE data (Supplementary Methods Section 3, equation 5) to obtain the purely T1-weighted signal S (TE = 0 ms). b: The original data with the shortest TE (2.8 ms) still shows T2*-induced signal attenuation. c: Monto Carlo simulations demonstrate that our optimized protocol (dual-bolus injection and optimized sequence settings) shows one-fold smaller standard deviation of kio estimation than the conventional scanning protocol (single-bolus injection, TR = 10 ms, FA = 10°. Std denotes the standard deviation). Box plot specifications: box bounds mean 25th and 75th percentile, center = 50th percentile, minima/maxima = center ± 1.5 × (75th percentile – 25th percentile), no whiskers shown. d-f: An example of a kio maone-foldone-foldp overlaid on T2-weighted image in the tumor region (d) and the model fittings of the DCE-MRI data with SS model for pixels located in the core (e, low kio = 1.6 s−1) and ring (f, high kio = 10.0 s−1) of the tumor. Here the raw data and the fitting results are shown as dots and continuous curves, respectively. Scale bar, 2 mm.

Source data

Extended Data Fig. 6 The kio map precisely reveals the intra-tumoral AQP4 distribution in each rat glioma model.

a-j: significant linear correlation between kio values and AQP4+ fractions was observed in each animal. Here, we used a series of concentric donut-shape ROIs to divide the tumor slice into six zones considering the ring-shape distribution of AQP4, as demonstrated in Fig. 5e. k. In the control group of TGN020 modulation (Fig. 6), the whole-tumor-averaged kio doesn’t show significant changes between the two days with the saline treatment. Paired two-sided, t-test, ns non-significant, p = 0.9588. The bar height and error bar width represent the mean and standard error of the mean, respectively. The data points overlay the corresponding box. n = 4.

Source data

Extended Data Fig. 7 A linear correlation is observed between kio and AQP4 expression in the rat orthotopic model of C6 glioma.

a, b: Typical examples of kio maps and AQP4 IHC results from two animals with small (a) and large kio (b) values. From up to down, they are the contrast-enhanced T1-weighted images (the position of tumor was illustrated with the white dashed circle), the kio maps overlaid on the T1-weighted images, and the typical AQP4 IHC results of the position pointed by the white arrows. MRI Scale bar, 2 mm; IHC Scale bar, 25 µm. c: A linear correlation is observed between the whole-tumor-averaged kio and of AQP4+ fractions in the seven rats of orthotopic glioma. The solid line reflects linear regression analysis and the two dashed curves denote 95% confidence intervals. n = 7.

Source data

Extended Data Fig. 8 The procedure of stereotactic biopsy in human glioma and the downstream analysis.

a: The placement of stereotactic frame from the frontal and lateral view. b: The illustration of trajectory for the biopsy entry point and the target on the MRI. c: The stereotactic biopsy platform. d, e: The view of aspiration side window cutting needle (d) and the acquired sample (e). f-i: examples of the downstream analysis for H&E, here, n = 35. Scale bar 50 µm. (f), IEM, here, n = 5. Scale bar 0.1 µm. (g), IHC, here, for AQP4, n = 45, for ZEB1, n = 10. Scale bar 50 µm. (h), and FACS (i) two biopsy points were obtained from one glioma patient, one sample for C6 TMZ 7 day. Scale bar 100 µm.

Source data

Extended Data Fig. 9 A special case of glioma patient and the sample-averaged statistics.

This is a recurrent glioblastoma patient who received radiofrequency ablation surgery with multiple needle tracts due to the large size of tumor region. In this special case, 10 biopsy samples were safely collected along the planned trajectory of radiofrequency ablation needles. a: Examples of kio maps and the positions to obtain the biopsy samples (white arrows). b: AQP4 IHC of the three biopsy points illustrated in a. Scale bar, 25 µm. c: A linear correlation is observed between kio and fractions of AQP4+ cells in the 10 stereotactic biopsy points from this patient. d: A linear correlation between sample-averaged kio and fractions of AQP4+ cells is still preserved in the 19 data points from 19 patients. Here, for the patient with multiple biopsies acquired, the averaged results from all biopsies of this patient was used as the representative biopsy result for this patient. The solid line reflects linear regression analysis and the two dashed curves denote 95% confidence intervals.

Source data

Extended Data Fig. 10 Comparison between conventional MRI and DCE-MRI-derived kio in predicting AQP4 distribution in human glioma.

Here the results using a random forest model evaluated with a five-fold cross validation method on the 45 stereotactic biopsy points were shown. a: the results of conventional MRI including contrast-enhanced T1-weighted imaging, T2-weighted imaging, apparent diffusion coefficient (ADC) and diffusion weighted imaging (DWI). b: the results of kio. c: the results of the combination of conventional MRI and kio. In a-c, the solid line reflects linear regression analysis between the predicted AQP4 and the observed AQP4. The two dashed curves denote 95% confidence intervals. R2 is the coefficient of determination. d: one example of the predicted AQP4 expression map in one glioma patient using kio only.

Source data

Supplementary information

Supplementary Information

Supplementary discussion, methods, figures, tables and references.

Reporting Summary

Source data

Source Data for Figs. 2–8 and Extended Data Figs. 1–10

Source data, statistics and unprocessed WBs.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jia, Y., Xu, S., Han, G. et al. Transmembrane water-efflux rate measured by magnetic resonance imaging as a biomarker of the expression of aquaporin-4 in gliomas. Nat. Biomed. Eng 7, 236–252 (2023). https://doi.org/10.1038/s41551-022-00960-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41551-022-00960-9

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer