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

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.

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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
  2. Shangchen Xu
  3. Guangxu Han
  4. Bao Wang
  5. Zejun Wang
  6. Chuanjin Lan
  7. Peng Zhao
  8. Meng Gao
  9. Yi Zhang
  10. Wenhong Jiang
  11. Biying Qiu
  12. Rui Liu
  13. Yi-Cheng Hsu
  14. Yi Sun
  15. Chong Liu
  16. Yingchao Liu
  17. Ruiliang Bai

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 toYingchao 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 (_R_1 ≡ 1/_T_1) 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, _k_io and other characters upon TMZ treatment on C6 cell line.

a-f: The changes in a, _k_io, 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 _k_io 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 _k_io (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 _k_io 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 _k_io 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 _k_io measurements in vivo.

a: The implementation of MGE sequence in Water-exchange DCE-MRI eliminates potential _T_2* artifacts caused by the contrast agent at 7 T by fitting the MGE data (Supplementary Methods Section 3, equation 5) to obtain the purely _T_1-weighted signal S (TE = 0 ms). b: The original data with the shortest TE (2.8 ms) still shows _T_2*-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 _k_io 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 _k_io maone-foldone-foldp overlaid on _T_2-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 _k_io = 1.6 s−1) and ring (f, high _k_io = 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 _k_io map precisely reveals the intra-tumoral AQP4 distribution in each rat glioma model.

a-j: significant linear correlation between _k_io 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 _k_io 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 _k_io and AQP4 expression in the rat orthotopic model of C6 glioma.

a, b: Typical examples of _k_io maps and AQP4 IHC results from two animals with small (a) and large _k_io (b) values. From up to down, they are the contrast-enhanced _T_1-weighted images (the position of tumor was illustrated with the white dashed circle), the _k_io maps overlaid on the _T_1-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 _k_io 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 _k_io 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 _k_io and fractions of AQP4+ cells in the 10 stereotactic biopsy points from this patient. d: A linear correlation between sample-averaged _k_io 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.

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Extended Data Fig. 10 Comparison between conventional MRI and DCE-MRI-derived _k_io 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 _T_1-weighted imaging, _T_2-weighted imaging, apparent diffusion coefficient (ADC) and diffusion weighted imaging (DWI). b: the results of _k_io. c: the results of the combination of conventional MRI and _k_io. 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. _R_2 is the coefficient of determination. d: one example of the predicted AQP4 expression map in one glioma patient using _k_io only.

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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

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