High-throughput, label-free, single-cell photoacoustic microscopy of intratumoral metabolic heterogeneity

Article metrics

Abstract

Intratumoral heterogeneity, which is manifested in almost all of the hallmarks of cancer, including the significantly altered metabolic profiles of cancer cells, represents a challenge to effective cancer therapy. High-throughput measurements of the metabolism of individual cancer cells would allow direct visualization and quantification of intratumoral metabolic heterogeneity, yet the throughputs of current measurement techniques are limited to about 120 cells per hour. Here, we show that single-cell photoacoustic microscopy can reach throughputs of approximately 12,000 cells per hour by trapping single cells with blood in an oxygen-diffusion-limited high-density microwell array and by using photoacoustic imaging to measure the haemoglobin oxygen change (that is, the oxygen consumption rate) in the microwells. We demonstrate the capability of this label-free technique by performing high-throughput single-cell oxygen-consumption-rate measurements of cultured cells and by imaging intratumoral metabolic heterogeneity in specimens from patients with breast cancer. High-throughput single-cell photoacoustic microscopy of oxygen consumption rates should enable the faster characterization of intratumoral metabolic heterogeneity.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: System schematic and working modes of SCM-PAM.
Fig. 2: SCM-PAM of single-cell trapping and oxygen sealing in a high-density microwell array.
Fig. 3: SCM-PAM of cellular metabolic heterogeneity in cultured cells.
Fig. 4: SCM-PAM of intratumoral metabolic heterogeneity in a breast cancer patient.
Fig. 5: Elevated and chaotic cellular metabolic heterogeneity in cancer measured by SCM-PAM.
Fig. 6: Oxygen consumption of cancer and normal cells in hypoxia measured by SCM-PAM.

Data availability

The authors declare that all data supporting the results in this study are available within the paper and its Supplementary information. The source data for the figures in this study are available in (identifier figshare)50.

References

  1. 1.

    Fisher, R., Pusztai, L. & Swanton, C. Cancer heterogeneity: implications for targeted therapeutics. Br. J. Cancer 108, 479–485 (2013).

  2. 2.

    Almendro, V., Marusyk, A. & Polyak, K. Cellular heterogeneity and molecular evolution in cancer. Annu. Rev. Pathol. 8, 277–302 (2013).

  3. 3.

    Zhao, Y., Butler, E. B. & Tan, M. Targeting cellular metabolism to improve cancer therapeutics. Cell Death Dis. 4, e532 (2013).

  4. 4.

    Robertson-Tessi, M., Gillies, R. J., Gatenby, R. A. & Anderson, A. R. A. Impact of metabolic heterogeneity on tumor growth, invasion, and treatment outcomes. Cancer Res. 75, 1567–1579 (2015).

  5. 5.

    Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 344, 1396–1401 (2014).

  6. 6.

    Sengupta, D. & Pratx, G. Imaging metabolic heterogeneity in cancer. Mol. Cancer 15, 4 (2016).

  7. 7.

    Hensley, C. T. et al. Metabolic heterogeneity in human lung tumors. Cell 164, 681–694 (2016).

  8. 8.

    Grimes, D. R., Warren, D. R. & Warren, S. Hypoxia imaging and radiotherapy: bridging the resolution gap. Br. J. Radiology 90, 20160939 (2017).

  9. 9.

    Xu, H. N., Zheng, G., Tchou, J., Nioka, S. & Li, L. Z. Characterizing the metabolic heterogeneity in human breast cancer xenografts by 3D high resolution fluorescence imaging. + 2, 73 (2013).

  10. 10.

    Georgakoudi, I. & Quinn, K. P. Optical imaging using endogenous contrast to assess metabolic state. Annu. Rev. Biomed. Eng. 14, 351–367 (2012).

  11. 11.

    Alhallak, K., Rebello, L. G., Muldoon, T. J., Quinn, K. P. & Rajaram, N. Optical redox ratio identifies metastatic potential-dependent changes in breast cancer cell metabolism. Biomed. Opt. Express 7, 4364–4374 (2016).

  12. 12.

    Hou, J. et al. Correlating two-photon excited fluorescence imaging of breast cancer cellular redox state with seahorse flux analysis of normalized cellular oxygen consumption. J. Biomed. Opt. 21, 060503 (2016).

  13. 13.

    Wagner, B. A., Venkataraman, S. & Buettner, G. R. The rate of oxygen utilization by cells. Free Radic. Biol. Med. 51, 700–712 (2011).

  14. 14.

    Ferrick, D. A., Neilson, A. & Beeson, C. Advances in measuring cellular bioenergetics using extracellular flux. Drug Discov. Today 13, 268–274 (2008).

  15. 15.

    Molter, T. W. et al. A microwell array device capable of measuring single-cell oxygen consumption rates. Sens. Actuators B 135, 678–686 (2009).

  16. 16.

    Osbourn, D. M., Sanger, R. H. & Smith, P. J. S. Determination of single-cell oxygen consumption with impedance feedback for control of sample-probe separation. Anal. Chem. 77, 6999–7004 (2005).

  17. 17.

    Kuang, Y. & Walt, D. R. Detecting oxygen consumption in the proximity of Saccharomyces cerevisiae cells using self‐assembled fluorescent nanosensors. Biotechnol. Bioeng. 96, 318–325 (2007).

  18. 18.

    Etzkorn, J. R. et al. Using micro-patterned sensors and cell self-assembly for measuring the oxygen consumption rate of single cells. J. Micromech. Microeng. 20, 095017 (2010).

  19. 19.

    Wang, L. V. & Yao, J. A practical guide to photoacoustic tomography in the life sciences. Nat. Methods 13, 627–638 (2016).

  20. 20.

    Guggenheim, J. A. et al. Ultrasensitive plano-concave optical microresonators for ultrasound sensing. Nat. Photonics 11, 714–719 (2017).

  21. 21.

    Yang, J. et al. Motionless volumetric photoacoustic microscopy with spatially invariant resolution. Nat. Commun. 8, 780 (2017).

  22. 22.

    Wong, T. T. W. et al. Fast label-free multilayered histology-like imaging of human breast cancer by photoacoustic microscopy. Sci. Adv. 3, e1602168 (2017).

  23. 23.

    Hai, P., Zhou, Y., Liang, J., Li, C. & Wang, L. V. Photoacoustic tomography of vascular compliance in humans. J. Biomed. Opt. 20, 126008 (2015).

  24. 24.

    Hai, P., Yao, J., Maslov, K. I., Zhou, Y. & Wang, L. V. Near-infrared optical-resolution photoacoustic microscopy. Opt. Lett. 39, 5192–5195 (2014).

  25. 25.

    Hu, S., Maslov, K. & Wang, L. V. Second-generation optical-resolution photoacoustic microscopy with improved sensitivity and speed. Opt. Lett. 36, 1134–1136 (2011).

  26. 26.

    Yao, J., Maslov, K. I., Zhang, Y., Xia, Y. & Wang, L. V. Label-free oxygen-metabolic photoacoustic microscopy in vivo. J. Biomed. Opt. 16, 076003 (2011).

  27. 27.

    Yang, M., Chadwick, A. E., Dart, C., Kamishima, T. & Quayle, J. M. Bioenergetic profile of human coronary artery smooth muscle cells and effect of metabolic intervention. PLoS ONE 12, 0177951 (2017).

  28. 28.

    Swartz, H. M. Measuring real levels of oxygen in vivo: opportunities and challenges. Biochem. Soc. Trans. 30, 248–252 (2002).

  29. 29.

    Wilson, D. F. et al. Oxygen distribution and vascular injury in the mouse eye measured by phosphorescence-lifetime imaging. Appl. Opt. 44, 5239–5248 (2005).

  30. 30.

    Yap, T. A. et al. Intratumor heterogeneity: seeing the wood for the trees. Sci. Transl. Med. 4, 127ps110 (2012).

  31. 31.

    Berg, C. P. et al. Human mature red blood cells express caspase-3 and caspase-8, but are devoid of mitochondrial regulators of apoptosis. Cell Death Differ. 8, 1197–1206 (2001).

  32. 32.

    Grimes, D. R., Kelly, C., Bloch, K. & Partridge, M. A method for estimating the oxygen consumption rate in multicellular tumour spheroids. J. R. Soc. Interface 11, 20131124 (2014).

  33. 33.

    Thomlinson, R. H. & Gray, L. H. The histological structure of some human lung cancers and the possible implications for radiotherapy. Br. J. Cancer 9, 539–549 (1955).

  34. 34.

    Campbell, P. J. et al. The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature 467, 1109–1113 (2010).

  35. 35.

    Clevers, H. The cancer stem cell: premises, promises and challenges. Nat. Med. 17, 313–319 (2011).

  36. 36.

    McGranahan, N. & Swanton, C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell 168, 613–628 (2017).

  37. 37.

    Meacham, C. E. & Morrison, S. J. Tumour heterogeneity and cancer cell plasticity. Nature 501, 328–337 (2013).

  38. 38.

    Gupta, P. B. et al. Stochastic state transitions give rise to phenotypic equilibrium in populations of cancer cells. Cell 146, 633–644 (2011).

  39. 39.

    Robin, E. D. & Wong, R. Mitochondrial DNA molecules and virtual number of mitochondria per cell in mammalian cells. J. Cell. Physiol. 136, 507–513 (1988).

  40. 40.

    Yao, J. et al. High-speed label-free functional photoacoustic microscopy of mouse brain in action. Nat. Methods 12, 407–410 (2015).

  41. 41.

    Yao, J., Wang, L., Li, C., Zhang, C. & Wang, L. V. Photoimprint photoacoustic microscopy for three-dimensional label-free subdiffraction imaging. Phys. Rev. Lett. 112, 014302 (2014).

  42. 42.

    Chatni, M. R. et al. Functional photoacoustic microscopy of pH. J. Biomed. Opt. 16, 100503 (2011).

  43. 43.

    Galluzzi, L., Kepp, O., Vander Heiden, M. G. & Kroemer, G. Metabolic targets for cancer therapy. Nat. Rev. Drug Discov. 12, 829–846 (2013).

  44. 44.

    Weinberg, S. E. & Chandel, N. S. Targeting mitochondria metabolism for cancer therapy. Nat. Chem. Biol. 11, 9–15 (2014).

  45. 45.

    Wong, A. H.-H. et al. Drug screening of cancer cell lines and human primary tumors using droplet microfluidics. Sci. Rep. 7, 9109 (2017).

  46. 46.

    Hai, P. et al. Label-free high-throughput detection and quantification of circulating melanoma tumor cell clusters by linear-array-based photoacoustic tomography. J. Biomed. Opt. 22, 41004 (2016).

  47. 47.

    Lin, R. et al. Longitudinal label-free optical-resolution photoacoustic microscopy of tumor angiogenesis in vivo. Quant. Imaging Med. Surg. 5, 23–29 (2015).

  48. 48.

    Luke, G. P. & Emelianov, S. Y. Label-free detection of lymph node metastases with US-guided functional photoacoustic imaging. Radiology 277, 435–442 (2015).

  49. 49.

    Cash, K. J., Li, C., Xia, J., Wang, L. V. & Clark, H. A. Optical drug monitoring: photoacoustic imaging of nanosensors to monitor therapeutic lithium in vivo. ACS Nano 9, 1692–1698 (2015).

  50. 50.

    Hai, P. et al. Dataset for high-throughput label-free single-cell photoacoustic microscopy of intratumoral metabolic heterogeneity. Figshare https://doi.org/10.6084/m9.figshare.7744004 (2019).

Download references

Acknowledgements

The authors appreciate the help received from J. Yao in the initial phase of the project and J. Liang in the data analysis while they were former members of our laboratory. We also thank J. Ballard for his close reading of the manuscript. This work was sponsored by National Science Foundation (grant nos 1255930 and 1255921) and National Institutes of Health (grant nos DP1 EB016986 (NIH Director’s Pioneer Award) and R01 CA186567 (NIH Director’s Transformative Research Award)).

Author information

P.H., J.Z. and L.V.W. conceived and designed the study. P.H. and T.I. developed the photoacoustic imaging system. S.X. fabricated the high-density microwell array. R.Z. cultured the cells. R.L.A. provided the excised breast cancer and normal tissues. P.H. and T.I. performed the experiments. P.H. and T.I. analysed the data. L.V.W. supervised the study. All authors wrote the manuscript.

Correspondence to Jun Zou or Lihong V. Wang.

Ethics declarations

Competing interests

L.V.W. has a financial interest in Microphotoacoustics, Inc.; CalPACT, LLC and Union Photoacoustic Technologies, Ltd. These companies did not support this work.

Additional information

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

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Further reading