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:

Nanosensor-based monitoring of autophagy-associated lysosomal acidification in vivo

Nature Chemical Biology volume 19, pages 1448–1457 (2023)Cite this article

Subjects

Abstract

Autophagy is a cellular process with important functions that drive neurodegenerative diseases and cancers. Lysosomal hyperacidification is a hallmark of autophagy. Lysosomal pH is currently measured by fluorescent probes in cell culture, but existing methods do not allow for quantitative, transient or in vivo measurements. In the present study, we developed near-infrared optical nanosensors using organic color centers (covalent sp3 defects on carbon nanotubes) to measure autophagy-mediated endolysosomal hyperacidification in live cells and in vivo. The nanosensors localize to the lysosomes, where the emission band shifts in response to local pH, enabling spatial, dynamic and quantitative mapping of subtle changes in lysosomal pH. Using the sensor, we observed cellular and intratumoral hyperacidification on administration of mTORC1 and V-ATPase modulators, revealing that lysosomal acidification mirrors the dynamics of S6K dephosphorylation and LC3B lipidation while diverging from p62 degradation. This sensor enables the transient and in vivo monitoring of the autophagy–lysosomal pathway.

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: Synthesis of pH-responsive OCC–DNA complexes.
Fig. 2: OCC–DNA complexes respond to endolysosomal pH.
Fig. 3: Nanosensor response under modulation of V-ATPase in live cells.
Fig. 4: Dynamic response to V-ATPase and mTORC modulation in live cells.
Fig. 5: In vivo nanosensor response to V-ATPase and mTORC modulation.
Fig. 6: In vivo dynamic monitoring of autophagy induction.

Similar content being viewed by others

Data availability

Source data are provided with this paper.

Code availability

LABVIEW code for data acquisition and MATLAB codes for data analysis in this article are available in the public GitHub repository (github.com/mijinee/HellerLab_MSKCC).

References

  1. Lawrence, R. E. & Zoncu, R. The lysosome as a cellular centre for signalling, metabolism and quality control. Nat. Cell Biol. 21, 133–142 (2019).

    Article  CAS  PubMed  Google Scholar 

  2. Mulcahy Levy, J. M. & Thorburn, A. Autophagy in cancer: moving from understanding mechanism to improving therapy responses in patients. Cell Death Differ. 27, 843–857 (2020).

    Article  PubMed  Google Scholar 

  3. Galluzzi, L. et al. Autophagy in malignant transformation and cancer progression. EMBO J. 34, 856–880 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Zhan, L. et al. Autophagy as an emerging therapy target for ovarian carcinoma. OncoTarget 7, 83476–83487 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Jung, S., Jeong, H. & Yu, S.-W. Autophagy as a decisive process for cell death. Exp. Mol. Med. 52, 921–930 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Lauzier, A. et al. Colorectal cancer cells respond differentially to autophagy inhibition in vivo. Sci. Rep. 9, 11316 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Klionsky, D. J. et al. Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition). Autophagy 17, 1–382 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Bellot, G. et al. Hypoxia-induced autophagy is mediated through hypoxia-inducible factor induction of BNIP3 and BNIP3L via their BH3 domains. Mol. Cell. Biol. 29, 2570–2581 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Chavez-Dominguez, R., Perez-Medina, M., Lopez-Gonzalez, J. S., Galicia-Velasco, M. & Aguilar-Cazares, D. The double-edge sword of autophagy in cancer: from tumor suppression to pro-tumor activity. Front. Oncol. 10, 578418 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Kimmelman, A. C. & White, E. Autophagy and tumor metabolism. Cell Metab. 25, 1037–1043 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Janji, B., Berchem, G. & Chouaib, S. Targeting autophagy in the tumor microenvironment: new challenges and opportunities for regulating tumor immunity. Front. Immunol. 9, 887 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Mizushima, N. & Murphy, L. O. Autophagy assays for biological discovery and therapeutic development. Trends Biochem. Sci. 45, 1080–1093 (2020).

    Article  CAS  PubMed  Google Scholar 

  13. Poillet-Perez, L. et al. Autophagy promotes growth of tumors with high mutational burden by inhibiting a T-cell immune response. Nat. Cancer 1, 923–934 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Kuma, A., Komatsu, M. & Mizushima, N. Autophagy-monitoring and autophagy-deficient mice. Autophagy 13, 1619–1628 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Yoshii, SaoriR. et al. Systemic analysis of Atg5-null mice rescued from neonatal lethality by transgenic ATG5 expression in neurons. Dev. Cell 39, 116–130 (2016).

    Article  CAS  PubMed  Google Scholar 

  16. Karsli-Uzunbas, G. et al. Autophagy is required for glucose homeostasis and lung tumor maintenance. Cancer Discov. 4, 914–927 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Yim, W. W.-Y. & Mizushima, N. Lysosome biology in autophagy. Cell Discov. 6, 6 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Williams, R. M. et al. Harnessing nanotechnology to expand the toolbox of chemical biology. Nat. Chem. Biol. 17, 129–137 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Aref, M. et al. Potentiometric pH nanosensor for intracellular measurements: real-time and continuous assessment of local gradients. Anal. Chem. 93, 15744–15751 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ma, L., Ouyang, Q., Werthmann, G. C., Thompson, H. M. & Morrow, E. M. Live-cell microscopy and fluorescence-based measurement of luminal pH in intracellular organelles. Front. Cell Dev. Biol. 5, 71 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Myochin, T. et al. Rational design of ratiometric near-infrared fluorescent pH probes with various pKa values, based on aminocyanine. J. Am. Chem. Soc. 133, 3401–3409 (2011).

    Article  CAS  PubMed  Google Scholar 

  22. Robinson, K. J. et al. Modified organosilica core–shell nanoparticles for stable pH sensing in biological solutions. ACS Sens. 3, 967–975 (2018).

    Article  CAS  PubMed  Google Scholar 

  23. Burgstaller, S. et al. pH-lemon, a fluorescent protein-based pH reporter for acidic compartments. ACS Sens. 4, 883–891 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Guha, S. et al. Approaches for detecting lysosomal alkalinization and impaired degradation in fresh and cultured RPE cells: evidence for a role in retinal degenerations. Exp. Eye Res. 126, 68–76 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Wäldchen, S., Lehmann, J., Klein, T., van de Linde, S. & Sauer, M. Light-induced cell damage in live-cell super-resolution microscopy. Sci. Rep. 5, 15348 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Laissue, P. P., Alghamdi, R. A., Tomancak, P., Reynaud, E. G. & Shroff, H. Assessing phototoxicity in live fluorescence imaging. Nat. Methods 14, 657–661 (2017).

    Article  CAS  PubMed  Google Scholar 

  27. Heller, D. A. et al. Peptide secondary structure modulates single-walled carbon nanotube fluorescence as a chaperone sensor for nitroaromatics. Proc. Natl Acad. Sci. USA 108, 8544–8549 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Kim, M. et al. Detection of ovarian cancer via the spectral fingerprinting of quantum-defect-modified carbon nanotubes in serum by machine learning. Nat. Biomed. Eng. 6, 267–275 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Bachilo, S. M. et al. Structure-assigned optical spectra of single-walled carbon nanotubes. Science 298, 2361 (2002).

    Article  CAS  PubMed  Google Scholar 

  30. Welsher, K., Sherlock, S. P. & Dai, H. Deep-tissue anatomical imaging of mice using carbon nanotube fluorophores in the second near-infrared window. Proc. Natl Acad. Sci. USA 108, 8943 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Mandal, A. K. et al. Fluorescent sp3 defect-tailored carbon nanotubes enable NIR-II single particle imaging in live brain slices at ultra-low excitation doses. Sci. Rep. 10, 5286 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Jena, P. V. et al. A carbon nanotube optical reporter maps endolysosomal lipid flux. ACS Nano 11, 10689–10703 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Galassi, T. V. et al. An optical nanoreporter of endolysosomal lipid accumulation reveals enduring effects of diet on hepatic macrophages in vivo. Sci. Transl. Med. 10, eaar2680 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Galassi, T. V. et al. Long-term in vivo biocompatibility of single-walled carbon nanotubes. PLoS ONE 15, e0226791 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Brozena, A. H., Kim, M., Powell, L. R. & Wang, Y. Controlling the optical properties of carbon nanotubes with organic colour-centre quantum defects. Nat. Rev. Chem. 3, 375–392 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Kwon, H. et al. Optical probing of local pH and temperature in complex fluids with covalently functionalized, semiconducting carbon nanotubes. J. Phys. Chem. C 119, 3733–3739 (2015).

    Article  CAS  Google Scholar 

  37. Piao, Y. et al. Brightening of carbon nanotube photoluminescence through the incorporation of sp3 defects. Nat. Chem. 5, 840–845 (2013).

    Article  CAS  PubMed  Google Scholar 

  38. Gravely, M., Safaee, M. M. & Roxbury, D. Biomolecular functionalization of a nanomaterial to control stability and retention within live cells. Nano Lett. 19, 6203–6212 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Roxbury, D., Jena, P. V., Shamay, Y., Horoszko, C. P. & Heller, D. A. Cell membrane proteins modulate the carbon nanotube optical bandgap via surface charge accumulation. ACS Nano 10, 499–506 (2016).

    Article  CAS  PubMed  Google Scholar 

  40. Hornung, V. et al. Silica crystals and aluminum salts activate the NALP3 inflammasome through phagosomal destabilization. Nat. Immunol. 9, 847–856 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Holtzmann, E. Lysosomes (Plenum, 1989).

  42. Alberts, B. et al. Molecular Biology of the Cell, 4th edn (Garland Science, 2002).

  43. Jin, H., Heller, D. A., Sharma, R. & Strano, M. S. Size-dependent cellular uptake and expulsion of single-walled carbon nanotubes: single particle tracking and a generic uptake model for nanoparticles. ACS Nano 3, 149–158 (2009).

    Article  CAS  PubMed  Google Scholar 

  44. Roxbury, D. et al. Hyperspectral microscopy of near-infrared fluorescence enables 17-chirality carbon nanotube imaging. Sci. Rep. 5, 14167 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Han, J. & Burgess, K. Fluorescent indicators for intracellular pH. Chem. Rev. 110, 2709–2728 (2010).

    Article  CAS  PubMed  Google Scholar 

  46. Yamamoto, A. et al. Bafilomycin A1 prevents maturation of autophagic vacuoles by inhibiting fusion between autophagosomes and lysosomes in rat hepatoma cell line, H-4-II-E cells. Cell Struct. Funct. 23, 33–42 (1998).

    Article  CAS  PubMed  Google Scholar 

  47. Mauvezin, C. & Neufeld, T. P. Bafilomycin A1 disrupts autophagic flux by inhibiting both V-ATPase-dependent acidification and Ca-P60A/SERCA-dependent autophagosome–lysosome fusion. Autophagy 11, 1437–1438 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Chung, C. Y.-S. et al. Covalent targeting of the vacuolar H+-ATPase activates autophagy via mTORC1 inhibition. Nat. Chem. Biol. 15, 776–785 (2019).

    Article  CAS  PubMed  Google Scholar 

  49. Frost, L. S., Dhingra, A., Reyes-Reveles, J. & Boesze-Battaglia, K. The use of DQ-BSA to monitor the turnover of autophagy-associated cargo. Methods Enzymol. 587, 43–54 (2017).

    Article  CAS  PubMed  Google Scholar 

  50. Corrotte, M., Fernandes, M. C., Tam, C. & Andrews, N. W. Toxin pores endocytosed during plasma membrane repair traffic into the lumen of MVBs for degradation. Traffic 13, 483–494 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Peña-Llopis, S. et al. Regulation of TFEB and V-ATPases by mTORC1. EMBO J. 30, 3242–3258 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Settembre, C. et al. TFEB links autophagy to lysosomal biogenesis. Science 332, 1429 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Thoreen, C. C. et al. An ATP-competitive mammalian target of rapamycin inhibitor reveals rapamycin-resistant functions of mTORC1*. J. Biol. Chem. 284, 8023–8032 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Sahani, M. H., Itakura, E. & Mizushima, N. Expression of the autophagy substrate SQSTM1/p62 is restored during prolonged starvation depending on transcriptional upregulation and autophagy-derived amino acids. Autophagy 10, 431–441 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Harvey, J. D. et al. A carbon nanotube reporter of microRNA hybridization events in vivo. Nat. Biomed. Eng. 1, 0041 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Zhang, X.-X. et al. pH-sensitive fluorescent dyes: are they really pH-sensitive in cells? Mol. Pharm. 10, 1910–1917 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Subbaiyan, N. K. et al. Role of surfactants and salt in aqueous two-phase separation of carbon nanotubes toward simple chirality isolation. ACS Nano 8, 1619–1628 (2014).

    Article  CAS  PubMed  Google Scholar 

  58. Quintero, B., Cabeza, M. C., Martínez, M. I., Gutiérrez, P. & Martínez, P. J. Dediazoniation of p-hydroxy and p-nitrobenzenediazonium ions in an aqueous medium: interference by the chelating agent diethylenetriaminepentaacetic acid. Can. J. Chem. 81, 832–839 (2003).

    Article  CAS  Google Scholar 

  59. Streit, J. K., Fagan, J. A. & Zheng, M. A low energy route to DNA-wrapped carbon nanotubes via replacement of bile salt surfactants. Anal. Chem. 89, 10496–10503 (2017).

    Article  CAS  PubMed  Google Scholar 

  60. Zheng, M. & Diner, B. A. Solution redox chemistry of carbon nanotubes. J. Am. Chem. Soc. 126, 15490–15494 (2004).

    Article  CAS  PubMed  Google Scholar 

  61. Bolte, S. & CordeliÈRes, F. P. A guided tour into subcellular colocalization analysis in light microscopy. J. Microsc. 224, 213–232 (2006).

    Article  CAS  PubMed  Google Scholar 

  62. Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank Memorial Sloan Kettering Cancer Center (MSKCC) Molecular Cytology Core Facility for assistance with AFM imaging, sample processing for immunofluorescence and confocal microscopy, and image analysis. We also thank the Center for Translational Pathology at Weill Cornell Medicine for immunohistochemistry. We thank C. O’Mara at the Daniel Bachovchin lab (MSKCC) for the NLRP3 inflammasome experiment. We thank P. Jena for assistance with image processing and analysis. The graphical abstract, Fig. 3a and Supplementary Figs. 2 and 11 were created with BioRender.com. This work was supported in part by the National Science Foundation CAREER Award (grant no. 1752506 to D.A.H.), the National Cancer Institute (grant no. R01-CA215719 to D.A.H. and Cancer Center Support grant no. P30-CA008748 to D.A.H., H.A. and Y.M.L.), the National Institutes of Health (NIH) Common Fund (grant no. DP2-HD075698 to D.A.H.), the Department of Defense Congressionally Directed Medical Research Program (W81XWH2210563 to D.A.H.), the American Cancer Society Research Scholar grant (no. GC230452 to D.A.H.), the Ara Parseghian Medical Research Fund (to D.A.H.), the Honorable Tina Brozman Foundation (to D.A.H.), the Ovarian Cancer Research Alliance and the Edmée Firth Fund for Research in Ovarian Cancer (grant no. CRDGAI-2023-3-1003 to D.A.H.), the Pershing Square Sohn Cancer Research Alliance (to D.A.H.), the New York State Biodefense Commercialization Fund (to D.A.H.), the Expect Miracles Foundation—Financial Services Against Cancer (to D.A.H.), the Louis and Rachel Rudin Foundation (to D.A.H.), the Experimental Therapeutics Center of MSKCC (to D.A.H. and Y.M.L.), Mr. William H. Goodwin and Mrs. Alice Goodwin and the Commonwealth Foundation for Cancer Research (to D.A.H. and Y.M.L.), the JPB Foundation (to Y.M.L.) and the William Randolph Hearst Fund in Experimental Therapeutics (to Y.M.L.). M.K. was supported by the NIH (grant no. K99-EB033580) and the Marie-Josée Kravis Women in Science Endeavor Postdoctoral Fellowship. Z.Y. was supported by the Ann Schreiber Mentored Investigator Award (Ovarian Cancer Research Fund) and Young Investigator 2019 (Kaleidoscope of Hope). R.L. was supported by an NIH T32 training grant (no. T32-GM73546), J.W. was supported by an NIH T32 training grant (no. T32-GM136640-Tan). D.W. was supported by NIH T32 training grants (nos. T32-GM141949 and T32-CA062948). R.F. was supported by the Alfred Benzon Foundation Fellowship. Y.H.W. acknowledges support from the National Science Foundation (grant nos. CHE-1904488 and CHE-2204202).

Author information

Author notes

  1. Zvi Yaari

    Present address: School of Pharmacy, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel

  2. These authors contributed equally: Mijin Kim, Chen Chen.

Authors and Affiliations

  1. Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Mijin Kim, Chen Chen, Zvi Yaari, Rune Frederiksen, Ewelina Randall, Jaina Wollowitz, Christian Cupo, Janki Shah, Daniel Worroll, Rachel E. Lagenbacher, Dana Goerzen, Yue-Ming Li, Heeseon An & Daniel A. Heller

  2. Weill Cornell Medicine, Cornell University, New York, NY, USA

    Chen Chen, Jaina Wollowitz, Rachel E. Lagenbacher, Dana Goerzen, Yue-Ming Li, Heeseon An & Daniel A. Heller

  3. Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Chen Chen & Jaina Wollowitz

  4. Department of Chemistry and Biochemistry, University of Maryland, College Park, MD, USA

    Xiaojian Wu & YuHuang Wang

  5. Maryland NanoCenter, University of Maryland, College Park, MD, USA

    YuHuang Wang

Authors
  1. Mijin Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chen Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zvi Yaari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rune Frederiksen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ewelina Randall
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jaina Wollowitz
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Christian Cupo
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xiaojian Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Janki Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Daniel Worroll
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Rachel E. Lagenbacher
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Dana Goerzen
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Yue-Ming Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Heeseon An
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. YuHuang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Daniel A. Heller
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.K., C. Chen, R.F., D.A.H., H.A. and Y.H.W. conceived the idea. M.K., C. Chen, R.F. and D.A.H. designed experiments. M.K., C. Chen, and D.A.H. analyzed the data. M.K., E.R. and X.W. performed nanosensor synthesis. M.K., C. Chen, R.F., E.R., J.W., C. Cupo, R.E.L., D.W.W., R.L. and D.G. performed in vitro experiments. M.K., C. Chen, Z.Y. and J.S. performed in vivo experiments. M.K., C. Chen and D.A.H. wrote the manuscript. Y.M.L., H.A. and Y.H.W. edited the manuscript.

Corresponding author

Correspondence to Daniel A. Heller.

Ethics declarations

Competing interests

D.A.H. is a cofounder and officer with equity interest in Lime Therapeutics, Inc., cofounder with equity interest in Selectin Therapeutics Inc. and Resident Diagnostics, Inc., and a member of the scientific advisory board of Concarlo Therapeutics, Inc., Nanorobotics Inc. and Mediphage Bioceuticals, Inc. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Chemical Biology thanks Jiajie Diao and the other, 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 Characterization of OCC-DNA response in various buffer/media conditions.

Emission wavelengths of a-c, E11 and d-f, E11- of the OCC-DNA complexes at varying buffer pH and media conditions in phosphate buffered saline. The metal ion concentrations tested are physiologically relevant ranges. All data are presented as mean values and error bars denote standard deviation from N = 3 technical replicates (a-f). g, Frequency distribution of standard deviations of ΔE ( = E11- – E11) wavelength shifts of triplicate measurements of a-f.

Source data

Extended Data Fig. 2 Protein concentration effects on OCC-DNA optical response.

The dynamic range of the OCC-DNA response to pH at increasing concentrations of: a, bovine serum albumin and b, fetal bovine serum. All data are presented as mean values and error bars denote standard deviation from N = 3 technical replicates (a,b).

Source data

Extended Data Fig. 3 Viscosity effects on OCC-DNA optical response.

Emission wavelengths of a, E11 and b, E11- of the OCC-DNA complexes at varying buffer pH and glycerol. All data are presented as mean values and error bars denote standard deviation from N = 3 technical replicates (a,b).

Source data

Extended Data Fig. 4 Cell viability in response to OCC-DNA complexes.

Single-dose (0.01 mg/L) OCC-DNA cell viability of SKOV3, OVCAR3, HEK293, HeLa, MEF, RM1, and Myc-CaP cell lines, measured via CellTiter-Glo 2.0, after 72 hours of incubation. No statistically significant differences were observed between the PBS control groups (gray) and the treatment groups (red) in all the tested cell lines. All data are presented as mean values and error bars denote standard deviation of triplicates for each condition.

Source data

Extended Data Fig. 5 OCC-DNA responses in 8 cell lines.

The emission response (ΔE = E11- – E11) of OCC-DNAs in live cells upon exposure to HEPES or MES buffer solutions of varying pHs with monensin (see Methods). All data are presented as mean values and error bars denote standard deviation from N = 3–25 biological replicates.

Source data

Extended Data Fig. 6 Inhibitor-mediated alterations of nanosensor response to pH.

The emission response (ΔE = E11- – E11) of nanosensors in live SKOV3 cells upon exposure to HEPES or MES buffer solutions of varying pHs with monensin (see Methods). Cells were treated with DMSO (black), 100 µM EN6 (red), 100 nM bafilomycin A1 (blue), 250 nM torin 1 (green) for 4 hours prior to pH measurements. Data are presented as mean values and error bars denote standard deviation from N = 25 each DMSO, EN6, and Baf A1 point, and N = 25, 24, and 21 for pH 7, 5.06, and 3.16 for torin 1 as biological replicates.

Source data

Extended Data Fig. 7 Nanosensor response in autophagy-defective cells.

a, ATG7 expression by western blotting confirmed the knockout of ATG7 in the HEK293T cell line used herein. The emission wavelength response (ΔE = E11- – E11) of OCC-DNAs in live b, wild type and c, ATG7-/- HEK293T cells upon exposure to HEPES or MES buffer solutions of varying pHs in the presence of monensin (see Methods). Cells were treated with DMSO (black) or 250 nM torin 1 (blue) for 4 hours prior to pH measurements. All data are presented as mean values and error bars denote standard deviation from N = 10 technical replicates (b,c). Original gel images are in Supplementary Fig. 24.

Source data

Extended Data Fig. 8 Time-dependent fluorescence intensity changes of intratumorally-injected nanosensors.

Quantification of total emission intensity of nanosensors in solid tumours of mice after injection. Fluorescence measurements were performed with a near-infrared preclinical hyperspectral imager with 730 nm excitation. Data are presented as mean values and error bars denote standard deviation from N = 5 biological replicates.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–24 and Supplementary References.

Reporting Summary

Supplementary Video 1

NIR fluorescence video of OCC–DNA complexes in live SKOV3 cells.

Supplementary Data 1

Source data for Supplementary Figs.

Source data

Fig. 1

Statistical source data.

Fig. 2

Statistical Source Data

Fig. 3

Statistical source data.

Fig. 4

Statistical source data.

Fig. 4

Unprocessed western blots/gels for actin, LC3B, P62 and pS6K.

Fig. 5

Statistical source data.

Fig. 6

Statistical source data.

Extended Data Fig. 1

Statistical source data.

Extended Data Fig. 2

Statistical source data.

Extended Data Fig. 3

Statistical source data.

Extended Data Fig. 4

Statistical source data.

Extended Data Fig. 5

Statistical source data.

Extended Data Fig. 6

Statistical source data.

Extended Data Fig. 7

Statistical source data.

Extended Data Fig. 7a

Unprocessed western blots/gels for actin and FIP200.

Extended Data Fig. 8

Statistical source data.

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

Kim, M., Chen, C., Yaari, Z. et al. Nanosensor-based monitoring of autophagy-associated lysosomal acidification in vivo. Nat Chem Biol 19, 1448–1457 (2023). https://doi.org/10.1038/s41589-023-01364-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41589-023-01364-9

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing