Nanoneedles enable spatiotemporal lipidomics of living tissues
Source abstract
Abstract
Abstract Spatial biology provides high-content diagnostic information by mapping the molecular composition of tissues. However, traditional spatial biology approaches typically require non-living samples, limiting temporal analysis. Here, to address this limitation, we present a workflow using porous silicon nanoneedles to repeatedly collect biomolecules from live brain tissues and map lipid distribution through desorption electrospray ionization mass spectrometry imaging. This method preserves the integrity of the original tissue while replicating its spatial molecular profile on the nanoneedle substrate, accurately reflecting lipid distribution and tissue morphology. Machine learning analysis of 23 human glioma biopsies demonstrated that nanoneedle sampling enables the precise classification of disease states. Furthermore, a spatiotemporal analysis of mouse gliomas treated with temozolomide revealed time- and treatment-dependent variations in lipid composition. Our approach enables non-destructive spatiotemporal lipidomics, advancing molecular diagnostics for precision medicine.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Electron Microscopy Techniques and Applications · Advanced biosensing and bioanalysis techniques · Ion-surface interactions and analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (51)
Show source-provided references
1. Kuett, L. et al. Three-dimensional imaging mass cytometry for highly multiplexed molecular and cellular mapping of tissues and the tumor microenvironment. Nat. Cancer 3, 122–133 (2022).10.1038/s43018-021-00301-w ↗
2. Ravi, V. M. et al. Spatially resolved multi-omics deciphers bidirectional tumor–host interdependence in glioblastoma. Cancer Cell 40, 639–655.e13 (2022).10.1016/j.ccell.2022.05.009 ↗
3. Moncada, R. et al. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat. Biotechnol. 38, 333–342 (2020).10.1038/s41587-019-0392-8 ↗
4. Bergholt, M. S. et al. Correlated heterospectral lipidomics for biomolecular profiling of remyelination in multiple sclerosis. ACS Cent. Sci. 4, 39–51 (2018).10.1021/acscentsci.7b00367 ↗
5. Jarmusch, A. K. et al. Lipid and metabolite profiles of human brain tumors by desorption electrospray ionization-MS. Proc. Natl Acad. Sci. USA 113, 1486–1491 (2016).10.1073/pnas.1523306113 ↗
6. Chiappini, C. Nanoneedle-based sensing in biological systems. ACS Sens. 2, 1086–1102 (2017).10.1021/acssensors.7b00350 ↗
7. Elnathan, R. et al. Biointerface design for vertical nanoprobes. Nat. Rev. Mater. 7, 953–973 (2022).10.1038/s41578-022-00464-7 ↗
8. Wang, Z. et al. High-throughput intracellular biopsy of microRNAs for dissecting the temporal dynamics of cellular heterogeneity. Sci. Adv. 6, eaba4971 (2020).10.1126/sciadv.aba4971 ↗
9. Chiappini, C. et al. Tutorial: using nanoneedles for intracellular delivery. Nat. Protoc. 16, 4539–4563 (2021).10.1038/s41596-021-00600-7 ↗
10. Marcuccio, F. et al. Single-cell nanobiopsy enables multigenerational longitudinal transcriptomics of cancer cells. Sci. Adv. 10, eadl0515 (2024).10.1126/sciadv.adl0515 ↗
11. Chen, W. et al. Live-seq enables temporal transcriptomic recording of single cells. Nature 608, 733–740 (2022).10.1038/s41586-022-05046-9 ↗
12. Chiappini, C. et al. Biodegradable silicon nanoneedles delivering nucleic acids intracellularly induce localized in vivo neovascularization. Nat. Mater. 14, 532–539 (2015).10.1038/nmat4249 ↗
13. Chiappini, C. et al. Mapping local cytosolic enzymatic activity in human esophageal mucosa with porous silicon nanoneedles. Adv. Mater. 27, 5147–5152 (2015).10.1002/adma.201501304 ↗
14. Gustafsson, O. J. R. et al. Metabolite mapping by consecutive nanostructure and silver-assisted mass spectrometry imaging on tissue sections. Rapid Commun. Mass Spectrom. 31, 991–1000 (2017).10.1002/rcm.7869 ↗
15. Rudd, D. et al. Mass spectrometry imaging reveals new biological roles for choline esters and Tyrian purple precursors in muricid molluscs. Sci. Rep. 5, 13408 (2015).10.1038/srep13408 ↗
16. Gimple, R. C. et al. Glioma stem cell–specific superenhancer promotes polyunsaturated fatty-acid synthesis to support EGFR signaling. Cancer Discov. 9, 1248–1267 (2019).10.1158/2159-8290.CD-19-0061 ↗
17. O’Neill, K. C., Liapis, E., Harris, B. T., Perlin, D. S. & Carter, C. L. Mass spectrometry imaging discriminates glioblastoma tumor cell subpopulations and different microvascular formations based on their lipid profiles. Sci. Rep. 12, 17069 (2022).10.1038/s41598-022-22093-4 ↗
18. Alfardus, H. et al. Intratumour heterogeneity in microRNAs expression regulates glioblastoma metabolism. Sci. Rep. 11, 15908 (2021).10.1038/s41598-021-95289-9 ↗
19. Wu, C., Ifa, D. R., Manicke, N. E. & Cooks, R. G. Rapid, direct analysis of cholesterol by charge labeling in reactive desorption electrospray ionization. Anal. Chem. 81, 7618–7624 (2009).10.1021/ac901003u ↗
20. Abbassi-Ghadi, N. et al. Repeatability and reproducibility of desorption electrospray ionization-mass spectrometry (DESI-MS) for the imaging analysis of human cancer tissue: a gateway for clinical applications. Anal. Methods 7, 71–80 (2015).10.1039/C4AY01770F ↗
21. Aissaoui, N., Bergaoui, L., Landoulsi, J., Lambert, J.-F. & Boujday, S. Silane layers on silicon surfaces: mechanism of interaction, stability, and influence on protein adsorption. Langmuir 28, 656–665 (2012).10.1021/la2036778 ↗
22. Nijdam, A. J. et al. Physicochemically modified silicon as a substrate for protein microarrays. Biomaterials 28, 550–558 (2007).10.1016/j.biomaterials.2006.08.051 ↗
23. Krishnan, S. T. et al. Nanostructured silicon enabled HR-MS for the label-free detection of biomarkers in colorectal cancer plasma small extracellular vesicles. J. Nanotheranostics 3, 189–202 (2022).10.3390/jnt3040013 ↗
24. Roberts, K. D. et al. A synthetic lipopeptide targeting top-priority multidrug-resistant Gram-negative pathogens. Nat. Commun. 13, 1625 (2022).10.1038/s41467-022-29234-3 ↗
25. Rudd, D. A. et al. Mapping insoluble indole metabolites in the gastrointestinal environment of a murine colorectal cancer model using desorption/ionisation on porous silicon imaging. Sci. Rep. 9, 12342 (2019).10.1038/s41598-019-48533-2 ↗
26. Pirro, V. et al. Analysis of human gliomas by swab touch spray-mass spectrometry: applications to intraoperative assessment of surgical margins and presence of oncometabolites. Analyst 142, 4058–4066 (2017).10.1039/C7AN01334E ↗
27. Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (Association for Computing Machinery, 2016); https://doi.org/10.1145/2939672.293978510.1145/2939672.2939785 ↗
28. Ke, G. et al. LightGBM: a highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems 30 (Curran Associates, 2017).
29. Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30 (Curran Associates, 2017).
30. Cui, Y. et al. Axonal degeneration in an in vitro model of ischemic white matter injury. Neurobiol. Dis. 134, 104672 (2020).10.1016/j.nbd.2019.104672 ↗
31. Notterpek, L. M., Bullock, P. N., Malek-Hedayat, S., Fisher, R. & Rome, L. H. Myelination in cerebellar slice cultures: development of a system amenable to biochemical analysis. J. Neurosci. Res. 36, 621–634 (1993).10.1002/jnr.490360603 ↗
32. Gaca-Tabaszewska, M., Bogusiewicz, J. & Bojko, B. Metabolomic and lipidomic profiling of gliomas—a new direction in personalized therapies. Cancers 14, 5041 (2022).10.3390/cancers14205041 ↗
33. Maimó-Barceló, A. et al. Polyunsaturated fatty acid-enriched lipid fingerprint of glioblastoma proliferative regions is differentially regulated according to glioblastoma molecular subtype. Int. J. Mol. Sci. 23, 2949 (2022).10.3390/ijms23062949 ↗
34. Kathman, S. G., Boshart, J., Jing, H. & Cravatt, B. F. Blockade of the lysophosphatidylserine lipase ABHD12 potentiates ferroptosis in cancer cells. ACS Chem. Biol. 15, 871–877 (2020).10.1021/acschembio.0c00086 ↗
35. Buccarelli, M. et al. Inhibition of autophagy increases susceptibility of glioblastoma stem cells to temozolomide by igniting ferroptosis. Cell Death Dis. 9, 1–17 (2018).10.1038/s41419-018-0864-7 ↗
36. Song, Q., Peng, S., Sun, Z., Heng, X. & Zhu, X. Temozolomide drives ferroptosis via a DMT1-dependent pathway in glioblastoma cells. Yonsei Med. J. 62, 843–849 (2021).10.3349/ymj.2021.62.9.843 ↗
37. Pearson, J. R. D. & Regad, T. Targeting cellular pathways in glioblastoma multiforme. Sign. Transduct. Target. Ther. 2, 1–11 (2017).
38. Tomar, V. S., Patil, V. & Somasundaram, K. Temozolomide induces activation of Wnt/β-catenin signaling in glioma cells via PI3K/Akt pathway: implications in glioma therapy. Cell Biol. Toxicol. 36, 273–278 (2020).10.1007/s10565-019-09502-7 ↗
39. Kim, H. et al. Flexible elastomer patch with vertical silicon nanoneedles for intracellular and intratissue nanoinjection of biomolecules. Sci. Adv. 4, eaau6972 (2018).10.1126/sciadv.aau6972 ↗
40. Wang, C. et al. Integrating porous silicon nanoneedles within medical devices for nucleic acid nanoinjection. ACS Nano 18, 14938–14953 (2024).10.1021/acsnano.4c00206 ↗
41. Chiappini, C., Liu, X., Fakhoury, J. R. & Ferrari, M. Biodegradable porous silicon barcode nanowires with defined geometry. Adv. Funct. Mater. 20, 2231–2239 (2010).10.1002/adfm.201000360 ↗
42. Chinwalla, A. T. et al. Initial sequencing and comparative analysis of the mouse genome. Nature 420, 520–562 (2002).10.1038/nature01262 ↗
43. Gangoso, E. et al. Glioblastomas acquire myeloid-affiliated transcriptional programs via epigenetic immunoediting to elicit immune evasion. Cell 184, 2454–2470.e26 (2021).10.1016/j.cell.2021.03.023 ↗
44. Helal, D. O. et al. A natural protein based platform for the delivery of temozolomide acid to glioma cells. Eur. J. Pharm. Biopharm. 169, 297–308 (2021).10.1016/j.ejpb.2021.10.007 ↗
45. Tortorella, S. et al. LipostarMSI: comprehensive, vendor-neutral software for visualization, data analysis, and automated molecular identification in mass spectrometry imaging. J. Am. Soc. Mass Spectrom. 31, 155–163 (2020).10.1021/jasms.9b00034 ↗
46. Wulff, J. E. & Mitchell, M. W. A comparison of various normalization methods for LC/MS metabolomics. Data Adv. Biosci. Biotechnol. 09, 339 (2018).10.4236/abb.2018.98022 ↗
47. Wijetunge, C. D. et al. EXIMS: an improved data analysis pipeline based on a new peak picking method for EXploring Imaging Mass Spectrometry data. Bioinformatics 31, 3198–3206 (2015).10.1093/bioinformatics/btv356 ↗
48. Jarmusch, A. K. et al. Differential lipid profiles of normal human brain matter and gliomas by positive and negative mode desorption electrospray ionization – mass spectrometry imaging. PLoS ONE 11, e0163180 (2016).10.1371/journal.pone.0163180 ↗
49. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
50. Akiba, T., Sano, S., Yanase, T., Ohta, T. & Koyama, M. Optuna: a next-generation hyperparameter optimization framework. In Proc. 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2623–2631 (Association for Computing Machinery, 2019); https://doi.org/10.1145/3292500.333070110.1145/3292500.3330701 ↗
51. Kim, H., Kim, E., Park, M., Lee, E. & Namkoong, K. Organotypic hippocampal slice culture from the adult mouse brain: a versatile tool for translational neuropsychopharmacology. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 41, 36–43 (2013).10.1016/j.pnpbp.2012.11.004 ↗