Decoding Endothelial Glycocalyx‐Nanoparticle Interactions via a Systematic Polymeric Nanoparticle Library and High‐Content Characterization
Source abstract
Abstract
ABSTRACT Understanding how nanoparticle physicochemical properties influence cellular interactions is critical for optimizing drug delivery systems. Here, we report the high‐throughput synthesis and biological characterization of a 42‐member library of self‐assembled polymeric nanoparticles that systematically vary in hydrodynamic diameter and surface charge. Using reversible addition‐fragmentation chain‐transfer (RAFT) polymerization and polymerization‐induced self‐assembly (PISA), nanoparticles were synthesized in microplate formats and characterized for cytotoxicity and uptake by endothelial cells expressing either immature or mature glycocalyces. We found that surface charge had a greater impact on cytotoxicity and cellular uptake than hydrodynamic diameter, with some cationic nanoparticles exhibiting higher toxicity and uptake. Notably, cells with a mature glycocalyx showed increased tolerance and uptake of cationic nanoparticles, suggesting a protective and regulatory role of the glycocalyx. A semi‐automated imaging workflow incorporating machine learning‐based cell segmentation enabled single‐cell quantification of nanoparticle uptake, revealing population‐level variability across the library. Protein corona formation in serum conditions further modulated nanoparticle surface charge and interactions. This study highlights the importance of integrating nanoparticle design with biological context and scalable analytical tools, offering a framework for optimizing nanoparticle formulations for targeted delivery across diverse cell types.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Nanoparticle-Based Drug Delivery · Polymer Surface Interaction Studies · Trauma, Hemostasis, Coagulopathy, Resuscitation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Biomedical Engineering University of New South Wales Sydney New South Wales Australia
Katherina Gaus Light Microscopy Facility University of New South Wales Sydney New South Wales Australia
School of Computer Science and Engineering University of New South Wales Sydney New South Wales Australia
Funding
Australian Research Council
References (63)
Show source-provided references
1. 10.1038/s41573-020-0090-810.1038/s41573-020-0090-8 ↗
2. 10.1063/5.007349410.1063/5.0073494 ↗
3. 10.1021/acsnano.9b0814210.1021/acsnano.9b08142 ↗
4. 10.1038/s41563-020-0676-x10.1038/s41563-020-0676-x ↗
5. 10.1021/acsnano.5b0132410.1021/acsnano.5b01324 ↗
6. 10.1021/nn302687n10.1021/nn302687n ↗
7. 10.1073/pnas.191201211710.1073/pnas.1912012117 ↗
8. 10.1152/ajplung.2001.280.6.L133510.1152/ajplung.2001.280.6.L1335 ↗
9. 10.1146/annurev.bioeng.9.060906.15195910.1146/annurev.bioeng.9.060906.151959 ↗
10. 10.1016/j.addr.2022.11419510.1016/j.addr.2022.114195 ↗
11. 10.1038/s41598-021-91231-110.1038/s41598-021-91231-1 ↗
12. 10.1016/j.actbio.2025.03.01210.1016/j.actbio.2025.03.012 ↗
13. 10.1021/acs.biomac.4c0047710.1021/acs.biomac.4c00477 ↗
14. 10.1371/journal.pone.016161010.1371/journal.pone.0161610 ↗
15. 10.1016/j.biomaterials.2009.06.04810.1016/j.biomaterials.2009.06.048 ↗
16. 10.1021/acsabm.4c0101210.1021/acsabm.4c01012 ↗
17. 10.2217/nnm-2016-033210.2217/nnm-2016-0332 ↗
18. 10.1021/acs.chemrev.3c0070510.1021/acs.chemrev.3c00705 ↗
19. 10.1039/C6CS00636A10.1039/C6CS00636A ↗
20. 10.1039/C8RE00066B10.1039/C8RE00066B ↗
21. 10.1073/pnas.110637910810.1073/pnas.1106379108 ↗
22. 10.1021/nn403370e10.1021/nn403370e ↗
23. 10.1038/s41551-023-01030-410.1038/s41551-023-01030-4 ↗
24. 10.1002/anie.20160011210.1002/anie.201600112 ↗
25. 10.1002/anie.20171104410.1002/anie.201711044 ↗
26. 10.1021/acs.macromol.7b0121910.1021/acs.macromol.7b01219 ↗
27. 10.1039/C7PY00007C10.1039/C7PY00007C ↗
28. 10.1021/acs.macromol.9b0056310.1021/acs.macromol.9b00563 ↗
29. 10.1039/C8RE00066B10.1039/C8RE00066B ↗
30. 10.1039/D0NR01627F10.1039/D0NR01627F ↗
31. Two‐step Protocol for Preparing Adherent Cells for High‐Throughput Flow Cytometry
32. 10.1039/D1NR00901J10.1039/D1NR00901J ↗
33. 10.1038/s41596-020-0313-910.1038/s41596-020-0313-9 ↗
34. Cell Segmentation in Microscopy Images using a SAM‐based U‐Net Architecture and a Novel Dataset
35. 10.1021/acs.macromol.5b0260210.1021/acs.macromol.5b02602 ↗
36. 10.1039/C7PY01161J10.1039/C7PY01161J ↗
37. 10.1002/anie.20241610610.1002/anie.202416106 ↗
38. 10.1039/C6PY00696E10.1039/C6PY00696E ↗
39. 10.1021/la203991y10.1021/la203991y ↗
40. 10.1021/acs.langmuir.9b0278910.1021/acs.langmuir.9b02789 ↗
41. 10.1161/CIRCRESAHA.108.19158510.1161/CIRCRESAHA.108.191585 ↗
42. 10.1021/acsanm.1c0098710.1021/acsanm.1c00987 ↗
43. 10.3390/nano1214247610.3390/nano12142476 ↗
44. 10.3109/17435390.2013.77346510.3109/17435390.2013.773465 ↗
45. 10.1021/acs.macromol.3c0122310.1021/acs.macromol.3c01223 ↗
46. 10.1016/j.taap.2010.04.01410.1016/j.taap.2010.04.014 ↗
47. 10.1073/pnas.070388210410.1073/pnas.0703882104 ↗
48. 10.1038/cr.2015.910.1038/cr.2015.9 ↗
49. 10.1021/acs.macromol.2c0085410.1021/acs.macromol.2c00854 ↗
50. 10.2147/IJN.S3611110.2147/IJN.S36111 ↗
51. 10.3389/fmedt.2023.119552910.3389/fmedt.2023.1195529 ↗
52. 10.1016/j.actbio.2022.11.01010.1016/j.actbio.2022.11.010 ↗
53. 10.1016/S0378-5173(01)00923-110.1016/S0378-5173(01)00923-1 ↗
54. 10.1039/C4TX00061G10.1039/C4TX00061G ↗
55. 10.1016/j.colsurfb.2023.11324510.1016/j.colsurfb.2023.113245 ↗
56. 10.1186/1477-3155-12-510.1186/1477-3155-12-5 ↗
57. 10.1039/D1NR07279J10.1039/D1NR07279J ↗
58. 10.1039/D2LC00799A10.1039/D2LC00799A ↗
59. 10.1039/C4NR05982D10.1039/C4NR05982D ↗
60. 10.1016/j.biomaterials.2021.12078010.1016/j.biomaterials.2021.120780 ↗
61. 10.1016/j.preme.2025.10003710.1016/j.preme.2025.100037 ↗
62. 10.1039/D2RA06888E10.1039/D2RA06888E ↗
63. 10.1016/j.biomaterials.2025.12371710.1016/j.biomaterials.2025.123717 ↗