Biological Logic Gate Using Gold Nanoparticles and Fluorescence Lifetime Imaging Microscopy
Source abstract
Abstract
Current medicine could greatly improve by intelligent treatment systems able to respond autonomously to early stages of diseases from within a patient. As an initial study en route to such a system, we describe biologically relevant logic gates based on gold nanoparticles (GNPs) and fluorescent molecules that are able to respond to multiple input parameters so as to detect specific biological conditions all through the lens of fluorescence lifetime (FLT) imaging microscopy (FLIM). By conjugating the pH-responsive Oregon Green 488 (OG) to the GNPs by a trypsin-cleavable peptide, we manufactured GNP–OG constructs, which are responsive to two separate inputs: surrounding pH and proteinase presence. The GNP–OG constructs can sensitively detect and distinguish between conditions of low pH and no enzyme, the presence of one of either raised pH or enzyme, and the presence of both. Additionally, the GNP–OG probes were tested on ex vivo mouse organs to demonstrate further biological relevance and successfully behaved as various logic gates would be expected in different organs where pH and enzyme conditions vary. Altogether, the GNP–OG constructs are shown to carry out logic gate behaviors, where the desired gate is defined by the FLT detected. Unlike previous biological logic gates, the GNP–OG constructs can realize AND, OR, NAND, NOR, XOR, and XNOR gates by choosing different FLT cutoffs alone. The constructs make for efficient fluorescent logic detectors independent of concentration and so can serve as a stepping stone toward more complex logic systems.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced biosensing and bioanalysis techniques · Gold and Silver Nanoparticles Synthesis and Applications · Advanced Nanomaterials in Catalysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Engineering and the Institute of Nanotechnology and Advanced Materials, Bar Ilan University, Ramat Gan 5290002, Israel
Department of Chemistry, Bar Ilan University, Ramat Gan 5290002, Israel
Funding
Ministry of Science, Technology and Space
References (70)
Show source-provided references
1. 10.1038/s41467-018-03705-y10.1038/s41467-018-03705-y ↗
2. 10.1021/acsnano.7b0529910.1021/acsnano.7b05299 ↗
3. 10.1016/j.bbagen.2017.01.02710.1016/j.bbagen.2017.01.027 ↗
4. 10.1146/annurev.bioeng.9.060906.15202510.1146/annurev.bioeng.9.060906.152025 ↗
5. 10.1038/nbt.333010.1038/nbt.3330 ↗
6. 10.1021/acsami.7b1645510.1021/acsami.7b16455 ↗
7. 10.1021/nl802915q10.1021/nl802915q ↗
8. 10.1021/ar960016n10.1021/ar960016n ↗
9. 10.1021/jp057170o10.1021/jp057170o ↗
10. 10.1016/j.ab.2004.11.02610.1016/j.ab.2004.11.026 ↗
11. 10.1016/j.mibio.2004.06.00210.1016/j.mibio.2004.06.002 ↗
12. 10.1038/nbt137710.1038/nbt1377 ↗
13. 10.1007/s12274-010-0045-110.1007/s12274-010-0045-1 ↗
14. 10.1155/2015/64671310.1155/2015/646713 ↗
15. 10.1515/nanoph-2017-003310.1515/nanoph-2017-0033 ↗
16. 10.1039/C5NR03119B10.1039/C5NR03119B ↗
17. 10.1117/12.228945910.1117/12.2289459 ↗
18. 10.2147/IJN.S2544610.2147/IJN.S25446 ↗
19. 10.1021/acsnano.5b0757610.1021/acsnano.5b07576 ↗
20. 10.1007/s12274-015-0891-y10.1007/s12274-015-0891-y ↗
21. 10.3390/ma911092610.3390/ma9110926 ↗
22. 10.1021/ph500214m10.1021/ph500214m ↗
23. Lakowicz J. R. · 200610.1007/978-0-387-46312-4 ↗
24. 10.1111/j.1365-2818.2012.03618.x10.1111/j.1365-2818.2012.03618.x ↗
25. 10.1002/jbio.20140013610.1002/jbio.201400136 ↗
26. 10.1063/1.275112510.1063/1.2751125 ↗
27. 10.1021/cm071510w10.1021/cm071510w ↗
28. 10.1021/ac800935610.1021/ac8009356 ↗
29. Chandran H. · 201210.1002/9783527645480.ch11 ↗
30. 10.1021/sb300111210.1021/sb3001112 ↗
31. 10.1039/C4CS00342J10.1039/C4CS00342J ↗
32. 10.1016/j.jconrel.2016.01.04510.1016/j.jconrel.2016.01.045 ↗
33. 10.1007/5346_2017_410.1007/5346_2017_4 ↗
34. 10.1038/nnano.2014.15610.1038/nnano.2014.156 ↗
35. 10.1021/ja904275210.1021/ja9042752 ↗
36. 10.1021/acsnano.6b0554110.1021/acsnano.6b05541 ↗
37. 10.1039/C8SC02079E10.1039/C8SC02079E ↗
38. 10.1039/c1cs15035a10.1039/c1cs15035a ↗
39. 10.1016/j.tips.2013.07.00210.1016/j.tips.2013.07.002 ↗
40. 10.1016/j.biomaterials.2014.11.05310.1016/j.biomaterials.2014.11.053 ↗
41. 10.2174/15701791179611723210.2174/157017911796117232 ↗
42. 10.1039/c0cc04441e10.1039/c0cc04441e ↗
43. 10.1007/s13404-012-0070-910.1007/s13404-012-0070-9 ↗
44. 10.1080/00387010.2013.78255710.1080/00387010.2013.782557 ↗
45. 10.1023/A:101687570957910.1023/A:1016875709579 ↗
46. 10.1021/nn502887j10.1021/nn502887j ↗
47. 10.1002/wnan.126710.1002/wnan.1267 ↗
48. 10.1002/bio.326210.1002/bio.3262 ↗
49. 10.1016/j.talanta.2018.09.11410.1016/j.talanta.2018.09.114 ↗
50. 10.1016/j.cej.2017.12.12310.1016/j.cej.2017.12.123 ↗
51. 10.1002/cphc.20090037510.1002/cphc.200900375 ↗
52. 10.1016/j.snb.2016.11.05010.1016/j.snb.2016.11.050 ↗
53. Wiesel-Kapah, I.; Kaminka, G. A.; Hachmon, G.; Agmon, N.; Bachelet, I. Rule-Based Programming of Molecular Robot Swarms for Biomedical Applications. Proceedings of the Twenty-Fifth International Joint Conference on Aritficial Intelligence (IJCAI-16); pp 3505–3512.
54. 10.1117/12.228709710.1117/12.2287097 ↗
55. 10.1002/jbio.20170008410.1002/jbio.201700084 ↗
56. Rick W. · 1974 · 101310.1016/B978-0-12-091302-2.50099-2 ↗
57. 10.1021/ja00904a00110.1021/ja00904a001 ↗
58. Johnson I. · 2010 · 66
59. 10.1016/S0003-2697(02)00325-110.1016/S0003-2697(02)00325-1 ↗
60. Bovey F. A. · 1960 · 4
61. 10.1177/00034894101190031010.1177/000348941011900310 ↗
62. 10.1155/2012/64690110.1155/2012/646901 ↗
63. 10.7150/thno.347810.7150/thno.3478 ↗
64. 10.1016/j.matbio.2015.09.00310.1016/j.matbio.2015.09.003 ↗
65. 10.1021/nl500573d10.1021/nl500573d ↗
66. 10.1186/s12951-014-0056-210.1186/s12951-014-0056-2 ↗
67. 10.1016/j.jconrel.2017.10.00910.1016/j.jconrel.2017.10.009 ↗
68. 10.1038/ncomms1499810.1038/ncomms14998 ↗
69. 10.1038/nnano.2014.5810.1038/nnano.2014.58 ↗
70. 10.1162/ARTL_a_0023510.1162/ARTL_a_00235 ↗