Computational On-Chip Imaging of Nanoparticles and Biomolecules using Ultraviolet Light

Handle URI:
http://hdl.handle.net/10754/623525
Title:
Computational On-Chip Imaging of Nanoparticles and Biomolecules using Ultraviolet Light
Authors:
Daloglu, Mustafa Ugur; Ray, Aniruddha; Gorocs, Zoltan; Xiong, Matthew; Malik, Ravinder; Bitan, Gal; McLeod, Euan; Ozcan, Aydogan
Abstract:
Significant progress in characterization of nanoparticles and biomolecules was enabled by the development of advanced imaging equipment with extreme spatial-resolution and sensitivity. To perform some of these analyses outside of well-resourced laboratories, it is necessary to create robust and cost-effective alternatives to existing high-end laboratory-bound imaging and sensing equipment. Towards this aim, we have designed a holographic on-chip microscope operating at an ultraviolet illumination wavelength (UV) of 266 nm. The increased forward scattering from nanoscale objects at this short wavelength has enabled us to detect individual sub-30 nm nanoparticles over a large field-of-view of >16 mm2 using an on-chip imaging platform, where the sample is placed at ≤0.5 mm away from the active area of an opto-electronic sensor-array, without any lenses in between. The strong absorption of this UV wavelength by biomolecules including nucleic acids and proteins has further enabled high-contrast imaging of nanoscopic aggregates of biomolecules, e.g., of enzyme Cu/Zn-superoxide dismutase, abnormal aggregation of which is linked to amyotrophic lateral sclerosis (ALS) - a fatal neurodegenerative disease. This UV-based wide-field computational imaging platform could be valuable for numerous applications in biomedical sciences and environmental monitoring, including disease diagnostics, viral load measurements as well as air- and water-quality assessment.
Citation:
Daloglu MU, Ray A, Gorocs Z, Xiong M, Malik R, et al. (2017) Computational On-Chip Imaging of Nanoparticles and Biomolecules using Ultraviolet Light. Scientific Reports 7: 44157. Available: http://dx.doi.org/10.1038/srep44157.
Publisher:
Springer Nature
Journal:
Scientific Reports
Issue Date:
9-Mar-2017
DOI:
10.1038/srep44157
Type:
Article
ISSN:
2045-2322
Sponsors:
The Ozcan Research Group at UCLA gratefully acknowledges the support of the Presidential Early Career Award for Scientists and Engineers (PECASE), the Army Research Office (ARO; W911NF-13-1-0419 and W911NF-13-1-0197), the ARO Life Sciences Division, the National Science Foundation (NSF) CBET Division Biophotonics Program, the NSF Emerging Frontiers in Research and Innovation (EFRI) Award, the NSF EAGER Award, NSF INSPIRE Award, NSF Partnerships for Innovation: Building Innovation Capacity (PFI:BIC) Program, Office of Naval Research (ONR), the National Institutes of Health (NIH), the Howard Hughes Medical Institute (HHMI), Vodafone Americas Foundation, the Mary Kay Foundation, Steven & Alexandra Cohen Foundation, and KAUST. The Bitan Research Group acknowledges support by RGK Foundation grant 20143057. This work is based upon research performed in a laboratory renovated by the National Science Foundation under Grant No. 0963183, which is an award funded under the American Recovery and Reinvestment Act of 2009 (ARRA). The authors acknowledge Zachary Scott Ballard and Wei Luo for their help with metal coating for SEM samples and Alborz Feizi for his helpful suggestions in LabVIEW programming.
Additional Links:
http://www.nature.com/articles/srep44157
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorDaloglu, Mustafa Uguren
dc.contributor.authorRay, Aniruddhaen
dc.contributor.authorGorocs, Zoltanen
dc.contributor.authorXiong, Matthewen
dc.contributor.authorMalik, Ravinderen
dc.contributor.authorBitan, Galen
dc.contributor.authorMcLeod, Euanen
dc.contributor.authorOzcan, Aydoganen
dc.date.accessioned2017-05-15T10:35:06Z-
dc.date.available2017-05-15T10:35:06Z-
dc.date.issued2017-03-09en
dc.identifier.citationDaloglu MU, Ray A, Gorocs Z, Xiong M, Malik R, et al. (2017) Computational On-Chip Imaging of Nanoparticles and Biomolecules using Ultraviolet Light. Scientific Reports 7: 44157. Available: http://dx.doi.org/10.1038/srep44157.en
dc.identifier.issn2045-2322en
dc.identifier.doi10.1038/srep44157en
dc.identifier.urihttp://hdl.handle.net/10754/623525-
dc.description.abstractSignificant progress in characterization of nanoparticles and biomolecules was enabled by the development of advanced imaging equipment with extreme spatial-resolution and sensitivity. To perform some of these analyses outside of well-resourced laboratories, it is necessary to create robust and cost-effective alternatives to existing high-end laboratory-bound imaging and sensing equipment. Towards this aim, we have designed a holographic on-chip microscope operating at an ultraviolet illumination wavelength (UV) of 266 nm. The increased forward scattering from nanoscale objects at this short wavelength has enabled us to detect individual sub-30 nm nanoparticles over a large field-of-view of >16 mm2 using an on-chip imaging platform, where the sample is placed at ≤0.5 mm away from the active area of an opto-electronic sensor-array, without any lenses in between. The strong absorption of this UV wavelength by biomolecules including nucleic acids and proteins has further enabled high-contrast imaging of nanoscopic aggregates of biomolecules, e.g., of enzyme Cu/Zn-superoxide dismutase, abnormal aggregation of which is linked to amyotrophic lateral sclerosis (ALS) - a fatal neurodegenerative disease. This UV-based wide-field computational imaging platform could be valuable for numerous applications in biomedical sciences and environmental monitoring, including disease diagnostics, viral load measurements as well as air- and water-quality assessment.en
dc.description.sponsorshipThe Ozcan Research Group at UCLA gratefully acknowledges the support of the Presidential Early Career Award for Scientists and Engineers (PECASE), the Army Research Office (ARO; W911NF-13-1-0419 and W911NF-13-1-0197), the ARO Life Sciences Division, the National Science Foundation (NSF) CBET Division Biophotonics Program, the NSF Emerging Frontiers in Research and Innovation (EFRI) Award, the NSF EAGER Award, NSF INSPIRE Award, NSF Partnerships for Innovation: Building Innovation Capacity (PFI:BIC) Program, Office of Naval Research (ONR), the National Institutes of Health (NIH), the Howard Hughes Medical Institute (HHMI), Vodafone Americas Foundation, the Mary Kay Foundation, Steven & Alexandra Cohen Foundation, and KAUST. The Bitan Research Group acknowledges support by RGK Foundation grant 20143057. This work is based upon research performed in a laboratory renovated by the National Science Foundation under Grant No. 0963183, which is an award funded under the American Recovery and Reinvestment Act of 2009 (ARRA). The authors acknowledge Zachary Scott Ballard and Wei Luo for their help with metal coating for SEM samples and Alborz Feizi for his helpful suggestions in LabVIEW programming.en
dc.publisherSpringer Natureen
dc.relation.urlhttp://www.nature.com/articles/srep44157en
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleComputational On-Chip Imaging of Nanoparticles and Biomolecules using Ultraviolet Lighten
dc.typeArticleen
dc.identifier.journalScientific Reportsen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionElectrical Engineering Department, University of California, Los Angeles, CA, 90095, USAen
dc.contributor.institutionBioengineering Department, University of California, Los Angeles, CA, 90095, USAen
dc.contributor.institutionCalifornia NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USAen
dc.contributor.institutionDepartment of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USAen
dc.contributor.institutionBrain Research Institute, University of California, Los Angeles, CA, 90095, USAen
dc.contributor.institutionMolecular Biology Institute, University of California, Los Angeles, CA, 90095, USAen
dc.contributor.institutionCollege of Optical Sciences, University of Arizona, Tucson, AZ 85721, USAen
dc.contributor.institutionDepartment of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USAen
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