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High-throughput smFRET analysis of freely diffusing nucleic acid molecules and associated proteins

Segal, Maya and Ingargiola, Antonino and Lerner, Eitan and Chung, SangYoon and White, Jonathan A. and Streets, Aaron and Weiss, S. and Michalet, X. (2019) High-throughput smFRET analysis of freely diffusing nucleic acid molecules and associated proteins. Methods, 169 . pp. 21-45. ISSN 1046-2023. https://resolver.caltech.edu/CaltechAUTHORS:20190528-091835048

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Abstract

Single-molecule Förster resonance energy transfer (smFRET) is a powerful technique for nanometer-scale studies of single molecules. Solution-based smFRET, in particular, can be used to study equilibrium intra- and intermolecular conformations, binding/unbinding events and conformational changes under biologically relevant conditions without ensemble averaging. However, single-spot smFRET measurements in solution are slow. Here, we detail a high-throughput smFRET approach that extends the traditional single-spot confocal geometry to a multispot one. The excitation spots are optically conjugated to two custom silicon single photon avalanche diode (SPAD) arrays. Two-color excitation is implemented using a periodic acceptor excitation (PAX), allowing distinguishing between singly- and doubly-labeled molecules. We demonstrate the ability of this setup to rapidly and accurately determine FRET efficiencies and population stoichiometries by pooling the data collected independently from the multiple spots. We also show how the high throughput of this approach can be used o increase the temporal resolution of single-molecule FRET population characterization from minutes to seconds. Combined with microfluidics, this high-throughput approach will enable simple real-time kinetic studies as well as powerful molecular screening applications.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.ymeth.2019.07.021DOIArticle
https://doi.org/10.1101/651869DOIDiscussion Paper
ORCID:
AuthorORCID
Segal, Maya0000-0002-1828-7682
Ingargiola, Antonino0000-0002-9348-1397
Lerner, Eitan0000-0002-3791-5277
Chung, SangYoon0000-0002-0592-4099
White, Jonathan A.0000-0002-7009-3332
Streets, Aaron0000-0002-3909-8389
Weiss, S.0000-0002-0720-5426
Michalet, X.0000-0001-6602-7693
Additional Information:© 2019 Elsevier Inc. Received 10 April 2019, Revised 24 June 2019, Accepted 22 July 2019, Available online 26 July 2019. We are grateful for the contribution of former lab members to the early developments of this technology as well as that of early users of the HT-smFRET setup. We also gratefully acknowledge the work of our POLIMI collaborators whose detectors have made this work possible and will fuel further progresses in years to come. We thank Mrs. Maya Lerner for preparation of illustrations for Fig. 9, panels A & B. This work was supported in part by NIH grants R01 GM095904, R01 GM069709, R01GM130942, by NSF awards MCB 1244175, MCB 1818147, EAGER 1842951, and by a seed grant from the UCLA Jonsson Comprehensive Cancer Center. S. Weiss discloses intellectual property used in the research reported here.
Funders:
Funding AgencyGrant Number
NIHR01GM095904
NIHR01GM069709
NIHR01GM130942
NSFMCB-1244175
NSFMCB-1818147
NSF20190026
UCLAUNSPECIFIED
Subject Keywords:Freely-diffusing; Single-molecule FRET; High-throughput; SPAD array
Record Number:CaltechAUTHORS:20190528-091835048
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190528-091835048
Official Citation:Maya Segal, Antonino Ingargiola, Eitan Lerner, SangYoon Chung, Jonathan A. White, Aaron Streets, S. Weiss, X. Michalet, High-throughput smFRET analysis of freely diffusing nucleic acid molecules and associated proteins, Methods, Volume 169, 2019, Pages 21-45, ISSN 1046-2023, https://doi.org/10.1016/j.ymeth.2019.07.021. (http://www.sciencedirect.com/science/article/pii/S1046202318304432)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:95808
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:28 May 2019 16:27
Last Modified:26 Nov 2019 23:18

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