Precise quantification of transcription factors in a surface-based single-molecule assay

• Verfasst von: Grußmayer, Kristin Stefanie [VerfasserIn]
• Verfasst von: Ehrhard, Tanja [VerfasserIn]
• Verfasst von: Lymperopoulos, Konstantinos [VerfasserIn]
• Verfasst von: Herten, Dirk-Peter [VerfasserIn]
• Titel: Precise quantification of transcription factors in a surface-based single-molecule assay
• Verf.angabe: Kristin S. Grußmayer, Tanja Ehrhard, Konstantinos Lymperopoulos, Dirk-Peter Herten
• E-Jahr: 2013
• Jahr: 15 August 2013
• Umfang: 7 S.
• Fussnoten: Gesehen am 16.02.2021
• Titel Quelle: Enthalten in: Biophysical chemistry
• Ort Quelle: Amsterdam [u.a.] : Elsevier Science, 1973
• Jahr Quelle: 2013
• Band/Heft Quelle: 184(2013), Seite 1-7
• ISSN Quelle: 1873-4200
• DOI: doi:10.1016/j.bpc.2013.07.015
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Biosensor
• Sach-SW: Quantitative detection
• Sach-SW: Single-molecule study
• Sach-SW: Surface immobilization
• Sach-SW: Transcription factor
• K10plus-PPN: 1746009667

Abstract

Biosensors have recognized a rapid development the last years in both industry and science. Recently, a single-molecule assay based on alternating laser excitation has been established for the quantitative detection of transcription factors. These proteins specifically recognize and bind DNA and play an important role in controlling gene expression. We implemented this assay format on a total internal reflection fluorescence microscope to detect transcription factors with immobilized single-molecule DNA biosensors. We quantify transcription factors via colocalization of the two halves of their binding site with immobilized single molecules of a two-color DNA biosensor. We could detect a model transcription factor, the bacterial lactose repressor, at different concentrations down to 150pM. We found that robust modeling of stoichiometry derived TIRF data is achieved with Student's t-distributions and nonlinear least-squares estimation with weights equal to the inverse of the expected number of bin entries. This significantly improved transcription factor concentration estimates with respect to distribution modeling with Gaussians without adding notable computational effort. The proposed model may enhance the precision of other single-molecule assays quantifying molecular distributions. Our measurements reliably confirm that the immobilized biosensor format is more sensitive than a previously published solution based approach.