Fluorescence correlation spectroscopy as tool for high-content-screening in yeast (HCS-FCS)

Christopher Wood; Joseph Huff; Will Marshall; Elden Qingfeng Yu; Jay Unruh; Brian Slaughter; Winfried Wiegraebe

doi:10.1117/12.873947

22 February 2011 Fluorescence correlation spectroscopy as tool for high-content-screening in yeast (HCS-FCS)

Christopher Wood, Joseph Huff, Will Marshall, Elden Qingfeng Yu, Jay Unruh, Brian Slaughter, Winfried Wiegraebe

Proceedings Volume 7905, Single Molecule Spectroscopy and Imaging IV; 79050H (2011) https://doi.org/10.1117/12.873947
Event: SPIE BiOS, 2011, San Francisco, California, United States

Abstract

To measure protein interactions, diffusion properties, and local concentrations in single cells, Fluorescence Correlation Spectroscopy (FCS) is a well-established and widely accepted method. However, measurements can take a long time and are laborious. Therefore investigations are typically limited to tens or a few hundred cells. We developed an automated system to overcome these limitations and make FCS available for High Content Screening (HCS). We acquired data in an auto-correlation screen of more than 4000 of the 6000 proteins of the yeast Saccharomyces cerevisiae, tagged with eGFP and expanded the HCS to use cross-correlation between eGFP and mCherry tagged proteins to screen for molecular interactions. We performed all high-content FCS screens (HCS-FCS) in a 96 well plate format. The system is based on an extended Carl Zeiss fluorescence correlation spectrometer ConfoCor 3 attached to a confocal microscope LSM 510. We developed image-processing software to control these hardware components. The confocal microscope obtained overview images and we developed an algorithm to search for and detect single cells. At each cell, we positioned a laser beam at a well-defined point and recorded the fluctuation signal. We used automatic scoring of the signal for quality control. All data was stored and organized in a database based on the open source Open Microscopy Environment (OME) platform. To analyze the data we used the image processing language IDL and the open source statistical software package R.

Citation Download Citation

Christopher Wood, Joseph Huff, Will Marshall, Elden Qingfeng Yu, Jay Unruh, Brian Slaughter, and Winfried Wiegraebe "Fluorescence correlation spectroscopy as tool for high-content-screening in yeast (HCS-FCS)", Proc. SPIE 7905, Single Molecule Spectroscopy and Imaging IV, 79050H (22 February 2011); https://doi.org/10.1117/12.873947

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