Automated quantitative live cell fluorescence microscopy

Abstract

Advances in microscopy automation and image analysis have given biologists the tools to attempt large scale systems-level experiments on biological systems using microscope image readout. Fluorescence microscopy has become a standard tool for assaying gene function in RNAi knockdown screens and protein localization studies in eukaryotic systems. Similar high throughput studies can be attempted in prokaryotes, though the difficulties surrounding work at the diffraction limit pose challenges, and targeting essential genes in a high throughput way can be difficult. Here we will discuss efforts to make live-cell fluorescent microscopy based experiments using genetically encoded fluorescent reporters an automated, high throughput, and quantitative endeavor amenable to systems-level experiments in bacteria. We emphasize a quantitative data reduction approach, using simulation to help develop biologically relevant cell measurements that completely characterize the cell image. We give an example of how this type of data can be directly exploited by statistical learning algorithms to discover functional pathways.

Figure 1.

Automated image analysis. Stored images are segmented by individual cell and parameterized using all available image information. Parameterized data is stored to disk for second-pass analysis where cell parameters are converted to biologically relevant metrics that can be used for quantitative phenotyping.

Figure 2.

Measurements made on an individual bacterial cell. (A) The membrane profile from a subpixel resolution spline fit to a phase-contrast image is combined with, (B) subpixel resolution measurements of fluorescent foci and delocalized fluorescent signal to provide, (C) a parameterized view of the cell including localized and delocalized fluorescent contributions.

Figure 3.

Using cell image simulation when developing algorithms. (A) An image simulation of a bacterial cell has been used to test a membrane-fitting algorithm. (B) The difference between the predicted and measured length and width of the cell as a function of threshold shows a systematic bias. (C) The effect on measuring a fluorescent spot relative to the measured pole of the cell. After correcting for the threshold effect a simulation run of 400 cells shows the resolution of the algorithms, given by the width of the residual histograms as shown.

Figure 4.

Polar localized protein concentration as a function of cell cycle. (A) An example of cell cycle dependent protein localization in Caulobacter. (B) An example single-cell image shows the correspondence with the visible changes in localized protein concentration (C) Automated analysis detects time variation in polar fluorescence intensities of the CpaE-CFP, PleC-YFP, and DivJ-RFP reporter fusions.

Figure 5.

Ensemble measurement of polar localized protein based on individual cell measurements. The polar fluorescence intensities of the CpaE-CFP, PleC-YFP, and DivJ-RFP reporter fusions are plotted for the nonmutagenized control strain (black) as well as gene disruptions in two separate open reading frames coding for the PodJ (blue) and TacA (red) proteins.

Figure 6.

Example of Genotype-Phenotype microscopy based analysis. A collection of automatically determined cell metrics is used to identify a functional cluster indicating a gene module coordinating temporal and spatial protein localization. Heat map representation of the calculated z-score values from localized protein abundance (L), delocalized protein (D), the fraction of cells identified as being biopolar (B), and the fraction similarly identified as monopolar (M) are shown for a highly significant gene cluster designated by the dendrogram.