Oufti: an integrated software package for high-accuracy, high-throughput quantitative microscopy analysis

Abstract

With the realization that bacteria display phenotypic variability among cells and exhibit complex subcellular organization critical for cellular function and behavior, microscopy has re-emerged as a primary tool in bacterial research during the last decade. However, the bottleneck in today's single-cell studies is quantitative image analysis of cells and fluorescent signals. Here, we address current limitations through the development of Oufti, a stand-alone, open-source software package for automated measurements of microbial cells and fluorescence signals from microscopy images. Oufti provides computational solutions for tracking touching cells in confluent samples, handles various cell morphologies, offers algorithms for quantitative analysis of both diffraction and non-diffraction-limited fluorescence signals and is scalable for high-throughput analysis of massive datasets, all with subpixel precision. All functionalities are integrated in a single package. The graphical user interface, which includes interactive modules for segmentation, image analysis and post-processing analysis, makes the software broadly accessible to users irrespective of their computational skills.

Fig. 1. Oufti detects individual cells in cell monolayers

(A) Monolayer of wild-type E. coli strain BW25113 in a microfluidic chamber. Scale bar on the bottom left represents 5 μm. (B) Same as (A), but with 322 curated cell contours (green) defined with Oufti using the subpixel algorithm.

Fig. 2. High throughput analysis of a microfluidic experiment with Oufti

(A) Oufti work-flow includes parallel computation, exploiting multiple threads on the user’s computer. Following image processing (cell segmentation, cell detection, cell mesh creation, cell joining and/or splitting, etc.), a data parser for the text-formatted output organizes data to be analyzed with various post-processing functions. (B) Wild-type E. coli strain BW25113 was grown in microfluidic chambers in M9 supplemented medium at 30°C for about 10 h. Cells were detected and tracked over time using the subpixel algorithm. The plot shows the growth of each cell (normalized by length at birth) during the 10-h experiment. (C–F) Note that all plots were created with MATLAB scripts using the Oufti output results. These plots are presented as examples of post-analysis that can be done with Oufti-generated datasets. (C) Scatter plot of cell volume at birth versus time (n = 2,234 cells). The distribution of cell volumes at birth is shown as a histogram along the y-axis of the scatter plot. (D) Scatter plot of the interdivision time versus the relative growth rate (n = 2,234 cells). The distribution of both parameters is shown as a histogram along the corresponding axis. The relative growth rate was calculated by fitting L_be^Bt to cell length as a function of time, where L_b is cell length at birth, B is the growth rate and t is time. (E) The red line ± 1 SD (gray shading) shows the average cell constriction profile for the detected 2,234 cells, from no detectable constriction (constriction degree = 0) to cell division (constriction degree = 1). (F) Degree of correlation (Kendall rank sum correlation coefficient) from one generation to another for the cell length at birth, the relative growth rate, the interdivision time and the normalized interdivision time.

Fig. 3. Precision and accuracy of the pixel and subpixel cell detection methods

(A) Variance of cell contour edge localization (14,660 edges from 116 cells, each sampled 301 times in a time-lapse experiment) from cell detection with the pixel and subpixel algorithms. Variance is measured in pixels² (bottom x axis) and nm² (top x axis). (B) Top left, simulated 2D phase-contrast image. The scale bar represents 1 μm. Top right, same but with the true edge location (red). Bottom left, same but with cell contour obtained with the pixel algorithm (blue). Bottom right, same but with cell contour obtained with subpixel algorithm (green). (C) Histogram of distance of cell contour edge localization to true cell edge coordinate (2,348,162 vertices from 22,500 cells). The localization error is measured in pixels (bottom x axis) and nm (top x axis).

Fig. 4. Oufti detects a variety of cell morphologies, even with the subpixel method

Phase contrast images of (A) E. coli minC mutant, (B) Borrelia burgdorferi, (C) C. crescentus and (D) E. coli rodZ mutant cells. The cell contours computed with Oufti’s subpixel algorithm are depicted with yellow or red lines. Cell contours were obtained without curation except for the irregularly shaped rodZ mutant cells.

Fig. 5. Detection and quantitative characterization of fluorescently labeled SgrS RNAs using spotDetection

(A) Probing E. coli SgrS small RNA using FISH microscopy reveals multiple fluorescent spots. The fluorescence image with Oufti cell contour is shown in log-scale to visualize the full spectrum of fluorescence spot intensities. (B) same as (A) but with spotDetection identification of fluorescent RNA foci (in red). (C) Localization error of spotDetection on diffraction-limited spots of varying signal-to-noise ratios (SN) from simulated images. Spots were detected using optimized parameters. Top inset shows example of single spots with corresponding SN values. Even when multiple Gaussian fitting was used, all spots were classified as single spots and fit with a single Gaussian. (D) Spot localization error for simulated spot pairs separated by a fixed distance d (top inset shows representative examples of spot pairs with their inter-spot distances). Graph inset shows the fraction of detected spot pairs as a function of inter-spot distance.

Fig. 6. Detection and cell cycle quantification of nucleoid areas using objectDetection

(A) Montage of fluorescence images from a microfluidic experiment showing E. coli cells with HU-mCherry-labeled nucleoids. The red and yellow contours show the cell and nucleoid contours, as determined by Oufti and its embedded objectDetection module, respectively. (B) Area of the cytoplasm (red) and nucleoid (blue) for four individual cells measured over the cell cycle. (C) Distribution of cell (red) and nucleoid (blue) areas for 740 cells are shown as intensity gradient. Black lines show the mean values. The inset plots the distribution of nucleoid-to-cell area ratio (gray) as intensity gradient and with mean (black line).

Fig. 7. The cellListFilter module

This module allows the user to quickly visualize the growth curves of all cells detected in a time-lapse experiment, as well as identify and select a subset of them for further refinement or analysis of the cell contours. (A) Typical output of the growth curve visualizing tool. (B) Cells with abnormal growth curves can be easily selected by choosing the growth curves that significantly deviate from exponential growth. A typical output is shown on the left panel where growth curves deviating from an exponential fit (root mean squared error RMSE >0.04) are highlighted in red on the left panel. The distribution of the RMSE values for all the growth curves fit is shown as a histogram on the right panel, with RMSE values > 0.04 highlighted in red. (C) The capability of selecting a subset of cells based on their growth rate or length at birth is illustrated with the blue and orange growth curves on the left panels, corresponding to the blue and orange bars on the histograms on the right panels. Cells and growth curves that are not included in the selection are represented with gray growth curves and bars.