Robust microbial cell segmentation by optical-phase thresholding with minimal processing requirements

Abstract

High-throughput imaging with single-cell resolution has enabled remarkable discoveries in cell physiology and Systems Biology investigations. A common, and often the most challenging step in all such imaging implementations, is the ability to segment multiple images to regions that correspond to individual cells. Here, a robust segmentation strategy for microbial cells using Quantitative Phase Imaging is reported. The proposed method enables a greater than 99% yeast cell segmentation success rate, without any computationally-intensive, post-acquisition processing. We also detail how the method can be expanded to bacterial cell segmentation with 98% success rates with substantially reduced processing requirements in comparison to existing methods. We attribute this improved performance to the remarkably uniform background, elimination of cell-to-cell and intracellular optical artifacts, and enhanced signal-to-background ratio-all innate properties of imaging in the optical-phase domain. © 2017 International Society for Advancement of Cytometry.

Keywords: image cytometry; label free; segmentation; single-cell.

Figure 1

Segmentation of individual yeast cells, using either fluorescence via a cell‐wall specific stain shown in (a), or Differential Interference Contrast (DIC) shown in (b). Both types of images were analyzed via max‐entropy thresholding and watershed algorithms (ImageJ)—shown in (c) and (d) for fluorescence and DIC respectively. Subsequently the computed regions of interest (ROI) were overlaid with the original images—shown in (e) and (f). While fluorescence exhibits a higher success rate (52%), both imaging modalities fail to properly segment all cells, evidencing the need for further image processing.

Figure 2

Schematic illustration of the optical‐phase thresholding implementation for segmenting individual microbial cells. The transmitted wavefront exhibits variable phase delays with the background being zero (ΔΦ _b), contrary to the non‐zero phase delay induced by the cell cytosol and intracellular organelles (ΔΦ ₁, ΔΦ ₂). [Color figure can be viewed at wileyonlinelibrary.com]

Figure 3

(a) A quantitative phase image of a budding S. cerevisiae cell at 100× magnification; scale bar (lower left) corresponds to 5 µm, and the calibration bar (right) exhibits the phase‐delay per pixel in radians. (b) A histogram representing the signal‐to‐background ratio (SBR) for n = 340 single‐cell observations. (c–f) The image processing pipeline including image acquisition (c), optical phase thresholding via “max‐entropy” (d), application of the “fill holes” and “watershed” algorithms (e), followed by cell segmentation coupled to size filtering with a 1,000 pixel threshold to eliminate any undesired, smaller‐sized background objects (f). [Color figure can be viewed at wileyonlinelibrary.com]

Figure 4

(a, b) Phase images of S. cerevisiae cells at 60× and 40× magnifications respectively; the scale bar (lower right) denotes 10 µm in both images. (c, d) The binary images of (a) and (b) after processing; the arrows indicate the cells that were unsuccessfully segmented. (e, f) Data‐overlaid boxcharts (10%–25%–75%–90%) illustrating the single‐cell phase‐delay under various iodixanol concentrations at 60× (g) and 40× (f) magnifications; 300 observations on average were acquired per iodixanol and magnification conditions. (g) The segmentation success rate at 60× and 40× magnifications as a function of the iodixanol concentration. [Color figure can be viewed at wileyonlinelibrary.com]