Image-Based Single Cell Profiling: High-Throughput Processing of Mother Machine Experiments

Abstract

Background: Microfluidic lab-on-chip technology combined with live-cell imaging has enabled the observation of single cells in their spatio-temporal context. The mother machine (MM) cultivation system is particularly attractive for the long-term investigation of rod-shaped bacteria since it facilitates continuous cultivation and observation of individual cells over many generations in a highly parallelized manner. To date, the lack of fully automated image analysis software limits the practical applicability of the MM as a phenotypic screening tool.

Results: We present an image analysis pipeline for the automated processing of MM time lapse image stacks. The pipeline supports all analysis steps, i.e., image registration, orientation correction, channel/cell detection, cell tracking, and result visualization. Tailored algorithms account for the specialized MM layout to enable a robust automated analysis. Image data generated in a two-day growth study (≈ 90 GB) is analyzed in ≈ 30 min with negligible differences in growth rate between automated and manual evaluation quality. The proposed methods are implemented in the software molyso (MOther machine AnaLYsis SOftware) that provides a new profiling tool to analyze unbiasedly hitherto inaccessible large-scale MM image stacks.

Conclusion: Presented is the software molyso, a ready-to-use open source software (BSD-licensed) for the unsupervised analysis of MM time-lapse image stacks. molyso source code and user manual are available at https://github.com/modsim/molyso.

Fig 1. Schematic explanations (central column) and debug output of molyso (data from Case Study A, right column).

Fig 2

Sample image (region of interest) as acquired from the experimental procedure (B). Characteristic mean intensity profiles in horizontal h(x) and vertical v(y) direction are shown in A and C, respectively. Pairwise differences of vertical mean intensity profiles f of adjacent strips are used for the orientation correction (D). Indicated by the dotted line, main features of the profiles are apparently shifted depending on the rotation of the image. This shift is used to find the orientation correction angle θ.

Fig 3

For a crop of a single growth channel (A) its Otsu binarization (D) is determined. In B and E the mean intensity profiles of both images are shown. The gray boxes in B denote detected cell borders, the splines fitted to the profile maxima and minima used for prominence calculation are indicated by dashed lines. Prominence per cell (C) and Otsu mean per cell (F) are step functions. The blackness threshold for candidate filtering is denoted by a horizontal line; the threshold for prominence is not included as it is much lower.

Fig 4. Kymograph of a time-resolved channel image collage taken from Case Study A, as produced by molyso.

Larger kymographs and further explanation are found in the S6 Fig.

Fig 5

A: Timing of division events along with division times determined with molyso automatically and manually with the ground truth mode. Division events appear after an initial lag phase. Corresponding kymographs of the analyzed channels are shown in S6 Fig. Studying the kymographs revealed the origin of erroneously detected division events: the medium flow shifts cells quickly to the end of the channel leading to false-too-low division times. B: Growth rates determined for data sub-sets and GT plotted versus the imaging interval.

Fig 6

A: Growth rate μ (solid line) as well as fluorescence values (dashed line) calculated by moving average of all analyzed cells (window size 25). Cells stop growing after medium change to production medium (solid vertical line). After a slight delay the production phase starts as indicated by an increase in fluorescent biosensor read-out. A growth rate is undefined if a time point failed to produce a minimal number of division events to eliminate spurious values due to artifacts. B: Three time points selected from the production phase: near beginning (t₁ = 25.4 h), near peak (t₂ = 30.4 h) and early reduction (t₃ = 35.4 h) (top row: phase contrast and fluorescence; bottom row: fluorescence only). C: Distribution of cell lengths before and after division during growth phase (t < t₁) (solid lines denote fitted normal distributions). D: Distribution of fluorescence values during growth and production phase.

Fig 7. Cell length over time graphs.

Asterisks denote detected cell division events. A and B are derived from Case Study A, and show high quality tracks. Good tracks show the typical sawtooth curve of a cell repeatedly growing in length, then dividing. C and D are derived from Case Study B, and show a good track, as well as a bad track. The bad track is an example for a typical artifact, e.g. produced by continuously detected top or bottom channel structure fragments. As artifact tracks differ in structure from good tracks, filtering them is straigthforward.