1 Image processing

1.1 Introduction

With the progress of technology, optical and fluorescence microscopy has become a fundamental tool for the characterization and understanding of the bacterial world. Microscopy has allowed humanity to extend its senses to observe the unknown world with exciting new perspectives that they might never otherwise have envisioned. Furthermore, microscopy offers a clear advantage over other techniques used to characterize bacteria since it can acquire data from living cells in spatial resolution.⁸

Including the discovery of fluorescent proteins (e.g., GFP and DsRed) and improvements in fluorescent reporters, it is possible to specifically label specific cellular components and track cellular functions.⁹ On the other hand, mechanical and intellectual development of microfluidic research techniques provides an excellent opportunity to overcome bio-medical and chemical techniques.¹⁰ Collectively, it is possible to study communities of bacteria at the level of individual cells.¹¹

Although all this technological development has provided a significant advance for the scientific community, after acquiring fluorescence images, the extraction of quantitative properties from these images is crucial, but unfortunately, a difficult step for analyzing experiments. Not so long ago, image analysis in biology relied on manual quantification. However, manual analysis suffers from two main problems: 1) accuracy and 2) scalability (that is, analyzing miles or more images). Fortunately, improvements in image accuracy and computational image analysis capabilities are revolutionizing the quantification of biological processes through.¹² Therefore, the manual correction required to analyze the experiments is minimal.

Here, we used a series of programs in \(\mu \mathrm{J}\) (https://github.com/ccg-esb-lab/uJ), which consists of an \(\mathrm{ImageJ}\) macro library (mainly) for quantifying unicellular bacterial dynamics in microfluidic devices.¹³ The specific steps used are described below and are summarized in Figure¹⁴(fig:).

1.2 Preprocessing

We exported the figures obtained by the NIS-Elements software (RRID:SCR_014329) from the microfluidics experiments in TIFF (Tagged Image File Format) format. Each figure was named as follows: experimentxyc1t001 where experiment indicates the name assigned to the experiment, xy the trap number, c the fluorescence channel, and t the passage of time.

Subsequently, we compile the images, rename them and save them as images in different folders. We maintained the classification by fluorescence channels and phase contrast, and within the channel folder, it is the sub-classification by trap number.

1.3 Segmentation

To determine which parts of the photographs correspond to cells, we carry out an image segmentation analysis. Segmentation consists of classification at the pixel level, which allows us to define the pixels that give identity to the limit of a cell, its interior, and the image’s background (everything that is not a cell). A new image is generated from the above, known as the segmentation mask, containing only the pixels that identify cells.

To build the segmentation mask, we used Deepcell.¹⁵ Deepcell is a network trained with a robust set of images that people previously classified as cells. However, the generation of the segmentation masks is not absolved of errors (see also Section 1.5). Sometimes we must correct them manually due to 1) mistakenly identifying two or more cells as one, 2) identifying two or more cells when it is only one cell, and 3) failing to identify a cell.

1.4 Tracking

From the image segmentation, we obtain ROI files (region of interest), which contain coordinates of the position of individual cells in each photograph.¹⁶ Tracking is the tracking of a region of interest in a consecutive series of images. In this case, the tracking generates the identification of the lineages, that is, the ancestry of each cell.

We read the ROI files in Python through the shapely package, which efficiently reconstructs polygons, thus calculating the length of the cells.¹⁷ Also, in Python using ROI files, we track cells with the k-nearest neighbors algorithm that uses various properties such as fluorescence intensity, length, and shape of each cell, to identify cell lineages.¹⁸

1.5 Manual corrections

For cell-tracking manual correction, we used Napari, an open-source python-based tool designed to explore, annotate, and analyze large multidimensional images.¹⁹ Our custom cell-viewer allows us to easy lineage data visualization, custom-plotting, and lineage-correction. Code for our cell-viewer is available on https://github.com/ccg-esb-lab/uJ/tree/master/single-channel.

We produced high-throughput data of thousands of cells with a single-cell resolution to the end of the lineages manual reconstruction. We obtained data about time-series of fluorescent intensity, morphological properties of individual cells (e.g., elongation, duplication rate), and time-resolved population-level statistics (e.g., probability of survival to the antibiotic shock).

1.6 Data extraction

We construct a file in columnar format through image processing that contains the information necessary to analyze each experiment (i.e., chromosomal and plasmids) in its different traps (i.e., XY identifier). See Table 1.1 for a full description of the output data. Subsequently, the table was analyzed in R for statistical computation and plotting (see Chapter 2).²⁰

Table 1.1: Resulting table from image processing.

Column	Description
experimentID	Unique identifier of the experiment.
trapID	Unique identifier of the trap used.
lineageID	Unique integer of the stem cell and its ancestry.
cellID	Unique identification number for each cell existing since the beginning of the experiment or generated later.
motherID	Represents the identification number of the stem cell that gave rise to the progeny.
trackID	Indicates the x-y coordinates where the cell being tracked starts.
roiID	Indicates the x-y position in which the cell is located, followed after each photograph.
frame	Number of the photograph in the sequence of photographs taken, indicating the elapsed time (10 minutes per frame).
length	Cell length.
division	Indicates cell division events, represented by the value 1 when they occur and 0 otherwise.
GFP	Represents the relative fluorescence intensity in each cell by green fluorescent protein (i.e., GFP).
DsRed	Represents the relative fluorescence intensity for cells generated by rhodamine’s internalization (i.e., DsRed); an indicator of cell death events.
tracking_score	Determine how good or bad the tracking of a cell was.
state	Indicates the state of the cell determined from its length and fluorescence thresholds. -1 for death, 0 for normal, and 1 for filamentation (see Section 2.2 for detailed information).