Time-lapse videos and snapshot images of mycobacterial cells alone or during infection under drug treatment

Original approaches are needed to cope with drug-recalcitrant infections that threaten global health, including tuberculosis. Here, we implemented a drug-discovery strategy based on a customized microfluidic dynamic single-cell screening for pheno-tuning compounds (µDeSCRiPTor). Our strategy assesses dynamic phenotypic changes occurring at the level of single mycobacterial cells upon exposure to pheno-tuning compounds, via time-lapse microscopy. The goal was to identify compounds that synergize with existing anti-tubercular drugs, ultimately making the bacterial population more homogeneously susceptible to therapy. Among the four identified hits, we focused on M06, which shows promise for improving tuberculosis therapy., Dynamic microfluidic screening videos (Dataset 1). A Mycobacterium smegmatis fluorescent reporter of DNA damage response (RecA-GFP), also constitutively expressing a red-fluorescent marker (mCherrycyt) was analyzed by multi-phasic time-resolved microscopy using a customized multi-condition microfluidic platform. Cells were dispensed into the 32 microfluidic growth chambers and imaged at regular time intervals using a motorized inverted epifluorescence microscope, equipped with a 100X oil objective. Fluorescence imaging conditions were: GFP/FITC [Excitation (Ex) at 475/28 nm, Emission (Em) at 525/48 nm] 50% transmittance (T), 150 msec; mCherry (Ex 575/25, Em 625/45), 50% T, 150 msec. Coordinates of the location of individual bacteria or small groups of bacteria, referred to as points, were recorded and monitored throughout the three phases of the experiment: exponential growth in fresh medium; exposure to subinhibitory concentrations of 72 test compounds or control conditions; and washin..., Open-source image analysis software, such as ImageJ, can be used to open both .tif and .dv files., # **Time-lapse videos and snapshot images of mycobacterial cells alone or during infection under drug treatment**

This dataset includes four distinct datasets as described below.

## Dataset 1: Dynamic microfluidic screening videos

The two directories contain 15 folders, one for each experiment defined by a unique alphanumeric code. Each folder contains 3 associated .tif files per each experimental point: one image stack for the green channel (GFP); one image stack for the red channel (mCherry); and one image stack for the binary mask (Mask). Each file is named as follows: **Experiment ID_Point_nnn_Condition_File type**. Only those points that were used for analysis are included in the directory. **Dataset 1** is associated with **Figure 1** of the paper.

Experiment N, Experiment ID, Image acquisition frequency (min), Exposure start-frame, Exposure end-frame
1 S1R1 30 14 24
2 S1R2 30 13 24
3 S2R1 30 10 21
4 S2R2 20 10 27
5 S3R1 30 14 24
6 S3R2 30 13 23
7 S4R1 20 6 22
8 S4R2 30 13 24
...