Fabrice Gielen

Evaluating the impact of bacteriophages on bacterial communities is required to assess the future utility of phage therapy. Methods able to study bacterial polycultures in the presence of phages are useful to mimic evolutionary pressures found in natural environments and recapitulate complex ecological contexts. Bacteriophages can drive rapid genetic and phenotypic changes in host cells. However, the presence of other bacteria can also impact bacterial densities and community structure and classical methods remain lengthy and resource intensive. Here we introduce a microdroplet-based encapsulation method in which bacterial co-cultures are imaged using Z-stack brightfield microscopy. The method relies on automated droplet imaging using an AI-based autofocus function, coupled with morphology-based deep learning models for accurate identification of two morphologically distinct bacterial species. We monitor the interactions between bacterial mono- or co-cultures of P. aeruginosa and S. aureus in the presence of a P. aeruginosa phage growing in 11 picolitre droplets for up to 20 h. We demonstrate quantification of growth rates, bacterial densities and lysis dynamics of the two species without the need for plating. We show that a potent lytic phage of P. aeruginosa can keep its density low long-term when in the presence of S. aureus .

Evaluating the impact of bacteriophages on bacterial communities is required to assess the future utility of phage therapy. Methods able to study bacterial polycultures in the presence of phages are useful to mimic evolutionary pressures found in natural environments and recapitulate complex ecological contexts. Bacteriophages can drive rapid genetic and phenotypic changes in host cells. However, the presence of other bacteria can also impact bacterial densities and community structure, and classical methods remain lengthy and resource intensive. Here, we introduce a microdroplet-based encapsulation method in which bacterial co-cultures are imaged using Z-stack brightfield microscopy. The method relies on automated droplet imaging using a novel AI-based autofocus function, coupled with morphology-based deep learning models for accurate identification of two morphologically distinct bacterial species. We show that we can monitor the relative growth dynamics of P. aeruginosa and S. aureus growing in 11 picolitre droplets for up to 24 hours. We demonstrate quantification of growth rates, bacterial densities and lysis dynamics of the two species without the need for plating. We show that a potent lytic phage of P. aeruginosa can either fully lyse the initial P. aeruginosa population or keep its density low long-term when in the presence of S. aureus .

The study of dose-response relationships underpins analytical biosciences. Droplet microfluidics platforms can automate the generation of microreactors encapsulating varying concentrations of an assay component, providing datasets across a large chemical space in a single experiment. A classical method consists in varying the flow rate of multiple solutions co-flowing into a single microchannel (producing different volume fractions) before encapsulating the contents into water-in-oil droplets. This process can be automated through controlling the pumping elements but lacks the ability to adapt to unpredictable experimental scenarios, often requiring constant human supervision. In this paper, we introduce an image-based, closed-loop control system for assessing and adjusting volume fractions, thereby generating unsupervised, uniform concentration gradients. We trained a shallow convolutional neural network to assess the position of the laminar flow interface between two co-flowing fluids and used this model to adjust flow rates in real-time. We apply the method to generate alginate microbeads in which HEK293FT cells could grow in three dimensions. The stiffnesses ranged from 50 Pa to close to 1 kPa in Young modulus and were encoded with a fluorescent marker. We trained deep learning models based on the YOLOv4 object detector to efficiently detect both microbeads and multicellular spheroids from high-content screening images. This allowed us to map relationships between hydrogel stiffness and multicellular spheroid growth.

Bacteria identification and counting at the small population scale is important to many applications in the food safety industry, the diagnostics of infectious diseases and the study and discovery of novel antimicrobial compounds. There is still a lack of easy to implement, fast and accurate methods to count populations of motile cells at the single-cell level. Here, we report a label-free method to count and localize bacterial cells freely swimming in microfluidic anchored picolitre droplets. We used the object detection oriented YOLOv4 deep learning framework for cell detection from bright-field images obtained with an automated Z-stack setup. The neural network was trained to recognize Escherichia coli cell morphology with an average precision of approximately 84%. This allowed us to accurately identify individual cell division events, enabling the study of stochastic bacterial growth starting from initial populations as low as one cell. This work also demonstrates the ability to study single cell lysis in the presence of T7 lytic bacterial viruses (phages). The high precision in cell numbers facilitated the visualization of bacteria-phage interactions over timescale of hours, paving the way towards deciphering phage life cycles in confined environments.

An alarming rise in antimicrobial resistance worldwide has spurred efforts into the search for alternatives to antibiotic treatments. The use of bacteriophages, bacterial viruses harmless to humans, represents a promising approach with potential to treat bacterial infections (phage therapy). Recent advances in microscopy-based single-cell techniques have allowed researchers to develop new quantitative methodologies for assessing the interactions between bacteria and phages, especially the ability of phages to eradicate bacterial pathogen populations and to modulate growth of both commensal and pathogen populations. Here we combine droplet microfluidics with fluorescence time-lapse microscopy to characterize the growth and lysis dynamics of the bacterium Escherichia coli confined in droplets when challenged with phage. We investigated phages that promote lysis of infected E. coli cells, specifically, a phage species with DNA genome, T7 (Escherichia virus T7) and two phage species with RNA genomes, MS2 (Emesvirus zinderi) and Qβ (Qubevirus durum). Our microfluidic trapping device generated and immobilized picoliter-sized droplets, enabling stable imaging of bacterial growth and lysis in a temperature-controlled setup. Temporal information on bacterial population size was recorded for up to 25 h, allowing us to determine growth rates of bacterial populations and helping us uncover the extent and speed of phage infection. In the long-term, the development of novel microfluidic single-cell and population-level approaches will expedite research towards fundamental understanding of the genetic and molecular basis of rapid phage-induced lysis and eco-evolutionary aspects of bacteria-phage dynamics, and ultimately help identify key factors influencing the success of phage therapy.

Cancer-associated fibroblasts (CAFs) are a crucial component in the tumor microenvironment influencing cancer progression. Besides shaping the extracellular matrix, these fibroblasts provide signaling factors to facilitate tumor survival and alter tumor behavior. In gastric cancer, one crucial signaling pathway influencing invasion and metastasis is the Wnt/Planar Cell Polarity (PCP) signaling. The crucial PCP ligand in this context is WNT5A, which is produced by the CAFs, and gastric cancer cells react upon this signal by enhanced polarized migration. Why gastric cancer cells respond to this signal is still unclear, as their expression level for the central WNT5A receptor, ROR2, is very low. Here, we show that CAFs display long and branched filopodia that form an extensive, complex network engulfing gastric cancer cells, such as the gastric cancer cell line AGS. CAFs have a significantly higher expression level of ROR2 than normal gastric fibroblasts and AGS cells. By high-resolution imaging, we observe a direct transfer of fluorescently tagged ROR2 from CAF to AGS cells by signaling filopodia, known as cytonemes. Surprisingly, we find that the transferred ROR2 complexes can activate Wnt/JNK signaling in AGS cells. Consistently, blockage of ROR2 function in the CAFs leads to reduced paracrine Wnt/JNK signaling, cell polarization, and migration of the receiving AGS cells. Complementary, enhanced migration via paracrine ROR2 transfer was observed in a zebrafish in vivo model. These findings demonstrate a fresh role for cytoneme-mediated signaling in the tumor microenvironment. Cytonemes convey Wnt receptors from CAFs to gastric cancer cells, allowing them to respond to Wnt/PCP signals.

An alarming rise in antimicrobial resistance worldwide has spurred efforts into the search for alternatives to antibiotic treatments. The use of bacteriophages, bacterial viruses harmless to humans, represents a promising approach with potential to treat bacterial infections (phage therapy). Recent advances in microscopy-based single-cell techniques have allowed researchers to develop new quantitative approaches for assessing the interactions between bacteria and phages, especially the ability of phages to eradicate bacterial pathogen populations. Here we combine droplet microfluidics with fluorescence time-lapse microscopy to characterize the growth and lysis dynamics of the bacterium Escherichia coli confined in droplets when challenged with phage. We investigated phages that promote lysis of infected E. coli cells, specifically, a phage species with DNA genome, T7 ( Escherichia virus T7 ) and two phage species with RNA genomes, MS2 ( Emesvirus zinderi ) and Qβ ( Qubevirus durum ). Our microfluidic trapping device generated and immobilized picoliter-sized droplets, enabling stable imaging of bacterial growth and lysis in a temperature-controlled setup. Temporal information on bacterial population size was recorded for up to 25 hours, allowing us to determine growth rates of bacterial populations helping us uncover the extent and speed of phage infection. In the long-term, the development of novel microfluidic and single-cell techniques will expedite research towards understanding the genetic and molecular basis of rapid phage-induced lysis, preempting bacterial resistance to phages and ultimately identifying key factors influencing the success of phage therapy.

Bacteria identification and counting at the small population scale is important to many applications in the food safety industry, the diagnostics of infectious diseases and the study and discovery of novel antimicrobial compounds. There is still a lack of easy to implement, fast and accurate methods to count populations of motile cells at the single-cell level. Here, we report a label-free method to count and localize bacterial cells freely swimming in microfluidic anchored picolitre droplets. We used the object detection oriented YOLOv4 deep learning framework for cell detection from bright-field images obtained with an automated Z-stack setup. The neural network was trained to recognize Escherichia coli cell morphology with an average precision of approximately 84%. This allowed us to accurately identify individual cell division events, enabling the study of stochastic bacterial growth starting from initial populations as low as one cell. This work also demonstrates the ability to study single cell lysis in the presence of T7 lytic bacterial viruses (phages). The high precision in cell numbers facilitated the visualization of bacteria-phage interactions over timescale of hours, paving the way towards deciphering phage life cycles in confined environments.

Microfluidic water-in-oil emulsion droplets are becoming a mainstay of experimental biology, where they replace the classical test tube. In most applications, such as ultrahigh-throughput directed evolution, the droplet content is identical for all compartmentalized assay reactions. When emulsion droplets are used for kinetics or other functional assays, though, concentration dependencies of initial rates that define Michaelis-Menten parameters are required. Droplet-on-demand systems satisfy this need, but extracting large amounts of data is challenging. Here, we introduce a multiplexed droplet absorbance detector, which─coupled to semi-automated droplet generation─forms a tubing-based droplet-on-demand system able to generate and extract quantitative datasets from defined concentration gradients across multiple series of droplets for multiple time points. The emergence of a product is detected by reading the absorbance of the droplet sets at multiple, adjustable time points by reversing the flow direction after each detection, so that the droplets pass a line scan camera multiple times. Detection multiplexing allows absorbance values at 12 distinct positions to be measured, and enzyme kinetics are recorded for label-free concentration gradients that are composed of about 60 droplets each, covering as many concentrations. With a throughput of around 8640 data points per hour, a 10-fold improvement compared to the previously reported single point detection method is achieved. In a single experiment, 12 full datasets of high-resolution and high-accuracy Michaelis-Menten kinetics were determined to demonstrate the potential for enzyme characterization for glycosidase substrates covering a range in enzymatic hydrolysis of 7 orders of magnitude in k_cat/K_M. The straightforward setup, high throughput, excellent data quality, and wide dynamic range that allows coverage of diverse activities suggest that this system may serve as a miniaturized spectrophotometer for detailed analysis of clones emerging from large-scale combinatorial experiments.

The movement trajectories of organisms serve as dynamic read-outs of their behaviour and physiology. For microorganisms this can be difficult to resolve due to their small size and fast movement. Here, we devise a novel droplet microfluidics assay to encapsulate single micron-sized algae inside closed arenas, enabling ultralong high-speed tracking of the same cell. Comparing two model species - Chlamydomonas reinhardtii (freshwater, 2 cilia), and Pyramimonas octopus (marine, 8 cilia), we detail their highly-stereotyped yet contrasting swimming behaviours and environmental interactions. By measuring the rates and probabilities with which cells transition between a trio of motility states (smooth-forward swimming, quiescence, tumbling or excitable backward swimming), we reconstruct the control network that underlies this gait switching dynamics. A simplified model of cell-roaming in circular confinement reproduces the observed long-term behaviours and spatial fluxes, including novel boundary circulation behaviour. Finally, we establish an assay in which pairs of droplets are fused on demand, one containing a trapped cell with another containing a chemical that perturbs cellular excitability, to reveal how aneural microorganisms adapt their locomotor patterns in real-time.