Growth-Associated Droplet Shrinkage for Bacterial Quantification, Growth Monitoring, and Separation by Ultrahigh-Throughput Microfluidics

Recent advances in microfluidic-based spectroscopic approaches for pathogen detection

Rapid identification of pathogens with higher sensitivity and specificity plays a significant role in maintaining public health, environmental monitoring, controlling food quality, and clinical diagnostics. Different methods have been widely used in food testing laboratories, quality control departments in food companies, hospitals, and clinical settings to identify pathogens. Some limitations in current pathogens detection methods are time-consuming, expensive, and laborious sample preparation, making it unsuitable for rapid detection. Microfluidics has emerged as a promising technology for biosensing applications due to its ability to precisely manipulate small volumes of fluids. Microfluidics platforms combined with spectroscopic techniques are capable of developing miniaturized devices that can detect and quantify pathogenic samples. The review focuses on the advancements in microfluidic devices integrated with spectroscopic methods for detecting bacterial microbes over the past five years. The review is based on several spectroscopic techniques, including fluorescence detection, surface-enhanced Raman scattering, and dynamic light scattering methods coupled with microfluidic platforms. The key detection principles of different approaches were discussed and summarized. Finally, the future possible directions and challenges in microfluidic-based spectroscopy for isolating and detecting pathogens using the latest innovations were also discussed.

Deep-qGFP: A Generalist Deep Learning Assisted Pipeline for Accurate Quantification of Green Fluorescent Protein Labeled Biological Samples in Microreactors

Absolute quantification of biological samples provides precise numerical expression levels, enhancing accuracy, and performance for rare templates. Current methodologies, however, face challenges-flow cytometers are costly and complex, whereas fluorescence imaging, relying on software or manual counting, is time-consuming and error-prone. It is presented that Deep-qGFP, a deep learning-aided pipeline for the automated detection and classification of green fluorescent protein (GFP) labeled microreactors, enables real-time absolute quantification. This approach achieves an accuracy of 96.23% and accurately measures the sizes and occupancy status of microreactors using standard laboratory fluorescence microscopes, providing precise template concentrations. Deep-qGFP demonstrates remarkable speed, quantifying over 2000 microreactors across ten images in just 2.5 seconds, with a dynamic range of 56.52-1569.43 copies µL-1 . The method demonstrates impressive generalization capabilities, successfully applied to various GFP-labeling scenarios, including droplet-based, microwell-based, and agarose-based applications. Notably, Deep-qGFP is the first all-in-one image analysis algorithm successfully implemented in droplet digital polymerase chain reaction (PCR), microwell digital PCR, droplet single-cell sequencing, agarose digital PCR, and bacterial quantification, without requiring transfer learning, modifications, or retraining. This makes Deep-qGFP readily applicable in biomedical laboratories and holds potential for broader clinical applications.

Microbial single-cell mass spectrometry: status, challenges, and prospects

Single-cell analysis uncovers phenotypic differences between cells in a population and dissects their individual physiological states and differences on all omics levels from genome to phenome. Spectrometric observation allows label-free analysis of the metabolome and proteome of individual cells, but is still mainly limited to the analysis of mammalian single cells. Recent progress in mass spectrometry approaches now enables the analysis of microbial single cells - mainly by miniaturizing cell handling, incubation, and improving chip-coupling concepts for analyte ionization by interfacing microfluidic chips and mass spectrometers. This review aims at distilling the enabling principles behind microbial single-cell mass spectrometry and puts them into perspective for the future of the field.

Droplet Surface Immunoassay by Relocation (D-SIRe) for High-Throughput Analysis of Cytosolic Proteins at the Single-Cell Level

Enzyme-linked immunosorbent assay (ELISA) is a central analytic method in biological science for the detection of proteins. Introduction of droplet-based microfluidics allowed the development of miniaturized, less-consuming, and more sensitive ELISA assays by coencapsulating the biological sample and antibody-functionalized particles. We report herein an alternative in-droplet immunoassay format, which avoids the use of particles. It exploits the oil/aqueous-phase interface as a protein capture and detection surface. This is achieved using tailored perfluorinated surfactants bearing azide-functionalized PEG-based polar headgroups, which spontaneously react when meeting at the droplet formation site, with strained alkyne-functionalized antibodies solubilized in the water phase. The resulting antibody-functionalized inner surface can then be used to capture a target protein. This surface capture process leads to concomitant relocation at the surface of a labeled detection antibody and in turn to a drastic change in the shape of the fluorescence signal from a convex shape (not captured) to a characteristic concave shape (captured). This novel droplet surface immunoassay by fluorescence relocation (D-SIRe) proved to be fast and sensitive at 2.3 attomoles of analyte per droplet. It was further demonstrated to allow detection of cytosolic proteins at the single bacteria level.

FluorMango, an RNA-Based Fluorogenic Biosensor for the Direct and Specific Detection of Fluoride

Nucleic acids are not only essential actors of cell life but also extremely appealing molecular objects in the development of synthetic molecules for biotechnological application, such as biosensors to report on the presence and concentration of a target ligand by emission of a measurable signal. In this work, FluorMango, a fluorogenic ribonucleic acid (RNA)-based biosensor specific for fluoride is introduced. The molecule consists of two RNA aptamer modules, a fluoride-specific sensor derived from the crcB riboswitch which changes its structure upon interaction with the target ion, and the light-up RNA Mango-III that emits fluorescence when complexed with a fluorogen. The two modules are connected by an optimized communication module identified by ultrahigh-throughput screening, which results in extremely high fluorescence of FluorMango in the presence of fluoride, and background fluorescence in its absence. The value and efficiency of this biosensor by direct monitoring of defluorinase activity in living bacterial cells is illustrated, and the use of this new tool in future screening campaigns aiming at discovering new defluorinase activities is discussed.

Droplet Detection and Sorting System in Microfluidics: A Review

Since being invented, droplet microfluidic technologies have been proven to be perfect tools for high-throughput chemical and biological functional screening applications, and they have been heavily studied and improved through the past two decades. Each droplet can be used as one single bioreactor to compartmentalize a big material or biological population, so millions of droplets can be individually screened based on demand, while the sorting function could extract the droplets of interest to a separate pool from the main droplet library. In this paper, we reviewed droplet detection and active sorting methods that are currently still being widely used for high-through screening applications in microfluidic systems, including the latest updates regarding each technology. We analyze and summarize the merits and drawbacks of each presented technology and conclude, with our perspectives, on future direction of development.

The Effect of Bacteria on the Stability of Microfluidic-Generated Water-in-Oil Droplet

Microencapsulation in emulsion droplets has great potential for various applications such as food which require formation of highly stable emulsions. Bacterial-emulsion interactions affect the physiological status of bacteria while bacterial cell characteristics such as surface-active properties and metabolic activity can affect emulsion stability. In this study, the viability and growth of two different bacterial species, Gram-negative Escherichia coli and Gram-positive Lactobacillus paracasei, encapsulated in water-in-oil (W/O) droplets or as planktonic cells, were monitored and their effect on droplet stability was determined. Microencapsulation of bacteria in W/O droplets with growth media or water was achieved by using a flow-focusing microfluidic device to ensure the production of highly monodispersed droplets. Stability of W/O droplets was monitored during 5 days of storage. Fluorescence microscopy was used to observe bacterial growth behaviour. Encapsulated cells showed different growth to planktonic cells. Encapsulated E. coli grew faster initially followed by a decline in viability while encapsulated L. paracasei showed a slow gradual growth throughout storage. The presence of bacteria increased droplet stability and a higher number of dead cells was found to provide better stability due to high affinity towards the interface. The stability of the droplets is also species dependent, with E. coli providing better stability as compared to Lactobacillus paracasei.