Label-Free Quantification of Small-Molecule Binding to Membrane Proteins on Single Cells by Tracking Nanometer-Scale Cellular Membrane Deformation

Measuring molecular binding to membrane proteins is critical for understanding cellular functions, validating biomarkers, and screening drugs. Despite the importance, developing such a capability has been a difficult challenge, especially for small-molecule binding to membrane proteins in their native cellular environment. Here we show that the binding of both large and small molecules to membrane proteins can be quantified on single cells by trapping single cells with a microfluidic device and detecting binding-induced cellular membrane deformation on the nanometer scale with label-free optical imaging. We develop a thermodynamic model to describe the binding-induced membrane deformation, validate the model by examining the dependence of membrane deformation on cell stiffness, membrane protein expression level, and binding affinity, and study four major types of membrane proteins, including glycoproteins, ion channels, G-protein coupled receptors, and tyrosine kinase receptors. The single-cell detection capability reveals the importance of local membrane environment on molecular binding and variability in the binding kinetics of different cell lines and heterogeneity of different cells within the same cell line

Microfluidic Device for Efficient Airborne Bacteria Capture and Enrichment

Highly efficient capture and enrichment is always the key for rapid analysis of airborne pathogens. Herein we report a simple microfluidic device which is capable of fast and efficient airborne bacteria capture and enrichment. The device was validated with <i>Escherichia coli</i> (<i>E. coli</i>) and <i>Mycobacterium smegmatis</i>. The results showed that the efficiency can reach close to 100% in 9 min. Compared with the traditional sediment method, there is also great improvement with capture limit. In addition, various flow rate and channel lengths have been investigated to obtain the optimized condition. The high capture and enrichment might be due to the chaotic vortex flow created in the microfluidic channel by the staggered herringbone mixer (SHM) structure, which is also confirmed with flow dynamic mimicking. The device is fabricated from polydimethylsiloxane (PDMS), simple, cheap, and disposable, perfect for field application, especially in developing countries with very limited modern instruments

Phenotypic Antimicrobial Susceptibility Testing with Deep Learning Video Microscopy

Timely determination of antimicrobial susceptibility for a bacterial infection enables precision prescription, shortens treatment time, and helps minimize the spread of antibiotic resistant infections. Current antimicrobial susceptibility testing (AST) methods often take several days and thus impede these clinical and health benefits. Here, we present an AST method by imaging freely moving bacterial cells in urine in real time and analyzing the videos with a deep learning algorithm. The deep learning algorithm determines if an antibiotic inhibits a bacterial cell by learning multiple phenotypic features of the cell without the need for defining and quantifying each feature. We apply the method to urinary tract infection, a common infection that affects millions of people, to determine the minimum inhibitory concentration of pathogens from both bacteria spiked urine and clinical infected urine samples for different antibiotics within 30 min and validate the results with the gold standard broth macrodilution method. The deep learning video microscopy-based AST holds great potential to contribute to the solution of increasing drug-resistant infections

Phenotypic Antimicrobial Susceptibility Testing with Deep Learning Video Microscopy

Timely determination of antimicrobial susceptibility for a bacterial infection enables precision prescription, shortens treatment time, and helps minimize the spread of antibiotic resistant infections. Current antimicrobial susceptibility testing (AST) methods often take several days and thus impede these clinical and health benefits. Here, we present an AST method by imaging freely moving bacterial cells in urine in real time and analyzing the videos with a deep learning algorithm. The deep learning algorithm determines if an antibiotic inhibits a bacterial cell by learning multiple phenotypic features of the cell without the need for defining and quantifying each feature. We apply the method to urinary tract infection, a common infection that affects millions of people, to determine the minimum inhibitory concentration of pathogens from both bacteria spiked urine and clinical infected urine samples for different antibiotics within 30 min and validate the results with the gold standard broth macrodilution method. The deep learning video microscopy-based AST holds great potential to contribute to the solution of increasing drug-resistant infections