This year, members of the Bailey Lab presented their work at the Karle Symposium and won awards for their hard work. Emily Mordan received second place among graduate student presenters for her talk, “Linear Gradient Compatible Refractive Index Based Detector for Polymer Composition Characterization.” Colleen, Cole, and Shannon all earned awards for their poster presentations, too!
Way to go, everyone! You can stay posted on future publications by following us here!
Members of the Bailey group just wrapped up presenting at UM’s Microfluidics in Biomedical Sciences Training Program Symposium. Among all of the thought-provoking presentations, Colleen Riordan was able to take home a best poster award for her presentation on a "Microfluidic Platform for Optimizing the Formation of Nanodisc Libraries from Whole Cell Lysate“. Steve Doonan walked away with an award for his talk titled “Development of the Droplet CAR-Wash Platform for Picoliter-Scale Epigenetic Analysis“.
The Bailey lab greatly appreciates the opportunity to share their work and learn from campus colleagues. For those interested in learning about microfluidics and seeing what MBSTP has to offer, you can follow this link http://umich.edu/~ufluids/ to find out more. See you next year!
Dr. Steve recently defended his thesis titled “Microfluidic Technologies for Bioanalytical Chemistry: Advancing Epigenetic Profiling via Chromatin Immunoprecipitation in Droplets."
Congrats, Steve, and good luck on the road ahead!
Dr. Mari recently defended her thesis titled “Development of the Silicon Photonic Microring Resonator Platform with Applications for the Detection of Nucleic Acids and Other Biopolymers."
Next up, she will be starting a post-doc at the Catalan Institute of Nanoscience and Nanotechnology. Congrats Mari!
ABSTRACT
To address current limitations in adapting solid phase sample capture and washing techniques to continuously flowing droplet microfluidics, we have developed the “Coalesce-Attract-Resegment Wash” (CAR-Wash) approach. This module provides efficient, high-throughput magnetic washing by electrocoalescing magnetic bead-laden input droplets with a washing buffer flow and magnetophoretically transporting beads through the buffer into a secondary droplet formation streamline. In this work, we first characterized the technology in terms of throughput, sample retention, and flow-based exclusion of waste volume, demonstrating >500 Hz droplet processing with >98% bead retention and >100-fold dilution in final droplets. Next, we showed that the technique can be adapted to alternative commercially available magnetic beads with lower magnetite content per particle. Then, we demonstrated the CAR-Wash module’s effectiveness in washing away a small molecule competitive inhibitor to restore the activity of magnetic bead-immobilized β-galactosidase. Finally, we applied the system to immunomagnetically enrich a Green Fluorescent Protein-Histone H2B fusion protein from cell lysate while washing away mCherry and other lysate components. We believe this approach will bridge the gap between powerful biochemical and bioanalytical techniques and current droplet microfluidic capabilities, and we envision future application in droplet-based immunoassays, solid phase extraction, and other complex, multi-step operations.
Here’s a snippet from the article:
Nearly a quarter of the world’s population has latent tuberculosis infection (LTBI). These people don’t have symptoms of the infection because the TB bacteria living inside them are dormant. In about 10% of those people, the bacteria will wake up, but diagnosing latent TB and predicting who is at risk for reactivating is hard.
A team led by Ryan C. Bailey of the University of Michigan and Patricio Escalante of Mayo Clinic reports an assay that reveals diagnostic signatures that doctors could use to both diagnose latent TB and evaluate a patient’s reactivation risk.
ABSTRACT
Latent tuberculosis infection (LTBI) is estimated in nearly one quarter of the world’s population, and of those immunocompetent and infected ~10% will proceed to active tuberculosis (TB). Current diagnostics cannot definitively identify LTBI and provide no insight into reactivation risk, thereby defining an unmet diagnostic challenge of incredible global significance. We introduce a new machine-learning-driven approach to LTBI diagnostics that leverages a high throughput, multiplexed cytokine detection technology and powerful bioinformatics to reveal multi-marker signatures for LTBI diagnosis and risk stratification. This approach is enabled through an individualized normalization procedure that allows disease-relevant biomarker signatures to be revealed despite heterogeneity in basal immune response. Specifically, cytokines secreted from antigen-challenged peripheral blood mononuclear cells were detected using silicon photonic sensor arrays and multidimensional data correlation of individually-normalized immune responses revealed signatures important for LTBI status. These results demonstrate a powerful combination of multiplexed biomarker detection technologies, precision immune normalization, and feature selection algorithms that revealed positively correlated multi-biomarker signatures for LTBI status and reactivation risk stratification from a relatively simple blood-based assay.
ABSTRACT
Molecular weight distribution (MWD) is often the most informative analytical parameter in polymer analysis with gel permeation chromatography (GPC) being the most common approach for determining the MWD for polymer samples. Many industrially relevant polymers lack chromogenic or fluorogenic signatures, precluding use of spectroscopy-based detection. Universal detectors, such as evaporative light scattering and charged aerosol detectors, are nonlinear, limiting quantitative polymer analysis. Differential refractive index (dRI) detectors show linear mass concentration sensitivity but are limited for some analyses given that they are incompatible with gradient-based separations, have limited dynamic range, and require extended thermal equilibration times. In this study, we investigated the utility of silicon photonic microring resonator arrays as a quantitative mass concentration detector for industrial polymer analysis. Microring resonators have optical properties that are sensitive to changes in refractive index, offer an extended dynamic range, broad solvent compatibility, and linear mass concentration detection for a range of molecular weights. Linear mass concentration detection for microrings was demonstrated through a series of isocratic GPC separations using narrow MWD polystyrene (PS) standards. This detection technology was then utilized in conjunction with GPC to analyze a series of broad MWD PS standards, with results in good agreement with dRI and UV. These results demonstrate the potential of the microring resonator platform as a detector for industrial polymer analysis.
