Ampk is an energy gatekeeper that responds to decreases in ATP by inhibiting energy-consuming anabolic processes and promoting energy-generating catabolic processes. Recently, we showed that Lkb1, an understudied kinase in B lymphocytes and a major upstream kinase for Ampk, had critical and unexpected roles in activating naïve B cells and in germinal center formation. Therefore, we examined whether Lkb1 activities during B cell activation depend on Ampk and report surprising Ampk activation with in vitro B cell stimulation in the absence of energy stress, coupled to rapid biomass accumulation. Despite Ampk activation and a controlling role for Lkb1 in B cell activation, Ampk knockout did not significantly affect B cell activation, differentiation, nutrient dynamics, gene expression, or humoral immune responses. Instead, Ampk loss specifically repressed the transcriptional expression of IgD and its regulator, Zfp318 . Results also reveal that early activation of Ampk by phenformin treatment impairs germinal center formation but does not significantly alter antibody responses. Combined, the data show an unexpectedly specific role for Ampk in the regulation of IgD expression during B cell activation.

Microfluidic devices are widely used for biomedical applications based on microscopy or other optical detection methods. However, the materials commonly used for microfabrication typically have a high refractive index relative to water, which can create artifacts at device edges and limit applicability to applications requiring high precision imaging or morphological feature detection. Here we present a soft lithography method to fabricate microfluidic devices out of MY133-V2000, a UV-curable, fluorinated polymer with low refractive index that is close to that of water (n = 1.33). The primary challenge in the use of this material (and fluorinated materials in general) is the low adhesion of the fluorinated material; we present several alternative fabrication methods we have tested to improve inter-layer adhesion. The close match between the refractive index of this material and aqueous solutions commonly used in biomedical applications enables fluorescence imaging at microchannel or other microfabricated edges without distortion. The close match in refractive index also enables quantitative phase microscopy (QPM) imaging across the full width of microchannels without error-inducing artifacts for measurement of cell biomass. Overall, our results demonstrate the utility of low-refractive index microfluidics for biological applications requiring high precision optical imaging.

The existing approaches to onychomycosis demonstrate limited success since the commonly used oral administration and topical cream only achieve temporary effective drug concentration at the fungal infection sites. An ideal therapeutic approach for onychomycosis should have (i) the ability to introduce antifungal drugs directly to the infected sites; (ii) finite intradermal sustainable release to maintain effective drug levels over prolonged time; (iii) a reporter system for monitoring maintenance of drug level; and (iv) minimum level of inflammatory responses at or around the fungal infection sites. To meet these expectations, we introduced ketoconazole-encapsulated cross-linked fluorescent supramolecular nanoparticles (KTZ⊂c-FSMNPs) as an intradermal controlled release solution for treating onychomycosis. A two-step synthetic approach was adopted to prepare a variety of KTZ⊂c-FSMNPs. Initial characterization revealed that 4800 nm KTZ⊂c-FSMNPs exhibited high KTZ encapsulation efficiency/capacity, optimal fluorescent property, and sustained KTZ release profile. Subsequently, 4800 nm KTZ⊂c-FSMNPs were chosen for in vivo studies using a mouse model, wherein the KTZ⊂c-FSMNPs were deposited intradermally via tattoo. The results obtained from (i) in vivo fluorescence imaging, (ii) high-performance liquid chromatography quantification of residual KTZ, (iii) matrix-assisted laser desorption/ionization mass spectrometry imaging mapping of KTZ distribution in intradermal regions around the tattoo site, and (iv) histology for assessment of local inflammatory responses and biocompatibility, suggest that 4800 nm KTZ⊂c-FSMNPs can serve as an effective treatment for onychomycosis.

Abstract. Standard algorithms for phase unwrapping often fail for interferometric quantitative phase imaging (QPI) of biological samples due to the variable morphology of these samples and the requirement to image at low light intensities to avoid phototoxicity. We describe a new algorithm combining random walk-based image segmentation with linear discriminant analysis (LDA)-based feature detection, using assumptions about the morphology of biological samples to account for phase ambiguities when standard methods have failed. We present three versions of our method: first, a method for LDA image segmentation based on a manually compiled training dataset; second, a method using a random walker (RW) algorithm informed by the assumed properties of a biological phase image; and third, an algorithm which combines LDA-based edge detection with an efficient RW algorithm. We show that the combination of LDA plus the RW algorithm gives the best overall performance with little speed penalty compared to LDA alone, and that this algorithm can be further optimized using a genetic algorithm to yield superior performance for phase unwrapping of QPI data from biological samples. © The Authors.Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Quantitative phase microscopy (QPM) enables studies of living biological systems without exogenous labels. To increase the utility of QPM, machine-learning methods have been adapted to extract additional information from the quantitative phase data. Previous QPM approaches focused on fluid flow systems or time-lapse images that provide high throughput data for cells at single time points, or of time-lapse images that require delayed post-experiment analyses, respectively. To date, QPM studies have not imaged specific cells over time with rapid, concurrent analyses during image acquisition. In order to study biological phenomena or cellular interactions over time, efficient time-dependent methods that automatically and rapidly identify events of interest are desirable. Here, we present an approach that combines QPM and machine learning to identify tumor-reactive T cell killing of adherent cancer cells rapidly, which could be used for identifying and isolating novel T cells and/or their T cell receptors for studies in cancer immunotherapy. We demonstrate the utility of this method by machine learning model training and validation studies using one melanoma-cognate T cell receptor model system, followed by high classification accuracy in identifying T cell killing in an additional, independent melanoma-cognate T cell receptor model system. This general approach could be useful for studying additional biological systems under label-free conditions over extended periods of examination.