Universal Pre-Mixing Dry-Film Stickers Capable of Retrofitting Existing Microfluidics

Integrating microfluidic mixers into lab-on-a-chip devices remains challenging yet important for numerous applications including dilutions, extractions, addition of reagents or drugs, and particle synthesis. High efficiency mixers utilize large or intricate geometries that are difficult to manufacture and co-implement with other lab-on-a-chip processes, leading to cumbersome two-chip solutions. To that end, we present a universal dry-film microfluidic mixing sticker that can retrofit pre-existing microfluidics and maintain high mixing performance over a range of flow rates and input component mixing ratio. To attach our pre-mixing sticker add-on module, one simply removes the backing material and presses the microfluidic sticker onto an existing microfluidic or substrate. Our key innovation centers around the multilayer use of laser-cut commercially available silicone-adhesive coated polymer sheets as microfluidic layers to create geometrically complex yet easy to assemble designs that can be adhered to a variety of surfaces, namely existing microfluidic devices. Our approach enabled us to assemble the well regarded yet difficult to manufacture “F-mixer” in minutes, and conceptually extend this design to create a novel space-saving spiral F-mixer. Computational Fluid Dynamic simulations and experimental results confirmed that both designs maintained high performance for 0.1<Re<10, and disparate input mixing ratios of 1:10. We then tested the integration of our system by using the pre-mixer to aid in the fluorescent tagging of proteins encapsulated in an existing microfluidic. When integrated with another microfluidic our pre-mixing sticker successfully combined primary and secondary antibodies to fluorescently tag micropatterned proteins with high spatial uniformity, unlike a traditional pre-mixing “T-mixer” sticker. Given the ease of this technology, we anticipate numerous applications for point of care devices, microphysiological-systems-on-a-chip, and microfluidic based biomedical research

Getting a good view: in vitro imaging of platelets under flow

As the anucleate cells responsible for hemostasis and thrombosis, platelets are exposed to a myriad of biophysical and biochemical stimuli within vasculature and heterogeneous blood clots. Highly controlled, reductionist in vitro imaging studies have been instrumental in providing a detailed and quantitative understanding of platelet biology and behavior, and have helped elucidate some surprising functions of platelets. In this review, we highlight the tools and approaches that enable visualization of platelets in conjunction with precise control over the local biofluidic and biochemical microenvironment. We also discuss next generation tools that add further control over microenvironment cell stiffness or enable visualization of the interactions between platelets and endothelial cells. Throughout the review, we include pragmatic knowledge on imaging systems, experimental conditions, and approaches that have proved to be useful to our in vitro imaging studies of platelets under flow

iCLOTS: open-source, artificial intelligence-enabled software for analyses of blood cells in microfluidic and microscopy-based assays

Abstract While microscopy-based cellular assays, including microfluidics, have significantly advanced over the last several decades, there has not been concurrent development of widely-accessible techniques to analyze time-dependent microscopy data incorporating phenomena such as fluid flow and dynamic cell adhesion. As such, experimentalists typically rely on error-prone and time-consuming manual analysis, resulting in lost resolution and missed opportunities for innovative metrics. We present a user-adaptable toolkit packaged into the open-source, standalone Interactive Cellular assay Labeled Observation and Tracking Software (iCLOTS). We benchmark cell adhesion, single-cell tracking, velocity profile, and multiscale microfluidic-centric applications with blood samples, the prototypical biofluid specimen. Moreover, machine learning algorithms characterize previously imperceptible data groupings from numerical outputs. Free to download/use, iCLOTS addresses a need for a field stymied by a lack of analytical tools for innovative, physiologically-relevant assays of any design, democratizing use of well-validated algorithms for all end-user biomedical researchers who would benefit from advanced computational methods