3D Imaging for Cleared Tissues and Thicker Samples on Confocal and Light-Sheet Microscopes.

Advances in fluorescence microscopy, specifically the development of confocal and light-sheet microscopes, have enabled researchers to harness tissue clearing techniques to image-stained intact tissue samples in 3D. Using these techniques, tissue structure and biomarker distributions in 3D structures are preserved, thus allowing researchers to gain a wealth of spatial information about their tissue of interest. However, the execution of imaging these larger tissue samples can be challenging. Broadly speaking, tissue clearing techniques unify the refractive indices inside tissue samples, thus enabling deep tissue imaging on a confocal or light-sheet microscope. Here, we provide an overview to tissue clearing and 3D immunohistochemistry staining in general and discuss some difficulties that researchers may encounter when using these techniques. We then focus on imaging CLARITY-processed samples with both confocal and light-sheet microscopes and optimizing the acquisition parameters, before noting potential issues that may come up in imaging.

Modeling negative cooperativity in streptavidin adsorption onto biotinylated microtubules.

The nanoscale architecture of binding sites can result in complex binding kinetics. Here, the adsorption of streptavidin and neutravidin to biotinylated microtubules is found to exhibit negative cooperativity due to electrostatic interactions and steric hindrance. This behavior is modeled by a newly developed kinetic analogue of the Fowler-Guggenheim adsorption model. The complex adsorption kinetics of streptavidin to biotinylated structures needs to be considered when these intermolecular bonds are employed in self-assembly and nanobiotechnology.

DIY liquid handling robots for integrated STEM education and life science research.

Automation has played a key role in improving the safety, accuracy, and efficiency of manufacturing and industrial processes and has the potential to greatly increase throughput in the life sciences. However, the lack of accessible entry-point automation hardware in life science research and STEM education hinders its widespread adoption and development for life science applications. Here we investigate the design of a low-cost (~$150) open-source DIY Arduino-controlled liquid handling robot (LHR) featuring plastic laser-cut parts. The robot moves in three axes with 0.5 mm accuracy and reliably dispenses liquid down to 20 µL. The open source, modular design allows for flexibility and easy modification. A block-based programming interface (Snap4Arduino) further extends the accessibility of this robot, encouraging adaptation and use by educators, hobbyists and beginner programmers. This robot was co-designed with teachers, and we detail the teachers' feedback in the context of a qualitative study. We conclude that affordable and accessible LHRs similar to this one could provide a useful educational tool to be deployed in classrooms, and LHR-based curricula may encourage interest in STEM and effectively introduce automation technology to life science enthusiasts.

Author Correction: First-hand, immersive full-body experiences with living cells through interactive museum exhibits.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Device and programming abstractions for spatiotemporal control of active micro-particle swarms.

We present a hardware setup and a set of executable commands for spatiotemporal programming and interactive control of a swarm of self-propelled microscopic agents inside a microfluidic chip. In particular, local and global spatiotemporal light stimuli are used to direct the motion of ensembles of Euglena gracilis, a unicellular phototactic organism. We develop three levels of programming abstractions (stimulus space, swarm space, and system space) to create a scripting language for directing swarms. We then implement a multi-level proof-of-concept biotic game using these commands to demonstrate their utility. These device and programming concepts will enhance our capabilities for manipulating natural and synthetic swarms, with future applications for on-chip processing, diagnostics, education, and research on collective behaviors.