Robotic platform for microinjection into single cells in intact tissue

Abstract

Microinjection into single cells in intact tissue allows the delivery of membrane-impermeant molecules such as nucleic acids and proteins is a powerful technique to study and manipulate the behavior of these cells and, if applicable, their progeny. However, a high level of skill is required to perform such microinjection and is a low-throughput and low-yield process. The automation of microinjection into cells in intact tissue would empower an increasing number of researchers to perform these challenging experiments and could potentially open up new avenues of experimentation. We have developed the 'Autoinjector', a robot that utilizes images acquired from a microscope to guide a microinjection needle into tissue to deliver femtoliter volumes of liquids into single cells. The robotic operation enables microinjection of hundreds of cells within a single organotypic slice, resulting in an overall yield that is an order of magnitude greater than manual microinjection. We validated the performance of the Autoinjector by microinjecting both apical progenitors (APs) and newborn neurons in the embryonic mouse telencephalon, APs in the embryonic mouse hindbrain, and neurons in fetal human brain tissue. We demonstrate the capability of the Autoinjector to deliver exogenous mRNA into APs. Further, we used the Autoinjector to systematically study gap-junctional communication between neural progenitors in the embryonic mouse telencephalon and found that apical contact is a characteristic feature of the cells that are part of a gap junction-coupled cell cluster. The throughput and versatility of the Autoinjector will not only render microinjection a broadly accessible high-performance cell manipulation technique but will also provide a powerful new platform for bioengineering and biotechnology for performing single-cell analyses in intact tissue.

Approaches for unveiling the kinetic mechanisms of voltage gated ion channels in neurons

“…By showing the live image and the recording electrode avatars in a 3D scene, the user is informed at all times where the actual electrodes are and where the live image is coming from. Since this original publication, other systems have been proposed that also function to automate the targeting and patch clamp process (Wu et al, 2016;Suk et al, 2017;Shull et al, 2018;Kolb et al, 2019), as well as new image analysis algorithms for cell identification (Lee and Rozell, 2017) and pipette detection (Koos et al, 2017). The pipette is rendered in the 3D scene viewer, and its position can be updated.…”