Pharmacological analysis of dopamine modulation in the Drosophila melanogaster larval heart

Abstract Dopamine (DA) and other neurotransmitters affect nonneuronal tissues in insects by circulating in the hemolymph. In several organisms, DA has been shown to modulate distinct aspects of cardiac function but the signal transduction pathways that mediate dopaminergic effects on the heart are not well characterized. Here, we used a semiintact Drosophila melanogaster larva preparation and drugs targeting DA receptors and canonical second messenger pathways to identify signaling cascades that mediate the effect of DA on a myogenic heart. DA has a positive chronotropic effect that is mimicked by SKF38393 (type-1 DA receptor agonist) and quinpirole (type-2 DA receptor agonist). SCH23390 and spiperone (type-1 and type-2 DA receptor antagonists) are moderately effective at inhibiting DA's effect. An adenylate cyclase inhibitor (SQ,22536) is also effective at blocking the stimulatory effect of DA but the drug has its own dose-dependent effect. Activation of protein kinase C with a diacylglycerol analog has a stimulatory effect on heart rate (HR). These results suggest that (1) both DA receptor subtypes are expressed in third instar larva cardiac myocytes to increase HR in response to rising levels of DA in the hemolymph, and (2) canonical second messenger pathways modulate HR in D. melanogaster larvae. Having these disparate signaling cascades converge toward a common modulatory function appears redundant, but in the context of multiple cardioactive chemicals this redundancy is likely to increase the fidelity of signal transduction

Microscope-Cockpit: Python-based bespoke microscopy for bio-medical science [version 2; peer review: 2 approved, 2 approved with reservations]

We have developed “Microscope-Cockpit” (Cockpit), a highly adaptable open source user-friendly Python-based Graphical User Interface (GUI) environment for precision control of both simple and elaborate bespoke microscope systems. The user environment allows next-generation near instantaneous navigation of the entire slide landscape for efficient selection of specimens of interest and automated acquisition without the use of eyepieces. Cockpit uses “Python-Microscope” (Microscope) for high-performance coordinated control of a wide range of hardware devices using open source software. Microscope also controls complex hardware devices such as deformable mirrors for aberration correction and spatial light modulators for structured illumination via abstracted device models. We demonstrate the advantages of the Cockpit platform using several bespoke microscopes, including a simple widefield system and a complex system with adaptive optics and structured illumination. A key strength of Cockpit is its use of Python, which means that any microscope built with Cockpit is ready for future customisation by simply adding new libraries, for example machine learning algorithms to enable automated microscopy decision making while imaging

Microscope-Cockpit: Python-based bespoke microscopy for bio-medical science

We have developed "Microscope-Cockpit" (Cockpit), a highly adaptable open source user-friendly Python-based Graphical User Interface (GUI) environment for precision control of both simple and elaborate bespoke microscope systems. The user environment allows next-generation near instantaneous navigation of the entire slide landscape for efficient selection of specimens of interest and automated acquisition without the use of eyepieces. Cockpit uses "Python-Microscope" (Microscope) for high-performance coordinated control of a wide range of hardware devices using open source software. Microscope also controls complex hardware devices such as deformable mirrors for aberration correction and spatial light modulators for structured illumination via abstracted device models. We demonstrate the advantages of the Cockpit platform using several bespoke microscopes, including a simple widefield system and a complex system with adaptive optics and structured illumination. A key strength of Cockpit is its use of Python, which means that any microscope built with Cockpit is ready for future customisation by simply adding new libraries, for example machine learning algorithms to enable automated microscopy decision making while imaging