GuPPy, a Python toolbox for the analysis of fiber photometry data
Fiber photometry (FP) is an adaptable method for recording in vivo neural activity in freely behaving animals. It has become a popular tool in neuroscience due to its ease of use, low cost, the ability to combine FP with freely moving behavior, among other advantages. However, analysis of FP data can be a challenge for new users, especially those with a limited programming background. Here, we present Guided Photometry Analysis in Python (GuPPy), a free and open-source FP analysis tool. GuPPy is provided as a Jupyter notebook, a well-commented interactive development environment (IDE) designed to operate across platforms. GuPPy presents the user with a set of graphic user interfaces (GUIs) to load data and provide input parameters. Graphs produced by GuPPy can be exported into various image formats for integration into scientific figures. As an open-source tool, GuPPy can be modified by users with knowledge of Python to fit their specific needs.
Multiphoton imaging with next-generation indicators
Two-photon (2P) in vivo functional imaging of genetically encoded fluorescent Ca2+indicators (GECIs) for neuronal activity has become a broadly applied standard tool in modern neuroscience, because it allows simultaneous imaging of the activity of many neurons at high spatial resolution within living animals. Unfortunately, the most commonly used light-sources – tunable femtosecond pulsed ti:sapphire lasers – can be prohibitively expensive for many labs and fall short of delivering sufficient powers for some new ultra-fast 2P microscopy modalities. Inexpensive homebuilt or industrial light sources such as Ytterbium fiber lasers (YbFLs) show great promise to overcome these limitations as they are becoming widely available at costs orders of magnitude lower and power outputs of up to many times higher than conventional ti:sapphire lasers. However, these lasers are typically bound to emitting a single wavelength (i.e., not tunable) centered around 1020-1060 nm, which fails to efficiently excite state of the art green GECIs such as jGCaMP7 or 8. To this end, we designed and characterized spectral variants (yellow CaMP = YCaMP) of the ultrasensitive genetically encoded calcium indicator jGCaMP7, that allows for efficient 2P-excitation at wavelengths above 1010nm. In this talk I will give a brief overview over some of the reasons why using a fiber laser for 2P excitation might be right for you. I will talk about the development of jYCaMP and some exciting new experimental avenues that it has opened while touching on the prospect that shifting biosensors yellow could have for the 2P imaging community. Please join me for an interesting and fun discussion on whether “yellow is the new green” after the talk!
Mobilefuge: A low-cost, portable, open source, 3D-printed centrifuge that can be used for purification of saliva samples for SARS-CoV2 detection
We made a low-cost centrifuge that can be useful for carrying out low-cost LAMP based detection of SARS-Cov2 virus in saliva. The 3D printed centrifuge (Mobilefuge) is portable, robust, stable, safe, easy to build and operate. The Mobilefuge doesn’t require soldering or programming skills and can be built without any specialised equipment, yet practical enough for high throughput use. More importantly, Mobilefuge can be powered from widely available USB ports, including mobile phones and associated power supplies. This allows the Mobilefuge to be used even in off-grid and resource limited settings. Website: https://www.cappa.ie/chinna-devarapu/
Non-Telecentric 2P microscopy for 3D random access mesoscale imaging
Ultra-low-cost, easily implemented and flexible two-photon scanning microscopy modification offering a several-fold expanded three-dimensional field of view that also maintains single-cell resolution. Application of our system for imaging neuronal activity has been demonstrated on mice, zebrafish and fruit flies. Website: https://github.com/BadenLab/nTCscope
An open-source experimental framework for automation of cell biology experiments
Modern biological methods often require a large number of experiments to be conducted. For example, dissecting molecular pathways involved in a variety of biological processes in neurons and non-excitable cells requires high-throughput compound library or RNAi screens. Another example requiring large datasets - modern data analysis methods such as deep learning. These have been successfully applied to a number of biological and medical questions. In this talk we will describe an open-source platform allowing such experiments to be automated. The platform consists of an XY stage, perfusion system and an epifluorescent microscope with autofocusing. It is extremely easy to build and can be used for different experimental paradigms, ranging from immunolabeling and routine characterisation of large numbers of cell lines to high-throughput imaging of fluorescent reporters.