Kamba, T. K. | bioRxiv Science

bioRxiv2021

An N-terminal Fusion Allele to Study Melanin Concentrating Hormone Receptor 1

Cilia on neurons play critical roles in both the development and function of the central nervous system (CNS). While it remains challenging to elucidate the precise roles for neuronal cilia, it is clear that a subset of G-protein-coupled receptors (GPCRs) preferentially localize to the cilia membrane. Further, ciliary GPCR signaling has been implicated in regulating a variety of behaviors. Melanin concentrating hormone receptor 1 (MCHR1), is a GPCR expressed centrally in rodents known to be enriched in cilia. Here we have used MCHR1 as a model ciliary GPCR to develop a strategy to fluorescently tag receptors expressed from the endogenous locus in vivo. Using CRISPR/Cas9, we inserted the coding sequence of the fluorescent protein mCherry into the N-terminus of Mchr1. Analysis of the fusion protein (mCherryMCHR1) revealed its localization to neuronal cilia in the CNS, across multiple developmental time points and in various regions of the adult brain. Our approach simultaneously produced fortuitous in/dels altering the Mchr1 start codon resulting in a new MCHR1 knockout line. Functional studies using electrophysiology show a significant alteration of synaptic strength in MCHR1 knockout mice. A reduction in strength is also detected in mice homozygous for the mCherry insertion, suggesting that while the strategy is useful for monitoring the receptor, activity could be altered. However, both lines should aid in studies of MCHR1 function and contribute to our understanding of MCHR1 signaling in the brain. Additionally, this approach could be expanded to aid in the study of other ciliary GPCRs.

neuroscience↗

bioRxiv2021

Artificial Intelligence Approaches to Assessing Primary Cilia

Cilia are microtubule based cellular appendages that function as signaling centers for a diversity of signaling pathways in many mammalian cell types. Cilia length is highly conserved, tightly regulated, and varies between different cell types and tissues and has been implicated in directly impacting their signaling capacity. For example, cilia have been shown to alter their lengths in response to activation of ciliary G protein-coupled receptors. However, accurately and reproducibly measuring the lengths of numerous cilia is a time-consuming and labor-intensive procedure. Current approaches are also error and bias prone. Artificial intelligence (Ai) programs can be utilized to overcome many of these challenges due to capabilities that permit assimilation, manipulation, and optimization of extensive data sets. Here, we demonstrate that an Ai module can be trained to recognize cilia in images from both in vivo and in vitro samples. After using the trained Ai to identify cilia, we are able to design and rapidly utilize applications that analyze hundreds of cilia in a single sample for length, fluorescence intensity and colocalization. This unbiased approach increased our confidence and rigor when comparing samples from different primary neuronal preps in vitro as well as across different brain regions within an animal and between animals. Moreover, this technique can be used to reliably analyze cilia dynamics from any cell type and tissue in a high-throughput manner across multiple samples and treatment groups. Ultimately, Ai-based approaches will likely become standard as most fields move toward less biased and more reproducible approaches for image acquisition and analysis. SUMMARYThe use of Artificial Intelligence (Ai) to analyze images is emerging as a powerful, less biased, and rapid approach compared with commonly used methods. Here we trained Ai to recognize a cellular organelle, primary cilia, and analyze properties such as length and staining intensity in a rigorous and reproducible manner.

cell biology↗