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Artificial Intelligence Approaches to Assessing Primary Cilia.

  • Bansal, Ruchi1
  • Engle, Staci E1
  • Kamba, Tisianna K1
  • Brewer, Kathryn M1
  • Lewis, Wesley R2
  • Berbari, Nicolas F3
  • 1 Department of Biology, Indiana University-Purdue University Indianapolis. , (India)
  • 2 Nikon Instruments Inc.
  • 3 Department of Biology, Indiana University-Purdue University Indianapolis; Stark Neurosciences Research Institute, Indiana University; Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine; [email protected] , (India)
Published Article
Journal of Visualized Experiments
MyJoVE Corporation
Publication Date
May 01, 2021
DOI: 10.3791/62521
PMID: 33999029


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 co-localization. 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.

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