Background

Primary cilia are microtubule-based sensory organelles that function as antenna-like structures emerging from the cell body. They detect mechanical and chemical signals from the environment and are involved in a range of biological processes critical for homeostasis (Goetz and Anderson, 2010; Anvarian et al., 2019). Not surprisingly, genetic cilia defects result in various clinical manifestations, called ciliopathies (Goetz and Anderson, 2010). Interestingly, the ciliary membrane hosts unique receptors and effector molecules that control a number of signaling pathways and biological processes. These include oncogenic receptor tyrosine kinases (RTKs), such as insulin-like growth factor receptor (IGF-1R) or platelet-derived growth factor receptor alpha (PDGFR-α), and G-protein coupled receptors (GPCRs), such as smoothened (SMO) (Christensen et al., 2012; Schou et al., 2015).

We previously reported that primary cilia mediate resistance to several kinase inhibitors in cancer cells (Jenks et al., 2018). Drug resistant cells show more cilia, longer cilia, the appearance of cilia terminal fragments (CTF), and increased activation of the cilia-specific Hedgehog signaling pathway (Jenks et al., 2018). Significantly, targeting cilia or ciliary signaling can sensitize drug-resistant cells to their respective inhibitors (Jenks et al., 2018). Interestingly, drug resistant cells show changes in the distribution of cilia proteins, including Kif7 and IFT81. While control cells showed Kif7 and IFT81 distributed along the cilia and at the cilia tip, drug resistant cells showed a significant decrease in the distribution of these proteins along the cilia (Jenks et al., 2018). In addition, we observed changes in a number of proteins and ciliary receptors in drug resistant cells, highlighting the importance of developing accurate image analysis tools for cilia research.

These results presented us with the challenge of being able to measure the distribution of proteins along the cilium. The accurate quantification of fluorescence intensity in microscopy images is an important part of cilia research. For this purpose, two open source software packages have been described (Lauring et al., 2019; Hansen et al., 2021). To accurately quantify the distribution of proteins along the cilia, we developed a series of MATLAB scripts that support cilia analysis using the improfile function of MATLAB and adaptive threshold for binarization. Our protocols are versatile, intuitive, and start with a manual script that is then fully automated. As cilia and cilia staining can be heterogeneous, our automated protocol can be adapted to different conditions by changing the pixel intensity threshold, named “level” in the script. These scripts are outlined here, along with a detailed step-by-step guide to use them with examples. A supplementary section with each script is included.