Artificial Intelligence Approaches to Assessing Primary Cilia

Abstract

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.

Figure 1.. Workflow for measuring cilia length and intensity using Ai.

(A) To train the Ai, binaries are drawn around the objects of interest (cilia) on the raw training images. Using the drawn binaries, Segment Ai is trained to recognize the shape and pixel intensities of the cilia. (B) Next, the trained Segment Ai is applied to raw experimental images. It draws binaries on objects it recognizes as cilia. These binaries can be refined to makes sure all and only cilia are being analyzed. (C) A GA3 program is constructed to analyze the intensity and length of objects recognized by the Ai. (D) The records are imported into a table in the software. This table can then be exported for further analysis.

Figure 2.. In vitro cilia length measurements.

Representative images of cilia in (A) IMCD cells (green, acetylated tubulin) (B) primary hypothalamic cultures (green, ACIII) and (C) hippocampal cultures (green, ACIII). A trained Ai was used to recognize cilia as shown in the binary mask (magenta) and then GA3 was used to measure cilia length. Distribution of cilia length is graphed as percentage of cilia in 0.5 or 1.0-micron bins. * indicates cytokinetic bridge properly not recognized by Ai. n=225 cilia in IMCD cells from 3 replicates, 54 cilia in hypothalamic and 139 cilia in hippocampal cultures from 3 animals. Scale bars 10 μm.

Figure 3.. In vivo cilia length measurements.

(A) Representative images of cilia (green, ACIII) in the ARC, PVN, and CA1 of adult mouse brain sections. (B) A trained Ai in NIS Elements was used to recognize cilia as shown in the binary mask (magenta) and then GA3 was used to measure cilia length. (C) Distribution of cilia length is graphed as percentage of cilia in one-micron bins. n= 68 cilia in ARC, 36 in PVN and 29 in CA1 from 3 animals. Scale bars 10 μm.

Figure 4.. Ai assisted cilia staining intensity measurements of hypothalamic neuronal cilia.

(A) Representative images of cilia (MCHR1, red) in the ARC and PVN of adult mouse brain sections. A trained Ai in NIS Elements was used to recognize cilia as shown in the binary mask (cyan) and then GA3 was used to measure the intensity of MCHR1 staining in cilia. (B) MCHR1 intensities are graphed as average ± S.E.M. Each dot represents a cilium. * p < 0.05, Student's t-test. (C) Distribution of MCHR1 intensity is graphed as percentage of cilia in bins of 0.2 x 10⁷ Arbitrary Units (A. U.). n= 53 cilia in ARC, 78 in PVN from 3 animals. Scale bars 10 μm.

Figure 5.. Ai assisted cilia colocalization analysis.

(A, B) Representative images of cilia in the ARC and PVN respectively. Cilia are labelled with ACIII (green) and MCHR1 (red). A trained Ai in NIS Elements was used to recognize cilia as shown in the binary mask (magenta for ACIII labelled cilia, cyan for MCHR1 labelled cilia). GA3 was used to recognize cilia that contained both ACIII and MCHR1. (C) Manders overlap coefficient (MOC) values are graphed as average ± S.E.M. Each dot represents a cilium. * p < 0.05, Student's t-test. (D) Scatter plot of MCHR1 intensity vs. ACIII intensity in ARC and PVN. Each dot represents a cilium. n= 72 cilia in ARC, 47 in PVN from 3 animals. Scale bars 10 μm.

Figure 6.. Cilia and basal body analysis.

(A) Representative images of cilia (red, ARL13B-mCherry) and basal body marker (green, Centrin2-GFP) in the ARC and PVN of P0 mice. A trained Ai was used to recognize cilia as shown in the binary mask (cyan). The binary mask for basal body (magenta) was drawn by thresholding in GA3 recipe. (B) Representative line scan intensity of a cilium. (C) ARL13B intensities at the proximal and distal ends of Ai identified cilia graphed as average ± S.E.M. The proximal and distal ends are defined as the region within the first 1 μm length and the last 1 μm length respectively from the base of the cilium. Each dot represents a cilium. * p < 0.05. n = 6 cilia in ARC from 2 animals and 21 cilia in PVN from 3 animals. Scale bars 10 μm.