Background

Gut motility is critical for digestive function, including fermentation and mixing, stool formation, and microbial composition. This contractile activity is predominantly regulated by the intrinsic network of neurons within the gut, the enteric nervous system (ENS) (Furness, 2012). Motility patterns vary in the different regions of the gastrointestinal (GI) tract. In the small intestine, contractile activity serves to maximise exposure to digestive enzymes and absorption of nutrients, whereas the colon is responsible for the formation and expulsion of faeces and absorption of water and electrolytes. Several studies have reported characteristics of contractile patterning in the small and large intestine of small animal species, e.g., rat, mouse, and guinea pig (Lyster et al., 1995; Tonini et al., 1996; Costa et al., 2013 and 2015; Swaminathan et al., 2016; Spencer et al., 2018; Li et al., 2019). However, contractile patterning of the caecum (the equivalent of the human appendix) is not well characterised. Caecal motility has been described in studies of chicken (Janssen et al., 2009; van Staaveren et al., 2020), guinea pig (Schulze‐Delrieu et al., 1996), and rabbit (Hulls et al., 2012 and 2016), but is yet to be modelled in mice or studied in humans.

GI dysmotility is a common comorbidity in many disorders including autism spectrum disorder (ASD; autism) (Gorrindo et al., 2012; McElhanon et al., 2014; Vuong and Hsiao, 2017; Lee et al., 2020) and Parkinson’s disease (Kupsky et al., 1987; Singaram et al., 1995; Cersosimo et al., 2013; Giancola et al., 2017). Given that changes in gut motility patterns can contribute to gastrointestinal symptoms that impact quality of life, such as constipation, diarrhoea, visceral pain, and/or bacterial overgrowth, the accurate measurement of motility patterns could assist in identifying potential therapeutic targets to treat these issues. It is therefore necessary to design and utilise unbiased methods to study gut motility patterns both to gain fundamental knowledge about the neural regulation of motility and to determine changes in these patterns in pre-clinical animal models of disease. Ex vivo motility assays enable the investigation of gut motility patterns in animal models (Roberts et al., 2007; Swaminathan et al., 2016). These techniques were first developed to investigate small intestinal peristalsis in guinea pigs (Hennig et al., 1999; Gwynne and Bornstein, 2007) and then expanded for use in the mouse colon (Swaminathan et al., 2016). This method has been primarily applied to segments of mouse and guinea pig colon (Roberts et al., 2007; Hosie et al., 2019; Leembruggen et al., 2020) and, in some studies, to segments of small bowel (Hennig et al., 1999; Gwynne et al., 2004; Neal et al., 2009). As mentioned previously, studies of caecal motility are restricted to rabbit and chicken (Schulze‐Delrieu et al., 1996; Janssen et al., 2009; Hulls et al., 2012 and 2016). Even though external innervation from the central nervous system (CNS) plays a role in modulating gut motility (Browning and Travagli, 2014), the ENS is capable of independently regulating GI function. Hence, a major advantage of the ex vivo video imaging technique reported here is the ability to measure gut motility in the absence of CNS inputs.

Video imaging combined with spatiotemporal mapping enables the quantitative assessment of gastrointestinal motility in animal models. Using these techniques, multiple contractility parameters such as propagation speed, magnitude, length, duration, diameter, and frequency of gastrointestinal contractions can be measured. However, the software platform we previously utilised for this purpose (Swaminathan et al., 2016) had limited functionality. A lack of compatibility with current software platforms and closed source code that prevented flexibility were major limitations. We found that although suitable for assessing intestinal motility patterns, the edge detection function within the previous version of the MATLAB-based software (Analyse2) could not accurately detect edges of tissue segments from irregular shaped gut regions, such as the caecum, due to the curved/nonlinear anatomy of this organ. In addition, the quality of the heatmaps generated using the previous edge detection function (Swaminathan et al., 2016) was lower, as 16-bit unsigned integer arrays led to a quantisation and pixelation of the resultant spatiotemporal heatmaps. Importantly, this edge detection code was not compatible across different operating systems (i.e., Windows and Macintosh), which reduced user accessibility. Here, we highlight the utility of the new MATLAB-based software interface, entitled GutMap, that enables i) accurate edge detection in multiple gut regions, including in combination with a novel mouse caecal motility protocol (which can additionally be applied to stomach motility), ii) output of high-resolution spatiotemporal heatmaps, and iii) usage across different operating systems.

GutMap is a user-friendly enhanced software interface (available on request) that enables a sensitive and robust approach for visualising and analysing regional gut motility patterns in rodents (with potential applications in other preclinical models following some modification). Data from video recordings of gut contractility patterns acquired from different regions of the GI tract (i.e., the small intestine, caecum, and colon) are converted to high-resolution spatiotemporal heatmaps. The GutMap resolution, measured in μm/pixel or mm/pixel as appropriate to the length scale of the experimental preparation, is entirely dependent on the resolution of the input video file. The spatiotemporal heatmaps generated are high quality as the edge data is stored in double precision arrays, providing a near-continuum of edge locations and thus greater precision of gut width measurements.

Spatiotemporal heatmaps display gut diameter and gut position plotted as a function of time and assign warm colours denoting gut contractions and cool colours indicating gut relaxation. GutMap can be used to measure multiple contractile parameters to characterise subtle changes in motility patterns and ENS activity. Novel features of GutMap include a video calibration function, real-time video tagging information that shows the video properties and edge detection dimensions, as well as a file queueing function for edge detection processing. These features ensure increased accuracy and consistency of measures (i.e., based on the initial tissue size calibration) for each video recording file, as well as streamlined heatmap generation. Heatmap analysis using GutMap enables full functionality of the previous Analyse2 module (Swaminathan et al., 2016), plus an additional novel analysis function that enables measurement of overall contractility of the gut tissue segment.

Here, we provide a comprehensive guide for investigating ex vivo gastrointestinal motility in rodent models and highlight the novelty of our caecal motility measurement protocol in female mouse GI preparations, using the novel video analysis software, GutMap. A detailed explanation is provided for the experimental setup and execution, which are adapted from studies performed in mouse colon (Swaminathan et al., 2016) and rabbit caecum (Hulls et al., 2012 and 2016). Here, we also outline detailed steps for generating and analysing spatiotemporal heatmaps using GutMap. We demonstrate that these processes enable the comparison of motility patterns in different gut regions (i.e., the small intestine, caecum, and colon) both in physiological conditions and in response to drug administration [i.e., the nitric oxide synthase (NOS) inhibitor drug, N_ω-Nitro-L-arginine (NOLA)].

The method can be further extended for use in various conditions including examining gut motility in response to different treatments and in other preclinical models of disease.