ThermoCyte: an inexpensive open-source temperature control system for in vitro live cell imaging

Live-cell imaging is a common technique in microscopy to investigate dynamic cellular behaviour and permits the accurate and relevant analysis of a wide range of cellular and tissue parameters, such as motility, cell division, wound healing responses, and calcium (Ca2+) signalling in cell lines, primary cell cultures, and ex vivo preparations. Furthermore, this can take place under many experimental conditions, making live-cell imaging indispensable for biological research. Systems which maintain cells at physiological conditions outside of a CO2 incubator are often bulky, expensive, and use proprietary components. Here we present an inexpensive, open-source temperature control system for in vitro live cell imaging. Our system ‘ThermoCyte’, which is constructed from standard electronic components, enables precise tuning, control, and logging of a temperature ‘set point’ for imaging cells at physiological temperature. We achieved stable thermal dynamics, with reliable temperature cycling and a standard deviation of 0.42°C over 1 hour. Furthermore, the device is modular in nature, and is adaptable to the researcher's specific needs. This represents simple, inexpensive, and reliable tool for laboratories to carry out custom live-cell imaging protocols, on a standard lab bench, at physiological temperature.

Methods

3D-printed stage-top (Fig. 1, B) was designed using Autodesk TinkerCad, and is made available as STL files.

ThermoCyte code (Fig. 1, C) was written in the Arduino IDE. All code is included unedited as .ino files.

Temperature values from ThermoCyte (Fig. 3, Fig. 9, C) were collected by logging raw temperature readings detected by Arduino thermocouples using 'putty', a free SSH and Telnet client. Logs are made available as comma-separated values file (.csv).

Circularity values (Fig. 5, E) were calculated using ImageJ 'shape descriptors' function. Circularity values are included as a comma-separated values (.csv)

Pixel intensity traces relating to calcium transients (Fig. 6, Fig. 8) were extracted from raw images. Photobleaching was corrected using napari bleach correct in Napari image viewer, specifically the 'histogram matching' function (python 3). Motion artefact was corrected using moco plugin in FIJI. Cells were segmented as objects using cellpose 2.0 (Python 3). Mean grey intensity values for each cell were extracted using FIJI, and these values for each cell (over time) are provided unedited as a comma-separated values (.csv).

Calcium Peak analysis (Fig. 7 A-D, Fig 8 C, D) was generated from raw pixel intensity data, provided in (5) above. This was carried out using custom Python code, supplied here as .py files (python 3).

Statistical analysis of calcium peak data (Fig. 7 A-D, Fig 8 C, D) was carried out using custom python code, supplied here as .py files (python 3).