Figure 1

CyberSco.Py framework. (A) Architecture. CyberSco.Py is built in Python and uses the web application library Flask to create a web user interface. Microscopy protocols are written into a YAML (human readable data serialization language) file, which can be interpreted by the Python core module of CyberSco.Py, which drives the various components of a IX81 fully automated microscope. The core module also drives a set of fluidic valves that can be used to switch the media flowing into a microfluidic device. A class in Python is associated to each device. Images obtained from the camera are analyzed in real-time by a U-NET deep learning model to segment yeast cells and/or detect specific events, depending on the pre-trained model selected by the user. The result of the analysis is used by the core module to update the current state of any devices under its control (see “Materials and methods” for more information). (B) Snapshot of the current user interface. The user interface is very simple by design and allows the user to choose between several pre-programmed event-based scenarios, for which the user must define various relevant parameters and condition switches. The simple drag and drop interface can be used to modify a given Multi-Dimensional Acquisition protocol to give more flexibility and to create more advanced protocols. The same interface can be used in “live mode” to view what is currently being imaged and check that the live image analysis is performing correctly. Once the program is launched, the computer takes control of the microscope and will adjust the image acquisition parameters based on the event-based scenario that has been selected. It is possible to code a novel scenario directly in Python and/or to manually adjust the thresholds and parameters used to detect events (e.g., number of cells, size of cells, etc.). The structure of a scenario consists of a list of instructions for the microscope (“make the autofocus”, “take a picture”, etc.) to be serially executed at each iteration, a conditional block, and an initialization block. Each scenario corresponds to a unique Python file with the same consistent structure. The user can also enter information about the projected experiment, as well as selecting modalities for monitoring the experiment remotely via email (selecting where to send the emails and at which frequency) and/or through a discussion channel (e.g., Microsoft Teams or Slack).