Fig 1.
Automated sampling and control system for FACS streamlines high-throughput cell sorting workflows.
(A-C) FACS automation case study and workflow. (A) Upstream sample preparation creates a library of engineered cell populations CRISPR-edited to express fluorescently tagged protein-coding genes. The cell pool contains a heterogeneous mixture of unedited (non-fluorescent) cells and successfully edited (fluorescent) cells. (B) The fluorescence signature of a representative sample of the cells is profiled via the cell sorter. (C) The profile is analyzed to specify and select the sub-population of interest within a sort gate. The cell sorter then selects those cells for collection and recovery, creating a homogenous population of successfully edited cells. (D) Design summary of the automated cell sorting system. A computer-aided design (CAD) rendering of the automated system and the real implementation highlight the details of the system (top left). The sample tubes are contained in a cooled tube housing, which agitates the samples to keep them in suspension. A robotic arm shuttles the samples between the tube housing and the cell sorter. Custom software controls the keyboard and mouse of the computer in order to interact with the sorter GUI. An algorithm analyzes the profiled cell data from (B) to generate a custom gate for each cell sample.
Fig 2.
Bespoke software controls the sorter GUI and reports on instrument status to the user.
(A) The automation software manipulates the sorter GUI using the PyAutoGUI Python package. The software identifies icons within the sorter GUI to operate the cell sorter (examples shown on the left). Additionally, the software enters texts and modifies the sorting settings between samples. (B) The software reports through instant messaging (Slack) on the status of a sorting run. It is capable of detecting errors with the sorter instrument and notifying the user of the problem (right panel).
Fig 3.
(A) Examples of sorting gates corresponding to the fluorescent tagging of genes with increasing expression level (left to right). As the expression of the fluorescently-tagged gene increases, the gate for the desired engineered population must shift such that only the brightest cells within the sample are collected based on the specific gating algorithm. The same cell samples were sorted both automatically and manually, confirming that the gating algorithm generates results consistent with manual sorting. (B) Automated gating strategy applied in the case study. Contour lines from the fluorescent signal from the cell sample profile (see Fig 1B) are used to define the shape and vertical position of the gate. The gate is translated and rotated such that it includes a user-defined percentage of the fluorescent population (in our case, top 1%). Alternative gating methods can be programmed into the automation control software.
Fig 4.
Sorting time is nominally similar between automated and manual sorting, but automation enables walk-away functionality.
(A) The time spent for each process to sort a sample is divided into five steps. For the case study defined in Fig 1A and S1 Fig, a complete sort takes on average 5.5 minutes, with 75% of the time spent profiling and sorting. The remainder of the time is spent transporting the samples, defining the gate for the sample, and setting up the sorter for each sample. (B) The same set of 12 samples were sorted with the automated system and manually. The total amount of time was comparable, but the user hands-on time for the automated system was reduced by 93%.