Application Note
Automation of iPSC culture, passaging, and expansion with the CellXpress.ai Automated Cell Culture System
- Automate and scale-up iPSC culture processes with a novel automated cell culture instrument
- Monitor iPSC cultures while gaining quality control with scheduled imaging and advanced image analysis
- Enable automated decision-making with machine learning-based algorithms
Introduction
iPSC-derived cell models are a popular tool enabling scientists to generate different cell types, organoids and tissues. Stem cells are also widely used to create disease- specific phenotypes with CRISPR gene editing to discover new drug targets using patient-derived samples. However, the process is often limited by labor-intensive and highly demanding culturing steps. To alleviate the limitations that come with labor-intensive protocols, we have developed the CellXpress.ai™ Automated Cell Culture System. This powerful new solution automates the entire cell culture process with an integrated incubator, liquid handler, and AI-powered, image-based decision-making. This hands- off system manages demanding feeding and passaging schedules by monitoring the development of cell cultures with periodic imaging and analysis, and leverages machine learning to initiate passaging, endpoint assay, or troubleshooting steps. In this application note, we present results from the automation of commonly used iPSC culture protocols, including cluster or single-cell passaging.
Methods
Materials
- iPSC cell line (our protocol was tested for two cell lines)
- 6-well plates Corning 3506
- Vitronectin, 1% solution
- PBS solution, any vendor
- ReLeSR™ reagent, STEMCELL Technologies
- mTeSR™ media, STEMCELL Technologies
- 1000 µL Tips
Automation of iPSC Workflow
The CellXpress.ai Automated Cell Culture System is a workcell for automated 2D and 3D cell culture. The system includes an automated imager, liquid handler, and incubator controlled by a unified AI powered software environment. The cell culture growth was monitored by periodic imaging and analysis. Automated decision- making was utilized to trigger passaging, and to notify the user of assay milestones or required intervention (e.g. refill consumables).
Cell culture protocols
IPSCs (ATCC 201B7) were cultured in Vitronectin (STEMCELL Technologies) in 6-well plates and passaged according to the STEMCELL Technologies protocols
for cluster or single-cell passaging. Here we present a description of cluster passaging method.1
Cell imaging and image analysis
Transmitted light images were acquired on the CellXpress.ai system using 4x or 2x magnifications. The CellXpress.ai system software was used for all analysis. Image analysis was done using pre-trained machine-learning protocols with IN Carta® Image Analysis Software.
Automation of iPSC culture
Automated workflows enabled the following steps:
- Seeding iPSCs cultured in 6-well format
- Automated media exchanges (every 24h)
- Automated imaging/analysis (every 12h)
- Plotting of growth curves and assessment of confluency, colony size, and cell differentiation
- Automated iPSC passaging
- Decision-making for passaging based on imaging data
Figure 1. CellXpress.ai system components and functionality
Figure 2. Schematic diagram of automated iPSC culture
Figure 3. Steps of the organoid culture and passaging protocol
Automated procedure for fragment passaging of iPSC
iPSC culture was performed according to the basic STEMCELL Technologies recommended protocol for human iPSC cells. The following steps can be optimized depending on iPSC and specific applications.
- All required reagents and consumables were place on the liquid handler deck including vitronectin coated plates (destination plates) before the start of passaging.
- mTesR was added to the destination plates and then transported to the incubator.
- iPSCs were grown to about 70% confluency in 6-well plates (source plate) that were used for passaging.
- The source plate containing iPSCs was was transported to the liquid handling deck and washed with PBS twice.
- For the aspiration and dispense steps, two tips per well were used to decrease time spent in the wash step. Plates were tilted during liquid aspiration.
- After washing, ReLeSR reagent was added and then removed after 3 minutes so only a thin film of ReLeSR was left on the cells. iPSCs were incubated for an additional ~3min.
- mTeSR was added to the cells and the suspension was mixed multiple times (10x) in the same plate to fragment the colonies.
- The pre-coated plates with mTeSR from the preparation step are brought to the deck and cell suspension aliquots are seeded into the wells of the destination plate (the volumes can be adjusted for different passaging ratios). To improve distribution of cells, cell suspension is dispensed in several positions of each target wells. The number of positions across the plate is user-defined.
Results
iPSC culture and monitoring
The protocol was tested with two commercially available iPSC cell lines and we automated basic iPSC protocols recommended by STEMCELL Technologies. The protocol was set up for media exchange of mTeSR every 24 hours on the liquid handler automated stem iPSCs were monitored by imaging every 12h.
Deep learning-based image segmentation was used to detect iPSC colonies, determine the cell area and confluency, and detection of cell phenotype changes or differentiation. The CellXpress.ai system software includes user-defined “widgets” for data visualization and review. iPSC growth curve, measured as the Area Sum (or confluency) over the time can be plotted in the software (Figure 4). Area Sum is the sum of areas of all colonies in the well. Error bars represent variability between the wells in same plate. Passaging events are marked by arrows.
Images acquired during monitoring were analyzed and measurements such as confluency may be used for automated decision making. Passaging steps can be triggered either by user decision (manual activation of the Passage step) or automatically depending on how the “rules” were configured. Figure 5 shows the user interface where decision parameters can be set up. In this example, 70% of cell confluency for 100% of wells were used as the decision-making point for automated passaging.
Upon decision point, user may select 2 options: notify user (by email) or proceed to the next phase (if configured as a passaging phase). The presence or percentage of differentiated cell can also be quantified during the image analysis step using pre-trained AI models in the software. (Figure 6). We developed two deep learning models for the analysis of iPSC colonies. The first model identifies colonies that shows typical stem cell morphologies while the second model identifies areas of differentiation. Together, these metrics may be used to trigger passaging, or to disregard wells with differentiated cells, or evaluate the extent of differentiation.
The process described here overcomes the challenges of manual handling of iPSC cells and enables automated iPSC culture and iPSC expansion. Cell culture automation offers great potential to reduce labor costs and increase productivity, throughput, and reproducibility.
Figure 4. Representative image of stem cell colonies and graph showing the total Area Sum or % confluency for iPSC culture in 6-well plate for two independent experiments. Passaging steps indicated with arrows. Error bars indicate SDEV between the wells.
Figure 5. Passaging triggered by rules based on selected measurements provided by image analysis. Decision-making software can select one or more criteria, combined by logical operations.
Figure 6. Pre-trained analysis models define stem cell colonies (in blue) and differentiated cell patches (in pink).
Summary
- iPSC-derived cell models are a popular tool to enable the generation of various cell types, micro-tissues, and disease phenotypes.
- To alleviate the limitations that come with labor-intensive protocols, we developed the CellXpress.ai Automated Cell Culture System.
- The new CellXpress.ai system automates the iPSC culture process, including passaging, media exchange, monitoring, and cell expansion.
- Automating the cell culture process has great potential to scale-up the creation of complex cell models and to enable a variety of drug discovery and precision medicine applications.