Establishing a reference focal plane using beads for trypan‐blue‐based viability measurements. (26th July 2021) - Peskin, Adele; Lund, Steven P.; Pierce, Laura; Kurbanov, Firdavs; Chan, Leo Li‐Ying; Halter, Michael; Elliott, John; Sarkar, Sumona; Chalfoun, Joe
Abstract: Trypan blue dye exclusion‐based cell viability measurements are highly dependent upon image quality and consistency. In order to make measurements repeatable, one must be able to reliably capture images at a consistent focal plane, and with signal‐to‐noise ratio within appropriate limits to support proper execution of image analysis routines. Imaging chambers and imaging systems used for trypan blue analysis can be inconsistent or can drift over time, leading to a need to assure the acquisition of images prior to automated image analysis. Although cell‐based autofocus techniques can be applied, the heterogeneity and complexity of the cell samples can make it difficult to assure the effectiveness, repeatability and accuracy of the routine for each measurement. Instead of auto‐focusing on cells in our images, we add control beads to the images, and use them to repeatedly return to a reference focal plane. We use bead image features that have stable profiles across a wide range of focal values and exposure levels. We created a predictive model based on image quality features computed over reference datasets. Because the beads have little variation, we can determine the reference plane from bead image features computed over a single‐shot image and can reproducibly return to that reference plane with each sample. The achieved accuracy (over 95%) is within the limits of the actuator repeatability. We demonstrate that a small number of beads (less than 3 beads per image) is needed to achieve this accuracy. We have also developed an open‐source Graphical User Interface called Bead Benchmarking‐Focus And Intensity Tool (BB‐FAIT) to implement these methods for a semi‐automated cell viability analyser. Lay Description: It is critical for the manufacturing and release of living cell‐based therapies to determine the viability, the ratio of living cells to the total number of cells (live and dead), in the therapy. Dead cells can be a safety concern for the patient, and dosing is often based on the number of living cells which are the active ingredient of the drug product. Currently, the most common approach to evaluating cell viability is based on the staining of cell samples with the trypan blue marker of cell membrane integrity: a loss in cell membrane integrity with cell death allows the dye into the cell, which can be seen using brightfield microscopy. To classify cells as live/dead, the brightness of the cells is evaluated and cells with bright centres are considered live, while those with dark centres are considered dead. Unfortunately, this approach of staining, imaging and classification is very sensitive to image acquisition settings, including image focus and brightness. This paper introduces a method to establish the required image quality for image viability analysis, providing a tool to return to image acquisition settings that will ensure image quality even when there is variability from sample to sample. In this method, polymeric beads are added to each cell sample prior to cell viability analysis. Using image processing, we extract key features from the beads in the image such as sharpness of the edges of the beads. The image features of the cells can vary significantly from sample to sample and under different cell conditions, but image features of beads have proved to be consistent across samples. We are thus able to collect reference datasets quantifying bead features over a wide range of image acquisition settings (brightness and focus), allowing us to establish a reference focal plan for image acquisition for any cell sample based on bead features. We show that with as few as three beads per image, the reference focal plane can be found from a single acquisition of beads image data over a wide range of image focuses and brightness, allowing users to consistently acquire images for cell viability that meet pre‐defined quality requirements.