Automated human induced pluripotent stem cell colony segmentation for use in cell culture automation applications

Abstract

Human induced pluripotent stem cells (hiPSCs) have demonstrated great promise for a variety of applications that include cell therapy and regenerative medicine. Production of clinical grade hiPSCs requires reproducible manufacturing methods with stringent quality-controls such as those provided by image-controlled robotic processing systems. In this paper we present an automated image analysis method for identifying and picking hiPSC colonies for clonal expansion using the CellX^TM robotic cell processing system. This method couples a light weight deep learning segmentation approach based on the U-Net architecture to automatically segment the hiPSC colonies in full field of view (FOV) high resolution phase contrast images with a standardized approach for suggesting pick locations. The utility of this method is demonstrated using images and data obtained from the CellX^TM system where clinical grade hiPSCs were reprogrammed, clonally expanded, and differentiated into retinal organoids for use in treatment of patients with inherited retinal degenerative blindness.

Keywords: Automated image analysis cell cultures; Deep learning; Human induced pluripotent stem cell (hiPSC) processing; Stem cell manufacturing.

Fig. 1.

A. Full FOV of phase-contrast image of hiPSC colonies in a single well 6-well cell culture. This image was acquired using CellX^™ system equipped with an Olympus IX83 with 4X objective and subsampled by a factor of two (image size = 7680 × 7400 pixels, pixel size=0.00227 mm) at the end of the reprogramming phase just prior to passaging. The imaging was restricted to the non-meniscal region of the cell culture. B. The full FOV image with manually defined colonies (yellow outlines) and the automatically segmented colonies (green outlines).

Fig. 2.

Example of manual annotation mask where only hiPSC cells in a colony were outlined. This image (image size = 500 × 575 pixels, pixel size=0.00227 mm) was cropped from a full FOV image acquired using CellX^™ system equipped with an Olympus IX83, 4X objective, and subsampled by a factor of two.

Fig. 3.

Schematic illustrating automated pick locations based on Pick Factor and Edge overlap.

Fig. 4.

Phase contrast images cropped from a full FOV image acquired using CellX^™ system equipped with an Olympus IX83, 4X objective, and subsampled by a factor of two (image size = 400 × 400 pixels, pixel size = 0.00227 mm) A. partially reprogrammed cells surrounded by fibroblasts that lacked a distinct outer boundary, B. corresponding outline (green) of this region from the automated segmentation, C. hiPSC colony with distinct outer boundary indicated by red arrow, and D. manual (yellow) and automated (green) outline of the hiPSC colony shown in C.

Fig. 5.

Phase contrast images cropped from a full FOV image acquired using CellX^™ system equipped with an Olympus IX83, 4X objective, and subsampled by a factor of two (image size = 540 × 700 pixels, pixel size = 0.00227 mm) A. hiPSC colony, and B. manual (yellow) and automated (green) segmentations. Red arrows point to regions where manual outlining included cells with a more variable morphology than the hiPSCs.

Fig. 6.

Distance between Reviewer-chosen multiple picks as a function of the number picks per colony.

Fig. 7.

Phase contrast images cropped from a full FOV image acquired using CellX^™ system equipped with an Olympus IX83, 4X objective, and subsampled by a factor of two (pixel size = 0.00227 mm). The automatically segmented hiPSC colony are outlined in yellow, the Reviewer-chosen picks in green, and the automated picks in light blue. Three different size colonies from different patient lines and wells are shown here. A. single pick colony (image size = 600 × 500), B. small multi-pick colony (image size = 600 × 815), and C. large multi-pick colony (image size = 1300 × 1430).