In-process evaluation of culture errors using morphology-based image analysis

Abstract

Introduction: Advancing industrial-scale manufacture of cells as therapeutic products is an example of the wide applications of regenerative medicine. However, one bottleneck in establishing stable and efficient cell manufacture is quality control. Owing to the lack of effective in-process measurement technology, analyzing the time-consuming and complex cell culture process that essentially determines cellular quality is difficult and only performed by manual microscopic observation. Our group has been applying advanced image-processing and machine-learning modeling techniques to construct prediction models that support quality evaluations during cell culture. In this study, as a model of errors during the cell culture process, intentional errors were compared to the standard culture and analyzed based only on the time-course morphological information of the cells.

Methods: Twenty-one lots of human mesenchymal stem cells (MSCs), including both bone-marrow-derived MSCs and adipose-derived MSCs, were cultured under 5 conditions (one standard and 4 types of intentional errors, such as clear failure of handlings and machinery malfunctions). Using time-course microscopic images, cell morphological profiles were quantitatively measured and utilized for visualization and prediction modeling. For visualization, modified principal component analysis (PCA) was used. For prediction modeling, linear regression analysis and the MT method were applied.

Results: By modified PCA visualization, the differences in cellular lots and culture conditions were illustrated as traits on a morphological transition line plot and found to be effective descriptors for discriminating the deviated samples in a real-time manner. In prediction modeling, both the cell growth rate and error condition discrimination showed high accuracy (>80%), which required only 2 days of culture. Moreover, we demonstrated the applicability of different concepts of machine learning using the MT method, which is effective for manufacture processes that mostly collect standard data but not a large amount of failure data.

Conclusions: Morphological information that can be quantitatively acquired during cell culture has great potential as an in-process measurement tool for quality control in cell manufacturing processes.

Keywords: Cell manufacturing; In-process measurement; Mesenchymal stem cells; Morphological analysis; Non-invasive image analysis; Quality control.

Fig. 1

Schematic illustration of morphology-based analysis in this study. The analysis step consists of (1) Image acquisition, (2) Image processing and morphology measurement, and (3) Visualization, or (4) Prediction modeling. In visualization, we used the modified PCA plot. In prediction modeling, we constructed a growth rate prediction regression model, culture condition discriminant regression model, and discriminant scoring model using the MT method.

Fig. 2

Morphology-based analysis of growth rate performance. (A) Growth rate performance among the 21 lots of MSCs used in this study. (B) Representative phase contrast images of MSC lots (Lot 6 and 5) and images of morphological transition analysis. Scale bar = 200 μm. All images were prepared with the same scale. (C) Representative image of morphological transition analysis applied to group A (good growth lots) and group B (bad growth lots). (D) Prediction performance of growth rate-prediction models examining the time-course window effect.

Fig. 3

Morphology-based analysis of errors in cell culture. (A) Representative phase contrast images of MSCs under 5 types of culture conditions (Standard and 4 types of intentional errors). Scale bar = 200 μm. All images were prepared with the same scale. (B) Representative visualization image of morphological transition analysis applied to the 5 culture conditions. (C) Prediction performances of culture condition discrimination models examining the time-course window effect.

Fig. 4

Morphology-based discriminant scoring of error samples using the MT method. (A) Schematic concept of the MT method using morphological information. (B) Time-course discriminant scoring by the MT method for detecting error samples from the 5 culture conditions. Each dot indicates an image converted into 28 morphological parameters (total 63 images per condition). The Y-axis indicates the discrimination score calculated from the MT method, which reflects the Mahalanobis information distance from the centroid of unit space to the sample. (C) Bar plot of discriminant scoring by the MT-method. **p < 0.01, ***p < 0.001. The Y-axis indicates the discrimination score calculated from the MT method.