Morphology-based noninvasive early prediction of serial-passage potency enhances the selection of clone-derived high-potency cell bank from mesenchymal stem cells

Abstract

Background Rapidly expanding clones (RECs) are one of the single-cell-derived mesenchymal stem cell clones sorted from human bone marrow mononuclear cells (BMMCs), which possess advantageous features. The RECs exhibit long-lasting proliferation potency that allows more than 10 repeated serial passages in vitro, considerably benefiting the manufacturing process of allogenic MSC-based therapeutic products. Although RECs aid the preparation of large-variation clone libraries for a greedy selection of better-quality clones, such a selection is only possible by establishing multiple-candidate cell banks for quality comparisons. Thus, there is a high demand for a novel method that can predict “low-risk and high-potency clones” early and in a feasible manner given the excessive cost and effort required to maintain such an establishment. Methods LNGFR and Thy-1 co-positive cells from BMMCs were single-cell-sorted into 96-well plates, and only fast-growing clones that reached confluency in 2 weeks were picked up and passaged as RECs. Fifteen RECs were prepared as passage 3 (P3) cryostock as the primary cell bank. From this cryostock, RECs were passaged until their proliferation limitation; their serial-passage limitation numbers were labeled as serial-passage potencies. At the P1 stage, phase-contrast microscopic images were obtained over 6–90 h to identify time-course changes of 24 morphological descriptors describing cell population information. Machine learning models were constructed using the morphological descriptors for predicting serial-passage potencies. The time window and field-of-view-number effects were evaluated to identify the most efficient image data usage condition for realizing high-performance serial-passage potency models. Results Serial-passage test results indicated variations of 7–13-repeated serial-passage potencies within RECs. Such potency values were predicted quantitatively with high performance (RMSE < 1.0) from P1 morphological profiles using a LASSO model. The earliest and minimum effort predictions require 6–30 h with 40 FOVs and 6–90 h with 15 FOVs, respectively. Conclusion We successfully developed a noninvasive morphology-based machine learning model to enhance the efficiency of establishing cell banks with single-cell-derived RECs for quantitatively predicting the future serial-passage potencies of clones. Conventional methods that can make noninvasive and quantitative predictions without wasting precious cells in the early stage are lacking; the proposed method will provide a more efficient and robust cell bank establishment process for allogenic therapeutic product manufacturing.

Robotics-driven manufacturing of cartilaginous microtissues for the bio-assembly of skeletal implants

“…In addition, the integration of process analytical technologies (PAT) (60)(61)(62)(63) and the definition of biological critical quality attributes by morphometrics, spectroscopic techniques or secreted proteins could be a next step to ensure a predictive outcome upon implantation (64)(65)(66)(67)(68)(69). On line, non-invasive monitoring techniques, such as the current approach, can produce large quantities of data on the process and biology that can be used as input for machine learning algorithms.…”

Morphology-Based Prediction of Proliferation and Differentiation Potencies of Porcine Muscle Stem Cells for Cultured Meat Production

“…It was also necessary to predict the quality potencies of pMuSCs based on cumulative time-course morphological parameters. MSC quality potencies can also be predicted through this process. − …”

“…The term “morphology” is used to refer to the identifiable biological phenotypes derived from images, including metrics of shape, intensity, and texture features . Morphological descriptors of cells have been found to exhibit quantitative correlations with the quality attributes of human mesenchymal stem cells (hMSCs). − In addition, advances in machine learning could be used to establish significant relationships between morphological parameters derived from cell images and the prediction of cellular quality attributes. − Studies showed that the combination of cell morphology and machine learning techniques can predict numerous quality attributes of hMSCs, such as proliferation potency, multiple differentiation potencies, immunosuppressive ability, and so forth. ,,, The “serial-passage potency” of hMSCs, which is typically measured only at the end of the cell expansion, has been predicted during the early stages of expansion . Numerous studies have focused on predicting the quality of hMSCs for advancing cell therapies, while studies on predicting proliferation and differentiation potencies of pMuSCs for cultured meat are limited.…”

“…17,18,20,25 The "serial-passage potency" of hMSCs, which is typically measured only at the end of the cell expansion, has been predicted during the early stages of expansion. 26 Numerous studies have focused on predicting the quality of hMSCs for advancing cell therapies, while studies on predicting proliferation and differentiation potencies of pMuSCs for cultured meat are limited.…”