TY - JOUR

T1 - Quality Control of Human Pluripotent Stem Cell Colonies by Computational Image Analysis Using Convolutional Neural Networks

AU - Mamaeva, Anastasiya

AU - Krasnova, Olga

AU - Khvorova, Irina

AU - Kozlov, Konstantin

AU - Gursky, Vitaly

AU - Samsonova, Maria

AU - Tikhonova, Olga

AU - Neganova, Irina

PY - 2022/12/21

Y1 - 2022/12/21

N2 - Human pluripotent stem cells are promising for a wide range of research and therapeutic purposes. Their maintenance in culture requires the deep control of their pluripotent and clonal status. A non-invasive method for such control involves day-to-day observation of the morphological changes, along with imaging colonies, with the subsequent automatic assessment of colony phenotype using image analysis by machine learning methods. We developed a classifier using a convolutional neural network and applied it to discriminate between images of human embryonic stem cell (hESC) colonies with "good" and "bad" morphological phenotypes associated with a high and low potential for pluripotency and clonality maintenance, respectively. The training dataset included the phase-contrast images of hESC line H9, in which the morphological phenotype of each colony was assessed through visual analysis. The classifier showed a high level of accuracy (89%) in phenotype prediction. By training the classifier on cropped images of various sizes, we showed that the spatial scale of ~144 μm was the most informative in terms of classification quality, which was an intermediate size between the characteristic diameters of a single cell (~15 μm) and the entire colony (~540 μm). We additionally performed a proteomic analysis of several H9 cell samples used in the computational analysis and showed that cells of different phenotypes differentiated at the molecular level. Our results indicated that the proposed approach could be used as an effective method of non-invasive automated analysis to identify undesirable developmental anomalies during the propagation of pluripotent stem cells.

AB - Human pluripotent stem cells are promising for a wide range of research and therapeutic purposes. Their maintenance in culture requires the deep control of their pluripotent and clonal status. A non-invasive method for such control involves day-to-day observation of the morphological changes, along with imaging colonies, with the subsequent automatic assessment of colony phenotype using image analysis by machine learning methods. We developed a classifier using a convolutional neural network and applied it to discriminate between images of human embryonic stem cell (hESC) colonies with "good" and "bad" morphological phenotypes associated with a high and low potential for pluripotency and clonality maintenance, respectively. The training dataset included the phase-contrast images of hESC line H9, in which the morphological phenotype of each colony was assessed through visual analysis. The classifier showed a high level of accuracy (89%) in phenotype prediction. By training the classifier on cropped images of various sizes, we showed that the spatial scale of ~144 μm was the most informative in terms of classification quality, which was an intermediate size between the characteristic diameters of a single cell (~15 μm) and the entire colony (~540 μm). We additionally performed a proteomic analysis of several H9 cell samples used in the computational analysis and showed that cells of different phenotypes differentiated at the molecular level. Our results indicated that the proposed approach could be used as an effective method of non-invasive automated analysis to identify undesirable developmental anomalies during the propagation of pluripotent stem cells.

KW - Humans

KW - Proteomics

KW - Pluripotent Stem Cells/metabolism

KW - Neural Networks, Computer

KW - Embryonic Stem Cells

KW - Quality Control

U2 - 10.3390/ijms24010140

DO - 10.3390/ijms24010140

M3 - Article

C2 - 36613583

VL - 24

JO - International Journal of Molecular Sciences

JF - International Journal of Molecular Sciences

SN - 1422-0067

IS - 1

M1 - 140

ER -