Computational image analysis of colony and nuclear morphology to evaluate human induced pluripotent stem cells

The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review

BACKGROUND

Stem cell research, particularly in the domain of induced pluripotent stem cell (iPSC) technology, has shown significant progress. The integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has played a pivotal role in refining iPSC classification, monitoring cell functionality, and conducting genetic analysis. These enhancements are broadening the applications of iPSC technology in disease modelling, drug screening, and regenerative medicine. This review aims to explore the role of AI in the advancement of iPSC research.

METHODS

In December 2023, data were collected from three electronic databases (PubMed, Web of Science, and Science Direct) to investigate the application of AI technology in iPSC processing.

RESULTS

This systematic scoping review encompassed 79 studies that met the inclusion criteria. The number of research studies in this area has increased over time, with the United States emerging as a leading contributor in this field. AI technologies have been diversely applied in iPSC technology, encompassing the classification of cell types, assessment of disease-specific phenotypes in iPSC-derived cells, and the facilitation of drug screening using iPSC. The precision of AI methodologies has improved significantly in recent years, creating a foundation for future advancements in iPSC-based technologies.

CONCLUSIONS

Our review offers insights into the role of AI in regenerative and personalized medicine, highlighting both challenges and opportunities. Although still in its early stages, AI technologies show significant promise in advancing our understanding of disease progression and development, paving the way for future clinical applications.

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

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 CellXTM 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 CellXTM 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.

Morphological Signal Processing for Phenotype Recognition of Human Pluripotent Stem Cells Using Machine Learning Methods

Human pluripotent stem cells have the potential for unlimited proliferation and controlled differentiation into various somatic cells, making them a unique tool for regenerative and personalized medicine. Determining the best clone selection is a challenging problem in this field and requires new sensing instruments and methods able to automatically assess the state of a growing colony ('phenotype') and make decisions about its destiny. One possible solution for such label-free, non-invasive assessment is to make phase-contrast images and/or videos of growing stem cell colonies, process the morphological parameters ('morphological portrait', or signal), link this information to the colony phenotype, and initiate an automated protocol for the colony selection. As a step in implementing this strategy, we used machine learning methods to find an effective model for classifying the human pluripotent stem cell colonies of three lines according to their morphological phenotype ('good' or 'bad'), using morphological parameters from the previously published data as predictors. We found that the model using cellular morphological parameters as predictors and artificial neural networks as the classification method produced the best average accuracy of phenotype prediction (67%). When morphological parameters of colonies were used as predictors, logistic regression was the most effective classification method (75% average accuracy). Combining the morphological parameters of cells and colonies resulted in the most effective model, with a 99% average accuracy of phenotype prediction. Random forest was the most efficient classification method for the combined data. We applied feature selection methods and showed that different morphological parameters were important for phenotype recognition via either cellular or colonial parameters. Our results indicate a necessity for retaining both cellular and colonial morphological information for predicting the phenotype and provide an optimal choice for the machine learning method. The classification models reported in this study could be used as a basis for developing and/or improving automated solutions to control the quality of human pluripotent stem cells for medical purposes.

Methylated histones on mitotic chromosomes promote topoisomerase IIα function for high fidelity chromosome segregation

DNA Topoisomerase IIα (TopoIIα) decatenates sister chromatids, allowing their segregation in mitosis. Without the TopoIIα Strand Passage Reaction (SPR), chromosome bridges and ultra-fine DNA bridges (UFBs) arise in anaphase. The TopoIIα C-terminal domain is dispensable for the SPR in vitro but essential for mitotic functions in vivo. Here, we present evidence that the Chromatin Tether (ChT) within the CTD interacts with specific methylated nucleosomes and is crucial for high-fidelity chromosome segregation. Mutation of individual αChT residues disrupts αChT-nucleosome interaction, induces loss of segregation fidelity and reduces association of TopoIIα with chromosomes. Specific methyltransferase inhibitors reducing histone H3 or H4 methylation decreased TopoIIα at centromeres and increased segregation errors. Methyltransferase inhibition did not further increase aberrant anaphases in the ChT mutants, indicating a functional connection. The evidence reveals novel cellular regulation whereby TopoIIα specifically interacts with methylated nucleosomes via the αChT to ensure high-fidelity chromosome segregation.

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

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.

A comprehensive review of computational and image analysis techniques for quantitative evaluation of striated muscle tissue architecture

Unbiased evaluation of morphology is crucial to understanding development, mechanics, and pathology of striated muscle tissues. Indeed, the ability of striated muscles to contract and the strength of their contraction is dependent on their tissue-, cellular-, and cytoskeletal-level organization. Accordingly, the study of striated muscles often requires imaging and assessing aspects of their architecture at multiple different spatial scales. While an expert may be able to qualitatively appraise tissues, it is imperative to have robust, repeatable tools to quantify striated myocyte morphology and behavior that can be used to compare across different labs and experiments. There has been a recent effort to define the criteria used by experts to evaluate striated myocyte architecture. In this review, we will describe metrics that have been developed to summarize distinct aspects of striated muscle architecture in multiple different tissues, imaged with various modalities. Additionally, we will provide an overview of metrics and image processing software that needs to be developed. Importantly to any lab working on striated muscle platforms, characterization of striated myocyte morphology using the image processing pipelines discussed in this review can be used to quantitatively evaluate striated muscle tissues and contribute to a robust understanding of the development and mechanics of striated muscles.

Prognostic Analysis of Human Pluripotent Stem Cells Based on Their Morphological Portrait and Expression of Pluripotent Markers

The ability of human pluripotent stem cells for unlimited proliferation and self-renewal promotes their application in the fields of regenerative medicine. The morphological assessment of growing colonies and cells, as a non-invasive method, allows the best clones for further clinical applications to be safely selected. For this purpose, we analyzed seven morphological parameters of both colonies and cells extracted from the phase-contrast images of human embryonic stem cell line H9, control human induced pluripotent stem cell (hiPSC) line AD3, and hiPSC line HPCASRi002-A (CaSR) in various passages during their growth for 120 h. The morphological phenotype of each colony was classified using a visual analysis and associated with its potential for pluripotency and clonality maintenance, thus defining the colony phenotype as the control parameter. Using the analysis of variance for the morphological parameters of each line, we showed that selected parameters carried information about different cell lines and different phenotypes within each line. We demonstrated that a model of classification of colonies and cells by phenotype, built on the selected parameters as predictors, recognized the phenotype with an accuracy of 70-75%. In addition, we performed a qRT-PCR analysis of eleven pluripotency markers genes. By analyzing the variance of their expression in samples from different lines and with different phenotypes, we identified group-specific sets of genes that could be used as the most informative ones for the separation of the best clones. Our results indicated the fundamental possibility of constructing a morphological portrait of a colony informative for the automatic identification of the phenotype and for linking this portrait to the expression of pluripotency markers.

Semi-three-dimensional cultures using laminin 221 as a coating material for human induced pluripotent stem cells

It was previously believed that human induced pluripotent stem cells (hiPSCs) did not show adhesion to the coating material Laminin 221, which is known to have specific affinity for cardiomyocytes. In this study, we report that human mononuclear cell-derived hiPSCs, established with Sendai virus vector, form peninsular-like colonies rather than embryonic stem cell-like colonies; these peninsular-like colonies can be passaged more than 10 times after establishment. Additionally, initialization-deficient cells with residual Sendai virus vector adhered to the coating material Laminin 511 but not to Laminin 221. Therefore, the expression of undifferentiated markers tended to be higher in hiPSCs established on Laminin 221 than on Laminin 511. On Laminin 221, hiPSCs15M66 showed a semi-floating colony morphology. The expression of various markers of cell polarity was significantly lower in hiPSCs cultured on Laminin 221 than in hiPSCs cultured on Laminin 511. Furthermore, 201B7 and 15M66 hiPSCs showed 3D cardiomyocyte differentiation on Laminin 221. Thus, the coating material Laminin 221 provides semi-floating culture conditions for the establishment, culture and induced differentiation of hiPSCs.

Label-free morphological sub-population cytometry for sensitive phenotypic screening of heterogenous neural disease model cells

Label-free image analysis has several advantages with respect to the development of drug screening platforms. However, the evaluation of drug-responsive cells based exclusively on morphological information is challenging, especially in cases of morphologically heterogeneous cells or a small subset of drug-responsive cells. We developed a novel label-free cell sub-population analysis method called “in silico FOCUS (in silico analysis of featured-objects concentrated by anomaly discrimination from unit space)” to enable robust phenotypic screening of morphologically heterogeneous spinal and bulbar muscular atrophy (SBMA) model cells. This method with the anomaly discrimination concept can sensitively evaluate drug-responsive cells as morphologically anomalous cells through in silico cytometric analysis. As this algorithm requires only morphological information of control cells for training, no labeling or drug administration experiments are needed. The responses of SBMA model cells to dihydrotestosterone revealed that in silico FOCUS can identify the characteristics of a small sub-population with drug-responsive phenotypes to facilitate robust drug response profiling. The phenotype classification model confirmed with high accuracy the SBMA-rescuing effect of pioglitazone using morphological information alone. In silico FOCUS enables the evaluation of delicate quality transitions in cells that are difficult to profile experimentally, including primary cells or cells with no known markers.

Temporal and Locational Values of Images Affecting the Deep Learning of Cancer Stem Cell Morphology

Deep learning is being increasingly applied for obtaining digital microscopy image data of cells. Well-defined annotated cell images have contributed to the development of the technology. Cell morphology is an inherent characteristic of each cell type. Moreover, the morphology of a cell changes during its lifetime because of cellular activity. Artificial intelligence (AI) capable of recognizing a mouse-induced pluripotent stem (miPS) cell cultured in a medium containing Lewis lung cancer (LLC) cell culture-conditioned medium (cm), miPS-LLCcm cell, which is a cancer stem cell (CSC) derived from miPS cell, would be suitable for basic and applied science. This study aims to clarify the limitation of AI models constructed using different datasets and the versatility improvement of AI models. The trained AI was used to segment CSC in phase-contrast images using conditional generative adversarial networks (CGAN). The dataset included blank cell images that were used for training the AI but they did not affect the quality of predicting CSC in phase contrast images compared with the dataset without the blank cell images. AI models trained using images of 1-day culture could predict CSC in images of 2-day culture; however, the quality of the CSC prediction was reduced. Convolutional neural network (CNN) classification indicated that miPS-LLCcm cell image classification was done based on cultivation day. By using a dataset that included images of each cell culture day, the prediction of CSC remains to be improved. This is useful because cells do not change the characteristics of stem cells owing to stem cell marker expression, even if the cell morphology changes during culture.

Label-Free Imaging to Track Reprogramming of Human Somatic Cells

The process of reprogramming patient samples to human-induced pluripotent stem cells (iPSCs) is stochastic, asynchronous, and inefficient, leading to a heterogeneous population of cells. In this study, we track the reprogramming status of patient-derived erythroid progenitor cells (EPCs) at the single-cell level during reprogramming with label-free live-cell imaging of cellular metabolism and nuclear morphometry to identify high-quality iPSCs. EPCs isolated from human peripheral blood of three donors were used for our proof-of-principle study. We found distinct patterns of autofluorescence lifetime for the reduced form of nicotinamide adenine dinucleotide (phosphate) and flavin adenine dinucleotide during reprogramming. Random forest models classified iPSCs with ∼95% accuracy, which enabled the successful isolation of iPSC lines from reprogramming cultures. Reprogramming trajectories resolved at the single-cell level indicated significant reprogramming heterogeneity along different branches of cell states. This combination of micropatterning, autofluorescence imaging, and machine learning provides a unique, real-time, and nondestructive method to assess the quality of iPSCs in a biomanufacturing process, which could have downstream impacts in regenerative medicine, cell/gene therapy, and disease modeling.

Ribosomal protein L5 facilitates rDNA-bundled condensate and nucleolar assembly

High content image analysis, single molecule tracking, modeling, and DBA patient analysis revealed that ribosomal protein L5 facilitates rDNA-bundled condensate and nucleolar assembly.

The nucleolus is the site of ribosome assembly and formed through liquid–liquid phase separation. Multiple ribosomal DNA (rDNA) arrays are bundled in the nucleolus, but the underlying mechanism and significance are unknown. In the present study, we performed high-content screening followed by image profiling with the wndchrm machine learning algorithm. We revealed that cells lacking a specific 60S ribosomal protein set exhibited common nucleolar disintegration. The depletion of RPL5 (also known as uL18), the liquid–liquid phase separation facilitator, was most effective, and resulted in an enlarged and un-separated sub-nucleolar compartment. Single-molecule tracking analysis revealed less-constrained mobility of its components. rDNA arrays were also unbundled. These results were recapitulated by a coarse-grained molecular dynamics model. Transcription and processing of ribosomal RNA were repressed in these aberrant nucleoli. Consistently, the nucleoli were disordered in peripheral blood cells from a Diamond–Blackfan anemia patient harboring a heterozygous, large deletion in RPL5. Our combinatorial analyses newly define the role of RPL5 in rDNA array bundling and the biophysical properties of the nucleolus, which may contribute to the etiology of ribosomopathy.

Moving Towards Induced Pluripotent Stem Cell-based Therapies with Artificial Intelligence and Machine Learning

The advent of induced pluripotent stem cell (iPSC) technology, which allows to transform one cell type into another, holds the promise to produce therapeutic cells and organs on demand. Realization of this objective is contingent on the ability to demonstrate quality and safety of the cellular product for its intended use. Bottlenecks and backlogs to the clinical use of iPSCs have been fully outlined and a need has emerged for safer and standardized protocols to trigger cell reprogramming and functional differentiation. Amidst great challenges, in particular associated with lengthy culture time and laborious cell characterization, a demand for faster and more accurate methods for the validation of cell identity and function at different stages of the iPSC manufacturing process has risen. Artificial intelligence-based methods are proving helpful for these complex tasks and might revolutionize the way iPSCs are managed to create surrogate cells and organs. Here, we briefly review recent progress in artificial intelligence approaches for evaluation of iPSCs and their derivatives in experimental studies.

Induced Pluripotent Stem Cells Detection via Ensemble Yolo Network

Induced pluripotent stem cells (iPSCs) have huge potential in regenerative medicine research and industrial applications. However, building automatic method without using cell staining technique for iPSCs identification is an important challenge. To improve the efficiency of producing iPSCs, we build an accurate and noninvasive iPSCs colonies detection method via ensemble Yolo network based on the self-collected bright-field microscopy images. Meanwhile, test-time augmentation (TTA) is leveraged to further improve the detection result of our iPSCs colonies detection method. Extensive experimental results on our dataset demonstrate that our method obtains quite favorable detection performance with the highest F1 score of 0.867 and the highest mean average precision score of 0.898, which outperforms most mainstream methods.

Quality evaluation of induced pluripotent stem cell colonies by fusing multi-source features

BACKGROUND AND OBJECTIVE

Induced pluripotent stem cells (iPSCs) have great potential as the basis of regenerative medicine. In this paper, we propose an automatic quality evaluation model based on multi-source feature ensemble learning to divide the iPSC colonies into three categories: good, medium and bad.

METHODS

First, we obtained iPSCs samples using a Sendai virus reprogramming method. Second, we collected the bright field-images of iPSC colonies and processed them with adaptive gamma transform and data enhancement. The evaluation for the iPSC colony quality was further verified with living cell fluorescent staining, currently accepted as the optimal biological method. Third, multi-source features were extracted using three deep convolutional neural networks (DCNNs) and four traditional feature descriptors. Finally, we utilized a support vector machine (SVM) to perform classification. Before feeding into the SVM, the features were processed by principal component analysis algorithm to save computational cost and training time.

RESULTS

Experimental results on the collected iPSC dataset (46,500 images) show that the proposed method could obtain 95.55% classification accuracy.

CONCLUSIONS

Our study could provide a method to efficiently and quickly judge the biological quality of a single iPSC colony or populations and facilitate the large-scale iPSC manufacturing.

Molecular signature and colony morphology affect in vitro pluripotency of porcine induced pluripotent stem cells

Overall efficiency of cell reprogramming for porcine fibroblasts into induced pluripotent stem cells (iPSCs) is currently poor, and few cell lines have been established. This study examined gene expression during early phase of cellular reprogramming in the relationship to the iPSC colony morphology and in vitro pluripotent characteristics. Fibroblasts were reprogrammed with OCT4, SOX2, KLF4 and c-MYC. Two different colony morphologies referred to either compact (n = 10) or loose (n = 10) colonies were further examined for proliferative activity, gene expression and in vitro pluripotency. A total of 1,697 iPSC-like colonies (2.34%) were observed after gene transduction. The compact colonies contained with tightly packed cells with a distinct-clear border between the colony and feeder cells, while loose colonies demonstrated irregular colony boundary. For quantitative expression of genes responsible for early phase cell reprogramming, the Dppa2 and EpCAM were significantly upregulated while NR0B1 was downregulated in compact colonies compared with loose phenotype (p < .05). Higher proportion of compact iPSC phenotype (5 of 10, 50%) could be maintained in undifferentiated state for more than 50 passages compared unfavourably with loose morphology (3 of 10, 30%). All iPS cell lines obtained from these two types of colony morphologies expressed pluripotent genes and proteins (OCT4, NANOG and E-cadherin). In addition, they could aggregate and form three-dimensional structure of embryoid bodies. However, only compact iPSC colonies differentiated into three germ layers. Molecular signature of early phase of cell reprogramming coupled with primary colony morphology reflected the in vitro pluripotency of porcine iPSCs. These findings can be simply applied for pre-screening selection of the porcine iPSC cell line.

Altered cytoskeletal arrangement in induced pluripotent stem cells (iPSCs) and motor neurons from patients with riboflavin transporter deficiency

The cytoskeletal network plays a crucial role in differentiation, morphogenesis, function and homeostasis of the nervous tissue, so that alterations in any of its components may lead to neurodegenerative diseases. Riboflavin transporter deficiency (RTD), a childhood-onset disorder characterized by degeneration of motor neurons (MNs), is caused by biallelic mutations in genes encoding the human riboflavin (RF) transporters. In a patient- specific induced Pluripotent Stem Cells (iPSCs) model of RTD, we recently demonstrated altered cell-cell contacts, energy dysmetabolism and redox imbalance.The present study focusses on cytoskeletal composition and dynamics associated to RTD, utilizing patients' iPSCs and derived MNs. Abnormal expression and distribution of α- and β-tubulin (α- and β-TUB), as well as imbalanced tyrosination of α-TUB, accompanied by impaired ability to repolymerize after nocodazole treatment, were found in RTD patient-derived iPSCs. Following differentiation, MNs showed consistent changes in TUB content, which was associated with abnormal morphofunctional features, such as neurite length and Ca++ homeostasis, suggesting impaired differentiation.Beneficial effects of RF supplementation, alone or in combination with the antioxidant molecule N-acetyl-cystine (NAC), were assessed. RF administration resulted in partially improved cytoskeletal features in patients' iPSCs and MNs, suggesting that redundancy of transporters may rescue cell functionality in the presence of adequate concentrations of the vitamin. Moreover, supplementation with NAC was demonstrated to be effective in restoring all the considered parameters, when used in combination with RF, thus supporting the therapeutic use of both compounds.

PML nuclear bodies and chromatin dynamics: catch me if you can!

Eukaryotic cells compartmentalize their internal milieu in order to achieve specific reactions in time and space. This organization in distinct compartments is essential to allow subcellular processing of regulatory signals and generate specific cellular responses. In the nucleus, genetic information is packaged in the form of chromatin, an organized and repeated nucleoprotein structure that is a source of epigenetic information. In addition, cells organize the distribution of macromolecules via various membrane-less nuclear organelles, which have gathered considerable attention in the last few years. The macromolecular multiprotein complexes known as Promyelocytic Leukemia Nuclear Bodies (PML NBs) are an archetype for nuclear membrane-less organelles. Chromatin interactions with nuclear bodies are important to regulate genome function. In this review, we will focus on the dynamic interplay between PML NBs and chromatin. We report how the structure and formation of PML NBs, which may involve phase separation mechanisms, might impact their functions in the regulation of chromatin dynamics. In particular, we will discuss how PML NBs participate in the chromatinization of viral genomes, as well as in the control of specific cellular chromatin assembly pathways which govern physiological mechanisms such as senescence or telomere maintenance.

Deep-learning-based multi-class segmentation for automated, non-invasive routine assessment of human pluripotent stem cell culture status

Human induced pluripotent stem cells (hiPSCs) are capable of differentiating into a variety of human tissue cells. They offer new opportunities for personalized medicine and drug screening. This requires large quantities of high quality hiPSCs, obtainable only via automated cultivation. One of the major requirements of an automated cultivation is a regular, non-invasive analysis of the cell condition, e.g. by whole-well microscopy. However, despite the urgency of this requirement, there are currently no automatic, image-processing-based solutions for multi-class routine quantification of this nature. This paper describes a method to fully automate the cell state recognition based on phase contrast microscopy and deep-learning. This approach can be used for in process control during an automated hiPSC cultivation. The U-Net based algorithm is capable of segmenting important parameters of hiPSC colony formation and can discriminate between the classes hiPSC colony, single cells, differentiated cells and dead cells. The model achieves more accurate results for the classes hiPSC colonies, differentiated cells, single hiPSCs and dead cells than visual estimation by a skilled expert. Furthermore, parameters for each hiPSC colony are derived directly from the classification result such as roundness, size, center of gravity and inclusions of other cells. These parameters provide localized information about the cell state and enable well based treatment of the cell culture in automated processes. Thus, the model can be exploited for routine, non-invasive image analysis during an automated hiPSC cultivation. This facilitates the generation of high quality hiPSC derived products for biomedical purposes.

Profiling and quantification of pluripotency reprogramming reveal that WNT pathways and cell morphology have to be reprogramed extensively

Pluripotent state can be established via reprogramming of somatic nuclei by factors within an oocyte or by ectopic expression of a few transgenes. Considered as being extensive and intensive, the full complement of genes to be reprogrammed, however, has never been defined, nor has the degree of reprogramming been determined quantitatively. Here, we propose a new concept of reprogramome, which is defined as the full complement of genes to be reprogrammed to the expression levels found in pluripotent stem cells (PSCs). This concept in combination with RNA-seq enables us to precisely profile reprogramome and sub-reprogramomes, and study the reprogramming process with the help of other available tools such as GO analyses. With reprogramming of human fibroblasts into PSCs as an example, we have defined the full complement of the human fibroblast-to-PSC reprogramome. Furthermore, our analyses of the reprogramome revealed that WNT pathways and genes with roles in cellular morphogenesis should be extensively and intensely reprogrammed for the establishment of pluripotency. We further developed a new mathematical model to quantitate the overall reprogramming, as well as reprogramming in a specific cellular feature such as WNT signaling pathways and genes regulating cellular morphogenesis. We anticipate that our concept and mathematical model may be applied to study and quantitate other reprogramming (pluripotency reprogramming from other somatic cells, and lineage reprogramming), as well as transcriptional and epigenetic differences between any two types of cells including cancer cells and their normal counterparts.

Computational analysis of morphological and molecular features in gastric cancer tissues

Biological morphologies of cells and tissues represent their physiological and pathological conditions. The importance of quantitative assessment of morphological information has been highly recognized in clinical diagnosis and therapeutic strategies. In this study, we used a supervised machine learning algorithm wndchrm to classify hematoxylin and eosin (H&E)‐stained images of human gastric cancer tissues. This analysis distinguished between noncancer and cancer tissues with different histological grades. We then classified the H&E‐stained images by expression levels of cancer‐associated nuclear ATF7IP/MCAF1 and membranous PD‐L1 proteins using immunohistochemistry of serial sections. Interestingly, classes with low and high expressions of each protein exhibited significant morphological dissimilarity in H&E images. These results indicated that morphological features in cancer tissues are correlated with expression of specific cancer‐associated proteins, suggesting the usefulness of biomolecular‐based morphological classification.

The recent advances in the mathematical modelling of human pluripotent stem cells

Human pluripotent stem cells hold great promise for developments in regenerative medicine and drug design. The mathematical modelling of stem cells and their properties is necessary to understand and quantify key behaviours and develop non-invasive prognostic modelling tools to assist in the optimisation of laboratory experiments. Here, the recent advances in the mathematical modelling of hPSCs are discussed, including cell kinematics, cell proliferation and colony formation, and pluripotency and differentiation.

Quantification of the morphological characteristics of hESC colonies

The maintenance of the undifferentiated state in human embryonic stem cells (hESCs) is critical for further application in regenerative medicine, drug testing and studies of fundamental biology. Currently, the selection of the best quality cells and colonies for propagation is typically performed by eye, in terms of the displayed morphological features, such as prominent/abundant nucleoli and a colony with a tightly packed appearance and a well-defined edge. Using image analysis and computational tools, we precisely quantify these properties using phase-contrast images of hESC colonies of different sizes (0.1–1.1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\bf{\text{mm}}}}^{{\bf{2}}}$$\end{document}mm2) during days 2, 3 and 4 after plating. Our analyses reveal noticeable differences in their structure influenced directly by the colony area \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\boldsymbol{A}}$$\end{document}A. Large colonies (A > 0.6 mm²) have cells with smaller nuclei and a short intercellular distance when compared with small colonies (A < 0.2 mm²). The gaps between the cells, which are present in small and medium sized colonies with A ≤ 0.6 mm², disappear in large colonies (A > 0.6 mm²) due to the proliferation of the cells in the bulk. This increases the colony density and the number of nearest neighbours. We also detect the self-organisation of cells in the colonies where newly divided (smallest) cells cluster together in patches, separated from larger cells at the final stages of the cell cycle. This might influence directly cell-to-cell interactions and the community effects within the colonies since the segregation induced by size differences allows the interchange of neighbours as the cells proliferate and the colony grows. Our findings are relevant to efforts to determine the quality of hESC colonies and establish colony characteristics database.

Reprogrammed Cells Display Distinct Proteomic Signatures Associated with Colony Morphology Variability

Human induced pluripotent stem cells (hiPSCs) are of high interest because they can be differentiated into a vast range of different cell types. Ideally, reprogrammed cells should sustain long-term culturing in an undifferentiated state. However, some reprogrammed cell lines represent an unstable state by spontaneously differentiating and changing their cellular phenotype and colony morphology. This phenomenon is not fully understood, and no method is available to predict it reliably. In this study, we analyzed and compared the proteome landscape of 20 reprogrammed cell lines classified as stable and unstable based on long-term colony morphology. We identified distinct proteomic signatures associated with stable colony morphology and with unstable colony morphology, although the typical pluripotency markers (POU5F1, SOX2) were present with both morphologies. Notably, epithelial to mesenchymal transition (EMT) protein markers were associated with unstable colony morphology, and the transforming growth factor beta (TGFB) signalling pathway was predicted as one of the main regulator pathways involved in this process. Furthermore, we identified specific proteins that separated the stable from the unstable state. Finally, we assessed both spontaneous embryonic body (EB) formation and directed differentiation and showed that reprogrammed lines with an unstable colony morphology had reduced differentiation capacity. To conclude, we found that different defined patterns of colony morphology in reprogrammed cells were associated with distinct proteomic profiles and different outcomes in differentiation capacity.

Tracking and Predicting Human Somatic Cell Reprogramming Using Nuclear Characteristics

Reprogramming of human somatic cells to induced pluripotent stem cells (iPSCs) generates valuable resources for disease modeling, toxicology, cell therapy, and regenerative medicine. However, the reprogramming process can be stochastic and inefficient, creating many partially reprogrammed intermediates and non-reprogrammed cells in addition to fully reprogrammed iPSCs. Much of the work to identify, evaluate, and enrich for iPSCs during reprogramming relies on methods that fix, destroy, or singularize cell cultures, thereby disrupting each cell's microenvironment. Here, we develop a micropatterned substrate that allows for dynamic live-cell microscopy of hundreds of cell subpopulations undergoing reprogramming while preserving many of the biophysical and biochemical cues within the cells' microenvironment. On this substrate, we were able to both watch and physically confine cells into discrete islands during the reprogramming of human somatic cells from skin biopsies and blood draws obtained from healthy donors. Using high-content analysis, we identified a combination of eight nuclear characteristics that can be used to generate a computational model to predict the progression of reprogramming and distinguish partially reprogrammed cells from those that are fully reprogrammed. This approach to track reprogramming in situ using micropatterned substrates could aid in biomanufacturing of therapeutically relevant iPSCs and be used to elucidate multiscale cellular changes (cell-cell interactions as well as subcellular changes) that accompany human cell fate transitions.

Automated sensing and splitting of stem cell colonies on microraft arrays

Human induced pluripotent stem cells (hiPSCs) are widely used for disease modeling, tissue engineering, and clinical applications. Although the development of new disease-relevant or customized hiPSC lines is of high importance, current automated hiPSC isolation technologies rely largely on the fluorescent labeling of cells, thus limiting the cell line development from many applications. The objective of this research was to develop a platform for high-throughput hiPSC cytometry and splitting that utilized a label-free cell sensing approach. An image analysis pipeline utilizing background subtraction and standard deviation projections was implemented to detect hiPSC colonies from bright-field microscopy data. The pipeline was incorporated into an automated microscopy system coupling quad microraft cell-isolation arrays, computer-based vision, and algorithms for smart decision making and cell sorting. The pipeline exhibited a hiPSC detection specificity of 98% and a sensitivity of 88%, allowing for the successful tracking of growth for hundreds of microcolonies over 7 days. The automated platform split 170 mother colonies from a microarray within 80 min, and the harvested daughter biopsies were expanded into viable hiPSC colonies suitable for downstream assays, such as polymerase chain reaction (PCR) or continued culture. Transmitted light microscopy offers an alternative, label-free modality for isolating hiPSCs, yet its low contrast and specificity for adherent cells remain a challenge for automation. This novel approach to label-free sensing and microcolony subsampling with the preservation of the mother colony holds the potential for hiPSC colony screening based on a wide range of properties including those measurable only by a cell destructive assay.

Time-course colony tracking analysis for evaluating induced pluripotent stem cell culture processes

Increasing the yield and maintaining a high quality of induced pluripotent stem cells (iPSCs) is necessary for manufacturing iPSCs at the industrial scale. However, because iPSCs are delicate, it is important to evaluate their quality during processing. To examine the status of cultured iPSCs non-invasively, morphology-based iPSC colony evaluation may be an efficient technology for cellular status monitoring and analysis. In this study, we examined the effectiveness of time-course colony tracking analysis for evaluating the iPSC culture process. Particularly, we obtained detailed time-course data to evaluate the effect of the pipetting technique on cell dissociation before seeding. Although the pipetting process causes severe shear stress to cells, which affects their quality, these effects have not been quantitatively analyzed because of their complex and uncontrollable parameters. By analyzing the heterogeneity and time-course responses of individual colonies, our colony tracking analysis revealed a critically damaged population caused by pipetting stress which could not be detected in conventional bulk analysis. Moreover, by comprehensively analyzing colony tracking data, which links the time-course morphology and marker staining results with each colony, we found that colony morphology is only highly correlated with the undifferentiated marker in the final stage, with a lower correlation in the early stages. Thus, colony tracking analysis provides a way to quantify cellular morphological information when evaluating complex iPSC manufacturing processes.

The Genomic Health of Human Pluripotent Stem Cells: Genomic Instability and the Consequences on Nuclear Organization

Human pluripotent stem cells (hPSCs) are increasingly used for cell-based regenerative therapies worldwide, with embryonic and induced pluripotent stem cells as potential treatments for debilitating and chronic conditions, such as age-related macular degeneration, Parkinson's disease, spinal cord injuries, and type 1 diabetes. However, with the level of genomic anomalies stem cells generate in culture, their safety may be in question. Specifically, hPSCs frequently acquire chromosomal abnormalities, often with gains or losses of whole chromosomes. This review discusses how important it is to efficiently and sensitively detect hPSC aneuploidies, to understand how these aneuploidies arise, consider the consequences for the cell, and indeed the individual to whom aneuploid cells may be administered.

Cell type discrimination based on image features of molecular component distribution

Machine learning-based cell classifiers use cell images to automate cell-type discrimination, which is increasingly becoming beneficial in biological studies and biomedical applications. Brightfield or fluorescence images are generally employed as the classifier input variables. We propose to use Raman spectral images and a method to extract features from these spatial patterns and explore the value of this information for cell discrimination. Raman images provide information regarding distribution of chemical compounds of the considered biological entity. Since each spectral wavelength can be used to reconstruct the distribution of a given compound, spectral images provide multiple channels of information, each representing a different pattern, in contrast to brightfield and fluorescence images. Using a dataset of single living cells, we demonstrate that the spatial information can be ranked by a Fisher discriminant score, and that the top-ranked features can accurately classify cell types. This method is compared with the conventional Raman spectral analysis. We also propose to combine the information from whole spectral analyses and selected spatial features and show that this yields higher classification accuracy. This method provides the basis for a novel and systematic analysis of cell-type investigation using Raman spectral imaging, which may benefit several studies and biomedical applications.

Ki-67 and condensins support the integrity of mitotic chromosomes through distinct mechanisms

Although condensins play essential roles in mitotic chromosome assembly, Ki-67 (also known as MKI67), a protein localizing to the periphery of mitotic chromosomes, had also been shown to make a contribution to the process. To examine their respective roles, we generated a set of HCT116-based cell lines expressing Ki-67 and/or condensin subunits that were fused with an auxin-inducible degron for their conditional degradation. Both the localization and the dynamic behavior of Ki-67 on mitotic chromosomes were not largely affected upon depletion of condensin subunits, and vice versa. When both Ki-67 and SMC2 (a core subunit of condensins) were depleted, ball-like chromosome clusters with no sign of discernible thread-like structures were observed. This severe defective phenotype was distinct from that observed in cells depleted of either Ki-67 or SMC2 alone. Our results show that Ki-67 and condensins, which localize to the external surface and the central axis of mitotic chromosomes, respectively, have independent yet cooperative functions in supporting the structural integrity of mitotic chromosomes.

Deep vector-based convolutional neural network approach for automatic recognition of colonies of induced pluripotent stem cells

Pluripotent stem cells can potentially be used in clinical applications as a model for studying disease progress. This tracking of disease-causing events in cells requires constant assessment of the quality of stem cells. Existing approaches are inadequate for robust and automated differentiation of stem cell colonies. In this study, we developed a new model of vector–based convolutional neural network (V-CNN) with respect to extracted features of the induced pluripotent stem cell (iPSC) colony for distinguishing colony characteristics. A transfer function from the feature vectors to the virtual image was generated at the front of the CNN in order for classification of feature vectors of healthy and unhealthy colonies. The robustness of the proposed V-CNN model in distinguishing colonies was compared with that of the competitive support vector machine (SVM) classifier based on morphological, textural, and combined features. Additionally, five-fold cross-validation was used to investigate the performance of the V-CNN model. The precision, recall, and F-measure values of the V-CNN model were comparatively higher than those of the SVM classifier, with a range of 87–93%, indicating fewer false positives and false negative rates. Furthermore, for determining the quality of colonies, the V-CNN model showed higher accuracy values based on morphological (95.5%), textural (91.0%), and combined (93.2%) features than those estimated with the SVM classifier (86.7, 83.3, and 83.4%, respectively). Similarly, the accuracy of the feature sets using five-fold cross-validation was above 90% for the V-CNN model, whereas that yielded by the SVM model was in the range of 75–77%. We thus concluded that the proposed V-CNN model outperforms the conventional SVM classifier, which strongly suggests that it as a reliable framework for robust colony classification of iPSCs. It can also serve as a cost-effective quality recognition tool during culture and other experimental procedures.

Classification of C2C12 cells at differentiation by convolutional neural network of deep learning using phase contrast images

In the field of regenerative medicine, tremendous numbers of cells are necessary for tissue/organ regeneration. Today automatic cell-culturing system has been developed. The next step is constructing a non-invasive method to monitor the conditions of cells automatically. As an image analysis method, convolutional neural network (CNN), one of the deep learning method, is approaching human recognition level. We constructed and applied the CNN algorithm for automatic cellular differentiation recognition of myogenic C2C12 cell line. Phase-contrast images of cultured C2C12 are prepared as input dataset. In differentiation process from myoblasts to myotubes, cellular morphology changes from round shape to elongated tubular shape due to fusion of the cells. CNN abstract the features of the shape of the cells and classify the cells depending on the culturing days from when differentiation is induced. Changes in cellular shape depending on the number of days of culture (Day 0, Day 3, Day 6) are classified with 91.3% accuracy. Image analysis with CNN has a potential to realize regenerative medicine industry.

Method for evaluation of human induced pluripotent stem cell quality using image analysis based on the biological morphology of cells

We propose an image analysis method for quality evaluation of human pluripotent stem cells based on biologically interpretable features. It is important to maintain the undifferentiated state of induced pluripotent stem cells (iPSCs) while culturing the cells during propagation. Cell culture experts visually select good quality cells exhibiting the morphological features characteristic of undifferentiated cells. Experts have empirically determined that these features comprise prominent and abundant nucleoli, less intercellular spacing, and fewer differentiating cellular nuclei. We quantified these features based on experts' visual inspection of phase contrast images of iPSCs and found that these features are effective for evaluating iPSC quality. We then developed an iPSC quality evaluation method using an image analysis technique. The method allowed accurate classification, equivalent to visual inspection by experts, of three iPSC cell lines.

A Machine Learning Assisted, Label-free, Non-invasive Approach for Somatic Reprogramming in Induced Pluripotent Stem Cell Colony Formation Detection and Prediction

During cellular reprogramming, the mesenchymal-to-epithelial transition is accompanied by changes in morphology, which occur prior to iPSC colony formation. The current approach for detecting morphological changes associated with reprogramming purely relies on human experiences, which involve intensive amounts of upfront training, human error with limited quality control and batch-to-batch variations. Here, we report a time-lapse-based bright-field imaging analysis system that allows us to implement a label-free, non-invasive approach to measure morphological dynamics. To automatically analyse and determine iPSC colony formation, a machine learning-based classification, segmentation, and statistical modelling system was developed to guide colony selection. The system can detect and monitor the earliest cellular texture changes after the induction of reprogramming in human somatic cells on day 7 from the 20–24 day process. Moreover, after determining the reprogramming process and iPSC colony formation quantitatively, a mathematical model was developed to statistically predict the best iPSC selection phase independent of any other resources. All the computational detection and prediction experiments were evaluated using a validation dataset, and biological verification was performed. These algorithm-detected colonies show no significant differences (Pearson Coefficient) in terms of their biological features compared to the manually processed colonies using standard molecular approaches.

Condensin II plays an essential role in reversible assembly of mitotic chromosomes in situ

A modified protocol for inducing reversible assembly of mitotic chromosomes in situ is developed. As judged by this assay, which is combined with quantitative morphological analyses using a supervised machine-learning algorithm, condensin II plays a crucial role in both the recovery of chromatin shapes and the reorganization of chromosome axes.

Condensins I and II are multisubunit complexes that play a central role in mitotic chromosome assembly. Although both complexes become concentrated along the axial region of each chromatid by metaphase, it remains unclear exactly how such axes might assemble and contribute to chromosome shaping. To address these questions from a physico-chemical point of view, we have established a set of two-step protocols for inducing reversible assembly of chromosome structure in situ, namely within a whole cell. In this assay, mitotic chromosomes are first expanded in a hypotonic buffer containing a Mg²⁺-chelating agent and then converted into different shapes in a NaCl concentration-dependent manner. Both chromatin and condensin-positive chromosome axes are converted into near-original shapes at 100 mM NaCl. This assay combined with small interfering RNA depletion demonstrates that the recovery of chromatin shapes and the reorganization of axes are highly sensitive to depletion of condensin II but less sensitive to depletion of condensin I or topoisomerase IIα. Furthermore, quantitative morphological analyses using the machine-learning algorithm wndchrm support the notion that chromosome shaping is tightly coupled to the reorganization of condensin II-based axes. We propose that condensin II makes a primary contribution to mitotic chromosome architecture and maintenance in human cells.

Pluri-IQ: Quantification of Embryonic Stem Cell Pluripotency through an Image-Based Analysis Software

Image-based assays, such as alkaline phosphatase staining or immunocytochemistry for pluripotent markers, are common methods used in the stem cell field to assess pluripotency. Although an increased number of image-analysis approaches have been described, there is still a lack of software availability to automatically quantify pluripotency in large images after pluripotency staining. To address this need, we developed a robust and rapid image processing software, Pluri-IQ, which allows the automatic evaluation of pluripotency in large low-magnification images. Using mouse embryonic stem cells (mESC) as a model, we combined an automated segmentation algorithm with a supervised machine-learning platform to classify colonies as pluripotent, mixed, or differentiated. In addition, Pluri-IQ allows the automatic comparison between different culture conditions. This efficient user-friendly open-source software can be easily implemented in images derived from pluripotent cells or cells that express pluripotent markers (e.g., OCT4-GFP) and can be routinely used, decreasing image assessment bias.

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Open-source software to evaluate pluripotency in low-magnification images

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Automatic colony detection and segmentation

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Supervised machine-learning platform with high characterization accuracy

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Software tools for easy data validation, visualization, and data analysis comparison

Visualization of morphological categories of colonies for monitoring of effect on induced pluripotent stem cell culture status

From the recent advances, there are growing expectations toward the mass production of induced pluripotent stem cells (iPSCs) for varieties of applications. For such type of industrial cell manufacturing, the technology which can stabilize the production efficiency is strongly required. Since the present iPSC culture is covered by delicate manual operations, there are still quality differences in produced cells from same culture protocols. To monitor the culture process of iPSCs with the quantified data to evaluate the culture status, we here introduce image-based visualization method of morphological diversity of iPSC colonies. We have set three types of experiments to evaluate the influential factors in iPSC culture technique that may disturb the undifferentiation status of iPSC colonies: (Exp. 1) technical differences in passage skills, (Exp. 2) technical differences in feeder cell preparation, and (Exp. 3) technical differences in maintenance skills (medium exchange frequency with the combination of manual removal of morphologically irregular colonies). By measuring the all existing colonies from real-time microscopic images, the heterogenous change of colony morphologies in the culture vessel was visualized. By such visualization with morphologically categorized Manhattan chart, the difference between technical skills could be compared for evaluating appropriate cell processing.

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Morphological clustering enabled visualization of diversity of iPSC colonies.

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Morphological clustering can record and visualize the effects of culture skills.

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Comparison of culture skills reveals clue for designing automation protocol.

Image-based cell quality evaluation to detect irregularities under same culture process of human induced pluripotent stem cells

To meet the growing demand for human induced pluripotent stem cells (iPSCs) for various applications, technologies that enable the manufacturing of iPSCs on a large scale should be developed. There are several technological challenges in iPSC manufacturing technology. Image-based cell quality evaluation technology for monitoring iPSC quality in culture enables the manufacture of intact cells for further applications. Although several studies have reported the effectiveness of image-based evaluation of iPSCs, it remains challenging to detect irregularities that may arise using the same processing operations during quality evaluation of automated processing. In this study, we investigated the evaluation performance of image-based cell quality analysis in detecting small differences that can result from human measurement, even when the same protocol is followed. To imitate such culture conditions, by image-analysis guided colony pickup, we changed the proportions of morphologically different subpopulations: "good morphology, regular morphology correlated with undifferentiation marker expression" and "bad morphology, irregular morphology correlated with loss of undifferentiation marker expression". In addition, comprehensive gene-expression and metabolomics analyses were carried out for the same samples to investigate performance differences. Our data shows an example of investigating the usefulness and sensitivity of quality evaluation methods for iPSC quality monitoring.

Parametric analysis of colony morphology of non-labelled live human pluripotent stem cells for cell quality control

Given the difficulties inherent in maintaining human pluripotent stem cells (hPSCs) in a healthy state, hPSCs should be routinely characterized using several established standard criteria during expansion for research or therapeutic purposes. hPSC colony morphology is typically considered an important criterion, but it is not evaluated quantitatively. Thus, we designed an unbiased method to evaluate hPSC colony morphology. This method involves a combination of automated non-labelled live-cell imaging and the implementation of morphological colony analysis algorithms with multiple parameters. To validate the utility of the quantitative evaluation method, a parent cell line exhibiting typical embryonic stem cell (ESC)-like morphology and an aberrant hPSC subclone demonstrating unusual colony morphology were used as models. According to statistical colony classification based on morphological parameters, colonies containing readily discernible areas of differentiation constituted a major classification cluster and were distinguishable from typical ESC-like colonies; similar results were obtained via classification based on global gene expression profiles. Thus, the morphological features of hPSC colonies are closely associated with cellular characteristics. Our quantitative evaluation method provides a biological definition of ‘hPSC colony morphology’, permits the non-invasive monitoring of hPSC conditions and is particularly useful for detecting variations in hPSC heterogeneity.

MicroRNA-302 switch to identify and eliminate undifferentiated human pluripotent stem cells

The efficiency of pluripotent stem cell differentiation is highly variable, often resulting in heterogeneous populations that contain undifferentiated cells. Here we developed a sensitive, target-specific, and general method for removing undesired cells before transplantation. MicroRNA-302a-5p (miR-302a) is highly and specifically expressed in human pluripotent stem cells and gradually decreases to basal levels during differentiation. We synthesized a new RNA tool, miR-switch, as a live-cell reporter mRNA for miR-302a activity that can specifically detect human induced pluripotent stem cells (hiPSCs) down to a spiked level of 0.05% of hiPSCs in a heterogeneous population and can prevent teratoma formation in an in vivo tumorigenicity assay. Automated and selective hiPSC-elimination was achieved by controlling puromycin resistance using the miR-302a switch. Our system uniquely provides sensitive detection of pluripotent stem cells and partially differentiated cells. In addition to its ability to eliminate undifferentiated cells, miR-302a switch also holds great potential in investigating the dynamics of differentiation and/or reprograming of live-cells based on intracellular information.

Machine Learning Approach to Automated Quality Identification of Human Induced Pluripotent Stem Cell Colony Images

The focus of this research is on automated identification of the quality of human induced pluripotent stem cell (iPSC) colony images. iPS cell technology is a contemporary method by which the patient's cells are reprogrammed back to stem cells and are differentiated to any cell type wanted. iPS cell technology will be used in future to patient specific drug screening, disease modeling, and tissue repairing, for instance. However, there are technical challenges before iPS cell technology can be used in practice and one of them is quality control of growing iPSC colonies which is currently done manually but is unfeasible solution in large-scale cultures. The monitoring problem returns to image analysis and classification problem. In this paper, we tackle this problem using machine learning methods such as multiclass Support Vector Machines and several baseline methods together with Scaled Invariant Feature Transformation based features. We perform over 80 test arrangements and do a thorough parameter value search. The best accuracy (62.4%) for classification was obtained by using a k-NN classifier showing improved accuracy compared to earlier studies.

Loss of the integral nuclear envelope protein SUN1 induces alteration of nucleoli

A supervised machine learning algorithm, which is qualified for image classification and analyzing similarities, is based on multiple discriminative morphological features that are automatically assembled during the learning processes. The algorithm is suitable for population-based analysis of images of biological materials that are generally complex and heterogeneous. Here we used the algorithm wndchrm to quantify the effects on nucleolar morphology of the loss of the components of nuclear envelope in a human mammary epithelial cell line. The linker of nucleoskeleton and cytoskeleton (LINC) complex, an assembly of nuclear envelope proteins comprising mainly members of the SUN and nesprin families, connects the nuclear lamina and cytoskeletal filaments. The components of the LINC complex are markedly deficient in breast cancer tissues. We found that a reduction in the levels of SUN1, SUN2, and lamin A/C led to significant changes in morphologies that were computationally classified using wndchrm with approximately 100% accuracy. In particular, depletion of SUN1 caused nucleolar hypertrophy and reduced rRNA synthesis. Further, wndchrm revealed a consistent negative correlation between SUN1 expression and the size of nucleoli in human breast cancer tissues. Our unbiased morphological quantitation strategies using wndchrm revealed an unexpected link between the components of the LINC complex and the morphologies of nucleoli that serves as an indicator of the malignant phenotype of breast cancer cells.

Evaluating Cell Processes, Quality, and Biomarkers in Pluripotent Stem Cells Using Video Bioinformatics

There is a foundational need for quality control tools in stem cell laboratories engaged in basic research, regenerative therapies, and toxicological studies. These tools require automated methods for evaluating cell processes and quality during in vitro passaging, expansion, maintenance, and differentiation. In this paper, an unbiased, automated high-content profiling toolkit, StemCellQC, is presented that non-invasively extracts information on cell quality and cellular processes from time-lapse phase-contrast videos. Twenty four (24) morphological and dynamic features were analyzed in healthy, unhealthy, and dying human embryonic stem cell (hESC) colonies to identify those features that were affected in each group. Multiple features differed in the healthy versus unhealthy/dying groups, and these features were linked to growth, motility, and death. Biomarkers were discovered that predicted cell processes before they were detectable by manual observation. StemCellQC distinguished healthy and unhealthy/dying hESC colonies with 96% accuracy by non-invasively measuring and tracking dynamic and morphological features over 48 hours. Changes in cellular processes can be monitored by StemCellQC and predictions can be made about the quality of pluripotent stem cell colonies. This toolkit reduced the time and resources required to track multiple pluripotent stem cell colonies and eliminated handling errors and false classifications due to human bias. StemCellQC provided both user-specified and classifier-determined analysis in cases where the affected features are not intuitive or anticipated. Video analysis algorithms allowed assessment of biological phenomena using automatic detection analysis, which can aid facilities where maintaining stem cell quality and/or monitoring changes in cellular processes are essential. In the future StemCellQC can be expanded to include other features, cell types, treatments, and differentiating cells.