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

A secreted proteomic footprint for stem cell pluripotency

With a view to developing a much-needed non-invasive method for monitoring the healthy pluripotent state of human stem cells in culture, we undertook proteomic analysis of the waste medium from cultured embryonic (Man-13) and induced (Rebl.PAT) human pluripotent stem cells (hPSCs). Cells were grown in E8 medium to maintain pluripotency, and then transferred to FGF2 and TGFβ deficient E6 media for 48 hours to replicate an early, undirected dissolution of pluripotency. We identified a distinct proteomic footprint associated with early loss of pluripotency in both hPSC lines, and a strong correlation with changes in the transcriptome. We demonstrate that multiplexing of four E8- against four E6- enriched secretome biomarkers provides a robust, diagnostic metric for the pluripotent state. These biomarkers were further confirmed by Western blotting which demonstrated consistent correlation with the pluripotent state across cell lines, and in response to a recovery assay.

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.

TNTdetect.AI: A Deep Learning Model for Automated Detection and Counting of Tunneling Nanotubes in Microscopy Images

BACKGROUND

Tunneling nanotubes (TNTs) are cellular structures connecting cell membranes and mediating intercellular communication. TNTs are manually identified and counted by a trained investigator; however, this process is time-intensive. We therefore sought to develop an automated approach for quantitative analysis of TNTs.

METHODS

We used a convolutional neural network (U-Net) deep learning model to segment phase contrast microscopy images of both cancer and non-cancer cells. Our method was composed of preprocessing and model development. We developed a new preprocessing method to label TNTs on a pixel-wise basis. Two sequential models were employed to detect TNTs. First, we identified the regions of images with TNTs by implementing a classification algorithm. Second, we fed parts of the image classified as TNT-containing into a modified U-Net model to estimate TNTs on a pixel-wise basis.

RESULTS

The algorithm detected 49.9% of human expert-identified TNTs, counted TNTs, and calculated the number of TNTs per cell, or TNT-to-cell ratio (TCR); it detected TNTs that were not originally detected by the experts. The model had 0.41 precision, 0.26 recall, and 0.32 f-1 score on a test dataset. The predicted and true TCRs were not significantly different across the training and test datasets (p = 0.78).

CONCLUSIONS

Our automated approach labeled and detected TNTs and cells imaged in culture, resulting in comparable TCRs to those determined by human experts. Future studies will aim to improve on the accuracy, precision, and recall of the algorithm.

Bioinformatics in bioscience and bioengineering: Recent advances, applications, and perspectives

Recent advances have led to the emergence of highly comprehensive and analytical approaches, such as omics analysis and high-resolution, time-resolved bioimaging analysis. These technologies have made it possible to obtain vast data from a single measurement. Subsequently, large datasets have pioneered the data-driven approach, an alternative to the traditional hypothesis-testing system, for researchers. However, processing, interpreting, and elucidating enormous datasets is no longer possible without computation. Bioinformatics is a field that has developed over long periods, intending to understand biological phenomena using methods collected from information science and statistics, thus solving this proposed research challenge. This review presents the latest methodologies and applications in sequencing, imaging, and mass spectrometry that were developed using bioinformatics. We presented the features of individual techniques and outlines in each part, avoiding the use of complex algorithms and formulas to allow beginning researchers to understand an overview. In the section on sequencing, we focused on comparative genomic, transcriptomic, and bacterial microbiome analyses, which are frequently used as applications of next-generation sequencing. Bioinformatic methods for handling sequence data and case studies were described. In the section on imaging, we introduced the analytical methods and microscopy imaging informatics techniques used in animal cell biology and plant physiology. We introduce informatics technologies for maximizing the value of measured data, including predicting the structure of unknown molecules and untargeted analysis in the section on mass spectrometry. Finally, we discuss the future outlook of this field. We anticipate that this review will assist biologists in using bioinformatics more effectively.

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.

Patient derived stem cells for discovery and validation of novel pathogenic variants in inherited retinal disease

Our understanding of inherited retinal disease has benefited immensely from molecular genetic analysis over the past several decades. New technologies that allow for increasingly detailed examination of a patient's DNA have expanded the catalog of genes and specific variants that cause retinal disease. In turn, the identification of pathogenic variants has allowed the development of gene therapies and low-cost, clinically focused genetic testing. Despite this progress, a relatively large fraction (at least 20%) of patients with clinical features suggestive of an inherited retinal disease still do not have a molecular diagnosis today. Variants that are not obviously disruptive to the codon sequence of exons can be difficult to distinguish from the background of benign human genetic variations. Some of these variants exert their pathogenic effect not by altering the primary amino acid sequence, but by modulating gene expression, isoform splicing, or other transcript-level mechanisms. While not discoverable by DNA sequencing methods alone, these variants are excellent targets for studies of the retinal transcriptome. In this review, we present an overview of the current state of pathogenic variant discovery in retinal disease and identify some of the remaining barriers. We also explore the utility of new technologies, specifically patient-derived induced pluripotent stem cell (iPSC)-based modeling, in further expanding the catalog of disease-causing variants using transcriptome-focused methods. Finally, we outline bioinformatic analysis techniques that will allow this new method of variant discovery in retinal disease. As the knowledge gleaned from previous technologies is informing targets for therapies today, we believe that integrating new technologies, such as iPSC-based modeling, into the molecular diagnosis pipeline will enable a new wave of variant discovery and expanded treatment of inherited retinal disease.

All models are wrong, but some are useful: Establishing standards for stem cell-based embryo models

Detailed studies of the embryo allow an increasingly mechanistic understanding of development, which has proved of profound relevance to human disease. The last decade has seen in vitro cultured stem cell-based models of embryo development flourish, which provide an alternative to the embryo for accessible experimentation. However, the usefulness of any stem cell-based embryo model will be determined by how accurately it reflects in vivo embryonic development, and/or the extent to which it facilitates new discoveries. Stringent benchmarking of embryo models is thus an important consideration for this growing field. Here we provide an overview of means to evaluate both the properties of stem cells, the building blocks of most embryo models, as well as the usefulness of current and future in vitro embryo models.

UV/ozone surface modification combined with atmospheric pressure plasma irradiation for cell culture plastics to improve pluripotent stem cell culture

Culturing pluripotent stem cells effectively requires substrates coated with feeder cell layers or cell-adhesive matrices. It is difficult to employ pluripotent stem cells as resources for regenerative medicine due to risks of culture system contamination by animal-derived factors, or the large costs associated with the use of adhesive matrices. To enable a coating-free culture system, we focused on UV/ozone surface modification and atmospheric pressure plasma treatment for polystyrene substrates, to improve adhesion and proliferation of pluripotent stem cells. In this study, to develop a feeder- and matrix coating-free culture system for embryonic stem cells (ESCs), mouse ESCs were cultured on polystyrene substrates that were surface-modified using UV/ozone-plasma combined treatment. mESCs could be successfully cultured under feeder-free conditions upon UV/ozone-plasma combined treatment of culture substrates, without any further chemical treatments, and showed similar proliferation rates to those of cells grown on the feeder cell layer or matrix-coated substrates.

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.

Effects of DMSO on the Pluripotency of Cultured Mouse Embryonic Stem Cells (mESCs)

DMSO is a commonly used solvent in biological studies, as it is an amphipathic molecule soluble in both aqueous and organic media. For that reason, it is the vehicle of choice for several water-insoluble substances used in research. At the molecular and cellular level, DMSO is a hydrogen-bound disrupter, an intercellular electrical uncoupler, and a cryoprotectant, among other properties. Importantly, DMSO often has overlooked side effects. In stem cell research, the literature is scarce, but there are reports on the effect of DMSO in human embryoid body differentiation and on human pluripotent stem cell priming towards differentiation, via modulation of cell cycle. However, in mouse embryonic stem cell (mESC) culture, there is almost no available information. Taking into consideration the almost ubiquitous use of DMSO in experiments involving mESCs, we aimed to understand the effect of very low doses of DMSO (0.0001%-0.2%), usually used to introduce pharmacological inhibitors/modulators, in mESCs cultured in two different media (2i and FBS-based media). Our results show that in the E14Tg2a mESC line used in this study, even the smallest concentration of DMSO had minor effects on the total number of cells in serum-cultured mESCs. However, these effects could not be explained by alterations in cell cycle or apoptosis. Furthermore, DMSO did not affect pluripotency or differentiation potential. All things considered, and although control experiments should be carried out in each cell line that is used, it is reasonable to conclude that DMSO at the concentrations used here has a minimal effect on this particular mESC line.

Metabolic characterization of a paused-like pluripotent state

Embryonic diapause is a conserved reproductive strategy in which development arrests at the blastocyst phase. Recently mammalian target of rapamycin (mTOR) inhibition was shown to induce diapause on mouse blastocysts and a paused-like state on mouse embryonic stem cells (mESCs). In this work, we aimed to further characterize this new paused-pluripotent state, focusing on its glycolytic and oxidative metabolic function. We therefore exposed mESCs, to the mTOR inhibitor INK-128 and evaluated proliferation, pluripotency status and energy-related metabolism, as well as the mTOR inhibition status and translational function. Unexpectedly, in our hands INK-128 did not inhibit the phosphorylation of mTOR or its downstream targets after 48 h. Accordingly, no alterations on protein translational function were observed. Nonetheless, INK-128 could still successfully induce a paused-like state in naïve mESCs regardless of their culturing conditions, by greatly slowing proliferation without affecting pluripotency status. This effect was more prevalent in 2i cultured cells. Interestingly, in this paused-like state, mESCs present a glucose-related hypometabolic profile, which is a hallmark of diapaused blastocysts, with decreased glycolytic and oxidative metabolism and decreased nutrient uptake. Despite the lack of mTOR inhibition and translational suppression, INK-128 still induced a paused-like pluripotent state through cell cycle and metabolic modulation, rather than by translational suppression, suggesting more than one avenue for this type of pluripotent phenotype.

Live-cell imaging of subcellular structures for quantitative evaluation of pluripotent stem cells

Pluripotent stem cells (PSCs) have various degrees of pluripotency, which necessitates selection of PSCs with high pluripotency before their application to regenerative medicine. However, the quality control processes for PSCs are costly and time-consuming, and it is essential to develop inexpensive and less laborious selection methods for translation of PSCs into clinical applications. Here we developed an imaging system, termed Phase Distribution (PD) imaging system, which visualizes subcellular structures quantitatively in unstained and unlabeled cells. The PD image and its derived PD index reflected the mitochondrial content, enabling quantitative evaluation of the degrees of somatic cell reprogramming and PSC differentiation. Moreover, the PD index allowed unbiased grouping of PSC colonies into those with high or low pluripotency without the aid of invasive methods. Finally, the PD imaging system produced three-dimensional images of PSC colonies, providing further criteria to evaluate pluripotency of PSCs. Thus, the PD imaging system may be utilized for screening of live PSCs with potentially high pluripotency prior to more rigorous quality control processes.

Cytoskeletal tension regulates mesodermal spatial organization and subsequent vascular fate

Morphogenesis during human development relies on the interplay between physiochemical cues that are mediated in part by cellular density and cytoskeletal tension. Here, we interrogated these factors on vascular lineage specification during human-induced pluripotent stem-cell (hiPSC) fate decision. We found that independent of chemical cues, spatially presented physical cues induce the self-organization of Brachyury-positive mesodermal cells, in a RhoA/Rho-associated kinase (ROCK)-dependent manner. Using unbiased support vector machine (SVM) learning, we found that density alone is sufficient to predict mesodermal fate. Furthermore, the long-withstanding presentation of spatial confinement during hiPSC differentiation led to an organized vascular tissue, reminiscent of native blood vessels, a process dependent on cell density as found by SVM analysis. Collectively, these results show how tension and density relate to vascular identity mirroring early morphogenesis. We propose that such a system can be applied to study other aspects of the stem-cell niche and its role in embryonic patterning.

Kinetic Analysis of Enzymes Immobilized in Porous Film Arrays

Measuring the catalytic activity of immobilized enzymes underpins development of biosensing, bioprocessing, and analytical chemistry tools. To expand the range of approaches available for measuring enzymatic activity, we report on a technique to probe activity of enzymes immobilized in porous materials in the absence of confounding mass transport artifacts. We measured reaction kinetics of calf intestinal alkaline phosphatase (CIAP) immobilized in benzophenone-modified polyacrylamide (BPMA-PAAm) gel films housed in an array of fluidically isolated chambers. To ensure kinetics measurements are not confounded by mass transport limitations, we employed Weisz's modulus (Φ), which compares observed enzyme-catalyzed reaction rates to characteristic substrate diffusion times. We characterized activity of CIAP immobilized in BPMA-PAAm gels in a reaction-limited regime (Φ ≪ 0.15 for all measurements), allowing us to isolate the effect of immobilization on enzymatic activity. Immobilization of CIAP in BPMA-PAAm gels produced a ∼2× loss in apparent enzyme-substrate affinity (Km) and ∼200× decrease in intrinsic catalytic activity (kcat) relative to in-solution measurements. As estimating Km and kcat requires multiple steps of data manipulation, we developed a computational approach (bootstrapping) to propagate uncertainty in calibration data through all data manipulation steps. Numerical simulation revealed that calibration error is only negligible when the normalized root-mean-squared error (NRMSE) in the calibration falls below 0.05%. Importantly, bootstrapping is independent of the mathematical model, and thus generalizable beyond enzyme kinetics studies. Furthermore, the measurement tool presented can be readily adapted to study other porous immobilization supports, facilitating rational design (immobilization method, geometry, enzyme loading) of immobilized-enzyme devices.