A Novel Automated High-Content Analysis Workflow Capturing Cell Population Dynamics from Induced Pluripotent Stem Cell Live Imaging Data

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.

Everything You Always Wanted to Know About Organoid-Based Models (and Never Dared to Ask)

Homeostatic functions of a living tissue, such as the gastrointestinal tract, rely on highly sophisticated and finely tuned cell-to-cell interactions. These crosstalks evolve and continuously are refined as the tissue develops and give rise to specialized cells performing general and tissue-specific functions. To study these systems, stem cell-based in vitro models, often called organoids, and non-stem cell-based primary cell aggregates (called spheroids) appeared just over a decade ago. These models still are evolving and gaining complexity, making them the state-of-the-art models for studying cellular crosstalk in the gastrointestinal tract, and to investigate digestive pathologies, such as inflammatory bowel disease, colorectal cancer, and liver diseases. However, the use of organoid- or spheroid-based models to recapitulate in vitro the highly complex structure of in vivo tissue remains challenging, and mainly restricted to expert developmental cell biologists. Here, we condense the founding knowledge and key literature information that scientists adopting the organoid technology for the first time need to consider when using these models for novel biological questions. We also include information that current organoid/spheroid users could use to add to increase the complexity to their existing models. We highlight the current and prospective evolution of these models through bridging stem cell biology with biomaterial and scaffold engineering research areas. Linking these complementary fields will increase the in vitro mimicry of in vivo tissue, and potentially lead to more successful translational biomedical applications. Deepening our understanding of the nature and dynamic fine-tuning of intercellular crosstalks will enable identifying novel signaling targets for new or repurposed therapeutics used in many multifactorial diseases.

Regression plane concept for analysing continuous cellular processes with machine learning

Biological processes are inherently continuous, and the chance of phenotypic discovery is significantly restricted by discretising them. Using multi-parametric active regression we introduce the Regression Plane (RP), a user-friendly discovery tool enabling class-free phenotypic supervised machine learning, to describe and explore biological data in a continuous manner. First, we compare traditional classification with regression in a simulated experimental setup. Second, we use our framework to identify genes involved in regulating triglyceride levels in human cells. Subsequently, we analyse a time-lapse dataset on mitosis to demonstrate that the proposed methodology is capable of modelling complex processes at infinite resolution. Finally, we show that hemocyte differentiation in Drosophila melanogaster has continuous characteristics.

Noninvasive measurement of cell/colony motion using image analysis methods to evaluate the proliferative capacity of oral keratinocytes as a tool for quality control in regenerative medicine

Image-based cell/colony analyses offer promising solutions to compensate for the lack of quality control (QC) tools for noninvasive monitoring of cultured cells, a regulatory challenge in regenerative medicine. Here, the feasibility of two image analysis algorithms, optical flow and normalised cross-correlation, to noninvasively measure cell/colony motion in human primary oral keratinocytes for screening the proliferative capacity of cells in the early phases of cell culture were examined. We applied our software to movies converted from 96 consecutive time-lapse phase-contrast images of an oral keratinocyte culture. After segmenting the growing colonies, two indices were calculated based on each algorithm. The correlation between each index of the colonies and their proliferative capacity was evaluated. The software was able to assess cell/colony motion noninvasively, and each index reflected the observed cell kinetics. A positive linear correlation was found between cell/colony motion and proliferative capacity, indicating that both algorithms are potential tools for QC.

Automated and Reproducible Detection of Vascular Endothelial Growth Factor (VEGF) in Renal Tissue Sections

Background

Manual analysis of tissue sections, such as for pathological diagnosis, requires an analyst with substantial knowledge and experience. Reproducible image analysis of biological samples is steadily gaining scientific importance. The aim of the present study was to employ image analysis followed by machine learning to identify vascular endothelial growth factor (VEGF) in kidney tissue that had been subjected to hypoxia.

Methods

Light microscopy images of renal tissue sections stained for VEGF were analyzed. Subsequently, machine learning classified the cells as VEGF⁺ and VEGF⁻ cells.

Results

VEGF was detected and cells were counted with high sensitivity and specificity.

Conclusion

With great clinical, diagnostic, and research potential, automatic image analysis offers a new quantitative capability, thereby adding numerical information to a mostly qualitative diagnostic approach.

Integrated Multiparametric High-Content Profiling of Endothelial Cells

Endothelial cells (ECs) are widely heterogeneous at the cell level and serve different functions at the vessel and tissue levels. EC-forming colonies derived from induced pluripotent stem cells (iPSC-ECFCs) alongside models such as primary human umbilical vein ECs (HUVECs) are slowly becoming available for research with future applications in cell therapies, disease modeling, and drug discovery. We and others previously described high-content analysis approaches capturing unbiased morphology-based measurements coupled with immunofluorescence and used these for multidimensional reduction and population analysis. Here, we report a tailored workflow to characterize ECs. We acquire images at high resolution with high-magnification water-immersion objectives with Hoechst, vascular endothelial cadherin (VEC), and activated NOTCH staining. We hypothesize that via these key markers alone we would be able to distinguish and assess different EC populations. We used cell population software analysis to phenotype HUVECs and iPSC-ECFCs in the absence or presence of vascular endothelial growth factor (VEGF). To our knowledge, this study presents the first parallel quantitative high-content multiparametric profiling of EC models. Importantly, it highlights a simple strategy to benchmark ECs in different conditions and develop new approaches for biological research and translational applications for regenerative medicine.

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Morphology-Based Analysis of Myoblasts for Prediction of Myotube Formation

The development of new drugs depends on the efficiency of drug screening. Phenotype-based screening has attracted interest due to its considerable potency for the discovery of first-in-class drugs. In general, fluorescently labeled imagery is the leading technique for phenotype-based screening; however, there are growing requirements to understand total culture profiles, which are unclear after end-point assays. In this study, we demonstrate that morphology-based cellular evaluation of unlabeled cells is an efficient approach to evaluate myotube formation assays. One of our aims was to study the myogenic differentiation process in C2C12 cells to discern the differences between cellular responses to different medium conditions (serum concentrations and insulin dosages). Our results show that predictive morphological profiles that strongly correlate with myogenic differentiation can be generated from myotube images, even in the confluent stage. The differentiation rate after 14 days can be quantitatively predicted with the highest accuracy by means of images taken on days 0-11.5. In addition, for the application of our morphology-based cellular evaluation, the effect of cyclic guanosine monophosphate (cGMP) on myogenic differentiation was analyzed. Our results show that the quantitated morphological profile from these images can be an effective descriptor for analysis of the myotube-recovering effect of cGMP.