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

Profiling of collagen and extracellular matrix deposition from cell culture using in vitro ExtraCellular matrix mass spectrometry imaging (ivECM-MSI)

While numerous approaches have been reported towards understanding single cell regulation, there is limited understanding of single cell production of extracellular matrix phenotypes. Collagens are major proteins of the extracellular microenvironment extensively used in basic cell culture, tissue engineering, and biomedical applications. However, identifying compositional regulation of collagen remains challenging. Here, we report the development of In vitro ExtraCellular Matrix Mass Spectrometry Imaging (ivECM-MSI) as a tool to rapidly and simultaneously define collagen subtypes from coatings and basic cell culture applications. The tool uses the mass spectrometry imaging platform with reference libraries to produce visual and numerical data types. The method is highly integrated with basic in vitro strategies as it may be used with conventional cell chambers on minimal numbers of cells and with minimal changes to biological experiments. Applications tested include semi-quantitation of collagen composition in culture coatings, time course collagen deposition, deposition altered by gene knockout, and changes induced by drug treatment. This approach provides new access to proteomic information on how cell types respond to and change the extracellular microenvironment and provides a holistic understanding of both the cell and extracellular response.

Advancing skin model development: A focus on a self-assembled, induced pluripotent stem cell-derived, xeno-free approach

The demand for skin models as alternatives to animal testing has grown due to ethical concerns and the need for accurate substance evaluation. These alternatives, known as New Approach Methodologies (NAMs), are increasingly used for regulatory decisions. Current skin models from primary human cells often rely on bovine collagen, raising ethical issues. This study explores self-assembled skin models (SASM) as a new method, utilizing hair follicle-derived keratinocytes reprogrammed into induced pluripotent stem cells (iPSC) and differentiated into fibroblasts and keratinocytes. The model relies on the ability of fibroblasts to secrete collagen to produce a xeno-free dermal layer and on the differentiation of keratinocytes to create a functional epidermal layer. These layers exhibited confirmed metabolic activity and the capability to withstand test substances. The successful development of SASM underscores the significance of accurate alternatives in dermatological research, providing an ethical and reliable option for substance evaluation and regulatory testing.

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.

The importance of scoring recognition fitness in spheroid morphological analysis for robust label-free quality evaluation

Because of the growing demand for human cell spheroids as functional cellular components for both drug development and regenerative therapy, the technology to non-invasively evaluate their quality has emerged. Image-based morphology analysis of spheroids enables high-throughput screening of their quality. However, since spheroids are three-dimensional, their images can have poor contrast in their surface area, and therefore the total spheroid recognition by image processing is greatly dependent on human who design the filter-set to fit for their own definition of spheroid outline. As a result, the reproducibility of morphology measurement is critically affected by the performance of filter-set, and its fluctuation can disrupt the subsequent morphology-based analysis. Although the unexpected failure derived from the inconsistency of image processing result is a critical issue for analyzing large image data for quality screening, it has been tackled rarely. To achieve robust analysis performances using morphological features, we investigated the influence of filter-set's reproducibility for various types of spheroid data. We propose a new scoring index, the "recognition fitness deviation (RFD)," as a measure to quantitatively and comprehensively evaluate how reproductively a designed filter-set can work with data variations, such as the variations in replicate samples, in time-course samples, and in different types of cells (a total of six normal or cancer cell types). Our result shows that RFD scoring from 5000 images can automatically rank the best robust filter-set for obtaining the best 6-cell type classification model (94% accuracy). Moreover, the RFD score reflected the differences between the worst and the best classification models for morphologically similar spheroids, 60% and 89% accuracy respectively. In addition to RFD scoring, we found that using the time-course of morphological features can augment the fluctuations in spheroid recognitions leading to robust morphological analysis.