Unraveling the Mechanisms of Vestibular Neuron Formation from Human Induced Pluripotent Stem Cells.

The development of in vitro models that accurately recapitulate the complex cellular and molecular interactions of the inner ear is crucial for understanding inner ear development, function, and disease. In this study, we utilized a customized microfluidic platform to generate human induced pluripotent stem cell (hiPSC)-derived three-dimensional otic sensory neurons (OSNs). hiPSC-derived otic neuronal progenitors (ONPs) were cultured in hydrogel-embedded microfluidic channels over a 40-day period. Careful modulation of Wnt and Shh signaling pathways was used to influence dorsoventral patterning and direct differentiation toward a vestibular neuron lineage. After validating the microfluidic platform, OSN spheroid transcription factor and protein expression were assessed using real-time quantitative polymerase chain reaction (RT-qPCR), immunocytochemistry, and flow cytometry. The results demonstrated the successful differentiation of hiPSCs into ONPs and subsequent divergent differentiation into vestibular neuronal lineages, as evidenced by the expression of characteristic markers. Overall, our microfluidic platform provides a physiologically relevant environment for the culture and differentiation of hiPSCs, offering a valuable tool for studying inner ear development, disease and drug screening, and regenerative medicine applications.

Cellular morphological features are predictive markers of cancer cell state.

Even genetically identical cells have heterogeneous properties because of stochasticity in gene or protein expression. Single cell techniques that assay heterogeneous properties would be valuable for basic science and diseases like cancer, where accurate estimates of tumor properties is critical for accurate diagnosis and grading. Cell morphology is an emergent outcome of many cellular processes, potentially carrying information about cell properties at the single cell level. Here we study whether morphological parameters are sufficient for classification of single cells, using a set of 15 cell lines, representing three processes: (i) the transformation of normal cells using specific genetic mutations; (ii) metastasis in breast cancer and (iii) metastasis in osteosarcomas. Cellular morphology is defined as quantitative measures of the shape of the cell and the structure of the actin. We use a toolbox that calculates quantitative morphological parameters of cell images and apply it to hundreds of images of cells belonging to different cell lines. Using a combination of dimensional reduction and machine learning, we test whether these different processes have specific morphological signatures and whether single cells can be classified based on morphology alone. Using morphological parameters we could accurately classify cells as belonging to the correct class with high accuracy. Morphological signatures could distinguish between cells that were different only because of a different mutation on a parental line. Furthermore, both oncogenesis and metastasis appear to be characterized by stereotypical morphology changes. Thus, cellular morphology is a signature of cell phenotype, or state, at the single cell level.