Automated human induced pluripotent stem cell culture and sample preparation for 3D live-cell microscopy.

To produce abundant cell culture samples to generate large, standardized image datasets of human induced pluripotent stem (hiPS) cells, we developed an automated workflow on a Hamilton STAR liquid handler system. This was developed specifically for culturing hiPS cell lines expressing fluorescently tagged proteins, which we have used to study the principles by which cells establish and maintain robust dynamic localization of cellular structures. This protocol includes all details for the maintenance, passage and seeding of cells, as well as Matrigel coating of 6-well plastic plates and 96-well optical-grade, glass plates. We also developed an automated image-based hiPS cell colony segmentation and feature extraction pipeline to streamline the process of predicting cell count and selecting wells with consistent morphology for high-resolution three-dimensional (3D) microscopy. The imaging samples produced with this protocol have been used to study the integrated intracellular organization and cell-to-cell variability of hiPS cells to train and develop deep learning-based label-free predictions from transmitted-light microscopy images and to develop deep learning-based generative models of single-cell organization. This protocol requires some experience with robotic equipment. However, we provide details and source code to facilitate implementation by biologists less experienced with robotics. The protocol is completed in less than 10 h with minimal human interaction. Overall, automation of our cell culture procedures increased our imaging samples' standardization, reproducibility, scalability and consistency. It also reduced the need for stringent culturist training and eliminated culturist-to-culturist variability, both of which were previous pain points of our original manual pipeline workflow.

Rho-associated protein kinase inhibition enhances airway epithelial Basal-cell proliferation and lentivirus transduction.

The identification of factors that regulate airway epithelial cell proliferation and differentiation are essential for understanding the pathophysiology of airway diseases. Rho-associated protein kinases (ROCKs) are downstream effector proteins of RhoA GTPase that direct the functions of cell cytoskeletal proteins. ROCK inhibition with Y27632 has been shown to enhance the survival and cloning of human embryonic stem cells and pluripotent cells in other tissues. We hypothesized that Y27632 treatment exerts a similar effect on airway epithelial basal cells, which function as airway epithelial progenitor cells. Treatment with Y27632 enhanced basal-cell proliferation in cultured human tracheobronchial and mouse tracheal epithelial cells. ROCK inhibition accelerated the maturation of basal cells, characterized by a diminution of the cell size associated with cell compaction and the expression of E-cadherin at cell-cell junctions. Transient treatment of cultured basal cells with Y27632 did not affect subsequent ciliated or mucous cell differentiation under air-liquid interface conditions, and allowed for the initial use of lower numbers of human or mouse primary airway epithelial cells than otherwise possible. Moreover, the use of Y27632 during lentivirus-mediated transduction significantly improved posttransduction efficiency and the selection of a transduced cell population, as determined by reporter gene expression. These findings suggest an important role for ROCKs in the regulation of proliferation and maturation of epithelial basal cells, and demonstrate that the inhibition of ROCK pathways using Y27632 provides an adjunctive tool for the in vitro genetic manipulation of airway epithelial cells by lentivirus vectors.

Cell states beyond transcriptomics: Integrating structural organization and gene expression in hiPSC-derived cardiomyocytes.

Although some cell types may be defined anatomically or by physiological function, a rigorous definition of cell state remains elusive. Here, we develop a quantitative, imaging-based platform for the systematic and automated classification of subcellular organization in single cells. We use this platform to quantify subcellular organization and gene expression in >30,000 individual human induced pluripotent stem cell-derived cardiomyocytes, producing a publicly available dataset that describes the population distributions of local and global sarcomere organization, mRNA abundance, and correlations between these traits. While the mRNA abundance of some phenotypically important genes correlates with subcellular organization (e.g., the beta-myosin heavy chain, MYH7), these two cellular metrics are heterogeneous and often uncorrelated, which suggests that gene expression alone is not sufficient to classify cell states. Instead, we posit that cell state should be defined by observing full distributions of quantitative, multidimensional traits in single cells that also account for space, time, and function.