Integrated intracellular organization and its variations in human iPS cells.

Understanding how a subset of expressed genes dictates cellular phenotype is a considerable challenge owing to the large numbers of molecules involved, their combinatorics and the plethora of cellular behaviours that they determine1,2. Here we reduced this complexity by focusing on cellular organization-a key readout and driver of cell behaviour3,4-at the level of major cellular structures that represent distinct organelles and functional machines, and generated the WTC-11 hiPSC Single-Cell Image Dataset v1, which contains more than 200,000 live cells in 3D, spanning 25 key cellular structures. The scale and quality of this dataset permitted the creation of a generalizable analysis framework to convert raw image data of cells and their structures into dimensionally reduced, quantitative measurements that can be interpreted by humans, and to facilitate data exploration. This framework embraces the vast cell-to-cell variability that is observed within a normal population, facilitates the integration of cell-by-cell structural data and allows quantitative analyses of distinct, separable aspects of organization within and across different cell populations. We found that the integrated intracellular organization of interphase cells was robust to the wide range of variation in cell shape in the population; that the average locations of some structures became polarized in cells at the edges of colonies while maintaining the 'wiring' of their interactions with other structures; and that, by contrast, changes in the location of structures during early mitotic reorganization were accompanied by changes in their wiring.

A comprehensive analysis of gene expression changes in a high replicate and open-source dataset of differentiating hiPSC-derived cardiomyocytes.

We performed a comprehensive analysis of the transcriptional changes occurring during human induced pluripotent stem cell (hiPSC) differentiation to cardiomyocytes. Using single cell RNA-seq, we sequenced > 20,000 single cells from 55 independent samples representing two differentiation protocols and multiple hiPSC lines. Samples included experimental replicates ranging from undifferentiated hiPSCs to mixed populations of cells at D90 post-differentiation. Differentiated cell populations clustered by time point, with differential expression analysis revealing markers of cardiomyocyte differentiation and maturation changing from D12 to D90. We next performed a complementary cluster-independent sparse regression analysis to identify and rank genes that best assigned cells to differentiation time points. The two highest ranked genes between D12 and D24 (MYH7 and MYH6) resulted in an accuracy of 0.84, and the three highest ranked genes between D24 and D90 (A2M, H19, IGF2) resulted in an accuracy of 0.94, revealing that low dimensional gene features can identify differentiation or maturation stages in differentiating cardiomyocytes. Expression levels of select genes were validated using RNA FISH. Finally, we interrogated differences in cardiac gene expression resulting from two differentiation protocols, experimental replicates, and three hiPSC lines in the WTC-11 background to identify sources of variation across these experimental variables.

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.

Leptomeninges-Derived Induced Pluripotent Stem Cells and Directly Converted Neurons From Autopsy Cases With Varying Neuropathologic Backgrounds.

Patient-specific stem cell technology from skin and other biopsy sources has transformed in vitro models of neurodegenerative disease, permitting interrogation of the effects of complex human genetics on neurotoxicity. However, the neuropathologic changes that underlie cognitive and behavioral phenotypes can only be determined at autopsy. To better correlate the biology of derived neurons with age-related and neurodegenerative changes, we generated leptomeningeal cell lines from well-characterized research subjects that have undergone comprehensive postmortem neuropathologic examinations. In a series of proof of principle experiments, we reprogrammed autopsy leptomeningeal cell lines to human-induced pluripotent stem cells (hiPSCs) and differentiated these into neurons. We show that leptomeningeal-derived hiPSC lines can be generated from fresh and frozen leptomeninges, are pluripotent, and retain the karyotype of the starting cell population. Additionally, neurons differentiated from these hiPSCs are functional and produce measurable Alzheimer disease-relevant analytes (Aß and Tau). Finally, we used direct conversion protocols to transdifferentiate leptomeningeal cells to neurons. These resources allow the generation of in vitro models to test mechanistic hypotheses as well as diagnostic and therapeutic strategies in association with neuropathology, clinical and cognitive data, and biomarker studies, aiding in the study of late-onset Alzheimer disease and other age-related neurodegenerative diseases.