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.

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.

Oligogenic inheritance of a human heart disease involving a genetic modifier.

Complex genetic mechanisms are thought to underlie many human diseases, yet experimental proof of this model has been elusive. Here, we show that a human cardiac anomaly can be caused by a combination of rare, inherited heterozygous mutations. Whole-exome sequencing of a nuclear family revealed that three offspring with childhood-onset cardiomyopathy had inherited three missense single-nucleotide variants in the MKL2, MYH7, and NKX2-5 genes. The MYH7 and MKL2 variants were inherited from the affected, asymptomatic father and the rare NKX2-5 variant (minor allele frequency, 0.0012) from the unaffected mother. We used CRISPR-Cas9 to generate mice encoding the orthologous variants and found that compound heterozygosity for all three variants recapitulated the human disease phenotype. Analysis of murine hearts and human induced pluripotent stem cell-derived cardiomyocytes provided histologic and molecular evidence for the NKX2-5 variant's contribution as a genetic modifier.