The generation of a collection of human induced pluripotent stem cell (hiPSC) lines expressing endogenously GFP-tagged proteins using CRISPR/Cas9 methods is described. The methods used and the genomic and cell biological data validating the GFP-tagged hiPSC lines are also presented.

We present a CRISPR/Cas9 genome editing strategy to systematically tag endogenous proteins with fluorescent tags in human inducible pluripotent stem cells. To date we have generated multiple human iPSC lines with GFP tags for 10 proteins representing key cellular structures. The tagged proteins include alpha tubulin, beta actin, desmoplakin, fibrillarin, lamin B1, non-muscle myosin heavy chain IIB, paxillin, Sec61 beta, tight junction protein ZO1, and Tom20. Our genome editing methodology using Cas9 ribonuclear protein electroporation and fluorescence-based enrichment of edited cells resulted in <0.1-24% HDR across all experiments. Clones were generated from each edited population and screened for precise editing. ~25% of the clones contained precise mono-allelic edits at the targeted locus. Furthermore, 92% (36/39) of expanded clonal lines satisfied key quality control criteria including genomic stability, appropriate expression and localization of the tagged protein, and pluripotency. Final clonal lines corresponding to each of the 10 cellular structures are now available to the research community. The data described here, including our editing protocol, genetic screening, quality control assays, and imaging observations, can serve as an initial resource for genome editing in cell biology and stem cell research.

A continuing challenge in quantitative cell biology is the accurate and robust 3D segmentation of structures of interest from fluorescence microscopy images in an automated, reproducible, and widely accessible manner for subsequent interpretable data analysis. We describe the Allen Cell Structure Segmenter, a new Python-based open source toolkit developed for 3D segmentation of intracellular structures in fluorescence microscope images. This toolkit brings together classic image segmentation and iterative deep learning workflows first to generate initial high-quality 3D intracellular structure segmentations and then to easily curate these results to generate the ground truths for building robust and accurate deep learning models. The toolkit takes advantage of the high-replicate 3D live cell image data collected at the Allen Institute for Cell Science of over 30 endogenous fluorescently tagged human induced pluripotent stem cell (hiPSC) lines. Each cell line represents a different intracellular structure with one or more distinct localization patterns within undifferentiated hiPS cells and hiPSC-derived cardiomyocytes. The Allen Cell Structure Segmenter consists of two complementary elements, a classic image segmentation workflow with a restricted set of algorithms and parameters and an iterative deep learning segmentation workflow. We created a collection of 20 classic image segmentation workflows based on 20 distinct and representative intracellular structure localization patterns as a "lookup table" reference and starting point for users. The iterative deep learning workflow can take over when the classic segmentation workflow is insufficient. Two straightforward "human-in-the-loop" curation strategies convert a set of classic image segmentation workflow results into a set of 3D ground truth images for iterative model training without the need for manual painting in 3D. The deep learning model architectures used in this toolkit were designed and tested specifically for 3D fluorescence microscope images and implemented as readable scripts. This toolkit was applied to the robust segmentation of fluorescent lamin B1, which exhibits significant variability in its localization pattern during the cell cycle. The Allen Cell Structure Segmenter thus leverages state of the art computer vision algorithms in an accessible way to facilitate their application by the experimental biology researcher.

Summary We describe a multistep method for endogenous tagging of transcriptionally silent genes in human induced pluripotent stem cells (hiPSCs). A monomeric EGFP (mEGFP) fusion tag and a constitutively expressed mCherry fluorescence selection cassette were delivered in tandem via homology-directed repair to five genes not expressed in hiPSCs but important for cardiomyocyte sarcomere function: TTN , MYL7 , MYL2 , TNNI1 , and ACTN2 . CRISPR/Cas9 was used to deliver the selection cassette and subsequently mediate its excision via microhomology-mediated end-joining and non-homologous end-joining. Most excised clones were effectively tagged, and all properly tagged clones expressed the mEGFP fusion protein upon differentiation into cardiomyocytes, allowing live visualization of these cardiac proteins at the sarcomere. This methodology provides a broadly applicable strategy for endogenously tagging transcriptionally silent genes in hiPSCs, potentially enabling their systematic and dynamic study during differentiation and morphogenesis.

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.