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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.

Non-small cell lung cancer (NSCLC) is currently one of the malignant tumors with the highest incidence and mortality rate in China. Circular RNA hsa_circ_0000896 (circFARSA) has been reported as being an oncogene and a potential biomarker for NSCL. However, the functional role and action mechanism of circFARSA in NSCLC progression have not been fully elucidated. The present study demonstrated that circFRASA was upregulated in NSCLC tissues and cell lines, and its expression was positively correlated with poor prognosis of patients with NSCLC. Further experiments revealed that circFARSA knockdown inhibited cell proliferation, migration, and invasion

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. : Gunawardane and colleagues use CRISPR/Cas9 to deliver an excisable cassette to transcriptionally silent loci in hiPSCs, then accomplish excision of the cassette in a second step utilizing Cas9/CRISPR and the MMEJ and NHEJ DNA-repair pathways. Excision results in mEGFP tagging of the targeted loci. Upon differentiation, each of five tagged cell lines appropriately expresses a unique fluorescent fusion protein localized to the sarcomere in live cardiomyocytes. Keywords: CRISPR/Cas9, genome editing, cardiomyocyte differentiation, stem cells, iPSCs, MMEJ, live imaging, endogenous fluorescent tagging, mEGFP, HDR

Coxsackievirus A16 (CV-A16) has caused worldwide epidemics of hand, foot, and mouth disease (HFMD), particularly among infants and preschool children. Circular RNAs (circRNAs) are a recently identified class of noncoding RNA molecules that participate in the occurrence and development of numerous infectious diseases. To date, the function of circRNAs has been a heavily researched topic, and their role in CV-A16 infection has not been fully elucidated. In this study, the viral effects of CV-A16 on the cellular circRNA transcriptome were investigated using next-generation sequencing technology. In this study, a total of 8726, 8611 and 6826 circRNAs were identified in the control, CV-A16-12 h and CV-A16-24 h groups, respectively. Further analysis showed that 1769 and 1192 circRNAs were differentially expressed in the CV-A16-12 h and CV-A16-24 h groups, respectively. Moreover, the common differentially expressed circRNAs were screened out and used for functional annotation analysis, which demonstrated that the parent genes of differentially expressed circRNAs might be associated with the viral infection process, especially the “Immune system process” in GO analysis and the “Inflammation mediated by chemokine and cytokine signaling pathway” in KEGG analysis. Additionally, 6 dysregulated circRNAs were selected for validation and were verified to be consistent with the sequencing results. Additionally, we further constructed a circRNA-miRNA-mRNA regulatory network that suggested the putative function of circRNAs as cellular miRNA decoys to indirectly regulate gene expression during CV-A16 infection, especially the hsa_circ_0004447/hsa-miR-942-5p/MMP2, hsa_circ_0078617/hsa-miR-6780b-5p/MMP2 and hsa_circ_0078617/hsa-miR-5196-5p/MMP2 regulatory axes. Considering all of these results, to the best of our knowledge, this study is the first to present a comprehensive overview of circRNAs induced by CV-A16, and this research demonstrated that a network of enriched circRNAs and circRNA-associated competitive endogenous RNAs (ceRNAs) is involved in the regulation of CV-A16 infection, thereby helping to elucidate the mechanisms underlying CV-A16-host interactions.

Enterovirus A71 (EV71) remains the most common causative agent of hand, foot, and mouth disease (HFMD), and the neurological complications induced by EV71 are usually the leading cause of death in children with HFMD. However, the mechanism of nervous system changes caused by EV71 infection is still unclear. Therefore, in the current study, EV71 was inoculated into the human neuroblastoma cell line SH-SY5Y and subsequent transcriptome sequencing was used to examine the alterations of the transcriptome in infected SH-SY5Y cells. It is expected to determine the underlying mechanism of neurological diseases in response to EV71 infection. As a result, a total of 82,406,974, 112,410,808 and 87,780,371 clean reads were found in the control, EV71-12 h and EV71-24 h groups, respectively. Moreover, 160 and 745 differentially expressed genes were identified in the EV71-12 h and EV71-24 h groups, respectively, as compared to the control group. Next, to further explore the pathogenic mechanism triggered by EV71 infection, we mainly focused on the common differentially expressed genes at different time points of EV71 infection. And it was discovered that there were 95 common differentially expressed genes, which were used to conduct GO and pathway analysis. GO enrichment analysis demarcated related biological processes, molecular functions and cellular components, and KEGG pathway analysis enabled annotations of metabolic pathways and revealed interactions among the significantly enriched pathways. The results showed that the enriched GO term “Nervous system development” and enriched pathway “CCKR signaling map” might be important contributors to EV71-induced neuropathological mechanisms. In addition, we also screened 10 up- and down-regulated non-protein coding genes with significantly different expression in our transcriptome profiling, which suggested that these abnormally regulated non-protein-encoding genes might also play important roles in the pathogenesis of EV71 infection. Eventually, RT-qPCR technology was adopted to validate the transcriptome sequencing data and the experiment demonstrated that the RT-qPCR and transcriptome sequencing results were basically consistent. In summary, this is the first transcriptome analysis of SH-SY5Y cells in response to EV71 infection and provides valuable cues for further exploring the mechanism of nervous system changes caused by EV71 infection.

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 and Structure Segmenter (Segmenter), a Python-based open source toolkit developed for 3D segmentation of cells and 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 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. The Segmenter thus leverages state of the art computer vision algorithms in an accessible way to facilitate their application by the experimental biology researcher.We include two useful applications to demonstrate how we used the classic image segmentation and iterative deep learning workflows to solve more challenging 3D segmentation tasks. First, we introduce the ‘Training Assay’ approach, a new experimental-computational co-design concept to generate more biologically accurate segmentation ground truths. We combined the iterative deep learning workflow with three Training Assays to develop a robust, scalable cell and nuclear instance segmentation algorithm, which could achieve accurate target segmentation for over 98% of individual cells and over 80% of entire fields of view. Second, we demonstrate how to extend the lamin B1 segmentation model built from the iterative deep learning workflow to obtain more biologically accurate lamin B1 segmentation by utilizing multi-channel inputs and combining multiple ML models. The steps and workflows used to develop these algorithms are generalizable to other similar segmentation challenges. More information, including tutorials and code repositories, are available at allencell.org/segmenter.

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 induced pluripotent stem cells (hiPSC). To date, we have generated multiple hiPSC lines with monoallelic green fluorescent protein tags labeling 10 proteins representing major cellular structures. The tagged proteins include alpha tubulin, beta actin, desmoplakin, fibrillarin, nuclear lamin B1, nonmuscle myosin heavy chain IIB, paxillin, Sec61 beta, tight junction protein ZO1, and Tom20. Our genome-editing methodology using Cas9/crRNA ribonuclear protein and donor plasmid coelectroporation, followed by fluorescence-based enrichment of edited cells, typically resulted in