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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.

Background and Purpose: Ischemic injury triggers multiple pathological responses in the brain tissue, including spreading depolarizations across the cerebral cortex (cortical spreading depolarizations [CSD]). Microglia have been recently shown to play a significant role in the propagation of CSD. However, the intracellular responses of myeloid cells during ischemic stroke have not been investigated. Methods: We have studied intracellular calcium activity in cortical microglia in the stroke model of the middle cerebral artery occlusion, using the murine Polr2a-based and Cre-dependent GCaMP5 and tdTomato reporter (PC::G5-tdT). High-speed 2-photon microscopy through cranial windows was employed to record signals from genetically encoded indicators of calcium. Inflammatory stimuli and pharmacological inhibition were used to modulate microglial calcium responses in the somatosensory cortex. Results: In vivo imaging revealed periodical calcium activity in microglia during the hyperacute phase of ischemic stroke. This activity was more frequent during the first 6 hours after occlusion, but the amplitudes of calcium transients became larger at later time points. Consistent with CSD nature of these events, we reproducibly triggered comparable calcium transients with microinjections of potassium chloride (KCl) into adjacent cortical areas. Furthermore, lipopolysaccharide-induced peripheral inflammation, mimicking sterile inflammation during ischemic stroke, produced significantly greater microglial calcium transients during CSD. Finally, in vivo pharmacological analysis with CRAC (calcium release-activated channel) inhibitor CM-EX-137 demonstrated that CSD-associated microglial calcium transients after KCl microinjections are mediated at least in part by the CRAC mechanism. Conclusions: Our findings demonstrate that microglia participate in ischemic brain injury via previously undetected mechanisms, which may provide new avenues for therapeutic interventions.

SummaryWe present a quantitative co-analysis of RNA abundance and sarcomere organization in single cells and an integrated framework to predict subcellular organization states from gene expression. We used human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes expressing mEGFP-tagged alpha-actinin-2 to develop quantitative image analysis tools for systematic and automated classification of subcellular organization. This captured a wide range of sarcomeric organization states within cell populations that were previously difficult to quantify. We performed RNA FISH targeting genes identified by single cell RNA sequencing to simultaneously assess the relationship between transcript abundance and structural states in single cells. Co-analysis of gene expression and sarcomeric patterns in the same cells revealed biologically meaningful correlations that could be used to predict organizational states. This study establishes a framework for multi-dimensional analysis of single cells to study the relationships between gene expression and subcellular organization and to develop a more nuanced description of cell states.Graphical AbstractTranscriptional profiling and structural classification was performed on human induced pluripotent stem cell-derived cardiomyocytes to characterize the relationship between transcript abundance and subcellular organization.

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.

In this paper, we present a novel region-based segmentation method for cortical spreading depressions in 2-photon microscopy images. Fluorescent microscopy has become an important tool in neuroscience, but segmentation approaches are challenged by the opaque properties and structures of brain tissue. These challenges are made more extreme when segmenting events such as cortical spreading depressions, where low signal-to-noise ratios and intensity inhomogeneity dominate images. The method we propose uses a local intensity similarity measure that takes advantage of normalized Euclidean and geodesic distance maps of the image. This method provides a smooth segmentation boundary which is robust to the noise and inhomogeneity within cortical spreading depression images. Experimental results yielded a DICE index of 0.9859, an increase of 6% over the current state-of-the-art, and a reduction of root mean square error by 79.9%.