Cell states beyond transcriptomics: Integrating structural organization and gene expression in hiPSC-derived cardiomyocytes

Information-Distilled Generative Label-Free Morphological Profiling Encodes Cellular Heterogeneity

Image-based cytometry faces challenges due to technical variations arising from different experimental batches and conditions, such as differences in instrument configurations or image acquisition protocols, impeding genuine biological interpretation of cell morphology. Existing solutions, often necessitating extensive pre-existing data knowledge or control samples across batches, have proved limited, especially with complex cell image data. To overcome this, "Cyto-Morphology Adversarial Distillation" (CytoMAD), a self-supervised multi-task learning strategy that distills biologically relevant cellular morphological information from batch variations, is introduced to enable integrated analysis across multiple data batches without complex data assumptions or extensive manual annotation. Unique to CytoMAD is its "morphology distillation", symbiotically paired with deep-learning image-contrast translation-offering additional interpretable insights into label-free cell morphology. The versatile efficacy of CytoMAD is demonstrated in augmenting the power of biophysical imaging cytometry. It allows integrated label-free classification of human lung cancer cell types and accurately recapitulates their progressive drug responses, even when trained without the drug concentration information. CytoMAD  also allows joint analysis of tumor biophysical cellular heterogeneity, linked to epithelial-mesenchymal plasticity, that standard fluorescence markers overlook. CytoMAD can substantiate the wide adoption of biophysical cytometry for cost-effective diagnosis and screening.

MYBPC3-c.772G>A mutation results in haploinsufficiency and altered myosin cycling kinetics in a patient induced stem cell derived cardiomyocyte model of hypertrophic cardiomyopathy

Approximately 40% of hypertrophic cardiomyopathy (HCM) mutations are linked to the sarcomere protein cardiac myosin binding protein-C (cMyBP-C). These mutations are either classified as missense mutations or truncation mutations. One mutation whose nature has been inconsistently reported in the literature is the MYBPC3-c.772G > A mutation. Using patient-derived human induced pluripotent stem cells differentiated to cardiomyocytes (hiPSC-CMs), we have performed a mechanistic study of the structure-function relationship for this MYBPC3-c.772G > A mutation versus a mutation corrected, isogenic cell line. Our results confirm that this mutation leads to exon skipping and mRNA truncation that ultimately suggests ∼20% less cMyBP-C protein (i.e., haploinsufficiency). This, in turn, results in increased myosin recruitment and accelerated myofibril cycling kinetics. Our mechanistic studies suggest that faster ADP release from myosin is a primary cause of accelerated myofibril cross-bridge cycling due to this mutation. Additionally, the reduction in force generating heads expected from faster ADP release during isometric contractions is outweighed by a cMyBP-C phosphorylation mediated increase in myosin recruitment that leads to a net increase of myofibril force, primarily at submaximal calcium activations. These results match well with our previous report on contractile properties from myectomy samples of the patients from whom the hiPSC-CMs were generated, demonstrating that these cell lines are a good model to study this pathological mutation and extends our understanding of the mechanisms of altered contractile properties of this HCM MYBPC3-c.772G > A mutation.

Establishing a conceptual framework for holistic cell states and state transitions

Cell states were traditionally defined by how they looked, where they were located, and what functions they performed. In this post-genomic era, the field is largely focused on a molecular view of cell state. Moving forward, we anticipate that the observables used to define cell states will evolve again as single-cell imaging and analytics are advancing at a breakneck pace via the collection of large-scale, systematic cell image datasets and the application of quantitative image-based data science methods. This is, therefore, a key moment in the arc of cell biological research to develop approaches that integrate the spatiotemporal observables of the physical structure and organization of the cell with molecular observables toward the concept of a holistic cell state. In this perspective, we propose a conceptual framework for holistic cell states and state transitions that is data-driven, practical, and useful to enable integrative analyses and modeling across many data types.

Histone demethylase KDM5 regulates cardiomyocyte maturation by promoting fatty acid oxidation, oxidative phosphorylation, and myofibrillar organization

AIMS

Human pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) provide a platform to identify and characterize factors that regulate the maturation of CMs. The transition from an immature foetal to an adult CM state entails coordinated regulation of the expression of genes involved in myofibril formation and oxidative phosphorylation (OXPHOS) among others. Lysine demethylase 5 (KDM5) specifically demethylates H3K4me1/2/3 and has emerged as potential regulators of expression of genes involved in cardiac development and mitochondrial function. The purpose of this study is to determine the role of KDM5 in iPSC-CM maturation.

METHODS AND RESULTS

KDM5A, B, and C proteins were mainly expressed in the early post-natal stages, and their expressions were progressively downregulated in the post-natal CMs and were absent in adult hearts and CMs. In contrast, KDM5 proteins were persistently expressed in the iPSC-CMs up to 60 days after the induction of myogenic differentiation, consistent with the immaturity of these cells. Inhibition of KDM5 by KDM5-C70 -a pan-KDM5 inhibitor, induced differential expression of 2372 genes, including upregulation of genes involved in fatty acid oxidation (FAO), OXPHOS, and myogenesis in the iPSC-CMs. Likewise, genome-wide profiling of H3K4me3 binding sites by the cleavage under targets and release using nuclease assay showed enriched of the H3K4me3 peaks at the promoter regions of genes encoding FAO, OXPHOS, and sarcomere proteins. Consistent with the chromatin and gene expression data, KDM5 inhibition increased the expression of multiple sarcomere proteins and enhanced myofibrillar organization. Furthermore, inhibition of KDM5 increased H3K4me3 deposits at the promoter region of the ESRRA gene and increased its RNA and protein levels. Knockdown of ESRRA in KDM5-C70-treated iPSC-CM suppressed expression of a subset of the KDM5 targets. In conjunction with changes in gene expression, KDM5 inhibition increased oxygen consumption rate and contractility in iPSC-CMs.

CONCLUSION

KDM5 inhibition enhances maturation of iPSC-CMs by epigenetically upregulating the expressions of OXPHOS, FAO, and sarcomere genes and enhancing myofibril organization and mitochondrial function.

Spherical harmonics analysis reveals cell shape-fate relationships in zebrafish lateral line neuromasts

Cell shape is a powerful readout of cell state, fate and function. We describe a custom workflow to perform semi-automated, 3D cell and nucleus segmentation, and spherical harmonics and principal components analysis to distill cell and nuclear shape variation into discrete biologically meaningful parameters. We apply these methods to analyze shape in the neuromast cells of the zebrafish lateral line system, finding that shapes vary with cell location and identity. The distinction between hair cells and support cells accounted for much of the variation, which allowed us to train classifiers to predict cell identity from shape features. Using transgenic markers for support cell subpopulations, we found that subtypes had different shapes from each other. To investigate how loss of a neuromast cell type altered cell shape distributions, we examined atoh1a mutants that lack hair cells. We found that mutant neuromasts lacked the cell shape phenotype associated with hair cells, but did not exhibit a mutant-specific cell shape. Our results demonstrate the utility of using 3D cell shape features to characterize, compare and classify cells in a living developing organism.

Facilitating cell segmentation with the projection-enhancement network

Contemporary approaches to instance segmentation in cell science use 2D or 3D convolutional networks depending on the experiment and data structures. However, limitations in microscopy systems or efforts to prevent phototoxicity commonly require recording sub-optimally sampled data that greatly reduces the utility of such 3D data, especially in crowded sample space with significant axial overlap between objects. In such regimes, 2D segmentations are both more reliable for cell morphology and easier to annotate. In this work, we propose the projection enhancement network (PEN), a novel convolutional module which processes the sub-sampled 3D data and produces a 2D RGB semantic compression, and is trained in conjunction with an instance segmentation network of choice to produce 2D segmentations. Our approach combines augmentation to increase cell density using a low-density cell image dataset to train PEN, and curated datasets to evaluate PEN. We show that with PEN, the learned semantic representation in CellPose encodes depth and greatly improves segmentation performance in comparison to maximum intensity projection images as input, but does not similarly aid segmentation in region-based networks like Mask-RCNN. Finally, we dissect the segmentation strength against cell density of PEN with CellPose on disseminated cells from side-by-side spheroids. We present PEN as a data-driven solution to form compressed representations of 3D data that improve 2D segmentations from instance segmentation networks.

Evolving views of ionic, osmotic and acid-base regulation in aquatic animals

The regulation of ionic, osmotic and acid-base (IOAB) conditions in biological fluids is among the most fundamental functions in all organisms; being surrounded by water uniquely shapes the IOAB regulatory strategies of water-breathing animals. Throughout its centennial history, Journal of Experimental Biology has established itself as a premier venue for publication of comparative, environmental and evolutionary studies on IOAB regulation. This Review provides a synopsis of IOAB regulation in aquatic animals, some of the most significant research milestones in the field, and evolving views about the underlying cellular mechanisms and their evolutionary implications. It also identifies promising areas for future research and proposes ideas for enhancing the impact of aquatic IOAB research.

Spatial Transcriptomics-correlated Electron Microscopy maps transcriptional and ultrastructural responses to brain injury

Understanding the complexity of cellular function within a tissue necessitates the combination of multiple phenotypic readouts. Here, we developed a method that links spatially-resolved gene expression of single cells with their ultrastructural morphology by integrating multiplexed error-robust fluorescence in situ hybridization (MERFISH) and large area volume electron microscopy (EM) on adjacent tissue sections. Using this method, we characterized in situ ultrastructural and transcriptional responses of glial cells and infiltrating T-cells after demyelinating brain injury in male mice. We identified a population of lipid-loaded "foamy" microglia located in the center of remyelinating lesion, as well as rare interferon-responsive microglia, oligodendrocytes, and astrocytes that co-localized with T-cells. We validated our findings using immunocytochemistry and lipid staining-coupled single-cell RNA sequencing. Finally, by integrating these datasets, we detected correlations between full-transcriptome gene expression and ultrastructural features of microglia. Our results offer an integrative view of the spatial, ultrastructural, and transcriptional reorganization of single cells after demyelinating brain injury.

Histone demethylase KDM5 regulates cardiomyocyte maturation by promoting fatty acid oxidation, oxidative phosphorylation, and myofibrillar organization

Rationale

Human pluripotent stem cell-derived CMs (iPSC-CMs) are a valuable tool for disease modeling, cell therapy and to reconstruct the CM maturation process and identify, characterize factors that regulate maturation. The transition from immature fetal to adult CM entails coordinated regulation of the mature gene programming, which is characterized by the induction of myofilament and OXPHOS gene expression among others. Recent studies in Drosophila , C. elegans, and C2C12 myoblast cell lines have implicated the histone H3K4me3 demethylase KDM5 and its homologs, as a potential regulator of developmental gene program and mitochondrial function. We speculated that KDM5 may potentiate the maturation of iPSC-CMs by targeting a conserved epigenetic program that encompass mitochondrial OXPHOS and other CM specific maturation genes.

Objectives

The purpose of this study is to determine the role of KDM5 in iPSC-CM maturation.

Methods and Results

Immunoblot analysis revealed that KDM5A, B, and C expression was progressively downregulated in postnatal cardiomyocytes and absent in adult hearts and CMs. Additionally, KDM5 proteins were found to be persistently expressed in iPSC-CMs up to 60 days after the onset of myogenic differentiation, consistent with the immaturity of these cells. Inhibition of KDM5 by KDM5-C70 -a pan-KDM5 inhibitor-resulted in differential regulation of 2,372 genes including upregulation of Fatty acid oxidation (FAO), OXPHOS, and myogenic gene programs in iPSC-CMs. Likewise, genome-wide profiling of H3K4me3 binding sites by the CUT&RUN assay revealed enriched H3K4me3 peaks at the promoter regions of FAO, OXPHOS, and sarcomere genes. Consistent with the chromatin and gene expression data, KDM5 inhibition led to increased expression of multiple sarcomere proteins, enhanced myofibrillar organization and improved calcium handling. Furthermore, inhibition of KDM5 increased H3K4me3 deposits at the promoter region of the ESRRA gene, which is known to regulate OXPHOS and cardiomyocyte maturation, and resulted in its increased RNA and protein levels. Finally, KDM5 inhibition increased baseline, peak, and spare oxygen consumption rates in iPSC-CMs.

Conclusions

KDM5 regulates the maturation of iPSC-CMs by epigenetically regulating the expression of ESRRA, OXPHOS, FAO, and sarcomere genes and enhancing myofibril organization and mitochondrial function.

Immune heterogeneity in cardiovascular diseases from a single-cell perspective

A variety of immune cell subsets occupy different niches in the cardiovascular system, causing changes in the structure and function of the heart and vascular system, and driving the progress of cardiovascular diseases (CVDs). The immune cells infiltrating the injury site are highly diverse and integrate into a broad dynamic immune network that controls the dynamic changes of CVDs. Due to technical limitations, the effects and molecular mechanisms of these dynamic immune networks on CVDs have not been fully revealed. With recent advances in single-cell technologies such as single-cell RNA sequencing, systematic interrogation of the immune cell subsets is feasible and will provide insights into the way we understand the integrative behavior of immune populations. We no longer lightly ignore the role of individual cells, especially certain highly heterogeneous or rare subpopulations. We summarize the phenotypic diversity of immune cell subsets and their significance in three CVDs of atherosclerosis, myocardial ischemia and heart failure. We believe that such a review could enhance our understanding of how immune heterogeneity drives the progression of CVDs, help to elucidate the regulatory roles of immune cell subsets in disease, and thus guide the development of new immunotherapies.

Trajectory reconstruction identifies dysregulation of perinatal maturation programs in pluripotent stem cell-derived cardiomyocytes

A limitation in the application of pluripotent stem cell-derived cardiomyocytes (PSC-CMs) is the failure of these cells to achieve full functional maturity. The mechanisms by which directed differentiation differs from endogenous development, leading to consequent PSC-CM maturation arrest, remain unclear. Here, we generate a single-cell RNA sequencing (scRNA-seq) reference of mouse in vivo CM maturation with extensive sampling of previously difficult-to-isolate perinatal time periods. We subsequently generate isogenic embryonic stem cells to create an in vitro scRNA-seq reference of PSC-CM-directed differentiation. Through trajectory reconstruction, we identify an endogenous perinatal maturation program that is poorly recapitulated in vitro. By comparison with published human datasets, we identify a network of nine transcription factors (TFs) whose targets are consistently dysregulated in PSC-CMs across species. Notably, these TFs are only partially activated in common ex vivo approaches to engineer PSC-CM maturation. Our study can be leveraged toward improving the clinical viability of PSC-CMs.

Optofluidic imaging meets deep learning: from merging to emerging

Propelled by the striking advances in optical microscopy and deep learning (DL), the role of imaging in lab-on-a-chip has dramatically been transformed from a silo inspection tool to a quantitative "smart" engine. A suite of advanced optical microscopes now enables imaging over a range of spatial scales (from molecules to organisms) and temporal window (from microseconds to hours). On the other hand, the staggering diversity of DL algorithms has revolutionized image processing and analysis at the scale and complexity that were once inconceivable. Recognizing these exciting but overwhelming developments, we provide a timely review of their latest trends in the context of lab-on-a-chip imaging, or coined optofluidic imaging. More importantly, here we discuss the strengths and caveats of how to adopt, reinvent, and integrate these imaging techniques and DL algorithms in order to tailor different lab-on-a-chip applications. In particular, we highlight three areas where the latest advances in lab-on-a-chip imaging and DL can form unique synergisms: image formation, image analytics and intelligent image-guided autonomous lab-on-a-chip. Despite the on-going challenges, we anticipate that they will represent the next frontiers in lab-on-a-chip imaging that will spearhead new capabilities in advancing analytical chemistry research, accelerating biological discovery, and empowering new intelligent clinical applications.

Implications of CRISPR-Cas9 Genome Editing Methods in Atherosclerotic Cardiovascular Diseases

Today, new methods have been developed to treat or modify the natural course of cardiovascular diseases (CVDs), including atherosclerosis, by the clustered regularly interspaced short palindromic repeats-CRISPR-associated protein 9 (CRISPR-Cas9) system. Genome-editing tools are CRISPR-related palindromic short iteration systems such as CRISPR-Cas9, a valuable technology for achieving somatic and germinal genomic manipulation in model cells and organisms for various applications, including the creation of deletion alleles. Mutations in genomic deoxyribonucleic acid and new genes' placement have emerged. Based on World Health Organization fact sheets, 17.9 million people die from CVDs each year, an estimated 32% of all deaths worldwide. 85% of all CVD deaths are due to acute coronary events and strokes. This review discusses the applications of CRISPR-Cas9 technology throughout atherosclerotic disease research and the prospects for future in vivo genome editing therapies. We also describe several limitations that must be considered to achieve the full scientific and therapeutic potential of cardiovascular genome editing in the treatment of atherosclerosis.

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 review of computational and image analysis techniques for quantitative evaluation of striated muscle tissue architecture

Unbiased evaluation of morphology is crucial to understanding development, mechanics, and pathology of striated muscle tissues. Indeed, the ability of striated muscles to contract and the strength of their contraction is dependent on their tissue-, cellular-, and cytoskeletal-level organization. Accordingly, the study of striated muscles often requires imaging and assessing aspects of their architecture at multiple different spatial scales. While an expert may be able to qualitatively appraise tissues, it is imperative to have robust, repeatable tools to quantify striated myocyte morphology and behavior that can be used to compare across different labs and experiments. There has been a recent effort to define the criteria used by experts to evaluate striated myocyte architecture. In this review, we will describe metrics that have been developed to summarize distinct aspects of striated muscle architecture in multiple different tissues, imaged with various modalities. Additionally, we will provide an overview of metrics and image processing software that needs to be developed. Importantly to any lab working on striated muscle platforms, characterization of striated myocyte morphology using the image processing pipelines discussed in this review can be used to quantitatively evaluate striated muscle tissues and contribute to a robust understanding of the development and mechanics of striated muscles.

Multienzyme activity profiling for evaluation of cell‐to‐cell variability of metabolic state

In solid organs, cells of the same “type” can vary in their molecular phenotype. The basis of this state variation is being revealed by characterizing cell features including the expression pattern of mRNAs and the internal distribution of proteins. Here, the variability of metabolic state between cells is probed by enzyme activity profiling. We study individual cells of types that can be identified during the post‐mitotic phase of oogenesis in Xenopus laevis. Whole‐cell homogenates of isolated oocytes are used for kinetic analysis of enzymes, with a focus on the initial reaction rate. For each oocyte type studied, the activity signatures of glyceraldehyde 3‐phosphate dehydrogenase (GAPDH) and malate dehydrogenase 1 (MDH1) vary more between the homogenates of single oocytes than between repeat samplings of control homogenates. Unexpectedly, the activity signatures of GAPDH and MDH1 strongly co‐vary between oocytes of each type and change in strength of correlation during oogenesis. Therefore, variability of the kinetic behavior of these housekeeping enzymes between “identical” cells is physiologically programmed. Based on these findings, we propose that single‐cell profiling of enzyme kinetics will improve understanding of how metabolic state heterogeneity is related to heterogeneity revealed by omics methods including proteomics, epigenomics, and metabolomics.

Time-regulated transcripts with the potential to modulate human pluripotent stem cell-derived cardiomyocyte differentiation

Background

Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM) are a promising disease model, even though hiPSC-CMs cultured for extended periods display an undifferentiated transcriptional landscape. MiRNA–target gene interactions contribute to fine-tuning the genetic program governing cardiac maturation and may uncover critical pathways to be targeted.

Methods

We analyzed a hiPSC-CM public dataset to identify time-regulated miRNA–target gene interactions based on three logical steps of filtering. We validated this process in silico using 14 human and mouse public datasets, and further confirmed the findings by sampling seven time points over a 30-day protocol with a hiPSC-CM clone developed in our laboratory. We then added miRNA mimics from the top eight miRNAs candidates in three cell clones in two different moments of cardiac specification and maturation to assess their impact on differentiation characteristics including proliferation, sarcomere structure, contractility, and calcium handling.

Results

We uncovered 324 interactions among 29 differentially expressed genes and 51 miRNAs from 20,543 transcripts through 120 days of hiPSC-CM differentiation and selected 16 genes and 25 miRNAs based on the inverse pattern of expression (Pearson R-values < − 0.5) and consistency in different datasets. We validated 16 inverse interactions among eight genes and 12 miRNAs (Person R-values < − 0.5) during hiPSC-CMs differentiation and used miRNAs mimics to verify proliferation, structural and functional features related to maturation. We also demonstrated that miR-124 affects Ca²⁺ handling altering features associated with hiPSC-CMs maturation.

Conclusion

We uncovered time-regulated transcripts influencing pathways affecting cardiac differentiation/maturation axis and showed that the top-scoring miRNAs indeed affect primarily structural features highlighting their role in the hiPSC-CM maturation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13287-022-03138-x.

Self-supervised deep learning encodes high-resolution features of protein subcellular localization

Explaining the diversity and complexity of protein localization is essential to fully understand cellular architecture. Here we present cytoself, a deep-learning approach for fully self-supervised protein localization profiling and clustering. Cytoself leverages a self-supervised training scheme that does not require preexisting knowledge, categories or annotations. Training cytoself on images of 1,311 endogenously labeled proteins from the OpenCell database reveals a highly resolved protein localization atlas that recapitulates major scales of cellular organization, from coarse classes, such as nuclear and cytoplasmic, to the subtle localization signatures of individual protein complexes. We quantitatively validate cytoself’s ability to cluster proteins into organelles and protein complexes, showing that cytoself outperforms previous self-supervised approaches. Moreover, to better understand the inner workings of our model, we dissect the emergent features from which our clustering is derived, interpret them in the context of the fluorescence images, and analyze the performance contributions of each component of our approach.

Cytoself is a self-supervised deep learning-based approach for profiling and clustering protein localization from fluorescence images. Cytoself outperforms established approaches and can accurately predict protein subcellular localization.

Software Tool for Automatic Quantification of Sarcomere Length and Organization in Fixed and Live 2D and 3D Muscle Cell Cultures In Vitro

Sarcomeres are the structural units of the contractile apparatus in cardiac and skeletal muscle cells. Changes in sarcomere characteristics are indicative of changes in the sarcomeric proteins and function during development and disease. Assessment of sarcomere length, alignment, and organization provides insight into disease and drug responses in striated muscle cells and models, ranging from cardiomyocytes and skeletal muscle cells derived from human pluripotent stem cells to adult muscle cells isolated from animals or humans. However, quantification of sarcomere length is typically time consuming and prone to user-specific selection bias. Automated analysis pipelines exist but these often require either specialized software or programming experience. In addition, these pipelines are often designed for only one type of cell model in vitro. Here, we present an easy-to-implement protocol and software tool for automated sarcomere length and organization quantification in a variety of striated muscle in vitro models: Two dimensional (2D) cardiomyocytes, three dimensional (3D) cardiac microtissues, isolated adult cardiomyocytes, and 3D tissue engineered skeletal muscles. Based on an existing mathematical algorithm, this image analysis software (SotaTool) automatically detects the direction in which the sarcomere organization is highest over the entire image and outputs the length and organization of sarcomeres. We also analyzed videos of live cells during contraction, thereby allowing measurement of contraction parameters like fractional shortening, contraction time, relaxation time, and beating frequency. In this protocol, we give a step-by-step guide on how to prepare, image, and automatically quantify sarcomere and contraction characteristics in different types of in vitro models and we provide basic validation and discussion of the limitations of the software tool. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Staining and analyzing static hiPSC-CMs with SotaTool Alternate Protocol: Sample preparation, acquisition, and quantification of fractional shortening in live reporter hiPSC lines Support Protocol 1: Finding the image resolution Support Protocol 2: Advanced analysis settings Support Protocol 3: Finding sarcomere length in non-aligned cells.

DynaMorph: self-supervised learning of morphodynamic states of live cells

A cell’s shape and motion represent fundamental aspects of cell identity and can be highly predictive of function and pathology. However, automated analysis of the morphodynamic states remains challenging for most cell types, especially primary human cells where genetic labeling may not be feasible. To enable automated and quantitative analysis of morphodynamic states, we developed DynaMorph—a computational framework that combines quantitative live cell imaging with self-supervised learning. To demonstrate the robustness and utility of this approach, we used DynaMorph to annotate morphodynamic states observed with label-free measurements of optical density and anisotropy of live microglia isolated from human brain tissue. These cells show complex behavior and have varied responses to disease-relevant perturbations. DynaMorph generates quantitative morphodynamic representations that can be used to compare the effects of the perturbations. Using DynaMorph, we identify distinct morphodynamic states of microglia polarization and detect rare transition events between states. The concepts and the methods presented here can facilitate automated discovery of functional states of diverse cellular systems.

Advances in spatial transcriptomic data analysis

Spatial transcriptomics is a rapidly growing field that promises to comprehensively characterize tissue organization and architecture at the single-cell or subcellular resolution. Such information provides a solid foundation for mechanistic understanding of many biological processes in both health and disease that cannot be obtained by using traditional technologies. The development of computational methods plays important roles in extracting biological signals from raw data. Various approaches have been developed to overcome technology-specific limitations such as spatial resolution, gene coverage, sensitivity, and technical biases. Downstream analysis tools formulate spatial organization and cell-cell communications as quantifiable properties, and provide algorithms to derive such properties. Integrative pipelines further assemble multiple tools in one package, allowing biologists to conveniently analyze data from beginning to end. In this review, we summarize the state of the art of spatial transcriptomic data analysis methods and pipelines, and discuss how they operate on different technological platforms.

Applying Machine Learning to Stem Cell Culture and Differentiation

Machine learning techniques are increasingly becoming incorporated into biological research workflows in a variety of disciplines, most notably cancer research and drug discovery. Efforts in stem cell research comparatively lag behind. We detail key paradigms in machine learning, with a focus on equipping stem cell biologists with the understanding necessary to begin conceptualizing and designing machine learning workflows within their own domain of expertise. Supervised approaches in both regression and classification as well as unsupervised clustering techniques are all covered, with examples from across the biological sciences. High-throughput, high-content, multiplex assays for data acquisition are also discussed in the form of single-cell RNA sequencing and image-based approaches. Lastly, potential applications in stem cell biology, including the development of novel cell types, and improving model maturation are also discussed. Machine learning approaches applied in stem cell biology show promise in accelerating progress in developmental biology, drug screening, disease modeling, and personalized medicine. © 2021 Wiley Periodicals LLC.

Transcriptomic entropy benchmarks stem cell-derived cardiomyocyte maturation against endogenous tissue at single cell level

The immaturity of pluripotent stem cell (PSC)-derived tissues has emerged as a universal problem for their biomedical applications. While efforts have been made to generate adult-like cells from PSCs, direct benchmarking of PSC-derived tissues against in vivo development has not been established. Thus, maturation status is often assessed on an ad-hoc basis. Single cell RNA-sequencing (scRNA-seq) offers a promising solution, though cross-study comparison is limited by dataset-specific batch effects. Here, we developed a novel approach to quantify PSC-derived cardiomyocyte (CM) maturation through transcriptomic entropy. Transcriptomic entropy is robust across datasets regardless of differences in isolation protocols, library preparation, and other potential batch effects. With this new model, we analyzed over 45 scRNA-seq datasets and over 52,000 CMs, and established a cross-study, cross-species CM maturation reference. This reference enabled us to directly compare PSC-CMs with the in vivo developmental trajectory and thereby to quantify PSC-CM maturation status. We further found that our entropy-based approach can be used for other cell types, including pancreatic beta cells and hepatocytes. Our study presents a biologically relevant and interpretable metric for quantifying PSC-derived tissue maturation, and is extensible to numerous tissue engineering contexts.

Bayesian metamodeling of complex biological systems across varying representations

Comprehensive modeling of a whole cell requires an integration of vast amounts of information on various aspects of the cell and its parts. To divide and conquer this task, we introduce Bayesian metamodeling, a general approach to modeling complex systems by integrating a collection of heterogeneous input models. Each input model can in principle be based on any type of data and can describe a different aspect of the modeled system using any mathematical representation, scale, and level of granularity. These input models are 1) converted to a standardized statistical representation relying on probabilistic graphical models, 2) coupled by modeling their mutual relations with the physical world, and 3) finally harmonized with respect to each other. To illustrate Bayesian metamodeling, we provide a proof-of-principle metamodel of glucose-stimulated insulin secretion by human pancreatic β-cells. The input models include a coarse-grained spatiotemporal simulation of insulin vesicle trafficking, docking, and exocytosis; a molecular network model of glucose-stimulated insulin secretion signaling; a network model of insulin metabolism; a structural model of glucagon-like peptide-1 receptor activation; a linear model of a pancreatic cell population; and ordinary differential equations for systemic postprandial insulin response. Metamodeling benefits from decentralized computing, while often producing a more accurate, precise, and complete model that contextualizes input models as well as resolves conflicting information. We anticipate Bayesian metamodeling will facilitate collaborative science by providing a framework for sharing expertise, resources, data, and models, as exemplified by the Pancreatic β-Cell Consortium.

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.