Aligning Single-Cell Developmental and Reprogramming Trajectories Identifies Molecular Determinants of Myogenic Reprogramming Outcome

Dual blockade of IL-10 and PD-1 leads to control of SIV viral rebound following analytical treatment interruption

Human immunodeficiency virus (HIV) persistence during antiretroviral therapy (ART) is associated with heightened plasma interleukin-10 (IL-10) levels and PD-1 expression. We hypothesized that IL-10 and PD-1 blockade would lead to control of viral rebound following analytical treatment interruption (ATI). Twenty-eight ART-treated, simian immunodeficiency virus (SIV)mac239-infected rhesus macaques (RMs) were treated with anti-IL-10, anti-IL-10 plus anti-PD-1 (combo) or vehicle. ART was interrupted 12 weeks after introduction of immunotherapy. Durable control of viral rebound was observed in nine out of ten combo-treated RMs for >24 weeks post-ATI. Induction of inflammatory cytokines, proliferation of effector CD8+ T cells in lymph nodes and reduced expression of BCL-2 in CD4+ T cells pre-ATI predicted control of viral rebound. Twenty-four weeks post-ATI, lower viral load was associated with higher frequencies of memory T cells expressing TCF-1 and of SIV-specific CD4+ and CD8+ T cells in blood and lymph nodes of combo-treated RMs. These results map a path to achieve long-lasting control of HIV and/or SIV following discontinuation of ART.

Transcription reprogramming and endogenous DNA damage

Transcription reprogramming is essential to carry out a variety of cell dynamics such as differentiation and stress response. During reprogramming of transcription, a number of adverse effects occur and potentially compromise genomic stability. Formaldehyde as an obligatory byproduct is generated in the nucleus via oxidative protein demethylation at regulatory regions, leading to the formation of DNA crosslinking damage. Elevated levels of transcription activities can result in the accumulation of unscheduled R-loop. DNA strand breaks can form if processed 5-methylcytosines are exercised by DNA glycosylase during imprint reversal. When cellular differentiation involves a large number of genes undergoing transcription reprogramming, these endogenous DNA lesions and damage-prone structures may pose a significant threat to genome stability. In this review, we discuss how DNA damage is formed during cellular differentiation, cellular mechanisms for their removal, and diseases associated with transcription reprogramming.

Gene-level alignment of single-cell trajectories

Single-cell data analysis can infer dynamic changes in cell populations, for example across time, space or in response to perturbation, thus deriving pseudotime trajectories. Current approaches comparing trajectories often use dynamic programming but are limited by assumptions such as the existence of a definitive match. Here we describe Genes2Genes, a Bayesian information-theoretic dynamic programming framework for aligning single-cell trajectories. It is able to capture sequential matches and mismatches of individual genes between a reference and query trajectory, highlighting distinct clusters of alignment patterns. Across both real world and simulated datasets, it accurately inferred alignments and demonstrated its utility in disease cell-state trajectory analysis. In a proof-of-concept application, Genes2Genes revealed that T cells differentiated in vitro match an immature in vivo state while lacking expression of genes associated with TNF signaling. This demonstrates that precise trajectory alignment can pinpoint divergence from the in vivo system, thus guiding the optimization of in vitro culture conditions.

Cell State Transition Models Stratify Breast Cancer Cell Phenotypes and Reveal New Therapeutic Targets

Understanding signaling patterns of transformation and controlling cell phenotypes is a challenge of current biology. Here we applied a cell State Transition Assessment and Regulation (cSTAR) approach to a perturbation dataset of single cell phosphoproteomic patterns of multiple breast cancer (BC) and normal breast tissue-derived cell lines. Following a separation of luminal, basal, and normal cell states, we identified signaling nodes within core control networks, delineated causal connections, and determined the primary drivers underlying oncogenic transformation and transitions across distinct BC subtypes. Whereas cell lines within the same BC subtype have different mutational and expression profiles, the architecture of the core network was similar for all luminal BC cells, and mTOR was a main oncogenic driver. In contrast, core networks of basal BC were heterogeneous and segregated into roughly four major subclasses with distinct oncogenic and BC subtype drivers. Likewise, normal breast tissue cells were separated into two different subclasses. Based on the data and quantified network topologies, we derived mechanistic cSTAR models that serve as digital cell twins and allow the deliberate control of cell movements within a Waddington landscape across different cell states. These cSTAR models suggested strategies of normalizing phosphorylation networks of BC cell lines using small molecule inhibitors.

Next-generation direct reprogramming

Tissue repair is significantly compromised in the aging human body resulting in critical disease conditions (such as myocardial infarction or Alzheimer's disease) and imposing a tremendous burden on global health. Reprogramming approaches (partial or direct reprogramming) are considered fruitful in addressing this unmet medical need. However, the efficacy, cellular maturity and specific targeting are still major challenges of direct reprogramming. Here we describe novel approaches in direct reprogramming that address these challenges. Extracellular signaling pathways (Receptor tyrosine kinases, RTK and Receptor Serine/Theronine Kinase, RSTK) and epigenetic marks remain central in rewiring the cellular program to determine the cell fate. We propose that modern protein design technologies (AI-designed minibinders regulating RTKs/RSTK, epigenetic enzymes, or pioneer factors) have potential to solve the aforementioned challenges. An efficient transdifferentiation/direct reprogramming may in the future provide molecular strategies to collectively reduce aging, fibrosis, and degenerative diseases.

A novel protein encoded by circKANSL1L regulates skeletal myogenesis via the Akt-FoxO3 signaling axis

Skeletal muscle is one the largest organs of the body and is involved in animal production and human health. Circular RNAs (circRNAs) have been implicated in skeletal myogenesis through largely unknown mechanisms. Herein, we report the phenotypic and metabolomic analysis of porcine longissimus dorsi muscles in Lantang and Landrace piglets, revealing a high-content of slow-oxidative fibers responsible for high-quality meat product in Lantang piglets. Using single-cell transcriptomics, we identified four myogenesis-related cell types, and the Akt-FoxO3 signaling axis was the most significantly enriched pathway in each subpopulation in the different pig breeds, as well as in fast-twitch glycolytic fibers. Using the multi-dimensional bioinformatic tools of circRNAome-seq and Ribo-seq, we identified a novel circRNA, circKANSL1L, with a protein-coding ability in porcine muscles, whose expression level correlated with myoblast proliferation and differentiation in vitro, as well as the transformation between distinct mature myofibers in vivo. The protein product of circKANSL1L could interact with Akt to decrease the phosphorylation level of FoxO3, which subsequently promoted FoxO3 transcriptional activity to regulate skeletal myogenesis. Our results established the existence of a protein encoded by circKANSL1L and demonstrated its potential functions in myogenesis.

A cell atlas of the larval Aedes aegypti ventral nerve cord

Mosquito-borne diseases account for nearly 1 million human deaths annually, yet we have a limited understanding of developmental events that influence host-seeking behavior and pathogen transmission in mosquitoes. Mosquito-borne pathogens are transmitted during blood meals, hence adult mosquito behavior and physiology have been intensely studied. However, events during larval development shape adult traits, larvae respond to many of the same sensory cues as adults, and larvae are susceptible to infection by many of the same disease-causing agents as adults. Hence, a better understanding of larval physiology will directly inform our understanding of physiological processes in adults. Here, we use single cell RNA sequencing (scRNA-seq) to provide a comprehensive view of cellular composition in the Aedes aegypti larval ventral nerve cord (VNC), a central hub of sensory inputs and motor outputs which additionally controls multiple aspects of larval physiology. We identify more than 35 VNC cell types defined in part by neurotransmitter and neuropeptide expression. We also explore diversity among monoaminergic and peptidergic neurons that likely control key elements of larval physiology and developmental timing, and identify neuroblasts and immature neurons, providing a view of neuronal differentiation in the VNC. Finally, we find that larval cell composition, number, and position are preserved in the adult abdominal VNC, suggesting studies of larval VNC form and function will likely directly inform our understanding adult mosquito physiology. Altogether, these studies provide a framework for targeted analysis of VNC development and neuronal function in Aedes aegypti larvae.

ATM-deficiency-induced microglial activation promotes neurodegeneration in ataxia-telangiectasia

While ATM loss of function has long been identified as the genetic cause of ataxia-telangiectasia (A-T), how it leads to selective and progressive degeneration of cerebellar Purkinje and granule neurons remains unclear. ATM expression is enriched in microglia throughout cerebellar development and adulthood. Here, we find evidence of microglial inflammation in the cerebellum of patients with A-T using single-nucleus RNA sequencing. Pseudotime analysis revealed that activation of A-T microglia preceded upregulation of apoptosis-related genes in granule and Purkinje neurons and that microglia exhibited increased neurotoxic cytokine signaling to granule and Purkinje neurons in A-T. To confirm these findings experimentally, we performed transcriptomic profiling of A-T induced pluripotent stem cell (iPSC)-derived microglia, which revealed cell-intrinsic microglial activation of cytokine production and innate immune response pathways compared to controls. Furthermore, A-T microglia co-culture with either control or A-T iPSC-derived neurons was sufficient to induce cytotoxicity. Taken together, these studies reveal that cell-intrinsic microglial activation may promote neurodegeneration in A-T.

Transgene-free direct conversion of murine fibroblasts into functional muscle stem cells

Transcription factor-based cellular reprogramming provides an attractive approach to produce desired cell types for regenerative medicine purposes. Such cellular conversions are widely dependent on viral vectors to efficiently deliver and express defined factors in target cells. However, use of viral vectors is associated with unfavorable genomic integrations that can trigger deleterious molecular consequences, rendering this method a potential impediment to clinical applications. Here, we report on a highly efficient transgene-free approach to directly convert mouse fibroblasts into induced myogenic progenitor cells (iMPCs) by overexpression of synthetic MyoD-mRNA in concert with an enhanced small molecule cocktail. First, we performed a candidate compound screen and identified two molecules that enhance fibroblast reprogramming into iMPCs by suppression of the JNK and JAK/STAT pathways. Simultaneously, we developed an optimal transfection protocol to transiently overexpress synthetic MyoD-mRNA in fibroblasts. Combining these two techniques enabled robust and rapid reprogramming of fibroblasts into Pax7 positive iMPCs in as little as 10 days. Nascent transgene-free iMPCs proliferated extensively in vitro, expressed a suite of myogenic stem cell markers, and could differentiate into highly multinucleated and contractile myotubes. Furthermore, using global and single-cell transcriptome assays, we delineated gene expression changes associated with JNK and JAK/STAT pathway inhibition during reprogramming, and identified in iMPCs a Pax7+ stem cell subpopulation resembling satellite cells. Last, transgene-free iMPCs robustly engrafted skeletal muscles of a Duchenne muscular dystrophy mouse model, restoring dystrophin expression in hundreds of myofibers. In summary, this study reports on an improved and clinically safer approach to convert fibroblasts into myogenic stem cells that can efficiently contribute to muscle regeneration in vivo.

Aging disrupts gene expression timing during muscle regeneration

Skeletal muscle function and regenerative capacity decline during aging, yet factors driving these changes are incompletely understood. Muscle regeneration requires temporally coordinated transcriptional programs to drive myogenic stem cells to activate, proliferate, fuse to form myofibers, and to mature as myonuclei, restoring muscle function after injury. We assessed global changes in myogenic transcription programs distinguishing muscle regeneration in aged mice from young mice by comparing pseudotime trajectories from single-nucleus RNA sequencing of myogenic nuclei. Aging-specific differences in coordinating myogenic transcription programs necessary for restoring muscle function occur following muscle injury, likely contributing to compromised regeneration in aged mice. Differences in pseudotime alignment of myogenic nuclei when comparing aged with young mice via dynamic time warping revealed pseudotemporal differences becoming progressively more severe as regeneration proceeds. Disruptions in timing of myogenic gene expression programs may contribute to incomplete skeletal muscle regeneration and declines in muscle function as organisms age.

Somatic mutations alter the differentiation outcomes of iPSC-derived neurons

The use of induced pluripotent stem cells (iPSC) as models for development and human disease has enabled the study of otherwise inaccessible tissues. A remaining challenge in developing reliable models is our limited understanding of the factors driving irregular differentiation of iPSCs, particularly the impact of acquired somatic mutations. We leveraged data from a pooled dopaminergic neuron differentiation experiment of 238 iPSC lines profiled with single-cell RNA and whole-exome sequencing to study how somatic mutations affect differentiation outcomes. We found that deleterious somatic mutations in key developmental genes, notably the BCOR gene, are strongly associated with failure in dopaminergic neuron differentiation and a larger proliferation rate in culture. We further identified broad differences in cell type composition between incorrectly and successfully differentiating lines, as well as significant changes in gene expression contributing to the inhibition of neurogenesis. Our work calls for caution in interpreting differentiation-related phenotypes in disease-modeling experiments.

Active eosinophils regulate host defence and immune responses in colitis

In the past decade, single-cell transcriptomics has helped to uncover new cell types and states and led to the construction of a cellular compendium of health and disease. Despite this progress, some difficult-to-sequence cells remain absent from tissue atlases. Eosinophils-elusive granulocytes that are implicated in a plethora of human pathologies1-5-are among these uncharted cell types. The heterogeneity of eosinophils and the gene programs that underpin their pleiotropic functions remain poorly understood. Here we provide a comprehensive single-cell transcriptomic profiling of mouse eosinophils. We identify an active and a basal population of intestinal eosinophils, which differ in their transcriptome, surface proteome and spatial localization. By means of a genome-wide CRISPR inhibition screen and functional assays, we reveal a mechanism by which interleukin-33 (IL-33) and interferon-γ (IFNγ) induce the accumulation of active eosinophils in the inflamed colon. Active eosinophils are endowed with bactericidal and T cell regulatory activity, and express the co-stimulatory molecules CD80 and PD-L1. Notably, active eosinophils are enriched in the lamina propria of a small cohort of patients with inflammatory bowel disease, and are closely associated with CD4+ T cells. Our findings provide insights into the biology of eosinophils and highlight the crucial contribution of this cell type to intestinal homeostasis, immune regulation and host defence. Furthermore, we lay a framework for the characterization of eosinophils in human gastrointestinal diseases.

Emergent dynamics of adult stem cell lineages from single nucleus and single cell RNA-Seq of Drosophila testes

Proper differentiation of sperm from germline stem cells, essential for production of the next generation, requires dramatic changes in gene expression that drive remodeling of almost all cellular components, from chromatin to organelles to cell shape itself. Here, we provide a single nucleus and single cell RNA-seq resource covering all of spermatogenesis in Drosophila starting from in-depth analysis of adult testis single nucleus RNA-seq (snRNA-seq) data from the Fly Cell Atlas (FCA) study. With over 44,000 nuclei and 6000 cells analyzed, the data provide identification of rare cell types, mapping of intermediate steps in differentiation, and the potential to identify new factors impacting fertility or controlling differentiation of germline and supporting somatic cells. We justify assignment of key germline and somatic cell types using combinations of known markers, in situ hybridization, and analysis of extant protein traps. Comparison of single cell and single nucleus datasets proved particularly revealing of dynamic developmental transitions in germline differentiation. To complement the web-based portals for data analysis hosted by the FCA, we provide datasets compatible with commonly used software such as Seurat and Monocle. The foundation provided here will enable communities studying spermatogenesis to interrogate the datasets to identify candidate genes to test for function in vivo.

MyoD-Induced Trans-Differentiation: A Paradigm for Dissecting the Molecular Mechanisms of Cell Commitment, Differentiation and Reprogramming

The discovery of the skeletal muscle-specific transcription factor MyoD represents a milestone in the field of transcriptional regulation during differentiation and cell-fate reprogramming. MyoD was the first tissue-specific factor found capable of converting non-muscle somatic cells into skeletal muscle cells. A unique feature of MyoD, with respect to other lineage-specific factors able to drive trans-differentiation processes, is its ability to dramatically change the cell fate even when expressed alone. The present review will outline the molecular strategies by which MyoD reprograms the transcriptional regulation of the cell of origin during the myogenic conversion, focusing on the activation and coordination of a complex network of co-factors and epigenetic mechanisms. Some molecular roadblocks, found to restrain MyoD-dependent trans-differentiation, and the possible ways for overcoming these barriers, will also be discussed. Indeed, they are of critical importance not only to expand our knowledge of basic muscle biology but also to improve the generation skeletal muscle cells for translational research.

Alignment of single-cell trajectory trees with CAPITAL

Global alignment of complex pseudotime trajectories between different single-cell RNA-seq datasets is challenging, as existing tools mainly focus on linear alignment of single-cell trajectories. Here we present CAPITAL (comparative analysis of pseudotime trajectory inference with tree alignment), a method for comparing single-cell trajectories with tree alignment whereby branching trajectories can be automatically compared. Computational tests on synthetic datasets and authentic bone marrow cells datasets indicate that CAPITAL has achieved accurate and robust alignments of trajectory trees, revealing various gene expression dynamics including gene–gene correlation conservation between different species.

Global alignment of complex cell state trajectories between single-cell datasets remains challenging. Here, the authors present a computational method called CAPITAL to compare branching trajectories, and demonstrate that this method achieves accurate and robust alignments.

A natural transdifferentiation event involving mitosis is empowered by integrating signaling inputs with conserved plasticity factors

Transdifferentiation, or direct cell reprogramming, is the conversion of one fully differentiated cell type into another. Whether core mechanisms are shared between natural transdifferentiation events when occurring with or without cell division is unclear. We have previously characterized the Y-to-PDA natural transdifferentiation in Caenorhabditis elegans, which occurs without cell division and requires orthologs of vertebrate reprogramming factors. Here, we identify a rectal-to-GABAergic transdifferentiation and show that cell division is required but not sufficient for conversion. We find shared mechanisms, including erasure of the initial identity, which requires the conserved reprogramming factors SEM-4/SALL, SOX-2, CEH-6/OCT, and EGL-5/HOX. We also find three additional and parallel roles of the Wnt signaling pathway: selection of a specific daughter, removal of the initial identity, and imposition of the precise final subtype identity. Our results support a model in which levels and antagonistic activities of SOX-2 and Wnt signaling provide a timer for the acquisition of final identity.

Integrative molecular roadmap for direct conversion of fibroblasts into myocytes and myogenic progenitor cells

Transient MyoD overexpression in concert with small molecule treatment reprograms mouse fibroblasts into induced myogenic progenitor cells (iMPCs). However, the molecular landscape and mechanisms orchestrating this cellular conversion remain unknown. Here, we undertook an integrative multiomics approach to delineate the process of iMPC reprogramming in comparison to myogenic transdifferentiation mediated solely by MyoD. Using transcriptomics, proteomics, and genome-wide chromatin accessibility assays, we unravel distinct molecular trajectories that govern the two processes. Notably, only iMPC reprogramming is characterized by gradual up-regulation of muscle stem cell markers, unique signaling pathways, and chromatin remodelers in conjunction with exclusive chromatin opening in core myogenic promoters. In addition, we determine that the Notch pathway is indispensable for iMPC formation and self-renewal and further use the Notch ligand Dll1 to homogeneously propagate iMPCs. Collectively, this study charts divergent molecular blueprints for myogenic transdifferentiation or reprogramming and underpins the heightened capacity of iMPCs for capturing myogenesis ex vivo.

mRNA-1273 and BNT162b2 mRNA vaccines have reduced neutralizing activity against the SARS-CoV-2 Omicron variant

The BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna) vaccines generate potent neutralizing antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the global emergence of SARS-CoV-2 variants with mutations in the spike protein, the principal antigenic target of these vaccines, has raised concerns over the neutralizing activity of vaccine-induced antibody responses. The Omicron variant, which emerged in November 2021, consists of over 30 mutations within the spike protein. Here, we used an authentic live virus neutralization assay to examine the neutralizing activity of the SARS-CoV-2 Omicron variant against mRNA vaccine-induced antibody responses. Following the 2nd dose, we observed a 30-fold reduction in neutralizing activity against the omicron variant. Through six months after the 2nd dose, none of the sera from naïve vaccinated subjects showed neutralizing activity against the Omicron variant. In contrast, recovered vaccinated individuals showed a 22-fold reduction with more than half of the subjects retaining neutralizing antibody responses. Following a booster shot (3rd dose), we observed a 14-fold reduction in neutralizing activity against the omicron variant and over 90% of boosted subjects showed neutralizing activity against the omicron variant. These findings show that a 3rd dose is required to provide robust neutralizing antibody responses against the Omicron variant.

Dissecting dual roles of MyoD during lineage conversion to mature myocytes and myogenic stem cells

The generation of myotubes from fibroblasts upon forced MyoD expression is a classic example of transcription factor-induced reprogramming. We recently discovered that additional modulation of signaling pathways with small molecules facilitates reprogramming to more primitive induced myogenic progenitor cells (iMPCs). Here, we dissected the transcriptional and epigenetic dynamics of mouse fibroblasts undergoing reprogramming to either myotubes or iMPCs using a MyoD-inducible transgenic model. Induction of MyoD in fibroblasts combined with small molecules generated Pax7⁺ iMPCs with high similarity to primary muscle stem cells. Analysis of intermediate stages of iMPC induction revealed that extinction of the fibroblast program preceded induction of the stem cell program. Moreover, key stem cell genes gained chromatin accessibility prior to their transcriptional activation, and these regions exhibited a marked loss of DNA methylation dependent on the Tet enzymes. In contrast, myotube generation was associated with few methylation changes, incomplete and unstable reprogramming, and an insensitivity to Tet depletion. Finally, we showed that MyoD's ability to bind to unique bHLH targets was crucial for generating iMPCs but dispensable for generating myotubes. Collectively, our analyses elucidate the role of MyoD in myogenic reprogramming and derive general principles by which transcription factors and signaling pathways cooperate to rewire cell identity.

PseudoGA: cell pseudotime reconstruction based on genetic algorithm

Dynamic regulation of gene expression is often governed by progression through transient cell states. Bulk RNA-seq analysis can only detect average change in expression levels and is unable to identify this dynamics. Single cell RNA-seq presents an unprecedented opportunity that helps in placing the cells on a hypothetical time trajectory that reflects gradual transition of their transcriptomes. This continuum trajectory or ‘pseudotime’, may reveal the developmental pathway and provide us with information on dynamic transcriptomic changes and other biological processes. Existing approaches to build pseudotime heavily depend on reducing huge dimension to extremely low dimensional subspaces and may lead to loss of information. We propose PseudoGA, a genetic algorithm based approach to order cells assuming that gene expressions vary according to a smooth curve along the pseudotime trajectory. We observe superior accuracy of our method in simulated as well as benchmarking real datasets. Generality of the assumption behind PseudoGA and no dependence on dimensionality reduction technique make it a robust choice for pseudotime estimation from single cell transcriptome data. PseudoGA is also time efficient when applied to a large single cell RNA-seq data and adaptable to parallel computing. R code for PseudoGA is freely available at https://github.com/indranillab/pseudoga.

Coordination of endothelial cell positioning and fate specification by the epicardium

The organization of an integrated coronary vasculature requires the specification of immature endothelial cells (ECs) into arterial and venous fates based on their localization within the heart. It remains unclear how spatial information controls EC identity and behavior. Here we use single-cell RNA sequencing at key developmental timepoints to interrogate cellular contributions to coronary vessel patterning and maturation. We perform transcriptional profiling to define a heterogenous population of epicardium-derived cells (EPDCs) that express unique chemokine signatures. We identify a population of Slit2+ EPDCs that emerge following epithelial-to-mesenchymal transition (EMT), which we term vascular guidepost cells. We show that the expression of guidepost-derived chemokines such as Slit2 are induced in epicardial cells undergoing EMT, while mesothelium-derived chemokines are silenced. We demonstrate that epicardium-specific deletion of myocardin-related transcription factors in mouse embryos disrupts the expression of key guidance cues and alters EPDC-EC signaling, leading to the persistence of an immature angiogenic EC identity and inappropriate accumulation of ECs on the epicardial surface. Our study suggests that EC pathfinding and fate specification is controlled by a common mechanism and guided by paracrine signaling from EPDCs linking epicardial EMT to EC localization and fate specification in the developing heart.

It remains unclear how spatial information controls endothelial cell identity and behavior in the developing heart. Here the authors perform single cell RNA sequencing at key developmental timepoints in mice to interrogate cellular contributions to coronary vessel patterning and maturation in the epicardium.

Spearheading future omics analyses using dyngen, a multi-modal simulator of single cells

We present dyngen, a multi-modal simulation engine for studying dynamic cellular processes at single-cell resolution. dyngen is more flexible than current single-cell simulation engines, and allows better method development and benchmarking, thereby stimulating development and testing of computational methods. We demonstrate its potential for spearheading computational methods on three applications: aligning cell developmental trajectories, cell-specific regulatory network inference and estimation of RNA velocity.

Integrated analysis of Xist upregulation and X-chromosome inactivation with single-cell and single-allele resolution

To ensure dosage compensation between the sexes, one randomly chosen X chromosome is silenced in each female cell in the process of X-chromosome inactivation (XCI). XCI is initiated during early development through upregulation of the long non-coding RNA Xist, which mediates chromosome-wide gene silencing. Cell differentiation, Xist upregulation and gene silencing are thought to be coupled at multiple levels to ensure inactivation of exactly one out of two X chromosomes. Here we perform an integrated analysis of all three processes through allele-specific single-cell RNA-sequencing. Specifically, we assess the onset of random XCI in differentiating mouse embryonic stem cells, and develop dedicated analysis approaches. By exploiting the inter-cellular heterogeneity of XCI onset, we identify putative Xist regulators. Moreover, we show that transient Xist upregulation from both X chromosomes results in biallelic gene silencing right before transitioning to the monoallelic state, confirming a prediction of the stochastic model of XCI. Finally, we show that genetic variation modulates the XCI process at multiple levels, providing a potential explanation for the long-known X-controlling element (Xce) effect, which leads to preferential inactivation of a specific X chromosome in inter-strain crosses. We thus draw a detailed picture of the different levels of regulation that govern the initiation of XCI. The experimental and computational strategies we have developed here will allow us to profile random XCI in more physiological contexts, including primary human cells in vivo.

Current progress and potential opportunities to infer single-cell developmental trajectory and cell fate

Rapid technological advances in transcriptomics and lineage tracing technologies provide new opportunities to understand organismal development at the single-cell level. Building on these advances, various computational methods have been proposed to infer developmental trajectories and to predict cell fate. These methods have unveiled previously uncharacterized transitional cell types and differentiation processes. Importantly, the ability to recover cell states and trajectories has been evolving hand-in-hand with new technologies and diverse experimental designs; more recent methods can capture complex trajectory topologies and infer short- and long-term cell fate dynamics. Here, we summarize and categorize the most recent and popular computational approaches for trajectory inference based on the information they leverage and describe future challenges and opportunities for the development of new methods for reconstructing differentiation trajectories and inferring cell fates.

Single cell RNA-seq analysis of the flexor digitorum brevis mouse myofibers

Background

Skeletal muscle myofibers can be separated into functionally distinct cell types that differ in gene and protein expression. Current single cell expression data is generally based upon single nucleus RNA, rather than whole myofiber material. We examined if a whole-cell flow sorting approach could be applied to perform single cell RNA-seq (scRNA-seq) in a single muscle type.

Methods

We performed deep, whole cell, scRNA-seq on intact and fragmented skeletal myofibers from the mouse fast-twitch flexor digitorum brevis muscle utilizing a flow-gated method of large cell isolation. We performed deep sequencing of 763 intact and fragmented myofibers.

Results

Quality control metrics across the different gates indicated only 171 of these cells were optimal, with a median read count of 239,252 and an average of 12,098 transcripts per cell. scRNA-seq identified three clusters of myofibers (a slow/fast 2A cluster and two fast 2X clusters). Comparison to a public skeletal nuclear RNA-seq dataset demonstrated a diversity in transcript abundance by method. RISH validated multiple genes across fast and slow twitch skeletal muscle types.

Conclusion

This study introduces and validates a method to isolate intact skeletal muscle myofibers to generate deep expression patterns and expands the known repertoire of fiber-type-specific genes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13395-021-00269-2.

Computational principles and challenges in single-cell data integration

The development of single-cell multimodal assays provides a powerful tool for investigating multiple dimensions of cellular heterogeneity, enabling new insights into development, tissue homeostasis and disease. A key challenge in the analysis of single-cell multimodal data is to devise appropriate strategies for tying together data across different modalities. The term 'data integration' has been used to describe this task, encompassing a broad collection of approaches ranging from batch correction of individual omics datasets to association of chromatin accessibility and genetic variation with transcription. Although existing integration strategies exploit similar mathematical ideas, they typically have distinct goals and rely on different principles and assumptions. Consequently, new definitions and concepts are needed to contextualize existing methods and to enable development of new methods.

Induced lineage promiscuity undermines the efficiency of all-trans-retinoid-acid-induced differentiation of acute myeloid leukemia

All-trans retinoid acid (ATRA) can induce terminal differentiation of acute promyelocytic leukemia (APL), also known as the M3 subtype of acute myeloid leukemia (AML). However, non-APL types of AML respond poorly to ATRA-induced differentiation, and the mechanism underlying cell-type-specific resistance against ATRA remains unclear. Here, we use single-cell transcriptome analysis to compare the differentiation trajectories of two AML cell types during ATRA treatment. We show that in NB4 (APL/AML-M3) cells, ATRA activates canonical myeloid lineage factors—including SPI1, CEBPE, and STAT1—to direct near-normal differentiation toward mature granulocytes. By contrast, in HL60 (AML-M2) cells, ATRA-induced differentiation is incomplete and promiscuous, which is characterized by coinduction of both myelopoiesis and lymphopoiesis gene expression programs, as well as transient activation of cis-regulatory elements associated with myeloid differentiation. Our study suggests that the differentiation inducing capacity of ATRA in certain subtypes of AML may be compromised by therapy-induced lineage promiscuity.

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Single-cell analysis of ATRA-induced differentiation in two AML cell types

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In AML-M3/APL cells, ATRA induces a near normal trajectory of granulopoiesis

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In AML-M2 cells, ATRA induces incomplete differentiation and lineage promiscuity

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ATRA-induced lineage promiscuity involves transient cis-regulatory reprogramming

Differentiation reveals latent features of aging and an energy barrier in murine myogenesis

Skeletal muscle experiences a decline in lean mass and regenerative potential with age, in part due to intrinsic changes in progenitor cells. However, it remains unclear how age-related changes in progenitors manifest across a differentiation trajectory. Here, we perform single-cell RNA sequencing (RNA-seq) on muscle mononuclear cells from young and aged mice and profile muscle stem cells (MuSCs) and fibro-adipose progenitors (FAPs) after differentiation. Differentiation increases the magnitude of age-related change in MuSCs and FAPs, but it also masks a subset of age-related changes present in progenitors. Using a dynamical systems approach and RNA velocity, we find that aged MuSCs follow the same differentiation trajectory as young cells but stall in differentiation near a commitment decision. Our results suggest that differentiation reveals latent features of aging and that fate commitment decisions are delayed in aged myogenic cells in vitro.

Turning Nature's own processes into design strategies for living bone implant biomanufacturing: a decade of Developmental Engineering

A decade after the term developmental engineering (DE) was coined to indicate the use of developmental processes as blueprints for the design and development of engineered living implants, a myriad of proof-of-concept studies demonstrate the potential of this approach in small animal models. This review provides an overview of DE work, focusing on applications in bone regeneration. Enabling technologies allow to quantify the distance between in vitro processes and their developmental counterpart, as well as to design strategies to reduce that distance. By embedding Nature's robust mechanisms of action in engineered constructs, predictive large animal data and subsequent positive clinical outcomes can be gradually achieved. To this end, the development of next generation biofabrication technologies should provide the necessary scale and precision for robust living bone implant biomanufacturing.

scMC learns biological variation through the alignment of multiple single-cell genomics datasets

Distinguishing biological from technical variation is crucial when integrating and comparing single-cell genomics datasets across different experiments. Existing methods lack the capability in explicitly distinguishing these two variations, often leading to the removal of both variations. Here, we present an integration method scMC to remove the technical variation while preserving the intrinsic biological variation. scMC learns biological variation via variance analysis to subtract technical variation inferred in an unsupervised manner. Application of scMC to both simulated and real datasets from single-cell RNA-seq and ATAC-seq experiments demonstrates its capability of detecting context-shared and context-specific biological signals via accurate alignment.

Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview

Thanks to innovative sample-preparation and sequencing technologies, gene expression in individual cells can now be measured for thousands of cells in a single experiment. Since its introduction, single-cell RNA sequencing (scRNA-seq) approaches have revolutionized the genomics field as they created unprecedented opportunities for resolving cell heterogeneity by exploring gene expression profiles at a single-cell resolution. However, the rapidly evolving field of scRNA-seq invoked the emergence of various analytics approaches aimed to maximize the full potential of this novel strategy. Unlike population-based RNA sequencing approaches, scRNA seq necessitates comprehensive computational tools to address high data complexity and keep up with the emerging single-cell associated challenges. Despite the vast number of analytical methods, a universal standardization is lacking. While this reflects the fields' immaturity, it may also encumber a newcomer to blend in.In this review, we aim to bridge over the abovementioned hurdle and propose four ready-to-use pipelines for scRNA-seq analysis easily accessible by a newcomer, that could fit various biological data types. Here we provide an overview of the currently available single-cell technologies for cell isolation and library preparation and a step by step guide that covers the entire canonical analytic workflow to analyse scRNA-seq data including read mapping, quality controls, gene expression quantification, normalization, feature selection, dimensionality reduction, and cell clustering useful for trajectory inference and differential expression. Such workflow guidelines will escort novices as well as expert users in the analysis of complex scRNA-seq datasets, thus further expanding the research potential of single-cell approaches in basic science, and envisaging its future implementation as best practice in the field.

A replicating stem-like cell that contributes to bone morphogenetic protein 2-induced heterotopic bone formation

Bone morphogenetic protein 2 (BMP2)‐induced heterotopic bone formation (HBF) starts synchronously from zero upon BMP2 induction, which is advantageous for lineage tracking. The studies reported here in GLAST‐Cre^Ert2:tdTomato red (TR)^floxSTOPflox mice during BMP2‐induced HBF show 78.8 ± 11.6% of chondrocytes and 86.5 ± 1.9% of osteoblasts are TR⁺ after approximately 1 week. Clustering after single‐cell RNAseq resulted in nine cell types, and analysis revealed one as a highly replicating stem‐like cell (RSC). Pseudotiming suggested that the RSC transitions to a mesenchymal stem‐like cell that simultaneously expresses multiple osteoblast and chondrocyte transcripts (chondro‐osseous progenitor [COP]). RSCs and COPs were isolated using flow cytometry for unique surface markers. Isolated RSCs (GLAST‐TR⁺ Hmmr⁺ Cd200⁻) and COPs (GLAST‐TR⁺ Cd200⁺ Hmmr⁻) were injected into the muscle of mice undergoing HBF. Approximately 9% of the cells in heterotopic bone (HB) in mice receiving RSCs were GLAST‐TR⁺, compared with less than 0.5% of the cells in mice receiving COPs, suggesting that RSCs are many times more potent than COPs. Analysis of donor‐derived TR⁺ RSCs isolated from the engrafted HB showed approximately 50% were COPs and 45% were other cells, presumably mature bone cells, confirming the early nature of the RSCs. We next isolated RSCs from these mice (approximately 300) and injected them into a second animal, with similar findings upon analysis of HBF. Unlike other methodology, single cell RNAseq has the ability to detect rare cell populations such as RSCs. The fact that RSCs can be injected into mice and differentiate suggests their potential utility for tissue regeneration.

Advancing Stem Cell Research through Multimodal Single-Cell Analysis

Technological advances play a key role in furthering our understanding of stem cell biology, and advancing the prospects of regenerative therapies. Highly parallelized methods, developed in the last decade, can profile DNA, RNA, or proteins in thousands of cells and even capture data across two or more modalities (multiomics). This allows unbiased and precise definition of molecular cell states, thus allowing classification of cell types, tracking of differentiation trajectories, and discovery of underlying mechanisms. Despite being based on destructive techniques, novel experimental and bioinformatic approaches enable embedding and extraction of temporal information, which is essential for deconvolution of complex data and establishing cause and effect relationships. Here, we provide an overview of recent studies pertinent to stem cell biology, followed by an outlook on how further advances in single-cell molecular profiling and computational analysis have the potential to shape the future of both basic and translational research.

Sphetcher: Spherical Thresholding Improves Sketching of Single-Cell Transcriptomic Heterogeneity

The massive size of single-cell RNA sequencing datasets often exceeds the capability of current computational analysis methods to solve routine tasks such as detection of cell types. Recently, geometric sketching was introduced as an alternative to uniform subsampling. It selects a subset of cells (the sketch) that evenly cover the transcriptomic space occupied by the original dataset, to accelerate downstream analyses and highlight rare cell types. Here, we propose algorithm Sphetcher that makes use of the thresholding technique to efficiently pick representative cells within spheres (as opposed to the typically used equal-sized boxes) that cover the entire transcriptomic space. We show that the spherical sketch computed by Sphetcher constitutes a more accurate representation of the original transcriptomic landscape. Our optimization scheme allows to include fairness aspects that can encode prior biological or experimental knowledge. We show how a fair sampling can inform the inference of the trajectory of human skeletal muscle myoblast differentiation.

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Sphetcher distils large-scale scRNA-seq data down to a small selection of cells

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Spheres of small radius around selected cells cover the original transcriptomic space

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Selection enhances and accelerates downstream analysis such as trajectory inference

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Sphetcher can leverage existing annotation of known cell types

Single-cell RNA-seq analysis of Mesp1-induced skeletal myogenic development

The Mesp1 lineage contributes to cardiac, hematopoietic and skeletal myogenic development. Interestingly, muscle stem cells residing in craniofacial skeletal muscles primarily arise from Mesp1+ progenitors, but those in trunk and limb skeletal muscles do not. To gain insights into the difference between the head and trunk/limb muscle developmental processes, we studied Mesp1+ skeletal myogenic derivatives via single-cell RNA-seq and other strategies. Using a doxycycline-inducible Mesp1-expressing mouse embryonic stem cell line, we found that the development of Mesp1-induced skeletal myogenic progenitors can be characterized by dynamic expression of PDGFRα and VCAM1. Single-cell RNA-seq analysis further revealed the heterogeneous nature of these Mesp1+ derivatives, spanning pluripotent and mesodermal to mesenchymal and skeletal myogenic. We subsequently reconstructed the single-cell trajectories of these subpopulations. Our data thereby provide a cell fate projection of Mesp1-induced skeletal myogenesis.

A pooled single-cell genetic screen identifies regulatory checkpoints in the continuum of the epithelial-to-mesenchymal transition

Integrating single-cell trajectory analysis with pooled genetic screening could reveal the genetic architecture that guides cellular decisions in development and disease. We applied this paradigm to probe the genetic circuitry that controls epithelial-to-mesenchymal transition (EMT). We used single-cell RNA sequencing to profile epithelial cells undergoing a spontaneous spatially determined EMT in the presence or absence of transforming growth factor-β. Pseudospatial trajectory analysis identified continuous waves of gene regulation as opposed to discrete 'partial' stages of EMT. KRAS was connected to the exit from the epithelial state and the acquisition of a fully mesenchymal phenotype. A pooled single-cell CRISPR-Cas9 screen identified EMT-associated receptors and transcription factors, including regulators of KRAS, whose loss impeded progress along the EMT. Inhibiting the KRAS effector MEK and its upstream activators EGFR and MET demonstrates that interruption of key signaling events reveals regulatory 'checkpoints' in the EMT continuum that mimic discrete stages, and reconciles opposing views of the program that controls EMT.

Single-Cell RNA Sequencing-Based Computational Analysis to Describe Disease Heterogeneity

The trillions of cells in the human body can be viewed as elementary but essential biological units that achieve different body states, but the low resolution of previous cell isolation and measurement approaches limits our understanding of the cell-specific molecular profiles. The recent establishment and rapid growth of single-cell sequencing technology has facilitated the identification of molecular profiles of heterogeneous cells, especially on the transcription level of single cells [single-cell RNA sequencing (scRNA-seq)]. As a novel method, the robustness of scRNA-seq under changing conditions will determine its practical potential in major research programs and clinical applications. In this review, we first briefly presented the scRNA-seq-related methods from the point of view of experiments and computation. Then, we compared several state-of-the-art scRNA-seq analysis frameworks mainly by analyzing their performance robustness on independent scRNA-seq datasets for the same complex disease. Finally, we elaborated on our hypothesis on consensus scRNA-seq analysis and summarized the potential indicative and predictive roles of individual cells in understanding disease heterogeneity by single-cell technologies.

Ameliorating the Fibrotic Remodeling of the Heart through Direct Cardiac Reprogramming

Coronary artery disease is the most common form of cardiovascular diseases, resulting in the loss of cardiomyocytes (CM) at the site of ischemic injury. To compensate for the loss of CMs, cardiac fibroblasts quickly respond to injury and initiate cardiac remodeling in an injured heart. In the remodeling process, cardiac fibroblasts proliferate and differentiate into myofibroblasts, which secrete extracellular matrix to support the intact structure of the heart, and eventually differentiate into matrifibrocytes to form chronic scar tissue. Discovery of direct cardiac reprogramming offers a promising therapeutic strategy to prevent/attenuate this pathologic remodeling and replace the cardiac fibrotic scar with myocardium in situ. Since the first discovery in 2010, many progresses have been made to improve the efficiency and efficacy of reprogramming by understanding the mechanisms and signaling pathways that are activated during direct cardiac reprogramming. Here, we overview the development and recent progresses of direct cardiac reprogramming and discuss future directions in order to translate this promising technology into an effective therapeutic paradigm to reverse cardiac pathological remodeling in an injured heart.

Context-Specific Transcription Factor Functions Regulate Epigenomic and Transcriptional Dynamics during Cardiac Reprogramming

Ectopic expression of combinations of transcription factors (TFs) can drive direct lineage conversion, thereby reprogramming a somatic cell's identity. To determine the molecular mechanisms by which Gata4, Mef2c, and Tbx5 (GMT) induce conversion from a cardiac fibroblast toward an induced cardiomyocyte, we performed comprehensive transcriptomic, DNA-occupancy, and epigenomic interrogation throughout the reprogramming process. Integration of these datasets identified new TFs involved in cardiac reprogramming and revealed context-specific roles for GMT, including the ability of Mef2c and Tbx5 to independently promote chromatin remodeling at previously inaccessible sites. We also find evidence for cooperative facilitation and refinement of each TF's binding profile in a combinatorial setting. A reporter assay employing newly defined regulatory elements confirmed that binding of a single TF can be sufficient for gene activation, suggesting that co-binding events do not necessarily reflect synergy. These results shed light on fundamental mechanisms by which combinations of TFs direct lineage conversion.

Concepts and limitations for learning developmental trajectories from single cell genomics

Single cell genomics has become a popular approach to uncover the cellular heterogeneity of progenitor and terminally differentiated cell types with great precision. This approach can also delineate lineage hierarchies and identify molecular programmes of cell-fate acquisition and segregation. Nowadays, tens of thousands of cells are routinely sequenced in single cell-based methods and even more are expected to be analysed in the future. However, interpretation of the resulting data is challenging and requires computational models at multiple levels of abstraction. In contrast to other applications of single cell sequencing, where clustering approaches dominate, developmental systems are generally modelled using continuous structures, trajectories and trees. These trajectory models carry the promise of elucidating mechanisms of development, disease and stimulation response at very high molecular resolution. However, their reliable analysis and biological interpretation requires an understanding of their underlying assumptions and limitations. Here, we review the basic concepts of such computational approaches and discuss the characteristics of developmental processes that can be learnt from trajectory models.

Single cell analysis of adult mouse skeletal muscle stem cells in homeostatic and regenerative conditions

Dedicated stem cells ensure postnatal growth, repair and homeostasis of skeletal muscle. Following injury, muscle stem cells (MuSCs) exit from quiescence and divide to reconstitute the stem cell pool and give rise to muscle progenitors. The transcriptomes of pooled MuSCs have provided a rich source of information for describing the genetic programs of distinct static cell states; however, bulk microarray and RNA sequencing provide only averaged gene expression profiles, blurring the heterogeneity and developmental dynamics of asynchronous MuSC populations. Instead, the granularity required to identify distinct cell types, states, and their dynamics can be afforded by single cell analysis. We were able to compare the transcriptomes of thousands of MuSCs and primary myoblasts isolated from homeostatic or regenerating muscles by single cell RNA sequencing. Using computational approaches, we could reconstruct dynamic trajectories and place, in a pseudotemporal manner, the transcriptomes of individual MuSC within these trajectories. This approach allowed for the identification of distinct clusters of MuSCs and primary myoblasts with partially overlapping but distinct transcriptional signatures, as well as the description of metabolic pathways associated with defined MuSC states.

Single cell RNA-seq identifies the origins of heterogeneity in efficient cell transdifferentiation and reprogramming

Forced transcription factor expression can transdifferentiate somatic cells into other specialised cell types or reprogram them into induced pluripotent stem cells (iPSCs) with variable efficiency. To better understand the heterogeneity of these processes, we used single-cell RNA sequencing to follow the transdifferentation of murine pre-B cells into macrophages as well as their reprogramming into iPSCs. Even in these highly efficient systems, there was substantial variation in the speed and path of fate conversion. We predicted and validated that these differences are inversely coupled and arise in the starting cell population, with Myc^high large pre-BII cells transdifferentiating slowly but reprogramming efficiently and Myc^low small pre-BII cells transdifferentiating rapidly but failing to reprogram. Strikingly, differences in Myc activity predict the efficiency of reprogramming across a wide range of somatic cell types. These results illustrate how single cell expression and computational analyses can identify the origins of heterogeneity in cell fate conversion processes.

Expanding the Boundaries of RNA Sequencing as a Diagnostic Tool for Rare Mendelian Disease

Gene-panel and whole-exome analyses are now standard methodologies for mutation detection in Mendelian disease. However, the diagnostic yield achieved is at best 50%, leaving the genetic basis for disease unsolved in many individuals. New approaches are thus needed to narrow the diagnostic gap. Whole-genome sequencing is one potential strategy, but it currently has variant-interpretation challenges, particularly for non-coding changes. In this study we focus on transcriptome analysis, specifically total RNA sequencing (RNA-seq), by using monogenetic neuromuscular disorders as proof of principle. We examined a cohort of 25 exome and/or panel "negative" cases and provided genetic resolution in 36% (9/25). Causative mutations were identified in coding and non-coding exons, as well as in intronic regions, and the mutational pathomechanisms included transcriptional repression, exon skipping, and intron inclusion. We address a key barrier of transcriptome-based diagnostics: the need for source material with disease-representative expression patterns. We establish that blood-based RNA-seq is not adequate for neuromuscular diagnostics, whereas myotubes generated by transdifferentiation from an individual's fibroblasts accurately reflect the muscle transcriptome and faithfully reveal disease-causing mutations. Our work confirms that RNA-seq can greatly improve diagnostic yield in genetically unresolved cases of Mendelian disease, defines strengths and challenges of the technology, and demonstrates the suitability of cell models for RNA-based diagnostics. Our data set the stage for development of RNA-seq as a powerful clinical diagnostic tool that can be applied to the large population of individuals with undiagnosed, rare diseases and provide a framework for establishing minimally invasive strategies for doing so.