Responsiveness to perturbations is a hallmark of transcription factors that maintain cell identity in vitro

Molecular gatekeepers of endogenous adult mammalian cardiomyocyte proliferation

Irreversible cardiac fibrosis, cardiomyocyte death and chronic cardiac dysfunction after myocardial infarction pose a substantial global health-care challenge, with no curative treatments available. To regenerate the injured heart, cardiomyocytes must proliferate to replace lost myocardial tissue - a capability that adult mammals have largely forfeited to adapt to the demanding conditions of life. Using various preclinical models, our understanding of cardiomyocyte proliferation has progressed remarkably, leading to the successful reactivation of cell cycle induction in adult animals, with functional recovery after cardiac injury. Central to this success is the targeting of key pathways and structures that drive cardiomyocyte maturation after birth - nucleation and ploidy, sarcomere structure, developmental signalling, chromatin and epigenetic regulation, the microenvironment and metabolic maturation - forming a complex regulatory framework that allows efficient cellular contraction but restricts cardiomyocyte proliferation. In this Review, we explore the molecular pathways underlying these core mechanisms and how their manipulation can reactivate the cell cycle in cardiomyocytes, potentially contributing to cardiac repair.

Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells

BACKGROUND

Cells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and conditions. How the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear.

RESULTS

We analyze existing bulk and single-cell transcriptomic datasets to uncover the prevalence of transcriptional adaptation in mammalian systems across diverse contexts and cell types. We perform regulon gene expression analyses of transcription factor target sets in both bulk and pooled single-cell genetic perturbation datasets. Our results reveal greater robustness in expression of regulons of transcription factors exhibiting transcriptional adaptation compared to those of transcription factors that do not. Stochastic mathematical modeling of minimal compensatory gene networks qualitatively recapitulates several aspects of transcriptional adaptation, including paralog upregulation and robustness to mutation. Combined with machine learning analysis of network features of interest, our framework offers potential explanations for which regulatory steps are most important for transcriptional adaptation.

CONCLUSIONS

Our integrative approach identifies several putative hits-genes demonstrating possible transcriptional adaptation-to follow-up on experimentally and provides a formal quantitative framework to test and refine models of transcriptional adaptation.

Retrospective identification of cell-intrinsic factors that mark pluripotency potential in rare somatic cells

Pluripotency can be induced in somatic cells by the expression of OCT4, KLF4, SOX2, and MYC. Usually only a rare subset of cells reprogram, and the molecular characteristics of this subset remain unknown. We apply retrospective clone tracing to identify and characterize the rare human fibroblasts primed for reprogramming. These fibroblasts showed markers of increased cell cycle speed and decreased fibroblast activation. Knockdown of a fibroblast activation factor identified by our analysis increased the reprogramming efficiency. We provide evidence for a unified model in which cells can move into and out of the primed state over time, explaining how reprogramming appears deterministic at short timescales and stochastic at long timescales. Furthermore, inhibiting the activity of LSD1 enlarged the pool of cells that were primed for reprogramming. Thus, even homogeneous cell populations can exhibit heritable molecular variability that can dictate whether individual rare cells will reprogram or not.

Single-cell expression predicts neuron-specific protein homeostasis networks

The protein homeostasis network keeps proteins in their correct shapes and avoids unwanted aggregation. In turn, the accumulation of aberrantly misfolded proteins has been directly associated with the onset of ageing-associated neurodegenerative diseases such as Alzheimer's and Parkinson's. However, a detailed and rational understanding of how protein homeostasis is achieved in health, and how it can be targeted for therapeutic intervention in diseases remains missing. Here, large-scale single-cell expression data from the Allen Brain Map are analysed to investigate the transcription regulation of the core protein homeostasis network across the human brain. Remarkably, distinct expression profiles suggest specialized protein homeostasis networks with systematic adaptations in excitatory neurons, inhibitory neurons and non-neuronal cells. Moreover, several chaperones and Ubiquitin ligases are found transcriptionally coregulated with genes important for synapse formation and maintenance, thus linking protein homeostasis to the regulation of neuronal function. Finally, evolutionary analyses highlight the conservation of an elevated interaction density in the chaperone network, suggesting that one of the most exciting aspects of chaperone action may yet be discovered in their collective action at the systems level. More generally, our work highlights the power of computational analyses for breaking down complexity and gaining complementary insights into fundamental biological problems.

Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells

Cells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene can trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, to date, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and contexts. Moreover, how the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear. Here we combine computational analysis of existing datasets with stochastic mathematical modeling and machine learning to uncover the widespread prevalence of transcriptional adaptation in mammalian systems and the diverse single-cell manifestations of minimal compensatory gene networks. Regulon gene expression analysis of a pooled single-cell genetic perturbation dataset recapitulates important model predictions. Our integrative approach uncovers several putative hits-genes demonstrating possible transcriptional adaptation-to follow up on experimentally, and provides a formal quantitative framework to test and refine models of transcriptional adaptation.

Modeling diabetic endothelial dysfunction with patient-specific induced pluripotent stem cells

Diabetes is a known risk factor for various cardiovascular complications, mediated by endothelial dysfunction. Despite the high prevalence of this metabolic disorder, there is a lack of in vitro models that recapitulate the complexity of genetic and environmental factors associated with diabetic endothelial dysfunction. Here, we utilized human induced pluripotent stem cell (iPSC)-derived endothelial cells (ECs) to develop in vitro models of diabetic endothelial dysfunction. We found that the diabetic phenotype was recapitulated in diabetic patient-derived iPSC-ECs, even in the absence of a diabetogenic environment. Subsequent exposure to culture conditions that mimic the diabetic clinical chemistry induced a diabetic phenotype in healthy iPSC-ECs but did not affect the already dysfunctional diabetic iPSC-ECs. RNA-seq analysis revealed extensive transcriptome-wide differences between cells derived from healthy individuals and diabetic patients. The in vitro disease models were used as a screening platform which identified angiotensin receptor blockers (ARBs) that improved endothelial function in vitro for each patient. In summary, we present in vitro models of diabetic endothelial dysfunction using iPSC technology, taking into account the complexity of genetic and environmental factors in the metabolic disorder. Our study provides novel insights into the pathophysiology of diabetic endothelial dysfunction and highlights the potential of iPSC-based models for drug discovery and personalized medicine.

Diverse clonal fates emerge upon drug treatment of homogeneous cancer cells

Even among genetically identical cancer cells, resistance to therapy frequently emerges from a small subset of those cells1-7. Molecular differences in rare individual cells in the initial population enable certain cells to become resistant to therapy7-9; however, comparatively little is known about the variability in the resistance outcomes. Here we develop and apply FateMap, a framework that combines DNA barcoding with single-cell RNA sequencing, to reveal the fates of hundreds of thousands of clones exposed to anti-cancer therapies. We show that resistant clones emerging from single-cell-derived cancer cells adopt molecularly, morphologically and functionally distinct resistant types. These resistant types are largely predetermined by molecular differences between cells before drug addition and not by extrinsic factors. Changes in the dose and type of drug can switch the resistant type of an initial cell, resulting in the generation and elimination of certain resistant types. Samples from patients show evidence for the existence of these resistant types in a clinical context. We observed diversity in resistant types across several single-cell-derived cancer cell lines and cell types treated with a variety of drugs. The diversity of resistant types as a result of the variability in intrinsic cell states may be a generic feature of responses to external cues.

Retrospective identification of intrinsic factors that mark pluripotency potential in rare somatic cells

Pluripotency can be induced in somatic cells by the expression of the four "Yamanaka" factors OCT4, KLF4, SOX2, and MYC. However, even in homogeneous conditions, usually only a rare subset of cells admit reprogramming, and the molecular characteristics of this subset remain unknown. Here, we apply retrospective clone tracing to identify and characterize the individual human fibroblast cells that are primed for reprogramming. These fibroblasts showed markers of increased cell cycle speed and decreased fibroblast activation. Knockdown of a fibroblast activation factor identified by our analysis led to increased reprogramming efficiency, identifying it as a barrier to reprogramming. Changing the frequency of reprogramming by inhibiting the activity of LSD1 led to an enlarging of the pool of cells that were primed for reprogramming. Our results show that even homogeneous cell populations can exhibit heritable molecular variability that can dictate whether individual rare cells will reprogram or not.

BRD9-containing non-canonical BAF complex maintains somatic cell transcriptome and acts as a barrier to human reprogramming

Epigenetic reprogramming to pluripotency requires extensive remodeling of chromatin landscapes to silence existing cell-type-specific genes and activate pluripotency genes. ATP-dependent chromatin remodeling complexes are important regulators of chromatin structure and gene expression; however, the role of recently identified Bromodomain-containing protein 9 (BRD9) and the associated non-canonical BRG1-associated factors (ncBAF) complex in reprogramming remains unknown. Here, we show that genetic or chemical inhibition of BRD9, as well as ncBAF complex subunit GLTSCR1, but not the closely related BRD7, increase human somatic cell reprogramming efficiency and can replace KLF4 and c-MYC. We find that BRD9 is dispensable for human induced pluripotent stem cells under primed but not under naive conditions. Mechanistically, BRD9 inhibition downregulates fibroblast-related genes and decreases chromatin accessibility at somatic enhancers. BRD9 maintains the expression of transcriptional regulators MN1 and ZBTB38, both of which impede reprogramming. Collectively, these results establish BRD9 as an important safeguarding factor for somatic cell identity whose inhibition lowers chromatin-based barriers to reprogramming.

Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro

Technologies that profile chromatin modifications at single-cell resolution offer enormous promise for functional genomic characterization, but the sparsity of the measurements and integrating multiple binding maps represent substantial challenges. Here we introduce single-cell (sc)CUT&Tag-pro, a multimodal assay for profiling protein-DNA interactions coupled with the abundance of surface proteins in single cells. In addition, we introduce single-cell ChromHMM, which integrates data from multiple experiments to infer and annotate chromatin states based on combinatorial histone modification patterns. We apply these tools to perform an integrated analysis across nine different molecular modalities in circulating human immune cells. We demonstrate how these two approaches can characterize dynamic changes in the function of individual genomic elements across both discrete cell states and continuous developmental trajectories, nominate associated motifs and regulators that establish chromatin states and identify extensive and cell-type-specific regulatory priming. Finally, we demonstrate how our integrated reference can serve as a scaffold to map and improve the interpretation of additional scCUT&Tag datasets.

A novel classification method for NSCLC based on the background interaction network and the edge-perturbation matrix

The biological functional network of tumor tissues is relatively stable for a period of time and under different conditions, so the impact of tumor heterogeneity is effectively avoided. Based on edge perturbation, functional gene interaction networks were used to reveal the pathological environment of patients with non-small cell carcinoma at the individual level, and to identify cancer subtypes with the same or similar status, and then a multi-dimensional and multi-omics comprehensive analysis was put into practice. Two edge perturbation subtypes were identified through the construction of the background interaction network and the edge-perturbation matrix (EPM). Further analyses revealed clear differences between those two clusters in terms of prognostic survival, stemness indices, immune cell infiltration, immune checkpoint molecular expression, copy number alterations, mutation load, homologous recombination defects (HRD), neoantigen load, and chromosomal instability. Additionally, a risk prediction model based on TCGA for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) was successfully constructed and validated using the independent data set (GSE50081).

Cell type determination for cardiac differentiation occurs soon after seeding of human-induced pluripotent stem cells

BACKGROUND

Cardiac differentiation of human-induced pluripotent stem (hiPS) cells consistently produces a mixed population of cardiomyocytes and non-cardiac cell types, even when using well-characterized protocols. We sought to determine whether different cell types might result from intrinsic differences in hiPS cells prior to the onset of differentiation.

RESULTS

By associating individual differentiated cells that share a common hiPS cell precursor, we tested whether expression variability is predetermined from the hiPS cell state. In a single experiment, cells that shared a progenitor were more transcriptionally similar to each other than to other cells in the differentiated population. However, when the same hiPS cells were differentiated in parallel, we did not observe high transcriptional similarity across differentiations. Additionally, we found that substantial cell death occurs during differentiation in a manner that suggested all cells were equally likely to survive or die, suggesting that there is no intrinsic selection bias for cells descended from particular hiPS cell progenitors. We thus wondered how cells grow spatially during differentiation, so we labeled cells by expression of marker genes and found that cells expressing the same marker tended to occur in patches. Our results suggest that cell type determination across multiple cell types, once initiated, is maintained in a cell-autonomous manner for multiple divisions.

CONCLUSIONS

Altogether, our results show that while substantial heterogeneity exists in the initial hiPS cell population, it is not responsible for the variability observed in differentiated outcomes; instead, factors specifying the various cell types likely act during a window that begins shortly after the seeding of hiPS cells for differentiation.