Scaling up single-cell RNA-seq data analysis with CellBridge workflow

SUMMARY

Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of gene expression at the individual cell level, unraveling unprecedented insights into cellular heterogeneity. However, the analysis of scRNA-seq data remains a challenging and time-consuming task, often demanding advanced computational expertise, rendering it impractical for high-volume environments and applications. We present CellBridge, an automated workflow designed to simplify the standard procedures entailed in scRNA-seq data analysis, eliminating the need for specialized computational expertise. CellBridge utilizes state-of-the-art computational methods, integrating a range of advanced functionalities, covering the entire process from raw unaligned sequencing reads to cell type annotation. Hence, CellBridge accelerates the pace of discovery by seamlessly enabling insights into vast volumes of scRNA-seq data, without compromising workflow control and reproducibility.

AVAILABILITY AND IMPLEMENTATION

The source code, detailed documentation, and materials required to reproduce the results are available on GitHub and archived in Zenodo. For the CellBridge pre-processing step (v1.0.0), access the GitHub repository at https://github.com/Sanofi-Public/PMCB-ToBridge and the Zenodo archive at https://zenodo.org/records/10246161. For the CellBridge processing step (v1.0.0), visit the GitHub repository at https://github.com/Sanofi-Public/PMCB-CellBridge and the Zenodo archive at https://zenodo.org/records/10246046.

Molecular Features and Stages of Pulmonary Fibrosis Driven by Type 2 Inflammation

Systemic sclerosis (SSc) is a progressive, multiorgan disease with limited treatment options. Although a recent proof-of-concept study using romilkimab or SAR156597, a bispecific IL-4/IL-13 antibody, suggests a direct role of these cytokines in the pathophysiology of SSc, their contributions to the balance between inflammation and fibrosis are unclear. Here, we determine the roles of type 2 inflammation in fibrogenesis using FRA2-Tg (Fos-related antigen 2-overexpressing transgenic) mice, which develop spontaneous, age-dependent progressive lung fibrosis. We defined the molecular signatures of inflammation and fibrosis at three key stages in disease progression, corresponding to preonset, inflammatory dominant, and fibrosis dominant biology, and revealed an early increase in cytokine-cytokine receptor interactions and antigen-processing and presentation pathways followed by enhanced Th2- and M2 macrophage-driven type 2 responses. This type 2 inflammation progressed to extensive fibrotic pathology by 14-18 weeks of age, with these gene signatures overlapping significantly with those seen in the lungs of patients with SSc with interstitial lung disease (ILD). These changes were also evident in the histopathology, which showed perivascular and peribronchiolar inflammation with prominent eosinophilia and accumulation of profibrotic M2-like macrophages followed by rapid progression to fibrosis with thickened alveolar walls with multifocal fibrotic bands and signs of interstitial pneumonia. Critically, treatment with a bispecific antibody targeting IL-4 and IL-13 during the inflammatory phase abrogated the Th2 and M2 responses and led to near-complete abrogation of lung fibrosis. These data recapitulate important features of fibrotic progression in the lungs of patients with SSc-ILD and enhance our understanding of the progressive pathobiology of SSc. This study also further establishes FRA2-Tg mice as a valuable tool for testing future therapeutic agents in SSc-ILD.

A marker gene-based method for identifying the cell-type of origin from single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) experiments provide opportunities to peer into complex tissues at single-cell resolution. However, insightful biological interpretation of scRNA-seq data relies upon precise identification of cell types. The ability to identify the origin of a cell quickly and accurately will greatly improve downstream analyses. We present Sargent, a transformation-free, cluster-free, single-cell annotation algorithm for rapidly identifying the cell types of origin based on cell type-specific markers. We demonstrate Sargent's high accuracy by annotating simulated datasets. Further, we compare Sargent performance against expert-annotated scRNA-seq data from human organs including PBMC, heart, kidney, and lung. We demonstrate that Sargent retains both the flexibility and biological interpretability of cluster-based manual annotation. Additionally, the automation eliminates the labor intensive and potentially biased user annotation, producing robust, reproducible, and scalable outputs.•Sargent is a transformation-free, cluster-free, single-cell annotation algorithm for rapidly identifying the cell types of origin based on cell type-specific markers.•Sargent retains both the flexibility and biological interpretability of cluster-based manual annotation.•Automation eliminates the labor intensive and potentially biased user annotation, producing robust, reproducible, and scalable outputs.

DiSiR: fast and robust method to identify ligand–receptor interactions at subunit level from single-cell RNA-sequencing data

Most cell–cell interactions and crosstalks are mediated by ligand–receptor interactions. The advent of single-cell RNA-sequencing (scRNA-seq) techniques has enabled characterizing tissue heterogeneity at single-cell level. In the past few years, several methods have been developed to study ligand–receptor interactions at cell type level using scRNA-seq data. However, there is still no easy way to query the activity of a specific user-defined signaling pathway in a targeted way or to map the interactions of the same subunit with different ligands as part of different receptor complexes. Here, we present DiSiR, a fast and easy-to-use permutation-based software framework to investigate how individual cells are interacting with each other by analyzing signaling pathways of multi-subunit ligand-activated receptors from scRNA-seq data, not only for available curated databases of ligand–receptor interactions, but also for interactions that are not listed in these databases. We show that, when utilized to infer ligand–receptor interactions from both simulated and real datasets, DiSiR outperforms other well-known permutation-based methods, e.g. CellPhoneDB and ICELLNET. Finally, to demonstrate DiSiR’s utility in exploring data and generating biologically relevant hypotheses, we apply it to COVID lung and rheumatoid arthritis (RA) synovium scRNA-seq datasets and highlight potential differences between inflammatory pathways at cell type level for control versus disease samples.

Additive efficacy of a bispecific anti-TNF/IL-6 nanobody compound in translational models of rheumatoid arthritis

Rheumatoid arthritis (RA) is one of the most common autoimmune diseases affecting primarily the joints. Despite successful therapies including antibodies against tumor necrosis factor (TNF) and interleukin-6 (IL-6) receptor, only 20 to 30% of patients experience remission. We studied whether inhibiting both TNF and IL-6 would result in improved efficacy. Using backtranslation from single-cell RNA sequencing (scRNA-seq) data from individuals with RA, we hypothesized that TNF and IL-6 act synergistically on fibroblast-like synoviocytes (FLS) and T cells. Coculture of FLS from individuals with RA and T cells supported this hypothesis, revealing effects on both disease-driving pathways and biomarkers. Combining anti-TNF and anti-IL-6 antibodies in collagen-induced arthritis (CIA) mouse models resulted in sustained long-term remission, improved histology, and effects on bone remodeling pathways. These promising data initiated the development of an anti-TNF/IL-6 bispecific nanobody compound 1, with similar potencies against TNF and IL-6. We observed additive efficacy of compound 1 in a FLS/T cell coculture affecting arthritis and T helper 17 (T_H17) pathways. This nanobody compound transcript signature inversely overlapped with described RA endotypes, indicating a potential efficacy in a broader patient population. In summary, we showed superiority of a bispecific anti-TNF/IL-6 nanobody compound or combination treatment over monospecific treatments in both in vitro and in vivo models. We anticipate improved efficacy in upcoming clinical studies.

Microglia ferroptosis is regulated by SEC24B and contributes to neurodegeneration

Iron dysregulation has been implicated in multiple neurodegenerative diseases, including Parkinson's disease (PD). Iron-loaded microglia are frequently found in affected brain regions, but how iron accumulation influences microglia physiology and contributes to neurodegeneration is poorly understood. Here we show that human induced pluripotent stem cell-derived microglia grown in a tri-culture system are highly responsive to iron and susceptible to ferroptosis, an iron-dependent form of cell death. Furthermore, iron overload causes a marked shift in the microglial transcriptional state that overlaps with a transcriptomic signature found in PD postmortem brain microglia. Our data also show that this microglial response contributes to neurodegeneration, as removal of microglia from the tri-culture system substantially delayed iron-induced neurotoxicity. To elucidate the mechanisms regulating iron response in microglia, we performed a genome-wide CRISPR screen and identified novel regulators of ferroptosis, including the vesicle trafficking gene SEC24B. These data suggest a critical role for microglia iron overload and ferroptosis in neurodegeneration.

Macrophage-targeted therapy unlocks antitumoral crosstalk between IFN𝛾-secreting lymphocytes and IL12-producing dendritic cells

Macrophages often abound within tumors, express colony-stimulating factor 1 receptor (CSF1R), and are linked to adverse patient survival. Drugs blocking CSF1R signaling have been used to suppress tumor-promoting macrophage responses; however, their mechanisms of action remain incompletely understood. Here, we assessed the lung tumor immune microenvironment in mice treated with BLZ945, a prototypical small molecule CSF1R inhibitor, using single-cell RNA sequencing and mechanistic validation approaches. We showed that tumor control was not caused by CSF1R+ cell depletion; instead, CSF1R targeting reshaped the CSF1R+ cell landscape, which unlocked crosstalk between antitumoral CSF1R- cells. These cells included IFNγ-producing NK and T cells, and an IL12-producing dendritic cell subset, denoted as DC3, which were all necessary for CSF1R inhibitor-mediated lung tumor control. These data indicate that CSF1R targeting can activate a cardinal crosstalk between cells that are not macrophages and that are essential to mediate the effects of T cell-targeted immunotherapies and promote antitumor immunity.

Identification of a targetable KRAS-mutant epithelial population in non-small cell lung cancer

Lung cancer is the leading cause of cancer deaths. Tumor heterogeneity, which hampers development of targeted therapies, was herein deconvoluted via single cell RNA sequencing in aggressive human adenocarcinomas (carrying Kras-mutations) and comparable murine model. We identified a tumor-specific, mutant-KRAS-associated subpopulation which is conserved in both human and murine lung cancer. We previously reported a key role for the oncogene BMI-1 in adenocarcinomas. We therefore investigated the effects of in vivo PTC596 treatment, which affects BMI-1 activity, in our murine model. Post-treatment, MRI analysis showed decreased tumor size, while single cell transcriptomics concomitantly detected near complete ablation of the mutant-KRAS-associated subpopulation, signifying the presence of a pharmacologically targetable, tumor-associated subpopulation. Our findings therefore hold promise for the development of a targeted therapy for KRAS-mutant adenocarcinomas.

Optimum processing conditions for single cell RNA sequencing on frozen human PBMCs

The field of single cell RNA sequencing (sc-SEQ) has exploded in the past few years. From picking up single cells manually under a microscope, to droplet-based encapsulation of cells using microfluidics – this technology has improved in leaps and bounds. Common droplet-based technologies include inDrop, Drop-seq and 10X Genomics Chromium. All three technologies utilize microfluidics for encapsulating single cells & uniquely barcoded beads within an oil droplet. They differ in their bead material/manufacturing, barcode design and the range to which their operation can be customized by the end user. However, the performance of each sc-SEQ each technology is dependent on factors such as ability to obtain pure, viable single-cell suspension, and ability to accurately quantify the number of cells before running them through the machine. Here, we compare and contrast different conditions for cell processing that can affect single-cell sequencing results – including cell counting and purifying methods, as well as cell subtype enrichment kits; followed by single cell encapsulation, library preparation and analysis using 10X Genomics Chromium workflow.

Single-Cell Transcriptomics of Human and Mouse Lung Cancers Reveals Conserved Myeloid Populations across Individuals and Species

Tumor-infiltrating myeloid cells (TIMs) comprise monocytes, macrophages, dendritic cells, and neutrophils, and have emerged as key regulators of cancer growth. These cells can diversify into a spectrum of states, which might promote or limit tumor outgrowth but remain poorly understood. Here, we used single-cell RNA sequencing (scRNA-seq) to map TIMs in non-small-cell lung cancer patients. We uncovered 25 TIM states, most of which were reproducibly found across patients. To facilitate translational research of these populations, we also profiled TIMs in mice. In comparing TIMs across species, we identified a near-complete congruence of population structures among dendritic cells and monocytes; conserved neutrophil subsets; and species differences among macrophages. By contrast, myeloid cell population structures in patients' blood showed limited overlap with those of TIMs. This study determines the lung TIM landscape and sets the stage for future investigations into the potential of TIMs as immunotherapy targets.

Abstract 5864: Novel anti-BMI-1 therapy in non-small cell lung cancer

Lung cancer represents one of the most significant and lethal diseases worldwide. Advances in treatment protocols, the development of targeted agents, and our comprehensive understanding of the genetics and mechanisms driving this disease, have helped identify novel agents with therapeutic promise, yet mortality remains high. Thus, there is a real need to identify new therapeutic options that can target this disease to improve patient outcome. BMI-1 (B cell-specific Moloney murine leukemia virus integration site 1), a key component of the epigenetic Polycomb Repressive Complex 1, plays a substantial role in many solid tumors, including NSCLC. It represents a compelling therapeutic target for NSCLC cancer patients as the majority of NSCLCs (~75%) display positive BMI-1 protein expression. Several compounds that reduce levels of the oncogene BMI-1 protein have been discovered by PTC Therapeutics i.e., PTC596 and a related analog PTC-028. PTC596 completed Ph1 trials and is currently being further tested in Ph1b trials. By using xenografts and established murine models of NSCLC, we are investigating BMI-1 as a novel therapeutic target in lung cancer, the mechanisms through which BMI-1 confers tumorigenicity, and determine the efficacy of BMI-1 inhibition in in vivo models of lung cancer at the single cell level. One critical aspect of cancer development lies in the 3-dimensional network of interactions that occurs between cells within their microenvironment. Therefore, by means of droplet-based molecular barcoding techniques we are analyzing single cell transcripts and identifying the population clusters within the heterogeneous tumor cellular milieu, to determine which cellular populations exist within tumors and how they respond to anti-BMI-1 treatment. Our data show that mutant EGFR and K-Ras driven lung cancer transgenic mice, as well as xenograft mice, express high levels of BMI-1 protein. In addition, we show that tumor growth of both models is affected by PTC596 treatment, with a more rapid response than that of currently available therapeutics. Tumor growth was measured at different time-points by magnetic resonance imaging. Transcriptional deconvolution of single cell sequencing data has enabled us to identify tumor-associated pulmonary subpopulations displaying epithelial, immune, fibroblast, and endothelial features. Importantly, tumor associated epithelial cells display a positive BMI-1 signature, which we also identified in primary NSCLC samples, underscoring the beneficial effects of anti-BMI-1 therapy in NSCLC patients.

Citation Format: Indira Krishnan, Giorgia Maroni, Sean Clohessy, Virginia Savova, Mahmoud Bassal, Rapolas Zilionis, Eva Csizmadia, Clara M. Kerwin, Sun Choi, Claire V. Meyerovitz, Nicole Pandell, Chee W. Fhu, Junyan Zhang, Daniela S. Basseres, Cristina M. Magli, Julian Goggi, Robert S. Welner, Allon M. Klein, Marla Weetall, Art Branstrom, Raphael Bueno, Azhar Ali, Daniel G. Tenen, Elena Levantini. Novel anti-BMI-1 therapy in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5864.

Osteoblasts remotely supply lung tumors with cancer-promoting SiglecFhigh neutrophils

Bone marrow-derived myeloid cells can accumulate within tumors and foster cancer outgrowth. Local immune-neoplastic interactions have been intensively investigated, but the contribution of the systemic host environment to tumor growth remains poorly understood. Here, we show in mice and cancer patients (n = 70) that lung adenocarcinomas increase bone stromal activity in the absence of bone metastasis. Animal studies reveal that the cancer-induced bone phenotype involves bone-resident osteocalcin-expressing (Ocn⁺) osteoblastic cells. These cells promote cancer by remotely supplying a distinct subset of tumor-infiltrating SiglecF^high neutrophils, which exhibit cancer-promoting properties. Experimentally reducing Ocn⁺ cell numbers suppresses the neutrophil response and lung tumor outgrowth. These observations posit osteoblasts as remote regulators of lung cancer and identify SiglecF^high neutrophils as myeloid cell effectors of the osteoblast-driven protumoral response.

Risk alleles of genes with monoallelic expression are enriched in gain-of-function variants and depleted in loss-of-function variants for neurodevelopmental disorders

Over 3000 human genes can be expressed from a single allele in one cell, and from the other allele-or both-in neighboring cells. Little is known about the consequences of this epigenetic phenomenon, monoallelic expression (MAE). We hypothesized that MAE increases expression variability, with a potential impact on human disease. Here, we use a chromatin signature to infer MAE for genes in lymphoblastoid cell lines and human fetal brain tissue. We confirm that across clones MAE status correlates with expression level, and that in human tissue data sets, MAE genes show increased expression variability. We then compare mono- and biallelic genes at three distinct scales. In the human population, we observe that genes with polymorphisms influencing expression variance are more likely to be MAE (P<1.1 × 10-6). At the trans-species level, we find gene expression differences and directional selection between humans and chimpanzees more common among MAE genes (P<0.05). Extending to human disease, we show that MAE genes are under-represented in neurodevelopmental copy number variants (CNVs) (P<2.2 × 10-10), suggesting that pathogenic variants acting via expression level are less likely to involve MAE genes. Using neuropsychiatric single-nucleotide polymorphism (SNP) and single-nucleotide variant (SNV) data, we see that genes with pathogenic expression-altering or loss-of-function variants are less likely MAE (P<7.5 × 10-11) and genes with only missense or gain-of-function variants are more likely MAE (P<1.4 × 10-6). Together, our results suggest that MAE genes tolerate a greater range of expression level than biallelic expression (BAE) genes, and this information may be useful in prediction of pathogenicity.

Transcriptomic insights into genetic diversity of protein-coding genes in X. laevis

We characterize the genetic diversity of Xenopus laevis strains using RNA-seq data and allele-specific analysis. This data provides a catalogue of coding variation, which can be used for improving the genomic sequence, as well as for better sequence alignment, probe design, and proteomic analysis. In addition, we paint a broad picture of the genetic landscape of the species by functionally annotating different classes of mutations with a well-established prediction tool (PolyPhen-2). Further, we specifically compare the variation in the progeny of four crosses: inbred genomic (J)-strain, outbred albino (B)-strain, and two hybrid crosses of J and B strains. We identify a subset of mutations specific to the B strain, which allows us to investigate the selection pressures affecting duplicated genes in this allotetraploid. From these crosses we find the ratio of non-synonymous to synonymous mutations is lower in duplicated genes, which suggests that they are under greater purifying selection. Surprisingly, we also find that function-altering ("damaging") mutations constitute a greater fraction of the non-synonymous variants in this group, which suggests a role for subfunctionalization in coding variation affecting duplicated genes.

Single-cell barcoding and sequencing using droplet microfluidics

Single-cell RNA sequencing has recently emerged as a powerful tool for mapping cellular heterogeneity in diseased and healthy tissues, yet high-throughput methods are needed for capturing the unbiased diversity of cells. Droplet microfluidics is among the most promising candidates for capturing and processing thousands of individual cells for whole-transcriptome or genomic analysis in a massively parallel manner with minimal reagent use. We recently established a method called inDrops, which has the capability to index >15,000 cells in an hour. A suspension of cells is first encapsulated into nanoliter droplets with hydrogel beads (HBs) bearing barcoding DNA primers. Cells are then lysed and mRNA is barcoded (indexed) by a reverse transcription (RT) reaction. Here we provide details for (i) establishing an inDrops platform (1 d); (ii) performing hydrogel bead synthesis (4 d); (iii) encapsulating and barcoding cells (1 d); and (iv) RNA-seq library preparation (2 d). inDrops is a robust and scalable platform, and it is unique in its ability to capture and profile >75% of cells in even very small samples, on a scale of thousands or tens of thousands of cells.

Tumor-suppressor genes that escape from X-inactivation contribute to cancer sex bias

There is a striking and unexplained male predominance across many cancer types. A subset of X-chromosome genes can escape X-inactivation, which would protect females from complete functional loss by a single mutation. To identify putative 'escape from X-inactivation tumor-suppressor' (EXITS) genes, we examined somatic alterations from >4,100 cancers across 21 tumor types for sex bias. Six of 783 non-pseudoautosomal region (PAR) X-chromosome genes (ATRX, CNKSR2, DDX3X, KDM5C, KDM6A, and MAGEC3) harbored loss-of-function mutations more frequently in males (based on a false discovery rate < 0.1), in comparison to zero of 18,055 autosomal and PAR genes (Fisher's exact P < 0.0001). Male-biased mutations in genes that escape X-inactivation were observed in combined analysis across many cancers and in several individual tumor types, suggesting a generalized phenomenon. We conclude that biallelic expression of EXITS genes in females explains a portion of the reduced cancer incidence in females as compared to males across a variety of tumor types.

dbMAE: the database of autosomal monoallelic expression

Recently, data on ‘random’ autosomal monoallelic expression has become available for the entire genome in multiple human and mouse tissues and cell types, creating a need for better access and dissemination. The database of autosomal monoallelic expression (dbMAE; https://mae.hms.harvard.edu) incorporates data from multiple recent reports of genome-wide analyses. These include transcriptome-wide analyses of allelic imbalance in clonal cell populations based on sequence polymorphisms, as well as indirect identification, based on a specific chromatin signature present in MAE gene bodies. Currently, dbMAE contains transcriptome-wide chromatin identification calls for 8 human and 21 mouse tissues, and describes over 16 000 murine and ∼700 human cases of directly measured biased expression, compiled from allele-specific RNA-seq and genotyping array data. All data are manually curated. To ensure cross-publication uniformity, we performed re-analysis of transcriptome-wide RNA-seq data using the same pipeline. Data are accessed through an interface that allows for basic and advanced searches; all source references, including raw data, are clearly described and hyperlinked. This ensures the utility of the resource as an initial screening tool for those interested in investigating the role of monoallelic expression in their specific genes and tissues of interest.

Somatic Cell Fusions Reveal Extensive Heterogeneity in Basal-like Breast Cancer

Basal-like and luminal breast tumors have distinct clinical behavior and molecular profiles, yet the underlying mechanisms are poorly defined. To interrogate processes that determine these distinct phenotypes and their inheritance pattern, we generated somatic cell fusions and performed integrated genetic and epigenetic (DNA methylation and chromatin) profiling. We found that the basal-like trait is generally dominant and is largely defined by epigenetic repression of luminal transcription factors. Definition of super-enhancers highlighted a core program common in luminal cells but a high degree of heterogeneity in basal-like breast cancers that correlates with clinical outcome. We also found that protein extracts of basal-like cells are sufficient to induce a luminal-to-basal phenotypic switch, implying a trigger of basal-like autoregulatory circuits. We determined that KDM6A might be required for luminal-basal fusions, and we identified EN1, TBX18, and TCF4 as candidate transcriptional regulators of the luminal-to-basal switch. Our findings highlight the remarkable epigenetic plasticity of breast cancer cells.

Chromatin signature of widespread monoallelic expression

In mammals, numerous autosomal genes are subject to mitotically stable monoallelic expression (MAE), including genes that play critical roles in a variety of human diseases. Due to challenges posed by the clonal nature of MAE, very little is known about its regulation; in particular, no molecular features have been specifically linked to MAE. In this study, we report an approach that distinguishes MAE genes in human cells with great accuracy: a chromatin signature consisting of chromatin marks associated with active transcription (H3K36me3) and silencing (H3K27me3) simultaneously occurring in the gene body. The MAE signature is present in ∼20% of ubiquitously expressed genes and over 30% of tissue-specific genes across cell types. Notably, it is enriched among key developmental genes that have bivalent chromatin structure in pluripotent cells. Our results open a new approach to the study of MAE that is independent of polymorphisms, and suggest that MAE is linked to cell differentiation.

DOI:http://dx.doi.org/10.7554/eLife.01256.001

Understanding how genes are activated and silenced is one of the central challenges in modern biology. These processes underpin the development of a fertilized egg into a complex organism, and they can also lead to life-threatening diseases when they go wrong. There are two copies of each gene in a human cell, a maternal copy and a paternal copy, and it is thought that both copies are usually regulated together. However, there are exceptions to this rule: for certain genes only the maternal copy is expressed as a protein in some cells, whereas the paternal copy is expressed in other cells.

This form of gene regulation, which is called monoallelic expression, can result in neighboring cells heading down very different paths. In extreme cases, depending on the differences between the two copies of the gene, cells that express one copy may function normally, while cells where the other copy is activated will start forming tumors. However, despite these potentially grave consequences, and early results which suggested that monoallelic expression affected a large number of human and mouse genes, it has proved to be a major technical challenge to identify these genes in most cell types.

Now, Nag, Savova et al. have discovered a molecular signature that can be used to detect monoallelic expression. The signature was found in chromatin, the densely packed structure formed by DNA and proteins inside the cell nucleus. Nag, Savova et al. discovered that the genes that are subject to monoallelic expression are bound with proteins that are modified in two contrasting ways. One modification, which is usually a sign of gene silencing, is prevalent on the inactive copy of the gene, and the other, which often marks active genes, is chiefly present on the active copy.

Nag, Savova et al. report that these modifications are found in different sets of genes in different cell types, indicating distinct genome-wide patterns of monoallelic expression. The chromatin signature approach lets them estimate the fraction of human genes that are subject to monoallelic expression. This number is surprisingly high: about 20% of commonly expressed genes and more than one-third of tissue-specific genes. In a particularly intriguing finding, almost all bivalent genes—a subset of genes that are involved in determining the fate of cell during development—are estimated to become monoallelic when they are activated.

In addition to these unexpected findings, the chromatin signature approach opens the door to exploring monoallelic expression as a form of gene regulation in all types of cells and, ultimately, to understanding how it is involved in both normal development and in disease.

DOI:http://dx.doi.org/10.7554/eLife.01256.002

Autosomal monoallelic expression: genetics of epigenetic diversity?

In mammals, relative expression of the two parental alleles of many genes is controlled by one of three major epigenetic phenomena: X chromosome inactivation, imprinting, and mitotically stable autosomal monoallelic expression (MAE). MAE affects a large fraction of human autosomal genes and introduces enormous epigenetic heterogeneity in otherwise similar cell populations. Despite its prevalence, many functional and mechanistic aspects of MAE biology remain unknown. Several lines of evidence imply that MAE establishment and maintenance are controlled by a variety of genetic elements. Based on known genomic features regulating X-inactivation and imprinting, we outline likely features of MAE-controlling elements. We also assess implications of MAE for genotype-phenotype relationship, with a focus on haploinsufficiency.

A transgenic mouse marking live replicating cells reveals in vivo transcriptional program of proliferation

Most adult mammalian tissues are quiescent, with rare cell divisions serving to maintain homeostasis. At present, the isolation and study of replicating cells from their in vivo niche typically involves immunostaining for intracellular markers of proliferation, causing the loss of sensitive biological material. We describe a transgenic mouse strain, expressing a CyclinB1-GFP fusion reporter, that marks replicating cells in the S/G2/M phases of the cell cycle. Using flow cytometry, we isolate live replicating cells from the liver and compare their transcriptome to that of quiescent cells to reveal gene expression programs associated with cell proliferation in vivo. We find that replicating hepatocytes have reduced expression of genes characteristic of liver differentiation. This reporter system provides a powerful platform for gene expression and metabolic and functional studies of replicating cells in their in vivo niche.