Cerebro: interactive visualization of scRNA-seq data.

Despite the growing availability of sophisticated bioinformatic methods for the analysis of single-cell RNA-seq data, few tools exist that allow biologists without extensive bioinformatic expertise to directly visualize and interact with their own data and results. Here, we present Cerebro (cell report browser), a Shiny- and Electron-based standalone desktop application for macOS and Windows which allows investigation and inspection of pre-processed single-cell transcriptomics data without requiring bioinformatic experience of the user. Through an interactive and intuitive graphical interface, users can (i) explore similarities and heterogeneity between samples and cell clusters in two-dimensional or three-dimensional projections such as t-SNE or UMAP, (ii) display the expression level of single genes or gene sets of interest, (iii) browse tables of most expressed genes and marker genes for each sample and cluster and (iv) display trajectories calculated with Monocle 2. We provide three examples prepared from publicly available datasets to show how Cerebro can be used and which are its capabilities. Through a focus on flexibility and direct access to data and results, we think Cerebro offers a collaborative framework for bioinformaticians and experimental biologists that facilitates effective interaction to shorten the gap between analysis and interpretation of the data. AVAILABILITY AND IMPLEMENTATION: The Cerebro application, additional documentation, and example datasets are available at https://github.com/romanhaa/Cerebro. Similarly, the cerebroApp R package is available at https://github.com/romanhaa/cerebroApp. All components are released under the MIT License. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A knowledge-based framework for the discovery of cancer-predisposing variants using large-scale sequencing breast cancer data.

BACKGROUND: The landscape of cancer-predisposing genes has been extensively investigated in the last 30 years with various methodologies ranging from candidate gene to genome-wide association studies. However, sequencing data are still poorly exploited in cancer predisposition studies due to the lack of statistical power when comparing millions of variants at once. METHOD: To overcome these power limitations, we propose a knowledge-based framework founded on the characteristics of known cancer-predisposing variants and genes. Under our framework, we took advantage of a combination of previously generated datasets of sequencing experiments to identify novel breast cancer-predisposing variants, comparing the normal genomes of 673 breast cancer patients of European origin against 27,173 controls matched by ethnicity. RESULTS: We detected several expected variants on known breast cancer-predisposing genes, like BRCA1 and BRCA2, and 11 variants on genes associated with other cancer types, like RET and AKT1. Furthermore, we detected 183 variants that overlap with somatic mutations in cancer and 41 variants associated with 38 possible loss-of-function genes, including PIK3CB and KMT2C. Finally, we found a set of 19 variants that are potentially pathogenic, negatively correlate with age at onset, and have never been associated with breast cancer. CONCLUSIONS: In this study, we demonstrate the usefulness of a genomic-driven approach nested in a classic case-control study to prioritize cancer-predisposing variants. In addition, we provide a resource containing variants that may affect susceptibility to breast cancer.

The hidden genomic landscape of acute myeloid leukemia: subclonal structure revealed by undetected mutations.

The analyses carried out using 2 different bioinformatics pipelines (SomaticSniper and MuTect) on the same set of genomic data from 133 acute myeloid leukemia (AML) patients, sequenced inside the Cancer Genome Atlas project, gave discrepant results. We subsequently tested these 2 variant-calling pipelines on 20 leukemia samples from our series (19 primary AMLs and 1 secondary AML). By validating many of the predicted somatic variants (variant allele frequencies ranging from 100% to 5%), we observed significantly different calling efficiencies. In particular, despite relatively high specificity, sensitivity was poor in both pipelines resulting in a high rate of false negatives. Our findings raise the possibility that landscapes of AML genomes might be more complex than previously reported and characterized by the presence of hundreds of genes mutated at low variant allele frequency, suggesting that the application of genome sequencing to the clinic requires a careful and critical evaluation. We think that improvements in technology and workflow standardization, through the generation of clear experimental and bioinformatics guidelines, are fundamental to translate the use of next-generation sequencing from research to the clinic and to transform genomic information into better diagnosis and outcomes for the patient.

Genome-wide mapping of human DNA-replication origins: levels of transcription at ORC1 sites regulate origin selection and replication timing.

We report the genome-wide mapping of ORC1 binding sites in mammals, by chromatin immunoprecipitation and parallel sequencing (ChIP-seq). ORC1 binding sites in HeLa cells were validated as active DNA replication origins (ORIs) using Repli-seq, a method that allows identification of ORI-containing regions by parallel sequencing of temporally ordered replicating DNA. ORC1 sites were universally associated with transcription start sites (TSSs) of coding or noncoding RNAs (ncRNAs). Transcription levels at the ORC1 sites directly correlated with replication timing, suggesting the existence of two classes of ORIs: those associated with moderate/high transcription levels (≥1 RNA copy/cell), firing in early S and mapping to the TSSs of coding RNAs; and those associated with low transcription levels (<1 RNA copy/cell), firing throughout the entire S and mapping to TSSs of ncRNAs. These findings are compatible with a scenario whereby TSS expression levels influence the efficiency of ORC1 recruitment at G(1) and the probability of firing during S.

Myc-binding-site recognition in the human genome is determined by chromatin context.

Large-scale chromatin immunoprecipitation (ChIP) studies have been effective in unravelling the distribution of DNA-binding transcription factors along eukaryotic genomes, but specificity determinants remain elusive. Gene-regulatory regions display distinct histone variants and modifications (or marks). An attractive hypothesis is that these marks modulate protein recognition, but whether or not this applies to transcription factors remains unknown. Based on large-scale datasets and quantitative ChIP, we dissect the correlations between 35 histone marks and genomic binding by the transcription factor Myc. Our data reveal a relatively simple combinatorial organization of histone marks in human cells, with a few main groups of marks clustering on distinct promoter populations. A stretch of chromatin bearing high H3 K4/K79 methylation and H3 acetylation (or 'euchromatic island'), which is generally associated with a pre-engaged basal transcription machinery, is a strict pre-requisite for recognition of any target site by Myc (whether the consensus CACGTG or an alternative sequence). These data imply that tethering of a transcription factor to restricted chromatin domains is rate-limiting for sequence-specific DNA binding in vivo.

A Rare Subset of Primary Tumor Cells with Concomitant Hyperactivation of Extracellular Matrix Remodeling and dsRNA-IFN1 Signaling Metastasizes in Breast Cancer.

Metastatic breast cancer has a poor prognosis and is largely considered incurable. A better understanding of the molecular determinants of breast cancer metastasis could facilitate development of improved prevention and treatment strategies. We used lentiviral barcoding coupled to single-cell RNA sequencing to trace clonal and transcriptional evolution during breast cancer metastasis and showed that metastases derive from rare prometastatic clones that are underrepresented in primary tumors. Both low clonal fitness and high metastatic potential were independent of clonal origin. Differential expression and classification analyses revealed that the prometastatic phenotype was acquired by rare cells characterized by the concomitant hyperactivation of extracellular matrix remodeling and dsRNA-IFN signaling pathways. Notably, genetic silencing of key genes in these pathways (KCNQ1OT1 or IFI6, respectively) significantly impaired migration in vitro and metastasis in vivo, with marginal effects on cell proliferation and tumor growth. Gene expression signatures derived from the identified prometastatic genes predict metastatic progression in patients with breast cancer, independently of known prognostic factors. This study elucidates previously unknown mechanisms of breast cancer metastasis and provides prognostic predictors and therapeutic targets for metastasis prevention. SIGNIFICANCE: Transcriptional lineage tracing coupled with single-cell transcriptomics defined the transcriptional programs underlying metastatic progression in breast cancer, identifying prognostic signatures and prevention strategies.

Time makes histone H3 modifications drift in mouse liver.

To detect the epigenetic drift of time passing, we determined the genome-wide distributions of mono- and tri-methylated lysine 4 and acetylated and tri-methylated lysine 27 of histone H3 in the livers of healthy 3, 6 and 12 months old C57BL/6 mice. The comparison of different age profiles of histone H3 marks revealed global redistribution of histone H3 modifications with time, in particular in intergenic regions and near transcription start sites, as well as altered correlation between the profiles of different histone modifications. Moreover, feeding mice with caloric restriction diet, a treatment known to retard aging, reduced the extent of changes occurring during the first year of life in these genomic regions.

Aberrant activation of p53/p66Shc-mInsc axis increases asymmetric divisions and attenuates proliferation of aged mammary stem cells.

Aging is accompanied by the progressive decline in tissue regenerative capacity and functions of resident stem cells (SCs). Underlying mechanisms, however, remain unclear. Here we show that, during chronological aging, self-renewing mitoses of mammary SCs (MaSCs) are preferentially asymmetric and that their progeny divides less frequently, leading to decreased number of MaSCs and reduced regenerative potential. Underlying mechanisms are investigated in the p66Shc-/- mouse, which exhibits several features of delayed aging, including reduced involution of the mammary gland (MG). p66Shc is a mitochondrial redox sensor that activates a specific p53 transcriptional program, in which the aging-associated p44 isoform of p53 plays a pivotal role. We report here that aged p66Shc-/- MaSCs show increased symmetric divisions, increased proliferation and increased regenerative potential, to an extent reminiscent of young wild-type (WT) MaSCs. Mechanistically, we demonstrate that p66Shc, together with p53: (i) accumulates in the aged MG, (ii) sustains expression of the cell polarity determinant mInscuteable and, concomitantly, (iii) down-regulates critical cell cycle genes (e.g.,: Cdk1 and Cyclin A). Accordingly, overexpression of p53/p44 increases asymmetric divisions and decreases proliferation of young WT MaSCs in a p66Shc-dependent manner and overexpression of mInsc restores WT-like levels of asymmetric divisions in aged p66Shc-/- MaSCs. Notably, deletion of p66Shc has negligible effects in young MaSCs and MG development. These results demonstrate that MG aging is due to aberrant activation of p66Shc, which induces p53/p44 signaling, leading to failure of symmetric divisions, decreased proliferation and reduced regenerative potential of MaSCs.

AML1/ETO oncoprotein is directed to AML1 binding regions and co-localizes with AML1 and HEB on its targets.

A reciprocal translocation involving chromosomes 8 and 21 generates the AML1/ETO oncogenic transcription factor that initiates acute myeloid leukemia by recruiting co-repressor complexes to DNA. AML1/ETO interferes with the function of its wild-type counterpart, AML1, by directly targeting AML1 binding sites. However, transcriptional regulation determined by AML1/ETO probably relies on a more complex network, since the fusion protein has been shown to interact with a number of other transcription factors, in particular E-proteins, and may therefore target other sites on DNA. Genome-wide chromatin immunoprecipitation and expression profiling were exploited to identify AML1/ETO-dependent transcriptional regulation. AML1/ETO was found to co-localize with AML1, demonstrating that the fusion protein follows the binding pattern of the wild-type protein but does not function primarily by displacing it. The DNA binding profile of the E-protein HEB was grossly rearranged upon expression of AML1/ETO, and the fusion protein was found to co-localize with both AML1 and HEB on many of its regulated targets. Furthermore, the level of HEB protein was increased in both primary cells and cell lines expressing AML1/ETO. Our results suggest a major role for the functional interaction of AML1/ETO with AML1 and HEB in transcriptional regulation determined by the fusion protein.

XenoCell: classification of cellular barcodes in single cell experiments from xenograft samples.

BACKGROUND: Single-cell sequencing technologies provide unprecedented opportunities to deconvolve the genomic, transcriptomic or epigenomic heterogeneity of complex biological systems. Its application in samples from xenografts of patient-derived biopsies (PDX), however, is limited by the presence of cells originating from both the host and the graft in the analysed samples; in fact, in the bioinformatics workflows it is still a challenge discriminating between host and graft sequence reads obtained in a single-cell experiment. RESULTS: We have developed XenoCell, the first stand-alone pre-processing tool that performs fast and reliable classification of host and graft cellular barcodes from single-cell sequencing experiments. We show its application on a mixed species 50:50 cell line experiment from 10× Genomics platform, and on a publicly available PDX dataset obtained by Drop-Seq. CONCLUSIONS: XenoCell accurately dissects sequence reads from any host and graft combination of species as well as from a broad range of single-cell experiments and platforms. It is open source and available at https://gitlab.com/XenoCell/XenoCell .