LifeTime and improving European healthcare through cell-based interceptive medicine.

Here we describe the LifeTime Initiative, which aims to track, understand and target human cells during the onset and progression of complex diseases, and to analyse their response to therapy at single-cell resolution. This mission will be implemented through the development, integration and application of single-cell multi-omics and imaging, artificial intelligence and patient-derived experimental disease models during the progression from health to disease. The analysis of large molecular and clinical datasets will identify molecular mechanisms, create predictive computational models of disease progression, and reveal new drug targets and therapies. The timely detection and interception of disease embedded in an ethical and patient-centred vision will be achieved through interactions across academia, hospitals, patient associations, health data management systems and industry. The application of this strategy to key medical challenges in cancer, neurological and neuropsychiatric disorders, and infectious, chronic inflammatory and cardiovascular diseases at the single-cell level will usher in cell-based interceptive medicine in Europe over the next decade.

The Smurf transition: new insights on ageing from end-of-life studies in animal models.

PURPOSE OF REVIEW: Over the past 5 years, many articles were published concerning the prediction of high risk of mortality in apparently healthy adults, echoing the first description in 2011 of the Smurf phenotype, a harbinger of natural death in drosophila. RECENT FINDINGS: These recent findings suggest that the end-of-life is molecularly and physiologically highly stereotyped, evolutionarily conserved and predictable. SUMMARY: Taken altogether, these results from independent teams using multiple organisms including humans draw the lines of future directions in ageing research. The ability to identify and study individuals about to die of natural causes with no apparent diseases is a game-changer in this field. In addition, the public health applications are potentially of tremendous impact in our ageing societies and raise important ethical questions.

Establishment of X chromosome inactivation and epigenomic features of the inactive X depend on cellular contexts.

X chromosome inactivation (XCI) is an essential epigenetic process that ensures X-linked gene dosage equilibrium between sexes in mammals. XCI is dynamically regulated during development in a manner that is intimately linked to differentiation. Numerous studies, which we review here, have explored the dynamics of X inactivation and reactivation in the context of development, differentiation and diseases, and the phenotypic and molecular link between the inactive status, and the cellular context. Here, we also assess whether XCI is a uniform mechanism in mammals by analyzing epigenetic signatures of the inactive X (Xi) in different species and cellular contexts. It appears that the timing of XCI and the epigenetic signature of the inactive X greatly vary between species. Surprisingly, even within a given species, various Xi configurations are found across cellular states. We discuss possible mechanisms underlying these variations, and how they might influence the fate of the Xi.

Regional copy number-independent deregulation of transcription in cancer.

Genetic and epigenetic alterations have been identified that lead to transcriptional deregulation in cancers. Genetic mechanisms may affect single genes or regions containing several neighboring genes, as has been shown for DNA copy number changes. It was recently reported that epigenetic suppression of gene expression can also extend to a whole region; this is known as long-range epigenetic silencing. Various techniques are available for identifying regional genetic alterations, but no large-scale analysis has yet been carried out to obtain an overview of regional epigenetic alterations. We carried out an exhaustive search for regions susceptible to such mechanisms using a combination of transcriptome correlation map analysis and array CGH data for a series of bladder carcinomas. We validated one candidate region experimentally, demonstrating histone methylation leading to the loss of expression of neighboring genes without DNA methylation.

Genome architecture marked by retrotransposons modulates predisposition to DNA methylation in cancer.

Epigenetic silencing plays an important role in cancer development. An attractive hypothesis is that local DNA features may participate in differential predisposition to gene hypermethylation. We found that, compared with methylation-resistant genes, methylation-prone genes have a lower frequency of SINE and LINE retrotransposons near their transcription start site. In several large testing sets, this distribution was highly predictive of promoter methylation. Genome-wide analysis showed that 22% of human genes were predicted to be methylation-prone in cancer; these tended to be genes that are down-regulated in cancer and that function in developmental processes. Moreover, retrotransposon distribution marks a larger fraction of methylation-prone genes compared to Polycomb group protein (PcG) marking in embryonic stem cells; indeed, PcG marking and our predictive model based on retrotransposon frequency appear to be correlated but also complementary. In summary, our data indicate that retrotransposon elements, which are widespread in our genome, are strongly associated with gene promoter DNA methylation in cancer and may in fact play a role in influencing epigenetic regulation in normal and abnormal physiological states.

Long non-coding RNAs and human X-chromosome regulation: a coat for the active X chromosome.

In mammals, the genic disequilibrium between males (XY) and females (XX) is resolved through the inactivation of one of the X-chromosomes in females. X-chromosome inactivation (XCI) takes place in all mammalian species, but has mainly been studied in the mouse model where it was shown to be controlled by the interplay of several long non-coding RNA (lncRNA). However, recent data point toward the existence of species divergences among mammals in the strategies used to achieve XCI. The recent discovery of XACT, a novel lncRNA that coats the active X-chromosome specifically in human pluripotent cells, further highlights the existence of human-specific mechanisms of X-chromosome regulation. Here, we discuss the roles of lncRNAs in defining species-specific mechanisms controlling X-inactivation and explore the potential role of large lncRNAs in gene activation.

Multi-modal quantification of pathway activity with MAYA.

Signaling pathways can be activated through various cascades of genes depending on cell identity and biological context. Single-cell atlases now provide the opportunity to inspect such complexity in health and disease. Yet, existing reference tools for pathway scoring resume activity of each pathway to one unique common metric across cell types. Here, we present MAYA, a computational method that enables the automatic detection and scoring of the diverse modes of activation of biological pathways across cell populations. MAYA improves the granularity of pathway analysis by detecting subgroups of genes within reference pathways, each characteristic of a cell population and how it activates a pathway. Using multiple single-cell datasets, we demonstrate the biological relevance of identified modes of activation, the robustness of MAYA to noisy pathway lists and batch effect. MAYA can also predict cell types starting from lists of reference markers in a cluster-free manner. Finally, we show that MAYA reveals common modes of pathway activation in tumor cells across patients, opening the perspective to discover shared therapeutic vulnerabilities.

A benchmark of computational pipelines for single-cell histone modification data.

BACKGROUND: Single-cell histone post translational modification (scHPTM) assays such as scCUT&Tag or scChIP-seq allow single-cell mapping of diverse epigenomic landscapes within complex tissues and are likely to unlock our understanding of various mechanisms involved in development or diseases. Running scHTPM experiments and analyzing the data produced remains challenging since few consensus guidelines currently exist regarding good practices for experimental design and data analysis pipelines. RESULTS: We perform a computational benchmark to assess the impact of experimental parameters and data analysis pipelines on the ability of the cell representation to recapitulate known biological similarities. We run more than ten thousand experiments to systematically study the impact of coverage and number of cells, of the count matrix construction method, of feature selection and normalization, and of the dimension reduction algorithm used. This allows us to identify key experimental parameters and computational choices to obtain a good representation of single-cell HPTM data. We show in particular that the count matrix construction step has a strong influence on the quality of the representation and that using fixed-size bin counts outperforms annotation-based binning. Dimension reduction methods based on latent semantic indexing outperform others, and feature selection is detrimental, while keeping only high-quality cells has little influence on the final representation as long as enough cells are analyzed. CONCLUSIONS: This benchmark provides a comprehensive study on how experimental parameters and computational choices affect the representation of single-cell HPTM data. We propose a series of recommendations regarding matrix construction, feature and cell selection, and dimensionality reduction algorithms.