We present SCENIC, a computational method for simultaneous gene regulatory network reconstruction and cell-state identification from single-cell RNA-seq data (http://scenicaertslaborg) On a compendium of single-cell data from tumors and brain, we demonstrate that cis-regulatory analysis can be exploited to guide the identification of transcription factors and cell states SCENIC provides critical biological insights into the mechanisms driving cellular heterogeneity

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SCENIC: single-cell regulatory network inference and clustering.

The success of anticancer therapy is usually limited by the development of drug resistance. Such acquired resistance is driven, in part, by intratumoural heterogeneity - that is, the phenotypic diversity of cancer cells co-inhabiting a single tumour mass. The introduction of the cancer stem cell (CSC) concept, which posits the presence of minor subpopulations of CSCs that are uniquely capable of seeding new tumours, has provided a framework for understanding one dimension of intratumoural heterogeneity. This concept, taken together with the identification of the epithelial-to-mesenchymal transition (EMT) programme as a critical regulator of the CSC phenotype, offers an opportunity to investigate the nature of intratumoural heterogeneity and a possible mechanistic basis for anticancer drug resistance. In fact, accumulating evidence indicates that conventional therapies often fail to eradicate carcinoma cells that have entered the CSC state via activation of the EMT programme, thereby permitting CSC-mediated clinical relapse. In this Review, we summarize our current understanding of the link between the EMT programme and the CSC state, and also discuss how this knowledge can contribute to improvements in clinical practice.

EMT, CSCs, and drug resistance: the mechanistic link and clinical implications

Evidence-Based Diagnosis, Staging, and Treatment of Patients With Hepatocellular Carcinoma.

Single-cell RNA-seq allows building cell atlases of any given tissue and infer the dynamics of cellular state transitions during developmental or disease trajectories. Both the maintenance and transitions of cell states are encoded by regulatory programs in the genome sequence. However, this regulatory code has not yet been exploited to guide the identification of cellular states from single-cell RNA-seq data. Here we describe a computational resource, called SCENIC (Single Cell rEgulatory Network Inference and Clustering), for the simultaneous reconstruction of gene regulatory networks (GRNs) and the identification of stable cell states, using single-cell RNA-seq data. SCENIC outperforms existing approaches at the level of cell clustering and transcription factor identification. Importantly, we show that cell state identification based on GRNs is robust towards batch-effects and technical-biases. We applied SCENIC to a compendium of single-cell data from the mouse and human brain and demonstrate that the proper combinations of transcription factors, target genes, enhancers, and cell types can be identified. Moreover, we used SCENIC to map the cell state landscape in melanoma and identified a gene regulatory network underlying a proliferative melanoma state driven by MITF and STAT and a contrasting network controlling an invasive state governed by NFATC2 and NFIB. We further validated these predictions by showing that two transcription factors are predominantly expressed in early metastatic sentinel lymph nodes. In summary, SCENIC is the first method to analyze scRNA-seq data using a network-centric, rather than cell-centric approach. SCENIC is generic, easy to use, and flexible, and allows for the simultaneous tracing of genomic regulatory programs and the mapping of cellular identities emerging from these programs. Availability: SCENIC is available as an R workflow based on three new R/Bioconductor packages: GENIE3, RcisTarget and AUCell. As scalable alternative to GENIE3, we also provide GRNboost, paving the way towards the network analysis across millions of single cells.

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SCENIC: Single-Cell Regulatory Network Inference And Clustering

Advances in nanoparticle synthesis and engineering have produced nanoscale agents affording both therapeutic and diagnostic functions that are often referred to by the portmanteau 'nanotheranostics'. The field is associated with many applications in the clinic, especially in cancer management. These include patient stratification, drug-release monitoring, imaging-guided focal therapy and post-treatment response monitoring. Recent advances in nanotheranostics have expanded this notion and enabled the characterization of individual tumours, the prediction of nanoparticle-tumour interactions, and the creation of tailor-designed nanomedicines for individualized treatment. Some of these applications require breaking the dogma that a nanotheranostic must combine both therapeutic and diagnostic agents within a single, physical entity; instead, it can be a general approach in which diagnosis and therapy are interwoven to solve clinical issues and improve treatment outcomes. In this Review, we describe the evolution and state of the art of cancer nanotheranostics, with an emphasis on clinical impact and translation.

Rethinking cancer nanotheranostics.

The extent of tumor heterogeneity is an emerging theme that researchers are only beginning to understand. How genetic and epigenetic heterogeneity affects tumor evolution and clinical progression is unknown. The precise nature of the environmental factors that influence this heterogeneity is also yet to be characterized. Nature Medicine, Nature Biotechnology and the Volkswagen Foundation organized a meeting focused on identifying the obstacles that need to be overcome to advance translational research in and tumor heterogeneity. Once these key questions were established, the attendees devised potential solutions. Their ideas are presented here.

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Michael Doherty

Papers

Toward understanding and exploiting tumor heterogeneity

Gut dysbiosis in rheumatic diseases: A systematic review and meta-analysis of 92 observational studies