Author Correction: Tubular cell and keratinocyte single-cell transcriptomics applied to lupus nephritis reveal type I IFN and fibrosis relevant pathways.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Tubular cell and keratinocyte single-cell transcriptomics applied to lupus nephritis reveal type I IFN and fibrosis relevant pathways.

The molecular and cellular processes that lead to renal damage and to the heterogeneity of lupus nephritis (LN) are not well understood. We applied single-cell RNA sequencing (scRNA-seq) to renal biopsies from patients with LN and evaluated skin biopsies as a potential source of diagnostic and prognostic markers of renal disease. Type I interferon (IFN)-response signatures in tubular cells and keratinocytes distinguished patients with LN from healthy control subjects. Moreover, a high IFN-response signature and fibrotic signature in tubular cells were each associated with failure to respond to treatment. Analysis of tubular cells from patients with proliferative, membranous and mixed LN indicated pathways relevant to inflammation and fibrosis, which offer insight into their histologic differences. In summary, we applied scRNA-seq to LN to deconstruct its heterogeneity and identify novel targets for personalized approaches to therapy.

Elucidating synergistic dependencies in lung adenocarcinoma by proteome-wide signaling-network analysis.

To understand drug combination effect, it is necessary to decipher the interactions between drug targets-many of which are signaling molecules. Previously, such signaling pathway models are largely based on the compilation of literature data from heterogeneous cellular contexts. Indeed, de novo reconstruction of signaling interactions from large-scale molecular profiling is still lagging, compared to similar efforts in transcriptional and protein-protein interaction networks. To address this challenge, we introduce a novel algorithm for the systematic inference of protein kinase pathways, and applied it to published mass spectrometry-based phosphotyrosine profile data from 250 lung adenocarcinoma (LUAD) samples. The resulting network includes 43 TKs and 415 inferred, LUAD-specific substrates, which were validated at >60% accuracy by SILAC assays, including "novel' substrates of the EGFR and c-MET TKs, which play a critical oncogenic role in lung cancer. This systematic, data-driven model supported drug response prediction on an individual sample basis, including accurate prediction and validation of synergistic EGFR and c-MET inhibitor activity in cells lacking mutations in either gene, thus contributing to current precision oncology efforts.

A single-cell survey of the human first-trimester placenta and decidua.

The placenta and decidua interact dynamically to enable embryonic and fetal development. Here, we report single-cell RNA sequencing of 14,341 and 6754 cells from first-trimester human placental villous and decidual tissues, respectively. Bioinformatic analysis identified major cell types, many known and some subtypes previously unknown in placental villi and decidual context. Further detailed analysis revealed proliferating subpopulations, enrichment of cell type-specific transcription factors, and putative intercellular communication in the fetomaternal microenvironment. This study provides a blueprint to further the understanding of the roles of these cells in the placenta and decidua for maintenance of early gestation as well as pathogenesis in pregnancy-related disorders.

Single cell RNA sequencing to dissect the molecular heterogeneity in lupus nephritis.

Lupus nephritis is a leading cause of mortality among systemic lupus erythematosus (SLE) patients, and its heterogeneous nature poses a significant challenge to the development of effective diagnostics and treatments. Single cell RNA sequencing (scRNA-seq) offers a potential solution to dissect the heterogeneity of the disease and enables the study of similar cell types distant from the site of renal injury to identify novel biomarkers. We applied scRNA-seq to human renal and skin biopsy tissues and demonstrated that scRNA-seq can be performed on samples obtained during routine care. Chronicity index, IgG deposition, and quantity of proteinuria correlated with a transcriptomic-based score composed of IFN-inducible genes in renal tubular cells. Furthermore, analysis of cumulative expression profiles of single cell keratinocytes dissociated from nonlesional, non-sun-exposed skin of patients with lupus nephritis also revealed upregulation of IFN-inducible genes compared with keratinocytes isolated from healthy controls. This indicates the possible use of scRNA-seq analysis of skin biopsies as a biomarker of renal disease. These data support the potential utility of scRNA-seq to provide new insights into the pathogenesis of lupus nephritis and pave the way for exploiting a readily accessible tissue to reflect injury in the kidney.

Mechanism and Role of SOX2 Repression in Seminoma: Relevance to Human Germline Specification.

Human male germ cell tumors (GCTs) are derived from primordial germ cells (PGCs). The master pluripotency regulator and neuroectodermal lineage effector transcription factor SOX2 is repressed in PGCs and the seminoma (SEM) subset of GCTs. The mechanism of SOX2 repression and its significance to GC and GCT development currently are not understood. Here, we show that SOX2 repression in SEM-derived TCam-2 cells is mediated by the Polycomb repressive complex (PcG) and the repressive H3K27me3 chromatin mark that are enriched at its promoter. Furthermore, SOX2 repression in TCam-2 cells can be abrogated by recruitment of the constitutively expressed H3K27 demethylase UTX to the SOX2 promoter through retinoid signaling, leading to expression of neuronal and other lineage genes. SOX17 has been shown to initiate human PGC specification, with its target PRDM1 suppressing mesendodermal genes. Our results are consistent with a role for SOX2 repression in normal germline development by suppressing neuroectodermal genes.

Interrogation of a context-specific transcription factor network identifies novel regulators of pluripotency.

The predominant view of pluripotency regulation proposes a stable ground state with coordinated expression of key transcription factors (TFs) that prohibit differentiation. Another perspective suggests a more complexly regulated state involving competition between multiple lineage-specifying TFs that define pluripotency. These contrasting views were developed from extensive analyses of TFs in pluripotent cells in vitro. An experimentally validated, genome-wide repertoire of the regulatory interactions that control pluripotency within the in vivo cellular contexts is yet to be developed. To address this limitation, we assembled a TF interactome of adult human male germ cell tumors (GCTs) using the Algorithm for the Accurate Reconstruction of Cellular Pathways (ARACNe) to analyze gene expression profiles of 141 tumors comprising pluripotent and differentiated subsets. The network (GCT(Net)) comprised 1,305 TFs, and its ingenuity pathway analysis identified pluripotency and embryonal development as the top functional pathways. We experimentally validated GCT(Net) by functional (silencing) and biochemical (ChIP-seq) analysis of the core pluripotency regulatory TFs POU5F1, NANOG, and SOX2 in relation to their targets predicted by ARACNe. To define the extent of the in vivo pluripotency network in this system, we ranked all TFs in the GCT(Net) according to sharing of ARACNe-predicted targets with those of POU5F1 and NANOG using an odds-ratio analysis method. To validate this network, we silenced the top 10 TFs in the network in H9 embryonic stem cells. Silencing of each led to downregulation of pluripotency and induction of lineage; 7 of the 10 TFs were identified as pluripotency regulators for the first time.

The Cyni framework for network inference in Cytoscape.

MOTIVATION: Research on methods for the inference of networks from biological data is making significant advances, but the adoption of network inference in biomedical research practice is lagging behind. Here, we present Cyni, an open-source 'fill-in-the-algorithm' framework that provides common network inference functionality and user interface elements. Cyni allows the rapid transformation of Java-based network inference prototypes into apps of the popular open-source Cytoscape network analysis and visualization ecosystem. Merely placing the resulting app in the Cytoscape App Store makes the method accessible to a worldwide community of biomedical researchers by mouse click. In a case study, we illustrate the transformation of an ARACNE implementation into a Cytoscape app. AVAILABILITY AND IMPLEMENTATION: Cyni, its apps, user guides, documentation and sample code are available from the Cytoscape App Store http://apps.cytoscape.org/apps/cynitoolbox CONTACT: benno.schwikowski@pasteur.fr.

Gene expression analysis uncovers similarity and differences among Burkitt lymphoma subtypes.

Burkitt lymphoma (BL) is classified into 3 clinical subsets: endemic, sporadic, and immunodeficiency-associated BL. So far, possible differences in their gene expression profiles (GEPs) have not been investigated. We studied GEPs of BL subtypes, other B-cell lymphomas, and B lymphocytes; first, we found that BL is a unique molecular entity, distinct from other B-cell malignancies. Indeed, by unsupervised analysis all BLs clearly clustered apart of other lymphomas. Second, we found that BL subtypes presented slight differences in GEPs. Particularly, they differed for genes involved in cell cycle control, B-cell receptor signaling, and tumor necrosis factor/nuclear factor κB pathways. Notably, by reverse engineering, we found that endemic and sporadic BLs diverged for genes dependent on RBL2 activity. Furthermore, we found that all BLs were intimately related to germinal center cells, differing from them for molecules involved in cell proliferation, immune response, and signal transduction. Finally, to validate GEP, we applied immunohistochemistry to a large panel of cases and showed that RBL2 can cooperate with MYC in inducing a neoplastic phenotype in vitro and in vivo. In conclusion, our study provided substantial insights on the pathobiology of BLs, by offering novel evidences that may be relevant for its classification and possibly future treatment.

A human B-cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers.

Assembly of a transcriptional and post-translational molecular interaction network in B cells, the human B-cell interactome (HBCI), reveals a hierarchical, transcriptional control module, where MYB and FOXM1 act as synergistic master regulators of proliferation in the germinal center (GC). Eighty percent of genes jointly regulated by these transcription factors are activated in the GC, including those encoding proteins in a complex regulating DNA pre-replication, replication, and mitosis. These results indicate that the HBCI analysis can be used for the identification of determinants of major human cell phenotypes and provides a paradigm of general applicability to normal and pathologic tissues.

Reverse engineering cellular networks.

We describe a computational protocol for the ARACNE algorithm, an information-theoretic method for identifying transcriptional interactions between gene products using microarray expression profile data. Similar to other algorithms, ARACNE predicts potential functional associations among genes, or novel functions for uncharacterized genes, by identifying statistical dependencies between gene products. However, based on biochemical validation, literature searches and DNA binding site enrichment analysis, ARACNE has also proven effective in identifying bona fide transcriptional targets, even in complex mammalian networks. Thus we envision that predictions made by ARACNE, especially when supplemented with prior knowledge or additional data sources, can provide appropriate hypotheses for the further investigation of cellular networks. While the examples in this protocol use only gene expression profile data, the algorithm's theoretical basis readily extends to a variety of other high-throughput measurements, such as pathway-specific or genome-wide proteomics, microRNA and metabolomics data. As these data become readily available, we expect that ARACNE might prove increasingly useful in elucidating the underlying interaction models. For a microarray data set containing approximately 10,000 probes, reconstructing the network around a single probe completes in several minutes using a desktop computer with a Pentium 4 processor. Reconstructing a genome-wide network generally requires a computational cluster, especially if the recommended bootstrapping procedure is used.