Bi-species, fusion-mediated, somatic cell reprogramming allows precise, organism-specific tracking of unknown lineage drivers. The fusion of Tcf7l1^-/- murine embryonic stem cells with EBV-transformed human B cell lymphocytes, leads to the generation of bi-species heterokaryons. Human mRNA transcript profiling at multiple time points permits the tracking of the reprogramming of B cell nuclei to a multipotent state. Interrogation of a human B cell regulatory network with gene expression signatures identifies 8 candidate master regulator proteins. Of these 8 candidates, ectopic expression of BAZ2B, from the bromodomain family, efficiently reprograms hematopoietic committed progenitors into a multipotent state and significantly enhances their long-term clonogenicity, stemness, and engraftment in immunocompromised mice. Unbiased systems biology approaches let us identify the early driving events of human B cell reprogramming.

The sparse vascularity of pancreatic ductal adenocarcinoma (PDAC) presents a mystery: What prevents this aggressive malignancy from undergoing neoangiogenesis to counteract hypoxia and better support growth? An incidental finding from prior work on paracrine communication between malignant PDAC cells and fibroblasts revealed that inhibition of the Hedgehog (HH) pathway partially relieved angiosuppression, increasing tumor vascularity through unknown mechanisms. Initial efforts to study this phenotype were hindered by difficulties replicating the complex interactions of multiple cell types in vitro. Here we identify a cascade of paracrine signals between multiple cell types that act sequentially to suppress angiogenesis in PDAC. Malignant epithelial cells promote HH signaling in fibroblasts, leading to inhibition of noncanonical WNT signaling in fibroblasts and epithelial cells, thereby limiting VEGFR2-dependent activation of endothelial hypersprouting. This cascade was elucidated using human and murine PDAC explant models, which effectively retain the complex cellular interactions of native tumor tissues.Significance: We present a key mechanism of tumor angiosuppression, a process that sculpts the physiologic, cellular, and metabolic environment of PDAC. We further present a computational and experimental framework for the dissection of complex signaling cascades that propagate among multiple cell types in the tissue environment.This article is featured in Selected Articles from This Issue, p. 201

While single-cell RNA sequencing provides a new window on physiologic and pathologic tissue biology and heterogeneity, it suffers from low signal-to-noise ratio and a high dropout rate at the individual gene level, thus challenging quantitative analyses. To address this problem, we introduce PISCES (Protein-activity Inference for Single Cell Studies), an integrated analytical framework for the protein activity-based analysis of single cell subpopulations. PISCES leverages the assembly of lineage-specific gene regulatory networks, to accurately measure activity of each protein based on the expression its transcriptional targets (regulon), using the ARACNe and metaVIPER algorithms, respectively. It implements novel analytical and visualization functions, including activity-based cluster analysis, identification of cell state repertoires, and elucidation of master regulators of cell state and cell state transitions, with full interoperability with Seurat’s single-cell data format. Accuracy and reproducibility assessment, via technical and biological validation assays and by assessing concordance with antibody and CITE-Seq-based measurements, show dramatic improvement in the ability to identify rare subpopulations and to assess activity of key lineage markers, compared to gene expression analysis.

Gene sets are being increasingly leveraged to make high-level biological inferences from transcriptomic data; however, existing gene set analysis methods rely on overly conservative, heuristic approaches for quantifying the statistical significance of gene set enrichment. We created Nonparametric analytical-Rank-based Enrichment Analysis (NaRnEA) to facilitate accurate and robust gene set analysis with an optimal null model derived using the information theoretic Principle of Maximum Entropy. By measuring the differential activity of ~2500 transcriptional regulatory proteins based on the differential expression of each protein’s transcriptional targets between primary tumors and normal tissue samples in three cohorts from The Cancer Genome Atlas (TCGA), we demonstrate that NaRnEA critically improves in two widely used gene set analysis methods: Gene Set Enrichment Analysis (GSEA) and analytical-Rank-based Enrichment Analysis (aREA). We show that the NaRnEA-inferred differential protein activity is significantly correlated with differential protein abundance inferred from independent, phenotype-matched mass spectrometry data in the Clinical Proteomic Tumor Analysis Consortium (CPTAC), confirming the statistical and biological accuracy of our approach. Additionally, our analysis crucially demonstrates that the sample-shuffling empirical null models leveraged by GSEA and aREA for gene set analysis are overly conservative, a shortcoming that is avoided by the newly developed Maximum Entropy analytical null model employed by NaRnEA.

Nonparametric analytical Rank-based Enrichment Analysis (NaRnEA) is a novel gene set analysis method which leverages an analytical null model derived under the Principle of Maximum Entropy. NaRnEA critically improves over two widely used methods – Gene Set Enrichment Analysis (GSEA) and analytical Rank-based Enrichment Analysis (aREA) – as shown by differential activity measurement of ~2,500 transcriptional regulatory proteins across three cohorts in The Cancer Genome Atlas (TCGA) based on the enrichment of their transcriptional targets in differentially expressed genes. Phenotype-matched proteomic data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) was used to evaluate measurement accuracy. We show that the sample-shuffling empirical null models leveraged by GSEA and aREA are overly conservative, a shortcoming that is critically addressed by NaRnEA’s optimal analytical null model.