scMINER: a mutual information-based framework for identifying hidden drivers from single-cell omics data.

The sparse nature of single-cell omics data makes it challenging to dissect the wiring and rewiring of the transcriptional and signaling drivers that regulate cellular states. Many of the drivers, referred to as "hidden drivers", are difficult to identify via conventional expression analysis due to low expression and inconsistency between RNA and protein activity caused by post-translational and other modifications. To address this issue, we developed scMINER, a mutual information (MI)-based computational framework for unsupervised clustering analysis and cell-type specific inference of intracellular networks, hidden drivers and network rewiring from single-cell RNA-seq data. We designed scMINER to capture nonlinear cell-cell and gene-gene relationships and infer driver activities. Systematic benchmarking showed that scMINER outperforms popular single-cell clustering algorithms, especially in distinguishing similar cell types. With respect to network inference, scMINER does not rely on the binding motifs which are available for a limited set of transcription factors, therefore scMINER can provide quantitative activity assessment for more than 6,000 transcription and signaling drivers from a scRNA-seq experiment. As demonstrations, we used scMINER to expose hidden transcription and signaling drivers and dissect their regulon rewiring in immune cell heterogeneity, lineage differentiation, and tissue specification. Overall, activity-based scMINER is a widely applicable, highly accurate, reproducible and scalable method for inferring cellular transcriptional and signaling networks in each cell state from scRNA-seq data. The scMINER software is publicly accessible via: https://github.com/jyyulab/scMINER.

scMINER: a mutual information-based framework for identifying hidden drivers from single-cell omics data.

The sparse nature of single-cell omics data makes it challenging to dissect the wiring and rewiring of the transcriptional and signaling drivers that regulate cellular states. Many of the drivers, referred to as "hidden drivers", are difficult to identify via conventional expression analysis due to low expression and inconsistency between RNA and protein activity caused by post-translational and other modifications. To address this issue, we developed scMINER, a mutual information (MI)-based computational framework for unsupervised clustering analysis and cell-type specific inference of intracellular networks, hidden drivers and network rewiring from single-cell RNA-seq data. We designed scMINER to capture nonlinear cell-cell and gene-gene relationships and infer driver activities. Systematic benchmarking showed that scMINER outperforms popular single-cell clustering algorithms, especially in distinguishing similar cell types. With respect to network inference, scMINER does not rely on the binding motifs which are available for a limited set of transcription factors, therefore scMINER can provide quantitative activity assessment for more than 6,000 transcription and signaling drivers from a scRNA-seq experiment. As demonstrations, we used scMINER to expose hidden transcription and signaling drivers and dissect their regulon rewiring in immune cell heterogeneity, lineage differentiation, and tissue specification. Overall, activity-based scMINER is a widely applicable, highly accurate, reproducible and scalable method for inferring cellular transcriptional and signaling networks in each cell state from scRNA-seq data. The scMINER software is publicly accessible via: https://github.com/jyyulab/scMINER.

Integrative genomic analyses reveal mechanisms of glucocorticoid resistance in acute lymphoblastic leukemia.

Identification of genomic and epigenomic determinants of drug resistance provides important insights for improving cancer treatment. Using agnostic genome-wide interrogation of mRNA and miRNA expression, DNA methylation, SNPs, CNAs and SNVs/Indels in primary human acute lymphoblastic leukemia cells, we identified 463 genomic features associated with glucocorticoid resistance. Gene-level aggregation identified 118 overlapping genes, 15 of which were confirmed by genome-wide CRISPR screen. Collectively, this identified 30 of 38 (79%) known glucocorticoid-resistance genes/miRNAs and all 38 known resistance pathways, while revealing 14 genes not previously associated with glucocorticoid-resistance. Single cell RNAseq and network-based transcriptomic modelling corroborated the top previously undiscovered gene, CELSR2. Manipulation of CELSR2 recapitulated glucocorticoid resistance in human leukemia cell lines and revealed a synergistic drug combination (prednisolone and venetoclax) that mitigated resistance in mouse xenograft models. These findings illustrate the power of an integrative genomic strategy for elucidating genes and pathways conferring drug resistance in cancer cells.

SJARACNe: a scalable software tool for gene network reverse engineering from big data.

SUMMARY: Over the last two decades, we have observed an exponential increase in the number of generated array or sequencing-based transcriptomic profiles. Reverse engineering of biological networks from high-throughput gene expression profiles has been one of the grand challenges in systems biology. The Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) represents one of the most effective and widely-used tools to address this challenge. However, existing ARACNe implementations do not efficiently process big input data with thousands of samples. Here we present an improved implementation of the algorithm, SJARACNe, to solve this big data problem, based on sophisticated software engineering. The new scalable SJARACNe package achieves a dramatic improvement in computational performance in both time and memory usage and implements new features while preserving the network inference accuracy of the original algorithm. Given that large-sampled transcriptomic data is increasingly available and ARACNe is extremely demanding for network reconstruction, the scalable SJARACNe will allow even researchers with modest computational resources to efficiently construct complex regulatory and signaling networks from thousands of gene expression profiles. AVAILABILITY AND IMPLEMENTATION: SJARACNe is implemented in C++ (computational core) and Python (pipelining scripting wrapper, ≥3.6.1). It is freely available at https://github.com/jyyulab/SJARACNe. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Hippo/Mst signalling couples metabolic state and immune function of CD8α+ dendritic cells.

Dendritic cells orchestrate the crosstalk between innate and adaptive immunity. CD8α+ dendritic cells present antigens to CD8+ T cells and elicit cytotoxic T cell responses to viruses, bacteria and tumours 1 . Although lineage-specific transcriptional regulators of CD8α+ dendritic cell development have been identified 2 , the molecular pathways that selectively orchestrate CD8α+ dendritic cell function remain elusive. Moreover, metabolic reprogramming is important for dendritic cell development and activation3,4, but metabolic dependence and regulation of dendritic cell subsets are largely uncharacterized. Here we use a data-driven systems biology algorithm (NetBID) to identify a role of the Hippo pathway kinases Mst1 and Mst2 (Mst1/2) in selectively programming CD8α+ dendritic cell function and metabolism. Our NetBID analysis reveals a marked enrichment of the activities of Hippo pathway kinases in CD8α+ dendritic cells relative to CD8α- dendritic cells. Dendritic cell-specific deletion of Mst1/2-but not Lats1 and Lats2 (Lats1/2) or Yap and Taz (Yap/Taz), which mediate canonical Hippo signalling-disrupts homeostasis and function of CD8+ T cells and anti-tumour immunity. Mst1/2-deficient CD8α+ dendritic cells are impaired in presentation of extracellular proteins and cognate peptides to prime CD8+ T cells, while CD8α- dendritic cells that lack Mst1/2 have largely normal function. Mechanistically, compared to CD8α- dendritic cells, CD8α+ dendritic cells exhibit much stronger oxidative metabolism and critically depend on Mst1/2 signalling to maintain bioenergetic activities and mitochondrial dynamics for their functional capacities. Further, selective expression of IL-12 by CD8α+ dendritic cells depends on Mst1/2 and the crosstalk with non-canonical NF-κB signalling. Our findings identify Mst1/2 as selective drivers of CD8α+ dendritic cell function by integrating metabolic activity and cytokine signalling, and highlight that the interplay between immune signalling and metabolic reprogramming underlies the unique functions of dendritic cell subsets.