Bayesian correlation is a robust gene similarity measure for single-cell RNA-seq data

Level of M1 GABAB predicts micro offline consolidation of motor learning during wakefulness

The consolidation process stabilizes a new initially labile memory. This consolidation could operate on a shorter timescale during wakefulness after initial motor learning. Within micro-offline learning states, sequences of simple individual actions learned through interleaved practice are condensed into a unified skill through a time-dependent consolidation process occurring during wakeful periods. While emerging evidence links Glutamate and GABA modulations in the primary motor cortex (M1) to motor learning, its relationship with micro-offline consolidation processes in brief resting states during motor learning is unclear. To investigate this issue, we employed Transcranial magnetic stimulation (TMS) to evaluate whether interindividual variation of different neurotransmitters at rest influences motor learning consolidation in humans. Our results point to the role of GABAB in micro-offline motor consolidation processes during motor learning in M1. This finding could have an important impact on planning neuropharmacology or non-invasive brain stimulation approaches in clinical domains, such as post-stroke rehabilitation.

Global identification of mammalian host and nested gene pairs reveal tissue-specific transcriptional interplay

Nucleotide sequences along a gene provide instructions to transcriptional and cotranscriptional machinery allowing genome expansion into the transcriptome. Nucleotide sequence can often be shared between two genes and in some occurrences, a gene is located completely within a different gene; these are known as host/nested gene pairs. In these instances, if both genes are transcribed, overlap can result in a transcriptional crosstalk where genes regulate each other. Despite this, a comprehensive annotation of where such genes are located and their expression patterns is lacking. To address this, we provide an up-to-date catalog of host/nested gene pairs in mouse and human, showing that over a tenth of all genes contain a nested gene. We discovered that transcriptional co-occurrence is often tissue specific. This coexpression was especially prevalent within the transcriptionally permissive tissue, testis. We use this developmental system and scRNA-seq analysis to demonstrate that the coexpression of pairs can occur in single cells and transcription in the same place at the same time can enhance the transcript diversity of the host gene. In agreement, host genes are more transcript-diverse than the rest of the transcriptome. Host/nested gene configurations are common in both human and mouse, suggesting that interplay between gene pairs is a feature of the mammalian genome. This highlights the relevance of transcriptional crosstalk between genes which share nucleic acid sequence. The results and analysis are available on an Rshiny application (https://hngeneviewer.sites.er.kcl.ac.uk/hn_viewer/).

Ribonuclease inhibitor and angiogenin system regulates cell type-specific global translation

Translation of mRNAs is a fundamental process that occurs in all cell types of multicellular organisms. Conventionally, it has been considered a default step in gene expression, lacking specific regulation. However, recent studies have documented that certain mRNAs exhibit cell type-specific translation. Despite this, it remains unclear whether global translation is controlled in a cell type-specific manner. By using human cell lines and mouse models, we found that deletion of the ribosome-associated protein ribonuclease inhibitor 1 (RNH1) decreases global translation selectively in hematopoietic-origin cells but not in the non-hematopoietic-origin cells. RNH1-mediated cell type-specific translation is mechanistically linked to angiogenin-induced ribosomal biogenesis. Collectively, this study unravels the existence of cell type-specific global translation regulators and highlights the complex translation regulation in vertebrates.

Brooklyn plots to identify co-expression dysregulation in single cell sequencing

Altered open chromatin regions, impacting gene expression, is a feature of some human disorders. We discovered it is possible to detect global changes in genomically-related adjacent gene co-expression within single cell RNA sequencing (scRNA-seq) data. We built a software package to generate and test non-randomness using 'Brooklyn plots' to identify the percent of genes significantly co-expressed from the same chromosome in ∼10 MB intervals across the genome. These plots establish an expected low baseline of co-expression in scRNA-seq from most cell types, but, as seen in dilated cardiomyopathy cardiomyocytes, altered patterns of open chromatin appear. These may relate to larger regions of transcriptional bursting, observable in single cell, but not bulk datasets.

Pan-glioma analyses reveal species- and tumor-specific regulation of neuron-glioma synapse genes by lncRNAs

Gliomas are highly heterogeneous and aggressive. Malignant cells in gliomas can contact normal neurons through a synapse-like structure (called neuron-to-glioma synapse, NGS) to promote their proliferation, but it is unclear whether NGS gene expression and regulation show species- and tumor-specificity. This question is important in that many anti-cancer drugs are developed upon mouse models. To address this question, we conducted a pan-glioma analysis using nine scRNA-seq datasets from humans and mice. We also experimentally validated the key element of our methods and verified a key result using TCGA datasets of the same glioma types. Our analyses revealed that NGS gene expression and regulation by lncRNAs are highly species- and tumor-specific. Importantly, simian-specific lncRNAs are more involved in NGS gene regulation than lncRNAs conserved in mammals, and transgenic mouse gliomas have little in common with PDX mouse models and human gliomas in terms of NGS gene regulation. The analyses suggest that simian-specific lncRNAs are a new and rich class of potential targets for tumor-specific glioma treatment, and provide pertinent data for further experimentally and clinically exmining the targets.

Single-cell RNA sequencing data reveals rewiring of transcriptional relationships in Alzheimer's Disease associated with risk variants

Understanding how genetic risk variants contribute to Alzheimer's Disease etiology remains a challenge. Single-cell RNA sequencing (scRNAseq) allows for the investigation of cell type specific effects of genomic risk loci on gene expression. Using seven scRNAseq datasets totalling >1.3 million cells, we investigated differential correlation of genes between healthy individuals and individuals diagnosed with Alzheimer's Disease. Using the number of differential correlations of a gene to estimate its involvement and potential impact, we present a prioritization scheme for identifying probable causal genes near genomic risk loci. Besides prioritizing genes, our approach pin-points specific cell types and provides insight into the rewiring of gene-gene relationships associated with Alzheimer's.

Investigating the Complexity of Gene Co-expression Estimation for Single-cell Data

With the rapid advance of single-cell RNA sequencing (scRNA-seq) technology, understanding biological processes at a more refined single-cell level is becoming possible. Gene co-expression estimation is an essential step in this direction. It can annotate functionalities of unknown genes or construct the basis of gene regulatory network inference. This study thoroughly tests the existing gene co-expression estimation methods on simulation datasets with known ground truth co-expression networks. We generate these novel datasets using two simulation processes that use the parameters learned from the experimental data. We demonstrate that these simulations better capture the underlying properties of the real-world single-cell datasets than previously tested simulations for the task. Our performance results on tens of simulated and eight experimental datasets show that all methods produce estimations with a high false discovery rate potentially caused by high-sparsity levels in the data. Finally, we find that commonly used pre-processing approaches, such as normalization and imputation, do not improve the co-expression estimation. Overall, our benchmark setup contributes to the co-expression estimator development, and our study provides valuable insights for the community of single-cell data analyses.

How does the structure of data impact cell-cell similarity? Evaluating how structural properties influence the performance of proximity metrics in single cell RNA-seq data

Accurately identifying cell-populations is paramount to the quality of downstream analyses and overall interpretations of single-cell RNA-seq (scRNA-seq) datasets but remains a challenge. The quality of single-cell clustering depends on the proximity metric used to generate cell-to-cell distances. Accordingly, proximity metrics have been benchmarked for scRNA-seq clustering, typically with results averaged across datasets to identify a highest performing metric. However, the 'best-performing' metric varies between studies, with the performance differing significantly between datasets. This suggests that the unique structural properties of an scRNA-seq dataset, specific to the biological system under study, have a substantial impact on proximity metric performance. Previous benchmarking studies have omitted to factor the structural properties into their evaluations. To address this gap, we developed a framework for the in-depth evaluation of the performance of 17 proximity metrics with respect to core structural properties of scRNA-seq data, including sparsity, dimensionality, cell-population distribution and rarity. We find that clustering performance can be improved substantially by the selection of an appropriate proximity metric and neighbourhood size for the structural properties of a dataset, in addition to performing suitable pre-processing and dimensionality reduction. Furthermore, popular metrics such as Euclidean and Manhattan distance performed poorly in comparison to several lessor applied metrics, suggesting that the default metric for many scRNA-seq methods should be re-evaluated. Our findings highlight the critical nature of tailoring scRNA-seq analyses pipelines to the dataset under study and provide practical guidance for researchers looking to optimize cell-similarity search for the structural properties of their own data.

Internal checkpoint regulates T cell neoantigen reactivity and susceptibility to PD1 blockade

BACKGROUND

Adoptive transfer of tumor-infiltrating lymphocytes (TIL) fails to consistently elicit tumor rejection. Manipulation of intrinsic factors that inhibit T cell effector function and neoantigen recognition may therefore improve TIL therapy outcomes. We previously identified the cytokine-induced SH2 protein (CISH) as a key regulator of T cell functional avidity in mice. Here, we investigate the mechanistic role of CISH in regulating human T cell effector function in solid tumors and demonstrate that CRISPR/Cas9 disruption of CISH enhances TIL neoantigen recognition and response to checkpoint blockade.

METHODS

Single-cell gene expression profiling was used to identify a negative correlation between high CISH expression and TIL activation in patient-derived TIL. A GMP-compliant CRISPR/Cas9 gene editing process was developed to assess the impact of CISH disruption on the molecular and functional phenotype of human peripheral blood T cells and TIL. Tumor-specific T cells with disrupted Cish function were adoptively transferred into tumor-bearing mice and evaluated for efficacy with or without checkpoint blockade.

FINDINGS

CISH expression was associated with T cell dysfunction. CISH deletion using CRISPR/Cas9 resulted in hyper-activation and improved functional avidity against tumor-derived neoantigens without perturbing T cell maturation. Cish knockout resulted in increased susceptibility to checkpoint blockade in vivo.

CONCLUSIONS

CISH negatively regulates human T cell effector function, and its genetic disruption offers a novel avenue to improve the therapeutic efficacy of adoptive TIL therapy.

FUNDING

This study was funded by Intima Bioscience, U.S. and in part through the Intramural program CCR at the National Cancer Institute.

Vitamin D Deficiency During Development Permanently Alters Liver Cell Composition and Function

Vitamin D, a fat-soluble vitamin, plays a critical role in calcium homeostasis, the immune system, and normal development. Many epidemiological cohort studies globally have found high prevalence rates of vitamin D deficiency and insufficiency, recognized as an important health issue that needs to be solved. In particular, reproductive age and pregnant women low in vitamin D status may confer risks of diseases like obesity on their offspring. While observational studies have suggested associations between prenatal vitamin D deficiency and metabolic phenotypes in offspring, not yet determined is whether prenatal vitamin D deficiency permanently alters the development of the liver, a major metabolic organ. We tested the histopathology and the transcriptomic profiles of livers from male C57BL/6J mice exposed to prenatal vitamin D deficiency through a maternal dietary intervention model. We found that prenatal vitamin D deficiency increases the prevalence of histopathological changes in the liver, and alters its gene expression profile. Cell subtype proportion analysis showed that the liver of prenatal vitamin D deficiency alters non-parenchymal cells of the liver, specifically macrophages, a subset of endothelial cells, and dendritic cells. Our results indicate the long-term memory of prenatal vitamin D deficiency exposure in the adult liver, a potential contributor to offspring health risks.

Single-cell and bulk transcriptomics of the liver reveals potential targets of NASH with fibrosis

Fibrosis is characterized by the excessive production of collagen and other extracellular matrix (ECM) components and represents a leading cause of morbidity and mortality worldwide. Previous studies of nonalcoholic steatohepatitis (NASH) with fibrosis were largely restricted to bulk transcriptome profiles. Thus, our understanding of this disease is limited by an incomplete characterization of liver cell types in general and hepatic stellate cells (HSCs) in particular, given that activated HSCs are the major hepatic fibrogenic cell population. To help fill this gap, we profiled 17,810 non-parenchymal cells derived from six healthy human livers. In conjunction with public single-cell data of fibrotic/cirrhotic human livers, these profiles enable the identification of potential intercellular signaling axes (e.g., ITGAV-LAMC1, TNFRSF11B-VWF and NOTCH2-DLL4) and master regulators (e.g., RUNX1 and CREB3L1) responsible for the activation of HSCs during fibrogenesis. Bulk RNA-seq data of NASH patient livers and rodent models for liver fibrosis of diverse etiologies allowed us to evaluate the translatability of candidate therapeutic targets for NASH-related fibrosis. We identified 61 liver fibrosis-associated genes (e.g., AEBP1, PRRX1 and LARP6) that may serve as a repertoire of translatable drug target candidates. Consistent with the above regulon results, gene regulatory network analysis allowed the identification of CREB3L1 as a master regulator of many of the 61 genes. Together, this study highlights potential cell-cell interactions and master regulators that underlie HSC activation and reveals genes that may represent prospective hallmark signatures for liver fibrosis.

scLink: Inferring Sparse Gene Co-expression Networks from Single-cell Expression Data

A system-level understanding of the regulation and coordination mechanisms of gene expression is essential for studying the complexity of biological processes in health and disease. With the rapid development of single-cell RNA sequencing technologies, it is now possible to investigate gene interactions in a cell type-specific manner. Here we propose the scLink method, which uses statistical network modeling to understand the co-expression relationships among genes and construct sparse gene co-expression networks from single-cell gene expression data. We use both simulation and real data studies to demonstrate the advantages of scLink and its ability to improve single-cell gene network analysis. The scLink R package is available at https://github.com/Vivianstats/scLink.

Loss of Claudin-3 Impairs Hepatic Metabolism, Biliary Barrier Function, and Cell Proliferation in the Murine Liver

BACKGROUND & AIMS

Tight junctions in the liver are essential to maintain the blood-biliary barrier, however, the functional contribution of individual tight junction proteins to barrier and metabolic homeostasis remains largely unexplored. Here, we describe the cell type-specific expression of tight junction genes in the murine liver, and explore the regulation and functional importance of the transmembrane protein claudin-3 in liver metabolism, barrier function, and cell proliferation.

METHODS

The cell type-specific expression of hepatic tight junction genes is described using our mouse liver single-cell sequencing data set. Differential gene expression in Cldn3-/- and Cldn3+/+ livers was assessed in young and aged mice by RNA sequencing (RNA-seq), and hepatic tissue was analyzed for lipid content and bile acid composition. A surgical model of partial hepatectomy was used to induce liver cell proliferation.

RESULTS

Claudin-3 is a highly expressed tight junction protein found in the liver and is expressed predominantly in hepatocytes and cholangiocytes. The histology of Cldn3-/- livers showed no overt phenotype, and the canalicular tight junctions appeared intact. Nevertheless, by RNA-seq we detected a down-regulation of metabolic pathways in the livers of Cldn3-/- young and aged mice, as well as a decrease in lipid content and a weakened biliary barrier for primary bile acids, such as taurocholic acid, taurochenodeoxycholic acid, and taurine-conjugated muricholic acid. Coinciding with defects in the biliary barrier and lower lipid metabolism, there was a diminished hepatocyte proliferative response in Cldn3-/- mice after partial hepatectomy.

CONCLUSIONS

Our data show that, in the liver, claudin-3 is necessary to maintain metabolic homeostasis, retention of bile acids, and optimal hepatocyte proliferation during liver regeneration. The RNA-seq data set can be accessed at: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE159914.

Synergistic effect of the TLR5 agonist CBLB502 and its downstream effector IL-22 against liver injury

The toll-like receptor 5 (TLR5) agonist, CBLB502/Entolimod, is a peptide derived from bacterial flagellin and has been shown to protect against radiation-induced tissue damage in animal models. Here we investigated the protective mechanism of CBLB502 in the liver using models of ischemia-reperfusion injury and concanavalin A (ConA) induced immuno-hepatitis. We report that pretreatment of mice with CBLB502 provoked a concomitant activation of NF-κB and STAT3 signaling in the liver and reduced hepatic damage in both models. To understand the underlying mechanism, we screened for cytokines in the serum of CBLB502 treated animals and detected high levels of IL-22. There was no transcriptional upregulation of IL-22 in the liver, rather it was found in extrahepatic tissues, mainly the colon, mesenteric lymph nodes (MLN), and spleen. RNA-seq analysis on isolated hepatocytes demonstrated that the concomitant activation of NF-κB signaling by CBLB502 and STAT3 signaling by IL-22 produced a synergistic cytoprotective transcriptional signature. In IL-22 knockout mice, the loss of IL-22 resulted in a decrease of hepatic STAT3 activation, a reduction in the cytoprotective signature, and a loss of hepatoprotection following ischemia-reperfusion-induced liver injury. Taken together, these findings suggest that CBLB502 protects the liver by increasing hepatocyte resistance to acute liver injury through the cooperation of TLR5-NF-κB and IL-22-STAT3 signaling pathways.