T cell cytolytic capacity is independent of initial stimulation strength.

How cells respond to myriad stimuli with finite signaling machinery is central to immunology. In naive T cells, the inherent effect of ligand strength on activation pathways and endpoints has remained controversial, confounded by environmental fluctuations and intercellular variability within populations. Here we studied how ligand potency affected the activation of CD8+ T cells in vitro, through the use of genome-wide RNA, multi-dimensional protein and functional measurements in single cells. Our data revealed that strong ligands drove more efficient and uniform activation than did weak ligands, but all activated cells were fully cytolytic. Notably, activation followed the same transcriptional pathways regardless of ligand potency. Thus, stimulation strength did not intrinsically dictate the T cell-activation route or phenotype; instead, it controlled how rapidly and simultaneously the cells initiated activation, allowing limited machinery to elicit wide-ranging responses.

Transcription-factor-mediated supervision of global genome architecture maintains B cell identity.

Recent studies have elucidated cell-lineage-specific three-dimensional genome organization; however, how such specific architecture is established or maintained is unclear. We hypothesized that lineage-defining transcription factors maintain cell identity via global control of genome organization. These factors bind many genomic sites outside of the genes that they directly regulate and thus are potentially implicated in three-dimensional genome organization. Using chromosome-conformation-capture techniques, we show that the transcription factor Paired box 5 (Pax5) is critical for the establishment and maintenance of the global lineage-specific architecture of B cells. Pax5 was found to supervise genome architecture throughout B cell differentiation, until the plasmablast stage, in which Pax5 is naturally silenced and B cell-specific genome structure is lost. Crucially, Pax5 did not rely on ongoing transcription to organize the genome. These results implicate sequence-specific DNA-binding proteins in global genome organization to establish and maintain lineage fidelity.

Senescence-induced endothelial phenotypes underpin immune-mediated senescence surveillance.

Senescence is a stress-responsive tumor suppressor mechanism associated with expression of the senescence-associated secretory phenotype (SASP). Through the SASP, senescent cells trigger their own immune-mediated elimination, which if evaded leads to tumorigenesis. Senescent parenchymal cells are separated from circulating immunocytes by the endothelium, which is targeted by microenvironmental signaling. Here we show that SASP induces endothelial cell NF-κB activity and that SASP-induced endothelial expression of the canonical NF-κB component Rela underpins senescence surveillance. Using human liver sinusoidal endothelial cells (LSECs), we show that SASP-induced endothelial NF-κB activity regulates a conserved transcriptional program supporting immunocyte recruitment. Furthermore, oncogenic hepatocyte senescence drives murine LSEC NF-κB activity in vivo. Critically, we show two distinct endothelial pathways in senescence surveillance. First, endothelial-specific loss of Rela prevents development of Stat1-expressing CD4+ T lymphocytes. Second, the SASP up-regulates ICOSLG on LSECs, with the ICOS-ICOSLG axis contributing to senescence cell clearance. Our results show that the endothelium is a nonautonomous SASP target and an organizing center for immune-mediated senescence surveillance.

Transcription Factor PU.1 Promotes Conventional Dendritic Cell Identity and Function via Induction of Transcriptional Regulator DC-SCRIPT.

Dendritic cells (DCs) are can be broadly divided into conventional (cDC) and plasmacytoid (pDC) subsets. Despite the importance of this lineage diversity, its genetic basis is not fully understood. We found that conditional ablation of the Ets-family transcription factor PU.1 in DC-restricted progenitors led to increased pDC production at the expense of cDCs. PU.1 controlled many of the cardinal functions of DCs, such as antigen presentation by cDCs and type I interferon production by pDCs. Conditional ablation of PU.1 de-repressed the pDC transcriptional signature in cDCs. The combination of genome-wide mapping of PU.1 binding and gene expression analysis revealed a key role for PU.1 in maintaining cDC identity through the induction of the transcriptional regulator DC-SCRIPT. PU.1 activated DC-SCRIPT expression, which in turn promoted cDC formation, particularly of cDC1s, and repressed pDC development. Thus, cDC identity is regulated by a transcriptional node requiring PU.1 and DC-SCRIPT.

Orchestrating single-cell analysis with Bioconductor.

Recent technological advancements have enabled the profiling of a large number of genome-wide features in individual cells. However, single-cell data present unique challenges that require the development of specialized methods and software infrastructure to successfully derive biological insights. The Bioconductor project has rapidly grown to meet these demands, hosting community-developed open-source software distributed as R packages. Featuring state-of-the-art computational methods, standardized data infrastructure and interactive data visualization tools, we present an overview and online book (https://osca.bioconductor.org) of single-cell methods for prospective users.

Publisher Correction: Orchestrating single-cell analysis with Bioconductor.

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

SpatialExperiment: infrastructure for spatially-resolved transcriptomics data in R using Bioconductor.

SUMMARY: SpatialExperiment is a new data infrastructure for storing and accessing spatially-resolved transcriptomics data, implemented within the R/Bioconductor framework, which provides advantages of modularity, interoperability, standardized operations and comprehensive documentation. Here, we demonstrate the structure and user interface with examples from the 10x Genomics Visium and seqFISH platforms, and provide access to example datasets and visualization tools in the STexampleData, TENxVisiumData and ggspavis packages. AVAILABILITY AND IMPLEMENTATION: The SpatialExperiment, STexampleData, TENxVisiumData and ggspavis packages are available from Bioconductor. The package versions described in this manuscript are available in Bioconductor version 3.15 onwards. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

COMRADES determines in vivo RNA structures and interactions.

The structural flexibility of RNA underlies fundamental biological processes, but there are no methods for exploring the multiple conformations adopted by RNAs in vivo. We developed cross-linking of matched RNAs and deep sequencing (COMRADES) for in-depth RNA conformation capture, and a pipeline for the retrieval of RNA structural ensembles. Using COMRADES, we determined the architecture of the Zika virus RNA genome inside cells, and identified multiple site-specific interactions with human noncoding RNAs.

Genome-wide analysis reveals no evidence of trans chromosomal regulation of mammalian immune development.

It has been proposed that interactions between mammalian chromosomes, or transchromosomal interactions (also known as kissing chromosomes), regulate gene expression and cell fate determination. Here we aimed to identify novel transchromosomal interactions in immune cells by high-resolution genome-wide chromosome conformation capture. Although we readily identified stable interactions in cis, and also between centromeres and telomeres on different chromosomes, surprisingly we identified no gene regulatory transchromosomal interactions in either mouse or human cells, including previously described interactions. We suggest that advances in the chromosome conformation capture technique and the unbiased nature of this approach allow more reliable capture of interactions between chromosomes than previous methods. Overall our findings suggest that stable transchromosomal interactions that regulate gene expression are not present in mammalian immune cells and that lineage identity is governed by cis, not trans chromosomal interactions.

Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data.

By profiling the transcriptomes of individual cells, single-cell RNA sequencing provides unparalleled resolution to study cellular heterogeneity. However, this comes at the cost of high technical noise, including cell-specific biases in capture efficiency and library generation. One strategy for removing these biases is to add a constant amount of spike-in RNA to each cell and to scale the observed expression values so that the coverage of spike-in transcripts is constant across cells. This approach has previously been criticized as its accuracy depends on the precise addition of spike-in RNA to each sample. Here, we perform mixture experiments using two different sets of spike-in RNA to quantify the variance in the amount of spike-in RNA added to each well in a plate-based protocol. We also obtain an upper bound on the variance due to differences in behavior between the two spike-in sets. We demonstrate that both factors are small contributors to the total technical variance and have only minor effects on downstream analyses, such as detection of highly variable genes and clustering. Our results suggest that scaling normalization using spike-in transcripts is reliable enough for routine use in single-cell RNA sequencing data analyses.

Testing for differential abundance in mass cytometry data.

When comparing biological conditions using mass cytometry data, a key challenge is to identify cellular populations that change in abundance. Here, we present a computational strategy for detecting 'differentially abundant' populations by assigning cells to hyperspheres, testing for significant differences between conditions and controlling the spatial false discovery rate. Our method (http://bioconductor.org/packages/cydar) outperforms other approaches in simulations and finds novel patterns of differential abundance in real data.

Specificity of RNAi, LNA and CRISPRi as loss-of-function methods in transcriptional analysis.

Loss-of-function (LOF) methods such as RNA interference (RNAi), antisense oligonucleotides or CRISPR-based genome editing provide unparalleled power for studying the biological function of genes of interest. However, a major concern is non-specific targeting, which involves depletion of transcripts other than those intended. Little work has been performed to characterize the off-target effects of these common LOF methods at the whole-transcriptome level. Here, we experimentally compared the non-specific activity of RNAi, antisense oligonucleotides and CRISPR interference (CRISPRi). All three methods yielded non-negligible off-target effects in gene expression, with CRISPRi also exhibiting strong clonal effects. As an illustrative example, we evaluated the performance of each method for determining the role of an uncharacterized long noncoding RNA (lncRNA). Several LOF methods successfully depleted the candidate lncRNA but yielded different sets of differentially expressed genes as well as a different cellular phenotype upon depletion. Similar discrepancies between methods were observed with a protein-coding gene (Ch-TOG/CKAP5) and another lncRNA (MALAT1). We suggest that the differences between methods arise due to method-specific off-target effects and provide guidelines for mitigating such effects in functional studies. Our recommendations provide a framework with which off-target effects can be managed to improve functional characterization of genes of interest.

Homeopathic Clinical Features of 18 Patients in COVID-19 Outbreaks in Hong Kong.

BACKGROUND: Hong Kong is geographically located in the province of Guangdong which, after Hubei, has been the region of China second-most affected by the COVID-19 pandemic. Compared to the pathognomonic symptoms of the named disease, homeopathic symptoms are always more helpful for homeopathic prescriptions. AIM: This study reports and summarizes the homeopathic symptoms observed in 18 confirmed/suspected epidemiologically related cases in cluster outbreaks of COVID-19 in Hong Kong in early 2020. METHODS: Homeopathic symptoms from this case series were collected from 18 consecutive patients who, in addition to their concurrent conventional treatment or traditional Chinese medicine, actively sought help from homeopathy as an adjunctive measure for symptomatic relief from COVID-19. Cases were categorized according to outbreak clusters, focusing mainly on the homeopathic symptoms. In the analysis, frequency of all homeopathic medicines, common rubrics in all the cases, common rubrics in each of the top-ranked remedies, and differentiating symptoms for each top-ranked remedy were determined. RESULTS: Homeopathic symptoms of 18 cases, each identified as mild and belonging to one of six separate clusters, are reported. Eighteen common symptoms screened out of 79 selected rubrics constituted two sets of homeopathic symptom pictures: Bryonia alba (n = 4) and Gelsemium sempervirens (n = 12). Eight and seven differentiating features, respectively, were identified for Bryonia alba and Gelsemium sempervirens. CONCLUSION: The common symptoms of 18 mild COVID-19 cases constituted two sets of homeopathic symptom pictures, indicating Bryonia alba or Gelsemium sempervirens; they were indicated in 4 and 12 cases, respectively, out of the 18 in total.

Three-dimensional disorganization of the cancer genome occurs coincident with long-range genetic and epigenetic alterations.

A three-dimensional chromatin state underpins the structural and functional basis of the genome by bringing regulatory elements and genes into close spatial proximity to ensure proper, cell-type-specific gene expression profiles. Here, we performed Hi-C chromosome conformation capture sequencing to investigate how three-dimensional chromatin organization is disrupted in the context of copy-number variation, long-range epigenetic remodeling, and atypical gene expression programs in prostate cancer. We find that cancer cells retain the ability to segment their genomes into megabase-sized topologically associated domains (TADs); however, these domains are generally smaller due to establishment of additional domain boundaries. Interestingly, a large proportion of the new cancer-specific domain boundaries occur at regions that display copy-number variation. Notably, a common deletion on 17p13.1 in prostate cancer spanning the TP53 tumor suppressor locus results in bifurcation of a single TAD into two distinct smaller TADs. Change in domain structure is also accompanied by novel cancer-specific chromatin interactions within the TADs that are enriched at regulatory elements such as enhancers, promoters, and insulators, and associated with alterations in gene expression. We also show that differential chromatin interactions across regulatory regions occur within long-range epigenetically activated or silenced regions of concordant gene activation or repression in prostate cancer. Finally, we present a novel visualization tool that enables integrated exploration of Hi-C interaction data, the transcriptome, and epigenome. This study provides new insights into the relationship between long-range epigenetic and genomic dysregulation and changes in higher-order chromatin interactions in cancer.

beachmat: A Bioconductor C++ API for accessing high-throughput biological data from a variety of R matrix types.

Biological experiments involving genomics or other high-throughput assays typically yield a data matrix that can be explored and analyzed using the R programming language with packages from the Bioconductor project. Improvements in the throughput of these assays have resulted in an explosion of data even from routine experiments, which poses a challenge to the existing computational infrastructure for statistical data analysis. For example, single-cell RNA sequencing (scRNA-seq) experiments frequently generate large matrices containing expression values for each gene in each cell, requiring sparse or file-backed representations for memory-efficient manipulation in R. These alternative representations are not easily compatible with high-performance C++ code used for computationally intensive tasks in existing R/Bioconductor packages. Here, we describe a C++ interface named beachmat, which enables agnostic data access from various matrix representations. This allows package developers to write efficient C++ code that is interoperable with dense, sparse and file-backed matrices, amongst others. We evaluated the performance of beachmat for accessing data from each matrix representation using both simulated and real scRNA-seq data, and defined a clear memory/speed trade-off to motivate the choice of an appropriate representation. We also demonstrate how beachmat can be incorporated into the code of other packages to drive analyses of a very large scRNA-seq data set.

A non-canonical function of Ezh2 preserves immune homeostasis.

Enhancer of zeste 2 (Ezh2) mainly methylates lysine 27 of histone-H3 (H3K27me3) as part of the polycomb repressive complex 2 (PRC2) together with Suz12 and Eed. However, Ezh2 can also modify non-histone substrates, although it is unclear whether this mechanism has a role during development. Here, we present evidence for a chromatin-independent role of Ezh2 during T-cell development and immune homeostasis. T-cell-specific depletion of Ezh2 induces a pronounced expansion of natural killer T (NKT) cells, although Ezh2-deficient T cells maintain normal levels of H3K27me3. In contrast, removal of Suz12 or Eed destabilizes canonical PRC2 function and ablates NKT cell development completely. We further show that Ezh2 directly methylates the NKT cell lineage defining transcription factor PLZF, leading to its ubiquitination and subsequent degradation. Sustained PLZF expression in Ezh2-deficient mice is associated with the expansion of a subset of NKT cells that cause immune perturbation. Taken together, we have identified a chromatin-independent function of Ezh2 that impacts on the development of the immune system.

Repression of Igf1 expression by Ezh2 prevents basal cell differentiation in the developing lung.

Epigenetic mechanisms involved in the establishment of lung epithelial cell lineage identities during development are largely unknown. Here, we explored the role of the histone methyltransferase Ezh2 during lung lineage determination. Loss of Ezh2 in the lung epithelium leads to defective lung formation and perinatal mortality. We show that Ezh2 is crucial for airway lineage specification and alveolarization. Using optical projection tomography imaging, we found that branching morphogenesis is affected in Ezh2 conditional knockout mice and the remaining bronchioles are abnormal, lacking terminally differentiated secretory club cells. Remarkably, RNA-seq analysis revealed the upregulation of basal genes in Ezh2-deficient epithelium. Three-dimensional imaging for keratin 5 further showed the unexpected presence of a layer of basal cells from the proximal airways to the distal bronchioles in E16.5 embryos. ChIP-seq analysis indicated the presence of Ezh2-mediated repressive marks on the genomic loci of some but not all basal genes, suggesting an indirect mechanism of action of Ezh2. We found that loss of Ezh2 de-represses insulin-like growth factor 1 (Igf1) expression and that modulation of IGF1 signaling ex vivo in wild-type lungs could induce basal cell differentiation. Altogether, our work reveals an unexpected role for Ezh2 in controlling basal cell fate determination in the embryonic lung endoderm, mediated in part by repression of Igf1 expression.

Overcoming confounding plate effects in differential expression analyses of single-cell RNA-seq data.

An increasing number of studies are using single-cell RNA-sequencing (scRNA-seq) to characterize the gene expression profiles of individual cells. One common analysis applied to scRNA-seq data involves detecting differentially expressed (DE) genes between cells in different biological groups. However, many experiments are designed such that the cells to be compared are processed in separate plates or chips, meaning that the groupings are confounded with systematic plate effects. This confounding aspect is frequently ignored in DE analyses of scRNA-seq data. In this article, we demonstrate that failing to consider plate effects in the statistical model results in loss of type I error control. A solution is proposed whereby counts are summed from all cells in each plate and the count sums for all plates are used in the DE analysis. This restores type I error control in the presence of plate effects without compromising detection power in simulated data. Summation is also robust to varying numbers and library sizes of cells on each plate. Similar results are observed in DE analyses of real data where the use of count sums instead of single-cell counts improves specificity and the ranking of relevant genes. This suggests that summation can assist in maintaining statistical rigour in DE analyses of scRNA-seq data with plate effects.

Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R.

Motivation: Single-cell RNA sequencing (scRNA-seq) is increasingly used to study gene expression at the level of individual cells. However, preparing raw sequence data for further analysis is not a straightforward process. Biases, artifacts and other sources of unwanted variation are present in the data, requiring substantial time and effort to be spent on pre-processing, quality control (QC) and normalization. Results: We have developed the R/Bioconductor package scater to facilitate rigorous pre-processing, quality control, normalization and visualization of scRNA-seq data. The package provides a convenient, flexible workflow to process raw sequencing reads into a high-quality expression dataset ready for downstream analysis. scater provides a rich suite of plotting tools for single-cell data and a flexible data structure that is compatible with existing tools and can be used as infrastructure for future software development. Availability and Implementation: The open-source code, along with installation instructions, vignettes and case studies, is available through Bioconductor at http://bioconductor.org/packages/scater . Contact: davis@ebi.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

No counts, no variance: allowing for loss of degrees of freedom when assessing biological variability from RNA-seq data.

RNA sequencing (RNA-seq) is widely used to study gene expression changes associated with treatments or biological conditions. Many popular methods for detecting differential expression (DE) from RNA-seq data use generalized linear models (GLMs) fitted to the read counts across independent replicate samples for each gene. This article shows that the standard formula for the residual degrees of freedom (d.f.) in a linear model is overstated when the model contains fitted values that are exactly zero. Such fitted values occur whenever all the counts in a treatment group are zero as well as in more complex models such as those involving paired comparisons. This misspecification results in underestimation of the genewise variances and loss of type I error control. This article proposes a formula for the reduced residual d.f. that restores error control in simulated RNA-seq data and improves detection of DE genes in a real data analysis. The new approach is implemented in the quasi-likelihood framework of the edgeR software package. The results of this article also apply to RNA-seq analyses that apply linear models to log-transformed counts, such as those in the limma software package, and more generally to any count-based GLM where exactly zero fitted values are possible.