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edgeR is an R/Bioconductor software package for differential analyses of sequencing data in the form of read counts for genes or genomic features. Over the past 15 years, edgeR has been a popular choice for statistical analysis of data from sequencing technologies such as RNA-seq or ChIP-seq. edgeR pioneered the use of the negative binomial distribution to model read count data with replicates and the use of generalized linear models to analyze complex experimental designs. edgeR implements empirical Bayes moderation methods to allow reliable inference when the number of replicates is small. This article announces edgeR version 4, which includes new developments across a range of application areas. Infrastructure improvements include support for fractional counts, implementation of model fitting in C and a new statistical treatment of the quasi-likelihood pipeline that improves accuracy for small counts. The revised package has new functionality for differential methylation analysis, differential transcript expression, differential transcript and exon usage, testing relative to a fold-change threshold and pathway analysis. This article reviews the statistical framework and computational implementation of edgeR, briefly summarizing all the existing features and functionalities but with special attention to new features and those that have not been described previously., (© The Author(s) 2025. Published by Oxford University Press on behalf of Nucleic Acids Research.)

This qualitative study explores the lived experiences of parent-child dyads to understand the occurrence of parentification in Chinese families affected by parental depression. Utilizing purposive sampling, families were recruited from community mental health services in Hong Kong, focusing on parents with major depressive disorder and their adolescent children. Data were collected from 14 families through in-depth, semistructured interviews with adolescents (n = 8) and parents (n = 12), as well as dyadic interviews (n = 8), and analyzed utilizing thematic analysis. The central theme, "Complementary Dynamic Interaction: Rippled Relationship," captures the mutual, circular, and reciprocal interactions within parent-adolescent dyads in the context of parental depression. These findings highlight the unique dynamics of parentification in Chinese families affected by parental depression., (© 2024 American Association for Marriage and Family Therapy.)

Blood-borne pathogens can cause systemic inflammatory response syndrome (SIRS) followed by protracted, potentially lethal immunosuppression. The mechanisms responsible for impaired immunity post-SIRS remain unclear. We show that SIRS triggered by pathogen mimics or malaria infection leads to functional paralysis of conventional dendritic cells (cDCs). Paralysis affects several generations of cDCs and impairs immunity for 3-4 weeks. Paralyzed cDCs display distinct transcriptomic and phenotypic signatures and show impaired capacity to capture and present antigens in vivo. They also display altered cytokine production patterns upon stimulation. The paralysis program is not initiated in the bone marrow but during final cDC differentiation in peripheral tissues under the influence of local secondary signals that persist after resolution of SIRS. Vaccination with monoclonal antibodies that target cDC receptors or blockade of transforming growth factor β partially overcomes paralysis and immunosuppression. This work provides insights into the mechanisms of paralysis and describes strategies to restore immunocompetence post-SIRS., Competing Interests: Declaration of interests M.H.L., I.C., and K.S. are listed as inventors on patents relating to Clec9A antibodies., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Human mesenchymal stem cells (hMSCs) are currently being explored as a promising cell-based therapeutic modality for various diseases, with more market approvals for clinical use expected over the next few years. To facilitate this transition, addressing the bottlenecks of scale, lot-to-lot reproducibility, cost, regulatory compliance, and quality control is critical. These challenges can be addressed by closing the process and adopting automated manufacturing platforms. In this study, we developed a closed and semi-automated process for passaging and harvesting Wharton's jelly (WJ)-derived hMSCs (WJ-hMSCs) from multi-layered flasks using counterflow centrifugation. The WJ-hMSCs were expanded using regulatory compliant serum-free xeno-free (SFM XF) medium, and they showed comparable cell proliferation (population doubling) and morphology to WJ-hMSCs expanded in classic serum-containing media. Our closed semi-automated harvesting protocol demonstrated high cell recovery (~98%) and viability (~99%). The cells washed and concentrated using counterflow centrifugation maintained WJ-hMSC surface marker expression, colony-forming units (CFU-F), trilineage differentiation potential, and cytokine secretion profiles. The semi-automated cell harvesting protocol developed in the study can be easily applied for the small- to medium-scale processing of various adherent and suspension cells by directly connecting to different cell expansion platforms to perform volume reduction, washing, and harvesting with a low output volume.

Objectives: Induced pluripotent stem cells (iPSCs) generated by monolayer cultures is plagued by low efficiencies, high levels of manipulation and operator unpredictability. We have developed a platform, reprogramming, expansion, and differentiation on Microcarriers, to solve these challenges., Materials and Methods: Five sources of human somatic cells were reprogrammed, selected, expanded and differentiated in microcarriers suspension cultures., Results: Improvement of transduction efficiencies up to 2 times was observed. Accelerated reprogramming in microcarrier cultures was 7 days faster than monolayer, providing between 30 and 50-fold more clones to choose from fibroblasts, peripheral blood mononuclear cells, T cells and CD34+ stem cells. This was observed to be due to an earlier induction of genes (β-catenin, E-cadherin and EpCAM) on day 4 versus monolayer cultures which occurred on days 14 or later. Following that, faster induction and earlier stabilization of pluripotency genes occurred during the maturation phase of reprogramming. Integrated expansion without trypsinization and efficient differentiation, without embryoid bodies formation, to the three germ-layers, cardiomyocytes and haematopoietic stem cells were further demonstrated., Conclusions: Our method can solve the inherent problems of conventional monolayer cultures. It is highly efficient, cell dissociation free, can be operated with lower labor, and allows testing of differentiation efficiency without trypsinization and generation of embryoid bodies. It is also amenable to automation for processing more samples in a small footprint, alleviating many challenges of manual monolayer selection., (© 2022 The Authors. Cell Proliferation published by John Wiley & Sons Ltd.)

Objectives: Large-scale generation of universal red blood cells (RBCs) from O-negative (O-ve) human induced pluripotent stem cells (hiPSCs) holds the potential to alleviate worldwide shortages of blood and provide a safe and secure year-round supply. Mature RBCs and reticulocytes, the immature counterparts of RBCs generated during erythropoiesis, could also find important applications in research, for example in malaria parasite infection studies. However, one major challenge is the lack of a high-density culture platform for large-scale generation of RBCs in vitro., Materials and Methods: We generated 10 O-ve hiPSC clones and evaluated their potential for mesoderm formation and erythroid differentiation. We then used a perfusion bioreactor system to perform studies with high-density cultures of erythroblasts in vitro., Results: Based on their tri-lineage (and specifically mesoderm) differentiation potential, we isolated six hiPSC clones capable of producing functional erythroblasts. Using the best performing clone, we demonstrated the small-scale generation of high-density cultures of erythroblasts in a perfusion bioreactor system. After process optimization, we were able to achieve a peak cell density of 34.7 million cells/ml with 92.2% viability in the stirred bioreactor. The cells expressed high levels of erythroblast markers, showed oxygen carrying capacity, and were able to undergo enucleation., Conclusions: This study demonstrated a scalable platform for the production of functional RBCs from hiPSCs. The perfusion culture platform we describe here could pave the way for large volume-controlled bioreactor culture for the industrial generation of high cell density erythroblasts and RBCs., (© 2022 The Authors. Cell Proliferation published by John Wiley & Sons Ltd.)

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., (© The Author(s) 2022. Published by Oxford University Press.)

Transposable elements (TEs) regulate diverse biological processes, from early development to cancer. Expression of young TEs is difficult to measure with next-generation, single-cell sequencing technologies because their highly repetitive nature means that short complementary DNA reads cannot be unambiguously mapped to a specific locus. Single CELl LOng-read RNA-sequencing (CELLO-seq) combines long-read single cell RNA-sequencing with computational analyses to measure TE expression at unique loci. We used CELLO-seq to assess the widespread expression of TEs in two-cell mouse blastomeres as well as in human induced pluripotent stem cells. Across both species, old and young TEs showed evidence of locus-specific expression with simulations demonstrating that only a small number of very young elements in the mouse could not be mapped back to the reference with high confidence. Exploring the relationship between the expression of individual elements and putative regulators revealed large heterogeneity, with TEs within a class showing different patterns of correlation and suggesting distinct regulatory mechanisms., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Genome stability relies on proper coordination of mitosis and cytokinesis, where dynamic microtubules capture and faithfully segregate chromosomes into daughter cells. With a high-content RNAi imaging screen targeting more than 2,000 human lncRNAs, we identify numerous lncRNAs involved in key steps of cell division such as chromosome segregation, mitotic duration and cytokinesis. Here, we provide evidence that the chromatin-associated lncRNA, linc00899, leads to robust mitotic delay upon its depletion in multiple cell types. We perform transcriptome analysis of linc00899-depleted cells and identify the neuronal microtubule-binding protein, TPPP/p25, as a target of linc00899. We further show that linc00899 binds TPPP/p25 and suppresses its transcription. In cells depleted of linc00899, upregulation of TPPP/p25 alters microtubule dynamics and delays mitosis. Overall, our comprehensive screen uncovers several lncRNAs involved in genome stability and reveals a lncRNA that controls microtubule behaviour with functional implications beyond cell division.

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.

Droplet-based single-cell RNA sequencing protocols have dramatically increased the throughput of single-cell transcriptomics studies. A key computational challenge when processing these data is to distinguish libraries for real cells from empty droplets. Here, we describe a new statistical method for calling cells from droplet-based data, based on detecting significant deviations from the expression profile of the ambient solution. Using simulations, we demonstrate that EmptyDrops has greater power than existing approaches while controlling the false discovery rate among detected cells. Our method also retains distinct cell types that would have been discarded by existing methods in several real data sets.

Conventional human embryonic stem cells are considered to be primed pluripotent but can be induced to enter a naive state. However, the transcriptional features associated with naive and primed pluripotency are still not fully understood. Here we used single-cell RNA sequencing to characterize the differences between these conditions. We observed that both naive and primed populations were mostly homogeneous with no clear lineage-related structure and identified an intermediate subpopulation of naive cells with primed-like expression. We found that the naive-primed pluripotency axis is preserved across species, although the timing of the transition to a primed state is species specific. We also identified markers for distinguishing human naive and primed pluripotency as well as strong co-regulatory relationships between lineage markers and epigenetic regulators that were exclusive to naive cells. Our data provide valuable insights into the transcriptional landscape of human pluripotency at a cellular and genome-wide resolution., (Crown Copyright © 2019. Published by Elsevier Inc. All rights reserved.)

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.

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.

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.

Barcode swapping results in the mislabelling of sequencing reads between multiplexed samples on patterned flow-cell Illumina sequencing machines. This may compromise the validity of numerous genomic assays; however, the severity and consequences of barcode swapping remain poorly understood. We have used two statistical approaches to robustly quantify the fraction of swapped reads in two plate-based single-cell RNA-sequencing datasets. We found that approximately 2.5% of reads were mislabelled between samples on the HiSeq 4000, which is lower than previous reports. We observed no correlation between the swapped fraction of reads and the concentration of free barcode across plates. Furthermore, we have demonstrated that barcode swapping may generate complex but artefactual cell libraries in droplet-based single-cell RNA-sequencing studies. To eliminate these artefacts, we have developed an algorithm to exclude individual molecules that have swapped between samples in 10x Genomics experiments, allowing the continued use of cutting-edge sequencing machines for these assays.

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.

Data exploration is critical to the comprehension of large biological data sets generated by high-throughput assays such as sequencing. However, most existing tools for interactive visualisation are limited to specific assays or analyses. Here, we present the iSEE (Interactive SummarizedExperiment Explorer) software package, which provides a general visual interface for exploring data in a SummarizedExperiment object. iSEE is directly compatible with many existing R/Bioconductor packages for analysing high-throughput biological data, and provides useful features such as simultaneous examination of (meta)data and analysis results, dynamic linking between plots and code tracking for reproducibility. We demonstrate the utility and flexibility of iSEE by applying it to explore a range of real transcriptomics and proteomics data sets., Competing Interests: No competing interests were disclosed.

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., Competing Interests: The authors have declared that no competing interests exist.

Large-scale single-cell RNA sequencing (scRNA-seq) data sets that are produced in different laboratories and at different times contain batch effects that may compromise the integration and interpretation of the data. Existing scRNA-seq analysis methods incorrectly assume that the composition of cell populations is either known or identical across batches. We present a strategy for batch correction based on the detection of mutual nearest neighbors (MNNs) in the high-dimensional expression space. Our approach does not rely on predefined or equal population compositions across batches; instead, it requires only that a subset of the population be shared between batches. We demonstrate the superiority of our approach compared with existing methods by using both simulated and real scRNA-seq data sets. Using multiple droplet-based scRNA-seq data sets, we demonstrate that our MNN batch-effect-correction method can be scaled to large numbers of cells.

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.

Loss of cone photoreceptors, crucial for daylight vision, has the greatest impact on sight in retinal degeneration. Transplantation of stem cell-derived L/M-opsin cones, which form 90% of the human cone population, could provide a feasible therapy to restore vision. However, transcriptomic similarities between fetal and stem cell-derived cones remain to be defined, in addition to development of cone cell purification strategies. Here, we report an analysis of the human L/M-opsin cone photoreceptor transcriptome using an AAV2/9.pR2.1:GFP reporter. This led to the identification of a cone-enriched gene signature, which we used to demonstrate similar gene expression between fetal and stem cell-derived cones. We then defined a cluster of differentiation marker combination that, when used for cell sorting, significantly enriches for cone photoreceptors from the fetal retina and stem cell-derived retinal organoids, respectively. These data may facilitate more efficient isolation of human stem cell-derived cones for use in clinical transplantation studies., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

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., (© 2017 Lun et al.; Published by Cold Spring Harbor Laboratory Press.)

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., (© The Author 2017. Published by Oxford University Press.)

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.

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.

Plasmacytoid dendritic cells (pDCs) represent a unique immune cell type that responds to viral nucleic acids through the rapid production of type I interferons. Within the hematopoietic system, the transcription factor RUNX2 is exclusively expressed in pDCs and is required for their peripheral homeostasis. Here, we show that RUNX2 plays an essential role in promoting pDC localization and function. RUNX2 is required for the appropriate expression of the integrin-mediated adhesion machinery, as well as for the down-modulation of the chemokine receptor CXCR4, which allows pDC egress into the circulation. RUNX2 also facilitates the robust response to viral infection through the control of IRF7, the major regulator of type I interferon production. Mice lacking one copy of Runx2 have reduced numbers of peripheral pDCs and IFN-α expression, which might contribute to the reported difficulties of individuals with cleidocranial dysplasia, who are haploinsufficient for RUNX2, to clear viral infections., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)