Bias, robustness and scalability in single-cell differential expression analysis

Gene choice in cancer cells is exclusive in ion transport but concurrent in DNA replication

Cancers share common cellular and physiological features. Little is known about whether distinctive gene expression patterns can be displayed at the single-cell level by gene families in cancer cells. The expression of gene homologs within a family can exhibit concurrence and exclusivity. Concurrence can promote all-or-none expression patterns of related genes and underlie alternative physiological states. Conversely, exclusive gene families express the same or similar number of homologs in each cell, allowing a broad repertoire of cell identities to be generated. We show that gene families involved in the cell-cycle and antigen presentation are expressed concurrently. Concurrence in the DNA replication complex MCM reflects the replicative status of cells, including cell lines and cancer-derived organoids. Exclusive expression requires precise regulatory mechanism, but cancer cells retain this form of control for ion homeostasis and extend it to gene families involved in cell migration. Thus, the cell adhesion-based identity of healthy cells is transformed to an identity based on migration in the population of cancer cells, reminiscent of epithelial-mesenchymal transition.

Learning to quantify uncertainty in off-target activity for CRISPR guide RNAs

CRISPR-based genome editing technologies have revolutionised the field of molecular biology, offering unprecedented opportunities for precise genetic manipulation. However, off-target effects remain a significant challenge, potentially leading to unintended consequences and limiting the applicability of CRISPR-based genome editing technologies in clinical settings. Current literature predominantly focuses on point predictions for off-target activity, which may not fully capture the range of possible outcomes and associated risks. Here, we present crispAI, a neural network architecture-based approach for predicting uncertainty estimates for off-target cleavage activity, providing a more comprehensive risk assessment and facilitating improved decision-making in single guide RNA (sgRNA) design. Our approach makes use of the count noise model Zero Inflated Negative Binomial (ZINB) to model the uncertainty in the off-target cleavage activity data. In addition, we present the first-of-its-kind genome-wide sgRNA efficiency score, crispAI-aggregate, enabling prioritization among sgRNAs with similar point aggregate predictions by providing richer information compared to existing aggregate scores. We show that uncertainty estimates of our approach are calibrated and its predictive performance is superior to the state-of-the-art in silico off-target cleavage activity prediction methods. The tool and the trained models are available at https://github.com/furkanozdenn/crispr-offtarget-uncertainty.

Best practices for differential accessibility analysis in single-cell epigenomics

Differential accessibility (DA) analysis of single-cell epigenomics data enables the discovery of regulatory programs that establish cell type identity and steer responses to physiological and pathophysiological perturbations. While many statistical methods to identify DA regions have been developed, the principles that determine the performance of these methods remain unclear. As a result, there is no consensus on the most appropriate statistical methods for DA analysis of single-cell epigenomics data. Here, we present a systematic evaluation of statistical methods that have been applied to identify DA regions in single-cell ATAC-seq (scATAC-seq) data. We leverage a compendium of scATAC-seq experiments with matching bulk ATAC-seq or scRNA-seq in order to assess the accuracy, bias, robustness, and scalability of each statistical method. The structure of our experiments also provides the opportunity to define best practices for the analysis of scATAC-seq data beyond DA itself. We leverage this understanding to develop an R package implementing these best practices.

Sex-specific single cell-level transcriptomic signatures of Rett syndrome disease progression

Dominant X-linked diseases are uncommon due to female X chromosome inactivation (XCI). While random XCI usually protects females against X-linked mutations, Rett syndrome (RTT) is a female neurodevelopmental disorder caused by heterozygous MECP2 mutation. After 6-18 months of typical neurodevelopment, RTT girls undergo a poorly understood regression. We performed longitudinal snRNA-seq on cerebral cortex in a construct-relevant Mecp2e1 mutant mouse model of RTT, revealing transcriptional effects of cell type, mosaicism, and sex on progressive disease phenotypes. Across cell types, we observed sex differences in the number of differentially expressed genes (DEGs) with 6x more DEGs in mutant females than males. Unlike males, female DEGs emerged prior to symptoms, were enriched for homeostatic gene pathways in distinct cell types over time and correlated with disease phenotypes and human RTT cortical cell transcriptomes. Non-cell-autonomous effects were prominent and dynamic across disease progression of Mecp2e1 mutant females, indicating that wild-type-expressing cells normalize transcriptional homeostasis. These results advance our understanding of RTT progression and treatment.

Post-stroke hippocampal neurogenesis is impaired by microvascular dysfunction and PI3K signaling in cerebral amyloid angiopathy

Ischemic stroke and cerebral amyloid angiopathy (CAA) pose significant challenges in an aging population, particularly in post-stroke recovery. Using the 5xFAD mouse model, we explore the relationship between CAA, ischemic stroke, and tissue recovery. We hypothesize that amyloid-beta accumulation worsens stroke outcomes by inducing blood-brain barrier (BBB) dysfunction, leading to impaired neurogenesis. Our findings show that CAA exacerbates stroke outcomes, with mice exhibiting constricted BBB microvessels, reduced cerebral blood flow, and impaired tissue recovery. Transcriptional analysis shows that endothelial cells and neural progenitor cells (NPCs) in the hippocampus exhibit differential gene expression in response to CAA and stroke, specifically targeting the phosphatidylinositol 3-kinase (PI3K) pathway. In vitro experiments with human NPCs validate these findings, showing that disruption of the CXCL12-PIK3C2A-CREB3L2 axis impairs neurogenesis. Notably, PI3K pathway activation restores neurogenesis, highlighting a potential therapeutic approach. These results suggest that CAA combined with stroke induces microvascular dysfunction and aberrant neurogenesis through this specific pathway.

VEGF-A-mediated venous endothelial cell proliferation results in neoangiogenesis during neuroinflammation

Newly formed leaky vessels and blood-brain barrier (BBB) damage are present in demyelinating acute and chronic lesions in multiple sclerosis (MS) and experimental autoimmune encephalomyelitis (EAE). However, the endothelial cell subtypes and signaling pathways contributing to these leaky neovessels are unclear. Here, using single-cell transcriptional profiling and in vivo validation studies, we show that venous endothelial cells express neoangiogenesis gene signatures and show increased proliferation resulting in enlarged veins and higher venous coverage in acute and chronic EAE lesions in female adult mice. These changes correlate with the upregulation of vascular endothelial growth factor A (VEGF-A) signaling. We also confirmed increased expression of neoangiogenic markers in acute and chronic human MS lesions. Treatment with a VEGF-A blocking antibody diminishes the neoangiogenic transcriptomic signatures and vascular proliferation in female adult mice with EAE, but it does not restore BBB function or ameliorate EAE pathology. Our data demonstrate that venous endothelial cells contribute to neoangiogenesis in demyelinating neuroinflammatory conditions.

Automated single-cell omics end-to-end framework with data-driven batch inference

To facilitate single-cell multi-omics analysis and improve reproducibility, we present single-cell pipeline for end-to-end data integration (SPEEDI), a fully automated end-to-end framework for batch inference, data integration, and cell-type labeling. SPEEDI introduces data-driven batch inference and transforms the often heterogeneous data matrices obtained from different samples into a uniformly annotated and integrated dataset. Without requiring user input, it automatically selects parameters and executes pre-processing, sample integration, and cell-type mapping. It can also perform downstream analyses of differential signals between treatment conditions and gene functional modules. SPEEDI's data-driven batch-inference method works with widely used integration and cell-typing tools. By developing data-driven batch inference, providing full end-to-end automation, and eliminating parameter selection, SPEEDI improves reproducibility and lowers the barrier to obtaining biological insight from these valuable single-cell datasets. The SPEEDI interactive web application can be accessed at https://speedi.princeton.edu/. A record of this paper's transparent peer review process is included in the supplemental information.

A primer on single-cell RNA-seq analysis using dendritic cells as a case study

Recent advances in single-cell (sc) transcriptomics have revolutionized our understanding of dendritic cells (DCs), pivotal players of the immune system. ScRNA-sequencing (scRNA-seq) has unraveled a previously unrecognized complexity and heterogeneity of DC subsets, shedding light on their ontogeny and specialized roles. However, navigating the rapid technological progress and computational methods can be daunting for researchers unfamiliar with the field. This review aims to provide immunologists with a comprehensive introduction to sc transcriptomic analysis, offering insights into recent developments in DC biology. Addressing common analytical queries, we guide readers through popular tools and methodologies, supplemented with references to benchmarks and tutorials for in-depth understanding. By examining findings from pioneering studies, we illustrate how computational techniques have expanded our knowledge of DC biology. Through this synthesis, we aim to equip researchers with the necessary tools and knowledge to navigate and leverage scRNA-seq for unraveling the intricacies of DC biology and advancing immunological research.

Systematic comparison of sequencing-based spatial transcriptomic methods

Recent developments of sequencing-based spatial transcriptomics (sST) have catalyzed important advancements by facilitating transcriptome-scale spatial gene expression measurement. Despite this progress, efforts to comprehensively benchmark different platforms are currently lacking. The extant variability across technologies and datasets poses challenges in formulating standardized evaluation metrics. In this study, we established a collection of reference tissues and regions characterized by well-defined histological architectures, and used them to generate data to compare 11 sST methods. We highlighted molecular diffusion as a variable parameter across different methods and tissues, significantly affecting the effective resolutions. Furthermore, we observed that spatial transcriptomic data demonstrate unique attributes beyond merely adding a spatial axis to single-cell data, including an enhanced ability to capture patterned rare cell states along with specific markers, albeit being influenced by multiple factors including sequencing depth and resolution. Our study assists biologists in sST platform selection, and helps foster a consensus on evaluation standards and establish a framework for future benchmarking efforts that can be used as a gold standard for the development and benchmarking of computational tools for spatial transcriptomic analysis.

Deciphering bone marrow engraftment after allogeneic stem cell transplantation in humans using single-cell analyses

BACKGROUNDDonor cell engraftment is a prerequisite of successful allogeneic hematopoietic stem cell transplantation. Based on peripheral blood analyses, it is characterized by early myeloid recovery and T and B cell lymphopenia. However, cellular networks associated with bone marrow engraftment of allogeneic human cells have been poorly described.METHODSMass cytometry and CITE-Seq analyses were performed on bone marrow cells 3 months after transplantation in patients with acute myelogenous leukemia.RESULTSMass cytometric analyses in 26 patients and 20 healthy controls disclosed profound alterations in myeloid and B cell progenitors, with a shift toward terminal myeloid differentiation and decreased B cell progenitors. Unsupervised analysis separated recipients into 2 groups, one of them being driven by previous graft-versus-host disease (R2 patients). We then used single-cell CITE-Seq to decipher engraftment, which resolved 36 clusters, encompassing all bone marrow cellular components. Hematopoiesis in transplant recipients was sustained by committed myeloid and erythroid progenitors in a setting of monocyte-, NK cell-, and T cell-mediated inflammation. Gene expression revealed major pathways in transplant recipients, namely, TNF-α signaling via NF-κB and the IFN-γ response. The hallmark of allograft rejection was consistently found in clusters from transplant recipients, especially in R2 recipients.CONCLUSIONBone marrow cell engraftment of allogeneic donor cells is characterized by a state of emergency hematopoiesis in the setting of an allogeneic response driving inflammation.FUNDINGThis study was supported by the French National Cancer Institute (Institut National du Cancer; PLBIO19-239) and by an unrestricted research grant by Alexion Pharmaceuticals.

The landscape of RNA-binding proteins in mammalian spermatogenesis

Despite continuous expansion of the RNA-binding protein (RBP) world, there is a lack of systematic understanding of RBPs in mammalian testis, which harbors one of the most complex tissue transcriptomes. We adapted RNA interactome capture to mouse male germ cells, building an RBP atlas characterized by multiple layers of dynamics along spermatogenesis. Trapping of RNA-crosslinked peptides showed that the glutamic acid-arginine (ER) patch, a residue-coevolved polyampholytic element present in coiled-coils, enhances RNA binding of its host RBPs. Deletion of this element in NONO (non-POU domain-containing octamer-binding protein) led to a defective mitosis-to-meiosis transition due to compromised NONO-RNA interactions. Whole-exome sequencing of over 1000 infertile men revealed a prominent role of RBPs in the human genetic architecture of male infertility and identified risk ER patch variants.

An autoimmune transcriptional circuit drives FOXP3+ regulatory T cell dysfunction

Autoimmune diseases, among the most common disorders of young adults, are mediated by genetic and environmental factors. Although CD4+FOXP3+ regulatory T cells (Tregs) play a central role in preventing autoimmunity, the molecular mechanism underlying their dysfunction is unknown. Here, we performed comprehensive transcriptomic and epigenomic profiling of Tregs in the autoimmune disease multiple sclerosis (MS) to identify critical transcriptional programs regulating human autoimmunity. We found that up-regulation of a primate-specific short isoform of PR domain zinc finger protein 1 (PRDM1-S) induces expression of serum and glucocorticoid-regulated kinase 1 (SGK1) independent from the evolutionarily conserved long PRDM1, which led to destabilization of forkhead box P3 (FOXP3) and Treg dysfunction. This aberrant PRDM1-S/SGK1 axis is shared among other autoimmune diseases. Furthermore, the chromatin landscape profiling in Tregs from individuals with MS revealed enriched activating protein-1 (AP-1)/interferon regulatory factor (IRF) transcription factor binding as candidate upstream regulators of PRDM1-S expression and Treg dysfunction. Our study uncovers a mechanistic model where the evolutionary emergence of PRDM1-S and epigenetic priming of AP-1/IRF may be key drivers of dysfunctional Tregs in autoimmune diseases.

Polyploid cancer cells reveal signatures of chemotherapy resistance

Therapeutic resistance in cancer significantly contributes to mortality, with many patients eventually experiencing recurrence after initial treatment responses. Recent studies have identified therapy-resistant large polyploid cancer cells in patient tissues, particularly in late-stage prostate cancer, linking them to advanced disease and relapse. Here, we analyzed bone marrow aspirates from 44 advanced prostate cancer patients and found the presence of circulating tumor cells with increased genomic content (CTC-IGC) was significantly associated with poorer progression-free survival. Single cell copy number profiling of CTC-IGC displayed clonal origins with typical CTCs, suggesting complete polyploidization. Induced polyploid cancer cells from PC3 and MDA-MB-231 cell lines treated with docetaxel or cisplatin were examined through single cell DNA sequencing, RNA sequencing, and protein immunofluorescence. Novel RNA and protein markers, including HOMER1, TNFRSF9, and LRP1, were identified as linked to chemotherapy resistance. These markers were also present in a subset of patient CTCs and associated with recurrence in public gene expression data. This study highlights the prognostic significance of large polyploid tumor cells, their role in chemotherapy resistance, and their expression of markers tied to cancer relapse, offering new potential avenues for therapeutic development.

Microglia protect against age-associated brain pathologies

Microglia are brain-resident macrophages that contribute to central nervous system (CNS) development, maturation, and preservation. Here, we examine the consequences of permanent microglial deficiencies on brain aging using the Csf1rΔFIRE/ΔFIRE mouse model. In juvenile Csf1rΔFIRE/ΔFIRE mice, we show that microglia are dispensable for the transcriptomic maturation of other brain cell types. By contrast, with advancing age, pathologies accumulate in Csf1rΔFIRE/ΔFIRE brains, macroglia become increasingly dysregulated, and white matter integrity declines, mimicking many pathological features of human CSF1R-related leukoencephalopathy. The thalamus is particularly vulnerable to neuropathological changes in the absence of microglia, with atrophy, neuron loss, vascular alterations, macroglial dysregulation, and severe tissue calcification. We show that populating Csf1rΔFIRE/ΔFIRE brains with wild-type microglia protects against many of these pathological changes. Together with the accompanying study by Chadarevian and colleagues1, our results indicate that the lifelong absence of microglia results in an age-related neurodegenerative condition that can be counteracted via transplantation of healthy microglia.

Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics

Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.

Lysozyme 1 Inflamed CCR2+ Macrophages Promote Obesity-Induced Cardiac Dysfunction

BACKGROUND

Macrophages are key players in obesity-associated cardiovascular diseases, which are marked by inflammatory and immune alterations. However, the pathophysiological mechanisms underlying macrophage's role in obesity-induced cardiac inflammation are incompletely understood. Our study aimed to identify the key macrophage population involved in obesity-induced cardiac dysfunction and investigate the molecular mechanism that contributes to the inflammatory response.

METHODS

In this study, we used single-cell RNA-sequencing analysis of Cd45+CD11b+F4/80+ cardiac macrophages to explore the heterogeneity of cardiac macrophages. The CCR2+ (C-C chemokine receptor 2) macrophages were specifically removed by a dual recombinase approach, and the macrophage CCR2 was deleted to investigate their functions. We also performed cleavage under target and tagmentation analysis, chromatin immunoprecipitation-polymerase chain reaction, luciferase assay, and macrophage-specific lentivirus transfection to define the impact of lysozyme C in macrophages on obesity-induced inflammation.

RESULTS

We find that the Ccr2 cluster undergoes a functional transition from homeostatic maintenance to proinflammation. Our data highlight specific changes in macrophage behavior during cardiac dysfunction under metabolic challenge. Consistently, inducible ablation of CCR2+CX3CR1+ macrophages or selective deletion of macrophage CCR2 prevents obesity-induced cardiac dysfunction. At the mechanistic level, we demonstrate that the obesity-induced functional shift of CCR2-expressing macrophages is mediated by the CCR2/activating transcription factor 3/lysozyme 1/NF-κB (nuclear factor kappa B) signaling. Finally, we uncover a noncanonical role for lysozyme 1 as a transcription activator, binding to the RelA promoter, driving NF-κB signaling, and strongly promoting inflammation and cardiac dysfunction in obesity.

CONCLUSIONS

Our findings suggest that lysozyme 1 may represent a potential target for the diagnosis of obesity-induced inflammation and the treatment of obesity-induced heart disease.

RankCompV3: a differential expression analysis algorithm based on relative expression orderings and applications in single-cell RNA transcriptomics

BACKGROUND

Effective identification of differentially expressed genes (DEGs) has been challenging for single-cell RNA sequencing (scRNA-seq) profiles. Many existing algorithms have high false positive rates (FPRs) and often fail to identify weak biological signals.

RESULTS

We present a novel method for identifying DEGs in scRNA-seq data called RankCompV3. It is based on the comparison of relative expression orderings (REOs) of gene pairs which are determined by comparing the expression levels of a pair of genes in a set of single-cell profiles. The numbers of genes with consistently higher or lower expression levels than the gene of interest are counted in two groups in comparison, respectively, and the result is tabulated in a 3 × 3 contingency table which is tested by McCullagh's method to determine if the gene is dysregulated. In both simulated and real scRNA-seq data, RankCompV3 tightly controlled the FPR and demonstrated high accuracy, outperforming 11 other common single-cell DEG detection algorithms. Analysis with either regular single-cell or synthetic pseudo-bulk profiles produced highly concordant DEGs with the ground-truth. In addition, RankCompV3 demonstrates higher sensitivity to weak biological signals than other methods. The algorithm was implemented using Julia and can be called in R. The source code is available at https://github.com/pathint/RankCompV3.jl .

CONCLUSIONS

The REOs-based algorithm is a valuable tool for analyzing single-cell RNA profiles and identifying DEGs with high accuracy and sensitivity.

Population Diversity at the Single-Cell Level

Population-scale single-cell genomics is a transformative approach for unraveling the intricate links between genetic and cellular variation. This approach is facilitated by cutting-edge experimental methodologies, including the development of high-throughput single-cell multiomics and advances in multiplexed environmental and genetic perturbations. Examining the effects of natural or synthetic genetic variants across cellular contexts provides insights into the mutual influence of genetics and the environment in shaping cellular heterogeneity. The development of computational methodologies further enables detailed quantitative analysis of molecular variation, offering an opportunity to examine the respective roles of stochastic, intercellular, and interindividual variation. Future opportunities lie in leveraging long-read sequencing, refining disease-relevant cellular models, and embracing predictive and generative machine learning models. These advancements hold the potential for a deeper understanding of the genetic architecture of human molecular traits, which in turn has important implications for understanding the genetic causes of human disease.

scHD4E: Novel ensemble learning-based differential expression analysis method for single-cell RNA-sequencing data

Differential expression (DE) analysis between cell types for scRNA-seq data by capturing its complicated features is crucial. Recently, different methods have been developed for targeting the scRNA-seq data analysis based on different modeling frameworks, assumptions, strategies and test statistic in considering various data features. The scDEA is an ensemble learning-based DE analysis method developed recently, yielding p-values using Lancaster's combination, generated by 12 individual DE analysis methods, and producing more accurate and stable results than individual methods. The objective of our study is to propose a new ensemble learning-based DE analysis method, scHD4E, using top performers in only 4 separate methods. The top performer 4 methods have been selected through an evaluation process using six real scRNA-seq data sets. We conducted comprehensive experiments for five experimental data sets to evaluate our proposed method based on the sample size effects, batch effects, type I error control, gene ontology enrichment analysis, runtime, identified matched DE genes, and semantic similarity measurement between methods. We also perform similar analyses (except the last 3 terms) and compute performance measures like accuracy, F1 score, Mathew's correlation coefficient etc. for a simulated data set. The results show that scHD4E is performs better than all the individual and scDEA methods in all the above perspectives. We expect that scHD4E will serve the modern data scientists for detecting the DEGs in scRNA-seq data analysis. To implement our proposed method, a Github R package scHD4E and its shiny application has been developed, and available in the following links: https://github.com/bbiswas1989/scHD4E and https://github.com/bbiswas1989/scHD4E-Shiny.

Immunological landscape of human lymphoid explants during measles virus infection

In humans, lymph nodes are the primary site of measles virus (MeV) replication. To understand the immunological events that occur at this site, we infected human lymphoid tissue explants using a pathogenic strain of MeV that expresses GFP. We found that MeV infected 5%-15% of cells across donors. Using single-cell RNA-Seq and flow cytometry, we found that while most of the 29 cell populations identified in the lymphoid culture were susceptible to MeV, there was a broad preferential infection of B cells and reduced infection of T cells. Further subsetting of T cells revealed that this reduction may be driven by the decreased infection of naive T cells. Transcriptional changes in infected B cells were dominated by an interferon-stimulated gene (ISG) signature. To determine which of these ISGs were most substantial, we evaluated the proteome of MeV-infected Raji cells by mass spectrometry. We found that IFIT1, IFIT2, IFIT3, ISG15, CXCL10, MX2, and XAF1 proteins were the most highly induced and positively correlated with their expression in the transcriptome. These data provide insight into the immunological events that occur in lymph nodes during infection and may lead to the development of therapeutic interventions.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Leveraging neighborhood representations of single-cell data to achieve sensitive DE testing with miloDE

Single-cell RNA-sequencing enables testing for differential expression (DE) between conditions at a cell type level. While powerful, one of the limitations of such approaches is that the sensitivity of DE testing is dictated by the sensitivity of clustering, which is often suboptimal. To overcome this, we present miloDE-a cluster-free framework for DE testing (available as an open-source R package). We illustrate the performance of miloDE on both simulated and real data. Using miloDE, we identify a transient hemogenic endothelia-like state in mouse embryos lacking Tal1 and detect distinct programs during macrophage activation in idiopathic pulmonary fibrosis.

Bdelloid rotifers deploy horizontally acquired biosynthetic genes against a fungal pathogen

Coevolutionary antagonism generates relentless selection that can favour genetic exchange, including transfer of antibiotic synthesis and resistance genes among bacteria, and sexual recombination of disease resistance alleles in eukaryotes. We report an unusual link between biological conflict and DNA transfer in bdelloid rotifers, microscopic animals whose genomes show elevated levels of horizontal gene transfer from non-metazoan taxa. When rotifers were challenged with a fungal pathogen, horizontally acquired genes were over twice as likely to be upregulated as other genes - a stronger enrichment than observed for abiotic stressors. Among hundreds of upregulated genes, the most markedly overrepresented were clusters resembling bacterial polyketide and nonribosomal peptide synthetases that produce antibiotics. Upregulation of these clusters in a pathogen-resistant rotifer species was nearly ten times stronger than in a susceptible species. By acquiring, domesticating, and expressing non-metazoan biosynthetic pathways, bdelloids may have evolved to resist natural enemies using antimicrobial mechanisms absent from other animals.

Single-cell transcriptomic analysis reveals the antiangiogenic role of Mgarp in diabetic retinopathy

INTRODUCTION

Diabetic retinopathy (DR) is a common vascular complication of diabetes mellitus and a leading cause of vision loss worldwide. Endothelial cell (EC) heterogeneity has been observed in the pathogenesis of DR. Elucidating the underlying mechanisms governing EC heterogeneity may provide novel insights into EC-specific therapies for DR.

RESEARCH DESIGN AND METHODS

We used the single-cell data from the Gene Expression Omnibus database to explore EC heterogeneity between diabetic retinas and non-diabetic retinas and identify the potential genes involved in DR. CCK-8 assays, EdU assays, transwell assays, and tube formation assays were conducted to determine the role of the identified gene in angiogenic effects.

RESULTS

Our analysis identified three distinct EC subpopulations in retinas and revealed that Mitochondria-localized glutamic acid-rich protein (Mgarp) gene is potentially involved in the pathogenesis of DR. Silencing of Mgarp significantly suppressed the proliferation, migration, and tube formation capacities in retinal endothelial cells.

CONCLUSIONS

This study not only offers new insights into transcriptomic heterogeneity and pathological alteration of retinal ECs but also holds the promise to pave the way for antiangiogenic therapy by targeting EC-specific gene.

Directly selecting cell-type marker genes for single-cell clustering analyses

In single-cell RNA sequencing (scRNA-seq) studies, cell types and their marker genes are often identified by clustering and differentially expressed gene (DEG) analysis. A common practice is to select genes using surrogate criteria such as variance and deviance, then cluster them using selected genes and detect markers by DEG analysis assuming known cell types. The surrogate criteria can miss important genes or select unimportant genes, while DEG analysis has the selection-bias problem. We present Festem, a statistical method for the direct selection of cell-type markers for downstream clustering. Festem distinguishes marker genes with heterogeneous distribution across cells that are cluster informative. Simulation and scRNA-seq applications demonstrate that Festem can sensitively select markers with high precision and enables the identification of cell types often missed by other methods. In a large intrahepatic cholangiocarcinoma dataset, we identify diverse CD8+ T cell types and potential prognostic marker genes.

Single-cell RNA sequencing reveals plasmid constrains bacterial population heterogeneity and identifies a non-conjugating subpopulation

Transcriptional heterogeneity in isogenic bacterial populations can play various roles in bacterial evolution, but its detection remains technically challenging. Here, we use microbial split-pool ligation transcriptomics to study the relationship between bacterial subpopulation formation and plasmid-host interactions at the single-cell level. We find that single-cell transcript abundances are influenced by bacterial growth state and plasmid carriage. Moreover, plasmid carriage constrains the formation of bacterial subpopulations. Plasmid genes, including those with core functions such as replication and maintenance, exhibit transcriptional heterogeneity associated with cell activity. Notably, we identify a cell subpopulation that does not transcribe conjugal plasmid transfer genes, which may help reduce plasmid burden on a subset of cells. Our study advances the understanding of plasmid-mediated subpopulation dynamics and provides insights into the plasmid-bacteria interplay.

In vivo AAV-SB-CRISPR screens of tumor-infiltrating primary NK cells identify genetic checkpoints of CAR-NK therapy

Natural killer (NK) cells have clinical potential against cancer; however, multiple limitations hinder the success of NK cell therapy. Here, we performed unbiased functional mapping of tumor-infiltrating NK (TINK) cells using in vivo adeno-associated virus (AAV)-SB (Sleeping Beauty)-CRISPR (clustered regularly interspaced short palindromic repeats) screens in four solid tumor mouse models. In parallel, we characterized single-cell transcriptomic landscapes of TINK cells, which identified previously unexplored subpopulations of NK cells and differentially expressed TINK genes. As a convergent hit, CALHM2-knockout (KO) NK cells showed enhanced cytotoxicity and tumor infiltration in mouse primary NK cells and human chimeric antigen receptor (CAR)-NK cells. CALHM2 mRNA reversed the CALHM2-KO phenotype. CALHM2 KO in human primary NK cells enhanced their cytotoxicity, degranulation and cytokine production. Transcriptomics profiling revealed CALHM2-KO-altered genes and pathways in both baseline and stimulated conditions. In a solid tumor model resistant to unmodified CAR-NK cells, CALHM2-KO CAR-NK cells showed potent in vivo antitumor efficacy. These data identify endogenous genetic checkpoints that naturally limit NK cell function and demonstrate the use of CALHM2 KO for engineering enhanced NK cell-based immunotherapies.

SCTC: inference of developmental potential from single-cell transcriptional complexity

Inferring the developmental potential of single cells from scRNA-Seq data and reconstructing the pseudo-temporal path of cell development are fundamental but challenging tasks in single-cell analysis. Although single-cell transcriptional diversity (SCTD) measured by the number of expressed genes per cell has been widely used as a hallmark of developmental potential, it may lead to incorrect estimation of differentiation states in some cases where gene expression does not decrease monotonously during the development process. In this study, we propose a novel metric called single-cell transcriptional complexity (SCTC), which draws on insights from the economic complexity theory and takes into account the sophisticated structure information of scRNA-Seq count matrix. We show that SCTC characterizes developmental potential more accurately than SCTD, especially in the early stages of development where cells typically have lower diversity but higher complexity than those in the later stages. Based on the SCTC, we provide an unsupervised method for accurate, robust, and transferable inference of single-cell pseudotime. Our findings suggest that the complexity emerging from the interplay between cells and genes determines the developmental potential, providing new insights into the understanding of biological development from the perspective of complexity theory.

Massively parallel in vivo Perturb-seq reveals cell-type-specific transcriptional networks in cortical development

Leveraging AAVs' versatile tropism and labeling capacity, we expanded the scale of in vivo CRISPR screening with single-cell transcriptomic phenotyping across embryonic to adult brains and peripheral nervous systems. Through extensive tests of 86 vectors across AAV serotypes combined with a transposon system, we substantially amplified labeling efficacy and accelerated in vivo gene delivery from weeks to days. Our proof-of-principle in utero screen identified the pleiotropic effects of Foxg1, highlighting its tight regulation of distinct networks essential for cell fate specification of Layer 6 corticothalamic neurons. Notably, our platform can label >6% of cerebral cells, surpassing the current state-of-the-art efficacy at <0.1% by lentivirus, to achieve analysis of over 30,000 cells in one experiment and enable massively parallel in vivo Perturb-seq. Compatible with various phenotypic measurements (single-cell or spatial multi-omics), it presents a flexible approach to interrogate gene function across cell types in vivo, translating gene variants to their causal function.

Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) have led to groundbreaking advancements in life sciences. To develop bioinformatics tools for scRNA-seq and SRT data and perform unbiased benchmarks, data simulation has been widely adopted by providing explicit ground truth and generating customized datasets. However, the performance of simulation methods under multiple scenarios has not been comprehensively assessed, making it challenging to choose suitable methods without practical guidelines.

RESULTS

We systematically evaluated 49 simulation methods developed for scRNA-seq and/or SRT data in terms of accuracy, functionality, scalability, and usability using 152 reference datasets derived from 24 platforms. SRTsim, scDesign3, ZINB-WaVE, and scDesign2 have the best accuracy performance across various platforms. Unexpectedly, some methods tailored to scRNA-seq data have potential compatibility for simulating SRT data. Lun, SPARSim, and scDesign3-tree outperform other methods under corresponding simulation scenarios. Phenopath, Lun, Simple, and MFA yield high scalability scores but they cannot generate realistic simulated data. Users should consider the trade-offs between method accuracy and scalability (or functionality) when making decisions. Additionally, execution errors are mainly caused by failed parameter estimations and appearance of missing or infinite values in calculations. We provide practical guidelines for method selection, a standard pipeline Simpipe ( https://github.com/duohongrui/simpipe ; https://doi.org/10.5281/zenodo.11178409 ), and an online tool Simsite ( https://www.ciblab.net/software/simshiny/ ) for data simulation.

CONCLUSIONS

No method performs best on all criteria, thus a good-yet-not-the-best method is recommended if it solves problems effectively and reasonably. Our comprehensive work provides crucial insights for developers on modeling gene expression data and fosters the simulation process for users.

SifiNet: a robust and accurate method to identify feature gene sets and annotate cells

SifiNet is a robust and accurate computational pipeline for identifying distinct gene sets, extracting and annotating cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Uniquely, SifiNet bypasses the cell clustering stage, commonly integrated into other cellular annotation pipelines, thereby circumventing potential inaccuracies in clustering that may compromise subsequent analyses. Consequently, SifiNet has demonstrated superior performance in multiple experimental datasets compared with other state-of-the-art methods. SifiNet can analyze both single-cell RNA and ATAC sequencing data, thereby rendering comprehensive multi-omic cellular profiles. It is conveniently available as an open-source R package.

Hypoxia coordinates the spatial landscape of myeloid cells within glioblastoma to affect survival

Myeloid cells are highly prevalent in glioblastoma (GBM), existing in a spectrum of phenotypic and activation states. We now have limited knowledge of the tumor microenvironment (TME) determinants that influence the localization and the functions of the diverse myeloid cell populations in GBM. Here, we have utilized orthogonal imaging mass cytometry with single-cell and spatial transcriptomic approaches to identify and map the various myeloid populations in the human GBM tumor microenvironment (TME). Our results show that different myeloid populations have distinct and reproducible compartmentalization patterns in the GBM TME that is driven by tissue hypoxia, regional chemokine signaling, and varied homotypic and heterotypic cellular interactions. We subsequently identified specific tumor subregions in GBM, based on composition of identified myeloid cell populations, that were linked to patient survival. Our results provide insight into the spatial organization of myeloid cell subpopulations in GBM, and how this is predictive of clinical outcome.

ATG-101 Is a Tetravalent PD-L1×4-1BB Bispecific Antibody That Stimulates Antitumor Immunity through PD-L1 Blockade and PD-L1-Directed 4-1BB Activation

Immune checkpoint inhibitors (ICI) have transformed cancer treatment. However, only a minority of patients achieve a profound response. Many patients are innately resistant while others acquire resistance to ICIs. Furthermore, hepatotoxicity and suboptimal efficacy have hampered the clinical development of agonists of 4-1BB, a promising immune-stimulating target. To effectively target 4-1BB and treat diseases resistant to ICIs, we engineered ATG-101, a tetravalent "2+2″ PD-L1×4-1BB bispecific antibody. ATG-101 bound PD-L1 and 4-1BB concurrently, with a greater affinity for PD-L1, and potently activated 4-1BB+ T cells when cross-linked with PD-L1-positive cells. ATG-101 activated exhausted T cells upon PD-L1 binding, indicating a possible role in reversing T-cell dysfunction. ATG-101 displayed potent antitumor activity in numerous in vivo tumor models, including those resistant or refractory to ICIs. ATG-101 greatly increased the proliferation of CD8+ T cells, the infiltration of effector memory T cells, and the ratio of CD8+ T/regulatory T cells in the tumor microenvironment (TME), rendering an immunologically "cold" tumor "hot." Comprehensive characterization of the TME after ATG-101 treatment using single-cell RNA sequencing further revealed an altered immune landscape that reflected increased antitumor immunity. ATG-101 was well tolerated and did not induce hepatotoxicity in non-human primates. According to computational semimechanistic pharmacology modeling, 4-1BB/ATG-101/PD-L1 trimer formation and PD-L1 receptor occupancy were both maximized at around 2 mg/kg of ATG-101, providing guidance regarding the optimal biological dose for clinical trials. In summary, by localizing to PD-L1-rich microenvironments and activating 4-1BB+ immune cells in a PD-L1 cross-linking-dependent manner, ATG-101 safely inhibits growth of ICI resistant and refractory tumors.

SIGNIFICANCE

The tetravalent PD-L1×4-1BB bispecific antibody ATG-101 activates 4-1BB+ T cells in a PD-L1 cross-linking-dependent manner, minimizing the hepatotoxicity of existing 4-1BB agonists and suppressing growth of ICI-resistant tumors. See related commentary by Ha et al., p. 1546.

Quantifying 3'UTR length from scRNA-seq data reveals changes independent of gene expression

Although more than half of all genes generate transcripts that differ in 3'UTR length, current analysis pipelines only quantify the amount but not the length of mRNA transcripts. 3'UTR length is determined by 3' end cleavage sites (CS). We map CS in more than 200 primary human and mouse cell types and increase CS annotations relative to the GENCODE database by 40%. Approximately half of all CS are used in few cell types, revealing that most genes only have one or two major 3' ends. We incorporate the CS annotations into a computational pipeline, called scUTRquant, for rapid, accurate, and simultaneous quantification of gene and 3'UTR isoform expression from single-cell RNA sequencing (scRNA-seq) data. When applying scUTRquant to data from 474 cell types and 2134 perturbations, we discover extensive 3'UTR length changes across cell types that are as widespread and coordinately regulated as gene expression changes but affect mostly different genes. Our data indicate that mRNA abundance and mRNA length are two largely independent axes of gene regulation that together determine the amount and spatial organization of protein synthesis.

Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models

Spatial transcriptomics (ST) assays represent a revolution in how the architecture of tissues is studied by allowing for the exploration of cells in their spatial context. A common element in the analysis is delineating tissue domains or "niches" followed by detecting differentially expressed genes to infer the biological identity of the tissue domains or cell types. However, many studies approach differential expression analysis by using statistical approaches often applied in the analysis of non-spatial scRNA data (e.g., two-sample t-tests, Wilcoxon's rank sum test), hence neglecting the spatial dependency observed in ST data. In this study, we show that applying linear mixed models with spatial correlation structures using spatial random effects effectively accounts for the spatial autocorrelation and reduces inflation of type-I error rate observed in non-spatial based differential expression testing. We also show that spatial linear models with an exponential correlation structure provide a better fit to the ST data as compared to non-spatial models, particularly for spatially resolved technologies that quantify expression at finer scales (i.e., single-cell resolution).

Identification of clinically relevant T cell receptors for personalized T cell therapy using combinatorial algorithms

A central challenge in developing personalized cancer cell immunotherapy is the identification of tumor-reactive T cell receptors (TCRs). By exploiting the distinct transcriptomic profile of tumor-reactive T cells relative to bystander cells, we build and benchmark TRTpred, an antigen-agnostic in silico predictor of tumor-reactive TCRs. We integrate TRTpred with an avidity predictor to derive a combinatorial algorithm of clinically relevant TCRs for personalized T cell therapy and benchmark it in patient-derived xenografts.

BASiCS workflow: a step-by-step analysis of expression variability using single cell RNA sequencing data

Cell-to-cell gene expression variability is an inherent feature of complex biological systems, such as immunity and development. Single-cell RNA sequencing is a powerful tool to quantify this heterogeneity, but it is prone to strong technical noise. In this article, we describe a step-by-step computational workflow that uses the BASiCS Bioconductor package to robustly quantify expression variability within and between known groups of cells (such as experimental conditions or cell types). BASiCS uses an integrated framework for data normalisation, technical noise quantification and downstream analyses, propagating statistical uncertainty across these steps. Within a single seemingly homogeneous cell population, BASiCS can identify highly variable genes that exhibit strong heterogeneity as well as lowly variable genes with stable expression. BASiCS also uses a probabilistic decision rule to identify changes in expression variability between cell populations, whilst avoiding confounding effects related to differences in technical noise or in overall abundance. Using a publicly available dataset, we guide users through a complete pipeline that includes preliminary steps for quality control, as well as data exploration using the scater and scran Bioconductor packages. The workflow is accompanied by a Docker image that ensures the reproducibility of our results.

PAGER-scFGA: unveiling cell functions and molecular mechanisms in cell trajectories through single-cell functional genomics analysis

Background: Understanding how cells and tissues respond to stress factors and perturbations during disease processes is crucial for developing effective prevention, diagnosis, and treatment strategies. Single-cell RNA sequencing (scRNA-seq) enables high-resolution identification of cells and exploration of cell heterogeneity, shedding light on cell differentiation/maturation and functional differences. Recent advancements in multimodal sequencing technologies have focused on improving access to cell-specific subgroups for functional genomics analysis. To facilitate the functional annotation of cell groups and characterization of molecular mechanisms underlying cell trajectories, we introduce the Pathways, Annotated Gene Lists, and Gene Signatures Electronic Repository for Single-Cell Functional Genomics Analysis (PAGER-scFGA). Results: We have developed PAGER-scFGA, which integrates cell functional annotations and gene-set enrichment analysis into popular single-cell analysis pipelines such as Scanpy. Using differentially expressed genes (DEGs) from pairwise cell clusters, PAGER-scFGA infers cell functions through the enrichment of potential cell-marker genesets. Moreover, PAGER-scFGA provides pathways, annotated gene lists, and gene signatures (PAGs) enriched in specific cell subsets with tissue compositions and continuous transitions along cell trajectories. Additionally, PAGER-scFGA enables the construction of a gene subcellular map based on DEGs and allows examination of the gene functional compartments (GFCs) underlying cell maturation/differentiation. In a real-world case study of mouse natural killer (mNK) cells, PAGER-scFGA revealed two major stages of natural killer (NK) cells and three trajectories from the precursor stage to NK T-like mature stage within blood, spleen, and bone marrow tissues. As the trajectories progress to later stages, the DEGs exhibit greater divergence and variability. However, the DEGs in different trajectories still interact within a network during NK cell maturation. Notably, PAGER-scFGA unveiled cell cytotoxicity, exocytosis, and the response to interleukin (IL) signaling pathways and associated network models during the progression from precursor NK cells to mature NK cells. Conclusion: PAGER-scFGA enables in-depth exploration of functional insights and presents a comprehensive knowledge map of gene networks and GFCs, which can be utilized for future studies and hypothesis generation. It is expected to become an indispensable tool for inferring cell functions and detecting molecular mechanisms within cell trajectories in single-cell studies. The web app (accessible at https://au-singlecell.streamlit.app/) is publicly available.

Precision and Accuracy of Single-Cell/Nuclei RNA Sequencing Data

Single-cell/nuclei RNA sequencing (sc/snRNA-Seq) is widely used for profiling cell-type gene expressions in biomedical research. An important but underappreciated issue is the quality of sc/snRNA-Seq data that would impact the reliability of downstream analyses. Here we evaluated the precision and accuracy in 18 sc/snRNA-Seq datasets. The precision was assessed on data from human brain studies with a total of 3,483,905 cells from 297 individuals, by utilizing technical replicates. The accuracy was evaluated with sample-matched scRNA-Seq and pooled-cell RNA-Seq data of cultured mononuclear phagocytes from four species. The results revealed low precision and accuracy at the single-cell level across all evaluated data. Cell number and RNA quality were highlighted as two key factors determining the expression precision, accuracy, and reproducibility of differential expression analysis in sc/snRNA-Seq. This study underscores the necessity of sequencing enough high-quality cells per cell type per individual, preferably in the hundreds, to mitigate noise in expression quantification.

SifiNet: A robust and accurate method to identify feature gene sets and annotate cells

SifiNet is a robust and accurate computational pipeline for identifying distinct gene sets, extracting and annotating cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Uniquely, SifiNet bypasses the cell clustering stage, commonly integrated into other cellular annotation pipelines, thereby circumventing potential inaccuracies in clustering that may compromise subsequent analyses. Consequently, SifiNet has demonstrated superior performance in multiple experimental datasets compared with other state-of-the-art methods. SifiNet can analyze both single-cell RNA and ATAC sequencing data, thereby rendering comprehensive multiomic cellular profiles. It is conveniently available as an open-source R package.

Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment

The application of bioinformatics has revolutionized the practice of medicine in the past 20 years. From early studies that uncovered subtypes of cancer to broad efforts spearheaded by the Cancer Genome Atlas initiative, the use of bioinformatics strategies to analyse high-dimensional data has provided unprecedented insights into the molecular basis of disease. In addition to the identification of disease subtypes - which enables risk stratification - informatics analysis has facilitated the identification of novel risk factors and drivers of disease, biomarkers of progression and treatment response, as well as possibilities for drug repurposing or repositioning; moreover, bioinformatics has guided research towards precision and personalized medicine. Implementation of specific computational approaches such as artificial intelligence, machine learning and molecular subtyping has yet to become widespread in urology clinical practice for reasons of cost, disruption of clinical workflow and need for prospective validation of informatics approaches in independent patient cohorts. Solving these challenges might accelerate routine integration of bioinformatics into clinical settings.

Single-cell and spatial transcriptomics: Bridging current technologies with long-read sequencing

Single-cell technologies have transformed biomedical research over the last decade, opening up new possibilities for understanding cellular heterogeneity, both at the genomic and transcriptomic level. In addition, more recent developments of spatial transcriptomics technologies have made it possible to profile cells in their tissue context. In parallel, there have been substantial advances in sequencing technologies, and the third generation of methods are able to produce reads that are tens of kilobases long, with error rates matching the second generation short reads. Long reads technologies make it possible to better map large genome rearrangements and quantify isoform specific abundances. This further improves our ability to characterize functionally relevant heterogeneity. Here, we show how researchers have begun to combine single-cell, spatial transcriptomics, and long-read technologies, and how this is resulting in powerful new approaches to profiling both the genome and the transcriptome. We discuss the achievements so far, and we highlight remaining challenges and opportunities.

Recent advances in differential expression analysis for single-cell RNA-seq and spatially resolved transcriptomic studies

Differential expression (DE) analysis is a necessary step in the analysis of single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data. Unlike traditional bulk RNA-seq, DE analysis for scRNA-seq or SRT data has unique characteristics that may contribute to the difficulty of detecting DE genes. However, the plethora of DE tools that work with various assumptions makes it difficult to choose an appropriate one. Furthermore, a comprehensive review on detecting DE genes for scRNA-seq data or SRT data from multi-condition, multi-sample experimental designs is lacking. To bridge such a gap, here, we first focus on the challenges of DE detection, then highlight potential opportunities that facilitate further progress in scRNA-seq or SRT analysis, and finally provide insights and guidance in selecting appropriate DE tools or developing new computational DE methods.

Critical view on oligo(dT)-based RNA-seq: bias arising, modeling, and mitigating

The precise biological interpretation of oligo(dT)-based RNA sequencing (RNA-seq) datasets, particularly in single-cell RNA-seq (scRNA-seq), is invaluable for understanding complex biological systems. However, the presence of biases can lead to misleading results in downstream analysis. This study has now identified two additional biases that are not accounted for in established bias models: poly(A)-tail length bias and fixed-position GC-content bias. These biases have a significant negative impact on the overall quality of oligo(dT)-based RNA-seq data. To address these biases, we have developed a universal bias-mitigating method based on the lower-affinity binding of short and nonanchored oligo(dT) primers to poly(A) tails. This method significantly reduces poly(A) length bias and completely eliminates fixed-position GC bias. Furthermore, the use of short oligo(dT) with impartial binding behavior toward the diverse poly(A) tails renders RNA-seq with more reliable measurements. The findings of this study are particularly beneficial for scRNA-seq datasets, where accurate benchmarking is critical.

Dual fluorescence images, transport pathway, and blood-brain barrier penetration of B-Met-W/O/W SE

Borneol is an aromatic traditional Chinese medicine that can improve the permeability of the blood-brain barrier (BBB), enter the brain, and promote the brain tissue distribution of many other drugs. In our previous study, borneol-metformin hydrochloride water/oil/water composite submicron emulsion (B-Met-W/O/W SE) was prepared using borneol and SE to promote BBB penetration, which significantly increased the brain distribution of Met. However, the dynamic images, transport pathway (uptake and efflux), promotion of BBB permeability, and mechanisms of B-Met-W/O/W SE before and after entering cells have not been clarified. In this study, rhodamine B and coumarin-6 were selected as water-soluble and oil-soluble fluorescent probes to prepare B-Met-W/O/W dual-fluorescent SE (B-Met-W/O/W DFSE) with concentric circle imaging. B-Met-W/O/W SE can be well taken up by brain microvascular endothelial cells (BMECs). The addition of three inhibitors (chlorpromazine hydrochloride, methyl-β-cyclodextrin, and amiloride hydrochloride) indicated that its main pathway may be clathrin-mediated and fossa protein-mediated endocytosis. Meanwhile, B-Met-W/O/W SE was obviously shown to inhibit the efflux of BMECs. Next, BMECs were cultured in the Transwell chamber to establish a BBB model, and Western blot was employed to detect the protein expressions of Occludin, Zona Occludens 1 (ZO-1), and p-glycoprotein (P-gp) after B-Met-W/O/W SE treatment. The results showed that B-Met-W/O/W SE significantly down-regulated the expression of Occludin, ZO-1, and P-gp, which increased the permeability of BBB, promoted drug entry into the brain through BBB, and inhibited BBB efflux. Furthermore, 11 differentially expressed genes (DEGs) and 7 related signaling pathways in BMECs treated with B-W/O/W SE were detected by transcriptome sequencing and verified by quantitative real-time polymerase chain reaction (qRT-PCR). These results provide a scientific experimental basis for the dynamic monitoring, transmembrane transport mode, and permeation-promoting mechanism of B-Met-W/O/W SE as a new brain-targeting drug delivery system.

A comparison of marker gene selection methods for single-cell RNA sequencing data

BACKGROUND

The development of single-cell RNA sequencing (scRNA-seq) has enabled scientists to catalog and probe the transcriptional heterogeneity of individual cells in unprecedented detail. A common step in the analysis of scRNA-seq data is the selection of so-called marker genes, most commonly to enable annotation of the biological cell types present in the sample. In this paper, we benchmark 59 computational methods for selecting marker genes in scRNA-seq data.

RESULTS

We compare the performance of the methods using 14 real scRNA-seq datasets and over 170 additional simulated datasets. Methods are compared on their ability to recover simulated and expert-annotated marker genes, the predictive performance and characteristics of the gene sets they select, their memory usage and speed, and their implementation quality. In addition, various case studies are used to scrutinize the most commonly used methods, highlighting issues and inconsistencies.

CONCLUSIONS

Overall, we present a comprehensive evaluation of methods for selecting marker genes in scRNA-seq data. Our results highlight the efficacy of simple methods, especially the Wilcoxon rank-sum test, Student's t-test, and logistic regression.

Measuring the composition of the tumor microenvironment with transcriptome analysis: past, present and future

Interactions between tumor cells and immune cells in the tumor microenvironment (TME) play a vital role the mechanisms of immune evasion, by which cancer cells escape immune elimination. Thus, the characterization and quantification of different components in the TME is a hot topic in molecular biology and drug discovery. Since the development of transcriptome sequencing in bulk tissue, single cells and spatial dimensions, there are increasing methods emerging to deconvolute and subtype the TME. This review discusses and compares such computational strategies and downstream subtyping analyses. Integrative analyses of the transcriptome with other data, such as epigenetics and T-cell receptor sequencing, are needed to obtain comprehensive knowledge of the dynamic TME.

Compartmentalization and synergy of osteoblasts drive bone formation in the regenerating fin

Zebrafish regenerate their fins which involves a component of cell plasticity. It is currently unclear how regenerate cells divide labor to allow for appropriate growth and patterning. Here, we studied lineage relationships of fluorescence-activated cell sorting-enriched epidermal, bone-forming (osteoblast), and (non-osteoblast) blastemal fin regenerate cells by single-cell RNA sequencing, lineage tracing, targeted osteoblast ablation, and electron microscopy. Most osteoblasts in the outgrowing regenerate derive from osterix+ osteoblasts, while mmp9+ cells reside at segment joints. Distal blastema cells contribute to distal osteoblast progenitors, suggesting compartmentalization of the regenerating appendage. Ablation of osterix+ osteoblasts impairs segment joint and bone matrix formation and decreases regenerate length which is partially compensated for by distal regenerate cells. Our study characterizes expression patterns and lineage relationships of rare fin regenerate cell populations, indicates inherent detection and compensation of impaired regeneration, suggests variable dependence on growth factor signaling, and demonstrates zonation of the elongating fin regenerate.

Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data

Clustering is commonly used in single-cell RNA-sequencing (scRNA-seq) pipelines to characterize cellular heterogeneity. However, current methods face two main limitations. First, they require user-specified heuristics which add time and complexity to bioinformatic workflows; second, they rely on post-selective differential expression analyses to identify marker genes driving cluster differences, which has been shown to be subject to inflated false discovery rates. We address these challenges by introducing nonparametric clustering of single-cell populations (NCLUSION): an infinite mixture model that leverages Bayesian sparse priors to identify marker genes while simultaneously performing clustering on single-cell expression data. NCLUSION uses a scalable variational inference algorithm to perform these analyses on datasets with up to millions of cells. By analyzing publicly available scRNA-seq studies, we demonstrate that NCLUSION (i) matches the performance of other state-of-the-art clustering techniques with significantly reduced runtime and (ii) provides statistically robust and biologically relevant transcriptomic signatures for each of the clusters it identifies. Overall, NCLUSION represents a reliable hypothesis-generating tool for understanding patterns of expression variation present in single-cell populations.

Convergence of coronary artery disease genes onto endothelial cell programs

Linking variants from genome-wide association studies (GWAS) to underlying mechanisms of disease remains a challenge1-3. For some diseases, a successful strategy has been to look for cases in which multiple GWAS loci contain genes that act in the same biological pathway1-6. However, our knowledge of which genes act in which pathways is incomplete, particularly for cell-type-specific pathways or understudied genes. Here we introduce a method to connect GWAS variants to functions. This method links variants to genes using epigenomics data, links genes to pathways de novo using Perturb-seq and integrates these data to identify convergence of GWAS loci onto pathways. We apply this approach to study the role of endothelial cells in genetic risk for coronary artery disease (CAD), and discover 43 CAD GWAS signals that converge on the cerebral cavernous malformation (CCM) signalling pathway. Two regulators of this pathway, CCM2 and TLNRD1, are each linked to a CAD risk variant, regulate other CAD risk genes and affect atheroprotective processes in endothelial cells. These results suggest a model whereby CAD risk is driven in part by the convergence of causal genes onto a particular transcriptional pathway in endothelial cells. They highlight shared genes between common and rare vascular diseases (CAD and CCM), and identify TLNRD1 as a new, previously uncharacterized member of the CCM signalling pathway. This approach will be widely useful for linking variants to functions for other common polygenic diseases.