The CD4 Versus CD8 T Cell Fate Decision: A Multiomics-Informed Perspective.

The choice of developing thymocytes to become CD8+ cytotoxic or CD4+ helper T cells has been intensely studied, but many of the underlying mechanisms remain to be elucidated. Recent multiomics approaches have provided much higher resolution analysis of gene expression in developing thymocytes than was previously achievable, thereby offering a fresh perspective on this question. Focusing on our recent studies using CITE-seq (cellular indexing of transcriptomes and epitopes) analyses of mouse thymocytes, we present a detailed timeline of RNA and protein expression changes during CD8 versus CD4 T cell differentiation. We also revisit our current understanding of the links between T cell receptor signaling and expression of the lineage-defining transcription factors ThPOK and RUNX3. Finally, we propose a sequential selection model to explain the tight linkage between MHC-I versus MHC-II recognition and T cell lineage choice. This model incorporates key aspects of previously proposed kinetic signaling, instructive, and stochastic/selection models.

Automating the analysis of facial emotion expression dynamics: A computational framework and application in psychotic disorders.

Facial emotion expressions play a central role in interpersonal interactions; these displays are used to predict and influence the behavior of others. Despite their importance, quantifying and analyzing the dynamics of brief facial emotion expressions remains an understudied methodological challenge. Here, we present a method that leverages machine learning and network modeling to assess the dynamics of facial expressions. Using video recordings of clinical interviews, we demonstrate the utility of this approach in a sample of 96 people diagnosed with psychotic disorders and 116 never-psychotic adults. Participants diagnosed with schizophrenia tended to move from neutral expressions to uncommon expressions (e.g., fear, surprise), whereas participants diagnosed with other psychoses (e.g., mood disorders with psychosis) moved toward expressions of sadness. This method has broad applications to the study of normal and altered expressions of emotion and can be integrated with telemedicine to improve psychiatric assessment and treatment.

Identification, characterization and expression analysis of circRNA encoded by SARS-CoV-1 and SARS-CoV-2.

Virus-encoded circular RNA (circRNA) participates in the immune response to viral infection, affects the human immune system, and can be used as a target for precision therapy and tumor biomarker. The coronaviruses SARS-CoV-1 and SARS-CoV-2 (SARS-CoV-1/2) that have emerged in recent years are highly contagious and have high mortality rates. In coronaviruses, little is known about the circRNA encoded by the SARS-CoV-1/2. Therefore, this study explores whether SARS-CoV-1/2 encodes circRNA and characteristics and functions of circRNA. Based on RNA-seq data of SARS-CoV-1 and SARS-CoV-2 infections, we used circRNA identification tools (circRNA_finder, find_circ and CIRI2) to identify circRNAs. The number of circRNAs encoded by SARS-CoV-1 and SARS-CoV-2 was identified as 151 and 470, respectively. It can be found that SARS-CoV-2 shows more prominent circRNA encoding ability than SARS-CoV-1. Expression analysis showed that only a few circRNAs encoded by SARS-CoV-1/2 showed high expression levels, and the positive strand produced more abundant circRNAs. Then, based on the identified SARS-CoV-1/2-encoded circRNAs, we performed circRNA identification and characterization using the previously developed CirRNAPL. Finally, target gene prediction and functional enrichment analysis were performed. It was found that viral circRNA is closely related to cancer and has a potential role in regulating host cell functions. This study studied the characteristics and functions of viral circRNA encoded by coronavirus SARS-CoV-1/2, providing a valuable resource for further research on the function and molecular mechanism of coronavirus circRNA.

3t-seq: automatic gene expression analysis of single-copy genes, transposable elements, and tRNAs from RNA-seq data.

RNA sequencing is the gold-standard method to quantify transcriptomic changes between two conditions. The overwhelming majority of data analysis methods available are focused on polyadenylated RNA transcribed from single-copy genes and overlook transcripts from repeated sequences such as transposable elements (TEs). These self-autonomous genetic elements are increasingly studied, and specialized tools designed to handle multimapping sequencing reads are available. Transfer RNAs are transcribed by RNA polymerase III and are essential for protein translation. There is a need for integrated software that is able to analyze multiple types of RNA. Here, we present 3t-seq, a Snakemake pipeline for integrated differential expression analysis of transcripts from single-copy genes, TEs, and tRNA. 3t-seq produces an accessible report and easy-to-use results for downstream analysis starting from raw sequencing data and performing quality control, genome mapping, gene expression quantification, and statistical testing. It implements three methods to quantify TEs expression and one for tRNA genes. It provides an easy-to-configure method to manage software dependencies that lets the user focus on results. 3t-seq is released under MIT license and is available at https://github.com/boulardlab/3t-seq.

Molecular and Spatial Signatures of Mouse Embryonic Endothelial Cells at Single-Cell Resolution.

BACKGROUND: Mature endothelial cells (ECs) are heterogeneous, with subtypes defined by tissue origin and position within the vascular bed (ie, artery, capillary, vein, and lymphatic). How this heterogeneity is established during the development of the vascular system, especially arteriovenous specification of ECs, remains incompletely characterized. METHODS: We used droplet-based single-cell RNA sequencing and multiplexed error-robust fluorescence in situ hybridization to define EC and EC progenitor subtypes from E9.5, E12.5, and E15.5 mouse embryos. We used trajectory inference to analyze the specification of arterial ECs (aECs) and venous ECs (vECs) from EC progenitors. Network analysis identified candidate transcriptional regulators of arteriovenous differentiation, which we tested by CRISPR (clustered regularly interspaced short palindromic repeats) loss of function in human-induced pluripotent stem cells undergoing directed differentiation to aECs or vECs (human-induced pluripotent stem cell-aECs or human-induced pluripotent stem cell-vECs). RESULTS: From the single-cell transcriptomes of 7682 E9.5 to E15.5 ECs, we identified 19 EC subtypes, including Etv2+Bnip3+ EC progenitors. Spatial transcriptomic analysis of 15 448 ECs provided orthogonal validation of these EC subtypes and established their spatial distribution. Most embryonic ECs were grouped by their vascular-bed types, while ECs from the brain, heart, liver, and lung were grouped by their tissue origins. Arterial (Eln, Dkk2, Vegfc, and Egfl8), venous (Fam174b and Clec14a), and capillary (Kcne3) marker genes were identified. Compared with aECs, embryonic vECs and capillary ECs shared fewer markers than their adult counterparts. Early capillary ECs with venous characteristics functioned as a branch point for differentiation of aEC and vEC lineages. CONCLUSIONS: Our results provide a spatiotemporal map of embryonic EC heterogeneity at single-cell resolution and demonstrate that the diversity of ECs in the embryo arises from both tissue origin and vascular-bed position. Developing aECs and vECs share common venous-featured capillary precursors and are regulated by distinct transcriptional regulatory networks.

mTORC1 Signaling in Brain Endothelial Progenitors Contributes to CCM Pathogenesis.

BACKGROUND: Cerebral vascular malformations (CCMs) are primarily found within the brain, where they result in increased risk for stroke, seizures, and focal neurological deficits. The unique feature of the brain vasculature is the blood-brain barrier formed by the brain neurovascular unit. Recent studies suggest that loss of CCM genes causes disruptions of blood-brain barrier integrity as the inciting events for CCM development. CCM lesions are proposed to be initially derived from a single clonal expansion of a subset of angiogenic venous capillary endothelial cells (ECs) and respective resident endothelial progenitor cells (EPCs). However, the critical signaling events in the subclass of brain ECs/EPCs for CCM lesion initiation and progression are unclear. METHODS: Brain EC-specific CCM3-deficient (Pdcd10BECKO) mice were generated by crossing Pdcd10fl/fl mice with Mfsd2a-CreERT2 mice. Single-cell RNA-sequencing analyses were performed by the chromium single-cell platform (10× genomics). Cell clusters were annotated into EC subtypes based on visual inspection and GO analyses. Cerebral vessels were visualized by 2-photon in vivo imaging and tissue immunofluorescence analyses. Regulation of mTOR (mechanistic target of rapamycin) signaling by CCM3 and Cav1 (caveolin-1) was performed by cell biology and biochemical approaches. RESULTS: Single-cell RNA-sequencing analyses from P10 Pdcd10BECKO mice harboring visible CCM lesions identified upregulated CCM lesion signature and mitotic EC clusters but decreased blood-brain barrier-associated EC clusters. However, a unique EPC cluster with high expression levels of stem cell markers enriched with mTOR signaling was identified from early stages of the P6 Pdcd10BECKO brain. Indeed, mTOR signaling was upregulated in both mouse and human CCM lesions. Genetic deficiency of Raptor (regulatory-associated protein of mTOR), but not of Rictor (rapamycin-insensitive companion of mTOR), prevented CCM lesion formation in the Pdcd10BECKO model. Importantly, the mTORC1 (mTOR complex 1) pharmacological inhibitor rapamycin suppressed EPC proliferation and ameliorated CCM pathogenesis in Pdcd10BECKO mice. Mechanistic studies suggested that Cav1/caveolae increased in CCM3-depleted EPC-mediated intracellular trafficking and complex formation of the mTORC1 signaling proteins. CONCLUSIONS: CCM3 is critical for maintaining blood-brain barrier integrity and CCM3 loss-induced mTORC1 signaling in brain EPCs initiates and facilitates CCM pathogenesis.

Quantification Quality Control Emerges as a Crucial Factor to Enhance Single-Cell Proteomics Data Analysis.

Mass spectrometry (MS)-based single-cell proteomics (SCP) provides us the opportunity to unbiasedly explore biological variability within cells without the limitation of antibody availability. This field is rapidly developed with the main focuses on instrument advancement, sample preparation refinement, and signal boosting methods; however, the optimal data processing and analysis are rarely investigated which holds an arduous challenge because of the high proportion of missing values and batch effect. Here, we introduced a quantification quality control to intensify the identification of differentially expressed proteins (DEPs) by considering both within and across SCP data. Combining quantification quality control with isobaric matching between runs (IMBR) and PSM-level normalization, an additional 12% and 19% of proteins and peptides, with more than 90% of proteins/peptides containing valid values, were quantified. Clearly, quantification quality control was able to reduce quantification variations and q-values with the more apparent cell type separations. In addition, we found that PSM-level normalization performed similar to other protein-level normalizations but kept the original data profiles without the additional requirement of data manipulation. In proof of concept of our refined pipeline, six uniquely identified DEPs exhibiting varied fold-changes and playing critical roles for melanoma and monocyte functionalities were selected for validation using immunoblotting. Five out of six validated DEPs showed an identical trend with the SCP dataset, emphasizing the feasibility of combining the IMBR, cell quality control, and PSM-level normalization in SCP analysis, which is beneficial for future SCP studies.

Directed Differentiation of Human Induced Pluripotent Stem Cells to Heart Valve Cells.

BACKGROUND: A main obstacle in current valvular heart disease research is the lack of high-quality homogeneous functional heart valve cells. Human induced pluripotent stem cells (hiPSCs)-derived heart valve cells may help with this dilemma. However, there are no well-established protocols to induce hiPSCs to differentiate into functional heart valve cells, and the networks that mediate the differentiation have not been fully elucidated. METHODS: To generate heart valve cells from hiPSCs, we sequentially activated the Wnt, BMP4, VEGF (vascular endothelial growth factor), and NFATc1 signaling pathways using CHIR-99021, BMP4, VEGF-165, and forskolin, respectively. The transcriptional and functional similarity of hiPSC-derived heart valve cells compared with primary heart valve cells were characterized. Longitudinal single-cell RNA sequencing was used to uncover the trajectory, switch genes, pathways, and transcription factors of the differentiation. RESULTS: An efficient protocol was developed to induce hiPSCs to differentiate into functional hiPSC-derived valve endothelial-like cells and hiPSC-derived valve interstitial-like cells. After 6-day differentiation and CD144 magnetic bead sorting, ≈70% CD144+ cells and 30% CD144- cells were obtained. On the basis of single-cell RNA sequencing data, the CD144+ cells and CD144- cells were found to be highly similar to primary heart valve endothelial cells and primary heart valve interstitial cells in gene expression profile. Furthermore, CD144+ cells had the typical function of primary heart valve endothelial cells, including tube formation, uptake of low-density lipoprotein, generation of endothelial nitric oxide synthase, and response to shear stress. Meanwhile, CD144- cells could secret collagen and matrix metalloproteinases, and differentiate into osteogenic or adipogenic lineages like primary heart valve interstitial cells. Therefore, we identified CD144+ cells and CD144- cells as hiPSC-derived valve endothelial-like cells and hiPSC-derived valve interstitial-like cells, respectively. Using single-cell RNA sequencing analysis, we demonstrated that the trajectory of heart valve cell differentiation was consistent with embryonic valve development. We identified the main switch genes (NOTCH1, HEY1, and MEF2C), signaling pathways (TGF-ß, Wnt, and NOTCH), and transcription factors (MSX1, SP5, and MECOM) that mediated the differentiation. Finally, we found that hiPSC-derived valve interstitial-like cells might derive from hiPSC-derived valve endothelial-like cells undergoing endocardial-mesenchymal transition. CONCLUSIONS: In summary, this is the first study to report an efficient strategy to generate functional hiPSC-derived valve endothelial-like cells and hiPSC-derived valve interstitial-like cells from hiPSCs, as well as to elucidate the differentiation trajectory and transcriptional dynamics of hiPSCs differentiated into heart valve cells.

Interferon-γ inducible factor 16 (IFI16) restricts adeno-associated virus type 2 (AAV2) transduction in an immune-modulatory independent way.

We determined the transcription profile of adeno-associated virus type 2 (AAV2)-infected primary human fibroblasts. Subsequent analysis revealed that cells respond to AAV infection through changes in several significantly affected pathways, including cell cycle regulation, chromatin modulation, and innate immune responses. Various assays were performed to validate selected differentially expressed genes and to confirm not only the quality but also the robustness of the raw data. One of the genes upregulated in AAV2-infected cells was interferon-γ inducible factor 16 (IFI16). IFI16 is known as a multifunctional cytosolic and nuclear innate immune sensor for double-stranded as well as single-stranded DNA, exerting its effects through various mechanisms, such as interferon response, epigenetic modifications, or transcriptional regulation. IFI16 thereby constitutes a restriction factor for many different viruses among them, as shown here, AAV2 and thereof derived vectors. Indeed, the post-transcriptional silencing of IFI16 significantly increased AAV2 transduction efficiency, independent of the structure of the virus/vector genome. We also show that IFI16 exerts its inhibitory effect on AAV2 transduction in an immune-modulatory independent way by interfering with Sp1-dependent transactivation of wild-type AAV2 and AAV2 vector promoters. IMPORTANCE: Adeno-associated virus (AAV) vectors are among the most frequently used viral vectors for gene therapy. The lack of pathogenicity of the parental virus, the long-term persistence as episomes in non-proliferating cells, and the availability of a variety of AAV serotypes differing in their cellular tropism are advantageous features of this biological nanoparticle. To deepen our understanding of virus-host interactions, especially in terms of antiviral responses, we present here the first transcriptome analysis of AAV serotype 2 (AAV2)-infected human primary fibroblasts. Our findings indicate that interferon-γ inducible factor 16 acts as an antiviral factor in AAV2 infection and AAV2 vector-mediated cell transduction in an immune-modulatory independent way by interrupting the Sp1-dependent gene expression from viral or vector genomes.

UNMF: a unified nonnegative matrix factorization for multi-dimensional omics data.

Factor analysis, ranging from principal component analysis to nonnegative matrix factorization, represents a foremost approach in analyzing multi-dimensional data to extract valuable patterns, and is increasingly being applied in the context of multi-dimensional omics datasets represented in tensor form. However, traditional analytical methods are heavily dependent on the format and structure of the data itself, and if these change even slightly, the analyst must change their data analysis strategy and techniques and spend a considerable amount of time on data preprocessing. Additionally, many traditional methods cannot be applied as-is in the presence of missing values in the data. We present a new statistical framework, unified nonnegative matrix factorization (UNMF), for finding informative patterns in messy biological data sets. UNMF is designed for tidy data format and structure, making data analysis easier and simplifying the development of data analysis tools. UNMF can handle a wide range of data structures and formats, and works seamlessly with tensor data including missing observations and repeated measurements. The usefulness of UNMF is demonstrated through its application to several multi-dimensional omics data, offering user-friendly and unified features for analysis and integration. Its application holds great potential for the life science community. UNMF is implemented with R and is available from GitHub (https://github.com/abikoushi/moltenNMF).

Model-based evaluation of spatiotemporal data reduction methods with unknown ground truth through optimal visualization and interpretability metrics.

Optimizing and benchmarking data reduction methods for dynamic or spatial visualization and interpretation (DSVI) face challenges due to many factors, including data complexity, lack of ground truth, time-dependent metrics, dimensionality bias and different visual mappings of the same data. Current studies often focus on independent static visualization or interpretability metrics that require ground truth. To overcome this limitation, we propose the MIBCOVIS framework, a comprehensive and interpretable benchmarking and computational approach. MIBCOVIS enhances the visualization and interpretability of high-dimensional data without relying on ground truth by integrating five robust metrics, including a novel time-ordered Markov-based structural metric, into a semi-supervised hierarchical Bayesian model. The framework assesses method accuracy and considers interaction effects among metric features. We apply MIBCOVIS using linear and nonlinear dimensionality reduction methods to evaluate optimal DSVI for four distinct dynamic and spatial biological processes captured by three single-cell data modalities: CyTOF, scRNA-seq and CODEX. These data vary in complexity based on feature dimensionality, unknown cell types and dynamic or spatial differences. Unlike traditional single-summary score approaches, MIBCOVIS compares accuracy distributions across methods. Our findings underscore the joint evaluation of visualization and interpretability, rather than relying on separate metrics. We reveal that prioritizing average performance can obscure method feature performance. Additionally, we explore the impact of data complexity on visualization and interpretability. Specifically, we provide optimal parameters and features and recommend methods, like the optimized variational contractive autoencoder, for targeted DSVI for various data complexities. MIBCOVIS shows promise for evaluating dynamic single-cell atlases and spatiotemporal data reduction models.

Division-Independent Differentiation of Muscle Stem Cells During a Growth Stimulus.

Adult muscle stem cells (MuSCs) are known to replicate upon activation before differentiating and fusing to regenerate myofibers. It is unclear whether MuSC differentiation is intrinsically linked to cell division, which has implications for stem cell population maintenance. We use single-cell RNA-sequencing to identify transcriptionally diverse subpopulations of MuSCs after 5 days of a growth stimulus in adult muscle. Trajectory inference in combination with a novel mouse model for tracking MuSC-derived myonuclei and in vivo labeling of DNA replication revealed an MuSC population that exhibited division-independent differentiation and fusion. These findings demonstrate that in response to a growth stimulus in the presence of intact myofibers, MuSC division is not obligatory.

Single-Cell Meta-Analysis Uncovers the Pancreatic Endothelial Cell Transcriptomic Signature and Reveals a Key Role for NKX2-3 in PLVAP Expression.

BACKGROUND: The pancreatic vasculature displays tissue-specific physiological and functional adaptations that support rapid insulin response by ß-cells. However, the digestive enzymes have made it difficult to characterize pancreatic endothelial cells (ECs), resulting in the poor understanding of pancreatic EC specialization. METHODS: Available single-nuclei/single-cell RNA-sequencing data sets were mined to identify pancreatic EC-enriched signature genes and to develop an integrated atlas of human pancreatic ECs. We validated the findings using independent single-nuclei/single-cell RNA-sequencing data, bulk RNA-sequencing data of isolated ECs, spatial transcriptomics data, immunofluorescence, and RNAScope of selected markers. The TF (transcription factor) NKX2-3 was expressed in HUVECs via gene transfection, and the expression of pancreatic EC-enriched signature genes was assessed via RT-qPCR. RESULTS: We defined a pancreatic EC-enriched gene signature conserved across species and developmental stages that included genes involved in ECM (extracellular matrix) composition (COL15A1 and COL4A1), permeability and barrier function (PLVAP, EHD4, CAVIN3, HSPG2, ROBO4, HEG1, and CLEC14A), and key signaling pathways (S1P, TGF-ß [transforming growth factor-ß], RHO-RAC GTPase, PI3k-AKT, and PDGF [platelet-derived growth factor]). The integrated atlas revealed the vascular hierarchy within the pancreas. We identified and validated a specialized islet capillary subpopulation characterized by genes involved in permeability (PLVAP and EHD4), immune-modulation (FABP5, HLA-C, and B2M), ECM composition (SPARC and SPARCL1), IGF (insulin-like growth factor) signaling (IGFBP7), and membrane transport (SLCO2A1, SLC2A3, and CD320). Importantly, we identified NKX2-3 as a key TF enriched in pancreatic ECs. DNA-binding motif analysis found NKX2-3 motifs in ≈40% of the signature genes. Induction of NKX2-3 in HUVECs promoted the expression of the islet capillary EC-enriched genes PLVAP and SPARCL1. CONCLUSIONS: We defined a validated transcriptomic signature of pancreatic ECs and uncovered their intratissue transcriptomic heterogeneity. We showed that NKX2-3 acts upstream of PLVAP and provided a single-cell online resource that can be further explored by the community: https://vasconcelos.shinyapps.io/pancreatic_endothelial/.

Single-Cell Transcriptome Reveals Potential Mechanisms for Coronary Artery Lesions in Kawasaki Disease.

BACKGROUND: Coronary artery lesions (CALs) are the most common and major complication of Kawasaki disease (KD) in developed countries. However, the underlying immunologic mechanisms of CAL development in KD remain unclear. METHODS: Here, we conducted single-cell transcriptome analyses of 212 210 peripheral blood mononuclear cells collected from a cross-sectional cohort of 16 children, including 4 patients with KD with CALs, 5 patients with KD without CALs, 4 healthy controls, and 3 febrile controls. RESULTS: KD altered the proportion of peripheral blood mononuclear cells, including an increasing trend in inflammatory cells (megakaryocytes and monocytes) and a decreasing trend in lymphocytes (eg, CD4+ T, CD8+ T, mucosal-associated invariant T, natural killer, and γδ T cells), highlighting the potential presence of lymphopenia phenomenon in KD. Our data indicated the presence of inflammatory cytokine storm in patients with KD with CALs, caused by systemic upregulation of TNFSF13B (tumor necrosis factor superfamily member 13b), CXCL16 (C-X-C motif chemokine ligand 16), TNFSF10 (tumor necrosis factor superfamily member 10), and IL1RN (interleukin 1 receptor antagonist), mainly produced by monocytes (especially for the Mono_CD14-CD16 cluster) and megakaryocytes. We also found that myeloid cells of patients with KD, particularly in those with CALs, might play a role in vascular injury (eg, increased MMP [matrix metalloproteinase] 9, MMP17, and MMP25) and immune cell recruitment. The immune landscape of patients with KD with CALs was featured by lower exhaustion levels in natural killer cells, a high cytotoxic state in the CD8_Pro cluster, and activation of the complement system in monocytes. Additionally, the activation of B cells was more pronounced in the early stage of KD. CONCLUSIONS: Collectively, this study provides a comprehensive understanding of the roles of various immune cells and inflammatory cytokine storms in the development of CALs in KD and offers a valuable resource for identifying novel therapeutic targets for patients with KD with CALs.

Comparative transcriptomic analysis provides insights into the genetic networks regulating oil differential production in oil crops.

BACKGROUND: Plants differ more than threefold in seed oil contents (SOCs). Soybean (Glycine max), cotton (Gossypium hirsutum), rapeseed (Brassica napus), and sesame (Sesamum indicum) are four important oil crops with markedly different SOCs and fatty acid compositions. RESULTS: Compared to grain crops like maize and rice, expanded acyl-lipid metabolism genes and relatively higher expression levels of genes involved in seed oil synthesis (SOS) in the oil crops contributed to the oil accumulation in seeds. Here, we conducted comparative transcriptomics on oil crops with two different SOC materials. In common, DIHYDROLIPOAMIDE DEHYDROGENASE, STEAROYL-ACYL CARRIER PROTEIN DESATURASE, PHOSPHOLIPID:DIACYLGLYCEROL ACYLTRANSFERASE, and oil-body protein genes were both differentially expressed between the high- and low-oil materials of each crop. By comparing functional components of SOS networks, we found that the strong correlations between genes in "glycolysis/gluconeogenesis" and "fatty acid synthesis" were conserved in both grain and oil crops, with PYRUVATE KINASE being the common factor affecting starch and lipid accumulation. Network alignment also found a conserved clique among oil crops affecting seed oil accumulation, which has been validated in Arabidopsis. Differently, secondary and protein metabolism affected oil synthesis to different degrees in different crops, and high SOC was due to less competition of the same precursors. The comparison of Arabidopsis mutants and wild type showed that CINNAMYL ALCOHOL DEHYDROGENASE 9, the conserved regulator we identified, was a factor resulting in different relative contents of lignins to oil in seeds. The interconnection of lipids and proteins was common but in different ways among crops, which partly led to differential oil production. CONCLUSIONS: This study goes beyond the observations made in studies of individual species to provide new insights into which genes and networks may be fundamental to seed oil accumulation from a multispecies perspective.

Expression analysis of genes including Zfhx4 in mice and zebrafish reveals a temporospatial conserved molecular basis underlying craniofacial development.

BACKGROUND: Embryonic craniofacial development involves several cellular and molecular events that are evolutionarily conserved among vertebrates. Vertebrate models such as mice and zebrafish have been used to investigate the molecular and cellular etiologies underlying human craniofacial disorders, including orofacial clefts. However, the molecular mechanisms underlying embryonic development in these two species are unknown. Therefore, elucidating the shared mechanisms of craniofacial development between disease models is crucial to understanding the underlying mechanisms of phenotypes in individual species. RESULTS: We selected mice and zebrafish as model organisms to compare various events during embryonic craniofacial development. We identified genes (Sox9, Zfhx3 and 4, Cjun, and Six1) exhibiting similar temporal expression patterns between these species through comprehensive and stage-matched gene expression analyses. Expression analysis revealed similar gene expression in hypothetically corresponding tissues, such as the mice palate and zebrafish ethmoid plate. Furthermore, loss-of-function analysis of Zfhx4/zfhx4, a causative gene of human craniofacial anomalies including orofacial cleft, in both species resulted in deformed skeletal elements such as the palatine and ethmoid plate in mice and zebrafish, respectively. CONCLUSIONS: These results demonstrate that these disease models share common molecular mechanisms, highlighting their usefulness in modeling craniofacial defects in humans.

Unraveling the Immune Signature of Herpes Zoster: Insights Into the Pathophysiology and Human Leukocyte Antigen Risk Profile.

The varicella-zoster virus (VZV) infects >95% of the population. VZV reactivation causes herpes zoster (HZ), known as shingles, primarily affecting the elderly and individuals who are immunocompromised. However, HZ can occur in otherwise healthy individuals. We analyzed the immune signature and risk profile in patients with HZ using a genome-wide association study across different UK Biobank HZ cohorts. Additionally, we conducted one of the largest HZ human leukocyte antigen association studies to date, coupled with transcriptomic analysis of pathways underlying HZ susceptibility. Our findings highlight the significance of the major histocompatibility complex locus for HZ development, identifying 5 protective and 4 risk human leukocyte antigen alleles. This demonstrates that HZ susceptibility is largely governed by variations in the major histocompatibility complex. Furthermore, functional analyses revealed the upregulation of type I interferon and adaptive immune responses. These findings provide fresh molecular insights into the pathophysiology and activation of innate and adaptive immune responses triggered by symptomatic VZV reactivation.

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.

DNA-protein quasi-mapping for rapid differential gene expression analysis in non-model organisms.

BACKGROUND: Conventional differential gene expression analysis pipelines for non-model organisms require computationally expensive transcriptome assembly. We recently proposed an alternative strategy of directly aligning RNA-seq reads to a protein database, and demonstrated drastic improvements in speed, memory usage, and accuracy in identifying differentially expressed genes. RESULT: Here we report a further speed-up by replacing DNA-protein alignment by quasi-mapping, making our pipeline > 1000× faster than assembly-based approach, and still more accurate. We also compare quasi-mapping to other mapping techniques, and show that it is faster but at the cost of sensitivity. CONCLUSION: We provide a quick-and-dirty differential gene expression analysis pipeline for non-model organisms without a reference transcriptome, which directly quasi-maps RNA-seq reads to a reference protein database, avoiding computationally expensive transcriptome assembly.

RoPE: A robust profile likelihood method for differential gene expression analysis.

Variation in RNA-Seq data creates modeling challenges for differential gene expression (DE) analysis. Statistical approaches address conventional small sample sizes and implement empirical Bayes or non-parametric tests, but frequently produce different conclusions. Increasing sample sizes enable proposal of alternative DE paradigms. Here we develop RoPE, which uses a data-driven adjustment for variation and a robust profile likelihood ratio DE test. Simulation studies show RoPE can have improved performance over existing tools as sample size increases and has the most reliable control of error rates. Application of RoPE demonstrates that an active Pseudomonas aeruginosa infection downregulates the SLC9A3 Cystic Fibrosis modifier gene.