3D reconstruction of a gastrulating human embryo.

Progress in understanding early human development has been impeded by the scarcity of reference datasets from natural embryos, particularly those with spatial information during crucial stages like gastrulation. We conducted high-resolution spatial transcriptomics profiling on 38,562 spots from 62 transverse sections of an intact Carnegie stage (CS) 8 human embryo. From this spatial transcriptomic dataset, we constructed a 3D model of the CS8 embryo, in which a range of cell subtypes are identified, based on gene expression patterns and positional register, along the anterior-posterior, medial-lateral, and dorsal-ventral axis in the embryo. We further characterized the lineage trajectories of embryonic and extra-embryonic tissues and associated regulons and the regionalization of signaling centers and signaling activities that underpin lineage progression and tissue patterning during gastrulation. Collectively, the findings of this study provide insights into gastrulation and post-gastrulation development of the human embryo.

A multimodal zebrafish developmental atlas reveals the state-transition dynamics of late-vertebrate pluripotent axial progenitors.

Elucidating organismal developmental processes requires a comprehensive understanding of cellular lineages in the spatial, temporal, and molecular domains. In this study, we introduce Zebrahub, a dynamic atlas of zebrafish embryonic development that integrates single-cell sequencing time course data with lineage reconstructions facilitated by light-sheet microscopy. This atlas offers high-resolution and in-depth molecular insights into zebrafish development, achieved through the sequencing of individual embryos across ten developmental stages, complemented by reconstructions of cellular trajectories. Zebrahub also incorporates an interactive tool to navigate the complex cellular flows and lineages derived from light-sheet microscopy data, enabling in silico fate-mapping experiments. To demonstrate the versatility of our multimodal resource, we utilize Zebrahub to provide fresh insights into the pluripotency of neuro-mesodermal progenitors (NMPs) and the origins of a joint kidney-hemangioblast progenitor population.

Single-cell atlas reveals correlates of high cognitive function, dementia, and resilience to Alzheimer's disease pathology.

Alzheimer's disease (AD) is the most common cause of dementia worldwide, but the molecular and cellular mechanisms underlying cognitive impairment remain poorly understood. To address this, we generated a single-cell transcriptomic atlas of the aged human prefrontal cortex covering 2.3 million cells from postmortem human brain samples of 427 individuals with varying degrees of AD pathology and cognitive impairment. Our analyses identified AD-pathology-associated alterations shared between excitatory neuron subtypes, revealed a coordinated increase of the cohesin complex and DNA damage response factors in excitatory neurons and in oligodendrocytes, and uncovered genes and pathways associated with high cognitive function, dementia, and resilience to AD pathology. Furthermore, we identified selectively vulnerable somatostatin inhibitory neuron subtypes depleted in AD, discovered two distinct groups of inhibitory neurons that were more abundant in individuals with preserved high cognitive function late in life, and uncovered a link between inhibitory neurons and resilience to AD pathology.

Differential encoding in prefrontal cortex projection neuron classes across cognitive tasks.

Single-cell transcriptomics has been widely applied to classify neurons in the mammalian brain, while systems neuroscience has historically analyzed the encoding properties of cortical neurons without considering cell types. Here we examine how specific transcriptomic types of mouse prefrontal cortex (PFC) projection neurons relate to axonal projections and encoding properties across multiple cognitive tasks. We found that most types projected to multiple targets, and most targets received projections from multiple types, except PFC→PAG (periaqueductal gray). By comparing Ca2+ activity of the molecularly homogeneous PFC→PAG type against two heterogeneous classes in several two-alternative choice tasks in freely moving mice, we found that all task-related signals assayed were qualitatively present in all examined classes. However, PAG-projecting neurons most potently encoded choice in cued tasks, whereas contralateral PFC-projecting neurons most potently encoded reward context in an uncued task. Thus, task signals are organized redundantly, but with clear quantitative biases across cells of specific molecular-anatomical characteristics.

Comprehensive Multi-omics Analysis Reveals Mitochondrial Stress as a Central Biological Hub for Spaceflight Impact.

Spaceflight is known to impose changes on human physiology with unknown molecular etiologies. To reveal these causes, we used a multi-omics, systems biology analytical approach using biomedical profiles from fifty-nine astronauts and data from NASA's GeneLab derived from hundreds of samples flown in space to determine transcriptomic, proteomic, metabolomic, and epigenetic responses to spaceflight. Overall pathway analyses on the multi-omics datasets showed significant enrichment for mitochondrial processes, as well as innate immunity, chronic inflammation, cell cycle, circadian rhythm, and olfactory functions. Importantly, NASA's Twin Study provided a platform to confirm several of our principal findings. Evidence of altered mitochondrial function and DNA damage was also found in the urine and blood metabolic data compiled from the astronaut cohort and NASA Twin Study data, indicating mitochondrial stress as a consistent phenotype of spaceflight.

Microglial Transcriptional Signatures in the Central Nervous System: Toward A Future of Unraveling Their Function in Health and Disease.

Microglia, the resident immune cells of the central nervous system (CNS), are primarily derived from the embryonic yolk sac and make their way to the CNS during early development. They play key physiological and immunological roles across the life span, throughout health, injury, and disease. Recent transcriptomic studies have identified gene transcript signatures expressed by microglia that may provide the foundation for unprecedented insights into their functions. Microglial gene expression signatures can help distinguish them from macrophage cell types to a reasonable degree of certainty, depending on the context. Microglial expression patterns further suggest a heterogeneous population comprised of many states that vary according to the spatiotemporal context. Microglial diversity is most pronounced during development, when extensive CNS remodeling takes place, and following disease or injury. A next step of importance for the field will be to identify the functional roles performed by these various microglial states, with the perspective of targeting them therapeutically.

Alternative splicing and cancer: insights, opportunities, and challenges from an expanding view of the transcriptome.

Over the past decade there has been increased awareness of the potential role of alternative splicing in the etiology of cancer. In particular, advances in RNA-Sequencing technology and analysis has led to a wave of discoveries in the last few years regarding the causes and functional relevance of alternative splicing in cancer. Here we discuss the current understanding of the connections between splicing and cancer, with a focus on the most recent findings. We also discuss remaining questions and challenges that must be addressed in order to use our knowledge of splicing to guide the diagnosis and treatment of cancer.

Misexpression of inactive genes in whole blood is associated with nearby rare structural variants.

Gene misexpression is the aberrant transcription of a gene in a context where it is usually inactive. Despite its known pathological consequences in specific rare diseases, we have a limited understanding of its wider prevalence and mechanisms in humans. To address this, we analyzed gene misexpression in 4,568 whole-blood bulk RNA sequencing samples from INTERVAL study blood donors. We found that while individual misexpression events occur rarely, in aggregate they were found in almost all samples and a third of inactive protein-coding genes. Using 2,821 paired whole-genome and RNA sequencing samples, we identified that misexpression events are enriched in cis for rare structural variants. We established putative mechanisms through which a subset of SVs lead to gene misexpression, including transcriptional readthrough, transcript fusions, and gene inversion. Overall, we develop misexpression as a type of transcriptomic outlier analysis and extend our understanding of the variety of mechanisms by which genetic variants can influence gene expression.

The central renin-angiotensin system: A genetic pathway, functional decoding, and selective target engagement characterization in humans.

Accumulating evidence suggests that the brain renin angiotensin system (RAS) plays a pivotal role in the regulation of cognition and behavior as well as in the neuropathology of neurological and mental disorders. The angiotensin II type 1 receptor (AT1R) mediates most functional and neuropathology-relevant actions associated with the central RAS. However, an overarching comprehension to guide translation and utilize the therapeutic potential of the central RAS in humans is currently lacking. We conducted a comprehensive characterization of the RAS using an innovative combination of transcriptomic gene expression mapping, image-based behavioral decoding, and pre-registered randomized controlled discovery-replication pharmacological resting-state functional magnetic resonance imaging (fMRI) trials (N = 132) with a selective AT1R antagonist. The AT1R exhibited a particular dense expression in a subcortical network encompassing the thalamus, striatum, and amygdalo-hippocampal formation. Behavioral decoding of the AT1R gene expression brain map showed an association with memory, stress, reward, and motivational processes. Transient pharmacological blockade of the AT1R further decreased neural activity in subcortical systems characterized by a high AT1R expression, while increasing functional connectivity in the cortico-basal ganglia-thalamo-cortical circuitry. Effects of AT1R blockade on the network level were specifically associated with the transcriptomic signatures of the dopaminergic, opioid, acetylcholine, and corticotropin-releasing hormone signaling systems. The robustness of the results was supported in an independent pharmacological fMRI trial. These findings present a biologically informed comprehensive characterization of the central AT1R pathways and their functional relevance on the neural and behavioral level in humans.

Haplotype-resolved genome assembly and implementation of VitExpress, an open interactive transcriptomic platform for grapevine.

Haplotype-resolved genome assemblies were produced for Chasselas and Ugni Blanc, two heterozygous Vitis vinifera cultivars by combining high-fidelity long-read sequencing and high-throughput chromosome conformation capture (Hi-C). The telomere-to-telomere full coverage of the chromosomes allowed us to assemble separately the two haplo-genomes of both cultivars and revealed structural variations between the two haplotypes of a given cultivar. The deletions/insertions, inversions, translocations, and duplications provide insight into the evolutionary history and parental relationship among grape varieties. Integration of de novo single long-read sequencing of full-length transcript isoforms (Iso-Seq) yielded a highly improved genome annotation. Given its higher contiguity, and the robustness of the IsoSeq-based annotation, the Chasselas assembly meets the standard to become the annotated reference genome for V. vinifera. Building on these resources, we developed VitExpress, an open interactive transcriptomic platform, that provides a genome browser and integrated web tools for expression profiling, and a set of statistical tools (StatTools) for the identification of highly correlated genes. Implementation of the correlation finder tool for MybA1, a major regulator of the anthocyanin pathway, identified candidate genes associated with anthocyanin metabolism, whose expression patterns were experimentally validated as discriminating between black and white grapes. These resources and innovative tools for mining genome-related data are anticipated to foster advances in several areas of grapevine research.

Ageing induces tissue-specific transcriptomic changes in Caenorhabditis elegans.

Ageing is a complex process with common and distinct features across tissues. Unveiling the underlying processes driving ageing in individual tissues is indispensable to decipher the mechanisms of organismal longevity. Caenorhabditis elegans is a well-established model organism that has spearheaded ageing research with the discovery of numerous genetic pathways controlling its lifespan. However, it remains challenging to dissect the ageing of worm tissues due to the limited description of tissue pathology and access to tissue-specific molecular changes during ageing. In this study, we isolated cells from five major tissues in young and old worms and profiled the age-induced transcriptomic changes within these tissues. We observed a striking diversity of ageing across tissues and identified different sets of longevity regulators therein. In addition, we found novel tissue-specific factors, including irx-1 and myrf-2, which control the integrity of the intestinal barrier and sarcomere structure during ageing respectively. This study demonstrates the complexity of ageing across worm tissues and highlights the power of tissue-specific transcriptomic profiling during ageing, which can serve as a resource to the field.

High-throughput transcriptome analyses from ASPIRO, a phase 1/2/3 study of gene replacement therapy for X-linked myotubular myopathy.

X-linked myotubular myopathy (XLMTM) is a severe congenital disease characterized by profound muscle weakness, respiratory failure, and early death. No approved therapy for XLMTM is currently available. Adeno-associated virus (AAV)-mediated gene replacement therapy has shown promise as an investigational therapeutic strategy. We aimed to characterize the transcriptomic changes in muscle biopsies of individuals with XLMTM who received resamirigene bilparvovec (AT132; rAAV8-Des-hMTM1) in the ASPIRO clinical trial and to identify potential biomarkers that correlate with therapeutic outcome. We leveraged RNA-sequencing data from the muscle biopsies of 15 study participants and applied differential expression analysis, gene co-expression analysis, and machine learning to characterize the transcriptomic changes at baseline (pre-dose) and at 24 and 48 weeks after resamirigene bilparvovec dosing. As expected, MTM1 expression levels were significantly increased after dosing (p < 0.0001). Differential expression analysis identified upregulated genes after dosing that were enriched in several pathways, including lipid metabolism and inflammatory response pathways, and downregulated genes were enriched in cell-cell adhesion and muscle development pathways. Genes involved in inflammatory and immune pathways were differentially expressed between participants exhibiting ventilator support reduction of either greater or less than 6 h/day after gene therapy compared to pre-dosing. Co-expression analysis identified similarly regulated genes, which were grouped into modules. Finally, the machine learning model identified five genes, including MTM1, as potential RNA biomarkers to monitor the progress of AAV gene replacement therapy. These findings further extend our understanding of AAV-mediated gene therapy in individuals with XLMTM at the transcriptomic level.

Should we really use graph neural networks for transcriptomic prediction?

The recent development of deep learning methods have undoubtedly led to great improvement in various machine learning tasks, especially in prediction tasks. This type of methods have also been adapted to answer various problems in bioinformatics, including automatic genome annotation, artificial genome generation or phenotype prediction. In particular, a specific type of deep learning method, called graph neural network (GNN) has repeatedly been reported as a good candidate to predict phenotypes from gene expression because its ability to embed information on gene regulation or co-expression through the use of a gene network. However, up to date, no complete and reproducible benchmark has ever been performed to analyze the trade-off between cost and benefit of this approach compared to more standard (and simpler) machine learning methods. In this article, we provide such a benchmark, based on clear and comparable policies to evaluate the different methods on several datasets. Our conclusion is that GNN rarely provides a real improvement in prediction performance, especially when compared to the computation effort required by the methods. Our findings on a limited but controlled simulated dataset shows that this could be explained by the limited quality or predictive power of the input biological gene network itself.

A robust statistical approach for finding informative spatially associated pathways.

Spatial transcriptomics offers deep insights into cellular functional localization and communication by mapping gene expression to spatial locations. Traditional approaches that focus on selecting spatially variable genes often overlook the complexity of biological pathways and the interactions among genes. Here, we introduce a novel framework that shifts the focus towards directly identifying functional pathways associated with spatial variability by adapting the Brownian distance covariance test in an innovative manner to explore the heterogeneity of biological functions over space. Unlike most other methods, this statistical testing approach is free of gene selection and parameter selection and allows nonlinear and complex dependencies. It allows for a deeper understanding of how cells coordinate their activities across different spatial domains through biological pathways. By analyzing real human and mouse datasets, the method found significant pathways that were associated with spatial variation, as well as different pathway patterns among inner- and edge-cancer regions. This innovative framework offers a new perspective on analyzing spatial transcriptomic data, contributing to our understanding of tissue architecture and disease pathology. The implementation is publicly available at https://github.com/tianlq-prog/STpathway.

SEGCECO: Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication.

Recent advances in single-cell RNA sequencing technology have eased analyses of signaling networks of cells. Recently, cell-cell interaction has been studied based on various link prediction approaches on graph-structured data. These approaches have assumptions about the likelihood of node interaction, thus showing high performance for only some specific networks. Subgraph-based methods have solved this problem and outperformed other approaches by extracting local subgraphs from a given network. In this work, we present a novel method, called Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication (SEGCECO), which uses an attributed graph convolutional neural network to predict cell-cell communication from single-cell RNA-seq data. SEGCECO captures the latent and explicit attributes of undirected, attributed graphs constructed from the gene expression profile of individual cells. High-dimensional and sparse single-cell RNA-seq data make converting the data into a graphical format a daunting task. We successfully overcome this limitation by applying SoptSC, a similarity-based optimization method in which the cell-cell communication network is built using a cell-cell similarity matrix which is learned from gene expression data. We performed experiments on six datasets extracted from the human and mouse pancreas tissue. Our comparative analysis shows that SEGCECO outperforms latent feature-based approaches, and the state-of-the-art method for link prediction, WLNM, with 0.99 ROC and 99% prediction accuracy. The datasets can be found at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84133 and the code is publicly available at Github https://github.com/sheenahora/SEGCECO and Code Ocean https://codeocean.com/capsule/8244724/tree.

Blood Monocyte Phenotype Is A Marker of Cardiovascular Risk in Type 2 Diabetes.

BACKGROUND: Diabetes is a major risk factor for atherosclerotic cardiovascular diseases with a 2-fold higher risk of cardiovascular events in people with diabetes compared with those without. Circulating monocytes are inflammatory effector cells involved in both type 2 diabetes (T2D) and atherogenesis. METHODS: We investigated the relationship between circulating monocytes and cardiovascular risk progression in people with T2D, using phenotypic, transcriptomic, and metabolomic analyses. cardiovascular risk progression was estimated with coronary artery calcium score in a cohort of 672 people with T2D. RESULTS: Coronary artery calcium score was positively correlated with blood monocyte count and frequency of the classical monocyte subtype. Unsupervised k-means clustering based on monocyte subtype profiles revealed 3 main endotypes of people with T2D at varying risk of cardiovascular events. These observations were confirmed in a validation cohort of 279 T2D participants. The predictive association between monocyte count and major adverse cardiovascular events was validated through an independent prospective cohort of 757 patients with T2D. Integration of monocyte transcriptome analyses and plasma metabolomes showed a disruption of mitochondrial pathways (tricarboxylic acid cycle, oxidative phosphorylation pathway) that underlined a proatherogenic phenotype. CONCLUSIONS: In this study, we provide evidence that frequency and monocyte phenotypic profile are closely linked to cardiovascular risk in patients with T2D. The assessment of monocyte frequency and count is a valuable predictive marker for risk of cardiovascular events in patients with T2D. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04353869.

Constitutive expression of apple endo-POLYGALACTURONASE1 in fruit induces early maturation, alters skin structure and accelerates softening.

During fruit ripening, polygalacturonases (PGs) are key contributors to the softening process in many species. Apple is a crisp fruit that normally exhibits only minor changes to cell walls and limited fruit softening. Here, we explore the effects of PG overexpression during fruit development using transgenic apple lines overexpressing the ripening-related endo-POLYGALACTURONASE1 gene. MdPG1-overexpressing (PGox) fruit displayed early maturation/ripening with black seeds, conversion of starch to sugars and ethylene production occurring by 80 days after pollination (DAP). PGox fruit exhibited a striking, white-skinned phenotype that was evident from 60 DAP and most likely resulted from increased air spaces and separation of cells in the hypodermis due to degradation of the middle lamellae. Irregularities in the integrity of the epidermis and cuticle were also observed. By 120 DAP, PGox fruit cracked and showed lenticel-associated russeting. Increased cuticular permeability was associated with microcracks in the cuticle around lenticels and was correlated with reduced cortical firmness at all time points and extensive post-harvest water loss from the fruit, resulting in premature shrivelling. Transcriptomic analysis suggested that early maturation was associated with upregulation of genes involved in stress responses, and overexpression of MdPG1 also altered the expression of genes involved in cell wall metabolism (e.g. ß-galactosidase, MD15G1221000) and ethylene biosynthesis (e.g. ACC synthase, MD14G1111500). The results show that upregulation of PG not only has dramatic effects on the structure of the fruit outer cell layers, indirectly affecting water status and turgor, but also has unexpected consequences for fruit development.

A novel type of malformed floral organs mutant in barley was conferred by loss-of-function mutations of the MADS-box gene HvAGL6.

The advanced model of floral morphogenesis is based largely on data from Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa), but this process is less well understood in the Triticeae. Here, we investigated a sterile barley (Hordeum vulgare) mutant with malformed floral organs (designated mfo1), of which the paleae, lodicules, and stamens in each floret were all converted into lemma-like organs, and the ovary was abnormally shaped. Combining bulked-segregant analysis, whole-genome resequencing, and TILLING approaches, the mfo1 mutant was attributed to loss-of-function mutations in the MADS-box transcription factor gene HvAGL6, a key regulator in the ABCDE floral morphogenesis model. Through transcriptomic analysis between young inflorescences of wild-type and mfo1 plants, 380 genes were identified as differentially expressed, most of which function in DNA binding, protein dimerization, cell differentiation, or meristem determinacy. Regulatory pathway enrichment showed HvAGL6 associates with transcriptional abundance of many MADS-box genes, including the B-class gene HvMADS4. Mutants with deficiency in HvMADS4 exhibited the conversion of stamens into supernumerary pistils, producing multiple ovaries resembling the completely sterile multiple ovaries 3.h (mov3.h) mutant. These findings demonstrate that the regulatory model of floral morphogenesis is conserved across plant species and provides insights into the interactions between HvAGL6 and other MADS-box regulators.

Inter-species Transcriptomic Analysis Reveals a Constitutive Adaptation Against Oxidative Stress for the Highly Virulent Leptospira Species.

Transcriptomic analyses across large scales of evolutionary distance have great potential to shed light on regulatory evolution but are complicated by difficulties in establishing orthology and limited availability of accessible software. We introduce here a method and a graphical user interface wrapper, called Annotator-RNAtor, for performing interspecies transcriptomic analysis and studying intragenus evolution. The pipeline uses third-party software to infer homologous genes in various species and highlight differences in the expression of the core-genes. To illustrate the methodology and demonstrate its usefulness, we focus on the emergence of the highly virulent Leptospira subclade known as P1+, which includes the causative agents of leptospirosis. Here, we expand on the genomic study through the comparison of transcriptomes between species from P1+ and their related P1- counterparts (low-virulent pathogens). In doing so, we shed light on differentially expressed pathways and focused on describing a specific example of adaptation based on a differential expression of PerRA-controlled genes. We showed that P1+ species exhibit higher expression of the katE gene, a well-known virulence determinant in pathogenic Leptospira species correlated with greater tolerance to peroxide. Switching PerRA alleles between P1+ and P1- species demonstrated that the lower repression of katE and greater tolerance to peroxide in P1+ species was solely controlled by PerRA and partly caused by a PerRA amino-acid permutation. Overall, these results demonstrate the strategic fit of the methodology and its ability to decipher adaptive transcriptomic changes, not observable by comparative genome analysis, that may have been implicated in the emergence of these pathogens.

Genetics-informed precision treatment formulation in schizophrenia and bipolar disorder.

Genetically informed drug development and repurposing is an attractive prospect for improving patient outcomes in psychiatry; however, the effectiveness of these endeavors is confounded by heterogeneity. We propose an approach that links interventions implicated by disorder-associated genetic risk, at the population level, to a framework that can target these compounds to individuals. Specifically, results from genome-wide association studies are integrated with expression data to prioritize individual "directional anchor" genes for which the predicted risk-increasing direction of expression could be counteracted by an existing drug. While these compounds represent plausible therapeutic candidates, they are not likely to be equally efficacious for all individuals. To account for this heterogeneity, we constructed polygenic scores restricted to variants annotated to the network of genes that interact with each directional anchor gene. These metrics, which we call a pharmagenic enrichment score (PES), identify individuals with a higher burden of genetic risk, localized in biological processes related to the candidate drug target, to inform precision drug repurposing. We used this approach to investigate schizophrenia and bipolar disorder and reveal several compounds targeting specific directional anchor genes that could be plausibly repurposed. These genetic risk scores, mapped to the networks associated with target genes, revealed biological insights that cannot be observed in undifferentiated genome-wide polygenic risk score (PRS). For example, an enrichment of these partitioned scores in schizophrenia cases with otherwise low PRS. In summary, genetic risk could be used more specifically to direct drug repurposing candidates that target particular genes implicated in psychiatric and other complex disorders.