Temporal Dynamics Of Gene Expression Research Articles

STIGMA: Single-cell tissue-specific gene prioritization using machine learning

Clinical exome and genome sequencing have revolutionized the understanding of human disease genetics. Yet many genes remain functionally uncharacterized, complicating the establishment of causal disease links for genetic variants. While several scoring methods have been devised to prioritize these candidate genes, these methods fall short of capturing the expression heterogeneity across cell subpopulations within tissues. Here, we introduce single-cell tissue-specific gene prioritization using machine learning (STIGMA), an approach that leverages single-cell RNA-seq (scRNA-seq) data to prioritize candidate genes associated with rare congenital diseases. STIGMA prioritizes genes by learning the temporal dynamics of gene expression across cell types during healthy organogenesis. To assess the efficacy of our framework, we applied STIGMA to mouse limb and human fetal heart scRNA-seq datasets. In a cohort of individuals with congenital limb malformation, STIGMA prioritized 469 variants in 345 genes, with UBA2 as a notable example. For congenital heart defects, we detected 34 genes harboring nonsynonymous de novo variants (nsDNVs) in two or more individuals from a set of 7,958 individuals, including the ortholog of Prdm1, which is associated with hypoplastic left ventricle and hypoplastic aortic arch. Overall, our findings demonstrate that STIGMA effectively prioritizes tissue-specific candidate genes by utilizing single-cell transcriptome data. The ability to capture the heterogeneity of gene expression across cell populations makes STIGMA a powerful tool for the discovery of disease-associated genes and facilitates the identification of causal variants underlying human genetic disorders.

Deciphering temporal gene expression dynamics in multiple coral species exposed to heat stress: Implications for predicting resilience

Coral reefs are facing unprecedented threats due to global climate change, particularly elevated sea surface temperatures causing coral bleaching. Understanding coral responses at the molecular level is crucial for predicting their resilience and developing effective conservation strategies. In this study, we conducted a comprehensive gene expression analysis of four coral species to investigate their long-term molecular response to heat stress. We identified distinct gene expression patterns among the coral species, with laminar corals exhibiting a stronger response compared to branching corals. Heat shock proteins (HSPs) showed an overall decreasing expression trend, indicating the high energy cost associated with sustaining elevated HSP levels during prolonged heat stress. Peroxidases and oxidoreductases involved in oxidative stress response demonstrated significant upregulation, highlighting their role in maintaining cellular redox balance. Differential expression of genes related to calcium homeostasis and bioluminescence suggested distinct mechanisms for coping with heat stress among the coral species. Furthermore, the impact of heat stress on coral biomineralization varied, with downregulation of carbonic anhydrase and skeletal organic matrix proteins indicating reduced capacity for biomineralization in the later stages of heat stress. Our findings provide insights into the molecular mechanisms underlying coral responses to heat stress and highlight the importance of considering species-specific responses in assessing coral resilience. The identified biomarkers may serve as indicators of heat stress and contribute to early detection of coral bleaching events. These findings contribute to our understanding of coral resilience and provide a basis for future research aimed at enhancing coral survival in the face of climate change.

The Expression of Triticum aestivum Cysteine-Rich Receptor-like Protein Kinase Genes during Leaf Rust Fungal Infection.

Understanding the role of cysteine-rich receptor-like kinases (CRKs) in plant defense mechanisms is crucial for enhancing wheat resistance to leaf rust fungus infection. Here, we identified and verified 164 members of the CRK gene family using the Triticum aestivum reference version 2 collected from the international wheat genome sequencing consortium (IWGSC). The proteins exhibited characteristic features of CRKs, including the presence of signal peptides, cysteine-rich/stress antifungal/DUF26 domains, transmembrane domains, and Pkinase domains. Phylogenetic analysis revealed extensive diversification within the wheat CRK gene family, indicating the development of distinct specific functional roles to wheat plants. When studying the expression of the CRK gene family in near-isogenic lines (NILs) carrying Lr57- and Lr14a-resistant genes, Puccinia triticina, the causal agent of leaf rust fungus, triggered temporal gene expression dynamics. The upregulation of specific CRK genes in the resistant interaction indicated their potential role in enhancing wheat resistance to leaf rust, while contrasting gene expression patterns in the susceptible interaction highlighted potential susceptibility associated CRK genes. The study uncovered certain CRK genes that exhibited expression upregulation upon leaf rust infection and the Lr14a-resistant gene. The findings suggest that targeting CRKs may present a promising strategy for improving wheat resistance to rust diseases.

GrandR: a comprehensive package for nucleotide conversion RNA-seq data analysis

Metabolic labeling of RNA is a powerful technique for studying the temporal dynamics of gene expression. Nucleotide conversion approaches greatly facilitate the generation of data but introduce challenges for their analysis. Here we present grandR, a comprehensive package for quality control, differential gene expression analysis, kinetic modeling, and visualization of such data. We compare several existing methods for inference of RNA synthesis rates and half-lives using progressive labeling time courses. We demonstrate the need for recalibration of effective labeling times and introduce a Bayesian approach to study the temporal dynamics of RNA using snapshot experiments.

ESPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio–temporal architectures of cells

BackgroundBioinformatics capability to analyze spatio–temporal dynamics of gene expression is essential in understanding animal development. Animal cells are spatially organized as functional tissues where cellular gene expression data contain information that governs morphogenesis during the developmental process. Although several computational tissue reconstruction methods using transcriptomics data have been proposed, those methods have been ineffective in arranging cells in their correct positions in tissues or organs unless spatial information is explicitly provided.ResultsThis study demonstrates stochastic self-organizing map clustering with Markov chain Monte Carlo calculations for optimizing informative genes effectively reconstruct any spatio–temporal topology of cells from their transcriptome profiles with only a coarse topological guideline. The method, eSPRESSO (enhanced SPatial REconstruction by Stochastic Self-Organizing Map), provides a powerful in silico spatio–temporal tissue reconstruction capability, as confirmed by using human embryonic heart and mouse embryo, brain, embryonic heart, and liver lobule with generally high reproducibility (average max. accuracy = 92.0%), while revealing topologically informative genes, or spatial discriminator genes. Furthermore, eSPRESSO was used for temporal analysis of human pancreatic organoids to infer rational developmental trajectories with several candidate ‘temporal’ discriminator genes responsible for various cell type differentiations.ConclusionseSPRESSO provides a novel strategy for analyzing mechanisms underlying the spatio–temporal formation of cellular organizations.

Age-related telomere attrition in the human putamen.

Age is a major risk factor for neurodegenerative diseases. Shortening of leucocyte telomeres with advancing age, arguably a measure of "biological" age, is a known phenomenon and epidemiologically correlated with age-related disease. The main mechanism of telomere shortening is cell division, rendering telomere length in post-mitotic cells presumably stable. Longitudinal measurement of human brain telomere length is not feasible, and cross-sectional cortical brain samples so far indicated no attrition with age. Hence, age-related changes in telomere length in the brain and the association between telomere length and neurodegenerative diseases remain unknown. Here, we demonstrate that mean telomere length in the putamen, a part of the basal ganglia, physiologically shortens with age, like leukocyte telomeres. This was achieved by using matched brain and leukocyte-rich spleen samples from 98 post-mortem healthy human donors. Using spleen telomeres as a reference, we further found that mean telomere length was brain region-specific, as telomeres in the putamen were significantly shorter than in the cerebellum. Expression analyses of genes involved in telomere length regulation and oxidative phosphorylation revealed that both region- and age-dependent expression pattern corresponded with region-dependent telomere length dynamics. Collectively, our results indicate that mean telomere length in the human putamen physiologically shortens with advancing age and that both local and temporal gene expression dynamics correlate with this, pointing at a potential mechanism for the selective, age-related vulnerability of the nigro-striatal network.

Robust classification of wound healing stages in both mice and humans for acute and burn wounds based on transcriptomic data

BackgroundWound healing involves careful coordination among various cell types carrying out unique or even multifaceted functions. The abstraction of this complex dynamic process into four primary wound stages is essential to the study of wound care for timing treatment and tracking wound progression. For example, a treatment that may promote healing in the inflammatory stage may prove detrimental in the proliferative stage. Additionally, the time scale of individual responses varies widely across and within the same species. Therefore, a robust method to assess wound stages can help advance translational work from animals to humans.ResultsIn this work, we present a data-driven model that robustly identifies the dominant wound healing stage using transcriptomic data from biopsies gathered from mouse and human wounds, both burn and surgical. A training dataset composed of publicly available transcriptomic arrays is used to derive 58 shared genes that are commonly differentially expressed. They are divided into 5 clusters based on temporal gene expression dynamics. The clusters represent a 5-dimensional parametric space containing the wound healing trajectory. We then create a mathematical classification algorithm in the 5-dimensional space and demonstrate that it can distinguish between the four stages of wound healing: hemostasis, inflammation, proliferation, and remodeling.ConclusionsIn this work, we present an algorithm for wound stage detection based on gene expression. This work suggests that there are universal characteristics of gene expression in wound healing stages despite the seeming disparities across species and wounds. Our algorithm performs well for human and mouse wounds of both burn and surgical types. The algorithm has the potential to serve as a diagnostic tool that can advance precision wound care by providing a way of tracking wound healing progression with more accuracy and finer temporal resolution compared to visual indicators. This increases the potential for preventive action.

Monocyte, neutrophil, and whole blood transcriptome dynamics following ischemic stroke

BackgroundAfter ischemic stroke (IS), peripheral leukocytes infiltrate the damaged region and modulate the response to injury. Peripheral blood cells display distinctive gene expression signatures post-IS and these transcriptional programs reflect changes in immune responses to IS. Dissecting the temporal dynamics of gene expression after IS improves our understanding of immune and clotting responses at the molecular and cellular level that are involved in acute brain injury and may assist with time-targeted, cell-specific therapy.MethodsThe transcriptomic profiles from peripheral monocytes, neutrophils, and whole blood from 38 ischemic stroke patients and 18 controls were analyzed with RNA-seq as a function of time and etiology after stroke. Differential expression analyses were performed at 0–24 h, 24–48 h, and >48 h following stroke.ResultsUnique patterns of temporal gene expression and pathways were distinguished for monocytes, neutrophils, and whole blood with enrichment of interleukin signaling pathways for different time points and stroke etiologies. Compared to control subjects, gene expression was generally upregulated in neutrophils and generally downregulated in monocytes over all times for cardioembolic, large vessel, and small vessel strokes. Self-organizing maps identified gene clusters with similar trajectories of gene expression over time for different stroke causes and sample types. Weighted Gene Co-expression Network Analyses identified modules of co-expressed genes that significantly varied with time after stroke and included hub genes of immunoglobulin genes in whole blood.ConclusionsAltogether, the identified genes and pathways are critical for understanding how the immune and clotting systems change over time after stroke. This study identifies potential time- and cell-specific biomarkers and treatment targets.

Epigenomic dynamics of early Xenopus Embryos.

How the embryonic genome regulates accessibility to transcription factors is one of the major questions in understanding the spatial and temporal dynamics of gene expression during embryogenesis. Epigenomic analyses of embryonic chromatin provide molecular insights into cell-specific gene activities and genomic architectures. In recent years, significant advances have been made to elucidate the dynamic changes behind the activation of the zygotic genome in various model organisms. Here we provide an overview of the recent epigenomic studies pertaining to early Xenopus development.

Stability of scRNA-Seq Analysis Workflows is Susceptible to Preprocessing and is Mitigated by Regularized or Supervised Approaches.

Background:Statistical methods developed to address various questions in single-cell datasets show increased variability to different parameter regimes. In order to delineate further the robustness of commonly utilized methods for single-cell RNA-Seq, we aimed to comprehensively review scRNA-Seq analysis workflows in the setting of dimension reduction, clustering, and trajectory inference.Methods:We utilized datasets with temporal single-cell transcriptomics profiles from public repositories. Combining multiple methods at each level of the workflow, we have performed over 6k analysis and evaluated the results of clustering and pseudotime estimation using adjusted rand index and rank correlation metrics. We have further integrated neural network methods to assess whether models with increased complexity can show increased bias/variance trade-off.Results:Combinatorial workflows showed that utilizing non-linear dimension reduction techniques such as t-SNE and UMAP are sensitive to initial preprocessing steps hence clustering results on dimension reduced space of single-cell datasets should be utilized carefully. Similarly, pseudotime estimation methods that depend on previous non-linear dimension reduction steps can result in highly variable trajectories. In contrast, methods that avoid non-linearity such as WOT can result in repeatable inferences of temporal gene expression dynamics. Furthermore, imputation methods do not improve clustering or trajectory inference results substantially in terms of repeatability. In contrast, the selection of the normalization method shows an increased effect on downstream analysis where ScTransform reduces variability overall.

Comparative Analysis of the Apple Root Transcriptome as Affected by Rootstock Genotype and Brassicaceae Seed Meal Soil Amendment: Implications for Plant Health.

Brassicaceae seed meal (SM) soil amendment has been utilized as an effective strategy to control the biological complex of organisms, which includes oomycetes, fungi, and parasitic nematodes, that incites the phenomenon termed apple replant disease. Soil-borne disease control attained in response to Brassicaceae SM amendment is reliant on multiple chemical and biological attributes, including specific SM-generated modifications to the soil/rhizosphere microbiome. In this study, we conducted a comparative analyses of apple root gene expression as influenced by rootstock genotype combined with a seed meal (SM) soil amendment. Apple replant disease (ARD) susceptible (M.26) and tolerant (G.210) rootstocks cultivated in SM-amended soil exhibited differential gene expression relative to corresponding non-treated control (NTC) orchard soil. The temporal dynamics of gene expression indicated that the SM-amended soil system altered the trajectory of the root transcriptome in a genotype-specific manner. In both genotypes, the expression of genes related to plant defense and hormone signaling were altered in SM-amended soil, suggesting SM-responsive phytohormone regulation. Altered gene expression was temporally associated with changes in rhizosphere microbiome density and composition in the SM-treated soil. Gene expression analysis across the two rootstocks cultivated in the pathogen-infested NTC soil showed genotype-specific responses indicative of different defensive strategies. These results are consistent with previously described resistance mechanisms of ARD “tolerant” rootstock cultivars and also add to our understanding of the multiple mechanisms by which SM soil amendment and the resulting rhizosphere microbiome affect apple rootstock physiology. Future studies which assess transcriptomic and metagenomic data in parallel will be important for illuminating important connections between specific rhizosphere microbiota, gene-regulation, and plant health.

Integrative genomic analysis of early neurogenesis reveals a temporal genetic program for differentiation and specification of preplate and Cajal-Retzius neurons.

Neurogenesis in the developing neocortex begins with the generation of the preplate, which consists of early-born neurons including Cajal-Retzius (CR) cells and subplate neurons. Here, utilizing the Ebf2-EGFP transgenic mouse in which EGFP initially labels the preplate neurons then persists in CR cells, we reveal the dynamic transcriptome profiles of early neurogenesis and CR cell differentiation. Genome-wide RNA-seq and ChIP-seq analyses at multiple early neurogenic stages have revealed the temporal gene expression dynamics of early neurogenesis and distinct histone modification patterns in early differentiating neurons. We have identified a new set of coding genes and lncRNAs involved in early neuronal differentiation and validated with functional assays in vitro and in vivo. In addition, at E15.5 when Ebf2-EGFP+ cells are mostly CR neurons, single-cell sequencing analysis of purified Ebf2-EGFP+ cells uncovers molecular heterogeneities in CR neurons, but without apparent clustering of cells with distinct regional origins. Along a pseudotemporal trajectory these cells are classified into three different developing states, revealing genetic cascades from early generic neuronal differentiation to late fate specification during the establishment of CR neuron identity and function. Our findings shed light on the molecular mechanisms governing the early differentiation steps during cortical development, especially CR neuron differentiation.

Abstract P744: Gene Transcript Clusters Distinguish Time-Dependent Expression Patterns in Monocytes, Neutrophils and Whole Blood After Ischemic Stroke Injury

Introduction: After ischemic stroke (IS), peripheral leukocytes infiltrate the damaged region and modulate the response to injury. We previously showed that peripheral blood cells display different gene expression profiles after IS and these transcriptional programs reflect the changes in immune processes in response to IS. Dissecting the temporal dynamics of gene expression after IS improves our understanding of the changes of molecular and cellular pathways involved in acute brain injury. Methods: We analyzed the transcriptomic profiles of 33 IS patients in isolated monocytes, neutrophils and whole blood. RNA-sequencing was performed on all the stroke samples as well as 12 controls with vascular risk factors (diabetes and/or hypertension and/or hypercholesterolemia). To identify differentially expressed genes, subjects were split into time points (TPs) from stroke onset (TP1= 0-24 h; TP2= 24-48 h; and TP3= > 48 h), and controls were assigned TP0. A linear regression model including time and the interaction of diagnosis x TP with cutoff of p<0.02 and fold-change>|1.2| was used. Time dependent changes were analyzed using artificial neural networks to identify clusters of genes that behave in a similar way across TPs. Results: Unique patterns of temporal expression were distinguished for the three sample types. These include genes not expressed in TP0 that peak only within the first 24 h, others that peak or decrease in TP2 and TP3, and more complex patterns. Genes that peak at TP1 in monocytes and neutrophils are related to cell adhesion and leukocyte differentiation/migration, respectively. Early peaks in whole blood occur in genes related to transcriptional regulation. In monocytes, interleukin pathways are enriched across all TPs, whereas there is a trend of suppression after 24 h in neutrophils. The inflammasome pathway is enriched in the earlier TPs in neutrophils, while not enriched in monocytes until over 48 hours. Conclusion: Our analyses on gene expression dynamics and cluster patterns allow identification of key genes and pathways at different time points following ischemic injury that are valuable as IS biomarkers and may be possible treatment targets.

A sensitive mNeonGreen reporter system to measure transcriptional dynamics in Drosophila development

The gene regulatory network governing anterior–posterior axis formation in Drosophila is a well-established paradigm to study transcription in developmental biology. The rapid temporal dynamics of gene expression during early stages of development, however, are difficult to track with standard techniques. We optimized the bright and fast-maturing fluorescent protein mNeonGreen as a real-time, quantitative reporter of enhancer expression. We derive enhancer activity from the reporter fluorescence dynamics with high spatial and temporal resolution, using a robust reconstruction algorithm. By comparing our results with data obtained with the established MS2-MCP system, we demonstrate the higher detection sensitivity of our reporter. We used the reporter to quantify the activity of variants of a simple synthetic enhancer, and observe increased activity upon reduction of enhancer–promoter distance or addition of binding sites for the pioneer transcription factor Zelda. Our reporter system constitutes a powerful tool to study spatio-temporal gene expression dynamics in live embryos.

A Comprehensive Online Database for Exploring ∼20,000 Public Arabidopsis RNA-Seq Libraries

A Comprehensive Online Database for Exploring ∼20,000 Public Arabidopsis RNA-Seq Libraries

Single cell RNA-sequencing identified Dec2 as a suppressive factor for spermatogonial differentiation by inhibiting Sohlh1 expression

Gonocyte-to-spermatogonia transition is a critical fate determination process to initiate sperm production throughout the lifecycle. However, the molecular dynamics of this process has not been fully elucidated mainly due to the asynchronized differentiation stages of neonatal germ cells. In this study, we employed single cell RNA sequencing analyses of P1.5–5.5 germ cells to clarify the temporal dynamics of gene expression during gonocyte-to-spermatogonia transition. The analyses identified transcriptional modules, one of which regulates spermatogonial gene network in neonatal germ cells. Among them, we identified Dec2, a bHLH-type transcription factor, as a transcriptional repressor for a spermatogonial differentiation factor Sohlh1. Deficiency of Dec2 in mice induces significant reduction of undifferentiated spermatogonia, and transplantation assay using Dec2-depleted cells also demonstrated the impaired efficiency of engraftment, suggesting its role in maintaining spermatogonial stem cells (SSCs). Collectively, this study revealed the intrinsic role of a new SSC factor Dec2, which protects germ cells from inadequate differentiation during neonatal testis development.

Single cell transcriptomics reveals spatial and temporal dynamics of gene expression in the developing mouse spinal cord.

ABSTRACTThe coordinated spatial and temporal regulation of gene expression in the vertebrate neural tube determines the identity of neural progenitors and the function and physiology of the neurons they generate. Progress has been made deciphering the gene regulatory programmes that are responsible for this process; however, the complexity of the tissue has hampered the systematic analysis of the network and the underlying mechanisms. To address this, we used single cell mRNA sequencing to profile cervical and thoracic regions of the developing mouse neural tube between embryonic days 9.5-13.5. We confirmed that the data accurately recapitulates neural tube development, allowing us to identify new markers for specific progenitor and neuronal populations. In addition, the analysis highlighted a previously underappreciated temporal component to the mechanisms that generate neuronal diversity, and revealed common features in the sequence of transcriptional events that lead to the differentiation of specific neuronal subtypes. Together, the data offer insight into the mechanisms that are responsible for neuronal specification and provide a compendium of gene expression for classifying spinal cord cell types that will support future studies of neural tube development, function and disease.

CpG island composition differences are a source of gene expression noise indicative of promoter responsiveness

BackgroundPopulation phenotypic variation can arise from genetic differences between individuals, or from cellular heterogeneity in an isogenic group of cells or organisms. The emergence of gene expression differences between genetically identical cells is referred to as gene expression noise, the sources of which are not well understood.ResultsIn this work, by studying gene expression noise between multiple cell lineages and mammalian species, we find consistent evidence of a role for CpG islands as sources of gene expression noise. Variation in noise among CpG island promoters can be partially attributed to differences in island size, in which short islands have noisier gene expression. Building on these findings, we investigate the potential for short CpG islands to act as fast response elements to environmental stimuli. Specifically, we find that these islands are enriched amongst primary response genes in SWI/SNF-independent stimuli, suggesting that expression noise is an indicator of promoter responsiveness.ConclusionsThus, through the integration of single-cell RNA expression profiling, chromatin landscape and temporal gene expression dynamics, we have uncovered a role for short CpG island promoters as fast response elements.

Temporal Dynamics of Gene Expression During Endothelial Cell Differentiation From Human iPS Cells: A Comparison Study of Signalling Factors and Small Molecules.

Endothelial cell (EC) therapy may promote vascular growth or reendothelization in a variety of disease conditions. However, the production of a cell therapy preparation containing differentiated, dividing cells presenting typical EC phenotype, functional properties and chemokine profile is challenging. We focused on comparative analysis of seven small molecule-mediated differentiation protocols of ECs from human induced pluripotent stem cells. Differentiated cells showed a typical surface antigen pattern of ECs as characterized with flow cytometry analysis, functional properties, such as tube formation and ability to uptake acetylated LDL. Gene expression analysis by RNA sequencing revealed an efficient silencing of pluripotency genes and upregulation of genes related to cellular adhesion during differentiation. In addition, distinct patterns of transcription factor expression were identified during cellular reprogramming providing targets for more effective differentiation protocols in the future. Altogether, our results suggest that the most optimal EC differentiation protocol includes early inhibition of Rho-associated coiled-coil kinase and activation of cyclic AMP signaling, and inhibition of transforming growth factor beta signaling after mesodermal stage. These findings provide the first systematic characterization of the most potent signalling factors and small molecules used to generate ECs from human induced pluripotent stem cells and, consequently, this work improves the existing EC differentiation protocols and opens up new avenues for controlling cell fate for regenerative EC therapy.

Temporal dynamics of gene expression in heat-stressed Caenorhabditis elegans.

There is considerable insight into pathways and genes associated with heat-stress conditions. Most genes involved in stress response have been identified using mutant screens or gene knockdowns. Yet, there is limited understanding of the temporal dynamics of global gene expression in stressful environments. Here, we studied global gene expression profiles during 12 hours of heat stress in the nematode C. elegans. Using a high-resolution time series of increasing stress exposures, we found a distinct shift in gene expression patterns between 3–4 hours into the stress response, separating an initially highly dynamic phase from a later relatively stagnant phase. This turning point in expression dynamics coincided with a phenotypic turning point, as shown by a strong decrease in movement, survival and, progeny count in the days following the stress. Both detectable at transcriptional and phenotypic level, this study pin-points a relatively small time frame during heat stress at which enough damage is accumulated, making it impossible to recover the next few days.