Trendy: segmented regression analysis of expression dynamics in high-throughput ordered profiling experiments

Time-course characterization of whole-transcriptome dynamics of HepG2/C3A spheroids and its toxicological implications

Physiologically relevant in vitro models are a priority in predictive toxicology to replace and/or reduce animal experiments. The compromised toxicant metabolism of many immortalized human liver cell lines grown as monolayers as compared to in vivo metabolism limits their physiological relevance. However, recent efforts to culture liver cells in a 3D environment, such as spheroids, to better mimic the in vivo conditions, may enhance the toxicant metabolism of human liver cell lines. In this study, we characterized the dynamic changes in the transcriptome of HepG2/C3A hepatocarcinoma cell spheroids maintained in a clinostat system (CelVivo) to gain insight into the metabolic capacity of this model as a function of spheroid size and culture time. We assessed morphological changes (size, necrotic core), cell health, and proliferation rate from initial spheroid seeding to 35 days of continuous culture in conjunction with a time-course (0, 3, 7, 10, 14, 21, 28 days) of the transcriptome (TempO-Seq, BioSpyder). The phenotypic characteristics of HepG2/C3A growing in spheroids were comparable to monolayer growth until ∼Day 12 (Day 10-14) when a significant decrease in cell doubling rate was noted which was concurrent with down-regulation of cell proliferation and cell cycle pathways over this time period. Principal component analysis of the transcriptome data suggests that the Day 3, 7, and 10 spheroids are pronouncedly different from the Day 14, 21, and 28 spheroids in support of a biological transition time point during the long-term 3D spheroid cultures. The expression of genes encoding cellular components involved in toxicant metabolism and transport rapidly increased during the early time points of spheroids to peak at Day 7 or Day 10 as compared to monolayer cultures with a gradual decrease in expression with further culture, suggesting the most metabolically responsive time window for exposure studies. Overall, we provide baseline information on the cellular and molecular characterization, with a particular focus on toxicant metabolic capacity dynamics and cell growth, of HepG2/C3A 3D spheroid cultures over time.

Integrated Blood Transcriptome and Multi-Tissue Trace Mineral Analyses of Healthy Stocker Cattle Fed Complexed or Inorganic Trace Mineral Supplement

Supplementing trace minerals is common in managing bovine respiratory disease (BRD) in post-weaned cattle; however, its influence on host immunity and metabolism in high-risk cattle remains unclear. We aimed to assess the impact of three supplementation programs on liver and serum trace element concentrations and blood gene expression. Fifty-six high-risk beef steers were randomly assigned to one of three groups over 60 days: (1) sulfate-sourced Cu, Co, Mn, and Zn (INR), (2) amino acid-complexed Cu, Mn, Co, and Zn (AAC), or (3) AAC plus trace mineral and vitamin drench (COMBO). Serum and liver biopsies for Cu, Co, Mn, and Zn at d0, d28, and d60 were analyzed from cattle free of BRD (n = 9 INR; n = 6 AAC; n = 10 COMBO). Differences and correlations of mineral concentrations were analyzed via generalized linear mixed models and Spearman's rank coefficients, respectively (p < 0.05). Whole blood RNA samples from healthy cattle (n = 4 INR; n = 4 AAC; n = 4 COMBO) at d0, d13, d28, d45, and d60 were sequenced and analyzed for differentially expressed genes (DEGs) via glmmSeq (FDR < 0.05), edgeR (FDR < 0.10), and Trendy (p < 0.10). Serum and liver Cu and Co concentrations increased over time in all groups, with higher liver Cu in COMBO (487.985 μg/g) versus AAC (392.043 μg/g) at d60 (p = 0.013). Serum and liver Cu concentrations (ρ = 0.579, p = 6.59 × 10-8) and serum and liver Co concentrations (ρ = 0.466, p = 2.80 × 10-5) were linearly correlated. Minimal gene expression differences were found between AAC versus COMBO (n = 2 DEGs) and INR versus COMBO (n = 0 DEGs) over time. AAC versus INR revealed 107 DEGs (d13-d60) with increased traits in AAC including metabolism of carbohydrates/fat-soluble vitamins, antigen presentation, ATPase activity, and B- and T-cell activation, while osteoclast differentiation and neutrophil degranulation decreased in AAC compared to INR. Our study identifies gene expression differences in high-risk cattle fed inorganic or amino acid-complexed mineral supplements, revealing adaptive immune and metabolic mechanisms that may be improved by organically sourced supplementation.

Genome-wide analysis of transcription-coupled repair reveals novel transcription events in Caenorhabditis elegans

Bulky DNA adducts such as those induced by ultraviolet light are removed from the genomes of multicellular organisms by nucleotide excision repair, which occurs through two distinct mechanisms, global repair, requiring the DNA damage recognition-factor XPC (xeroderma pigmentosum complementation group C), and transcription-coupled repair (TCR), which does not. TCR is initiated when elongating RNA polymerase II encounters DNA damage, and thus analysis of genome-wide excision repair in XPC-mutants only repairing by TCR provides a unique opportunity to map transcription events missed by methods dependent on capturing RNA transcription products and thus limited by their stability and/or modifications (5'-capping or 3'-polyadenylation). Here, we have performed eXcision Repair-sequencing (XR-seq) in the model organism Caenorhabditis elegans to generate genome-wide repair maps in a wild-type strain with normal excision repair, a strain lacking TCR (csb-1), and a strain that only repairs by TCR (xpc-1). Analysis of the intersections between the xpc-1 XR-seq repair maps with RNA-mapping datasets (RNA-seq, long- and short-capped RNA-seq) reveal previously unrecognized sites of transcription and further enhance our understanding of the genome of this important model organism.

Single-cell profiling reveals transcriptome dynamics during bovine oocyte growth

BACKGROUND

Mammalian follicle development is characterized by extensive changes in morphology, endocrine responsiveness, and function, providing the optimum environment for oocyte growth, development, and resumption of meiosis. In cattle, the first signs of transcription activation in the oocyte are observed in the secondary follicle, later than during mouse and human oogenesis. While many studies have generated extensive datasets characterizing gene expression in bovine oocytes, they are mostly limited to the analysis of fully grown and matured oocytes. The aim of the present study was to apply single-cell RNA sequencing to interrogate the transcriptome of the growing bovine oocyte from the secondary follicle stage through to the mid-antral follicle stage.

RESULTS

Single-cell RNA-seq libraries were generated from oocytes of known diameters (< 60 to > 120 μm), and datasets were binned into non-overlapping size groups for downstream analysis. Combining the results of weighted gene co-expression network and Trendy analyses, and differently expressed genes (DEGs) between size groups, we identified a decrease in oxidative phosphorylation and an increase in maternal -genes and transcription regulators across the bovine oocyte growth phase. In addition, around 5,000 genes did not change in expression, revealing a cohort of stable genes. An interesting switch in gene expression profile was noted in oocytes greater than 100 μm in diameter, when the expression of genes related to cytoplasmic activities was replaced by genes related to nuclear activities (e.g., chromosome segregation). The highest number of DEGs were detected in the comparison of oocytes 100-109 versus 110-119 μm in diameter, revealing a profound change in the molecular profile of oocytes at the end of their growth phase.

CONCLUSIONS

The current study provides a unique dataset of the key genes and pathways characteristic of each stage of oocyte development, contributing an important resource for a greater understanding of bovine oogenesis.

Transcriptomic clock predicts vascular changes of prodromal diabetic retinopathy

Diabetic retinopathy is a common complication of long-term diabetes and that could lead to vision loss. Unfortunately, early diabetic retinopathy remains poorly understood. There is no effective way to prevent or treat early diabetic retinopathy until patients develop later stages of diabetic retinopathy. Elevated acellular capillary density is considered a reliable quantitative trait present in the early development of retinopathy. Hence, in this study, we interrogated whole retinal vascular transcriptomic changes via a Nile rat model to better understand the early pathogenesis of diabetic retinopathy. We uncovered the complexity of associations between acellular capillary density and the joint factors of blood glucose, diet, and sex, which was modeled through a Bayesian network. Using segmented regressions, we have identified different gene expression patterns and enriched Gene Ontology (GO) terms associated with acellular capillary density increasing. We developed a random forest regression model based on expression patterns of 14 genes to predict the acellular capillary density. Since acellular capillary density is a reliable quantitative trait in early diabetic retinopathy, and thus our model can be used as a transcriptomic clock to measure the severity of the progression of early retinopathy. We also identified NVP-TAE684, geldanamycin, and NVP-AUY922 as the top three potential drugs which can potentially attenuate the early DR. Although we need more in vivo studies in the future to support our re-purposed drugs, we have provided a data-driven approach to drug discovery.

Linked Mutations in the Ebola Virus Polymerase Are Associated with Organ Specific Phenotypes

Ebola virus (EBOV) causes a severe infection called Ebola virus disease (EVD). The pathogenesis of EBOV infection is complex, and outcome has been associated with a variety of immunological and cellular factors. Disease can result from several mechanisms, including direct organ and endothelial cell damage as a result of viral replication. During the2013 to 2016 Western Africa EBOV outbreak, several mutants emerged, with changes in the genes of nucleoprotein (NP), glycoprotein (GP), and the large (L) protein. Reverse genetic analysis has been used to investigate whether these mutations played any role in pathogenesis with mixed results depending on the experimental system used. Previous studies investigated the impact of three single nonsynonymous mutations (GP-A82V, NP-R111C, and L-D759G) on the fatality rate of mouse and ferret models and suggested that the L-D759G mutation decreased the virulence of EBOV. In this study, the effect of these three mutations was further evaluated by deep sequencing to determine viral population genetics and the host response in longitudinal samples of blood, liver, kidney, spleen, and lung tissues taken from the previous ferret model. The data indicated that the mutations were maintained in the different tissues, but the frequency of minor genomic mutations were different. In addition, compared to wild-type virus, the recombinant mutants had different within host effects, where the D759G (and accompanying Q986H) substitution in the L protein resulted in an upregulation of the immune response in the kidney, liver, spleen, and lungs. Together these studies provide insights into the biology of EBOV mutants both between and within hosts. IMPORTANCE Ebola virus infection can have dramatic effects on the human body which manifest in Ebola virus disease. The outcome of infection is either survival or death and in the former group with the potential of longer-term health consequences and persistent infection. Disease severity is undoubtedly associated with the host response, often with overt inflammatory responses correlated with poorer outcomes. The scale of the2013 to 2016 Western African Ebola virus outbreak revealed new aspects of viral biology. This included the emergence of mutants with potentially altered virulence. Biobanked tissue from ferret models of EBOV infected with different mutants that emerged in the Western Africa outbreak was used to investigate the effect of EBOV genomic variation in different tissues. Overall, the work provided insights into the population genetics of EBOV and showed that different organs in an animal model can respond differently to variants of EBOV.

The GRN concept as a guide for evolutionary developmental biology

Organismal phenotypes result largely from inherited developmental programs, usually executed during embryonic and juvenile life stages. These programs are not blank slates onto which natural selection can draw arbitrary forms. Rather, the mechanisms of development play an integral role in shaping phenotypic diversity and help determine the evolutionary trajectories of species. Modern evolutionary biology must, therefore, account for these mechanisms in both theory and in practice. The gene regulatory network (GRN) concept represents a potent tool for achieving this goal whose utility has grown in tandem with advances in "omic" technologies and experimental techniques. However, while the GRN concept is widely utilized, it is often less clear what practical implications it has for conducting research in evolutionary developmental biology. In this Perspective, we attempt to provide clarity by discussing how experiments and projects can be designed in light of the GRN concept. We first map familiar biological notions onto the more abstract components of GRN models. We then review how diverse functional genomic approaches can be directed toward the goal of constructing such models and discuss current methods for functionally testing evolutionary hypotheses that arise from them. Finally, we show how the major steps of GRN model construction and experimental validation suggest generalizable workflows that can serve as a scaffold for project design. Taken together, the practical implications that we draw from the GRN concept provide a set of guideposts for studies aiming at unraveling the molecular basis of phenotypic diversity.

Belayer: Modeling discrete and continuous spatial variation in gene expression from spatially resolved transcriptomics

Spatially resolved transcriptomics (SRT) technologies measure gene expression at known locations in a tissue slice, enabling the identification of spatially varying genes or cell types. Current approaches for these tasks assume either that gene expression varies continuously across a tissue or that a tissue contains a small number of regions with distinct cellular composition. We propose a model for SRT data from layered tissues that includes both continuous and discrete spatial variation in expression and an algorithm, Belayer, to learn the parameters of this model. Belayer models gene expression as a piecewise linear function of the relative depth of a tissue layer with possible discontinuities at layer boundaries. We use conformal maps to model relative depth and derive a dynamic programming algorithm to infer layer boundaries and gene expression functions. Belayer accurately identifies tissue layers and biologically meaningful spatially varying genes in SRT data from the brain and skin.

Single-cell generalized trend model (scGTM): a flexible and interpretable model of gene expression trend along cell pseudotime

MOTIVATION

Modeling single-cell gene expression trends along cell pseudotime is a crucial analysis for exploring biological processes. Most existing methods rely on nonparametric regression models for their flexibility; however, nonparametric models often provide trends too complex to interpret. Other existing methods use interpretable but restrictive models. Since model interpretability and flexibility are both indispensable for understanding biological processes, the single-cell field needs a model that improves the interpretability and largely maintains the flexibility of nonparametric regression models.

RESULTS

Here, we propose the single-cell generalized trend model (scGTM) for capturing a gene's expression trend, which may be monotone, hill-shaped or valley-shaped, along cell pseudotime. The scGTM has three advantages: (i) it can capture non-monotonic trends that are easy to interpret, (ii) its parameters are biologically interpretable and trend informative, and (iii) it can flexibly accommodate common distributions for modeling gene expression counts. To tackle the complex optimization problems, we use the particle swarm optimization algorithm to find the constrained maximum likelihood estimates for the scGTM parameters. As an application, we analyze several single-cell gene expression datasets using the scGTM and show that scGTM can capture interpretable gene expression trends along cell pseudotime and reveal molecular insights underlying biological processes.

AVAILABILITY AND IMPLEMENTATION

The Python package scGTM is open-access and available at https://github.com/ElvisCuiHan/scGTM.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A Brain Region-Specific Expression Profile for Genes Within Large Introgression Deserts and Under Positive Selection in Homo sapiens

Analyses of ancient DNA from extinct hominins have provided unique insights into the complex evolutionary history of Homo sapiens, intricately related to that of the Neanderthals and the Denisovans as revealed by several instances of admixture events. These analyses have also allowed the identification of introgression deserts: genomic regions in our species that are depleted of "archaic" haplotypes. The presence of genes like FOXP2 in these deserts has been taken to be suggestive of brain-related functional differences between Homo species. Here, we seek a deeper characterization of these regions and the specific expression trajectories of genes within them, taking into account signals of positive selection in our lineage. Analyzing publicly available transcriptomic data from the human brain at different developmental stages, we found that structures outside the cerebral neocortex, in particular the cerebellum, the striatum and the mediodorsal nucleus of the thalamus show the most divergent transcriptomic profiles when considering genes within large introgression deserts and under positive selection.

Time Series Transcriptome Analysis in Medicago truncatula Shoot and Root Tissue During Early Nodulation

In response to colonization by rhizobia bacteria, legumes are able to form nitrogen-fixing nodules in their roots, allowing the plants to grow efficiently in nitrogen-depleted environments. Legumes utilize a complex, long-distance signaling pathway to regulate nodulation that involves signals in both roots and shoots. We measured the transcriptional response to treatment with rhizobia in both the shoots and roots of Medicago truncatula over a 72-h time course. To detect temporal shifts in gene expression, we developed GeneShift, a novel computational statistics and machine learning workflow that addresses the time series replicate the averaging issue for detecting gene expression pattern shifts under different conditions. We identified both known and novel genes that are regulated dynamically in both tissues during early nodulation including leginsulin, defensins, root transporters, nodulin-related, and circadian clock genes. We validated over 70% of the expression patterns that GeneShift discovered using an independent M. truncatula RNA-Seq study. GeneShift facilitated the discovery of condition-specific temporally differentially expressed genes in the symbiotic nodulation biological system. In principle, GeneShift should work for time-series gene expression profiling studies from other systems.

TimesVector-Web: A Web Service for Analysing Time Course Transcriptome Data with Multiple Conditions

From time course gene expression data, we may identify genes that modulate in a certain pattern across time. Such patterns are advantageous to investigate the transcriptomic response to a certain condition. Especially, it is of interest to compare two or more conditions to detect gene expression patterns that significantly differ between them. Time course analysis can become difficult using traditional differentially expressed gene (DEG) analysis methods since they are based on pair-wise sample comparison instead of a series of time points. Most importantly, the related tools are mostly available as local Software, requiring technical expertise. Here, we present TimesVector-web, which is an easy to use web service for analysing time course gene expression data with multiple conditions. The web-service was developed to (1) alleviate the burden for analyzing multi-class time course data and (2) provide downstream analysis on the results for biological interpretation including TF, miRNA target, gene ontology and pathway analysis. TimesVector-web was validated using three case studies that use both microarray and RNA-seq time course data and showed that the results captured important biological findings from the original studies.

Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology

Significant innovations in next-generation sequencing techniques and bioinformatics tools have impacted our appreciation and understanding of RNA. Practical RNA sequencing (RNA-Seq) applications have evolved in conjunction with sequence technology and bioinformatic tools advances. In most projects, bulk RNA-Seq data is used to measure gene expression patterns, isoform expression, alternative splicing and single-nucleotide polymorphisms. However, RNA-Seq holds far more hidden biological information including details of copy number alteration, microbial contamination, transposable elements, cell type (deconvolution) and the presence of neoantigens. Recent novel and advanced bioinformatic algorithms developed the capacity to retrieve this information from bulk RNA-Seq data, thus broadening its scope. The focus of this review is to comprehend the emerging bulk RNA-Seq-based analyses, emphasizing less familiar and underused applications. In doing so, we highlight the power of bulk RNA-Seq in providing biological insights.

Integration of segmented regression analysis with weighted gene correlation network analysis identifies genes whose expression is remodeled throughout physiological aging in mouse tissues

Gene expression alterations occurring with aging have been described for a multitude of species, organs, and cell types. However, most of the underlying studies rely on static comparisons of mean gene expression levels between age groups and do not account for the dynamics of gene expression throughout the lifespan. These studies also tend to disregard the pairwise relationships between gene expression profiles, which may underlie commonly altered pathways and regulatory mechanisms with age. To overcome these limitations, we have combined segmented regression analysis with weighted gene correlation network analysis (WGCNA) to identify high-confidence signatures of aging in the brain, heart, liver, skeletal muscle, and pancreas of C57BL/6 mice in a publicly available RNA-Seq dataset (GSE132040). Functional enrichment analysis of the overlap of genes identified in both approaches showed that immune- and inflammation-related responses are prominently altered in the brain and the liver, while in the heart and the muscle, aging affects amino and fatty acid metabolism, and tissue regeneration, respectively, which reflects an age-related global loss of tissue function. We also explored sexual dimorphism in the aging mouse transcriptome and found the liver and the muscle to have the most pronounced gender differences in gene expression throughout the lifespan, particularly in proteostasis-related pathways. While the data showed little overlap among the age-dysregulated genes between tissues, aging triggered common biological processes in distinct tissues, which we highlight as important features of murine tissue physiological aging.

Interspecies chimeric conditions affect the developmental rate of human pluripotent stem cells

Human pluripotent stem cells hold significant promise for regenerative medicine. However, long differentiation protocols and immature characteristics of stem cell-derived cell types remain challenges to the development of many therapeutic applications. In contrast to the slow differentiation of human stem cells in vitro that mirrors a nine-month gestation period, mouse stem cells develop according to a much faster three-week gestation timeline. Here, we tested if co-differentiation with mouse pluripotent stem cells could accelerate the differentiation speed of human embryonic stem cells. Following a six-week RNA-sequencing time course of neural differentiation, we identified 929 human genes that were upregulated earlier and 535 genes that exhibited earlier peaked expression profiles in chimeric cell cultures than in human cell cultures alone. Genes with accelerated upregulation were significantly enriched in Gene Ontology terms associated with neurogenesis, neuron differentiation and maturation, and synapse signaling. Moreover, chimeric mixed samples correlated with in utero human embryonic samples earlier than human cells alone, and acceleration was dose-dependent on human-mouse co-culture ratios. The altered gene expression patterns and developmental rates described in this report have implications for accelerating human stem cell differentiation and the use of interspecies chimeric embryos in developing human organs for transplantation.

Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data

Dynamic studies in time course experimental designs and clinical approaches have been widely used by the biomedical community. These applications are particularly relevant in stimuli-response models under environmental conditions, characterization of gradient biological processes in developmental biology, identification of therapeutic effects in clinical trials, disease progressive models, cell-cycle, and circadian periodicity. Despite their feasibility and popularity, sophisticated dynamic methods that are well validated in large-scale comparative studies, in terms of statistical and computational rigor, are less benchmarked, comparing to their static counterparts. To date, a number of novel methods in bulk RNA-Seq data have been developed for the various time-dependent stimuli, circadian rhythms, cell-lineage in differentiation, and disease progression. Here, we comprehensively review a key set of representative dynamic strategies and discuss current issues associated with the detection of dynamically changing genes. We also provide recommendations for future directions for studying non-periodical, periodical time course data, and meta-dynamic datasets.

Identification and characterization of dynamically regulated hepatitis-related genes in a concanavalin A-induced liver injury model

Background: Concanavalin A (ConA)-induced liver damage of mice is a well-established murine model mimicking the human autoimmune hepatitis (AIH). However, the pathogenic genes of the liver injury remain to be revealed.

Methods: Using time-series liver transcriptome, top dynamic genes were inferred from a set of segmented regression models, and cross-checked by weighted correlation network analysis (WGCNA). AIH murine models created by ConA were used to verify the in vivo effect of these genes.

Results: We identified 115 top dynamic genes, of which most were overlapped with the hub genes determined by WGCNA. The expression of several top dynamic genes including Cd63, Saa3, Slc10a1, Nrxn1, Ugt2a3, were verified in vivo. Further, Cluster determinant 63 (Cd63) knockdown in mice treated with ConA showed significantly less liver pathology and inflammation as well as higher survival rates than the corresponding controls.

Conclusion: We have identified the top dynamic genes related to the process of acute liver injury, and highlighted a targeted strategy for Cd63 might have utility for the protection of hepatocellular damage.

TimeMeter assesses temporal gene expression similarity and identifies differentially progressing genes

Comparative time series transcriptome analysis is a powerful tool to study development, evolution, aging, disease progression and cancer prognosis. We develop TimeMeter, a statistical method and tool to assess temporal gene expression similarity, and identify differentially progressing genes where one pattern is more temporally advanced than the other. We apply TimeMeter to several datasets, and show that TimeMeter is capable of characterizing complicated temporal gene expression associations. Interestingly, we find: (i) the measurement of differential progression provides a novel feature in addition to pattern similarity that can characterize early developmental divergence between two species; (ii) genes exhibiting similar temporal patterns between human and mouse during neural differentiation are under strong negative (purifying) selection during evolution; (iii) analysis of genes with similar temporal patterns in mouse digit regeneration and axolotl blastema differentiation reveals common gene groups for appendage regeneration with potential implications in regenerative medicine.

An In Vitro Human Segmentation Clock Model Derived from Embryonic Stem Cells

Defects in somitogenesis result in vertebral malformations at birth known as spondylocostal dysostosis (SCDO). Somites are formed with a species-specific periodicity controlled by the “segmentation clock,” which comprises a group of oscillatory genes in the presomitic mesoderm. Here, we report that a segmentation clock model derived from human embryonic stem cells shows many hallmarks of the mammalian segmentation clock in vivo, including a dependence on the NOTCH and WNT signaling pathways. The gene expression oscillations are highly synchronized, displaying a periodicity specific to the human clock. Introduction of a point of mutation into HES7, a specific mutation previously associated with clinical SCDO, eliminated clock gene oscillations, successfully reproducing the defects in the segmentation clock. Thus, we provide a model for studying the previously inaccessible human segmentation clock to better understand the mechanisms contributing to congenital skeletal defects.

The segmentation clock is a molecular oscillator regulating the tempo of somite formation in a species-specific manner. Chu et al. report an embryonic-stem-cell-derived model system displaying a human-specific gene oscillation periodicity, which can shed light on human somitogenesis and model skeletal developmental disorders.

Trajectory-based differential expression analysis for single-cell sequencing data

Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the trajectory, or (ii) differentially expressed between lineages, to illuminate the underlying biological processes. Current data analysis procedures, however, either fail to exploit the continuous resolution provided by trajectory inference, or fail to pinpoint the exact types of differential expression. We introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression. By incorporating observation-level weights, the model additionally allows to account for zero inflation. We evaluate the method on simulated datasets and on real datasets from droplet-based and full-length protocols, and show that it yields biological insights through a clear interpretation of the data.

Circadian rhythms in the absence of the clock gene Bmal1

Circadian (~24 hour) clocks have a fundamental role in regulating daily physiology. The transcription factor BMAL1 is a principal driver of a molecular clock in mammals. Bmal1 deletion abolishes 24-hour activity patterning, one measure of clock output. We determined whether Bmal1 function is necessary for daily molecular oscillations in skin fibroblasts and liver slices. Unexpectedly, in Bmal1 knockout mice, both tissues exhibited 24-hour oscillations of the transcriptome, proteome, and phosphoproteome over 2 to 3 days in the absence of any exogenous drivers such as daily light or temperature cycles. This demonstrates a competent 24-hour molecular pacemaker in Bmal1 knockouts. We suggest that such oscillations might be underpinned by transcriptional regulation by the recruitment of ETS family transcription factors, and nontranscriptionally by co-opting redox oscillations.

Lag penalized weighted correlation for time series clustering

BACKGROUND

The similarity or distance measure used for clustering can generate intuitive and interpretable clusters when it is tailored to the unique characteristics of the data. In time series datasets generated with high-throughput biological assays, measurements such as gene expression levels or protein phosphorylation intensities are collected sequentially over time, and the similarity score should capture this special temporal structure.

RESULTS

We propose a clustering similarity measure called Lag Penalized Weighted Correlation (LPWC) to group pairs of time series that exhibit closely-related behaviors over time, even if the timing is not perfectly synchronized. LPWC aligns time series profiles to identify common temporal patterns. It down-weights aligned profiles based on the length of the temporal lags that are introduced. We demonstrate the advantages of LPWC versus existing time series and general clustering algorithms. In a simulated dataset based on the biologically-motivated impulse model, LPWC is the only method to recover the true clusters for almost all simulated genes. LPWC also identifies clusters with distinct temporal patterns in our yeast osmotic stress response and axolotl limb regeneration case studies.

CONCLUSIONS

LPWC achieves both of its time series clustering goals. It groups time series with correlated changes over time, even if those patterns occur earlier or later in some of the time series. In addition, it refrains from introducing large shifts in time when searching for temporal patterns by applying a lag penalty. The LPWC R package is available at https://github.com/gitter-lab/LPWC and CRAN under a MIT license.

Automated minute scale RNA-seq of pluripotent stem cell differentiation reveals early divergence of human and mouse gene expression kinetics

Pluripotent stem cells retain the developmental timing of their species of origin in vitro, an observation that suggests the existence of a cell-intrinsic developmental clock, yet the nature and machinery of the clock remain a mystery. We hypothesize that one possible component may lie in species-specific differences in the kinetics of transcriptional responses to differentiation signals. Using a liquid-handling robot, mouse and human pluripotent stem cells were exposed to identical neural differentiation conditions and sampled for RNA-sequencing at high frequency, every 4 or 10 minutes, for the first 10 hours of differentiation to test for differences in transcriptomic response rates. The majority of initial transcriptional responses occurred within a rapid window in the first minutes of differentiation for both human and mouse stem cells. Despite similarly early onsets of gene expression changes, we observed shortened and condensed gene expression patterns in mouse pluripotent stem cells compared to protracted trends in human pluripotent stem cells. Moreover, the speed at which individual genes were upregulated, as measured by the slopes of gene expression changes over time, was significantly faster in mouse compared to human cells. These results suggest that downstream transcriptomic response kinetics to signaling cues are faster in mouse versus human cells, and may offer a partial account for the vast differences in developmental rates across species.

Inferring Regulatory Programs Governing Region Specificity of Neuroepithelial Stem Cells during Early Hindbrain and Spinal Cord Development

Neuroepithelial stem cells (NSC) from different anatomical regions of the embryonic neural tube's rostrocaudal axis can differentiate into diverse central nervous system tissues, but the transcriptional regulatory networks governing these processes are incompletely understood. Here, we measure region-specific NSC gene expression along the rostrocaudal axis in a human pluripotent stem cell model of early central nervous system development over a 72-h time course, spanning the hindbrain to cervical spinal cord. We introduce Escarole, a probabilistic clustering algorithm for non-stationary time series, and combine it with prior-based regulatory network inference to identify genes that are regulated dynamically and predict their upstream regulators. We identify known regulators of patterning and neural development, including the HOX genes, and predict a direct regulatory connection between the transcription factor POU3F2 and target gene STMN2. We demonstrate that POU3F2 is required for expression of STMN2, suggesting that this regulatory connection is important for region specificity of NSCs.