Spatially varying cis-regulatory divergence in Drosophila embryos elucidates cis-regulatory logic

SEESAW: detecting isoform-level allelic imbalance accounting for inferential uncertainty

Detecting allelic imbalance at the isoform level requires accounting for inferential uncertainty, caused by multi-mapping of RNA-seq reads. Our proposed method, SEESAW, uses Salmon and Swish to offer analysis at various levels of resolution, including gene, isoform, and aggregating isoforms to groups by transcription start site. The aggregation strategies strengthen the signal for transcripts with high uncertainty. The SEESAW suite of methods is shown to have higher power than other allelic imbalance methods when there is isoform-level allelic imbalance. We also introduce a new test for detecting imbalance that varies across a covariate, such as time.

Single cell- and spatial 'Omics revolutionize physiology

Single cell multi- 'Omics and Spatial Transcriptomics are prominent technological highlights of recent years, and both fields still witness a ceaseless firework of novel approaches for high resolution profiling of additional omics layers. As all life processes in organs and organisms are based on the functions of their fundamental building blocks, the individual cells and their interactions, these methods are of utmost worth for the study of physiology in health and disease. Recent discoveries on embryonic development, tumor immunology, the detailed cellular composition and function of complex tissues like for example the kidney or the brain, different roles of the same cell type in different organs, the oncogenic program of individual tumor entities, or the architecture of immunopathology in infected tissue are based on single cell and spatial transcriptomics experiments. In this review, we will give a broad overview of technological concepts for single cell and spatial analysis, showing both advantages and limitations, and illustrate their impact with some particularly impressive case studies.

Airpart: interpretable statistical models for analyzing allelic imbalance in single-cell datasets

MOTIVATION

Allelic expression analysis aids in detection of cis-regulatory mechanisms of genetic variation, which produce allelic imbalance (AI) in heterozygotes. Measuring AI in bulk data lacking time or spatial resolution has the limitation that cell-type-specific (CTS), spatial- or time-dependent AI signals may be dampened or not detected.

RESULTS

We introduce a statistical method airpart for identifying differential CTS AI from single-cell RNA-sequencing data, or dynamics AI from other spatially or time-resolved datasets. airpart outputs discrete partitions of data, pointing to groups of genes and cells under common mechanisms of cis-genetic regulation. In order to account for low counts in single-cell data, our method uses a Generalized Fused Lasso with Binomial likelihood for partitioning groups of cells by AI signal, and a hierarchical Bayesian model for AI statistical inference. In simulation, airpart accurately detected partitions of cell types by their AI and had lower Root Mean Square Error (RMSE) of allelic ratio estimates than existing methods. In real data, airpart identified differential allelic imbalance patterns across cell states and could be used to define trends of AI signal over spatial or time axes.

AVAILABILITY AND IMPLEMENTATION

The airpart package is available as an R/Bioconductor package at https://bioconductor.org/packages/airpart.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Molecular Evolution across Mouse Spermatogenesis

Genes involved in spermatogenesis tend to evolve rapidly, but we lack a clear understanding of how protein sequences and patterns of gene expression evolve across this complex developmental process. We used fluorescence-activated cell sorting (FACS) to generate expression data for early (meiotic) and late (postmeiotic) cell types across 13 inbred strains of mice (Mus) spanning ∼7 My of evolution. We used these comparative developmental data to investigate the evolution of lineage-specific expression, protein-coding sequences, and expression levels. We found increased lineage specificity and more rapid protein-coding and expression divergence during late spermatogenesis, suggesting that signatures of rapid testis molecular evolution are punctuated across sperm development. Despite strong overall developmental parallels in these components of molecular evolution, protein and expression divergences were only weakly correlated across genes. We detected more rapid protein evolution on the X chromosome relative to the autosomes, whereas X-linked gene expression tended to be relatively more conserved likely reflecting chromosome-specific regulatory constraints. Using allele-specific FACS expression data from crosses between four strains, we found that the relative contributions of different regulatory mechanisms also differed between cell types. Genes showing cis-regulatory changes were more common late in spermatogenesis, and tended to be associated with larger differences in expression levels and greater expression divergence between species. In contrast, genes with trans-acting changes were more common early and tended to be more conserved across species. Our findings advance understanding of gene evolution across spermatogenesis and underscore the fundamental importance of developmental context in molecular evolutionary studies.

Contributions of cis- and trans-Regulatory Evolution to Transcriptomic Divergence across Populations in the Drosophila mojavensis Larval Brain

Natural selection on gene expression was originally predicted to result primarily in cis- rather than trans-regulatory evolution, due to the expectation of reduced pleiotropy. Despite this, numerous studies have ascribed recent evolutionary divergence in gene expression predominantly to trans-regulation. Performing RNA-seq on single isofemale lines from genetically distinct populations of the cactophilic fly Drosophila mojavensis and their F₁ hybrids, we recapitulated this pattern in both larval brains and whole bodies. However, we demonstrate that improving the measurement of brain expression divergence between populations by using seven additional genotypes considerably reduces the estimate of trans-regulatory contributions to expression evolution. We argue that the finding of trans-regulatory predominance can result from biases due to environmental variation in expression or other sources of noise, and that cis-regulation is likely a greater contributor to transcriptional evolution across D. mojavensis populations. Lastly, we merge these lines of data to identify several previously hypothesized and intriguing novel candidate genes, and suggest that the integration of regulatory and population-level transcriptomic data can provide useful filters for the identification of potentially adaptive genes.

Primate cell fusion disentangles gene regulatory divergence in neurodevelopment

Among primates, humans display a unique trajectory of development that is responsible for the many traits specific to our species. However, the inaccessibility of primary human and chimpanzee tissues has limited our ability to study human evolution. Comparative in vitro approaches using primate-derived induced pluripotent stem cells have begun to reveal species differences on the cellular and molecular levels1,2. In particular, brain organoids have emerged as a promising platform to study primate neural development in vitro3-5, although cross-species comparisons of organoids are complicated by differences in developmental timing and variability of differentiation6,7. Here we develop a new platform to address these limitations by fusing human and chimpanzee induced pluripotent stem cells to generate a panel of tetraploid hybrid stem cells. We applied this approach to study species divergence in cerebral cortical development by differentiating these cells into neural organoids. We found that hybrid organoids provide a controlled system for disentangling cis- and trans-acting gene-expression divergence across cell types and developmental stages, revealing a signature of selection on astrocyte-related genes. In addition, we identified an upregulation of the human somatostatin receptor 2 gene (SSTR2), which regulates neuronal calcium signalling and is associated with neuropsychiatric disorders8,9. We reveal a human-specific response to modulation of SSTR2 function in cortical neurons, underscoring the potential of this platform for elucidating the molecular basis of human evolution.

Uncovering an Organ's Molecular Architecture at Single-Cell Resolution by Spatially Resolved Transcriptomics

Revealing fine-scale cellular heterogeneity among spatial context and the functional and structural foundations of tissue architecture is fundamental within biological research and pharmacology. Unlike traditional approaches involving single molecules or bulk omics, cutting-edge, spatially resolved transcriptomics techniques offer near-single-cell or even subcellular resolution within tissues. Massive information across higher dimensions along with position-coordinating labels can better map the whole 3D transcriptional landscape of tissues. In this review, we focus on developments and strategies in spatially resolved transcriptomics, compare the cell and gene throughput and spatial resolution in detail for existing methods, and highlight the enormous potential in biomedical research.

Evolution of larval segment position across 12 Drosophila species

Many developmental traits that are critical to the survival of the organism are also robust. These robust traits are resistant to phenotypic change in the face of variation. This presents a challenge to evolution. In this article, we asked whether and how a well‐established robust trait, Drosophila segment patterning, changed over the evolutionary history of the genus. We compared segment position scaled to body length at the first‐instar larval stage among 12 Drosophila species. We found that relative segment position has changed many times across the phylogeny. Changes were frequent, but primarily small in magnitude. Phylogenetic analysis demonstrated that rates of change in segment position are variable along the Drosophila phylogenetic tree, and that these changes can occur in short evolutionary timescales. Correlation between position shifts of segments decreased as the distance between two segments increased, suggesting local control of segment position. The posterior‐most abdominal segment showed the highest magnitude of change on average, had the highest rate of evolution between species, and appeared to be evolving more independently as compared to the rest of the segments. This segment was exceptionally elongated in the cactophilic species in our dataset, raising questions as to whether this change may be adaptive.

Variable levels of drift in tunicate cardiopharyngeal gene regulatory elements

BACKGROUND

Mutations in gene regulatory networks often lead to genetic divergence without impacting gene expression or developmental patterning. The rules governing this process of developmental systems drift, including the variable impact of selective constraints on different nodes in a gene regulatory network, remain poorly delineated.

RESULTS

Here we examine developmental systems drift within the cardiopharyngeal gene regulatory networks of two tunicate species, Corella inflata and Ciona robusta. Cross-species analysis of regulatory elements suggests that trans-regulatory architecture is largely conserved between these highly divergent species. In contrast, cis-regulatory elements within this network exhibit distinct levels of conservation. In particular, while most of the regulatory elements we analyzed showed extensive rearrangements of functional binding sites, the enhancer for the cardiopharyngeal transcription factor FoxF is remarkably well-conserved. Even minor alterations in spacing between binding sites lead to loss of FoxF enhancer function, suggesting that bound trans-factors form position-dependent complexes.

CONCLUSIONS

Our findings reveal heterogeneous levels of divergence across cardiopharyngeal cis-regulatory elements. These distinct levels of divergence presumably reflect constraints that are not clearly associated with gene function or position within the regulatory network. Thus, levels of cis-regulatory divergence or drift appear to be governed by distinct structural constraints that will be difficult to predict based on network architecture.