Single-cell co-expression analysis reveals that transcriptional modules are shared across cell types in the brain

Reimagining Cortical Connectivity by Deconstructing Its Molecular Logic into Building Blocks

Comprehensive maps of neuronal connectivity provide a foundation for understanding the structure of neural circuits. In a circuit, neurons are diverse in morphology, electrophysiology, gene expression, activity, and other neuronal properties. Thus, constructing a comprehensive connectivity map requires associating various properties of neurons, including their connectivity, at cellular resolution. A commonly used approach is to use the gene expression profiles as an anchor to which all other neuronal properties are associated. Recent advances in genomics and anatomical techniques dramatically improved the ability to determine and associate the long-range projections of neurons with their gene expression profiles. These studies revealed unprecedented details of the gene-projection relationship, but also highlighted conceptual challenges in understanding this relationship. In this article, I delve into the findings and the challenges revealed by recent studies using state-of-the-art neuroanatomical and transcriptomic techniques. Building upon these insights, I propose an approach that focuses on understanding the gene-projection relationship through basic features in gene expression profiles and projections, respectively, that associate with underlying cellular processes. I then discuss how the developmental trajectories of projections and gene expression profiles create additional challenges and necessitate interrogating the gene-projection relationship across time. Finally, I explore complementary strategies that, together, can provide a comprehensive view of the gene-projection relationship.

Identifying Reproducible Transcription Regulator Coexpression Patterns with Single Cell Transcriptomics

The proliferation of single cell transcriptomics has potentiated our ability to unveil patterns that reflect dynamic cellular processes, rather than cell type compositional effects that emerge from bulk tissue samples. In this study, we leverage a broad collection of single cell RNA-seq data to identify the gene partners whose expression is most coordinated with each human and mouse transcription regulator (TR). We assembled 120 human and 103 mouse scRNA-seq datasets from the literature (>28 million cells), constructing a single cell coexpression network for each. We aimed to understand the consistency of TR coexpression profiles across a broad sampling of biological contexts, rather than examine the preservation of context-specific signals. Our workflow therefore explicitly prioritizes the patterns that are most reproducible across cell types. Towards this goal, we characterize the similarity of each TR's coexpression within and across species. We create single cell coexpression rankings for each TR, demonstrating that this aggregated information recovers literature curated targets on par with ChIP-seq data. We then combine the coexpression and ChIP-seq information to identify candidate regulatory interactions supported across methods and species. Finally, we highlight interactions for the important neural TR ASCL1 to demonstrate how our compiled information can be adopted for community use.

Cross-expression analysis reveals patterns of coordinated gene expression in spatial transcriptomics

Spatial transcriptomics promises to transform our understanding of tissue biology by molecularly profiling individual cells in situ. A fundamental question they allow us to ask is how nearby cells orchestrate their gene expression. To investigate this, we introduce cross-expression, a novel framework for discovering gene pairs that coordinate their expression across neighboring cells. Just as co-expression quantifies synchronized gene expression within the same cells, cross-expression measures coordinated gene expression between spatially adjacent cells, allowing us to understand tissue gene expression programs with single cell resolution. Using this framework, we recover ligand-receptor partners and discover gene combinations marking anatomical regions. More generally, we create cross-expression networks to find gene modules with orchestrated expression patterns. Finally, we provide an efficient R package to facilitate cross-expression analysis, quantify effect sizes, and generate novel visualizations to better understand spatial gene expression programs.

Exploring Lignin Biosynthesis Genes in Rice: Evolution, Function, and Expression

Lignin is nature's second most abundant vascular plant biopolymer, playing significant roles in mechanical support, water transport, and stress responses. This study identified 90 lignin biosynthesis genes in rice based on phylogeny and motif constitution, and they belong to PAL, C4H, 4CL, HCT, C3H, CCoAOMT, CCR, F5H, COMT, and CAD families. Duplication events contributed largely to the expansion of these gene families, such as PAL, CCoAOMT, CCR, and CAD families, mainly attributed to tandem and segmental duplication. Microarray data of 33 tissue samples covering the entire life cycle of rice suggested fairly high PAL, HCT, C3H, CCoAOMT, CCR, COMT, and CAD gene expressions and rather variable C4H, 4CL, and F5H expressions. Some members of lignin-related genes (OsCCRL11, OsHCT1/2/5, OsCCoAOMT1/3/5, OsCOMT, OsC3H, OsCAD2, and OsPAL1/6) were expressed in all tissues examined. The expression patterns of lignin-related genes can be divided into two major groups with eight subgroups, each showing a distinct co-expression in tissues representing typically primary and secondary cell wall constitutions. Some lignin-related genes were strongly co-expressed in tissues typical of secondary cell walls. Combined HPLC analysis showed increased lignin monomer (H, G, and S) contents from young to old growth stages in five genotypes. Based on 90 genes' microarray data, 27 genes were selected for qRT-PCR gene expression analysis. Four genes (OsPAL9, OsCAD8C, OsCCR8, and OsCOMTL4) were significantly negatively correlated with lignin monomers. Furthermore, eleven genes were co-expressed in certain genotypes during secondary growth stages. Among them, six genes (OsC3H, OsCAD2, OsCCR2, OsCOMT, OsPAL2, and OsPAL8) were overlapped with microarray gene expressions, highlighting their importance in lignin biosynthesis.

Gene regulatory networks in disease and ageing

The precise control of gene expression is required for the maintenance of cellular homeostasis and proper cellular function, and the declining control of gene expression with age is considered a major contributor to age-associated changes in cellular physiology and disease. The coordination of gene expression can be represented through models of the molecular interactions that govern gene expression levels, so-called gene regulatory networks. Gene regulatory networks can represent interactions that occur through signal transduction, those that involve regulatory transcription factors, or statistical models of gene-gene relationships based on the premise that certain sets of genes tend to be coexpressed across a range of conditions and cell types. Advances in experimental and computational technologies have enabled the inference of these networks on an unprecedented scale and at unprecedented precision. Here, we delineate different types of gene regulatory networks and their cell-biological interpretation. We describe methods for inferring such networks from large-scale, multi-omics datasets and present applications that have aided our understanding of cellular ageing and disease mechanisms.

Exploring the role of transcriptional and post-transcriptional processes in mRNA co-expression

Co-expression of two or more genes at the single-cell level is usually associated with functional co-regulation. While mRNA co-expression-measured as the correlation in mRNA levels-can be influenced by both transcriptional and post-transcriptional events, transcriptional regulation is typically considered dominant. We review and connect the literature describing transcriptional and post-transcriptional regulation of co-expression. To enhance our understanding, we integrate four datasets spanning single-cell gene expression data, single-cell promoter activity data and individual transcript half-lives. Confirming expectations, we find that positive co-expression necessitates promoter coordination and similar mRNA half-lives. Surprisingly, negative co-expression is favored by differences in mRNA half-lives, contrary to initial predictions from stochastic simulations. Notably, this association manifests specifically within clusters of genes. We further observe a striking compensation between promoter coordination and mRNA half-lives, which additional stochastic simulations suggest might give rise to the observed co-expression patterns. These findings raise intriguing questions about the functional advantages conferred by this compensation between distal kinetic steps.

Individual variation in preference behavior in sailfin fish refines the neurotranscriptomic pathway for mate preference

Social interactions can drive distinct gene expression profiles which may vary by social context. Here we use female sailfin molly fish (Poecilia latipinna) to identify genomic profiles associated with preference behavior in distinct social contexts: male interactions (mate choice) versus female interactions (shoaling partner preference). We measured the behavior of 15 females interacting in a non-contact environment with either two males or two females for 30 min followed by whole-brain transcriptomic profiling by RNA sequencing. We profiled females that exhibited high levels of social affiliation and great variation in preference behavior to identify an order of magnitude more differentially expressed genes associated with behavioral variation than by differences in social context. Using a linear model (limma), we took advantage of the individual variation in preference behavior to identify unique gene sets that exhibited distinct correlational patterns of expression with preference behavior in each social context. By combining limma and weighted gene co-expression network analyses (WGCNA) approaches we identified a refined set of 401 genes robustly associated with mate preference that is independent of shoaling partner preference or general social affiliation. While our refined gene set confirmed neural plasticity pathways involvement in moderating female preference behavior, we also identified a significant proportion of discovered that our preference-associated genes were enriched for 'immune system' gene ontology categories. We hypothesize that the association between mate preference and transcriptomic immune function is driven by the less well-known role of these genes in neural plasticity which is likely involved in higher-order learning and processing during mate choice decisions.

Charting plant gene functions in the multi-omics and single-cell era

Despite the increased access to high-quality plant genome sequences, the set of genes with a known function remains far from complete. With the advent of novel bulk and single-cell omics profiling methods, we are entering a new era where advanced and highly integrative functional annotation strategies are being developed to elucidate the functions of all plant genes. Here, we review different multi-omics approaches to improve functional and regulatory gene characterization and highlight the power of machine learning and network biology to fully exploit the complementary information embedded in different omics layers. Finally, we discuss the potential of emerging single-cell methods and algorithms to further increase the resolution, allowing generation of functional insights about plant biology.

The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest

Much of the complexity within cells arises from functional and regulatory interactions among proteins. The core of these interactions is increasingly known, but novel interactions continue to be discovered, and the information remains scattered across different database resources, experimental modalities and levels of mechanistic detail. The STRING database (https://string-db.org/) systematically collects and integrates protein-protein interactions-both physical interactions as well as functional associations. The data originate from a number of sources: automated text mining of the scientific literature, computational interaction predictions from co-expression, conserved genomic context, databases of interaction experiments and known complexes/pathways from curated sources. All of these interactions are critically assessed, scored, and subsequently automatically transferred to less well-studied organisms using hierarchical orthology information. The data can be accessed via the website, but also programmatically and via bulk downloads. The most recent developments in STRING (version 12.0) are: (i) it is now possible to create, browse and analyze a full interaction network for any novel genome of interest, by submitting its complement of encoded proteins, (ii) the co-expression channel now uses variational auto-encoders to predict interactions, and it covers two new sources, single-cell RNA-seq and experimental proteomics data and (iii) the confidence in each experimentally derived interaction is now estimated based on the detection method used, and communicated to the user in the web-interface. Furthermore, STRING continues to enhance its facilities for functional enrichment analysis, which are now fully available also for user-submitted genomes.

DeepVelo: Single-cell transcriptomic deep velocity field learning with neural ordinary differential equations

Recent advances in single-cell sequencing technologies have provided unprecedented opportunities to measure the gene expression profile and RNA velocity of individual cells. However, modeling transcriptional dynamics is computationally challenging because of the high-dimensional, sparse nature of the single-cell gene expression measurements and the nonlinear regulatory relationships. Here, we present DeepVelo, a neural network-based ordinary differential equation that can model complex transcriptome dynamics by describing continuous-time gene expression changes within individual cells. We apply DeepVelo to public datasets from different sequencing platforms to (i) formulate transcriptome dynamics on different time scales, (ii) measure the instability of cell states, and (iii) identify developmental driver genes via perturbation analysis. Benchmarking against the state-of-the-art methods shows that DeepVelo can learn a more accurate representation of the velocity field. Furthermore, our perturbation studies reveal that single-cell dynamical systems could exhibit chaotic properties. In summary, DeepVelo allows data-driven discoveries of differential equations that delineate single-cell transcriptome dynamics.

scHumanNet: a single-cell network analysis platform for the study of cell-type specificity of disease genes

A major challenge in single-cell biology is identifying cell-type-specific gene functions, which may substantially improve precision medicine. Differential expression analysis of genes is a popular, yet insufficient approach, and complementary methods that associate function with cell type are required. Here, we describe scHumanNet (https://github.com/netbiolab/scHumanNet), a single-cell network analysis platform for resolving cellular heterogeneity across gene functions in humans. Based on cell-type-specific gene networks (CGNs) constructed under the guidance of the HumanNet reference interactome, scHumanNet displayed higher functional relevance to the cellular context than CGNs built by other methods on single-cell transcriptome data. Cellular deconvolution of gene signatures based on network compactness across cell types revealed breast cancer prognostic markers associated with T cells. scHumanNet could also prioritize genes associated with particular cell types using CGN centrality and identified the differential hubness of CGNs between disease and healthy conditions. We demonstrated the usefulness of scHumanNet by uncovering T-cell-specific functional effects of GITR, a prognostic gene for breast cancer, and functional defects in autism spectrum disorder genes specific for inhibitory neurons. These results suggest that scHumanNet will advance our understanding of cell-type specificity across human disease genes.

In search of a Drosophila core cellular network with single-cell transcriptome data

Along with specialized functions, cells of multicellular organisms also perform essential functions common to most if not all cells. Whether diverse cells do this by using the same set of genes, interacting in a fixed coordinated fashion to execute essential functions, or a subset of genes specific to certain cells, remains a central question in biology. Here, we focus on gene coexpression to search for a core cellular network across a whole organism. Single-cell RNA-sequencing measures gene expression of individual cells, enabling researchers to discover gene expression patterns that contribute to the diversity of cell functions. Current efforts to study cellular functions focus primarily on identifying differentially expressed genes across cells. However, patterns of coexpression between genes are probably more indicative of biological processes than are the expression of individual genes. We constructed cell-type-specific gene coexpression networks using single-cell transcriptome datasets covering diverse cell types from the fruit fly, Drosophila melanogaster. We detected a set of highly coordinated genes preserved across cell types and present this as the best estimate of a core cellular network. This core is very small compared with cell-type-specific gene coexpression networks and shows dense connectivity. Gene members of this core tend to be ancient genes and are enriched for those encoding ribosomal proteins. Overall, we find evidence for a core cellular network in diverse cell types of the fruit fly. The topological, structural, functional, and evolutionary properties of this core indicate that it accounts for only a minority of essential functions.

Shared regulation and functional relevance of local gene co-expression revealed by single cell analysis

Most human genes are co-expressed with a nearby gene. Previous studies have revealed this local gene co-expression to be widespread across chromosomes and across dozens of tissues. Yet, so far these studies used bulk RNA-seq, averaging gene expression measurements across millions of cells, thus being unclear if this co-expression stems from transcription events in single cells. Here, we leverage single cell datasets in >85 individuals to identify gene co-expression across cells, unbiased by cell-type heterogeneity and benefiting from the co-occurrence of transcription events in single cells. We discover >3800 co-expressed gene pairs in two human cell types, induced pluripotent stem cells (iPSCs) and lymphoblastoid cell lines (LCLs) and (i) compare single cell to bulk RNA-seq in identifying local gene co-expression, (ii) show that many co-expressed genes – but not the majority – are composed of functionally related genes and (iii) using proteomics data, provide evidence that their co-expression is maintained up to the protein level. Finally, using single cell RNA-sequencing (scRNA-seq) and single cell ATAC-sequencing (scATAC-seq) data for the same single cells, we identify gene-enhancer associations and reveal that >95% of co-expressed gene pairs share regulatory elements. These results elucidate the potential reasons for co-expression in single cell gene regulatory networks and warrant a deeper study of shared regulatory elements, in view of explaining disease comorbidity due to affecting several genes. Our in-depth view of local gene co-expression and regulatory element co-activity advances our understanding of the shared regulatory architecture between genes.

Using single-cell data from cell lines, the co-expression of genes and co-activity of regulatory elements is analyzed, providing insight into shared architecture and regulation between genes.

Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data

BACKGROUND

Constructing gene coexpression networks is a powerful approach for analyzing high-throughput gene expression data towards module identification, gene function prediction, and disease-gene prioritization. While optimal workflows for constructing coexpression networks, including good choices for data pre-processing, normalization, and network transformation, have been developed for microarray-based expression data, such well-tested choices do not exist for RNA-seq data. Almost all studies that compare data processing and normalization methods for RNA-seq focus on the end goal of determining differential gene expression.

RESULTS

Here, we present a comprehensive benchmarking and analysis of 36 different workflows, each with a unique set of normalization and network transformation methods, for constructing coexpression networks from RNA-seq datasets. We test these workflows on both large, homogenous datasets and small, heterogeneous datasets from various labs. We analyze the workflows in terms of aggregate performance, individual method choices, and the impact of multiple dataset experimental factors. Our results demonstrate that between-sample normalization has the biggest impact, with counts adjusted by size factors producing networks that most accurately recapitulate known tissue-naive and tissue-aware gene functional relationships.

CONCLUSIONS

Based on this work, we provide concrete recommendations on robust procedures for building an accurate coexpression network from an RNA-seq dataset. In addition, researchers can examine all the results in great detail at https://krishnanlab.github.io/RNAseq_coexpression to make appropriate choices for coexpression analysis based on the experimental factors of their RNA-seq dataset.

A critical period of translational control during brain development at codon resolution

Translation modulates the timing and amplification of gene expression after transcription. Brain development requires uniquely complex gene expression patterns, but large-scale measurements of translation directly in the prenatal brain are lacking. We measure the reactants, synthesis and products of mRNA translation spanning mouse neocortex neurogenesis, and discover a transient window of dynamic regulation at mid-gestation. Timed translation upregulation of chromatin-binding proteins like Satb2, which is essential for neuronal subtype differentiation, restricts protein expression in neuronal lineages despite broad transcriptional priming in progenitors. In contrast, translation downregulation of ribosomal proteins sharply decreases ribosome biogenesis, coinciding with a major shift in protein synthesis dynamics at mid-gestation. Changing activity of eIF4EBP1, a direct inhibitor of ribosome biogenesis, is concurrent with ribosome downregulation and affects neurogenesis of the Satb2 lineage. Thus, the molecular logic of brain development includes the refinement of transcriptional programs by translation. Modeling of the developmental neocortex translatome is provided as an open-source searchable resource at https://shiny.mdc-berlin.de/cortexomics .