Assessing evolutionary and developmental transcriptome dynamics in homologous cell types

Leveraging neighborhood representations of single-cell data to achieve sensitive DE testing with miloDE

Single-cell RNA-sequencing enables testing for differential expression (DE) between conditions at a cell type level. While powerful, one of the limitations of such approaches is that the sensitivity of DE testing is dictated by the sensitivity of clustering, which is often suboptimal. To overcome this, we present miloDE-a cluster-free framework for DE testing (available as an open-source R package). We illustrate the performance of miloDE on both simulated and real data. Using miloDE, we identify a transient hemogenic endothelia-like state in mouse embryos lacking Tal1 and detect distinct programs during macrophage activation in idiopathic pulmonary fibrosis.

CD38+ Alveolar macrophages mediate early control of M. tuberculosis proliferation in the lung

Tuberculosis, caused by M.tuberculosis (Mtb), remains an enduring global health challenge, especially given the limited efficacy of current therapeutic interventions. Much of existing research has focused on immune failure as a driver of tuberculosis. However, the crucial role of host macrophage biology in controlling the disease remains underappreciated. While we have gained deeper insights into how alveolar macrophages (AMs) interact with Mtb, the precise AM subsets that mediate protection and potentially prevent tuberculosis progression have yet to be identified. In this study, we employed multi-modal scRNA-seq analyses to evaluate the functional roles of diverse macrophage subpopulations across different infection timepoints, allowing us to delineate the dynamic landscape of controller and permissive AM populations during the course of infection. Our analyses at specific time-intervals post-Mtb challenge revealed macrophage populations transitioning between distinct anti- and pro-inflammatory states. Notably, early in Mtb infection, CD38- AMs showed a muted response. As infection progressed, we observed a phenotypic shift in AMs, with CD38+ monocyte-derived AMs (moAMs) and a subset of tissue-resident AMs (TR-AMs) emerging as significant controllers of bacterial growth. Furthermore, scATAC-seq analysis of naïve lungs demonstrated that CD38+ TR-AMs possessed a distinct chromatin signature prior to infection, indicative of epigenetic priming and predisposition to a pro-inflammatory response. BCG intranasal immunization increased the numbers of CD38+ macrophages, substantially enhancing their capability to restrict Mtb growth. Collectively, our findings emphasize the pivotal, dynamic roles of different macrophage subsets in TB infection and reveal rational pathways for the development of improved vaccines and immunotherapeutic strategies.

Single-cell RNA-sequencing identifies unique cell-specific gene expression profiles in high-grade cardiac allograft vasculopathy

Background

Cardiac allograft vasculopathy (CAV), a diffuse thickening of the intima of the coronary arteries and microvasculature, is the leading cause of late graft failure and mortality after heart transplantation (HT). Diagnosis involves invasive coronary angiography, which carries substantial risk, and minimally-invasive approaches to CAV diagnosis are urgently needed. Using single-cell RNA-sequencing in peripheral blood mononuclear cells (PBMCs), we sought to identify cell-specific gene expression profiles in CAV.

Methods

Whole blood was collected from 22 HT recipients with angiographically-confirmed CAV and 18 HT recipients without CAV. PBMCs were isolated and subjected to single-cell RNA-sequencing using a 10X Genomics microfluidic platform. Downstream analyses focused on differential expression of genes, cell compositional changes, and T cell receptor repertoire analyses.

Results

Across 40 PBMC samples, we isolated 134,984 cells spanning 8 major clusters and 31 subclusters of cell types. Compositional analyses showed subtle, but significant increases in CD4+ T central memory cells, and CD14+ and CD16+ monocytes in high-grade CAV (CAV-2 and CAV-3) as compared to low-grade or absent CAV. After adjusting for age, gender, and prednisone use, 745 genes were differentially expressed in a cell-specific manner in high-grade CAV. Weighted gene co-expression network analyses showed enrichment for putative pathways involved in inflammation and angiogenesis. There were no significant differences in T cell clonality or diversity with increasing CAV severity.

Conclusions

Unbiased whole transcriptomic analyses at single-cell resolution identify unique, cell-specific gene expression patterns in CAV, suggesting the potential utility of peripheral gene expression biomarkers in diagnosing CAV.

Building and analyzing metacells in single-cell genomics data

The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells-and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data ( https://github.com/GfellerLab/MetacellAnalysisTutorial ) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods ( https://github.com/GfellerLab/MetacellAnalysisToolkit ).

scCDC: a computational method for gene-specific contamination detection and correction in single-cell and single-nucleus RNA-seq data

In droplet-based single-cell and single-nucleus RNA-seq assays, systematic contamination of ambient RNA molecules biases the quantification of gene expression levels. Existing methods correct the contamination for all genes globally. However, there lacks specific evaluation of correction efficacy for varying contamination levels. Here, we show that DecontX and CellBender under-correct highly contaminating genes, while SoupX and scAR over-correct lowly/non-contaminating genes. Here, we develop scCDC as the first method to detect the contamination-causing genes and only correct expression levels of these genes, some of which are cell-type markers. Compared with existing decontamination methods, scCDC excels in decontaminating highly contaminating genes while avoiding over-correction of other genes.

Functional genomics and systems biology in human neuroscience

Neuroscience research has entered a phase of key discoveries in the realm of neurogenomics owing to strong financial and intellectual support for resource building and tool development. The previous challenge of tissue heterogeneity has been met with the application of techniques that can profile individual cells at scale. Moreover, the ability to perturb genes, gene regulatory elements and neuronal activity in a cell-type-specific manner has been integrated with gene expression studies to uncover the functional underpinnings of the genome at a systems level. Although these insights have necessarily been grounded in model systems, we now have the opportunity to apply these approaches in humans and in human tissue, thanks to advances in human genetics, brain imaging and tissue collection. We acknowledge that there will probably always be limits to the extent to which we can apply the genomic tools developed in model systems to human neuroscience; however, as we describe in this Perspective, the neuroscience field is now primed with an optimal foundation for tackling this ambitious challenge. The application of systems-level network analyses to these datasets will facilitate a deeper appreciation of human neurogenomics that cannot otherwise be achieved from directly observable phenomena.

Aberrant pace of cortical neuron development in brain organoids from patients with 22q11.2 deletion syndrome and schizophrenia

Adults and children afflicted with the 22q11.2 deletion syndrome (22q11.2DS) exhibit cognitive, social, and emotional impairments, and are at significantly heightened risk for schizophrenia (SCZ). The impact of this deletion on early human brain development, however, has remained unclear. Here we harness organoid models of the developing human cerebral cortex, cultivated from subjects with 22q11.2DS and SCZ, as well as unaffected control samples, to identify cell-type-specific developmental abnormalities arising from this genomic lesion. Leveraging single-cell RNA-sequencing in conjunction with experimental validation, we find that the loss of genes within the 22q11.2 locus leads to a delayed development of cortical neurons. This compromised development was reflected in an elevated proportion of actively proliferating neural progenitor cells, coupled with a decreased fraction of more mature neurons. Furthermore, we identify perturbed molecular imprints linked to neuronal maturation, observe the presence of sparser neurites, and note a blunted amplitude in glutamate-induced Ca2+ transients. The aberrant transcription program underlying impaired development contains molecular signatures significantly enriched in neuropsychiatric genetic liability. MicroRNA profiling and target gene investigation suggest that microRNA dysregulation may drive perturbations of genes governing the pace at which maturation unfolds. Using protein-protein interaction network analysis we define complementary effects stemming from additional genes residing within the deleted locus. Our study uncovers reproducible neurodevelopmental and molecular alterations due to 22q11.2 deletions. These findings have the potential to facilitate disease modeling and promote the pursuit of therapeutic interventions.

scGEM: Unveiling the Nested Tree-Structured Gene Co-Expressing Modules in Single Cell Transcriptome Data

BACKGROUND

Single-cell transcriptome analysis has fundamentally changed biological research by allowing higher-resolution computational analysis of individual cells and subsets of cell types. However, few methods have met the need to recognize and quantify the underlying cellular programs that determine the specialization and differentiation of the cell types.

METHODS

In this study, we present scGEM, a nested tree-structured nonparametric Bayesian model, to reveal the gene co-expression modules (GEMs) reflecting transcriptome processes in single cells.

RESULTS

We show that scGEM can discover shared and specialized transcriptome signals across different cell types using peripheral blood mononuclear single cells and early brain development single cells. scGEM outperformed other methods in perplexity and topic coherence (p < 0.001) on our simulation data. Larger datasets, deeper trees and pre-trained models are shown to be positively associated with better scGEM performance. The GEMs obtained from triple-negative breast cancer single cells exhibited better correlations with lymphocyte infiltration (p = 0.009) and the cell cycle (p < 0.001) than other methods in additional validation on the bulk RNAseq dataset.

CONCLUSIONS

Altogether, we demonstrate that scGEM can be used to model the hidden cellular functions of single cells, thereby unveiling the specialization and generalization of transcriptomic programs across different types of cells.

Integrative in situ mapping of single-cell transcriptional states and tissue histopathology in a mouse model of Alzheimer's disease

Complex diseases are characterized by spatiotemporal cellular and molecular changes that may be difficult to comprehensively capture. However, understanding the spatiotemporal dynamics underlying pathology can shed light on disease mechanisms and progression. Here we introduce STARmap PLUS, a method that combines high-resolution spatial transcriptomics with protein detection in the same tissue section. As proof of principle, we analyze brain tissues of a mouse model of Alzheimer's disease at 8 and 13 months of age. Our approach provides a comprehensive cellular map of disease progression. It reveals a core-shell structure where disease-associated microglia (DAM) closely contact amyloid-β plaques, whereas disease-associated astrocyte-like (DAA-like) cells and oligodendrocyte precursor cells (OPCs) are enriched in the outer shells surrounding the plaque-DAM complex. Hyperphosphorylated tau emerges mainly in excitatory neurons in the CA1 region and correlates with the local enrichment of oligodendrocyte subtypes. The STARmap PLUS method bridges single-cell gene expression profiles with tissue histopathology at subcellular resolution, providing a tool to pinpoint the molecular and cellular changes underlying pathology.

Identification of Transcription Factor Networks during Mouse Hindlimb Development

Mammalian hindlimb development involves a variety of cells and the regulation of spatiotemporal molecular events, but regulatory networks of transcription factors contributing to hindlimb morphogenesis are not well understood. Here, we identified transcription factor networks during mouse hindlimb morphology establishment through transcriptome analysis. We used four stages of embryonic hindlimb transcription profiles acquired from the Gene Expression Omnibus database (GSE30138), including E10.5, E11.5, E12.5 and E13.5, to construct a gene network using Weighted Gene Co-expression Network Analysis (WGCNA), and defined seven stage-associated modules. After filtering 7625 hub genes, we further prioritized 555 transcription factors with AnimalTFDB3.0. Gene ontology enrichment showed that transcription factors of different modules were enriched in muscle tissue development, connective tissue development, embryonic organ development, skeletal system morphogenesis, pattern specification process and urogenital system development separately. Six regulatory networks were constructed with key transcription factors, which contribute to the development of different tissues. Knockdown of four transcription factors from regulatory networks, including Sox9, Twist1, Snai2 and Klf4, showed that the expression of limb-development-related genes was also inhibited, which indicated the crucial role of transcription factor networks in hindlimb development.

Transcriptome and Metabolome Analysis Provides Insights into the Heterosis of Yield and Quality Traits in Two Hybrid Rice Varieties (Oryza sativa L.)

Heterosis is a common biological phenomenon that is useful for breeding superior lines. Using heterosis to increase the yield and quality of crops is one of the main achievements of modern agricultural science. In this study, we analysed the transcriptome and metabolome of two three-line hybrid rice varieties, Taiyou 871 (TY871), and Taiyou 398 (TY398) and the parental grain endosperm using RNA-seq (three biological repeats per variety) and untargeted metabolomic (six biological repeats per variety) methods. TY871 and TY398 showed specific heterosis in yield and quality. Transcriptome analysis of the hybrids revealed 638 to 4059 differentially expressed genes in the grain when compared to the parents. Metabolome analysis of the hybrids revealed 657 to 3714 differential grain metabolites when compared to the parents. The honeydew1 and grey60 module core genes Os04g0350700 and Os05g0154700 are involved in the regulation of awn development, grain size, and grain number, as well as the regulation of grain length and plant height, respectively. Rice grain length may be an important indicator for improving the quality of three-line hybrid rice. In addition, the rice quality-related metabolite NEG_M341T662 was highly connected to the module core genes Os06g0254300 and Os03g0168100. The functions of Os06g0254300 and Os03g0168100 are EF-hand calcium binding protein and late embroideries absolute protein repeat containing protein, respectively. These genes may play a role in the formation of rice quality. We constructed a gene and metabolite coexpression network, which provides a scientific basis for the utilization of heterosis in producing high-yield and high-quality hybrid rice.

The emerging double-edged sword role of Sirtuins in the gastric inflammation-carcinoma sequence revealed by bulk and single-cell transcriptomes

Histone modification and the inflammation-carcinoma sequence (ICS) have been acknowledgedly implicated in gastric carcinogenesis. However, the extremum expression of some histone modification genes (HMGs) in intestinal metaplasia (IM) rather than GC obscures the roles of HMGs in ICS. In this study, we assumed an explanation that the roles of HMGs in ICS were stage specific. Bulk RNA-seq on endoscopy biopsy samples from a total of 50 patients was accompanied by reanalysis of a set of published single-cell transcriptomes, which cross-sectionally profiled the transcriptomic features of chronic superficial gastritis (SG), atrophy gastritis (AG), IM, and early gastric cancer (GC). Differential analysis observed significantly peaked expression of SIRT6 and SIRT7 at IM. Weighted correlation network analysis on bulk transcriptome recognized significant correlations between SIRT1/6 and IM. The single-cell atlas identified one subgroup of B cells expressing high level of TFF1 (TFF1^hi naive B cell) that theoretically played important roles in defending microbial infection, while SIRT6 displayed a positive correlation with TFF1^low naive B cells. Moreover, gene set enrichment analysis at different lesions (SG-AG, AG-IM, and IM-GC) highlighted that gene sets contributing to IM, e.g., Brush Border, were largely enriched from co-expressing genes of Sirtuins (SIRTs) in AG-IM. Surveys of the genes negatively correlated with SIRT6 in public databases considered SIRT6 as tumor suppressors, which was confirmed by the cell proliferation and migration assays after transient transfection of SIRT6 overexpression vector into AGS cells. All the above observations were then confirmed by serial section-based immunohistochemistry against Ki-67, MUC2, MUC5AC, p53, and SIRT6 on the endoscopic submucosal dissection tissue. By contrast, the expression of the other HMGs varied even opposite within same family. Taken together, this study preliminarily demonstrated the two-edged sword role of SIRTs in ICS and, by extension, showed that the roles of HMGs in ICS were probably stage specific. Our study may provide new insights into and attract attention on gastric prevention and therapy targeting HMGs.

Gene expression data analysis using Hellinger correlation in weighted gene co-expression networks (WGCNA)

Weighted gene co-expression network analysis (WGCNA) is used to detect clusters with highly correlated genes. Measurements of correlation most typically rely on linear relationships. However, a linear relationship does not always model pairwise functional-related dependence between genes. In this paper, we first compared 6 different correlation methods in their ability to capture complex dependence between genes in three different tissues. Next, we compared their gene-pairwise coefficient results and corresponding WGCNA results. Finally, we applied a recently proposed correlation method, Hellinger correlation, as a more sensitive correlation measurement in WGCNA. To test this method, we constructed gene networks containing co-expression gene modules from RNA-seq data of human frontal cortex from Alzheimer's disease patients. To test the generality, we also used a microarray data set from human frontal cortex, single cell RNA-seq data from human prefrontal cortex, RNA-seq data from human temporal cortex, and GTEx data from heart. The Hellinger correlation method captures essentially similar results as other linear correlations in WGCNA, but provides additional new functional relationships as exemplified by uncovering a link between inflammation and mitochondria function. We validated the network constructed with the microarray and single cell sequencing data sets and a RNA-seq dataset of temporal cortex. We observed that this new correlation method enables the detection of non-linear biologically meaningful relationships among genes robustly and provides a complementary new approach to WGCNA. Thus, the application of Hellinger correlation to WGCNA provides a more flexible correlation approach to modelling networks in gene expression analysis that uncovers novel network relationships.

Tracing the cell-type-specific modules of immune responses during COVID-19 progression using scDisProcema

COVID-19 has caused severe threats to lives and damage to property worldwide. The immunopathology of the disease is of particular concern. Currently, researchers have used gene co-expression networks (GCNs) to deepen the study of molecular mechanisms of immune responses to COVID-19. However, most efforts have not fully explored dynamic changes of cell-type-specific molecular networks in the disease process. This study proposes a GCN construction pipeline named single-cell Disease Progression cellular module analysis (scDisProcema), which can trace dynamic changes of immune system response during disease progression using single-cell data. Here, scDisProcema considers changes in cell fate and expression patterns during disease development, identifying gene modules responsible for different immune cells. The hub genes are screened for each module by the specific expression level and the intercellular connectivity of modules. Based on functional items enriched by each gene module, we elucidate the biological processes of different cells involved in disease development and explain the molecular mechanisms underlying the process of cell depletion or proliferation caused by disease. Compared with traditional WGCNA methods, scDisProcema can make more convenient use of the heterogeneity information provided by scRNA-seq data and has great potential in exploring molecular changes during disease progression and organ development.

Marsupials and Multi-Omics: Establishing New Comparative Models of Neural Crest Patterning and Craniofacial Development

Studies across vertebrates have revealed significant insights into the processes that drive craniofacial morphogenesis, yet we still know little about how distinct facial morphologies are patterned during development. Studies largely point to evolution in GRNs of cranial progenitor cell types such as neural crest cells, as the major driver underlying adaptive cranial shapes. However, this hypothesis requires further validation, particularly within suitable models amenable to manipulation. By utilizing comparative models between related species, we can begin to disentangle complex developmental systems and identify the origin of species-specific patterning. Mammals present excellent evolutionary examples to scrutinize how these differences arise, as sister clades of eutherians and marsupials possess suitable divergence times, conserved cranial anatomies, modular evolutionary patterns, and distinct developmental heterochrony in their NCC behaviours and craniofacial patterning. In this review, I lend perspectives into the current state of mammalian craniofacial biology and discuss the importance of establishing a new marsupial model, the fat-tailed dunnart, for comparative research. Through detailed comparisons with the mouse, we can begin to decipher mammalian conserved, and species-specific processes and their contribution to craniofacial patterning and shape disparity. Recent advances in single-cell multi-omics allow high-resolution investigations into the cellular and molecular basis of key developmental processes. As such, I discuss how comparative evolutionary application of these tools can provide detailed insights into complex cellular behaviours and expression dynamics underlying adaptive craniofacial evolution. Though in its infancy, the field of "comparative evo-devo-omics" presents unparalleled opportunities to precisely uncover how phenotypic differences arise during development.