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Single-cell RNA sequencing reveals a high-resolution cell atlas of petals in Prunus mume at different flowering development stages.
Prunus mume (mei), a traditional ornamental plant in China, is renowned for its fragrant flowers, primarily emitted by its petals. However, the cell types of mei petals and where floral volatile synthesis occurs are rarely reported. The study used single-cell RNA sequencing to characterize the gene expression landscape in petals of P. mume 'Fenhong Zhusha' at budding stage (BS) and full-blooming stage (FS). Six major cell types of petals were identified: epidermal cells (ECs), parenchyma cells (PCs), xylem parenchyma cells, phloem parenchyma cells, xylem vessels and fibers, and sieve elements and companion cells complex. Cell-specific marker genes in each cell type were provided. Floral volatiles from mei petals were measured at four flowering development stages, and their emissions increased from BS to FS, and decreased at the withering stage. Fifty-eight differentially expressed genes (DEGs) in benzenoid/phenylpropanoid pathway were screened using bulk RNA-seq data. Twenty-eight DEGs expression increased from BS to FS, indicating that they might play roles in floral volatile synthesis in P. mume, among which PmBAHD3 would participate in benzyl acetate synthesis. ScRNA-seq data showed that 27 DEGs mentioned above were expressed variously in different cell types. In situ hybridization confirmed that PmPAL2, PmCAD1, PmBAHD3,5, and PmEGS1 involved in floral volatile synthesis in mei petals are mainly expressed in EC, PC, and most vascular tissues, consistent with scRNA-seq data. The result indicates that benzyl acetate and eugenol, the characteristic volatiles in mei, are mostly synthesized in these cell types. The first petal single-cell atlas was constructed, offering new insights into the molecular mechanism of floral volatile synthesis.
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Single cell atlas for 11 non-model mammals, reptiles and birds.
The availability of viral entry factors is a prerequisite for the cross-species transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Large-scale single-cell screening of animal cells could reveal the expression patterns of viral entry genes in different hosts. However, such exploration for SARS-CoV-2 remains limited. Here, we perform single-nucleus RNA sequencing for 11 non-model species, including pets (cat, dog, hamster, and lizard), livestock (goat and rabbit), poultry (duck and pigeon), and wildlife (pangolin, tiger, and deer), and investigated the co-expression of ACE2 and TMPRSS2. Furthermore, cross-species analysis of the lung cell atlas of the studied mammals, reptiles, and birds reveals core developmental programs, critical connectomes, and conserved regulatory circuits among these evolutionarily distant species. Overall, our work provides a compendium of gene expression profiles for non-model animals, which could be employed to identify potential SARS-CoV-2 target cells and putative zoonotic reservoirs.
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Application of CIBERSORTx and BayesPrism to deconvolution of bulk RNA-seq data from human myocardium and skeletal muscle.
RNA-sequencing (RNA-seq) is an important tool to explore molecular mechanisms of disease. Technological advances mean this can be performed at the single-cell level, but the large sample sizes needed in clinical studies are currently prohibitively expensive and complex. Deconvolution of bulk RNA-seq offers an opportunity to bridge this gap by defining the cell lineage composition of samples. This approach is widely used in immunology studies, but currently there are no validated pipelines for researchers analysing human myocardium or skeletal muscle. Here, we describe the application and in silico validation of two pipelines to deconvolute human right atrium, left ventricle and skeletal muscle bulk RNA-seq data. Specifically, we have defined the major cell lineages of these tissues using single cell/nucleus RNA-seq data from the Heart Cell Atlas, which are then applied during deconvolution using the CIBERSORTx or BayesPrism deconvolution packages. Both pipelines gave robust estimates of the proportion of all major cell lineages in these tissues. We demonstrate their value in defining age- and sex-differences in tissue composition using bulk RNA-seq data from the GTEx consortium. Our validated pipelines can be rapidly applied by researchers working with existing or novel bulk RNA-seq of myocardium or skeletal muscle to gain novel insights.
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Standardizing single-cell approaches to osteoarthritis: Toward a comprehensive cellular atlas.
Osteoarthritis (OA) is a degenerative joint disease marked by progressive cartilage degradation and complex cellular heterogeneity. In recent years, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for dissecting the cellular composition of the osteoarthritic joint. However, constructing a complete and coherent picture of the OA single-cell landscape remains challenging, akin to assembling a puzzle from multiple sets, each with pieces of varying shapes and sizes. This editorial outlines the next steps in advancing single-cell research in OA, emphasizing the need for standardized cell type annotations and comprehensive, integrative access to both historical and newly generated datasets.
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Exploring the utility of snRNA-seq in profiling human bladder tissue: A comprehensive comparison with scRNA-seq.
Single cell sequencing technologies have revolutionized our understanding of biology by mapping cell diversity and gene expression in healthy and diseased tissues. While single-cell RNA sequencing (scRNA-seq) has been widely used, interest in single-nucleus RNA sequencing (snRNA-seq) is growing due to its benefits, including the ability to analyze archival tissues and capture rare cell types that are challenging to dissociate. However, comparative studies across tissues have yielded mixed results, with some reporting enhanced cell type retention using snRNA-seq while others finding cell type identification to be challenging in snRNA-seq data. The GUDMAP consortium aims to construct a molecular atlas of the lower urinary tract (LUT); thus, we set out to determine the strengths and limitations of each approach in characterizing LUT cell types. Using the human bladder, we determined that scRNA-seq offered more discriminative gene sets for identification while snRNA-seq could facilitate capture of previously underrepresented cell types.
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Comprehensive single-cell transcriptional profiling of a multicellular organism.
To resolve cellular heterogeneity, we developed a combinatorial indexing strategy to profile the transcriptomes of single cells or nuclei, termed sci-RNA-seq (single-cell combinatorial indexing RNA sequencing). We applied sci-RNA-seq to profile nearly 50,000 cells from the nematode Caenorhabditis elegans at the L2 larval stage, which provided >50-fold "shotgun" cellular coverage of its somatic cell composition. From these data, we defined consensus expression profiles for 27 cell types and recovered rare neuronal cell types corresponding to as few as one or two cells in the L2 worm. We integrated these profiles with whole-animal chromatin immunoprecipitation sequencing data to deconvolve the cell type-specific effects of transcription factors. The data generated by sci-RNA-seq constitute a powerful resource for nematode biology and foreshadow similar atlases for other organisms.
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The Human Protein Atlas - an important resource for basic and clinical research.
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Novel insights into adipose tissue heterogeneity.
When normalized to volume, adipose tissue is comprised mainly of large lipid metabolizing and storing cells called adipocytes. Strikingly, the numerical representation of non-adipocytes, composed of a wide variety of cell types found in the so-called stromal vascular fraction (SVF), outnumber adipocytes by far. Besides its function in energy storage, adipose tissue has emerged as a versatile organ that regulates systemic metabolism and has therefore constituted an attractive target for the treatment of metabolic diseases. Recent high-resolution single cells/nucleus RNA seq data exemplify an intriguingly profound diversity of both adipocytes and SVF cells in all adipose depots, and the current data, while limited, demonstrate the significance of the intra-tissue cell composition in shaping the overall functionality of this tissue. Due to the complexity of adipose tissue, our understanding of the biological relevance of this heterogeneity and plasticity is fractional. Therefore, establishing atlases of adipose tissue cell heterogeneity is the first step towards generating an understanding of these functionalities. In this review, we will describe the current knowledge on adipose tissue cell composition and the heterogeneity of single-cell RNA sequencing, including the technical limitations.
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An Atlas of Thyroid Hormone Receptors' Target Genes in Mouse Tissues.
We gathered available RNA-seq and ChIP-seq data in a single database to better characterize the target genes of thyroid hormone receptors in several cell types. This database can serve as a resource to analyze the mode of action of thyroid hormone (T3). Additionally, it is an easy-to-use and convenient tool to obtain information on specific genes regarding T3 regulation or to extract large gene lists of interest according to the users' criteria. Overall, this atlas is a unique compilation of recent sequencing data focusing on T3, its receptors, modes of action, targets and roles, which may benefit researchers within the field. A preliminary analysis indicates extensive variations in the repertoire of target genes where transcription is upregulated by chromatin-bound nuclear receptors. Although it has a major influence, chromatin accessibility is not the only parameter that determines the cellular selectivity of the hormonal response.
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A comprehensive cell atlas of fall armyworm (Spodoptera frugiperda) larval gut and fat body via snRNA-Seq.
The midgut and fat body of insects control key physiological processes, including growth, digestion, metabolism, and stress response. Single-nucleus RNA sequencing (snRNA-seq) is a promising way to reveal organ complexity at the cellular level, yet data for lepidopteran insects are lacking. We utilized snRNA-seq to assess cellular diversity in the midgut and fat body of Spodoptera frugiperda. Our study identified 20 distinct clusters in the midgut, including enterocytes, enteroendocrine, stem-like cells, and muscle cells, and 27 clusters in the fat body, including adipocytes, hemocytes, and epithelial cells. This dataset, containing all identified cell types in midgut and fat body, is valuable for characterizing the cellular composition of these organs and uncovering new cell-specific biomarkers. This cellular atlas enhances our understanding of cellular heterogeneity of fat and midgut, serving as a basis for future functional and comparative analyses. As the first snRNA-seq study on the midgut and fat body of S. frugiperda, it will also support future research, contribute to lepidopteran studies, and aid in developing targeted pest control strategies.