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Using Single-Cell RNA-Seq Data to Trace Tissue Cells Responsive to Thyroid Hormones.
Thyroid hormones mediate a remarkable range of functions in many tissues and organ systems through the thyroid hormone receptors-THRA and THRB. Tissues and organs are composed of heterogeneous cells of different cell types. These different cell types have varying receptor expression abilities, which lead to variable responses in thyroid hormone regulation. The tissue-specific Thra and Thrb gene expression patterns help us understand the action of thyroid hormones at the tissue level. However, the situation becomes complicated if we wish to focus on tissues more closely to trace the responsive cells, which is a vital step in the process of understanding the molecular mechanism of diseases related to thyroid hormone regulation. Single-cell RNA sequencing technology is a powerful tool used to profile gene expression programs in individual cells. The Tabula Muris Consortium generates a single-cell transcriptomic atlas across the life span of Mus musculus that includes data from 23 tissues and organs. It provides an unprecedented opportunity to understand thyroid hormone regulation at the cell type resolution. We demonstrated the approaches that allow application of the single-cell RNA-Seq data generated by the Tabula Muris Consortium to trace responsive cells in tissues. First, employing the single-cell RNA-Seq data, we calculated the ability of different cell types to express Thra and Thrb, which direct us to the cell types sensitive to thyroid hormone regulation in tissues and organs. Next, using a cell clustering algorithm, we explored the subtypes with low Thra or Thrb expression within the different cell types and identified the potentially responsive cell subtypes. Finally, in the liver tissue treated with thyroid hormones, using the single-cell RNA-Seq data, we successfully traced the responsive cell types. We acknowledge that the computational predictions reported here need to be further validated using wet-lab experiments. However, we believe our results provide powerful information and will be beneficial for wet lab researchers.
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Evaluation of deep learning-based feature selection for single-cell RNA sequencing data analysis.
BACKGROUND: Feature selection is an essential task in single-cell RNA-seq (scRNA-seq) data analysis and can be critical for gene dimension reduction and downstream analyses, such as gene marker identification and cell type classification. Most popular methods for feature selection from scRNA-seq data are based on the concept of differential distribution wherein a statistical model is used to detect changes in gene expression among cell types. Recent development of deep learning-based feature selection methods provides an alternative approach compared to traditional differential distribution-based methods in that the importance of a gene is determined by neural networks. RESULTS: In this work, we explore the utility of various deep learning-based feature selection methods for scRNA-seq data analysis. We sample from Tabula Muris and Tabula Sapiens atlases to create scRNA-seq datasets with a range of data properties and evaluate the performance of traditional and deep learning-based feature selection methods for cell type classification, feature selection reproducibility and diversity, and computational time. CONCLUSIONS: Our study provides a reference for future development and application of deep learning-based feature selection methods for single-cell omics data analyses.
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Dissociation of Tissues for Single-Cell Analysis.
It is now a routine to carry out single-cell RNA-Seq to define the gene expression patterns of thousands of cells, thereby revolutionizing many areas of research. Projects are underway to use these techniques to create an atlas of the expressed genes in all cell types of the human body. Here we describe cold-active protease methods for single-cell dissociation of organs and tissues that better preserve the in vivo gene expression patterns.
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Single-cell RNA sequencing reveals a high-resolution cell atlas of xylem in Populus.
High-throughput single-cell RNA sequencing (scRNA-seq) has advantages over traditional RNA-seq to explore spatiotemporal information on gene dynamic expressions in heterogenous tissues. We performed Drop-seq, a method for the dropwise sequestration of single cells for sequencing, on protoplasts from the differentiating xylem of Populus alba × Populus glandulosa. The scRNA-seq profiled 9,798 cells, which were grouped into 12 clusters. Through characterization of differentially expressed genes in each cluster and RNA in situ hybridizations, we identified vessel cells, fiber cells, ray parenchyma cells and xylem precursor cells. Diffusion pseudotime analyses revealed the differentiating trajectory of vessels, fiber cells and ray parenchyma cells and indicated a different differentiation process between vessels and fiber cells, and a similar differentiation process between fiber cells and ray parenchyma cells. We identified marker genes for each cell type (cluster) and key candidate regulators during developmental stages of xylem cell differentiation. Our study generates a high-resolution expression atlas of wood formation at the single cell level and provides valuable information on wood formation.
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Development of a single-cell atlas for woodland strawberry (Fragaria vesca) leaves during early Botrytis cinerea infection using single-cell RNA-seq.
Pathogen invasion leads to fast, local-to-systemic signal transduction that initiates plant defense responses. Despite tremendous progress in past decades, aspects of this process remain unknown, such as which cell types respond first and how signals are transferred among cell types. Here, we used single-cell RNA-seq of >50 000 single cells to document the gene expression landscape in leaves of woodland strawberry during infection by Botrytis cinerea and identify major cell types. We constructed a single-cell atlas and characterized the distinct gene expression patterns of hydathode, epidermal, and mesophyll cells during the incubation period of B. cinerea infection. Pseudotime trajectory analysis revealed signals of the transition from normal functioning to defense response in epidermal and mesophyll cells upon B. cinerea infection. Genes related to disease resistance showed different expression patterns among cell types: disease resistance-related genes and genes encoding transcription factors were highly expressed in individual cell types and interacted to trigger plant systemic immunity to B. cinerea. This is the first report to document the single-cell transcriptional landscape of the plant pathogenic invasion process; it provides new insights into the holistic dynamics of host-pathogen interactions and can guide the identification of genes and the formulation of strategies for resistant cultivar development.
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Enhanced interpretation of immune cell phenotype and function through a rhesus macaque single-cell atlas.
Single-cell RNA sequencing (scRNA-seq) allows cell classification using genome-wide transcriptional state; however, high-dimensional transcriptomic profiles, and the unsupervised analyses employed to interpret them, provide a systematically different view of biology than well-established functional/lineage definitions of immunocytes. Understanding these differences and limits is essential for accurate interpretation of these rich data. We present the Rhesus Immune Reference Atlas (RIRA), the first immune-focused macaque single-cell multi-tissue atlas. We contrasted transcriptional profiles against immune lineages, using surface protein and marker genes as ground truth. While the pattern of clustering can align with cell type, this is not always true. Especially within T and natural killer (NK) cells, many functionally distinct subsets lack defining markers, and strong shared expression programs, such as cytotoxicity, result in systematic intermingling by unsupervised clustering. We identified gene programs with high discriminatory/diagnostic value, including multi-gene signatures that model T/NK cell maturation. Directly measuring these diagnostic programs complements unsupervised analyses.
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SCDevDB: A Database for Insights Into Single-Cell Gene Expression Profiles During Human Developmental Processes.
Single-cell RNA-seq studies profile thousands of cells in developmental processes. Current databases for human single-cell expression atlas only provide search and visualize functions for a selected gene in specific cell types or subpopulations. These databases are limited to technical properties or visualization of single-cell RNA-seq data without considering the biological relations of their collected cell groups. Here, we developed a database to investigate single-cell gene expression profiling during different developmental pathways (SCDevDB). In this database, we collected 10 human single-cell RNA-seq datasets, split these datasets into 176 developmental cell groups, and constructed 24 different developmental pathways. SCDevDB allows users to search the expression profiles of the interested genes across different developmental pathways. It also provides lists of differentially expressed genes during each developmental pathway, T-distributed stochastic neighbor embedding maps showing the relationships between developmental stages based on these differentially expressed genes, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analysis results of these differentially expressed genes. This database is freely available at https://scdevdb.deepomics.org.
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Gene expression atlas of a developing tissue by single cell expression correlation analysis.
The Drosophila wing disc has been a fundamental model system for the discovery of key signaling pathways and for our understanding of developmental processes. However, a complete map of gene expression in this tissue is lacking. To obtain a gene expression atlas in the wing disc, we employed single cell RNA sequencing (scRNA-seq) and developed a method for analyzing scRNA-seq data based on gene expression correlations rather than cell mapping. This enables us to compute expression maps for all detected genes in the wing disc and to discover 824 genes with spatially restricted expression patterns. This approach identifies clusters of genes with similar expression patterns and functional relevance. As proof of concept, we characterize the previously unstudied gene CG5151 and show that it regulates Wnt signaling. Our method will enable the leveraging of scRNA-seq data for generating expression atlases of undifferentiated tissues during development.
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Single-cell RNA-sequencing of retrieved human oocytes and eggs in clinical practice and for human ovarian cell atlasing.
With the advancement of single-cell separation techniques and high-throughput sequencing platforms, single-cell RNA-sequencing (scRNA-seq) has emerged as a vital technology for understanding tissue and organ systems at cellular resolution. Through transcriptional analysis, it is possible to characterize unique or rare cell types, interpret their interactions, and reveal novel functional states or shifts in developmental stages. As such, this technology is uniquely suited for studying the cells within the human ovary. The ovary is a cellularly heterogeneous organ that houses follicles, the reproductive and endocrine unit that consists of an oocyte surrounded by hormone-producing support cells, as well as many other cell populations constituting stroma, vasculature, lymphatic, and immune components. Here we review studies that have utilized scRNA-seq technology to analyze cells from healthy human ovaries and discuss the single-cell isolation techniques used. We identified two overarching applications for scRNA-seq in the human ovary. The first applies this technology to investigate transcriptional differences in oocytes/eggs from patients undergoing in vitro fertilization treatments to ultimately improve clinical outcomes. The second utilizes scRNA-seq for the pursuit of creating a comprehensive single-cell atlas of the human ovary. The knowledge gained from these studies underscores the importance of scRNA-seq technologies in unlocking a new biological understanding of the human ovary.
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Time-resolved single-cell atlas identifies the spatiotemporal transcription dynamics in vernalization response in Brassica rapa.
Many temperate plants require vernalization, a prolonged low-temperature period, to accelerate flowering. Vernalization is a quantitative process whereby extended cold exposure establishes a stable transcriptional repression, with the degree of silencing correlating with the length of cold treatment. While much is known about the genes regulating this process, the expression dynamics at the single-cell level remain elusive. Using single-cell RNA sequencing, we analyze the vernalization response in Brassica rapa. Our data show that mesophyll cells exhibit the most significant changes in gene expression at low temperatures, whereas vasculature exhibits higher expression levels of flowering-related genes. Mesophyll trajectory analyses suggest that B. rapa plants undergo a biphasic response to chill stress during vernalization. Tissue-wide BrFLC expression changes result from variations in the proportion of expressing cells, supporting the quantitative nature of vernalization through digital cell responses. This study provides valuable resources and insights into the spatiotemporal regulation of flowering during vernalization.