Single-nucleus RNA-seq resolves spatiotemporal developmental trajectories in the tomato shoot apex

Single cell transcriptomics is revolutionizing our understanding of development and response to environmental cues1–3. Recent advances in single cell RNA sequencing (scRNA-seq) technology have enabled profiling gene expression pattern of heterogenous tissues and organs at single cellular level and have been widely applied in human and animal research4,5. Nevertheless, the existence of cell walls significantly encumbered its application in plant research. Protoplasts have been applied for scRNA-seq analysis, but mostly restricted to tissues amenable for wall digestion, such as root tips6–10. However, many cell types are resistant to protoplasting, and protoplasting may yield ectopic gene expression and bias proportions of cell types. Here we demonstrate a method with minimal artifacts for high-throughput single-nucleus RNA sequencing (snRNA-Seq) that we use to profile tomato shoot apex cells. The obtained high-resolution expression atlas identifies numerous distinct cell types covering major shoot tissues and developmental stages, delineates developmental trajectories of mesophyll cells, vasculature cells, epidermal cells, and trichome cells. In addition, we identify key developmental regulators and reveal their hierarchy. Collectively, this study demonstrates the power of snRNA-seq to plant research and provides an unprecedented spatiotemporal gene expression atlas of heterogeneous shoot cells.

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Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

10.1101/786285 ◽

2019 ◽

Cited By ~ 4

Author(s):

Marcus Alvarez ◽

Elior Rahmani ◽

Brandon Jew ◽

Kristina M. Garske ◽

Zong Miao ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Cell Types ◽

Supervised Machine Learning ◽

Data Sets ◽

Rna Seq ◽

Novel Approach ◽

Single Nucleus ◽

Downstream Analysis

AbstractSingle-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. Contrary to single-cell RNA seq (scRNA-seq), we observe that snRNA-seq is commonly subject to contamination by high amounts of extranuclear background RNA, which can lead to identification of spurious cell types in downstream clustering analyses if overlooked. We present a novel approach to remove debris-contaminated droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: 1) human differentiating preadipocytes in vitro, 2) fresh mouse brain tissue, and 3) human frozen adipose tissue (AT) from six individuals. All three data sets showed various degrees of extranuclear RNA contamination. We observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq data, we also successfully applied DIEM to single-cell data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.

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Single Cell, Single Nucleus and Spatial RNA Sequencing of the Human Liver Identifies Hepatic Stellate Cell and Cholangiocyte Heterogeneity

10.1101/2021.03.27.436882 ◽

2021 ◽

Author(s):

Tallulah S Andrews ◽

Jawairia Atif ◽

Jeff C Liu ◽

Catia T Perciani ◽

Xue-Zhong Ma ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Human Liver ◽

Stellate Cell ◽

Parenchymal Cell ◽

Cell Types ◽

Cell Populations ◽

Healthy Human ◽

Single Nucleus

The critical functions of the human liver are coordinated through the interactions of hepatic parenchymal and non-parenchymal cells. Recent advances in single cell transcriptional approaches have enabled an examination of the human liver with unprecedented resolution. However, dissociation related cell perturbation can limit the ability to fully capture the human liver's parenchymal cell fraction, which limits the ability to comprehensively profile this organ. Here, we report the transcriptional landscape of 73,295 cells from the human liver using matched single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq). The addition of snRNA-seq enabled the characterization of interzonal hepatocytes at single-cell resolution, revealed the presence of rare subtypes of hepatic stellate cells previously only seen in disease, and detection of cholangiocyte progenitors that had only been observed during in vitro differentiation experiments. However, T and B lymphocytes and NK cells were only distinguishable using scRNA-seq, highlighting the importance of applying both technologies to obtain a complete map of tissue-resident cell-types. We validated the distinct spatial distribution of the hepatocyte, cholangiocyte and stellate cell populations by an independent spatial transcriptomics dataset and immunohistochemistry. Our study provides a systematic comparison of the transcriptomes captured by scRNA-seq and snRNA-seq and delivers a high-resolution map of the parenchymal cell populations in the healthy human liver.

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Dynamics of gene expression in single root cells ofA. thaliana

10.1101/448514 ◽

2018 ◽

Cited By ~ 3

Author(s):

Ken Jean-Baptiste ◽

José L. McFaline-Figueroa ◽

Cristina M. Alexandre ◽

Michael W. Dorrity ◽

Lauren Saunders ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Developmental Trajectories ◽

Cell Types ◽

Cell Type ◽

Specific Expression ◽

Root Cells ◽

Cell Lineages ◽

Cell Type Specific Expression ◽

Cell Type Specific

ABSTRACTSingle-cell RNA-seq can yield high-resolution cell-type-specific expression signatures that reveal new cell types and the developmental trajectories of cell lineages. Here, we apply this approach toA. thalianaroot cells to capture gene expression in 3,121 root cells. We analyze these data with Monocle 3, which orders single cell transcriptomes in an unsupervised manner and uses machine learning to reconstruct single-cell developmental trajectories along pseudotime. We identify hundreds of genes with cell-type-specific expression, with pseudotime analysis of several cell lineages revealing both known and novel genes that are expressed along a developmental trajectory. We identify transcription factor motifs that are enriched in early and late cells, together with the corresponding candidate transcription factors that likely drive the observed expression patterns. We assess and interpret changes in total RNA expression along developmental trajectories and show that trajectory branch points mark developmental decisions. Finally, by applying heat stress to whole seedlings, we address the longstanding question of possible heterogeneity among cell types in the response to an abiotic stress. Although the response of canonical heat shock genes dominates expression across cell types, subtle but significant differences in other genes can be detected among cell types. Taken together, our results demonstrate that single-cell transcriptomics holds promise for studying plant development and plant physiology with unprecedented resolution.

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Power Analysis of Single Cell RNA-Sequencing Experiments

10.1101/073692 ◽

2016 ◽

Cited By ~ 6

Author(s):

Valentine Svensson ◽

Kedar Nath Natarajan ◽

Lam-Ha Ly ◽

Ricardo J Miragaia ◽

Charlotte Labalette ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Developmental Process ◽

Cell Types ◽

Rna Degradation ◽

Published Data ◽

Minute Amount ◽

Trade Offs ◽

Single Cell Rna Sequencing

AbstractHigh-throughput single cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, and has revealed new cell types, and new insights into developmental process and stochasticity in gene expression. There are now several published scRNA-seq protocols, which all sequence transcriptomes from a minute amount of starting material. Therefore, a key question is how these methods compare in terms of sensitivity of detection of mRNA molecules, and accuracy of quantification of gene expression. Here, we assessed the sensitivity and accuracy of many published data sets based on standardized spike-ins with a uniform raw data processing pipeline. We developed a flexible and fast UMI counting tool (https://github.com/vals/umis) which is compatible with all UMI based protocols. This allowed us to relate these parameters to sequencing depth, and discuss the trade offs between the different methods. To confirm our results, we performed experiments on cells from the same population using three different protocols. We also investigated the effect of RNA degradation on spike-in molecules, and the average efficiency of scRNA-seq on spike-in molecules versus endogenous RNAs.

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A survey of the mouse hindbrain in the fed and fasted state using single-nucleus RNA sequencing

10.1101/2021.03.11.434948 ◽

2021 ◽

Author(s):

Georgina K.C. Dowsett ◽

Brian Y.H. Lam ◽

John Tadross ◽

Irene Cimino ◽

Debra Rimmington ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Area Postrema ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Fasted State ◽

Single Nucleus ◽

Human Use ◽

Obesity Drugs

AbstractObjectiveThe area postrema (AP) and the nucleus tractus solitaris (NTS), located in the hindbrain, are key nuclei that sense and integrate peripheral nutritional signals and, consequently, regulate feeding behaviour. While single cell transcriptomics have been used in mice to reveal the gene expression profile and heterogeneity of key hypothalamic populations, similar in-depth studies have not yet been performed in the hindbrain.MethodsUsing single-nucleus RNA sequencing, we provide a detailed survey of 16,034 cells within the AP and NTS of the mouse, in the fed and fasted state.ResultsOf these, 8910 are neurons that group into 30 clusters, with 4289 coming from mice fedad libitumand 4621 from overnight fasted mice. 7124 nuclei are from non-neuronal cells, including oligodendrocytes, astrocytes and microglia. Interestingly, we identified that the oligodendrocyte population was particularly transcriptionally sensitive to an overnight fast. The receptors GLP1R, GIPR, GFRAL and CALCR, which bind GLP1, GIP, GDF15 and amylin respectively, are all expressed in the hindbrain and are major targets for anti-obesity therapeutics. We characterise the transcriptomes of these four populations and show that their gene expression profiles are not dramatically altered by an overnight fast. Notably, we find that roughly half of cells that express GIPR are oligodendrocytes. Additionally, we profile POMC expressing neurons within the hindbrain and demonstrate that 84% of POMC neurons express either PCSK1, PSCK2 or both, implying that melanocortin peptides are likely produced by these neurons.ConclusionWe provide a detailed single-cell level characterisation of AP and NTS cells expressing receptors for key anti-obesity drugs that are either already approved for human use or are in clinical trials. This resource will help delineate the mechanisms underlying the effectiveness of these compounds, and also prove useful in the continued search for other novel therapeutic targets.

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Single-cell RNA sequencing reveals heterogeneous tumor and immune cell populations in early-stage lung adenocarcinomas harboring EGFR mutations

Oncogene ◽

10.1038/s41388-020-01528-0 ◽

2020 ◽

Cited By ~ 1

Author(s):

Di He ◽

Di Wang ◽

Ping Lu ◽

Nan Yang ◽

Zhigang Xue ◽

...

Keyword(s):

Gene Expression ◽

T Cells ◽

Single Cell ◽

Tumor Cells ◽

Rna Sequencing ◽

Early Stage ◽

Cell Types ◽

Egfr Mutations ◽

Advanced Tumor ◽

Single Cell Rna Sequencing

Abstract Lung adenocarcinoma (LUAD) harboring EGFR mutations prevails in Asian population. However, the inter-patient and intra-tumor heterogeneity has not been addressed at single-cell resolution. Here we performed single-cell RNA sequencing (scRNA-seq) of total 125,674 cells from seven stage-I/II LUAD samples harboring EGFR mutations and five tumor-adjacent lung tissues. We identified diverse cell types within the tumor microenvironment (TME) in which myeloid cells and T cells were the most abundant stromal cell types in tumors and adjacent lung tissues. Within tumors, accompanied by an increase in CD1C+ dendritic cells, the tumor-associated macrophages (TAMs) showed pro-tumoral functions without signature gene expression of defined M1 or M2 polarization. Tumor-infiltrating T cells mainly displayed exhausted and regulatory T-cell features. The adenocarcinoma cells can be categorized into different subtypes based on their gene expression signatures in distinct pathways such as hypoxia, glycolysis, cell metabolism, translation initiation, cell cycle, and antigen presentation. By performing pseudotime trajectory, we found that ELF3 was among the most upregulated genes in more advanced tumor cells. In response to secretion of inflammatory cytokines (e.g., IL1B) from immune infiltrates, ELF3 in tumor cells was upregulated to trigger the activation of PI3K/Akt/NF-κB pathway and elevated expression of proliferation and anti-apoptosis genes such as BCL2L1 and CCND1. Taken together, our study revealed substantial heterogeneity within early-stage LUAD harboring EGFR mutations, implicating complex interactions among tumor cells, stromal cells and immune infiltrates in the TME.

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Single-nucleus RNA sequencing of mouse auditory cortex reveals critical period triggers and brakes

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1920433117 ◽

2020 ◽

Vol 117 (21) ◽

pp. 11744-11752 ◽

Cited By ~ 6

Author(s):

Brian T. Kalish ◽

Tania R. Barkat ◽

Erin E. Diel ◽

Elizabeth J. Zhang ◽

Michael E. Greenberg ◽

...

Keyword(s):

Gene Expression ◽

Auditory Cortex ◽

Rna Sequencing ◽

Critical Period ◽

Neural Circuit ◽

Developmental Process ◽

Brain Plasticity ◽

Cell Types ◽

Altered Expression ◽

Single Nucleus

Auditory experience drives neural circuit refinement during windows of heightened brain plasticity, but little is known about the genetic regulation of this developmental process. The primary auditory cortex (A1) of mice exhibits a critical period for thalamocortical connectivity between postnatal days P12 and P15, during which tone exposure alters the tonotopic topography of A1. We hypothesized that a coordinated, multicellular transcriptional program governs this window for patterning of the auditory cortex. To generate a robust multicellular map of gene expression, we performed droplet-based, single-nucleus RNA sequencing (snRNA-seq) of A1 across three developmental time points (P10, P15, and P20) spanning the tonotopic critical period. We also tone-reared mice (7 kHz pips) during the 3-d critical period and collected A1 at P15 and P20. We identified and profiled both neuronal (glutamatergic and GABAergic) and nonneuronal (oligodendrocytes, microglia, astrocytes, and endothelial) cell types. By comparing normal- and tone-reared mice, we found hundreds of genes across cell types showing altered expression as a result of sensory manipulation during the critical period. Functional voltage-sensitive dye imaging confirmed GABA circuit function determines critical period onset, while Nogo receptor signaling is required for its closure. We further uncovered previously unknown effects of developmental tone exposure on trajectories of gene expression in interneurons, as well as candidate genes that might execute tonotopic plasticity. Our single-nucleus transcriptomic resource of developing auditory cortex is thus a powerful discovery platform with which to identify mediators of tonotopic plasticity.

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Single cell RNA sequencing identifies IGFBP5 and QKI as ciliated epithelial cell genes associated with severe COPD

Respiratory Research ◽

10.1186/s12931-021-01675-2 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Xiuying Li ◽

Guillaume Noell ◽

Tracy Tabib ◽

Alyssa D. Gregory ◽

Humberto E. Trejo Bittar ◽

...

Keyword(s):

Gene Expression ◽

Epithelial Cells ◽

Single Cell ◽

Rna Sequencing ◽

Cell Types ◽

Cell Type ◽

Protein Levels ◽

Single Cell Rna Sequencing ◽

Ciliated Epithelial Cells ◽

Severe Copd

Abstract Background Whole lung tissue transcriptomic profiling studies in chronic obstructive pulmonary disease (COPD) have led to the identification of several genes associated with the severity of airflow limitation and/or the presence of emphysema, however, the cell types driving these gene expression signatures remain unidentified. Methods To determine cell specific transcriptomic changes in severe COPD, we conducted single-cell RNA sequencing (scRNA seq) on n = 29,961 cells from the peripheral lung parenchymal tissue of nonsmoking subjects without underlying lung disease (n = 3) and patients with severe COPD (n = 3). The cell type composition and cell specific gene expression signature was assessed. Gene set enrichment analysis (GSEA) was used to identify the specific cell types contributing to the previously reported transcriptomic signatures. Results T-distributed stochastic neighbor embedding and clustering of scRNA seq data revealed a total of 17 distinct populations. Among them, the populations with more differentially expressed genes in cases vs. controls (log fold change >|0.4| and FDR = 0.05) were: monocytes (n = 1499); macrophages (n = 868) and ciliated epithelial cells (n = 590), respectively. Using GSEA, we found that only ciliated and cytotoxic T cells manifested a trend towards enrichment of the previously reported 127 regional emphysema gene signatures (normalized enrichment score [NES] = 1.28 and = 1.33, FDR = 0.085 and = 0.092 respectively). Among the significantly altered genes present in ciliated epithelial cells of the COPD lungs, QKI and IGFBP5 protein levels were also found to be altered in the COPD lungs. Conclusions scRNA seq is useful for identifying transcriptional changes and possibly individual protein levels that may contribute to the development of emphysema in a cell-type specific manner.

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AdRoit is an accurate and robust method to infer complex transcriptome composition

Communications Biology ◽

10.1038/s42003-021-02739-1 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Tao Yang ◽

Nicole Alessandri-Haber ◽

Wen Fury ◽

Michael Schaner ◽

Robert Breese ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Adaptive Learning ◽

Expression Profiles ◽

Cell Types ◽

Transcriptome Data ◽

Robust Method ◽

Gene Expressions ◽

Tissue Organization

AbstractBulk RNA sequencing provides the opportunity to understand biology at the whole transcriptome level without the prohibitive cost of single cell profiling. Advances in spatial transcriptomics enable to dissect tissue organization and function by genome-wide gene expressions. However, the readout of both technologies is the overall gene expression across potentially many cell types without directly providing the information of cell type constitution. Although several in-silico approaches have been proposed to deconvolute RNA-Seq data composed of multiple cell types, many suffer a deterioration of performance in complex tissues. Here we present AdRoit, an accurate and robust method to infer the cell composition from transcriptome data of mixed cell types. AdRoit uses gene expression profiles obtained from single cell RNA sequencing as a reference. It employs an adaptive learning approach to alleviate the sequencing technique difference between the single cell and the bulk (or spatial) transcriptome data, enhancing cross-platform readout comparability. Our systematic benchmarking and applications, which include deconvoluting complex mixtures that encompass 30 cell types, demonstrate its preferable sensitivity and specificity compared to many existing methods as well as its utilities. In addition, AdRoit is computationally efficient and runs orders of magnitude faster than most methods.

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Bayesian estimation of cell-type-specific gene expression per bulk sample with prior derived from single-cell data

10.1101/2020.08.05.238949 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jiebiao Wang ◽

Kathryn Roeder ◽

Bernie Devlin

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Cell Types ◽

Autism Spectrum ◽

Specific Cell ◽

Expression Data ◽

Cell Type ◽

Disease Etiology ◽

Cell Type Specific

AbstractWhen assessed over a large number of samples, bulk RNA sequencing provides reliable data for gene expression at the tissue level. Single-cell RNA sequencing (scRNA-seq) deepens those analyses by evaluating gene expression at the cellular level. Both data types lend insights into disease etiology. With current technologies, however, scRNA-seq data are known to be noisy. Moreover, constrained by costs, scRNA-seq data are typically generated from a relatively small number of subjects, which limits their utility for some analyses, such as identification of gene expression quantitative trait loci (eQTLs). To address these issues while maintaining the unique advantages of each data type, we develop a Bayesian method (bMIND) to integrate bulk and scRNA-seq data. With a prior derived from scRNA-seq data, we propose to estimate sample-level cell-type-specific (CTS) expression from bulk expression data. The CTS expression enables large-scale sample-level downstream analyses, such as detecting CTS differentially expressed genes (DEGs) and eQTLs. Through simulations, we demonstrate that bMIND improves the accuracy of sample-level CTS expression estimates and power to discover CTS-DEGs when compared to existing methods. To further our understanding of two complex phenotypes, autism spectrum disorder and Alzheimer’s disease, we apply bMIND to gene expression data of relevant brain tissue to identify CTS-DEGs. Our results complement findings for CTS-DEGs obtained from snRNA-seq studies, replicating certain DEGs in specific cell types while nominating other novel genes in those cell types. Finally, we calculate CTS-eQTLs for eleven brain regions by analyzing GTEx V8 data, creating a new resource for biological insights.

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