Quantitative cell release from plant tissues for single-cell genomics

ABSTRACTSingle-cell RNA-sequencing (scRNA-seq) can provide invaluable insight into cell development, cell type identification, and plant evolution. However, the resilience of the cell wall makes it difficult to dissociate plant tissues and release individual cells. Here, we show that plant tissues can be rapidly and quantitatively dissociated if the tissues are fixed prior to enzymatic digestion. Fixation enables digestion at high temperatures at which enzymatic activity is optimal and stabilizes the plant cell cytoplasm, rendering cells resistant to mechanical shear force. This protocol was applied to maize anthers and provided suitable single-cell expression data for the identification of the tapetum, endothecium, meiocytes, and epidermis, while providing putative marker genes and gene ontology information for the identification of unknown cell types. This approach also preserves morphology of the isolated cells, permitting many cell types to be identified without staining. Our fixation-based protocol can be applied to a range of plant species and tissues with minimal optimization.

Download Full-text

Molecular characteristics and spatial distribution of adult human corneal cell subtypes

Scientific Reports ◽

10.1038/s41598-021-94933-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ann J. Ligocki ◽

Wen Fury ◽

Christian Gutierrez ◽

Christina Adler ◽

Tao Yang ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cross Sections ◽

Cell Types ◽

Marker Genes ◽

Molecular Characteristics ◽

Transcriptional Level ◽

Human Cornea ◽

Adult Human ◽

And Migration

AbstractBulk RNA sequencing of a tissue captures the gene expression profile from all cell types combined. Single-cell RNA sequencing identifies discrete cell-signatures based on transcriptomic identities. Six adult human corneas were processed for single-cell RNAseq and 16 cell clusters were bioinformatically identified. Based on their transcriptomic signatures and RNAscope results using representative cluster marker genes on human cornea cross-sections, these clusters were confirmed to be stromal keratocytes, endothelium, several subtypes of corneal epithelium, conjunctival epithelium, and supportive cells in the limbal stem cell niche. The complexity of the epithelial cell layer was captured by eight distinct corneal clusters and three conjunctival clusters. These were further characterized by enriched biological pathways and molecular characteristics which revealed novel groupings related to development, function, and location within the epithelial layer. Moreover, epithelial subtypes were found to reflect their initial generation in the limbal region, differentiation, and migration through to mature epithelial cells. The single-cell map of the human cornea deepens the knowledge of the cellular subsets of the cornea on a whole genome transcriptional level. This information can be applied to better understand normal corneal biology, serve as a reference to understand corneal disease pathology, and provide potential insights into therapeutic approaches.

Download Full-text

JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation

10.1101/2020.10.06.327601 ◽

2020 ◽

Author(s):

Mohit Goyal ◽

Guillermo Serrano ◽

Ilan Shomorony ◽

Mikel Hernaez ◽

Idoia Ochoa

Keyword(s):

Single Cell ◽

Cell Types ◽

Marker Genes ◽

Specific Marker ◽

Rna Seq ◽

Batch Effects ◽

Cell Type ◽

Latent Space ◽

Cell Type Specific ◽

Low Dimensional

AbstractSingle-cell RNA-seq is a powerful tool in the study of the cellular composition of different tissues and organisms. A key step in the analysis pipeline is the annotation of cell-types based on the expression of specific marker genes. Since manual annotation is labor-intensive and does not scale to large datasets, several methods for automated cell-type annotation have been proposed based on supervised learning. However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic profiles, such as differentiating cells. We propose JIND, a framework for automated cell-type identification based on neural networks that directly learns a low-dimensional representation (latent code) in which cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profile of unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration. Availability: https://github.com/mohit1997/JIND.

Download Full-text

Revealing immune responses in the Mycobacterium avium subsp. paratuberculosis-infected THP-1 cells using single cell RNA-sequencing

PLoS ONE ◽

10.1371/journal.pone.0254194 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0254194

Author(s):

Hong-Tae Park ◽

Woo Bin Park ◽

Suji Kim ◽

Jong-Sung Lim ◽

Gyoungju Nah ◽

...

Keyword(s):

Crohn’S Disease ◽

Crohn's Disease ◽

Single Cell ◽

Mycobacterium Avium ◽

Expression Patterns ◽

Cell Types ◽

Marker Genes ◽

Specific Marker ◽

Cell Type ◽

Cytokines And Chemokines

Mycobacterium avium subsp. paratuberculosis (MAP) is a causative agent of Johne’s disease, which is a chronic and debilitating disease in ruminants. MAP is also considered to be a possible cause of Crohn’s disease in humans. However, few studies have focused on the interactions between MAP and human macrophages to elucidate the pathogenesis of Crohn’s disease. We sought to determine the initial responses of human THP-1 cells against MAP infection using single-cell RNA-seq analysis. Clustering analysis showed that THP-1 cells were divided into seven different clusters in response to phorbol-12-myristate-13-acetate (PMA) treatment. The characteristics of each cluster were investigated by identifying cluster-specific marker genes. From the results, we found that classically differentiated cells express CD14, CD36, and TLR2, and that this cell type showed the most active responses against MAP infection. The responses included the expression of proinflammatory cytokines and chemokines such as CCL4, CCL3, IL1B, IL8, and CCL20. In addition, the Mreg cell type, a novel cell type differentiated from THP-1 cells, was discovered. Thus, it is suggested that different cell types arise even when the same cell line is treated under the same conditions. Overall, analyzing gene expression patterns via scRNA-seq classification allows a more detailed observation of the response to infection by each cell type.

Download Full-text

A computational method to aid the design and analysis of single cell RNA-seq experiments for cell type identification

10.1101/247114 ◽

2018 ◽

Cited By ~ 1

Author(s):

Douglas Abrams ◽

Parveen Kumar ◽

R. Krishna Murthy Karuturi ◽

Joshy George

Keyword(s):

Experimental Design ◽

Single Cell ◽

Single Cells ◽

Cell Types ◽

Cell Number ◽

Fold Change ◽

Computational Method ◽

Marker Genes ◽

Cell Type ◽

Estimate Sample Size

AbstractBackgroundThe advent of single cell RNA sequencing (scRNA-seq) enabled researchers to study transcriptomic activity within individual cells and identify inherent cell types in the sample. Although numerous computational tools have been developed to analyze single cell transcriptomes, there are no published studies and analytical packages available to guide experimental design and to devise suitable analysis procedure for cell type identification.ResultsWe have developed an empirical methodology to address this important gap in single cell experimental design and analysis into an easy-to-use tool called SCEED (Single Cell Empirical Experimental Design and analysis). With SCEED, user can choose a variety of combinations of tools for analysis, conduct performance analysis of analytical procedures and choose the best procedure, and estimate sample size (number of cells to be profiled) required for a given analytical procedure at varying levels of cell type rarity and other experimental parameters. Using SCEED, we examined 3 single cell algorithms using 48 simulated single cell datasets that were generated for varying number of cell types and their proportions, number of genes expressed per cell, number of marker genes and their fold change, and number of single cells successfully profiled in the experiment.ConclusionsBased on our study, we found that when marker genes are expressed at fold change of 4 or more than the rest of the genes, either Seurat or Simlr algorithm can be used to analyze single cell dataset for any number of single cells isolated (minimum 1000 single cells were tested). However, when marker genes are expected to be only up to fC 2 upregulated, choice of the single cell algorithm is dependent on the number of single cells isolated and proportion of rare cell type to be identified. In conclusion, our work allows the assessment of various single cell methods and also aids in examining the single cell experimental design.

Download Full-text

ICTD: A semi-supervised cell type identification and deconvolution method for multi-omics data

10.1101/426593 ◽

2018 ◽

Cited By ~ 2

Author(s):

Wennan Chang ◽

Changlin Wan ◽

Xiaoyu Lu ◽

Szu-wei Tu ◽

Yifan Sun ◽

...

Keyword(s):

Single Cell ◽

Cell Types ◽

Training Data ◽

Marker Genes ◽

Cell Detection ◽

Omics Data ◽

Deconvolution Method ◽

Cell Type ◽

Data Set ◽

Cell Type Specific

AbstractWe developed a novel deconvolution method, namely Inference of Cell Types and Deconvolution (ICTD) that addresses the fundamental issue of identifiability and robustness in current tissue data deconvolution problem. ICTD provides substantially new capabilities for omics data based characterization of a tissue microenvironment, including (1) maximizing the resolution in identifying resident cell and sub types that truly exists in a tissue, (2) identifying the most reliable marker genes for each cell type, which are tissue and data set specific, (3) handling the stability problem with co-linear cell types, (4) co-deconvoluting with available matched multi-omics data, and (5) inferring functional variations specific to one or several cell types. ICTD is empowered by (i) rigorously derived mathematical conditions of identifiable cell type and cell type specific functions in tissue transcriptomics data and (ii) a semi supervised approach to maximize the knowledge transfer of cell type and functional marker genes identified in single cell or bulk cell data in the analysis of tissue data, and (iii) a novel unsupervised approach to minimize the bias brought by training data. Application of ICTD on real and single cell simulated tissue data validated that the method has consistently good performance for tissue data coming from different species, tissue microenvironments, and experimental platforms. Other than the new capabilities, ICTD outperformed other state-of-the-art devolution methods on prediction accuracy, the resolution of identifiable cell, detection of unknown sub cell types, and assessment of cell type specific functions. The premise of ICTD also lies in characterizing cell-cell interactions and discovering cell types and prognostic markers that are predictive of clinical outcomes.

Download Full-text

Integrated Single Cell Atlas of Endothelial Cells of the Human Lung

Circulation ◽

10.1161/circulationaha.120.052318 ◽

2021 ◽

Author(s):

Jonas C. Schupp ◽

Taylor S. Adams ◽

Carlos Cosme Jr. ◽

Micha Sam Brickman Raredon ◽

Yifan Yuan ◽

...

Keyword(s):

Endothelial Cells ◽

Pulmonary Hypertension ◽

Single Cell ◽

Differential Expression ◽

Human Lung ◽

Differential Expression Analysis ◽

Cell Types ◽

Marker Genes ◽

Lung Endothelium ◽

Lung Endothelial Cells

Background: The cellular diversity of the lung endothelium has not been systematically characterized in humans. Here, we provide a reference atlas of human lung endothelial cells (ECs) to facilitate a better understanding of the phenotypic diversity and composition of cells comprising the lung endothelium. Methods: We reprocessed human control single cell RNA sequencing (scRNAseq) data from six datasets. EC populations were characterized through iterative clustering with subsequent differential expression analysis. Marker genes were validated by fluorescent microscopy and in situ hybridization. scRNAseq of primary lung ECs cultured in-vitro was performed. The signaling network between different lung cell types was studied. For cross species analysis or disease relevance, we applied the same methods to scRNAseq data obtained from mouse lungs or from human lungs with pulmonary hypertension. Results: Six lung scRNAseq datasets were reanalyzed and annotated to identify over 15,000 vascular EC cells from 73 individuals. Differential expression analysis of EC revealed signatures corresponding to endothelial lineage, including pan-endothelial, pan-vascular and subpopulation-specific marker gene sets. Beyond the broad cellular categories of lymphatic, capillary, arterial and venous ECs, we found previously indistinguishable subpopulations: among venous EC, we identified two previously indistinguishable populations, pulmonary-venous ECs (COL15A1neg) localized to the lung parenchyma and systemic-venous ECs (COL15A1pos) localized to the airways and the visceral pleura; among capillary EC, we confirmed their subclassification into recently discovered aerocytes characterized by EDNRB, SOSTDC1 and TBX2 and general capillary EC. We confirmed that all six endothelial cell types, including the systemic-venous EC and aerocytes, are present in mice and identified endothelial marker genes conserved in humans and mice. Ligand-receptor connectome analysis revealed important homeostatic crosstalk of EC with other lung resident cell types. scRNAseq of commercially available primary lung ECs demonstrated a loss of their native lung phenotype in culture. scRNAseq revealed that the endothelial diversity is maintained in pulmonary hypertension. Our manuscript is accompanied by an online data mining tool (www.LungEndothelialCellAtlas.com). Conclusions: Our integrated analysis provides the comprehensive and well-crafted reference atlas of lung endothelial cells in the normal lung and confirms and describes in detail previously unrecognized endothelial populations across a large number of humans and mice.

Download Full-text

PATH-26. EVALUATING THE DISTRIBUTION AND PROGNOSIS OF IMMUNE CELL SIGNATURES OF THE TUMOR MICRO-ENVIRONMENT IN DIFFUSE GLIOMAS

Neuro-Oncology ◽

10.1093/neuonc/noab196.478 ◽

2021 ◽

Vol 23 (Supplement_6) ◽

pp. vi120-vi120

Author(s):

Bharati Mehani ◽

Saleembhasha Asanigari ◽

Hye-Jung Chung ◽

Kenneth Aldape

Keyword(s):

Gene Expression ◽

Single Cell ◽

Immune Cell ◽

Tumor Grade ◽

Cell Types ◽

Idh Mutation ◽

Mutation Status ◽

Cluster Analyses ◽

Diffuse Gliomas ◽

Cell Expression

Abstract The tumor micro-environment (TME) plays an important role in the biology of cancer, including gliomas. Single cell studies have highlighted the role of specific TME components in gliomas, and the methods to deconvolve bulk profiling data may serve to complement these studies on clinically annotated tumors. In this study, we estimated cell type proportions in 3 large glioma datasets (TCGA, CGGA-325, CGGA-693) using CIBERSORTx. Using a signature matrix comprising 22 immune cell types, we identified IDH mutation status-specific immune cell distributions and found that the proportions of 10 cell types were significantly different between IDHmut and IDHwt tumors across the 3 datasets. Looking further within IDHmut tumors, we found that monocytes were enriched in 1p/19q non-co-deleted tumors across the 3 glioma datasets, consistent with prior single cell studies. We then examined estimated gene expression among immune cell types relative to IDH mutation status and found clear separation of gene expression in 15 of 22 cell types in all 3 datasets. When we applied these 22 gene expression signatures in each tumor sample onto cluster-of-cluster analyses to identify tumor groups with distinct immune signature patterns, we found that samples were distributed largely according to the IDH status in all 3 datasets, confirming that immune cell expression is distinct based on IDH status. Among IDH-specific groups, cluster-of-cluster analyses showed that immune cell-based cluster groups had distinct survival outcomes, and that IDHwt samples were distributed significantly based on tumor grades as well as based on EGFR overexpression. Among IDHmut tumors, the distributions of tumor grade and 1p/19q co-deletion status were significantly different in the immune-based clusters in 2 of the 3 datasets examined. Overall, these results highlight the biological and clinical significance of the immune cell environment in gliomas, including distinctions based on IDH mutation status as well as prognosis within IDH-specific groups.

Download Full-text

Evaluation of single-cell classifiers for single-cell RNA sequencing data sets

Briefings in Bioinformatics ◽

10.1093/bib/bbz096 ◽

2019 ◽

Vol 21 (5) ◽

pp. 1581-1595 ◽

Cited By ~ 6

Author(s):

Xinlei Zhao ◽

Shuang Wu ◽

Nan Fang ◽

Xiao Sun ◽

Jue Fan

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Reference Data ◽

Predictive Accuracy ◽

Cell Types ◽

Superior Performance ◽

Marker Genes ◽

Data Sets ◽

Sequencing Data ◽

Single Cell Rna Sequencing

Abstract Single-cell RNA sequencing (scRNA-seq) has been rapidly developing and widely applied in biological and medical research. Identification of cell types in scRNA-seq data sets is an essential step before in-depth investigations of their functional and pathological roles. However, the conventional workflow based on clustering and marker genes is not scalable for an increasingly large number of scRNA-seq data sets due to complicated procedures and manual annotation. Therefore, a number of tools have been developed recently to predict cell types in new data sets using reference data sets. These methods have not been generally adapted due to a lack of tool benchmarking and user guidance. In this article, we performed a comprehensive and impartial evaluation of nine classification software tools specifically designed for scRNA-seq data sets. Results showed that Seurat based on random forest, SingleR based on correlation analysis and CaSTLe based on XGBoost performed better than others. A simple ensemble voting of all tools can improve the predictive accuracy. Under nonideal situations, such as small-sized and class-imbalanced reference data sets, tools based on cluster-level similarities have superior performance. However, even with the function of assigning ‘unassigned’ labels, it is still challenging to catch novel cell types by solely using any of the single-cell classifiers. This article provides a guideline for researchers to select and apply suitable classification tools in their analysis workflows and sheds some lights on potential direction of future improvement on classification tools.

Download Full-text

Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling

Nature Communications ◽

10.1038/s41467-019-12917-9 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 16

Author(s):

Qingnan Liang ◽

Rachayata Dharmat ◽

Leah Owen ◽

Akbar Shakoor ◽

Yumei Li ◽

...

Keyword(s):

Single Cell ◽

Transcriptome Profiling ◽

Cell Types ◽

Retinal Cell ◽

Peripheral Retina ◽

Marker Genes ◽

Rna Seq ◽

Cell Type ◽

Retinal Tissue ◽

The Individual

AbstractSingle-cell RNA-seq is a powerful tool in decoding the heterogeneity in complex tissues by generating transcriptomic profiles of the individual cell. Here, we report a single-nuclei RNA-seq (snRNA-seq) transcriptomic study on human retinal tissue, which is composed of multiple cell types with distinct functions. Six samples from three healthy donors are profiled and high-quality RNA-seq data is obtained for 5873 single nuclei. All major retinal cell types are observed and marker genes for each cell type are identified. The gene expression of the macular and peripheral retina is compared to each other at cell-type level. Furthermore, our dataset shows an improved power for prioritizing genes associated with human retinal diseases compared to both mouse single-cell RNA-seq and human bulk RNA-seq results. In conclusion, we demonstrate that obtaining single cell transcriptomes from human frozen tissues can provide insight missed by either human bulk RNA-seq or animal models.

Download Full-text