Molecular characteristics and spatial distribution of adult human corneal cell subtypes

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.

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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.

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Information Theoretic Feature Selection Methods for Single Cell RNA-Sequencing

10.1101/646919 ◽

2019 ◽

Author(s):

Umang Varma ◽

Justin Colacino ◽

Anna Gilbert

Keyword(s):

Feature Selection ◽

Single Cell ◽

Rna Sequencing ◽

Complex Mixture ◽

Cell Types ◽

Marker Genes ◽

Selection Methods ◽

Information Theoretic ◽

Single Cell Rna Sequencing ◽

Information Theoretic Methods

AbstractSingle cell RNA-sequencing (scRNA-seq) technologies have generated an expansive amount of new biological information, revealing new cellular populations and hierarchical relationships. A number of technologies complementary to scRNA-seq rely on the selection of a smaller number of marker genes (or features) to accurately differentiate cell types within a complex mixture of cells. In this paper, we benchmark differential expression methods against information-theoretic feature selection methods to evaluate the ability of these algorithms to identify small and efficient sets of genes that are informative about cell types. Unlike differential methods, that are strictly binary and univariate, information-theoretic methods can be used as any combination of binary or multiclass and univariate or multivariate. We show for some datasets, information theoretic methods can reveal genes that are both distinct from those selected by traditional algorithms and that are as informative, if not more, of the class labels. We also present detailed and principled theoretical analyses of these algorithms. All information theoretic methods in this paper are implemented in our PicturedRocks Python package that is compatible with the widely used scanpy package.

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Single-cell transcriptome profiling of an adult human cell atlas of 15 major organs

10.1101/2020.03.18.996975 ◽

2020 ◽

Author(s):

Shuai He ◽

Lin-He Wang ◽

Yang Liu ◽

Yi-Qi Li ◽

Hai-Tian Chen ◽

...

Keyword(s):

Single Cell ◽

Human Cell ◽

Regulatory Networks ◽

Transcriptome Profiling ◽

Cell Types ◽

Cell Receptor ◽

Marker Genes ◽

Human Organs ◽

Adult Human ◽

Receptor Repertoire

ABSTRACTBackgroundAs core units of organ tissues, cells of various types play their harmonious rhythms to maintain the homeostasis of the human body. It is essential to identify the characteristics of cells in human organs and their regulatory networks for understanding the biological mechanisms related to health and disease. However, a systematic and comprehensive single-cell transcriptional profile across multiple organs of a normal human adult is missing.ResultsWe perform single-cell transcriptomes of 84,363 cells derived from 15 tissue organs of one adult donor and generate an adult human cell atlas. The adult human cell atlas depicts 252 subtypes of cells, including major cell types such as T, B, myeloid, epithelial, and stromal cells, as well as novel COCH+ fibroblasts and FibSmo cells, each of which is distinguished by multiple marker genes and transcriptional profiles. These collectively contribute to the heterogeneity of major human organs. Moreover, T cell and B cell receptor repertoire comparisons and trajectory analyses reveal direct clonal sharing of T and B cells with various developmental states among different tissues. Furthermore, novel cell markers, transcription factors and ligand-receptor pairs are identified with potential functional regulations in maintaining the homeostasis of human cells among tissues.ConclusionsThe adult human cell atlas reveals the inter- and intra-organ heterogeneity of cell characteristics and provides a useful resource in uncovering key events during the development of human diseases in the context of the heterogeneity of cells and organs.

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Deciphering cell lineage specification of human lung adenocarcinoma with single-cell RNA sequencing

10.21203/rs.3.rs-127270/v1 ◽

2021 ◽

Author(s):

Zhoufeng Wang ◽

Zhe Li ◽

Kun Zhou ◽

Li Zhang ◽

Ying Yang ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Early Stage ◽

Precancerous Lesions ◽

Cell Types ◽

Marker Genes ◽

Adenocarcinoma In Situ ◽

Invasive Adenocarcinoma ◽

Single Cell Rna Sequencing ◽

Clinical Stages

Abstract Lung adenocarcinomas (LUAD) start as precancerous lesions such as atypical adenomatous hyperplasia (AAH), develop stepwise into adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA), then eventually progress toward invasive adenocarcinoma (IA). To date the cellular heterogeneity across these distinct clinical stages and the underlying molecular events driving tumor progression remain largely unclear. In this study, we performed single-cell RNA sequencing on 52 specimens from 25 patients spanning the four clinical stages. By assessing the expression pattern of marker genes among 268,471 cells, we identified 16 major cell types. We demonstrated that AT2 feature cell types (AT2-like cells) were associated with malignant composition. AT2-like subcluster emerged first in AAH and partially lost AT2 cell transcriptional identity, accompanied with a gain of stemness during cell transition. In addition, genes related to energy metabolism, ribosome synthesis were upregulated in the early stage of LUAD, leading us to identify new markers including miRNA10 and β-hydroxybutyric acid to diagnose early-stage LUAD noninvasively in the blood. We also identified MDK and TIMP1 as potential biomarkers to facilitate our understanding of LUAD pathogenesis. Taken together, our data identified a new mechanism in LUAD evolution, and provided a robust basis for diagnosis and treatment of LUAD.

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Automated identification of Cell Types in Single Cell RNA Sequencing

10.1101/532093 ◽

2019 ◽

Cited By ~ 3

Author(s):

Feiyang Ma ◽

Matteo Pellegrini

Keyword(s):

Neural Network ◽

Single Cell ◽

Rna Sequencing ◽

Immune Cell ◽

Cell Types ◽

Marker Genes ◽

Complex Data ◽

Cell Type ◽

Human T Cell ◽

Single Cell Rna Sequencing

AbstractCell type identification is one of the major goals in single cell RNA sequencing (scRNA-seq). Current methods for assigning cell types typically involve the use of unsupervised clustering, the identification of signature genes in each cluster, followed by a manual lookup of these genes in the literature and databases to assign cell types. However, there are several limitations associated with these approaches, such as unwanted sources of variation that influence clustering and a lack of canonical markers for certain cell types. Here, we present ACTINN (Automated Cell Type Identification using Neural Networks), which employs a neural network with 3 hidden layers, trains on datasets with predefined cell types, and predicts cell types for other datasets based on the trained parameters. We trained the neural network on a mouse cell type atlas (Tabula Muris Atlas) and a human immune cell dataset, and used it to predict cell types for mouse leukocytes, human PBMCs and human T cell sub types. The results showed that our neural network is fast and accurate, and should therefore be a useful tool to complement existing scRNA-seq pipelines.Author SummarySingle cell RNA sequencing (scRNA-seq) provides high resolution profiling of the transcriptomes of individual cells, which inevitably results in high volumes of data that require complex data processing pipelines. Usually, one of the first steps in the analysis of scRNA-seq is to assign individual cells to known cell types. To accomplish this, traditional methods first group the cells into different clusters, then find marker genes, and finally use these to manually assign cell types for each cluster. Thus these methods require prior knowledge of cell type canonical markers, and some level of subjectivity to make the cell type assignments. As a result, the process is often laborious and requires domain specific expertise, which is a barrier for inexperienced users. By contrast, our neural network ACTINN automatically learns the features for each predefined cell type and uses these features to predict cell types for individual cells. This approach is computationally efficient and requires no domain expertise of the tissues being studied. We believe ACTINN allows users to rapidly identify cell types in their datasets, thus rendering the analysis of their scRNA-seq datasets more efficient.

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Characterization of human-iPSCs derived spinal motor neurons by single-cell RNA sequencing

10.1101/2019.12.28.889972 ◽

2019 ◽

Author(s):

Louise Thiry ◽

Regan Hamel ◽

Stefano Pluchino ◽

Thomas Durcan ◽

Stefano Stifani

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Motor Neurons ◽

Cell Types ◽

Molecular Characteristics ◽

Specific Cell ◽

Lateral Motor Column ◽

Human Ipscs ◽

Single Cell Rna Sequencing ◽

Human Ipsc

AbstractHuman induced pluripotent stem cells (iPSCs) offer the opportunity to generate specific cell types from healthy and diseased individuals, allowing the study of mechanisms of early human development, modelling a variety of human diseases, and facilitating the development of new therapeutics. Human iPSC-based applications are often limited by the variability among iPSC lines originating from a single donor, as well as the heterogeneity among specific cell types that can be derived from iPSCs. The ability to deeply phenotype different iPSC-derived cell types is therefore of primary importance to the successful and informative application of this technology. Here we describe a combination of motor neuron (MN) derivation and single-cell RNA sequencing approaches to generate and characterize specific MN subtypes obtained from human iPSCs. Our studies provide evidence for rapid and robust generation of MN progenitor cells that can give rise to a heterogenous population of brainstem and spinal cord MNs. Approximately 58% of human iPSC-derived MNs display molecular characteristics of lateral motor column MNs, ∼19% of induced MNs resemble hypaxial motor column MNs, while ∼6% of induced MNs have features of medial motor column MNs. The present study has the potential to improve our understanding of iPSC-derived MN subtype function and dysfunction, possibly leading to improved iPSC-based applications for the study of human MN biology and diseases.

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NS-Forest: A machine learning method for the objective identification of minimum marker gene combinations for cell type determination from single cell RNA sequencing

10.1101/2020.09.23.308932 ◽

2020 ◽

Author(s):

Brian Aevermann ◽

Yun Zhang ◽

Mark Novotny ◽

Trygve Bakken ◽

Jeremy Miller ◽

...

Keyword(s):

Machine Learning ◽

Single Cell ◽

Rna Sequencing ◽

Marker Gene ◽

Cell Types ◽

Biological Research ◽

Marker Genes ◽

Cell Type ◽

Type Identity ◽

Wide Range

AbstractSingle cell genomics is rapidly advancing our knowledge of cell phenotypic types and states. Driven by single cell/nucleus RNA sequencing (scRNA-seq) data, comprehensive atlas projects covering a wide range of organisms and tissues are currently underway. As a result, it is critical that the cell transcriptional phenotypes discovered are defined and disseminated in a consistent and concise manner. Molecular biomarkers have historically played an important role in biological research, from defining immune cell-types by surface protein expression to defining diseases by molecular drivers. Here we describe a machine learning-based marker gene selection algorithm, NS-Forest version 2.0, which leverages the non-linear attributes of random forest feature selection and a binary expression scoring approach to discover the minimal marker gene expression combinations that precisely captures the cell type identity represented in the complete scRNA-seq transcriptional profiles. The marker genes selected provide a barcode of the necessary and sufficient characteristics for semantic cell type definition and serve as useful tools for downstream biological investigation. The use of NS-Forest to identify marker genes for human brain middle temporal gyrus cell types reveals the importance of cell signaling and non-coding RNAs in neuronal cell type identity.

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Single-cell transcriptome profiling of an adult human cell atlas of 15 major organs

Genome Biology ◽

10.1186/s13059-020-02210-0 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Shuai He ◽

Lin-He Wang ◽

Yang Liu ◽

Yi-Qi Li ◽

Hai-Tian Chen ◽

...

Keyword(s):

Single Cell ◽

Human Cell ◽

Regulatory Networks ◽

Transcriptome Profiling ◽

Cell Types ◽

Cell Receptor ◽

Marker Genes ◽

Human Organs ◽

Adult Human ◽

Receptor Repertoire

Abstract Background As core units of organ tissues, cells of various types play their harmonious rhythms to maintain the homeostasis of the human body. It is essential to identify the characteristics of cells in human organs and their regulatory networks for understanding the biological mechanisms related to health and disease. However, a systematic and comprehensive single-cell transcriptional profile across multiple organs of a normal human adult is missing. Results We perform single-cell transcriptomes of 84,363 cells derived from 15 tissue organs of one adult donor and generate an adult human cell atlas. The adult human cell atlas depicts 252 subtypes of cells, including major cell types such as T, B, myeloid, epithelial, and stromal cells, as well as novel COCH+ fibroblasts and FibSmo cells, each of which is distinguished by multiple marker genes and transcriptional profiles. These collectively contribute to the heterogeneity of major human organs. Moreover, T cell and B cell receptor repertoire comparisons and trajectory analyses reveal direct clonal sharing of T and B cells with various developmental states among different tissues. Furthermore, novel cell markers, transcription factors, and ligand-receptor pairs are identified with potential functional regulations in maintaining the homeostasis of human cells among tissues. Conclusions The adult human cell atlas reveals the inter- and intra-organ heterogeneity of cell characteristics and provides a useful resource in uncovering key events during the development of human diseases in the context of the heterogeneity of cells and organs.

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Big data and single cell transcriptomics: implications for ontological representation

10.1101/257352 ◽

2018 ◽

Author(s):

Brian D. Aevermann ◽

Mark Novotny ◽

Trygve Bakken ◽

Jeremy A. Miller ◽

Alexander D. Diehl ◽

...

Keyword(s):

Big Data ◽

Single Cell ◽

Rna Sequencing ◽

Transcriptional Profiling ◽

Cell Types ◽

The Body ◽

Marker Genes ◽

Cell Type ◽

Type Classes ◽

Cellular Phenotypes

AbstractCells are fundamental functional units of multicellular organisms, with different cell types playing distinct physiological roles in the body. The recent advent of single cell transcriptional profiling using RNA sequencing is producing “big data”, enabling the identification of novel human cell types at an unprecedented rate. In this review, we summarize recent work characterizing cell types in the human central nervous and immune systems using single cell and single nuclei RNA sequencing, and discuss the implications that these discoveries are having on the representation of cell types in the reference Cell Ontology (CL). We propose a method based on random forest machine learning for identifying sets of necessary and sufficient marker genes that can be used to assemble consistent and reproducible cell type definitions for incorporation into the CL. The representation of defined cell type classes and their relationships in the CL using this strategy will make the cell type classes findable, accessible, interoperable, and reusable (FAIR), allowing the CL to serve as a reference knowledgebase of information about the role that distinct cellular phenotypes play in human health and disease.

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Defining the variety of cell types in developing and adult human kidneys by single-cell RNA sequencing

npj Regenerative Medicine ◽

10.1038/s41536-021-00156-w ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

A. Schumacher ◽

M. B. Rookmaaker ◽

J. A. Joles ◽

R. Kramann ◽

T. Q. Nguyen ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Renal Cell ◽

Early Stage ◽

Kidney Tissue ◽

Cell Types ◽

Intermediate Cell ◽

Adult Human ◽

Single Cell Rna Sequencing

AbstractThe kidney is among the most complex organs in terms of the variety of cell types. The cellular complexity of human kidneys is not fully unraveled and this challenge is further complicated by the existence of multiple progenitor pools and differentiation pathways. Researchers disagree on the variety of renal cell types due to a lack of research providing a comprehensive picture and the challenge to translate findings between species. To find an answer to the number of human renal cell types, we discuss research that used single-cell RNA sequencing on developing and adult human kidney tissue and compares these findings to the literature of the pre-single-cell RNA sequencing era. We find that these publications show major steps towards the discovery of novel cell types and intermediate cell stages as well as complex molecular signatures and lineage pathways throughout development. The variety of cell types remains variable in the single-cell literature, which is due to the limitations of the technique. Nevertheless, our analysis approaches an accumulated number of 41 identified cell populations of renal lineage and 32 of non-renal lineage in the adult kidney, and there is certainly much more to discover. There is still a need for a consensus on a variety of definitions and standards in single-cell RNA sequencing research, such as the definition of what is a cell type. Nevertheless, this early-stage research already proves to be of significant impact for both clinical and regenerative medicine, and shows potential to enhance the generation of sophisticated in vitro kidney tissue.

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