Single-nucleus transcriptomic atlas of spinal cord neuron in human

Despite the recognized importance of spinal cord in sensory processing, motor behaviors and/or neural diseases, the underlying neuronal clusters remain elusive. Recently, several studies attempted to define the neuronal types and functional heterogeneity in spinal cord using single cell and/or single-nucleus RNA-sequencing in varied animal models. However, the molecular evidence of neuronal heterogeneity in human spinal cord has not been established yet. Here we sought to classify spinal cord neurons from human donors by high-throughput single-nucleus RNA-sequencing. The functional heterogeneity of identified cell types and signaling pathways that connecting neuronal subtypes were explored. Moreover, we also compared human results with previous single-cell transcriptomic profiles of mouse spinal cord. As a result, we generated the first comprehensive atlas of human spinal cord neurons and defined 18 neuronal clusters. In addition to identification of the new and functionally-distinct neuronal subtypes, our results also provide novel marker genes for previously known neuronal types. The comparation with mouse transcriptomic profiles revealed an overall similarity in the cellular composition of spinal cord between the two species. In summary, these results illustrate the complexity and diversity of neuronal types in human spinal cord and will provide an important resource for future researches to explore the molecular mechanism underlying several spinal cord physiology and diseases.

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Temporal single-cell transcriptomes of zebrafish spinal cord pMN progenitors reveal distinct neuronal and glial progenitor populations

10.1101/2021.04.28.441874 ◽

2021 ◽

Author(s):

Kayt Scott ◽

Rebecca O’Rourke ◽

Caitlin C. Winkler ◽

Christina A. Kearns ◽

Bruce Appel

Keyword(s):

Spinal Cord ◽

Transcription Factor ◽

Single Cell ◽

Progenitor Cells ◽

Rna Sequencing ◽

Motor Neurons ◽

Marker Genes ◽

List Type ◽

Fate Mapping ◽

Single Cell Rna Sequencing

AbstractVentral spinal cord progenitor cells, which express the basic helix loop helix transcription factor Olig2, sequentially produce motor neurons and oligodendrocyte precursor cells (OPCs). Following specification some OPCs differentiate as myelinating oligodendrocytes while others persist as OPCs. Though a considerable amount of work has described the molecular profiles that define motor neurons, OPCs, and oligodendrocytes, less is known about the progenitors that produce them. To identify the developmental origins and transcriptional profiles of motor neurons and OPCs, we performed single-cell RNA sequencing on isolated pMN cells from embryonic zebrafish trunk tissue at stages that encompassed motor neurogenesis, OPC specification, and initiation of oligodendrocyte differentiation. Downstream analyses revealed two distinct pMN progenitor populations: one that appears to produce neurons and one that appears to produce OPCs. This latter population, called Pre-OPCs, is marked by expression of GS Homeobox 2 (gsx2), a gene that encodes a homeobox transcription factor. Using fluorescent in situ hybridizations, we identified gsx2-expressing Pre-OPCs in the spinal cord prior to expression of canonical OPC marker genes. Our data therefore reveal heterogeneous gene expression profiles among pMN progenitors, supporting prior fate mapping evidence.HighlightsSingle-cell RNA sequencing reveals the developmental trajectories of neurons and glia that arise from spinal cord pMN progenitor cells in zebrafish embryosTranscriptionally distinct subpopulations of pMN progenitors are the apparent sources of neurons or oligodendrocytes, consistent with fate mapping datagsx2 expression marks pMN progenitors that produce oligodendrocyte lineage cells

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Single-cell RNA sequencing reveals time- and sex-specific responses of spinal cord microglia to peripheral nerve injury and links ApoE to neuropathic pain

10.1101/2020.12.09.418541 ◽

2020 ◽

Author(s):

Shannon Tansley ◽

Sonali Uttam ◽

Alba Ureña Guzmán ◽

Moein Yaqubi ◽

Alain Pacis ◽

...

Keyword(s):

Spinal Cord ◽

Neuropathic Pain ◽

Peripheral Nerve ◽

Single Cell ◽

Rna Sequencing ◽

Nerve Injury ◽

Peripheral Nerve Injury ◽

Single Cell Analysis ◽

Human Spinal Cord ◽

Single Cell Rna Sequencing

AbstractActivation of microglia in the spinal cord following peripheral nerve injury is critical for the development of long-lasting pain hypersensitivity. However, it remains unknown whether distinct microglia subpopulations or states contribute to different stages of pain development and maintenance. We show, using single-cell RNA-sequencing, that nerve injury induces the generation of a male-specific inflammatory microglia subtype, and demonstrate increased proliferation of microglia in males as compared to females. We also show time- and sex-specific transcriptional changes in different microglial subpopulations following injury. Apolipoprotein E (Apoe) is the top upregulated gene in microglia at chronic time points after nerve injury in mice and polymorphisms in the APOE gene in humans are associated with chronic pain. Single-cell analysis of human spinal cord microglia reveals a subpopulation with a disease-related transcriptional signature. Our data provide a detailed analysis of transcriptional states of mouse and human spinal cord microglia, and identify a previously unrecognized role for ApoE in neuropathic pain.

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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.

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There are no A1 and A2 astrocytes in brain

10.21203/rs.3.rs-737027/v1 ◽

2021 ◽

Author(s):

Shuang-qi Gao

Keyword(s):

Endothelial Cells ◽

Single Cell ◽

Rna Sequencing ◽

Cell Sorting ◽

Reactive Astrocytes ◽

Marker Genes ◽

Set Up ◽

The Brain

Abstract Objectives The subsets of astrocytes in the brain have not been fully elucidated. Using bulk RNA sequencing, reactive astrocytes were divided into A1 versus A2. However, using single-cell RNAseq (ScRNAseq), astrocytes were divided into over two subsets. Our aim was to set up the correspondence between the fluorescent-activated cell sorting (FACS)-bulk RNAseq and ScRNAseq data. Results We found that most of reactive astrocytes (RAs) marker genes were expressed in endothelial cells but not in astrocytes, suggesting those marker genes are not suitable for astrocytic activation. The absence of A1 and A2 astrocytes in the brain.

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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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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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Deciphering the functional heterogeneity of skin fibroblasts using single‐cell RNA sequencing

The FASEB Journal ◽

10.1096/fj.201902001rr ◽

2020 ◽

Vol 34 (3) ◽

pp. 3677-3692 ◽

Cited By ~ 13

Author(s):

Vera Vorstandlechner ◽

Maria Laggner ◽

Polina Kalinina ◽

Werner Haslik ◽

Christine Radtke ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Skin Fibroblasts ◽

Functional Heterogeneity ◽

Single Cell Rna Sequencing

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Single nucleus RNA-sequencing defines unexpected diversity of cholinergic neuron types in the adult mouse spinal cord

10.1101/2020.07.16.193292 ◽

2020 ◽

Cited By ~ 1

Author(s):

Mor R. Alkaslasi ◽

Zoe E. Piccus ◽

Hanna Silberberg ◽

Li Chen ◽

Yajun Zhang ◽

...

Keyword(s):

Spinal Cord ◽

Rna Sequencing ◽

Motor Neurons ◽

Cholinergic Neurons ◽

Functional Roles ◽

Cholinergic Interneurons ◽

Rich Diversity ◽

Degenerative Disorders ◽

Nuclear Rna ◽

Single Nucleus

AbstractIn vertebrates, motor control relies on cholinergic neurons in the spinal cord that have been extensively studied over the past hundred years, yet the full heterogeneity of these neurons and their different functional roles in the adult remain to be defined. Here, we developed a targeted single nuclear RNA sequencing approach and used it to identify an array of cholinergic interneurons, visceral and skeletal motor neurons. Our data expose markers for distinguishing these classes of cholinergic neurons and their extremely rich diversity. Specifically, visceral motor neurons, which provide autonomic control, could be divided into more than a dozen transcriptomic classes with anatomically restricted localization along the spinal cord. The complexity of the skeletal motor neurons was also reflected in our analysis with alpha, beta, and gamma subtypes clearly distinguished. In combination, our data provide a comprehensive transcriptomic description of this important population of neurons that control many aspects of physiology and movement and encompass the cellular substrates for debilitating degenerative disorders.

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Complementary Roles for Single-Nucleus and Single-Cell RNA Sequencing in Kidney Disease Research

Journal of the American Society of Nephrology ◽

10.1681/asn.2019020112 ◽

2019 ◽

Vol 30 (4) ◽

pp. 712-713 ◽

Cited By ~ 11

Author(s):

Eoin D. O’Sullivan ◽

Katie J. Mylonas ◽

Jeremy Hughes ◽

David A. Ferenbach

Keyword(s):

Kidney Disease ◽

Single Cell ◽

Rna Sequencing ◽

Disease Research ◽

Single Cell Rna Sequencing ◽

Single Nucleus

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Unveiling the heterogeneity of NKT cells in the liver through single cell RNA sequencing

Scientific Reports ◽

10.1038/s41598-020-76659-1 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Hao Shen ◽

Chan Gu ◽

Tao Liang ◽

Haifeng Liu ◽

Fan Guo ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cell Subset ◽

Nkt Cells ◽

Organ Distribution ◽

Marker Genes ◽

Type I ◽

Nkt Cell ◽

Single Cell Rna Sequencing

Abstract CD1d-dependent type I NKT cells, which are activated by lipid antigen, are known to play important roles in innate and adaptive immunity, as are a portion of type II NKT cells. However, the heterogeneity of NKT cells, especially NKT-like cells, remains largely unknown. Here, we report the profiling of NKT (NK1.1+CD3e+) cells in livers from wild type (WT), Jα18-deficient and CD1d-deficient mice by single-cell RNA sequencing. Unbiased transcriptional clustering revealed distinct cell subsets. The transcriptomic profiles identified the well-known CD1d-dependent NKT cells and defined two CD1d-independent NKT cell subsets. In addition, validation of marker genes revealed the differential organ distribution and landscape of NKT cell subsets during liver tumor progression. More importantly, we found that CD1d-independent Sca-1−CD62L+ NKT cells showed a strong ability to secrete IFN-γ after costimulation with IL-2, IL-12 and IL-18 in vitro. Collectively, our findings provide a comprehensive characterization of NKT cell heterogeneity and unveil a previously undefined functional NKT cell subset.

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