Single-cell dissection of the human cerebrovasculature in health and disease

SummaryDespite the importance of the blood-brain barrier in maintaining normal brain physiology and in understanding neurodegeneration and CNS drug delivery, human cerebrovascular cells remain poorly characterized due to their sparsity and dispersion. Here, we perform the first single-cell characterization of the human cerebrovasculature using both ex vivo fresh-tissue experimental enrichment and post mortem in silico sorting of human cortical tissue samples. We capture 31,812 cerebrovascular cells across 17 subtypes, including three distinct subtypes of perivascular fibroblasts as well as vasculature-coupled neurons and glia. We uncover human-specific expression patterns along the arteriovenous axis and determine previously uncharacterized cell type-specific markers. We use our newly discovered human-specific signatures to study changes in 3,945 cerebrovascular cells of Huntington’s disease patients, which reveal an activation of innate immune signaling in vascular and vasculature-coupled cell types and the concomitant reduction to proteins critical for maintenance of BBB integrity. Finally, our study provides a comprehensive resource molecular atlas of the human cerebrovasculature to guide future biological and therapeutic studies.

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Glioma biopsies Classification Using Raman Spectroscopy and Machine Learning Models on Fresh Tissue Samples

Cancers ◽

10.3390/cancers13051073 ◽

2021 ◽

Vol 13 (5) ◽

pp. 1073

Author(s):

Marco Riva ◽

Tommaso Sciortino ◽

Riccardo Secoli ◽

Ester D’Amico ◽

Sara Moccia ◽

...

Keyword(s):

Machine Learning ◽

Raman Spectroscopy ◽

Brain Tissue ◽

Ex Vivo ◽

Biological Significance ◽

Normal Brain ◽

Fresh Tissue ◽

Tissue Samples ◽

Glioma Resection

Identifying tumor cells infiltrating normal-appearing brain tissue is critical to achieve a total glioma resection. Raman spectroscopy (RS) is an optical technique with potential for real-time glioma detection. Most RS reports are based on formalin-fixed or frozen samples, with only a few studies deployed on fresh untreated tissue. We aimed to probe RS on untreated brain biopsies exploring novel Raman bands useful in distinguishing glioma and normal brain tissue. Sixty-three fresh tissue biopsies were analyzed within few minutes after resection. A total of 3450 spectra were collected, with 1377 labelled as Healthy and 2073 as Tumor. Machine learning methods were used to classify spectra compared to the histo-pathological standard. The algorithms extracted information from 60 different Raman peaks identified as the most representative among 135 peaks screened. We were able to distinguish between tumor and healthy brain tissue with accuracy and precision of 83% and 82%, respectively. We identified 19 new Raman shifts with known biological significance. Raman spectroscopy was effective and accurate in discriminating glioma tissue from healthy brain ex-vivo in fresh samples. This study added new spectroscopic data that can contribute to further develop Raman Spectroscopy as an intraoperative tool for in-vivo glioma detection.

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Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression

Nature Communications ◽

10.1038/ncomms9687 ◽

2015 ◽

Vol 6 (1) ◽

Cited By ~ 125

Author(s):

Jong Kyoung Kim ◽

Aleksandra A. Kolodziejczyk ◽

Tomislav Ilicic ◽

Sarah A. Teichmann ◽

John C. Marioni

Keyword(s):

Single Cell ◽

Regulatory Networks ◽

Large Fraction ◽

Expression Patterns ◽

Cell Types ◽

Specific Expression ◽

Allele Specific Expression ◽

Technical Noise ◽

Allele Specific ◽

High Level

Abstract Single-cell RNA-sequencing (scRNA-seq) facilitates identification of new cell types and gene regulatory networks as well as dissection of the kinetics of gene expression and patterns of allele-specific expression. However, to facilitate such analyses, separating biological variability from the high level of technical noise that affects scRNA-seq protocols is vital. Here we describe and validate a generative statistical model that accurately quantifies technical noise with the help of external RNA spike-ins. Applying our approach to investigate stochastic allele-specific expression in individual cells, we demonstrate that a large fraction of stochastic allele-specific expression can be explained by technical noise, especially for lowly and moderately expressed genes: we predict that only 17.8% of stochastic allele-specific expression patterns are attributable to biological noise with the remainder due to technical noise.

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Finding cell-specific expression patterns in the early Ciona embryo with single-cell RNA-seq

10.1101/197699 ◽

2017 ◽

Author(s):

Garth R. Ilsley ◽

Ritsuko Suyama ◽

Takeshi Noda ◽

Nori Satoh ◽

Nicholas M. Luscombe

Keyword(s):

Gene Expression ◽

Single Cell ◽

Expression Patterns ◽

Cell Types ◽

Specific Gene ◽

Rna Seq ◽

Cell Stage ◽

Specific Expression ◽

Temporal Gene Expression

AbstractSingle-cell RNA-seq has been established as a reliable and accessible technique enabling new types of analyses, such as identifying cell types and studying spatial and temporal gene expression variation and change at single-cell resolution. Recently, single-cell RNA-seq has been applied to developing embryos, which offers great potential for finding and characterising genes controlling the course of development along with their expression patterns. In this study, we applied single-cell RNA-seq to the 16-cell stage of the Ciona embryo, a marine chordate and performed a computational search for cell-specific gene expression patterns. We recovered many known expression patterns from our single-cell RNA-seq data and despite extensive previous screens, we succeeded in finding new cell-specific patterns, which we validated by in situ and single-cell qPCR.

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NKCC1, an Elusive Molecular Target in Brain Development: Making Sense of the Existing Data

Cells ◽

10.3390/cells9122607 ◽

2020 ◽

Vol 9 (12) ◽

pp. 2607

Author(s):

Mari A. Virtanen ◽

Pavel Uvarov ◽

Christian A. Hübner ◽

Kai Kaila

Keyword(s):

Single Cell ◽

Expression Patterns ◽

Cell Types ◽

Brain Cell ◽

Specific Expression ◽

Knock Out ◽

Making Sense ◽

Cell Type Specific Expression ◽

Endogenous Proteins ◽

Gaba Transmission

Ionotropic GABA transmission is mediated by anion (mainly Cl−)-permeable GABAA receptors (GABAARs). In immature neurons, GABA exerts depolarizing and sometimes functionally excitatory actions, based on active uptake of Cl− by the Na-K-2Cl cotransporter NKCC1. While functional evidence firmly shows NKCC1-mediated ion transport in immature and diseased neurons, molecular detection of NKCC1 in the brain has turned out to be extremely difficult. In this review, we describe the highly inconsistent data that are available on the cell type-specific expression patterns of the NKCC1 mRNA and protein in the CNS. We discuss the major technical caveats, including a lack of knock-out-controlled immunohistochemistry in the forebrain, possible effects of alternative splicing on the binding of antibodies and RNA probes, and the wide expression of NKCC1 in different cell types, which make whole-tissue analyses of NKCC1 useless for studying its neuronal expression. We also review novel single-cell RNAseq data showing that most of the NKCC1 in the adult CNS may, in fact, be expressed in non-neuronal cells, especially in glia. As future directions, we suggest single-cell NKCC1 mRNA and protein analyses and the use of genetically tagged endogenous proteins or systematically designed novel antibodies, together with proper knock-out controls, for the visualization of endogenous NKCC1 in distinct brain cell types and their subcellular compartments.

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Molecular taxonomy of human ocular outflow tissues defined by single-cell transcriptomics

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2001896117 ◽

2020 ◽

Vol 117 (23) ◽

pp. 12856-12867 ◽

Cited By ~ 5

Author(s):

Gaurang Patel ◽

Wen Fury ◽

Hua Yang ◽

Maria Gomez-Caraballo ◽

Yu Bai ◽

...

Keyword(s):

Single Cell ◽

Expression Profiles ◽

Expression Patterns ◽

Molecular Taxonomy ◽

Cell Types ◽

Primary Component ◽

Second Primary ◽

Specific Expression ◽

Component Structure ◽

Outflow Pathway

The conventional outflow pathway is a complex tissue responsible for maintaining intraocular pressure (IOP) homeostasis. The coordinated effort of multiple cells with differing responsibilities ensures healthy outflow function and IOP maintenance. Dysfunction of one or more resident cell types results in ocular hypertension and risk for glaucoma, a leading cause of blindness. In this study, single-cell RNA sequencing was performed to generate a comprehensive cell atlas of human conventional outflow tissues. We obtained expression profiles of 17,757 genes from 8,758 cells from eight eyes of human donors representing the outflow cell transcriptome. Upon clustering analysis, 12 distinct cell types were identified, and region-specific expression of candidate genes was mapped in human tissues. Significantly, we identified two distinct expression patterns (myofibroblast- and fibroblast-like) from cells located in the trabecular meshwork (TM), the primary structural component of the conventional outflow pathway. We also located Schwann cell and macrophage signatures in the TM. The second primary component structure, Schlemm’s canal, displayed a unique combination of lymphatic/blood vascular gene expression. Other expression clusters corresponded to cells from neighboring tissues, predominantly in the ciliary muscle/scleral spur, which together correspond to the uveoscleral outflow pathway. Importantly, the utility of our atlas was demonstrated by mapping glaucoma-relevant genes to outflow cell clusters. Our study provides a comprehensive molecular and cellular classification of conventional and unconventional outflow pathway structures responsible for IOP homeostasis.

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Potential Networks of Nitrogen-Phosphorus-Potassium Channels and Transporters in Arabidopsis Roots at a Single Cell Resolution

Frontiers in Plant Science ◽

10.3389/fpls.2021.689545 ◽

2021 ◽

Vol 12 ◽

Author(s):

Dhondup Lhamo ◽

Sheng Luan

Keyword(s):

Single Cell ◽

Expression Patterns ◽

Cell Types ◽

Long Distance ◽

Specific Expression ◽

Root Cells ◽

Long Distance Transport ◽

Npk Uptake ◽

Transcriptomics Data ◽

Different Cell Types

Nitrogen (N), phosphorus (P), and potassium (K) are three major macronutrients essential for plant life. These nutrients are acquired and transported by several large families of transporters expressed in plant roots. However, it remains largely unknown how these transporters are distributed in different cell-types that work together to transfer the nutrients from the soil to different layers of root cells and eventually reach vasculature for massive flow. Using the single cell transcriptomics data from Arabidopsis roots, we profiled the transcriptional patterns of putative nutrient transporters in different root cell-types. Such analyses identified a number of uncharacterized NPK transporters expressed in the root epidermis to mediate NPK uptake and distribution to the adjacent cells. Some transport genes showed cortex- and endodermis-specific expression to direct the nutrient flow toward the vasculature. For long-distance transport, a variety of transporters were shown to express and potentially function in the xylem and phloem. In the context of subcellular distribution of mineral nutrients, the NPK transporters at subcellular compartments were often found to show ubiquitous expression patterns, which suggests function in house-keeping processes. Overall, these single cell transcriptomic analyses provide working models of nutrient transport from the epidermis across the cortex to the vasculature, which can be further tested experimentally in the future.

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Identifying cell types from single-cell data based on similarities and dissimilarities between cells

BMC Bioinformatics ◽

10.1186/s12859-020-03873-z ◽

2021 ◽

Vol 22 (S3) ◽

Author(s):

Yuanyuan Li ◽

Ping Luo ◽

Yi Lu ◽

Fang-Xiang Wu

Keyword(s):

Gene Expression ◽

Single Cell ◽

Spectral Clustering ◽

Incidence Matrix ◽

Expression Patterns ◽

Cell Types ◽

Clustering Method ◽

Different Types ◽

Cell Data ◽

Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

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miRNA Expression Characterizes Histological Subtypes and Metastasis in Penile Squamous Cell Carcinoma

Cancers ◽

10.3390/cancers13061480 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1480

Author(s):

Hiresh Ayoubian ◽

Joana Heinzelmann ◽

Sebastian Hölters ◽

Oybek Khalmurzaev ◽

Alexey Pryalukhin ◽

...

Keyword(s):

Microarray Data ◽

Expression Patterns ◽

Hpv Infection ◽

Differentially Expressed ◽

Histological Subtypes ◽

Specific Expression ◽

Tissue Samples ◽

Qrt Pcr ◽

Differentially Expressed Mirnas ◽

The Impact

Although microRNAs are described as promising biomarkers in many tumor types, little is known about their role in PSCC. Thus, we attempted to identify miRNAs involved in tumor development and metastasis in distinct histological subtypes considering the impact of HPV infection. In a first step, microarray analyses were performed on RNA from formalin-fixed, paraffin-embedded tumor (22), and normal (8) tissue samples. Microarray data were validated for selected miRNAs by qRT-PCR on an enlarged cohort, including 27 tumor and 18 normal tissues. We found 876 significantly differentially expressed miRNAs (p ≤ 0.01) between HPV-positive and HPV-negative tumor samples by microarray analysis. Although no significant differences were detected between normal and tumor tissue in the whole cohort, specific expression patterns occurred in distinct histological subtypes, such as HPV-negative usual PSCC (95 differentially expressed miRNAs, p ≤ 0.05) and HPV-positive basaloid/warty subtypes (247 differentially expressed miRNAs, p ≤ 0.05). Selected miRNAs were confirmed by qRT-PCR. Furthermore, microarray data revealed 118 miRNAs (p ≤ 0.01) that were significantly differentially expressed in metastatic versus non-metastatic usual PSCC. The lower expression levels for miR-137 and miR-328-3p in metastatic usual PSCC were validated by qRT-PCR. The results of this study confirmed that specific miRNAs could serve as potential diagnostic and prognostic markers in single PSCC subtypes and are associated with HPV-dependent pathways.

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Identification of cell-type-specific marker genes from co-expression patterns in tissue samples

Bioinformatics ◽

10.1093/bioinformatics/btab257 ◽

2021 ◽

Author(s):

Yixuan Qiu ◽

Jiebiao Wang ◽

Jing Lei ◽

Kathryn Roeder

Keyword(s):

Single Cell ◽

Expression Patterns ◽

R Package ◽

Supplementary Information ◽

Marker Genes ◽

Specific Marker ◽

Cell Type ◽

Correlation Pattern ◽

Tissue Samples ◽

Bulk Data

Abstract Motivation Marker genes, defined as genes that are expressed primarily in a single cell type, can be identified from the single cell transcriptome; however, such data are not always available for the many uses of marker genes, such as deconvolution of bulk tissue. Marker genes for a cell type, however, are highly correlated in bulk data, because their expression levels depend primarily on the proportion of that cell type in the samples. Therefore, when many tissue samples are analyzed, it is possible to identify these marker genes from the correlation pattern. Results To capitalize on this pattern, we develop a new algorithm to detect marker genes by combining published information about likely marker genes with bulk transcriptome data in the form of a semi-supervised algorithm. The algorithm then exploits the correlation structure of the bulk data to refine the published marker genes by adding or removing genes from the list. Availability and implementation We implement this method as an R package markerpen, hosted on CRAN (https://CRAN.R-project.org/package=markerpen). Supplementary information Supplementary data are available at Bioinformatics online.

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A novel mouse model based on intersectional genetics enables unambiguous in vivo discrimination between plasmacytoid and other dendritic cells and their comparative characterization

10.1101/2022.01.07.475382 ◽

2022 ◽

Author(s):

Michael Valente ◽

Nils Collinet ◽

Thien-Phong Vu Manh ◽

Karima Naciri ◽

Gilles Bessou ◽

...

Keyword(s):

Dendritic Cells ◽

Steady State ◽

Mouse Model ◽

Single Cell ◽

Ex Vivo ◽

High Specificity ◽

Type I ◽

Specific Expression ◽

Physiological Functions

Plasmacytoid dendritic cells (pDC) were identified about 20 years ago, based on their unique ability to rapidly produce copious amounts of all subsets of type I and type III interferon (IFN-I/III) upon virus sensing, while being refractory to infection. Yet, the identity and physiological functions of pDC are still a matter of debate, in a large part due to their lack of specific expression of any single cell surface marker or gene that would allow to track them in tissues and to target them in vivo with high specificity and penetrance. Indeed, recent studies showed that previous methods that were used to identify or deplete pDC also targeted other cell types, including pDC-like cells and transitional DC (tDC) that were proposed to be responsible for all the antigen presentation ability previously attributed to steady state pDC. Hence, improving our understanding of the nature and in vivo choreography of pDC physiological functions requires the development of novel tools to unambiguously identify and track these cells, including in comparison to pDC-like cells and tDC. Here, we report successful generation of a pDC-reporter mouse model, by using an intersectional genetic strategy based on the unique co-expression of Siglech and Pacsin1 in pDC. This pDC-Tomato mouse strain allows specific ex vivo and in situ detection of pDC. Breeding them with Zbtb46GFP mice allowed side-by-side purification and transcriptional profiling by single cell RNA sequencing of bona fide pDC, pDC-like cells and tDC, in comparison to type 1 and 2 conventional DC (cDC1 and cDC2), both at steady state and during a viral infection, revealing diverging activation patterns of pDC-like cells and tDC. Finally, by breeding pDC-Tomato mice with Ifnb1EYFP mice, we determined the choreography of pDC recruitment to the micro-anatomical sites of viral replication in the spleen, with initially similar but later divergent behaviors of the pDC that engaged or not into IFN-I production. Our novel pDC-Tomato mouse model, and newly identified gene modules specific to combinations of DC types and activations states, will constitute valuable resources for a deeper understanding of the functional division of labor between DC types and its molecular regulation at homeostasis and during viral infections.

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