Transcriptional landscape of human microglia reveals robust gene expression signatures that implicates age, sex and APOE-related immunometabolic pathway perturbations

Microglia have fundamental roles in health and disease, however effects of age, sex and genetic factors on human microglia have not been fully explored. We applied bulk and single cell approaches to comprehensively characterize human microglia transcriptomes and their associations with age, sex and APOE. We identified a novel microglial signature, characterized its expression in bulk data from 1,306 brain samples across 6 regions and in single cell microglia transcriptome. We discovered microglial co-expression network modules associated with age, sex and APOE-ε4 that are enriched for lipid and carbohydrate metabolism genes. Integrated analyses of modules with single cell transcriptomes revealed significant overlap between age-associated module genes and both pro-inflammatory and disease-associated microglial clusters. These modules and clusters harbor known neurodegenerative disease genes including APOE, PLCG2 and BIN1. These data represent a well-characterized human microglial transcriptome resource; and highlight age, sex and APOE-related microglial immunometabolism perturbations with potential relevance in neurodegeneration.

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Transcriptional landscape of human microglia reveals robust gene expression signatures that implicates age, sex and APOE-related immunometabolic pathway perturbations

10.21203/rs.3.rs-523348/v1 ◽

2021 ◽

Author(s):

Tulsi Patel ◽

Troy Carnwath ◽

Xue Wang ◽

Mariet Allen ◽

Sarah Lincoln ◽

...

Keyword(s):

Single Cell ◽

Genetic Factors ◽

Disease Genes ◽

Human Microglia ◽

Bulk Data ◽

Network Modules ◽

Health And Disease ◽

Transcriptome Resource ◽

Carbohydrate Metabolism Genes ◽

Transcriptional Landscape

Abstract Microglia have fundamental roles in health and disease, however effects of age, sex and genetic factors on human microglia have not been fully explored. We applied bulk and single cell approaches to comprehensively characterize human microglia transcriptomes and their associations with age, sex and APOE. We identified a novel microglial signature, characterized its expression in bulk data from 1,306 brain samples across 6 regions and in single cell microglia transcriptome. We discovered microglial co-expression network modules associated with age, sex and APOE-ε4 that are enriched for lipid and carbohydrate metabolism genes. Integrated analyses of modules with single cell transcriptomes revealed significant overlap between age-associated module genes and both pro-inflammatory and disease-associated microglial clusters. These modules and clusters harbor known neurodegenerative disease genes including APOE, PLCG2 and BIN1. These data represent a well-characterized human microglial transcriptome resource; and highlight age, sex and APOE-related microglial immunometabolism perturbations with potential relevance in neurodegeneration.

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Single-cell biology to decode the immune cellular composition of kidney inflammation

Cell and Tissue Research ◽

10.1007/s00441-021-03483-y ◽

2021 ◽

Author(s):

Yu Zhao ◽

Ulf Panzer ◽

Stefan Bonn ◽

Christian F. Krebs

Keyword(s):

Single Cell ◽

Cell Biology ◽

Computational Analysis ◽

Immune Cell ◽

Therapeutic Interventions ◽

Experimental Setup ◽

Disease Etiology ◽

Rna Profiling ◽

Immune Mediated ◽

Health And Disease

AbstractSingle-cell biology is transforming the ability of researchers to understand cellular signaling and identity across medical and biological disciplines. Especially for immune-mediated diseases, a single-cell look at immune cell subtypes, signaling, and activity might yield fundamental insights into the disease etiology, mechanisms, and potential therapeutic interventions. In this review, we highlight recent advances in the field of single-cell RNA profiling and their application to understand renal function in health and disease. With a focus on the immune system, in particular on T cells, we propose some key directions of understanding renal inflammation using single-cell approaches. We detail the benefits and shortcomings of the various technological approaches outlined and give advice on potential pitfalls and challenges in experimental setup and computational analysis. Finally, we conclude with a brief outlook into a promising future for single-cell technologies to elucidate kidney function.

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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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Cell type identification for single cell RNA data by bulk data reference projection

10.1109/bibm52615.2021.9669148 ◽

2021 ◽

Author(s):

Oleg Sysoev ◽

Danuta Gawel ◽

Sandra Lilja ◽

Samuel Schafer ◽

Mikael Benson

Keyword(s):

Single Cell ◽

Cell Type ◽

Bulk Data

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Human microglia regional heterogeneity and phenotypes determined by multiplexed single-cell mass cytometry

Nature Neuroscience ◽

10.1038/s41593-018-0290-2 ◽

2018 ◽

Vol 22 (1) ◽

pp. 78-90 ◽

Cited By ~ 101

Author(s):

Chotima Böttcher ◽

◽

Stephan Schlickeiser ◽

Marjolein A. M. Sneeboer ◽

Desiree Kunkel ◽

...

Keyword(s):

Single Cell ◽

Cell Mass ◽

Mass Cytometry ◽

Regional Heterogeneity ◽

Human Microglia

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Multiplexed Profiling of Single-cell Extracellular Vesicles Secretion

10.1101/459438 ◽

2018 ◽

Author(s):

Yahui Ji ◽

Dongyuan Qi ◽

Linmei Li ◽

Haoran Su ◽

Xiaojie Li ◽

...

Keyword(s):

Single Cell ◽

Cell Lines ◽

Extracellular Vesicles ◽

Single Cells ◽

Cell Communication ◽

Deep Understanding ◽

Surface Markers ◽

Cell Heterogeneity ◽

Health And Disease ◽

Cytokines Secretion

AbstractExtracellular vesicles (EVs) are important intercellular mediators regulating health and disease. Conventional EVs surface marker profiling, which was based on population measurements, masked the cell-to-cell heterogeneity in the quantity and phenotypes of EVs secretion. Herein, by using spatially patterned antibodies barcode, we realized multiplexed profiling of single-cell EVs secretion from more than 1000 single cells simultaneously. Applying this platform to profile human oral squamous cell carcinoma (OSCC) cell lines led to deep understanding of previously undifferentiated single cell heterogeneity underlying EVs secretion. Notably, we observed the decrement of certain EV phenotypes (e.g. CD63+EVs) were associated with the invasive feature of both OSCC cell lines and primary OSCC cells. We also realized multiplexed detection of EVs secretion and cytokines secretion simultaneously from the same single cells to investigate multidimensional spectrum of intercellular communications, from which we resolved three functional subgroups with distinct secretion profiles by visualized clustering. In particular, we found EVs secretion and cytokines secretion were generally dominated by different cell subgroups. The technology introduced here enables comprehensive evaluation of EVs secretion heterogeneity at single cell level, which may become an indispensable tool to complement current single cell analysis and EV research.SignificanceExtracellular vesicles (EVs) are cell derived nano-sized particles medicating cell-cell communication and transferring biology information molecules like nucleic acids to regulate human health and disease. Conventional methods for EV surface markers profiling can’t tell the differences in the quantity and phenotypes of EVs secretion between cells. To address this need, we developed a platform for profiling an array of surface markers on EVs from large numbers of single cells, enabling more comprehensive monitoring of cellular communications. Single cell EVs secretion assay led to previously unobserved cell heterogeneity underlying EVs secretion, which might open up new avenues for studying cell communication and cell microenvironment in both basic and clinical research.

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Spatial and single-cell transcriptional landscape of human cerebellar development

10.1101/2020.06.30.174391 ◽

2020 ◽

Author(s):

Kimberly A. Aldinger ◽

Zach Thomson ◽

Parthiv Haldipur ◽

Mei Deng ◽

Andrew E. Timms ◽

...

Keyword(s):

Single Cell ◽

Emotional Regulation ◽

Cell Types ◽

Molecular Organization ◽

Cerebellar Development ◽

Disease Genes ◽

Spatially Resolved ◽

Human Cerebellum ◽

Spatial Composition ◽

And Function

ABSTRACTCerebellar development and function require precise regulation of molecular and cellular programs to coordinate motor functions and integrate network signals required for cognition and emotional regulation. However, molecular understanding of human cerebellar development is limited. Here, we combined spatially resolved and single-cell transcriptomics to systematically map the molecular, cellular, and spatial composition of early and mid-gestational human cerebellum. This enabled us to transcriptionally profile major cell types and examine the dynamics of gene expression within cell types and lineages across development. The resulting ‘Developmental Cell Atlas of the Human Cerebellum’ demonstrates that the molecular organization of the cerebellar anlage reflects cytoarchitecturally distinct regions and developmentally transient cell types that are insufficiently captured in bulk transcriptional profiles. By mapping disease genes onto cell types, we implicate the dysregulation of specific cerebellar cell types, especially Purkinje cells, in pediatric and adult neurological disorders. These data provide a critical resource for understanding human cerebellar development with implications for the cellular basis of cerebellar diseases.

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The genetic factors involved in functional food efficacy on cardiovascular disease etiology

SURG Journal ◽

10.21083/surg.v11i0.4152 ◽

2019 ◽

Vol 11 ◽

Author(s):

Maarij Siddiqi

Keyword(s):

Risk Factors ◽

Cardiovascular Disease ◽

Nutritional Value ◽

Functional Foods ◽

Genetic Factors ◽

Dietary Guidelines ◽

Disease Etiology ◽

Modifiable Factor ◽

Health And Disease ◽

The Relationship

While the impacts of modifiable and non-modifiable risk factors on chronic diseases such as cardiovascular disease (CVD) are widely established, the interactions between such coexisting risk factors and their subsequent effects on the promotion or suppression of CVD are less known. As part of the diet, functional foods are considered a modifiable factor that influence health beyond their basic nutritional value. The relationship between these functional foods and the underlying genome, along with their joint implication in health and disease, forms the focus of the emerging field of nutrigenomics. Reviewed in this paper are some prominent gene-diet interactions demonstrated in CVD etiology. Specifically, the interaction between foods such as phytosterols and isoflavones with genetic factors of the consuming population are examined in relation to CVD. By determining how nutritional intake affects genetics and vice versa, we create the potential to offer improved dietary guidelines to certain individuals, subgroups, or populations in order to maximize health benefits of specific diets.

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A Novel Method to Identify the Differences Between Two Single Cell Groups at Single Gene, Gene Pair, and Gene Module Levels

Frontiers in Genetics ◽

10.3389/fgene.2021.648898 ◽

2021 ◽

Vol 12 ◽

Author(s):

Lingyu Cui ◽

Bo Wang ◽

Changjing Ren ◽

Ailan Wang ◽

Hong An ◽

...

Keyword(s):

Single Cell ◽

Molecular Mechanisms ◽

Single Gene ◽

Cell Types ◽

Human Pancreas ◽

Biological Difference ◽

Cell Clusters ◽

Cell Groups ◽

Network Modules ◽

Differential Gene

Single-cell sequencing technology can not only view the heterogeneity of cells from a molecular perspective, but also discover new cell types. Although there are many effective methods on dropout imputation, cell clustering, and lineage reconstruction based on single cell RNA sequencing (RNA-seq) data, there is no systemic pipeline on how to compare two single cell clusters at the molecular level. In the study, we present a novel pipeline on comparing two single cell clusters, including calling differential gene expression, coexpression network modules, and so on. The pipeline could reveal mechanisms behind the biological difference between cell clusters and cell types, and identify cell type specific molecular mechanisms. We applied the pipeline to two famous single-cell databases, Usoskin from mouse brain and Xin from human pancreas, which contained 622 and 1,600 cells, respectively, both of which were composed of four types of cells. As a result, we identified many significant differential genes, differential gene coexpression and network modules among the cell clusters, which confirmed that different cell clusters might perform different functions.

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