The interplay between microglial states and major risk factors in Alzheimer’s disease through the eyes of single-cell RNA-sequencing: beyond black and white

Microglia constitute ~10–20% of glial cells in the adult human brain. They are the resident phagocytic immune cells of the central nervous system and play an integral role as first responders during inflammation. Microglia are commonly classified as “HM” (homeostatic), “M1” (classically activated proinflammatory), or “M2” (alternatively activated). Multiple single-cell RNA-sequencing studies suggest that this discrete classification system does not accurately and fully capture the vast heterogeneity of microglial states in the brain. In fact, a recent single-cell RNA-sequencing study showed that microglia exist along a continuous spectrum of states. This spectrum spans heterogeneous populations of homeostatic and neuropathology-associated microglia in both healthy and Alzheimer’s disease (AD) mouse brains. Major risk factors, such as sex, age, and genes, modulate microglial states, suggesting that shifts along the trajectory might play a causal role in AD pathogenesis. This study provides important insight into the cellular mechanisms of AD and underlines the potential of novel cell-based therapies for AD.

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Single cell RNA sequencing of human microglia uncovers a subset that is associated with Alzheimer’s disease

Alzheimer s & Dementia ◽

10.1002/alz.038589 ◽

2020 ◽

Vol 16 (S2) ◽

Author(s):

Marta Olah ◽

Vilas Menon ◽

Naomi Habib ◽

Mariko Taga ◽

Yiyi Ma ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Single Cell ◽

Rna Sequencing ◽

Human Microglia ◽

Single Cell Rna Sequencing

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Single-cell RNA sequencing reveals B cell–related molecular biomarkers for Alzheimer’s disease

Experimental & Molecular Medicine ◽

10.1038/s12276-021-00714-8 ◽

2021 ◽

Author(s):

Liu-Lin Xiong ◽

Lu-Lu Xue ◽

Ruo-Lan Du ◽

Rui-Ze Niu ◽

Li Chen ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

B Cells ◽

B Cell ◽

Single Cell ◽

Rna Sequencing ◽

Early Stage ◽

Diagnostic Process ◽

Sequencing Analysis ◽

Single Cell Rna Sequencing

AbstractIn recent years, biomarkers have been integrated into the diagnostic process and have become increasingly indispensable for obtaining knowledge of the neurodegenerative processes in Alzheimer’s disease (AD). Peripheral blood mononuclear cells (PBMCs) in human blood have been reported to participate in a variety of neurodegenerative activities. Here, a single-cell RNA sequencing analysis of PBMCs from 4 AD patients (2 in the early stage, 2 in the late stage) and 2 normal controls was performed to explore the differential cell subpopulations in PBMCs of AD patients. A significant decrease in B cells was detected in the blood of AD patients. Furthermore, we further examined PBMCs from 43 AD patients and 41 normal subjects by fluorescence activated cell sorting (FACS), and combined with correlation analysis, we found that the reduction in B cells was closely correlated with the patients’ Clinical Dementia Rating (CDR) scores. To confirm the role of B cells in AD progression, functional experiments were performed in early-stage AD mice in which fibrous plaques were beginning to appear; the results demonstrated that B cell depletion in the early stage of AD markedly accelerated and aggravated cognitive dysfunction and augmented the Aβ burden in AD mice. Importantly, the experiments revealed 18 genes that were specifically upregulated and 7 genes that were specifically downregulated in B cells as the disease progressed, and several of these genes exhibited close correlation with AD. These findings identified possible B cell-based AD severity, which are anticipated to be conducive to the clinical identification of AD progression.

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Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer’s disease

Nature Communications ◽

10.1038/s41467-020-19737-2 ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

Marta Olah ◽

Vilas Menon ◽

Naomi Habib ◽

Mariko F. Taga ◽

Yiyi Ma ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Single Cell ◽

Rna Sequencing ◽

Independent Set ◽

Neurosurgical Procedures ◽

Human Microglia ◽

Single Cell Rna Sequencing ◽

Single Nucleus ◽

Disease Related Genes

AbstractThe extent of microglial heterogeneity in humans remains a central yet poorly explored question in light of the development of therapies targeting this cell type. Here, we investigate the population structure of live microglia purified from human cerebral cortex samples obtained at autopsy and during neurosurgical procedures. Using single cell RNA sequencing, we find that some subsets are enriched for disease-related genes and RNA signatures. We confirm the presence of four of these microglial subpopulations histologically and illustrate the utility of our data by characterizing further microglial cluster 7, enriched for genes depleted in the cortex of individuals with Alzheimer’s disease (AD). Histologically, these cluster 7 microglia are reduced in frequency in AD tissue, and we validate this observation in an independent set of single nucleus data. Thus, our live human microglia identify a range of subtypes, and we prioritize one of these as being altered in AD.

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Characterization of Alzheimer’s Disease-Associated Excitatory Neurons via Single-Cell RNA Sequencing Analysis

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2021.742176 ◽

2021 ◽

Vol 13 ◽

Author(s):

Fanghong Shao ◽

Meiting Wang ◽

Qi Guo ◽

Bowen Zhang ◽

Xiangting Wang

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Single Cell ◽

Rna Sequencing ◽

Neuronal Cell ◽

Cell Populations ◽

Sequencing Analysis ◽

Single Cell Rna Sequencing ◽

Excitatory Neurons

The detailed characteristics of neuronal cell populations in Alzheimer’s disease (AD) using single-cell RNA sequencing have not been fully elucidated. To explore the characterization of neuronal cell populations in AD, this study utilized the publicly available single-nucleus RNA-sequencing datasets in the transgenic model of 5X familial Alzheimer’s disease (5XFAD) and wild-type mice to reveal an AD-associated excitatory neuron population (C3:Ex.Neuron). The relative abundance of C3:Ex.Neuron increased at 1.5 months and peaked at 4.7 months in AD mice. Functional pathways analyses showed that the pathways positively related to neurodegenerative disease progression were downregulated in the C3:Ex.Neuron at 1.5 months in AD mice. Based on the differentially expressed genes among the C3:Ex.Neuron, four subtypes (C3.1–4) were identified, which exhibited distinct abundance regulatory patterns during the development of AD. Among these subtypes, the C3.1 neurons [marked by netrin G1 (Ntng1)] exhibited a similar regulatory pattern as the C3:Ex.Neuron in abundance during the development of AD. In addition, our gene set variation analysis (GSEA) showed that the C3.1 neurons, instead of other subtypes of the C3:Ex.Neuron, possessed downregulated AD pathways at an early stage (1.5 months) of AD mice. Collectively, our results identified a previously unidentified subset of excitatory neurons and provide a potential application of these neurons to modulate the disease susceptibility.

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Human dermal fibroblast subpopulations are conserved across single-cell RNA sequencing studies

Journal of Investigative Dermatology ◽

10.1016/j.jid.2020.11.028 ◽

2020 ◽

Author(s):

Alex M. Ascensión ◽

Sandra Fuertes-Álvarez ◽

Olga Ibañez-Solé ◽

Ander Izeta ◽

Marcos J. Araúzo-Bravo

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Dermal Fibroblast ◽

Human Dermal Fibroblast ◽

Single Cell Rna Sequencing ◽

Sequencing Studies

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Controlling for confounding effects in single cell RNA sequencing studies using both control and target genes

10.1101/045070 ◽

2016 ◽

Author(s):

Mengjie Chen ◽

Xiang Zhou

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Target Genes ◽

Expectation Maximization Algorithm ◽

Data Sets ◽

Single Cell Rna Sequencing ◽

Sequencing Studies ◽

Order Of Magnitude ◽

The Rich ◽

Downstream Analysis

Single cell RNA sequencing (scRNAseq) technique is becoming increasingly popular for unbiased and high-resolutional transcriptome analysis of heterogeneous cell populations. Despite its many advantages, scRNAseq, like any other genomic sequencing technique, is susceptible to the influence of confounding effects. Controlling for confounding effects in scRNAseq data is thus a crucial step for proper data normalization and accurate downstream analysis. Several recent methodological studies have demonstrated the use of control genes for controlling for confounding effects in scRNAseq studies; the control genes are used to infer the confounding effects, which are then used to normalize target genes of primary interest. However, these methods can be suboptimal as they ignore the rich information contained in the target genes. Here, we develop an alternative statistical method, which we refer to as scPLS, for more accurate inference of confounding effects. Our method is based on partial least squares and models control and target genes jointly to better infer and control for confounding effects. To accompany our method, we develop a novel expectation maximization algorithm for scalable inference. Our algorithm is an order of magnitude faster than standard ones, making scPLS applicable to hundreds of cells and hundreds of thousands of genes. With extensive simulations and comparisons with other methods, we demonstrate the effectiveness of scPLS. Finally, we apply scPLS to analyze two scRNAseq data sets to illustrate its benefits in removing technical confounding effects as well as for removing cell cycle effects.

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Multimodal single-cell/nucleus RNA sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease

Genome Research ◽

10.1101/gr.272484.120 ◽

2021 ◽

pp. gr.272484.120 ◽

Cited By ~ 1

Author(s):

Jielin Xu ◽

Pengyue Zhang ◽

Yin Huang ◽

Yadi Zhou ◽

Yuan Hou ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Data Analysis ◽

Single Cell ◽

Rna Sequencing ◽

Cell Nucleus ◽

Drug Repurposing ◽

Molecular Networks ◽

Sequencing Data ◽

Sequencing Data Analysis

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Detection and removal of barcode swapping in single-cell RNA-seq data

10.1101/177048 ◽

2017 ◽

Cited By ~ 5

Author(s):

Jonathan A. Griffiths ◽

Arianne C. Richard ◽

Karsten Bach ◽

Aaron T.L. Lun ◽

John C Marioni

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Flow Cell ◽

Rna Seq ◽

Genomic Assays ◽

Single Cell Rna Sequencing ◽

Sequencing Studies ◽

Continued Use ◽

Statistical Approaches ◽

Transcriptomic Studies

AbstractBarcode swapping results in the mislabeling of sequencing reads between multiplexed samples on the new patterned flow cell Illumina sequencing machines. This may compromise the validity of numerous genomic assays, especially for single-cell studies where many samples are routinely multiplexed together. The severity and consequences of barcode swapping for single-cell transcriptomic studies remain poorly understood. We have used two statistical approaches to robustly quantify the fraction of swapped reads in each of two plate-based single-cell RNA sequencing datasets. We found that approximately 2.5% of reads were mislabeled between samples on the HiSeq 4000 machine, which is lower than previous reports. We observed no correlation between the swapped fraction of reads and the concentration of free barcode across plates. Furthermore, we have demonstrated that barcode swapping may generate complex but artefactual cell libraries in droplet-based single-cell RNA sequencing studies. To eliminate these artefacts, we have developed an algorithm to exclude individual molecules that have swapped between samples in 10X Genomics experiments, exploiting the combinatorial complexity present in the data. This permits the continued use of cutting-edge sequencing machines for droplet-based experiments while avoiding the confounding effects of barcode swapping.

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SNV identification from single-cell RNA sequencing data

Human Molecular Genetics ◽

10.1093/hmg/ddz207 ◽

2019 ◽

Vol 28 (21) ◽

pp. 3569-3583 ◽

Cited By ~ 3

Author(s):

Patricia M Schnepp ◽

Mengjie Chen ◽

Evan T Keller ◽

Xiang Zhou

Keyword(s):

Dna Sequencing ◽

Single Cell ◽

Rna Sequencing ◽

Single Cells ◽

Specific Gene ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Single Cell Rna Sequencing ◽

Sequencing Studies ◽

Genomic Regions

Abstract Integrating single-cell RNA sequencing (scRNA-seq) data with genotypes obtained from DNA sequencing studies facilitates the detection of functional genetic variants underlying cell type-specific gene expression variation. Unfortunately, most existing scRNA-seq studies do not come with DNA sequencing data; thus, being able to call single nucleotide variants (SNVs) from scRNA-seq data alone can provide crucial and complementary information, detection of functional SNVs, maximizing the potential of existing scRNA-seq studies. Here, we perform extensive analyses to evaluate the utility of two SNV calling pipelines (GATK and Monovar), originally designed for SNV calling in either bulk or single-cell DNA sequencing data. In both pipelines, we examined various parameter settings to determine the accuracy of the final SNV call set and provide practical recommendations for applied analysts. We found that combining all reads from the single cells and following GATK Best Practices resulted in the highest number of SNVs identified with a high concordance. In individual single cells, Monovar resulted in better quality SNVs even though none of the pipelines analyzed is capable of calling a reasonable number of SNVs with high accuracy. In addition, we found that SNV calling quality varies across different functional genomic regions. Our results open doors for novel ways to leverage the use of scRNA-seq for the future investigation of SNV function.

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