Absence of microglia promotes diverse pathologies and early lethality in Alzheimer’s disease mice

Shabestari, Sepideh Kiani; Morabito, Samuel; Danhash, Emma; McQuade, Amanda; Sanchez, Jessica; Miyoshi, Emily; Chadarevian, Jean Paul; Claes, Christel; Coburn, Morgan A.; Hasselmann, Jonathan; Hidalgo, Jorge Luis Silva; Tran, Kayla Nhi; Martini, Alessandra Cadete; Rothermich, Winston Chang; Pascual, Jesse; Head, Elizabeth; Hume, David; Pridans, Clare; Davtyan, Hayk; Swarup, Vivek; Blurton‐Jones, Mathew

doi:10.1016/j.celrep.2022.110961

Cited by 71 publications

(53 citation statements)

References 89 publications

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“…Contrary to other approaches, combinatorial barcoding does not require any complex microfluidics or other specialized equipment. Over the past few years, this approach has been widely adopted to study developmental processes and disease progression at scale [13][14][15][16][17][18][19][20][21][22][23][24][25][26] .…”

Section: Introductionmentioning

confidence: 99%

High sensitivity single cell RNA sequencing with split pool barcoding

Tran¹,

Papalexi²,

Schroeder³

et al. 2022

Preprint

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Single cell RNA sequencing (scRNA-seq) has become a core tool for researchers to understand biology. As scRNA-seq has become more ubiquitous, many applications demand higher scalability and sensitivity. Split-pool combinatorial barcoding makes it possible to scale projects to hundreds of samples and millions of cells, overcoming limitations of previous droplet based technologies. However, there is still a need for increased sensitivity for both droplet and combinatorial barcoding based scRNA-seq technologies. To meet this need, here we introduce an updated combinatorial barcoding method for scRNA-seq with dramatically improved sensitivity. To assess performance, we profile a variety of sample types, including cell lines, human peripheral blood mononuclear cells (PBMCs), mouse brain nuclei, and mouse liver nuclei. When compared to the previously best performing approach, we find up to a 2.6-fold increase in unique transcripts detected per cell and up to a 1.8-fold increase in genes detected per cell. These improvements to transcript and gene detection increase the resolution of the resulting data, making it easier to distinguish cell types and states in heterogeneous samples. Split-pool combinatorial barcoding already enables scaling to millions of cells, the ability to perform scRNA-seq on previously fixed and frozen samples, and access to scRNA-seq without the need to purchase specialized lab equipment. Our hope is that by combining these previous advantages with the dramatic improvements to sensitivity presented here, we will elevate the standards and capabilities of scRNA-seq for the broader community.

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Section: Introductionmentioning

confidence: 99%

High sensitivity single cell RNA sequencing with split pool barcoding

Tran¹,

Papalexi²,

Schroeder³

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…2b, S6c). We compared these gene modules to cluster marker genes from a whole mouse brain snRNA-seq dataset (23) and found significant correspondences, such as the striatum module SM7 and medium spiny neurons (Fisher’s exact test FDR < 0.05, Fig. S6d).…”

Section: Resultsmentioning

confidence: 99%

“…5j). We projected the microglial consensus modules into a dataset of the PFC in aged human samples (49), the superior frontal gyrus (SFG) and entorhinal cortex (EC) in AD samples (50), the occipital cortex (OC) and the occipitotemporal cortex (OTC) in human AD samples (51), the PFC from 5xFAD mice (11), and whole brain samples from 5xFAD mice (23) (Fig. 5h).…”

Section: Resultsmentioning

confidence: 99%

“…Table 1 details the different datasets used throughout this manuscript. We used several published datasets generated by our own group (12, 23, 49), and sequencing data was not re-downloaded for these studies. For all human snRNA-seq datasets, we applied a uniform processing pipeline to process each dataset starting from the raw sequencing data and resulting in an anndata object (73) containing UMI counts, normalized gene expression, cluster identities, and cell type annotations.…”

Section: Methodsmentioning

confidence: 99%

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High dimensional co-expression networks enable discovery of transcriptomic drivers in complex biological systems

Morabito

Reese

Rahimzadeh

et al. 2022

Preprint

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Biological systems are immensely complex, organized into a multi-scale hierarchy of functional units based on tightly-regulated interactions between distinct molecules, cells, organs, and organisms. While experimental methods enable transcriptome-wide measurements across millions of cells, the most ubiquitous bioinformatic tools do not support systems-level analysis. Here we present hdWGCNA, a comprehensive framework for analyzing co-expression networks in high dimensional transcriptomics data such as single-cell and spatial RNA-seq. hdWGCNA provides built-in functions for network inference, gene module identification, functional gene enrichment analysis, statistical tests for network reproducibility, and data visualization. In addition to conventional single-cell RNA-seq, hdWGCNA is capable of performing isoform-level network analysis using long-read single-cell data. We showcase hdWGCNA using publicly available single-cell datasets from Autism spectrum disorder and Alzheimer's disease brain samples, identifying disease-relevant co-expression network modules in specific cell populations. hdWGCNA is directly compatible with Seurat, a widely-used R package for single-cell and spatial transcriptomics analysis, and we demonstrate the scalability of hdWGCNA by analyzing a dataset containing nearly one million cells.

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“…Increased numbers of astrocytes and microglia were observed in the vicinity of Aβ plaques in postmortem AD mouse model brains and patients with AD [18]. Microglia are resident macrophages in the central nervous system (CNS) that are important for maintaining brain homeostasis [18] but have also been implicated in the pathophysiology of AD [8,22]. Recent single-cell sequencing transcriptomics for disease-associated microglia (DAM) represents transcriptionally distinct and neurodegeneration-specific microglial profiles with potential significance in AD signatures, including TREM2, CD33, and ApoE [8,21,51,13].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of CB₂R expression and pyridine-based radiotracers in brains from a mouse model of Alzheimer’s disease

Kecheliev

Spinelli

Herde

et al. 2022

Preprint

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Neuroinflammation plays an important role in the pathophysiology of Alzheimer′s disease. The cannabinoid type 2 receptor (CB2R) is an emerging target for neuroinflammation and therapeutics of Alzheimer′s disease. Here, we aimed to assess the alterations in brain CB2R levels and evaluate novel CB2R imaging tracers in the arcAβ mouse model of Alzheimer′s disease amyloidosis. Immunohistochemical staining for Aβ deposits (6E10), microgliosis (anti-Iba1 and anti-CD68 antibodies), astrocytes (GFAP) and the anti-CB2R antibody was performed on brain slices from arcAβ mice 17 months of age. Autoradiography using the CB2R imaging probes [18F]RoSMA-18-d6, [11C]RSR-056 and [11C]RS-028 and mRNA analysis were performed in brain tissue from arcAβ and nontransgenic littermate (NTL) mice at 6, 17, and 24 months of age. Specific increased CB2R immunofluorescence intensities on the increased number of GFAP-positive astrocytes and Iba1-positive microglia were detected in the hippocampus and cortex of 17-month-old arcAβ mice compared to NTL mice. CB2R immunofluorescence was higher in the glial cells inside 6E10-positive amyloid-β deposits than peri-plaque with a low background. Ex vivo autoradiography showed that the binding of [18F]RoSMA-18-d6 and [11C]RSR-056 was comparable in arcAβ and NTL mice at 6, 17 and 24 months. The level of Cnr2 mRNA expression in the brain was not significantly different between arcAβ and NTL mice at 6, 17 or 24 months. In conclusion, we demonstrated pronounced specific increases in microglial and astroglial CB2R expression levels in a model of AD-related cerebral amyloidosis/AD mouse model, emphasizing CB2R as a suitable target for imaging neuroinflammation.

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Absence of microglia promotes diverse pathologies and early lethality in Alzheimer’s disease mice

Cited by 71 publications

References 89 publications

High sensitivity single cell RNA sequencing with split pool barcoding

High sensitivity single cell RNA sequencing with split pool barcoding

High dimensional co-expression networks enable discovery of transcriptomic drivers in complex biological systems

Evaluation of CB₂R expression and pyridine-based radiotracers in brains from a mouse model of Alzheimer’s disease

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Absence of microglia promotes diverse pathologies and early lethality in Alzheimer’s disease mice

Cited by 71 publications

References 89 publications

High sensitivity single cell RNA sequencing with split pool barcoding

High sensitivity single cell RNA sequencing with split pool barcoding

High dimensional co-expression networks enable discovery of transcriptomic drivers in complex biological systems

Evaluation of CB2R expression and pyridine-based radiotracers in brains from a mouse model of Alzheimer’s disease

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Evaluation of CB₂R expression and pyridine-based radiotracers in brains from a mouse model of Alzheimer’s disease