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Method Article
We provide a protocol for isolation of microglia from different dissected regions of an adult mouse brain hemisphere, followed by semi-automated library preparation for deep single-cell RNA sequencing of full-length transcriptomes. This method will help to elucidate functional heterogeneity of microglia in health and disease.
As resident macrophages in the central nervous system, microglia actively control brain development and homeostasis, and their dysfunctions may drive human diseases. Considerable advances have been made to uncover the molecular signatures of homeostatic microglia as well as alterations of their gene expression in response to environmental stimuli. With the advent and maturation of single-cell genomic methodologies, it is increasingly recognized that heterogenous microglia may underlie the diverse roles they play in different developmental and pathological conditions. Further dissection of such heterogeneity can be achieved through efficient isolation of microglia from a given region of interest, followed by sensitive profiling of individual cells. Here, we provide a detailed protocol for the rapid isolation of microglia from different brain regions in a single adult mouse brain hemisphere. We also demonstrate how to use these sorted microglia for plate-based deep single-cell RNA sequencing. We discuss the adaptability of this method to other scenarios and provide guidelines for improving the system to accommodate large-scale studies.
Microglia, representing 5%−10% of all neural cells, are resident macrophages scattered throughout the central nervous system (CNS)1. Protected behind blood-brain barrier, typical microglia in a healthy adult brain contain many fine processes that rapidly extend and retract to interact with neurons and other glial cells in the parenchyma. Microglia can also adopt the amoeboid morphology associated with increased phagocytic function during specific developmental stages or upon immune challenges in injury and disease1,2,3,4. Recent exciting discoveries have clearly demonstrated that microglia are by no means passive bystanders to brain-derived or pathological signals, but play pivotal roles in controlling brain development and homeostasis, for instance, by supporting neuronal survival, pruning immature synapses, promoting oligodendrocyte lineage cells differentiation as well as angiogenesis1. As more functions of microglia are elucidated, the excitement is further fueled by human genetics studies, which showed that many neurodegenerative disease risk genes, such as TREM2, are predominantly or exclusively expressed by microglia5,6,7. Given their significance in development and plausible disease-driving roles, tremendous effort has recently been put towards our understanding of microglial gene regulation and function in hope of finding new therapeutic targets for neurodegenerative diseases1,8.
RNA sequencing (RNA-seq) allows unbiased characterization of cell type-specific gene expression, which in turn guides scientists to investigate gene functions in dense cellular networks7. RNA-seq had been mostly done on bulk samples, leading to the discovery of a homeostatic microglial gene signature that distinguishes them from other neural and immune cells9. However, such an approach could overlook molecular and functional differences among microglia, especially those transiently present in development, or associated with aging and disease. Indeed, single-cell RNA-seq (scRNA-seq) offers the sensitivity and resolution that have revolutionized the field by revealing previously underappreciated heterogeneity of microglia in a variety of contexts2,3,10. In addition, due to the presence of other similar immune cells at the CNS-circulation interface, scRNA-seq provides information aiding the design of new tools to separate and functionally dissect these related cells with little prior knowledge2,11.
A diverse array of scRNA-seq platforms have been invented, each suitable for certain applications12. In general, droplet-based methods, such as 10x Genomics, are higher in throughput with (tens of) thousands of cells sequenced in each run, and they are less selective for the input which may contain mixed cell populations requiring broad categorization. Plate-based methods provide higher sensitivity and read depth13,14, usually targeting specific populations from cell sorting to reveal subtle differences or rare transcripts. Given the small percentage of microglial cells, particularly those development- or disease-associated subpopulations, among all CNS cell types, it is often desirable to isolate microglia from a specific region of interest and obtain deep and full-length transcriptomic information in order to understand their heterogeneity.
Here, we provide details on how to isolate microglia from different mouse brain regions dissected from a single hemisphere, which are used for single-cell (or bulk) RNA-seq following a semi-automated plate-based library preparation procedure. The other hemisphere can then be used for histological validation. Streamlined from a previously published method9, this isolation protocol aims to maximize the yield from small amount of starting materials, and meanwhile maintain endogenous microglial gene expression profiles. We use fluorescence-activated cell sorting (FACS) to enrich microglia (or other related immune cells of interest) into 96-well plates and miniaturize the volumes of reagents for library preparation in order to increase throughput. We highlight this sensitive scRNA-seq platform, although other plate-based strategies may be applied. This method can be easily adapted to isolate microglia from other dissected tissues, such as injury or disease foci, and the age of the mouse can vary across almost any postnatal stages. Efficient isolation of regional microglia for single-cell transcriptomics studies will facilitate better understanding of their functions in health and disease.
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All procedures involving rodents conformed to Stanford University guidelines, which comply with national and state laws and policies. All animal procedures were approved by Stanford University's Administrative Panel on Laboratory Animal Care.
NOTE: All solution and buffer compositions are provided in Table of Materials.
1. Preparation on the Day of Cell Isolation
2. Brain Region Dissection
NOTE: This step should take ~30 min.
3. Mechanical Tissue Dissociation
NOTE: Keep cells and reagents cool all the time except during staining steps. This step should take ~30 min.
4. Myelin Removal
NOTE: This step should take ~60 min.
5. Staining for Fluorescence-activated Cell Sorting
NOTE: This step should take ~40 min.
6. Index FACS Sorting
NOTE: This step should take ~1 h.
7. Single-cell RNA-sequencing Library Preparation
NOTE: Here, the published protocol14 is followed to generate scRNA-seq libraries with the aid of liquid handling robotics and a few modifications. In this article, the procedure is only briefly described, and the differences are highlighted. Processing 4 plates simultaneously takes about 2.5 days.
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This protocol describes a method to isolate and sort microglia from different brain regions in one adult perfused brain hemisphere, followed by scRNA-seq. We use douncing to create single cell suspension and also as a first step to enrich microglia. Insufficient or over-douncing reduces the yield. In addition, adult mouse brains contain high levels of myelin, which can also reduce sorting efficiency and yield if not removed properly. Therefore, we examine the cell suspension under microscope by using trypan blue and a he...
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Microglia actively interact with other cell types in the CNS, and they are very sensitive to environmental stimuli. In order to minimize inflammatory responses and aberrant changes in their gene expression during the isolation process, this protocol has been streamlined from a previously published method9, and it is now suitable to isolate microglia from multiple regions of a single mouse brain hemisphere in parallel. The tissues and reagents are kept at cold temperature and experiments are perfor...
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The authors have nothing to disclose.
We thank Mariko L. Bennett, Liana Nicole Bonanno, and Spyros Darmanis for their help during the development of this protocol. We also thank the Stanford Shared FACS Facility, particularly Meredith Weglarz and Lisa Nichols; Yen Tran, Michael Eckart from Stanford Protein and Nucleic Acid Facility (PAN) for their great support for the filming. This work is funded by the JPB Foundation and Vincent J. Coates Foundation.
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Name | Company | Catalog Number | Comments |
5 M Betaine | Sigma-Aldrich | Cat# B0300-5VL | |
10 mM dNTP mix | Thermo Fisher Scientific | Cat# R0192 | |
0.5 M EDTA, pH 8.0 | Thermo Fisher Scientific | Cat# 15575020 | |
10X Hanks’ Balanced Salt Solution | Thermo Fisher Scientific | Cat# 14185-052 | |
1 M HEPES | Thermo Fisher Scientific | Cat# 15630080 | |
1X KAPA HIFI Hotstart Master Mix | Kapa Biosciences | Cat# KK2602 | |
5 mL Round Bottom Polystyrene Tube, with Cell Strainer Cap | Corning | Cat# 352235 | |
AATI, High Sensitivity NGS Fragment Analysis Kit (1 bp – 6,000 bp) | Advanced Analytical | Cat# DNF-474-1000 | |
Bovine Serum Albumin | Sigma Aldrich | Cat# A8806 | |
DNase I | Worthington | Cat# LS002007 | Working solution: 12500 units/ml |
DTT, Molecular Grade | Promega | Cat# P1171 | |
ERCC RNA Spike-In Mix | Thermo Fisher Scientific | Cat# 4456740 | |
Fetal Bovine Serum | Thermo Fisher Scientific | Cat# 10437-028 | |
Illumina XT Index Kit v2 Set A (96 indexes) | Illumina | Cat# FC-131-2001 | |
Illumina XT Index Kit v2 Set B (96 indexes) | Illumina | Cat# FC-131-2002 | |
Illumina XT Index Kit v2 Set C (96 indexes) | Illumina | Cat# FC-131-2003 | |
Illumina XT Index Kit v2 Set D (96 indexes) | Illumina | Cat# FC-131-2004 | |
Lambda Exonuclease (5 U/μl) | New England BioLabs | Cat# M0262S | |
Mouse Fc block | BD Pharmingen | Cat# 553142 | |
Myelin removal beads | Miltenyl Biotec | Cat# 130-096-433 | |
Nextera XT DNA Sample Prep Kit | Illumina | Cat# FC-131-1096 | |
NextSeq 500/550 High Output Kit v2.5 (150 Cycles) | Illumina | Cat# 20024907 | |
PBS (10X), pH 7.4 | Thermo Fisher Scientific | Cat# 70011044 | |
PCRClean DX beads | Aline Biosciences | Cat# C-1003-50 | |
Propidium Iodide | Thermo Fisher Scientific | Cat# P3566 | Staining: 1:1000 |
Qubit dsDNA HS Assay Kit | Thermal Fisher Scientific | Cat# Q32851 | |
Rat monoclonal anti mouse/human CD11b, Brilliant Violet 421 (clone M1/70) | BioLegend | Cat# 101236; RRID: AB_11203704 | Staining: 1:300 |
Rat monoclonal anti mouse CD45, PE/Cy7 (clone 30-F11) | Thermo Fisher Scientific | Cat# 25-0451-82; RRID: AB_469625 | Staining: 1:300 |
Recombinant RNase Inhibitor | Takara Bio | Cat# 2313B | |
SMARTScribe Reverse Transcriptase (100 U/μl) | Clontech | Cat# 639538 | Containing 5x First strand buffer |
Oligonucleotides | |||
0.1 μM ISPCR Oligo: 5' - AAGCAGTGGTATCAA CGCAGAGT-3' | (Picelli et al., 2014) | ||
Oligo-dT30VN primer: 5' - AAGCAGTGGTATCAACGCA GAGTACT 30 VN-3' | (Picelli et al., 2014) | ||
TSO 5' - AAGCAGTGGTATCAACGCAGA GTACATrGrG+G-3' ("r" is forribobases and "+" is for an LNA base) | (Picelli et al., 2014) | ||
Solutions | |||
FACS buffer | Recipe: sterile-filtered 1% FBS, 2 mM EDTA, 25 mM HEPES in 1X PBS | ||
MCS buffer | Recipe: sterile-filtered 0.5% BSA, 2 mM EDTA in 1X PBS | ||
Medium A | Recipe: 15 mM HEPES, 0.5% glucose in 1X HBSS without phenol red | ||
Plates | |||
384-well Rigi-Plate PCR Microplates, Axygen Scientific | VWR | 89005-556 | |
Hard-shell 96-well PCR plates | Bio-Rad | HSP9631 | |
Others | |||
Dumont #55 forceps | Fine Science Tools | 11295-51 | |
Dounce homogenizer, 2 ml | Wheaton | 357422 | |
Large depletion column | Miltenyi Biotec | 130-042-901 | |
Large selection column | Miltenyi Biotec | 130-042-401 | |
MACS MultiStand | Miltenyi Biotec | 130-042-303 | |
QuadroMACS Separator | Miltenyi Biotec | 130-090-976 | |
RNAzap | Thermo Fisher Scientific | AM9780 | |
Strainer (70 μm) | Falcon | 352350 | |
Equipment | |||
BD FACSAria II | BD Biosciences | http://www.bdbiosciences.com/ | |
Bioanalyzer | Agilent | 2100 | |
Fragment Analyzer | Agilent | 5300 | |
Mosquito HTS nanoliter pipetting robot | TTP Labtech | https://www.ttplabtech.com/ | |
Qubit 4 Fluorometer | Thermo Fisher Scientific | Q33226 |
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