Abstract
The advance of single-cell RNA-sequencing technologies in the past years has enabled unprecedented insights into the complexity and heterogeneity of microglial cell states in the homeostatic and diseased brain. This includes rather complex proteomic, metabolomic, morphological, transcriptomic, and epigenetic adaptations to external stimuli and challenges resulting in a novel concept of core microglia properties and functions. To uncover the regulatory programs facilitating the rapid transcriptomic adaptation in response to changes in the local microenvironment, the accessibility of gene bodies and gene regulatory elements can be assessed. Here, we describe the application of a previously published method for simultaneous high-throughput ATAC and RNA expression with sequencing (SHARE-seq) on microglia nuclei isolated from frozen mouse brain tissue.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Prinz M, Priller J (2014) Microglia and brain macrophages in the molecular age: from origin to neuropsychiatric disease. Nat Rev Neurosci 15(5):300–312. https://doi.org/10.1038/nrn3722
Tremblay M-È (2011) The role of microglia at synapses in the healthy CNS: novel insights from recent imaging studies. Neuron Glia Biol 7(1):67–76. https://doi.org/10.1017/S1740925X12000038
Lawson LJ, Perry VH, Dri P, Gordon S (1990) Heterogeneity in the distribution and morphology of microglia in the normal adult mouse brain. Neuroscience 39(1):151–170. https://doi.org/10.1016/0306-4522(90)90229-w
Schmid CD, Sautkulis LN, Danielson PE, Cooper J, Hasel KW, Hilbush BS, Sutcliffe JG, Carson MJ (2002) Heterogeneous expression of the triggering receptor expressed on myeloid cells-2 on adult murine microglia. J Neurochem 83(6):1309–1320. https://doi.org/10.1046/j.1471-4159.2002.01243.x
Nimmerjahn A, Kirchhoff F, Helmchen F (2005) Resting microglial cells are highly dynamic surveillants of brain parenchyma in vivo. Science 308(5726):1314–1318. https://doi.org/10.1126/science.1110647
Davalos D, Grutzendler J, Yang G, Kim JV, Zuo Y, Jung S, Littman DR, Dustin ML, Gan W-B (2005) ATP mediates rapid microglial response to local brain injury in vivo. Nat Neurosci 8(6):752–758. https://doi.org/10.1038/nn1472
Edler MK, Mhatre-Winters I, Richardson JR (2021) Microglia in aging and Alzheimer's disease: a comparative species review. Cell 10(5). https://doi.org/10.3390/cells10051138
Matcovitch-Natan O, Winter DR, Giladi A, Vargas Aguilar S, Spinrad A, Sarrazin S, Ben-Yehuda H, David E, Zelada González F, Perrin P, Keren-Shaul H, Gury M, Lara-Astaiso D, Thaiss CA, Cohen M, Bahar Halpern K, Baruch K, Deczkowska A, Lorenzo-Vivas E, Itzkovitz S, Elinav E, Sieweke MH, Schwartz M, Amit I (2016) Microglia development follows a stepwise program to regulate brain homeostasis. Science 353(6301):aad8670. https://doi.org/10.1126/science.aad8670
Silvin A, Uderhardt S, Piot C, Da Mesquita S, Yang K, Geirsdottir L, Mulder K, Eyal D, Liu Z, Bridlance C, Thion MS, Zhang XM, Kong WT, Deloger M, Fontes V, Weiner A, Ee R, Dress R, Hang JW, Balachander A, Chakarov S, Malleret B, Dunsmore G, Cexus O, Chen J, Garel S, Dutertre CA, Amit I, Kipnis J, Ginhoux F (2022) Dual ontogeny of disease-associated microglia and disease inflammatory macrophages in aging and neurodegeneration. Immunity 55(8):1448–1465.e1446. https://doi.org/10.1016/j.immuni.2022.07.004
Olah M, Patrick E, Villani A-C, Xu J, White CC, Ryan KJ, Piehowski P, Kapasi A, Nejad P, Cimpean M, Connor S, Yung CJ, Frangieh MA (2018) A transcriptomic atlas of aged human microglia. Nat Commun 9(1):539. https://doi.org/10.1038/s41467-018-02926-5
Hammond TR, Dufort C, Dissing-Olesen L, Giera S, Young A, Wysoker A, Walker AJ, Gergits F, Segel M, Nemesh J, Marsh SE, Saunders A, Macosko E, Ginhoux F, Chen J, Franklin RJM, Piao X (2019) Single-cell RNA sequencing of microglia throughout the mouse lifespan and in the injured brain reveals complex cell-state changes. Immunity 50(1):253–271.e256. https://doi.org/10.1016/j.immuni.2018.11.004
Keren-Shaul H, Spinrad A, Weiner A, Matcovitch-Natan O, Dvir-Szternfeld R, Ulland TK, David E, Baruch K, Lara-Astaiso D, Toth B, Itzkovitz S, Colonna M, Schwartz M, Amit I (2017) A unique microglia type associated with restricting development of Alzheimer's disease. Cell 169(7):1276–1290.e1217. https://doi.org/10.1016/j.cell.2017.05.018
Masuda T, Sankowski R, Staszewski O, Böttcher C, Amann L, Sagar SC, Nessler S, Kunz P, van Loo G, Coenen VA, Reinacher PC, Michel A, Sure U, Gold R, Grün D, Priller J, Stadelmann C, Prinz M (2019) Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution. Nature 566(7744):388–392. https://doi.org/10.1038/s41586-019-0924-x
Yeh H, Ikezu T (2019) Transcriptional and epigenetic regulation of microglia in health and disease. Trends Mol Med 25(2):96–111. https://doi.org/10.1016/j.molmed.2018.11.004
Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ (2013) Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA -binding proteins and nucleosome position. Nat Methods 10(12):1213–1218. https://doi.org/10.1038/nmeth.2688
Lareau CA, Duarte FM, Chew JG, Kartha VK, Burkett ZD, Kohlway AS, Pokholok D, Aryee MJ, Steemers FJ, Lebofsky R, Buenrostro JD (2019) Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility. Nat Biotechnol 37(8):916–924. https://doi.org/10.1038/s41587-019-0147-6
Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM, Hao Y, Stoeckius M, Smibert P, Satija R (2019) Comprehensive integration of single-cell data. Cell 177(7):1888–1902.e1821. https://doi.org/10.1016/j.cell.2019.05.031
Duren Z, Chen X, Zamanighomi M, Zeng W, Satpathy AT, Chang HY, Wang Y, Wong WH (2018) Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations. Proc Natl Acad Sci U S A 115(30):7723–7728. https://doi.org/10.1073/pnas.1805681115
Liu J, Huang Y, Singh R, Vert J-P, Noble WS (2019) Jointly embedding multiple single-cell omics measurements. Algor Bioinform 143. https://doi.org/10.4230/LIPIcs.WABI.2019.10
Welch JD, Kozareva V, Ferreira A, Vanderburg C, Martin C, Macosko EZ (2019) Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177(7):1873–1887.e1817. https://doi.org/10.1016/j.cell.2019.05.006
Lin Y, Wu T-Y, Wan S, Yang JYH, Wong WH, Wang YXR (2022) scJoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning. Nat Biotechnol 40(5):703–710. https://doi.org/10.1038/s41587-021-01161-6
Zhang Z, Yang C, Zhang X (2022) scDART: integrating unmatched scRNA-seq and scATAC-seq data and learning cross-modality relationship simultaneously. Genome Biol 23(1):139. https://doi.org/10.1186/s13059-022-02706-x
Cao J, Cusanovich DA, Ramani V, Aghamirzaie D, Pliner HA, Hill AJ, Daza RM, McFaline-Figueroa JL, Packer JS, Christiansen L, Steemers FJ, Adey AC, Trapnell C, Shendure J (2018) Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361(6409):1380–1385. https://doi.org/10.1126/science.aau0730
Liu L, Liu C, Quintero A, Wu L, Yuan Y, Wang M, Cheng M, Leng L, Xu L, Dong G, Li R, Liu Y, Wei X, Xu J, Chen X, Lu H, Chen D, Wang Q, Zhou Q, Lin X, Li G, Liu S, Wang Q, Wang H, Fink JL, Gao Z, Liu X, Hou Y, Zhu S, Yang H, Ye Y, Lin G, Chen F, Herrmann C, Eils R, Shang Z, Xu X (2019) Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity. Nat Commun 10(1):470. https://doi.org/10.1038/s41467-018-08205-7
Zhu C, Yu M, Huang H, Juric I, Abnousi A, Hu R, Lucero J, Behrens MM, Hu M, Ren B (2019) An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome. Nat Struct Mol Biol 26(11):1063–1070. https://doi.org/10.1038/s41594-019-0323-x
Chen S, Lake BB, Zhang K (2019) High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell. Nat Biotechnol 37(12):1452–1457. https://doi.org/10.1038/s41587-019-0290-0
Ma S, Zhang B, LaFave LM, Earl AS, Chiang Z, Hu Y, Ding J, Brack A, Kartha VK, Tay T, Law T, Lareau C, Hsu Y-C, Regev A, Buenrostro JD (2020) Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 183(4):1103–1116.e1120. https://doi.org/10.1016/j.cell.2020.09.056
Zheng GXY, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, Ziraldo SB, Wheeler TD, McDermott GP, Zhu J, Gregory MT, Shuga J, Montesclaros L, Underwood JG, Masquelier DA, Nishimura SY, Schnall-Levin M, Wyatt PW, Hindson CM, Bharadwaj R, Wong A, Ness KD, Beppu LW, Deeg HJ, McFarland C, Loeb KR, Valente WJ, Ericson NG, Stevens EA, Radich JP, Mikkelsen TS, Hindson BJ, Bielas JH (2017) Massively parallel digital transcriptional profiling of single cells. Nat Commun 8:14049. https://doi.org/10.1038/ncomms14049
Rosenberg AB, Roco CM, Muscat RA, Kuchina A, Sample P, Yao Z, Graybuck LT, Peeler DJ, Mukherjee S, Chen W, Pun SH, Sellers DL, Tasic B, Seelig G (2018) Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360(6385):176–182. https://doi.org/10.1126/science.aam8999
Nerlov C, Graf T (1998) PU.1 induces myeloid lineage commitment in multipotent hematopoietic progenitors. Genes Dev 12(15):2403–2412. https://doi.org/10.1101/gad.12.15.2403
Picelli S, Björklund AK, Reinius B, Sagasser S, Winberg G, Sandberg R (2014) Tn5 transposase and tagmentation procedures for massively scaled sequencing projects. Genome Res 24(12):2033–2040. https://doi.org/10.1101/gr.177881.114
Merkel D (2014) Docker: lightweight Linux containers for consistent development and deployment. Linux J 2014(239):2.
Renaud G, Stenzel U, Maricic T, Wiebe V, Kelso J (2015) deML: robust demultiplexing of Illumina sequences using a likelihood-based approach. Bioinformatics 31(5):770–772. https://doi.org/10.1093/bioinformatics/btu719
Hao Y, Hao S, Andersen-Nissen E, Mauck WM, Zheng S, Butler A, Lee MJ, Wilk AJ, Darby C, Zager M, Hoffman P, Stoeckius M, Papalexi E, Mimitou EP, Jain J, Srivastava A, Stuart T, Fleming LM, Yeung B, Rogers AJ, McElrath JM, Blish CA, Gottardo R, Smibert P, Satija R (2021) Integrated analysis of multimodal single-cell data. Cell 184(13):3573–3587.e3529. https://doi.org/10.1016/j.cell.2021.04.048
Konishi H, Kobayashi M, Kunisawa T, Imai K, Sayo A, Malissen B, Crocker PR, Sato K, Kiyama H (2017) Siglec-H is a microglia-specific marker that discriminates microglia from CNS-associated macrophages and CNS-infiltrating monocytes. Glia 65(12):1927–1943. https://doi.org/10.1002/glia.23204
Stuart T, Srivastava A, Madad S, Lareau CA, Satija R (2021) Single-cell chromatin state analysis with Signac. Nat Methods 18(11):1333–1341. https://doi.org/10.1038/s41592-021-01282-5
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic Supplementary Material
Supplementary Table 1
Overview of all necessary oligonucleotides and primers as published by Ma and colleagues in 2020 [27] (XLSX 9 kb)
Supplementary Table 2
Sequences for barcoding oligonucleotides as published by Ma and colleagues in 2020 [27] (XLSX 17 kb)
Supplementary Table 3
Sequences for the library-specific Ad1 primer as published by Ma and colleagues in 2020 [27] (XLSX 10 kb)
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Scholz, R., Brösamle, D., Yuan, X., Neher, J.J., Beyer, M. (2024). Combined Analysis of mRNA Expression and Open Chromatin in Microglia. In: Mass, E. (eds) Tissue-Resident Macrophages. Methods in Molecular Biology, vol 2713. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3437-0_35
Download citation
DOI: https://doi.org/10.1007/978-1-0716-3437-0_35
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-3436-3
Online ISBN: 978-1-0716-3437-0
eBook Packages: Springer Protocols