Cross-Laboratory Analysis of Brain Cell Type Transcriptomes with Applications to Interpretation of Bulk Tissue Data

Abstract

Establishing the molecular diversity of cell types is crucial for the study of the nervous system. We compiled a cross-laboratory database of mouse brain cell type-specific transcriptomes from 36 major cell types from across the mammalian brain using rigorously curated published data from pooled cell type microarray and single-cell RNA-sequencing (RNA-seq) studies. We used these data to identify cell type-specific marker genes, discovering a substantial number of novel markers, many of which we validated using computational and experimental approaches. We further demonstrate that summarized expression of marker gene sets (MGSs) in bulk tissue data can be used to estimate the relative cell type abundance across samples. To facilitate use of this expanding resource, we provide a user-friendly web interface at www.neuroexpresso.org .

Shown for reading. Its publisher allows quoting it, not republishing it.

References 87

1. 1.

   Anandasabapathy N, Victora GD, Meredith M, Feder R, Dong B, Kluger C, Yao K, Dustin ML, Nussenzweig MC, Steinman RM, Liu K (2011 ) Flt3L controls the development of radiosensitive dendritic cells in the meninges and choroid plexus of the steady-state mouse brain. J Exp Med 208:1695–1705.

   Not in our library10.1084/jem.20102657

2. 2.

   Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, Yefanov A, Lee H, Zhang N, Robertson CL, Serova N, Davis S, Soboleva A (2013 ) NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res 41:D991–D995.

   Not in our library10.1093/nar/gks1193

3. 3.

   Beckervordersandforth R, Tripathi P, Ninkovic J, Bayam E, Lepier A, Stempfhuber B, Kirchhoff F, Hirrlinger J, Haslinger A, Lie DC, Beckers J, Yoder B, Irmler M, Götz M (2010 ) In vivo fate mapping and expression analysis reveals molecular hallmarks of prospectively isolated adult neural stem cells. Cell Stem Cell 7:744–758.

   Not in our library10.1016/j.stem.2010.11.017

4. 4.

   Bellesi M, Pfister-Genskow M, Maret S, Keles S, Tononi G, Cirelli C (2013 ) Effects of sleep and wake on oligodendrocytes and their precursors. J Neurosci 33:14288–14300.

   Not in our library10.1523/jneurosci.5102-12.2013

5. 5.

   Benjamini Y, Hochberg Y (1995 ) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol 57:289–300.

   Not in our library

6. 6.

   Bennett ML, Bennett FC, Liddelow SA, Ajami B, Zamanian JL, Fernhoff NB, Mulinyawe SB, Bohlen CJ, Adil A, Tucker A, Weissman IL, Chang EF, Li G, Grant GA, Hayden Gephart MG, Barres BA (2016 ) New tools for studying microglia in the mouse and human CNS. Proc Natl Acad Sci USA 113:E1738–E1746.

   Not in our library10.1073/pnas.1525528113

7. 7.

   Cahoy JD, Emery B, Kaushal A, Foo LC, Zamanian JL, Christopherson KS, Xing Y, Lubischer JL, Krieg PA, Krupenko SA, Thompson WJ, Barres BA (2008 ) A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J Neurosci 28:264–278.

   Not in our library10.1523/jneurosci.4178-07.2008

8. 8.

   Carvalho BS, Irizarry RA (2010 ) A framework for oligonucleotide microarray preprocessing. Bioinformatics 26:2363–2367.

   Not in our library10.1093/bioinformatics/btq431

9. 9.

   Celio MR, Heizmann CW (1981 ) Calcium-binding protein parvalbumin as a neuronal marker. Nature 293:300–302.

   Not in our library10.1038/293300a0

10. 10.
    Hipposeq: a comprehensive RNA-seq database of gene expression in hippocampal principal neurons · 2016

    No notice
11. 11.

    Chikina M, Zaslavsky E, Sealfon SC (2015 ) CellCODE: a robust latent variable approach to differential expression analysis for heterogeneous cell populations. Bioinformatics 31:1584–1591.

    Not in our library10.1093/bioinformatics/btv015

12. 12.

    Chung CY, Seo H, Sonntag KC, Brooks A, Lin L, Isacson O (2005 ) Cell type-specific gene expression of midbrain dopaminergic neurons reveals molecules involved in their vulnerability and protection. Hum Mol Genet 14:1709–1725.

    Not in our library10.1093/hmg/ddi178

13. 13.

    Dalal J, Roh JH, Maloney SE, Akuffo A, Shah S, Yuan H, Wamsley B, Jones WB, Strong C, de G, Gray PA, Holtzman DM, Heintz N, Dougherty JD (2013 ) Translational profiling of hypocretin neurons identifies candidate molecules for sleep regulation. Genes Dev 27:565–578.

    Not in our library10.1101/gad.207654.112

14. 14.

    Darmanis S, Sloan SA, Zhang Y, Enge M, Caneda C, Shuer LM, Gephart MGH, Barres BA, Quake SR (2015 ) A survey of human brain transcriptome diversity at the single cell level. Proc Natl Acad Sci USA 112:7285–7290.

    Not in our library10.1073/pnas.1507125112

15. 15.

    Dougherty JD, Maloney SE, Wozniak DF, Rieger MA, Sonnenblick L, Coppola G, Mahieu NG, Zhang J, Cai J, Patti GJ, Abrahams BS, Geschwind DH, Heintz N (2013 ) The disruption of Celf6, a gene identified by translational profiling of serotonergic neurons, results in autism-related behaviors. J Neurosci 33:2732–2753.

    Not in our library10.1523/jneurosci.4762-12.2013

16. 16.

    Doyle JP, Dougherty JD, Heiman M, Schmidt EF, Stevens TR, Ma G, Bupp S, Shrestha P, Shah RD, Doughty ML, Gong S, Greengard P, Heintz N (2008 ) Application of a translational profiling approach for the comparative analysis of CNS cell types. Cell 135:749–762.

    Not in our library10.1016/j.cell.2008.10.029

17. 17.

    Edgar R, Domrachev M, Lash AE (2002 ) Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30:207–210.

    Not in our library10.1093/nar/30.1.207

18. 18.

    Enjin A, Rabe N, Nakanishi ST, Vallstedt A, Gezelius H, Memic F, Lind M, Hjalt T, Tourtellotte WG, Bruder C, Eichele G, Whelan PJ, Kullander K (2010 ) Identification of novel spinal cholinergic genetic subtypes disclose Chodl and Pitx2 as markers for fast motor neurons and partition cells. J Comp Neurol 518:2284–2304.

    Not in our library10.1002/cne.22332

19. 19.

    Erny D, Hrabě de Angelis AL, Jaitin D, Wieghofer P, Staszewski O, David E, Keren-Shaul H, Mahlakoiv T, Jakobshagen K, Buch T, Schwierzeck V, Utermöhlen O, Chun E, Garrett WS, McCoy KD, Diefenbach A, Staeheli P, Stecher B, Amit I, Prinz M (2015 ) Host microbiota constantly control maturation and function of microglia in the CNS. Nat Neurosci 18:965–977.

    Not in our library10.1038/nn.4030

20. 20.

    Fomchenko EI, Dougherty JD, Helmy KY, Katz AM, Pietras A, Brennan C, Huse JT, Milosevic A, Holland EC (2011 ) Recruited cells can become transformed and overtake PDGF-induced murine gliomas in vivo during tumor progression. PLoS One 6:e20605.

    Not in our library10.1371/journal.pone.0020605

21. 21.

    Galloway JN, Shaw C, Yu P, Parghi D, Poidevin M, Jin P, Nelson DL (2014 ) CGG repeats in RNA modulate expression of TDP-43 in mouse and fly models of fragile X tremor ataxia syndrome. Hum Mol Genet 23:5906–5915.

    Not in our library10.1093/hmg/ddu314

22. 22.

    Görlich A, Antolin-Fontes B, Ables JL, Frahm S, Slimak MA, Dougherty JD, Ibañez-Tallon I (2013 ) Reexposure to nicotine during withdrawal increases the pacemaking activity of cholinergic habenular neurons. Proc Natl Acad Sci USA 110:17077–17082.

    Not in our library10.1073/pnas.1313103110

23. 23.

    Grange P, Bohland JW, Okaty BW, Sugino K, Bokil H, Nelson SB, Ng L, Hawrylycz M, Mitra PP (2014 ) Cell-type–based model explaining coexpression patterns of genes in the brain. Proc Natl Acad Sci USA 111:5397–5402.

    Not in our library10.1073/pnas.1312098111

24. 24.

    Gudi V, Moharregh-Khiabani D, Skripuletz T, Koutsoudaki PN, Kotsiari A, Skuljec J, Trebst C, Stangel M (2009 ) Regional differences between grey and white matter in cuprizone induced demyelination. Brain Res 1283:127–138.

    Not in our library10.1016/j.brainres.2009.06.005

25. 25.

    Hagenauer MH, Li JZ, Walsh DM, Vawter MP, Thompson RC, Turner CA, Bunney WE, Myers RM, Barchas JD, Schatzberg AF, Watson SJ, Akil H (2016 ) Inference of cell-type composition from human brain transcriptomic datasets illuminates the effects of age, manner death, dissection, and psychiatric diagnosis. bioRxiv 089391.

    Not in our library10.1371/journal.pone.0200003

26. 26.

    Halliwell B (2003 ) Oxidative stress in cell culture: an under-appreciated problem? FEBS Lett 540:3–6.

    Not in our library10.1016/s0014-5793(03)00235-7

27. 27.

    Handley A, Schauer T, Ladurner AG, Margulies CE (2015 ) Designing cell-type-specific genome-wide experiments. Mol Cell 58:621–631.

    Not in our library10.1016/j.molcel.2015.04.024

28. 28.

    Heiman M, Heilbut A, Francardo V, Kulicke R, Fenster RJ, Kolaczyk ED, Mesirov JP, Surmeier DJ, Cenci MA, Greengard P (2014 ) Molecular adaptations of striatal spiny projection neurons during levodopa-induced dyskinesia. Proc Natl Acad Sci USA 111:4578–4583.

    Not in our library10.1073/pnas.1401819111

29. 29.

    Holtman IR, Noback M, Bijlsma M, Duong KN, van der Geest MA, Ketelaars PT, Brouwer N, Vainchtein ID, Eggen BJL, Boddeke HWGM (2015 ) Glia open access database (GOAD): a comprehensive gene expression encyclopedia of glia cells in health and disease. Glia 63:1495–1506.

    Not in our library10.1002/glia.22810

30. 30.

    Hu H, Gan J, Jonas P (2014 ) Fast-spiking, parvalbumin+ GABAergic interneurons: from cellular design to microcircuit function. Science 345:1255263.

    Not in our library10.1126/science.1255263

31. 31.

    Januszyk M, Rennert RC, Sorkin M, Maan ZN, Wong LK, Whittam AJ, Whitmore A, Duscher D, Gurtner GC (2015 ) Evaluating the effect of cell culture on gene expression in primary tissue samples using microfluidic-based single cell transcriptional analysis. Microarrays 4:540–550.

    Not in our library10.3390/microarrays4040540

32. 32.

    Jones MJ, Islam SA, Edgar RD, Kobor MS (2017 ) Adjusting for cell type composition in DNA methylation data using a regression-based approach. Methods Mol Biol 1589:99–106.

    Not in our library10.1007/7651_2015_262

33. 33.

    Kawaguchi Y, Katsumaru H, Kosaka T, Heizmann CW, Hama K (1987 ) Fast spiking cells in rat hippocampus (CA1 region) contain the calcium-binding protein parvalbumin. Brain Res 416:369–374.

    Not in our library10.1016/0006-8993(87)90921-8

34. 34.

    Kuhn A, Thu D, Waldvogel HJ, Faull RLM, Luthi-Carter R (2011 ) Population-specific expression analysis (PSEA) reveals molecular changes in diseased brain. Nat Methods 8:945–947.

    Not in our library10.1038/nmeth.1710

35. 35.
    A Genomic Pathway Approach to a Complex Disease: Axon Guidance and Parkinson Disease · 2007

    No notice
36. 36.

    Lobo MK, Karsten SL, Gray M, Geschwind DH, Yang XW (2006 ) FACS-array profiling of striatal projection neuron subtypes in juvenile and adult mouse brains. Nat Neurosci 9:443–452.

    Not in our library10.1038/nn1654

37. 37.

    Maechler M, Rousseeuw PR, Struyf A, Gonzalez J (2016 ) Cluster: “Finding Groups in Data”: Cluster Analysis Extended Rousseeuw et al.

    Not in our library

38. 38.

    Margolis EB, Lock H, Hjelmstad GO, Fields HL (2006 ) The ventral tegmental area revisited: is there an electrophysiological marker for dopaminergic neurons? J Physiol 577:907–924.

    Not in our library10.1113/jphysiol.2006.117069

39. 39.

    Maze I, Chaudhury D, Dietz DM, Von Schimmelmann M, Kennedy PJ, Lobo MK, Sillivan SE, Miller ML, Bagot RC, Sun H, Turecki G, Neve RL, Hurd YL, Shen L, Han M-H, Schaefer A, Nestler EJ (2014 ) G9a influences neuronal subtype specification in striatum. Nat Neurosci 17:533–539.

    Not in our library10.1038/nn.3670

40. 40.

    Moran LB, Duke DC, Deprez M, Dexter DT, Pearce RKB, Graeber MB (2006 ) Whole genome expression profiling of the medial and lateral substantia nigra in Parkinson’s disease. Neurogenetics 7:1–11.

    Not in our library10.1007/s10048-005-0020-2

Cited by papers we hold 11

• Astrocytes in human central nervous system diseases: a frontier for new therapies · 2023
  No notice
• Bulk and Single-Nucleus Transcriptomics Highlight Intra-Telencephalic and Somatostatin Neurons in Alzheimer’s Disease · 2022
  Not checked
• Comprehensive evaluation of deconvolution methods for human brain gene expression · 2022
  No notice
• Molecular characterization of the stress network in individuals at risk for schizophrenia · 2021
  Not checked
• Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data · 2021
  No notice
• Decoding the Transcriptional Response to Ischemic Stroke in Young and Aged Mouse Brain · 2020
  Not checked
• A single-cell atlas of the human substantia nigra reveals cell-specific pathways associated with neurological disorders · 2020
  No notice
• CSF1R inhibitor JNJ-40346527 attenuates microglial proliferation and neurodegeneration in P301S mice · 2019
  No notice
• Transcriptional profiling of microglia; current state of the art and future perspectives · 2019
  No notice
• Early life stress alters transcriptomic patterning across reward circuitry in male and female mice · 2019
  No notice
• Neuropathological and transcriptomic characteristics of the aged brain · 2017
  No notice

Reads

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.