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Mixed-species RNA-seq for elucidation of non-cell-autonomous control of gene transcription

Abstract

Transcriptomic changes induced in one cell type by another mediate many biological processes in the brain and elsewhere; however, achieving artifact-free physical separation of cell types to study them is challenging and generally allows for analysis of only a single cell type. We describe an approach using a co-culture of distinct cell types from different species that enables physical cell sorting to be replaced by in silico RNA sequencing (RNA-seq) read sorting, which is possible because of evolutionary divergence of messenger RNA (mRNA) sequences. As an exemplary experiment, we describe the co-culture of purified neurons, astrocytes, and microglia from different species (12–14 d). We describe how to use our Python tool, Sargasso, to separate the reads from conventional RNA-seq according to species and to eliminate any artifacts borne of imperfect genome annotation (10 h). We show how this procedure, which requires no special skills beyond those that might normally be expected of wet lab and bioinformatics researchers, enables the simultaneous transcriptomic profiling of different cell types, revealing the distinct influence of microglia on astrocytic and neuronal transcriptomes under inflammatory conditions.

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Fig. 1: Determination of the presence of expected cell markers and the absence of markers of contaminating cell types.
Fig. 2: Differential gene expression in microglia, astrocytes, and neurons in co-culture.

References

  1. 1.

    Denef, C. Contact-dependent signaling. Cell Commun. Insights 6, 1–11 (2014).

    CAS  Article  Google Scholar 

  2. 2.

    Bell, K. F. & Hardingham, G. E. The influence of synaptic activity on neuronal health. Curr. Opin. Neurobiol. 21, 299–305 (2011).

    CAS  Article  Google Scholar 

  3. 3.

    West, A. E. & Greenberg, M. E. Neuronal activity-regulated gene transcription in synapse development and cognitive function. Cold Spring Harb. Perspect. Biol. 3, https://doi.org/10.1101/cshperspect.a005744 (2011).

    Article  Google Scholar 

  4. 4.

    Hardingham, G. E. & Lipton, S. A. Regulation of neuronal oxidative and nitrosative stress by endogenous protective pathways and disease processes. Antioxid. Redox. Signal. 14, 1421–1424 (2011).

    CAS  Article  Google Scholar 

  5. 5.

    Bell, K. F. & Hardingham, G. E. CNS peroxiredoxins and their regulation in health and disease. Antioxid. Redox. Signal. 14, 1467–1477 (2011).

    CAS  Article  Google Scholar 

  6. 6.

    Baxter, P. S. et al. Synaptic NMDA receptor activity is coupled to the transcriptional control of the glutathione system. Nat. Commun. 6, 6761 (2015).

    CAS  Article  Google Scholar 

  7. 7.

    Hasel, P. et al. Neurons and neuronal activity control gene expression in astrocytes to regulate their development and metabolism. Nat. Commun. 8, 15132 (2017).

    Article  Google Scholar 

  8. 8.

    Mensch, S. et al. Synaptic vesicle release regulates myelin sheath number of individual oligodendrocytes in vivo. Nat. Neurosci. 18, 628–630 (2015).

    CAS  Article  Google Scholar 

  9. 9.

    Hardingham, G. E. & Do, K. Q. Linking early-life NMDAR hypofunction and oxidative stress in schizophrenia pathogenesis. Nat. Rev. Neurosci. 17, 125–134 (2016).

    CAS  Article  Google Scholar 

  10. 10.

    Bell, K. F., Fowler, J. H., Al-Mubarak, B., Horsburgh, K. & Hardingham, G. E. Activation of Nrf2-regulated glutathione pathway genes by ischemic preconditioning. Oxid. Med. Cell. Longev. 2011, 689524 (2011).

    Article  Google Scholar 

  11. 11.

    Baxter, P. S. & Hardingham, G. E. Adaptive regulation of the brain’s antioxidant defences by neurons and astrocytes. Free Radic. Biol. Med. 100, 147–152 (2016).

    CAS  Article  Google Scholar 

  12. 12.

    Allen, N. J. et al. Astrocyte glypicans 4 and 6 promote formation of excitatory synapses via GluA1 AMPA receptors. Nature 486, 410–414 (2012).

    CAS  Article  Google Scholar 

  13. 13.

    Clarke, L. E. & Barres, B. A. Emerging roles of astrocytes in neural circuit development. Nat. Rev. Neurosci. 14, 311–321 (2013).

    CAS  Article  Google Scholar 

  14. 14.

    Schafer, D. P. & Stevens, B. Microglia function in central nervous system development and plasticity. Cold Spring Harb. Perspect. Biol. 7, a020545 (2015).

    Article  Google Scholar 

  15. 15.

    Salter, M. W. & Stevens, B. Microglia emerge as central players in brain disease. Nat. Med. 23, 1018–1027 (2017).

    CAS  Article  Google Scholar 

  16. 16.

    Perry, V. H. & Holmes, C. Microglial priming in neurodegenerative disease. Nat. Rev. Neurol. 10, 217–224 (2014).

    CAS  Article  Google Scholar 

  17. 17.

    Hoarau, J. J. et al. Activation and control of CNS innate immune responses in health and diseases: a balancing act finely tuned by neuroimmune regulators (NIReg). CNS Neurol. Disord. Drug Targets 10, 25–43 (2011).

    CAS  Article  Google Scholar 

  18. 18.

    Liddelow, S. A. et al. Neurotoxic reactive astrocytes are induced by activated microglia. Nature 541, 481–487 (2017).

    CAS  Article  Google Scholar 

  19. 19.

    Miron, V. E. et al. M2 microglia and macrophages drive oligodendrocyte differentiation during CNS remyelination. Nat. Neurosci. 16, 1211–1218 (2013).

    CAS  Article  Google Scholar 

  20. 20.

    Baxter, P. S., Martel, M. A., McMahon, A., Kind, P. C. & Hardingham, G. E. Pituitary adenylate cyclase-activating peptide induces long-lasting neuroprotection through the induction of activity-dependent signaling via the cyclic AMP response element-binding protein-regulated transcription co-activator 1. J. Neurochem. 118, 365–378 (2011).

    CAS  Article  Google Scholar 

  21. 21.

    Bell, K. F. et al. Neuronal development is promoted by weakened intrinsic antioxidant defences due to epigenetic repression of Nrf2. Nat. Commun. 6, 7066 (2015).

    CAS  Article  Google Scholar 

  22. 22.

    Edman, S. et al. TCN 201 selectively blocks GluN2A-containing NMDARs in a GluN1 co-agonist dependent but non-competitive manner. Neuropharmacology 63, 441–449 (2012).

    CAS  Article  Google Scholar 

  23. 23.

    Wilhelm, B. T., Marguerat, S., Goodhead, I. & Bahler, J. Defining transcribed regions using RNA-seq. Nat. Protoc. 5, 255–266 (2010).

    CAS  Article  Google Scholar 

  24. 24.

    Okaty, B. W., Sugino, K. & Nelson, S. B. A quantitative comparison of cell-type-specific microarray gene expression profiling methods in the mouse brain. PLoS ONE 6, e16493 (2011).

    CAS  Article  Google Scholar 

  25. 25.

    Okaty, B. W., Sugino, K. & Nelson, S. B. Cell type-specific transcriptomics in the brain. J. Neurosci. 31, 6939–6943 (2011).

    CAS  Article  Google Scholar 

  26. 26.

    Doyle, J. P. et al. Application of a translational profiling approach for the comparative analysis of CNS cell types. Cell 135, 749–762 (2008).

    CAS  Article  Google Scholar 

  27. 27.

    Ahdesmaki, M. J., Gray, S. R., Johnson, J. H. & Lai, Z. Disambiguate: an open-source application for disambiguating two species in next generation sequencing data from grafted samples. F1000Res. 5, 2741 (2016).

    Article  Google Scholar 

  28. 28.

    Conway, T. et al. Xenome--a tool for classifying reads from xenograft samples. Bioinformatics 28, i172–i178 (2012).

    CAS  Article  Google Scholar 

  29. 29.

    Kumar, S., Stecher, G., Suleski, M. & Hedges, S. B. TimeTree: a resource for timelines, timetrees, and divergence times. Mol. Biol. Evol. 34, 1812–1819 (2017).

    Article  Google Scholar 

  30. 30.

    Puddifoot, C. et al. PGC-1α negatively regulates extrasynaptic NMDAR activity and excitotoxicity. J. Neurosci. 32, 6995–7000 (2012).

    CAS  Article  Google Scholar 

  31. 31.

    Chow, J. C., Young, D. W., Golenbock, D. T., Christ, W. J. & Gusovsky, F. Toll-like receptor-4 mediates lipopolysaccharide-induced signal transduction. J. Biol. Chem. 274, 10689–10692 (1999).

    CAS  Article  Google Scholar 

  32. 32.

    Qiu, J. et al. Evidence for evolutionary divergence of activity-dependent gene expression in developing neurons. eLife 5, https://doi.org/10.7554/eLife.20337 (2016).

  33. 33.

    Espuny-Camacho, I. et al. Hallmarks of Alzheimer’s disease in stem-cell-derived human neurons transplanted into mouse brain. Neuron 93, 1066–1081.e8 (2017).

    CAS  Article  Google Scholar 

  34. 34.

    Wang, S. et al. Human iPSC-derived oligodendrocyte progenitor cells can myelinate and rescue a mouse model of congenital hypomyelination. Cell Stem Cell 12, 252–264 (2013).

    CAS  Article  Google Scholar 

  35. 35.

    Han, X. et al. Forebrain engraftment by human glial progenitor cells enhances synaptic plasticity and learning in adult mice. Cell Stem Cell 12, 342–353 (2013).

    CAS  Article  Google Scholar 

  36. 36.

    O’Doherty, A. et al. An aneuploid mouse strain carrying human chromosome 21 with Down syndrome phenotypes. Science 309, 2033–2037 (2005).

    Article  Google Scholar 

  37. 37.

    Kim, K. M., Abdelmohsen, K., Mustapic, M., Kapogiannis, D. & Gorospe, M. RNA in extracellular vesicles. Wiley Interdiscip. Rev. RNA 8, https://doi.org/10.1002/wrna.1413 (2017).

    Article  Google Scholar 

  38. 38.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  Article  Google Scholar 

  39. 39.

    Tarasov, A., Vilella, A. J., Cuppen, E., Nijman, I. J. & Prins, P. Sambamba: fast processing of NGS alignment formats. Bioinformatics 31, 2032–2034 (2015).

    CAS  Article  Google Scholar 

  40. 40.

    Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).

    CAS  Article  Google Scholar 

  41. 41.

    Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res. 4, 1521 (2015).

    Article  Google Scholar 

  42. 42.

    Yates, A. et al. Ensembl 2016. Nucleic Acids Res. 44, D710–D716 (2016).

    CAS  Article  Google Scholar 

  43. 43.

    McKenzie, G. J. et al. Nuclear Ca2+ and CaM kinase IV specify hormonal- and Notch-responsiveness. J. Neurochem. 93, 171–185 (2005).

    CAS  Article  Google Scholar 

  44. 44.

    Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    CAS  Article  Google Scholar 

  45. 45.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  Google Scholar 

  46. 46.

    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  Article  Google Scholar 

  47. 47.

    Anders, S. et al. Count-based differential expression analysis of RNA sequencing data using R and bioconductor. Nat. Protoc. 8, 1765–1786 (2013).

    Article  Google Scholar 

  48. 48.

    Akiyama, H. et al. Expression of intercellular adhesion molecule (ICAM)-1 by a subset of astrocytes in Alzheimer disease and some other degenerative neurological disorders. Acta Neuropathol. 85, 628–634 (1993).

    CAS  Article  Google Scholar 

  49. 49.

    Bonneh-Barkay, D., Wang, G., Starkey, A., Hamilton, R. L. & Wiley, C. A. In vivo CHI3L1 (YKL-40) expression in astrocytes in acute and chronic neurological diseases. J. Neuroinflammation. 7, 34 (2010).

    Article  Google Scholar 

Download references

Acknowledgements

This work was funded by UK Dementia Research Institute partner funders: the Medical Research Council, Alzheimer’s Research UK, and the Alzheimer’s Society, as well as by the Wellcome Trust and the Simons Initiative for the Developing Brain. We are grateful to X. He for comments on the manuscript.

Author information

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Authors

Contributions

J.Q., P.B., and P.H. developed and validated the mixed-species co-culture systems. O.D., S.H., and T.I.S. developed the Sargasso tool and analyzed the data. G.E.H. conceived and directed the project, and analyzed the data. G.E.H., J.Q., and O.D. wrote the manuscript.

Corresponding authors

Correspondence to Owen Dando or Giles E. Hardingham.

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The authors declare no competing interests.

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Related links

Key references using this protocol

Hasel, P. et al. Nat. Commun. 8, 15132 (2017) https://doi.org/10.1038/ncomms15132

Qiu, J. et al. eLife 5, e20337 (2016) https://doi.org/10.7554/eLife.20337

Integrated supplementary information

Supplementary Figure 1 Impact of Sargasso species disambiguation on gene expression quantification.

a, Single-species RNA-seq reads (rat samples MGLmonoCTR1–3 from ArrayExpress accession E-MTAB-5987 (this study), human samples GSM2285374–7 from Gene Expression Omnibus series GSE85839 and mouse samples CTR1-34316426, CTR2-34335325 and CTR3-34312414 from ArrayExpress accession E-MTAB-548932), were taken, and we calculated the % reads lost for each protein-coding gene expressed >1 FPKM (by normal mapping–defined as mapping which requires perfect match to target species, but no disambiguation between any other species) when performing the Sargasso pipeline requiring disambiguation of reads from the other two species. A cumulative distribution plot for genes against % of reads lost is shown. b,c, For rat RNA-seq reads (rat samples MGLmonoCTR1–3 from E-MTAB-5987), gene FPKM values were calculated using normal mapping and Sargasso mapping, requiring disambiguation of reads from mouse and human genomes. FPKM values were plotted against each other for all 12,432 protein-coding genes expressed > 1 FPKM (by normal mapping) (B). In (C), the FPKM (Sargasso) was calculated as a % of the FPKM (normal mapping), and a frequency distribution of the genes shown (with 5% bin size). d,e, For human RNA-seq reads (samples GSM2285374–7 from GSE85839), gene FPKM values were calculated using normal mapping and Sargasso mapping, requiring disambiguation of reads from rat and mouse genomes. FPKM values were plotted against each other for all 11,668 protein-coding genes expressed > 1 FPKM (by normal mapping) (D). In (E), the FPKM (Sargasso) was calculated as a % of the FPKM (normal mapping), and a frequency distribution of the genes shown (with 5% bin size). f,g, For mouse RNA-seq reads (mouse samples CTR1-34316426, CTR2-34335325 and CTR3-34312414 from E-MTAB-5489), gene FPKM values were calculated using normal mapping and Sargasso mapping, requiring disambiguation of reads from rat and human genomes. FPKM values were plotted against each other for all 11,971 protein-coding genes expressed > 1 FPKM (by normal mapping) (F). In (G), the FPKM (Sargasso) was calculated as a % of the FPKM (normal mapping), and a frequency distribution of the genes shown (with 5% bin size).

Supplementary Figure 2 Impact of the Sargasso species disambiguation on transcript expression quantification.

a,b, For rat RNA-seq reads (rat samples MGLmonoCTR1–3 from E-MTAB-5987), FPKM values of all transcripts of protein-coding genes (as annotated by Ensembl) were calculated using normal mapping and Sargasso mapping, requiring disambiguation of reads from mouse and human genomes. FPKM values were plotted against each other for all those 16,169 transcripts expressed > 1 FPKM (by normal mapping) (A). In (B), the FPKM (Sargasso) was calculated as a % of the FPKM (normal mapping), and a frequency distribution of the transcripts shown. c,d, For human RNA-seq reads (samples GSM2285374–7 from GSE85839), FPKM values of all transcripts of protein-coding genes (as annotated by Ensembl) were calculated using normal mapping and Sargasso mapping, requiring disambiguation of reads from mouse and rat genomes. FPKM values were plotted against each other for all those 34,149 transcripts expressed > 1 FPKM (by normal mapping) (C). In (D), the FPKM (Sargasso) was calculated as a % of the FPKM (normal mapping), and a frequency distribution of the transcripts shown. e,f, For mouse RNA-seq reads (mouse samples CTR1-34316426, CTR2-34335325 and CTR3-34312414 from E-MTAB-5489), FPKM values of all transcripts of protein-coding genes (as annotated by Ensembl) were calculated using normal mapping and Sargasso mapping, requiring disambiguation of reads from human and rat genomes. FPKM values were plotted against each other for all those 34,024 transcripts expressed > 1 FPKM (by normal mapping) (E). In (F), the FPKM (Sargasso) was calculated as a % of the FPKM (normal mapping), and a frequency distribution of the transcripts shown.

Supplementary Figure 3 Impact of Sargasso species disambiguation on stimulus-induced gene- and transcript- level fold induction quantification.

a,b, For rat RNA-seq reads (rat control samples MGLmonoCTR1–3, and LPS-treated samples MGLmonoLPS1–3, E-MTAB-5987 (this publication-sample sets 6a and 6b: see Supplementary Results), DESeq2 Log2-fold change (LPS vs. Con) was calculated both at the gene level (A) and transcript level (B), using normal mapping, and for Sargasso mapping requiring disambiguation of reads from mouse and human genomes. For all genes/transcripts expressed > 1FPKM on average across the samples (by normal mapping), DESeq2 Log2-fold change for the two approaches was plotted against each other, and a correlation coefficient calculated. c,d, For mouse RNA-seq reads (mouse DIV4 cortical neuron control samples, and high K+-treated samples, both from E-MTAB-548932), DESeq2 Log2-fold change (high K+ vs. Con) was calculated both at the gene level (C) and transcript level (D), using normal mapping, and for Sargasso mapping requiring disambiguation of reads from rat and human genomes. For all genes/transcripts expressed > 1FPKM on average across the samples (by normal mapping), DESeq2 Log2-fold change for the two approaches was plotted against each other, and a correlation coefficient calculated. e,f, For human RNA-seq reads (human ES cell-derived neuron control samples, and high K+-treated samples, both from E-MTAB-548932), DESeq2 Log2-fold change (high K+ vs. Con) was calculated both at the gene level (E) and transcript level (F), using normal mapping, and for Sargasso mapping requiring disambiguation of reads from rat and mouse genomes. For all genes/transcripts expressed > 1FPKM on average across the samples (by normal mapping), DESeq2 Log2-fold change for the two approaches was plotted against each other, and a correlation coefficient calculated.

Supplementary Figure 4 Sargasso species disambiguation of human RNA-seq reads against species of varying evolutionary distance.

a, Human RNA-seq reads (Gene Expression Omnibus samples GSM2285374–7), were taken, and we calculated the % reads lost for each protein-coding gene expressed > 1 FPKM (by normal mapping – defined as mapping which requires perfect match to target species, but no disambiguation between any other species, 11,661 genes in total) when performing the Sargasso pipeline requiring disambiguation of reads against mouse, rat, macaque, or chimpanzee. A cumulative distribution plot for genes against % of reads lost is shown for each of the four Sargasso species disambiguation procedures. b-e, For the human RNA-seq reads in (A), FPKM values of all protein-coding genes were calculated using normal mapping and Sargasso mapping, requiring disambiguation of reads from mouse (B), rat (C), macaque (D) and chimpanzee (E). FPKM values were plotted against each other for all those 11,661 genes expressed > 1 FPKM (by normal mapping), and the correlation coefficient calculated.

Supplementary Figure 5 Expression changes induced by LPS in microglia are not influenced by having other cells present from different species.

a,b, Differential gene expression analysis between samples 5a and 5b, co-cultures of rat microglia, neurons and astrocytes (±LPS). We took the list of genes significantly induced (304 genes) or repressed (113 genes) > 4-fold by LPS treatment in rat microglia in the mixed species co-culture (samples 2a vs. 2b) and then looked at the LPS-dependent regulation of the subset of these genes whose induction could be tracked in a single species co-culture (5a vs. 5b) by virtue of their expression being > 5-fold higher in a pure microglial culture, than the mixed microglia-astrocyte-neuron co-culture (samples 5a vs. 6a), and expressed at least 1 FPKM in mono-cultured microglia. Applying these criteria meant that we could, to a first approximation, monitor the regulation of 108/304 LPS-induced genes (A), and 44/113 LPS-repressed genes (B), in microglia in a single species microglia-neuron-astrocyte co-culture. The DESeq2 Log2fold-change is shown for each of the 108 LPS-induced genes (A) and 44 LPS-repressed genes (B), and a P value calculated (paired t-test, samples 5a vs. 5b, n = 108 (A), n = 44 (B)).

Supplementary Figure 6 Activated microglia induce largely distinct specific transcriptional responses in neurons and astrocytes.

For genes induced (>1.5-fold) by microglia in astrocytes (Fig. 2d) and in neurons (Fig. 2f), the fold change in neurons is plotted against that in astrocytes. The top-right quadrant formed by the crossed dotted lines includes those genes induced >1.5-fold by microglia in both neurons and astrocytes, a relatively small number of genes.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6, Supplementary Results

Reporting Summary

Supplementary Software

Version 1.1 of the Sargasso software and documentation (doi:10.5281/zenodo.260123)

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Qiu, J., Dando, O., Baxter, P.S. et al. Mixed-species RNA-seq for elucidation of non-cell-autonomous control of gene transcription. Nat Protoc 13, 2176–2199 (2018). https://doi.org/10.1038/s41596-018-0029-2

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