Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue


Transcriptome profiling of single cells resident in their natural microenvironment depends upon RNA capture methods that are both noninvasive and spatially precise. We engineered a transcriptome in vivo analysis (TIVA) tag, which upon photoactivation enables mRNA capture from single cells in live tissue. Using the TIVA tag in combination with RNA sequencing (RNA-seq), we analyzed transcriptome variance among single neurons in culture and in mouse and human tissue in vivo. Our data showed that the tissue microenvironment shapes the transcriptomic landscape of individual cells. The TIVA methodology is, to our knowledge, the first noninvasive approach for capturing mRNA from live single cells in their natural microenvironment.

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Figure 1: The TIVA tag is a multifunctional, caged mRNA-capture molecule.
Figure 2: Validation of the TIVA tag in solution.
Figure 3: Validation of uptake and uncaging of TIVA tag in live cells.
Figure 4: TIVA tag capture of mRNA from single neurons in mouse hippocampal slices.
Figure 5: TIVA tag capture of mRNA from cells in human live brain tissue specimen obtained from biopsy of the right frontal cortex from a subject undergoing surgery for communicating hydrocephalus.
Figure 6: Bimodal transcripts in single hippocampal neurons in tissue and in culture.

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  1. 1

    Kaern, M., Elston, T.C., Blake, W.J. & Collins, J.J. Stochasticity in gene expression: from theories to phenotypes. Nat. Rev. Genet. 6, 451–464 (2005).

    CAS  Article  Google Scholar 

  2. 2

    Raj, A. & van Oudenaarden, A. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 135, 216–226 (2008).

    CAS  Article  Google Scholar 

  3. 3

    Eldar, A. & Elowitz, M.B. Functional roles for noise in genetic circuits. Nature 467, 167–173 (2010).

    CAS  Article  Google Scholar 

  4. 4

    Elowitz, M.B., Levine, A.J., Siggia, E.D. & Swain, P.S. Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002).

    CAS  Article  Google Scholar 

  5. 5

    Flatz, L. et al. Single-cell gene-expression profiling reveals qualitatively distinct CD8 T cells elicited by different gene-based vaccines. Proc. Natl. Acad. Sci. USA 108, 5724–5729 (2011).

    CAS  Article  Google Scholar 

  6. 6

    Taniguchi, Y. et al. Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science 329, 533–538 (2010).

    CAS  Article  Google Scholar 

  7. 7

    Pedraza, J.M. & van Oudenaarden, A. Noise propagation in gene networks. Science 307, 1965–1969 (2005).

    CAS  Article  Google Scholar 

  8. 8

    Cahoy, J.D. et al. A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J. Neurosci. 28, 264–278 (2008).

    CAS  Article  Google Scholar 

  9. 9

    Lovatt, D. et al. The transcriptome and metabolic gene signature of protoplasmic astrocytes in the adult murine cortex. J. Neurosci. 27, 12255–12266 (2007).

    CAS  Article  Google Scholar 

  10. 10

    Sugino, K. et al. Molecular taxonomy of major neuronal classes in the adult mouse forebrain. Nat. Neurosci. 9, 99–107 (2006).

    CAS  Article  Google Scholar 

  11. 11

    Eberwine, J. et al. Quantitative biology of single neurons. J. R. Soc. Interface 9, 3165–3183 (2012).

    CAS  Article  Google Scholar 

  12. 12

    Espina, V. et al. Laser-capture microdissection. Nat. Protoc. 1, 586–603 (2006).

    CAS  Article  Google Scholar 

  13. 13

    Tang, F. et al. mRNA-seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

    CAS  Article  Google Scholar 

  14. 14

    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 

  15. 15

    Joliot, A. & Prochiantz, A. Transduction peptides: from technology to physiology. Nat. Cell Biol. 6, 189–196 (2004).

    CAS  Article  Google Scholar 

  16. 16

    Kumar, P. et al. Transvascular delivery of small interfering RNA to the central nervous system. Nature 448, 39–43 (2007).

    CAS  Article  Google Scholar 

  17. 17

    Zeng, F. et al. A protocol for PAIR: PNA-assisted identification of RNA binding proteins in living cells. Nat. Protoc. 1, 920–927 (2006).

    CAS  Article  Google Scholar 

  18. 18

    Zielinski, J. et al. In vivo identification of ribonucleoprotein-RNA interactions. Proc. Natl. Acad. Sci. USA 103, 1557–1562 (2006).

    CAS  Article  Google Scholar 

  19. 19

    Adams, S.R. & Tsien, R.Y. Controlling cell chemistry with caged compounds. Annu. Rev. Physiol. 55, 755–784 (1993).

    CAS  Article  Google Scholar 

  20. 20

    Tang, X. & Dmochowski, I.J. Synthesis of light-activated antisense oligodeoxynucleotide. Nat. Protoc. 1, 3041–3048 (2006).

    CAS  Article  Google Scholar 

  21. 21

    Dmochowski, I.J. & Tang, X. Taking control of gene expression with light-activated oligonucleotides. Biotechniques 43, 161–165 (2007).

    CAS  Article  Google Scholar 

  22. 22

    Madani, F., Lindberg, S., Langel, U., Futaki, S. & Graslund, A. Mechanisms of cellular uptake of cell-penetrating peptides. J. Biophys. 2011, 414729 (2011).

    Article  Google Scholar 

  23. 23

    Svensen, N., Walton, J.G. & Bradley, M. Peptides for cell-selective drug delivery. Trends Pharmacol. Sci. 33, 186–192 (2012).

    CAS  Article  Google Scholar 

  24. 24

    Roy, R., Hohng, S. & Ha, T. A practical guide to single-molecule FRET. Nat. Methods 5, 507–516 (2008).

    CAS  Article  Google Scholar 

  25. 25

    Eberwine, J. et al. Analysis of gene expression in single live neurons. Proc. Natl. Acad. Sci. USA 89, 3010–3014 (1992).

    CAS  Article  Google Scholar 

  26. 26

    Morris, J., Singh, J.M. & Eberwine, J.H. Transcriptome analysis of single cells. J. Vis. Exp. 2011, 2634 (2011).

    Google Scholar 

  27. 27

    Ramskold, D. et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782 (2012).

    Article  Google Scholar 

  28. 28

    Griffith, M. et al. Alternative expression analysis by RNA sequencing. Nat. Methods 7, 843–847 (2010).

    CAS  Article  Google Scholar 

  29. 29

    Zheng, W., Chung, L.M. & Zhao, H. Bias detection and correction in RNA-Sequencing data. BMC Bioinformatics 12, 290 (2011).

    CAS  Article  Google Scholar 

  30. 30

    Adiconis, X. et al. Comparative analysis of RNA sequencing methods for degraded or low-input samples. Nat. Methods 10, 623–629 (2013).

    CAS  Article  Google Scholar 

  31. 31

    Gertz, J. et al. Transposase mediated construction of RNA-seq libraries. Genome Res. 22, 134–141 (2012).

    CAS  Article  Google Scholar 

  32. 32

    Ellis-Davies, G.C. Caged compounds: photorelease technology for control of cellular chemistry and physiology. Nat. Methods 4, 619–628 (2007).

    CAS  Article  Google Scholar 

  33. 33

    Zhang, S.C. Defining glial cells during CNS development. Nat. Rev. Neurosci. 2, 840–843 (2001).

    CAS  Article  Google Scholar 

  34. 34

    Pribyl, T.M. et al. Expression of the myelin basic protein gene locus in neurons and oligodendrocytes in the human fetal central nervous system. J. Comp. Neurol. 374, 342–353 (1996).

    CAS  Article  Google Scholar 

  35. 35

    Landry, C.F. et al. Myelin basic protein gene expression in neurons: developmental and regional changes in protein targeting within neuronal nuclei, cell bodies, and processes. The J. Neurosci. 16, 2452–2462 (1996).

    CAS  Article  Google Scholar 

  36. 36

    Vives, V., Alonso, G., Solal, A.C., Joubert, D. & Legraverend, C. Visualization of S100B-positive neurons and glia in the central nervous system of EGFP transgenic mice. J. Comp. Neurol. 457, 404–419 (2003).

    CAS  Article  Google Scholar 

  37. 37

    West, A.E., Griffith, E.C. & Greenberg, M.E. Regulation of transcription factors by neuronal activity. Nat. Rev. Neurosci. 3, 921–931 (2002).

    CAS  Article  Google Scholar 

  38. 38

    Turner, J.J. et al. Cell-penetrating peptide conjugates of peptide nucleic acids (PNA) as inhibitors of HIV-1 Tat-dependent trans-activation in cells. Nucleic Acids Res. 33, 6837–6849 (2005).

    CAS  Article  Google Scholar 

  39. 39

    Richards, J.L., Tang, X., Turetsky, A. & Dmochowski, I.J. RNA bandages for photoregulating in vitro protein synthesis. Bioorg. Med. Chem. Lett. 18, 6255–6258 (2008).

    CAS  Article  Google Scholar 

  40. 40

    Cummings, D.D., Wilcox, K.S. & Dichter, M.A. Calcium-dependent paired-pulse facilitation of miniature EPSC frequency accompanies depression of EPSCs at hippocampal synapses in culture. J. Neurosci. 16, 5312–5323 (1996).

    CAS  Article  Google Scholar 

  41. 41

    Grant, G.R. et al. Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM). Bioinformatics 27, 2518–2528 (2011).

    CAS  Article  Google Scholar 

  42. 42

    Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

    CAS  Article  Google Scholar 

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We thank J. Cheung-Lau for assistance with in vitro FRET measurements. Funding was provided by the PhRMA foundation to D.L., US National Institutes of Health (NIH) R01 GM083030 to I.J.D., McKnight Foundation Technology Innovations Award to I.J.D. and J.E., U01MH098953 to J.K. and J.E. and NIH DP004117 to J.E. This project is funded, in part, by the Penn Genome Frontiers Institute under a grant with the Pennsylvania Department of Health, which disclaims responsibility for any analyses, interpretations or conclusions.

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All authors contributed to the writing of the manuscript. D.L. performed dispersed-cell TIVA experiments and some computational analysis; B.K.R. contributed TIVA-tag characterization and supplied TIVA tag; J.L. perfomed slice experiments and the bulk of TIVA uncaging; H.D. performed the bulk of the computational analysis; T.K.K. performed TIVA-mediated RNA amplifications; S.F. contributed computational analysis; C.F. contributed some control samples; J.M.S. contributed TIVA-mediated RNA amplifications; J.A.W., M.S.G. and A.V.U. organized human tissue use; S.B.Y. and J.C.G. contributed TIVA tag; P.T.B. contributed TIVA-mediated RNA amplifications; J.K. directed the computational analysis; J.Y.S. contributed the TIVA uncaging parameters and oversaw the biophotonics; I.J.D. designed experiments and contributed oversight of TIVA-tag synthesis; J.E. designed experiments and contributed oversight of the biological experiments.

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Correspondence to James Eberwine.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 and Supplementary Tables 1, 2, 4 and 5 (PDF 4202 kb)

Supplementary Table 3

Environment specific expressed genes in single neurons from culture vs. tissue (XLSX 293 kb)

Supplementary Table 6

List of 645 bimodal genes (XLSX 76 kb)

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Lovatt, D., Ruble, B., Lee, J. et al. Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue. Nat Methods 11, 190–196 (2014).

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