Abstract

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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Accessions

Primary accessions

Gene Expression Omnibus

References

  1. 1.

    , , & Stochasticity in gene expression: from theories to phenotypes. Nat. Rev. Genet. 6, 451–464 (2005).

  2. 2.

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

  3. 3.

    & Functional roles for noise in genetic circuits. Nature 467, 167–173 (2010).

  4. 4.

    , , & Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002).

  5. 5.

    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).

  6. 6.

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

  7. 7.

    & Noise propagation in gene networks. Science 307, 1965–1969 (2005).

  8. 8.

    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).

  9. 9.

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

  10. 10.

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

  11. 11.

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

  12. 12.

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

  13. 13.

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

  14. 14.

    , & A quantitative comparison of cell-type-specific microarray gene expression profiling methods in the mouse brain. PLoS ONE 6, e16493 (2011).

  15. 15.

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

  16. 16.

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

  17. 17.

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

  18. 18.

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

  19. 19.

    & Controlling cell chemistry with caged compounds. Annu. Rev. Physiol. 55, 755–784 (1993).

  20. 20.

    & Synthesis of light-activated antisense oligodeoxynucleotide. Nat. Protoc. 1, 3041–3048 (2006).

  21. 21.

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

  22. 22.

    , , , & Mechanisms of cellular uptake of cell-penetrating peptides. J. Biophys. 2011, 414729 (2011).

  23. 23.

    , & Peptides for cell-selective drug delivery. Trends Pharmacol. Sci. 33, 186–192 (2012).

  24. 24.

    , & A practical guide to single-molecule FRET. Nat. Methods 5, 507–516 (2008).

  25. 25.

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

  26. 26.

    , & Transcriptome analysis of single cells. J. Vis. Exp. 2011, 2634 (2011).

  27. 27.

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

  28. 28.

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

  29. 29.

    , & Bias detection and correction in RNA-Sequencing data. BMC Bioinformatics 12, 290 (2011).

  30. 30.

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

  31. 31.

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

  32. 32.

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

  33. 33.

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

  34. 34.

    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).

  35. 35.

    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).

  36. 36.

    , , , & Visualization of S100B-positive neurons and glia in the central nervous system of EGFP transgenic mice. J. Comp. Neurol. 457, 404–419 (2003).

  37. 37.

    , & Regulation of transcription factors by neuronal activity. Nat. Rev. Neurosci. 3, 921–931 (2002).

  38. 38.

    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).

  39. 39.

    , , & RNA bandages for photoregulating in vitro protein synthesis. Bioorg. Med. Chem. Lett. 18, 6255–6258 (2008).

  40. 40.

    , & Calcium-dependent paired-pulse facilitation of miniature EPSC frequency accompanies depression of EPSCs at hippocampal synapses in culture. J. Neurosci. 16, 5312–5323 (1996).

  41. 41.

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

  42. 42.

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

Download references

Acknowledgements

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.

Author information

Author notes

    • Ditte Lovatt
    •  & Brittani K Ruble

    These authors contributed equally to this work.

    • Ivan J Dmochowski
    •  & James Eberwine

    These authors jointly directed this work.

Affiliations

  1. Department of Pharmacology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

    • Ditte Lovatt
    • , Jaehee Lee
    • , Tae Kyung Kim
    • , Jennifer M Spaethling
    • , Peter T Buckley
    • , Jai-Yoon Sul
    •  & James Eberwine
  2. Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

    • Brittani K Ruble
    • , Sean B Yeldell
    • , Julianne C Griepenburg
    •  & Ivan J Dmochowski
  3. Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

    • Hannah Dueck
    • , Stephen Fisher
    • , Chantal Francis
    •  & Junhyong Kim
  4. Department of Neurosurgery, University of Pennsylvania Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

    • John A Wolf
    • , M Sean Grady
    •  & Alexandra V Ulyanova
  5. PENN Genome Frontiers Institute, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

    • Junhyong Kim
    •  & James Eberwine

Authors

  1. Search for Ditte Lovatt in:

  2. Search for Brittani K Ruble in:

  3. Search for Jaehee Lee in:

  4. Search for Hannah Dueck in:

  5. Search for Tae Kyung Kim in:

  6. Search for Stephen Fisher in:

  7. Search for Chantal Francis in:

  8. Search for Jennifer M Spaethling in:

  9. Search for John A Wolf in:

  10. Search for M Sean Grady in:

  11. Search for Alexandra V Ulyanova in:

  12. Search for Sean B Yeldell in:

  13. Search for Julianne C Griepenburg in:

  14. Search for Peter T Buckley in:

  15. Search for Junhyong Kim in:

  16. Search for Jai-Yoon Sul in:

  17. Search for Ivan J Dmochowski in:

  18. Search for James Eberwine in:

Contributions

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.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to James Eberwine.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–8 and Supplementary Tables 1, 2, 4 and 5

Excel files

  1. 1.

    Supplementary Table 3

    Environment specific expressed genes in single neurons from culture vs. tissue

  2. 2.

    Supplementary Table 6

    List of 645 bimodal genes

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nmeth.2804

Further reading