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Neuronal subtype-specific growth cone and soma purification from mammalian CNS via fractionation and fluorescent sorting for subcellular analyses and spatial mapping of local transcriptomes and proteomes

Nature Protocols volume 17, pages 222–251 (2022)Cite this article

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Abstract

During neuronal development, growth cones (GCs) of projection neurons navigate complex extracellular environments to reach distant targets, thereby generating extraordinarily complex circuitry. These dynamic structures located at the tips of axonal projections respond to substrate-bound as well as diffusible guidance cues in a neuronal subtype– and stage-specific manner to construct highly specific and functional circuitry. In vitro studies of the past decade indicate that subcellular localization of specific molecular machinery in GCs underlies the precise navigational control that occurs during circuit ‘wiring’. Our laboratory has recently developed integrated experimental and analytical approaches enabling high-depth, quantitative proteomic and transcriptomic investigation of subtype- and stage-specific GC molecular machinery directly from the rodent central nervous system (CNS) in vivo. By using these approaches, a pure population of GCs and paired somata can be isolated from any neuronal subtype of the CNS that can be fluorescently labeled. GCs are dissociated from parent axons using fluid shear forces, and a bulk GC fraction is isolated by buoyancy ultracentrifugation. Subtype-specific GCs and somata are purified by recently developed fluorescent small particle sorting and established FACS of neurons and are suitable for downstream analyses of proteins and RNAs, including small RNAs. The isolation of subtype-specific GCs and parent somata takes ~3 h, plus sorting time, and ~1–2 h for subsequent extraction of molecular contents. RNA library preparation and sequencing can take several days to weeks, depending on the turnaround time of the core facility involved.

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Fig. 1: Paired isolation of subpopulation-specific GCs and somata enables mapping of subcellular proteomes and transcriptomes.
Fig. 2: A combination of fluorescent labeling of a neuronal subtype of interest; parallel preparation and isolation of labeled, axonal GCs and parent somata; and proteomic/transcriptomic analyses enables deep investigation of subtype-specific subcellular molecular machinery.
Fig. 3: In utero electroporation enables selective labeling of somata and GCs of projection neurons, in this case callosal projection neurons, in their endogenous anatomical context.
Fig. 4: Subcellular fractionation enables isolation of membrane-sealed GC particles, which protect their molecular cargo and pass stringent quality-control metrics.
Fig. 5: FSPS of GCF and FACS of dissociated somata enable isolation of subtype-specific GCs and parent somata.
Fig. 6: High-depth transcriptomic and proteomic analyses of paired GC and soma samples enables identification of compartment-specific transcriptomes/proteomes as well as subcellular localization analysis.
Fig. 7: Molecular candidates of interest identified in subcellular transcriptomic or proteomic analyses are validated and further investigated using in vitro and in vivo approaches.

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Data availability

Most data included were first described in our earlier paper13, and the figure legends describe specifically where each piece of data included was published previously. Examples of datasets generated and analyzed by using the presented approaches are also available in the Harvard Dataverse repository (https://doi.org/10.7910/DVN/ISOEB6 (ref. 13).

References

  1. Cajal, S. R. A quelle époque apparaissent les expansions des cellules nerveuses de la moëlle épinière du poulet?. Anat. Anz. 5, 609–613, 621–631 (1890).

    Google Scholar 

  2. Menon, S. & Gupton, S. L. Building blocks of functioning brain: cytoskeletal dynamics in neuronal development. Int. Rev. Cell Mol. Biol. 322, 183–245 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Tamariz, E. & Varela-Echavarria, A. The discovery of the growth cone and its influence on the study of axon guidance. Front. Neuroanat. 9, 51 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Vitriol, E. A. & Zheng, J. Q. Growth cone travel in space and time: the cellular ensemble of cytoskeleton, adhesion, and membrane. Neuron 73, 1068–1081 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Ye, X., Qiu, Y., Gao, Y., Wan, D. & Zhu, H. A subtle network mediating axon guidance: intrinsic dynamic structure of growth cone, attractive and repulsive molecular cues, and the intermediate role of signaling pathways. Neural Plast. 2019, 1719829 (2019).

    PubMed  PubMed Central  Google Scholar 

  6. Dent, E. W., Gupton, S. L. & Gertler, F. B. The growth cone cytoskeleton in axon outgrowth and guidance. Cold Spring Harb. Perspect. Biol. 3, a001800 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Bellon, A. & Mann, F. Keeping up with advances in axon guidance. Curr. Opin. Neurobiol. 53, 183–191 (2018).

    Article  CAS  PubMed  Google Scholar 

  8. Kaplan, A., Kent, C. B., Charron, F. & Fournier, A. E. Switching responses: spatial and temporal regulators of axon guidance. Mol. Neurobiol. 49, 1077–1086 (2014).

    Article  CAS  PubMed  Google Scholar 

  9. Stoeckli, E. T. Understanding axon guidance: are we nearly there yet? Development 145, dev151415 (2018).

    Article  PubMed  CAS  Google Scholar 

  10. Costa, C. J. & Willis, D. E. To the end of the line: axonal mRNA transport and local translation in health and neurodegenerative disease. Dev. Neurobiol. 78, 209–220 (2018).

    Article  CAS  PubMed  Google Scholar 

  11. Holt, C. E. & Schuman, E. M. The central dogma decentralized: new perspectives on RNA function and local translation in neurons. Neuron 80, 648–657 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Sasaki, Y. Local translation in growth cones and presynapses, two axonal compartments for local neuronal functions. Biomolecules 10, 668 (2020).

    Article  CAS  PubMed Central  Google Scholar 

  13. Poulopoulos, A. et al. Subcellular transcriptomes and proteomes of developing axon projections in the cerebral cortex. Nature 565, 356–360 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Lohse, K. et al. Axonal origin and purity of growth cones isolated from fetal rat brain. Brain Res. Dev. Brain Res. 96, 83–96 (1996).

    Article  CAS  PubMed  Google Scholar 

  15. Pfenninger, K. H., Ellis, L., Johnson, M. P., Friedman, L. B. & Somlo, S. Nerve growth cones isolated from fetal rat brain: subcellular fractionation and characterization. Cell 35, 573–584 (1983).

    Article  CAS  PubMed  Google Scholar 

  16. Catapano, L. A., Arnold, M. W., Perez, F. A. & Macklis, J. D. Specific neurotrophic factors support the survival of cortical projection neurons at distinct stages of development. J. Neurosci. 21, 8863–8872 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Ellis, L., Katz, F. & Pfenninger, K. H. Nerve growth cones isolated from fetal rat brain. II. Cyclic adenosine 3′:5′-monophosphate (cAMP)-binding proteins and cAMP-dependent protein phosphorylation. J. Neurosci. 5, 1393–1401 (1985).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Ellis, L., Wallis, I., Abreu, E. & Pfenninger, K. H. Nerve growth cones isolated from fetal rat brain. IV. Preparation of a membrane subfraction and identification of a membrane glycoprotein expressed on sprouting neurons. J. Cell Biol. 101, 1977–1989 (1985).

    Article  CAS  PubMed  Google Scholar 

  19. Katz, F., Ellis, L. & Pfenninger, K. H. Nerve growth cones isolated from fetal rat brain. III. Calcium-dependent protein phosphorylation. J. Neurosci. 5, 1402–1411 (1985).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Brittis, P. A., Lu, Q. & Flanagan, J. G. Axonal protein synthesis provides a mechanism for localized regulation at an intermediate target. Cell 110, 223–235 (2002).

    Article  CAS  PubMed  Google Scholar 

  21. Farina, K. L., Huttelmaier, S., Musunuru, K., Darnell, R. & Singer, R. H. Two ZBP1 KH domains facilitate beta-actin mRNA localization, granule formation, and cytoskeletal attachment. J. Cell Biol. 160, 77–87 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Gumy, L. F. et al. Transcriptome analysis of embryonic and adult sensory axons reveals changes in mRNA repertoire localization. RNA 17, 85–98 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Leung, K. M. et al. Asymmetrical β-actin mRNA translation in growth cones mediates attractive turning to netrin-1. Nat. Neurosci. 9, 1247–1256 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Leung, L. C. et al. Coupling of NF-protocadherin signaling to axon guidance by cue-induced translation. Nat. Neurosci. 16, 166–173 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Minis, A. et al. Subcellular transcriptomics-dissection of the mRNA composition in the axonal compartment of sensory neurons. Dev. Neurobiol. 74, 365–381 (2014).

    Article  CAS  PubMed  Google Scholar 

  26. Taylor, A. M. et al. Axonal mRNA in uninjured and regenerating cortical mammalian axons. J. Neurosci. 29, 4697–4707 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Wu, K. Y. et al. Local translation of RhoA regulates growth cone collapse. Nature 436, 1020–1024 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Yoon, B. C. et al. Local translation of extranuclear lamin B promotes axon maintenance. Cell 148, 752–764 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Zivraj, K. H. et al. Subcellular profiling reveals distinct and developmentally regulated repertoire of growth cone mRNAs. J. Neurosci. 30, 15464–15478 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Arlotta, P. et al. Neuronal subtype-specific genes that control corticospinal motor neuron development in vivo. Neuron 45, 207–221 (2005).

    Article  CAS  PubMed  Google Scholar 

  31. Clare, A. J., Day, R. C., Empson, R. M. & Hughes, S. M. Transcriptome profiling of layer 5 intratelencephalic projection neurons from the mature mouse motor cortex. Front. Mol. Neurosci. 11, 410 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kawasawa, Y. I. et al. RNA-seq analysis of developing olfactory bulb projection neurons. Mol. Cell. Neurosci. 74, 78–86 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Molyneaux, B. J. et al. Novel subtype-specific genes identify distinct subpopulations of callosal projection neurons. J. Neurosci. 29, 12343–12354 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Cagnetta, R., Frese, C. K., Shigeoka, T., Krijgsveld, J. & Holt, C. E. Rapid cue-specific remodeling of the nascent axonal proteome. Neuron 99, 29–46.e4 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Shigeoka, T. et al. Dynamic axonal translation in developing and mature visual circuits. Cell 166, 181–192 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Saito, T. & Nakatsuji, N. Efficient gene transfer into the embryonic mouse brain using in vivo electroporation. Dev. Biol. 240, 237–246 (2001).

    Article  CAS  PubMed  Google Scholar 

  37. Trichas, G., Begbie, J. & Srinivas, S. Use of the viral 2A peptide for bicistronic expression in transgenic mice. BMC Biol. 6, 40 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Filipe, V., Hawe, A. & Jiskoot, W. Critical evaluation of Nanoparticle Tracking Analysis (NTA) by NanoSight for the measurement of nanoparticles and protein aggregates. Pharm. Res. 27, 796–810 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Wang, L., Wang, S. & Li, W. RSeQC: quality control of RNA-seq experiments. Bioinformatics 28, 2184–2185 (2012).

    Article  CAS  PubMed  Google Scholar 

  40. Hatch, J., Poulopoulos, A., Engmann, A. K. & Macklis, J. D. Growth cone molecular machinery locally implements the development of subtype-specific neocortical circuitry. Soc. Neurosci. Abstr. 365, 11 (2019).

    Google Scholar 

  41. Spandidos, A., Wang, X., Wang, H. & Seed, B. PrimerBank: a resource of human and mouse PCR primer pairs for gene expression detection and quantification. Nucleic Acids Res. 38, D792–D799 (2010).

    Article  CAS  PubMed  Google Scholar 

  42. Cox, J. et al. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol. Cell. Proteom. 13, 2513–2526 (2014).

    Article  CAS  Google Scholar 

  43. Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    Article  CAS  PubMed  Google Scholar 

  44. Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).

    Article  CAS  PubMed  Google Scholar 

  45. Zhang, X. et al. Proteome-wide identification of ubiquitin interactions using UbIA-MS. Nat. Protoc. 13, 530–550 (2018).

    Article  CAS  PubMed  Google Scholar 

  46. Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).

    Article  CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  48. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We dedicate this paper to the memory of John J. Hatch, who believed deeply in sharing resources, expertise, data and his excitement and exceptional abilities to enable others to deeply, rigorously and creatively investigate the subcellular biology of polarized cells, developing neurons and circuits in particular. We thank our laboratory colleague Dustin Tillman for a representative image of the hydrolysis protection assay. We thank Joyce LaVecchio and Nema Kheradmand of the HSCRB-HSCI Flow Cytometry Core; Emma White, Christian Daly and Claire Hartmann of the Harvard Bauer Sequencing Core; William Lane, John Neveu and Bogdan Budnik of the Harvard FAS CSB Mass Spectrometry and Proteomics Resource Lab; Anna-Katerina Hadjantonakis (Memorial Sloan Kettering) and Shankar Srinivas (Oxford) for reagents. Analytic tools and infrastructure in the Harvard Chan Bioinformatics Core were partially supported by funds from the Harvard NeuroDiscovery Center and the Harvard Stem Cell Institute. This work was supported by the following grants to J.D.M.: NIH Pioneer Award DP1NS106665; Paul G. Allen Frontiers Group—Allen Distinguished Investigator award and Brain Research Foundation Scientific Innovations Award; Max and Anne Wien Professor of Life Sciences fund; Emily and Robert Pearlstein Fund; and additional infrastructure support from NIH grants NS045523, NS104055, NS075672 and NS049553. J.J.H. was partially supported by NIH individual Training Grant F31 NS103262. A.K.E. was partially supported by the Jean-Jacques & Felicia Lopez-Loreta Foundation, the Travis Roy Foundation (to J.D.M.) and a Swiss National Science Foundation Fellowship. A.P. was partially supported by a European Molecular Biology Long-Term Fellowship and a Human Frontiers Science Program Long-Term Fellowship. A.J.M. was partially supported by NIH Training Grant T32 AG000222.

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Author notes

  1. Alexandros Poulopoulos

    Present address: Department of Pharmacology, University of Maryland School of Medicine, Baltimore, MD, USA

  2. Alexander J. Murphy

    Present address: Takeda Pharmaceutical Company Limited, Cambridge, MA, USA

  3. These authors contributed equally: Anne K. Engmann, John J. Hatch.

  4. Deceased: John J. Hatch.

Authors and Affiliations

  1. Department of Stem Cell and Regenerative Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA

    Anne K. Engmann, John J. Hatch, Prakruti Nanda, Priya Veeraraghavan, Abdulkadir Ozkan, Alexandros Poulopoulos, Alexander J. Murphy & Jeffrey D. Macklis

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Contributions

A.K.E., J.J.H., A.P., P.V. and J.D.M. wrote the manuscript. A.P., A.J.M., J.J.H., A.O. and A.K.E. performed experiments and analyzed data. A.P., J.J.H., P.N. and A.K.E. designed figures.

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Correspondence to Jeffrey D. Macklis.

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Poulopoulos, A. et al. Nature 565, 356–360 (2019) https://doi.org/10.1038/s41586-018-0847-y

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Engmann, A.K., Hatch, J.J., Nanda, P. et al. Neuronal subtype-specific growth cone and soma purification from mammalian CNS via fractionation and fluorescent sorting for subcellular analyses and spatial mapping of local transcriptomes and proteomes. Nat Protoc 17, 222–251 (2022). https://doi.org/10.1038/s41596-021-00638-7

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