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Multiplexed Cre-dependent selection yields systemic AAVs for targeting distinct brain cell types

Nature Methods volume 17, pages 541–550 (2020)Cite this article

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Abstract

Recombinant adeno-associated viruses (rAAVs) are efficient gene delivery vectors via intravenous delivery; however, natural serotypes display a finite set of tropisms. To expand their utility, we evolved AAV capsids to efficiently transduce specific cell types in adult mouse brains. Building upon our Cre-recombination-based AAV targeted evolution (CREATE) platform, we developed Multiplexed-CREATE (M-CREATE) to identify variants of interest in a given selection landscape through multiple positive and negative selection criteria. M-CREATE incorporates next-generation sequencing, synthetic library generation and a dedicated analysis pipeline. We have identified capsid variants that can transduce the central nervous system broadly, exhibit bias toward vascular cells and astrocytes, target neurons with greater specificity or cross the blood–brain barrier across diverse murine strains. Collectively, the M-CREATE methodology accelerates the discovery of capsids for use in neuroscience and gene-therapy applications.

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Fig. 1: Workflow of M-CREATE and analysis of 7-mer-i selection in R1.
Fig. 2: R2 capsid selections by synthetic pool and PCR pool methods.
Fig. 3: Selected AAV capsids form sequence families and include variants for brain-wide transduction of vasculature.
Fig. 4: Characterization of R2 brain libraries and identification of capsids with broad CNS tropism.
Fig. 5: Recovery of AAV-PHP.B variants including one with high specificity for neurons.
Fig. 6: Tropism of variants from distinct families across mouse strains.

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Data availability

The NGS datasets using the synthetic pool and PCR pool selection methods that are reported in this article are available under the SRA accession code PRJNA610987. The following vector plasmids are deposited on Addgene for distribution (http://www.addgene.org) AAV-PHP.V1: 127847, AAV-PHP.V2: 127848, AAV-PHP.B4: 127849, and AAV-PHP.N: 127851, and viruses may be available for commonly packaged genomes. Other plasmids or viruses not available at Addgene may be requested from Caltech, CLOVER Center (http://clover.caltech.edu/). GenBank: AAV-PHP.V1: MT162422, AAV-PHP.V2: MT162423, AAV-PHP.N: MT162424, AAV- PHP.C1: MT162425, AAV-PHP.C2: MT162426, AAV-PHP.C3: MT162427, AAV-PHP.B4: MT162428, AAV-PHP.B5: MT162429, AAV-PHP.B6: MT162430, AAV-PHP.B7: MT162431 and AAV-PHP.B8: MT162432.

Code availability

The codes used for M-CREATE data analysis were written in python or MATLAB and are made available on GitHub: https://github.com/GradinaruLab/mCREATE. The custom MATLAB scripts to generate HCR probes is accessible through GitHub on a different repository: https://github.com/GradinaruLab/HCRprobe.

References

  1. Wu, Z., Asokan, A. & Samulski, R. J. Adeno-associated virus serotypes: vector toolkit for human gene therapy. Mol. Ther. J. Am. Soc. Gene Ther. 14, 316–327 (2006).

    Article  CAS  Google Scholar 

  2. Naso, M. F., Tomkowicz, B., Perry, W. L. & Strohl, W. R. Adeno-associated virus (AAV) as a vector for gene therapy. Biodrugs 31, 317–334 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Daya, S. & Berns, K. I. Gene therapy using adeno-associated virus vectors. Clin. Microbiol. Rev. 21, 583–593 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Gaj, T., Epstein, B. E. & Schaffer, D. V. Genome engineering using adeno-associated virus: basic and clinical research applications. Mol. Ther. 24, 458–464 (2016).

    Article  CAS  PubMed  Google Scholar 

  5. Deverman, B. E., Ravina, B. M., Bankiewicz, K. S., Paul, S. M. & Sah, D. W. Y. Gene therapy for neurological disorders: progress and prospects. Nat. Rev. Drug Discov. 17, 767 (2018).

    Article  CAS  PubMed  Google Scholar 

  6. Sen, D. Improving clinical efficacy of adeno associated vectors by rational capsid bioengineering. J. Biomed. Sci. 21, 103 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Lee, E. J., Guenther, C. M. & Suh, J. Adeno-associated virus (AAV) vectors: rational design strategies for capsid engineering. Curr. Opin. Biomed. Eng. 7, 58–63 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Bartlett, J. S., Kleinschmidt, J., Boucher, R. C. & Samulski, R. J. Targeted adeno-associated virus vector transduction of nonpermissive cells mediated by a bispecific F(ab’gamma)2 antibody. Nat. Biotechnol. 17, 181–186 (1999).

    Article  CAS  PubMed  Google Scholar 

  9. Davidsson, M. et al. A systematic capsid evolution approach performed in vivo for the design of AAV vectors with tailored properties and tropism. Proc. Natl Acad. Sci. USA 116, 27053–27062 (2019).

    Article  CAS  PubMed Central  Google Scholar 

  10. Bedbrook, C. N., Deverman, B. E. & Gradinaru, V. Viral strategies for targeting the central and peripheral nervous systems. Annu. Rev. Neurosci. 41, 323–348 (2018).

    Article  CAS  PubMed  Google Scholar 

  11. Kotterman, M. A. & Schaffer, D. V. Engineering adeno-associated viruses for clinical gene therapy. Nat. Rev. Genet. 15, 445–451 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Grimm, D. et al. In vitro and in vivo gene therapy vector evolution via multispecies interbreeding and retargeting of adeno-associated viruses. J. Virol. 82, 5887–5911 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Maheshri, N., Koerber, J. T., Kaspar, B. K. & Schaffer, D. V. Directed evolution of adeno-associated virus yields enhanced gene delivery vectors. Nat. Biotechnol. 24, 198–204 (2006).

    Article  CAS  PubMed  Google Scholar 

  14. Excoffon, K. J. D. A. et al. Directed evolution of adeno-associated virus to an infectious respiratory virus. Proc. Natl Acad. Sci. USA 106, 3865–3870 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Pulicherla, N. et al. Engineering liver-detargeted AAV9 vectors for cardiac and musculoskeletal gene transfer. Mol. Ther. J. Am. Soc. Gene Ther. 19, 1070–1078 (2011).

    Article  CAS  Google Scholar 

  16. Ying, Y. et al. Heart-targeted adeno-associated viral vectors selected by in vivo biopanning of a random viral display peptide library. Gene Ther. 17, 980–990 (2010).

    Article  CAS  PubMed  Google Scholar 

  17. Müller, O. J. et al. Random peptide libraries displayed on adeno-associated virus to select for targeted gene therapy vectors. Nat. Biotechnol. 21, 1040–1046 (2003).

    Article  PubMed  CAS  Google Scholar 

  18. Ogden, P. J., Kelsic, E. D., Sinai, S. & Church, G. M. Comprehensive AAV capsid fitness landscape reveals a viral gene and enables machine-guided design. Science 366, 1139–1143 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Pekrun, K. et al. Using a barcoded AAV capsid library to select for clinically relevant gene therapy vectors. JCI Insight 4, pii: 131610 (2019).

    Article  Google Scholar 

  20. Dalkara, D. et al. In vivo-directed evolution of a new adeno-associated virus for therapeutic outer retinal gene delivery from the vitreous. Sci. Transl. Med. 5, 189ra76 (2013).

    Article  PubMed  CAS  Google Scholar 

  21. Davis, A. S. et al. Rational design and engineering of a modified adeno-associated virus (AAV1)-based vector system for enhanced retrograde gene delivery. Neurosurgery 76, 216–225 (2015). discussion 225.

    Article  PubMed  Google Scholar 

  22. Tordo, J. et al. A novel adeno-associated virus capsid with enhanced neurotropism corrects a lysosomal transmembrane enzyme deficiency. Brain 141, 2014–2031 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Ojala, D. S. et al. In vivo selection of a computationally designed SCHEMA AAV library yields a novel variant for infection of adult neural stem cells in the SVZ. Mol. Ther. J. Am. Soc. Gene Ther. 26, 304–319 (2018).

    Article  CAS  Google Scholar 

  24. Tervo, D. G. R. et al. A designer AAV variant permits efficient retrograde access to projection neurons. Neuron 92, 372–382 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Körbelin, J. et al. A brain microvasculature endothelial cell-specific viral vector with the potential to treat neurovascular and neurological diseases. EMBO Mol. Med. 8, 609–625 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Deverman, B. E. et al. Cre-dependent selection yields AAV variants for widespread gene transfer to the adult brain. Nat. Biotechnol. 34, 204–209 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Chan, K. Y. et al. Engineered AAVs for efficient noninvasive gene delivery to the central and peripheral nervous systems. Nat. Neurosci. 20, 1172–1179 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Lee, Y., Messing, A., Su, M. & Brenner, M. GFAP promoter elements required for region-specific and astrocyte-specific expression. Glia 56, 481–493 (2008).

    Article  PubMed  Google Scholar 

  29. Kisanuki, Y. Y. et al. Tie2-Cre transgenic mice: a new model for endothelial cell-lineage analysis in vivo. Dev. Biol. 230, 230–242 (2001).

    Article  CAS  PubMed  Google Scholar 

  30. de Leeuw, C. N. et al. rAAV-compatible MiniPromoters for restricted expression in the brain and eye. Mol. Brain 9, 52 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Rincon, M. Y. et al. Widespread transduction of astrocytes and neurons in the mouse central nervous system after systemic delivery of a self-complementary AAV-PHP.B vector. Gene Ther. 25, 83 (2018).

    Article  CAS  PubMed  Google Scholar 

  32. Hordeaux, J. et al. The GPI-Linked protein LY6A drives AAV-PHP.B transport across the blood-brain barrier. Mol. Ther. 27, 912–921 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Matsuzaki, Y. et al. Neurotropic properties of AAV-PHP.B are shared among diverse inbred strains of mice. Mol. Ther. 27, 700–704 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Huang, Q. et al. Delivering genes across the blood-brain barrier: LY6A, a novel cellular receptor for AAV-PHP.B capsids. PLoS One 14, e0225206 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Challis, R. C. et al. Systemic AAV vectors for widespread and targeted gene delivery in rodents. Nat. Protoc. 14, 379–414 (2019).

  36. Batista, A. R. et al. Ly6a differential expression in blood–brain barrier is responsible for strain specific central nervous system transduction profile of AAV-PHP.B. Hum. Gene Ther. 31, 90–102 (2019).

  37. Hordeaux, J. et al. The neurotropic properties of AAV-PHP.B are limited to C57BL/6J mice. Mol. Ther. 26, 664–668 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Sweeney, M. D., Zhao, Z., Montagne, A., Nelson, A. R. & Zlokovic, B. V. Blood–brain barrier: from physiology to disease and back. Physiol. Rev. 99, 21–78 (2019).

    Article  CAS  PubMed  Google Scholar 

  39. Betley, J. N. & Sternson, S. M. Adeno-associated viral vectors for mapping, monitoring and manipulating neural circuits. Hum. Gene Ther. 22, 669–677 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Sweeney, M. D., Sagare, A. P. & Zlokovic, B. V. Blood–brain barrier breakdown in Alzheimer disease and other neurodegenerative disorders. Nat. Rev. Neurol. 14, 133–150 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Lykken, E. A., Shyng, C., Edwards, R. J., Rozenberg, A. & Gray, S. J. Recent progress and considerations for AAV gene therapies targeting the central nervous system. J. Neurodev. Disord. 10, 16 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Vlasov, K., Van Dort, C. J. & Solt, K. in Methods in Enzymology vol. 603 (eds. Eckenhoff, R. G. & Dmochowski, I. J.) 181–196 (Academic Press, 2018).

  43. Hwang, B., Lee, J. H. & Bang, D. Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp. Mol. Med. 50, 96 (2018).

    Article  PubMed Central  CAS  Google Scholar 

  44. Paulk, N. K. et al. Bioengineered viral platform for intramuscular passive vaccine delivery to human skeletal muscle. Mol. Ther. Methods Clin. Dev. 10, 144–155 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Harris, J. A. et al. Anatomical characterization of Cre driver mice for neural circuit mapping and manipulation. Front. Neural Circuits 8, 76 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Garcia, A. D. R., Doan, N. B., Imura, T., Bush, T. G. & Sofroniew, M. V. GFAP-expressing progenitors are the principal source of constitutive neurogenesis in adult mouse forebrain. Nat. Neurosci. 7, 1233–1241 (2004).

    Article  CAS  PubMed  Google Scholar 

  47. Zhu, Y. et al. Ablation of NF1 function in neurons induces abnormal development of cerebral cortex and reactive gliosis in the brain. Genes Dev. 15, 859–876 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Madisen, L. et al. A robust and high-throughput Cre reporting and characterization system for the whole mouse brain. Nat. Neurosci. 13, 133–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  49. Choi, H. M. T. et al. Third-generation in situ hybridization chain reaction: multiplexed, quantitative, sensitive, versatile, robust. Development 145, dev165753 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Renier, N. et al. iDISCO: a simple, rapid method to immunolabel large tissue samples for volume imaging. Cell 159, 896–910 (2014).

    Article  CAS  PubMed  Google Scholar 

  51. Hama, H. et al. ScaleS: an optical clearing palette for biological imaging. Nat. Neurosci. 18, 1518–1529 (2015).

    Article  CAS  PubMed  Google Scholar 

  52. DiMattia, M. A. et al. Structural insight into the unique properties of adeno-associated virus serotype 9. J. Virol. 86, 6947–6958 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Schneider, T. D. & Stephens, R. M. Sequence logos: a new way to display consensus sequences. Nucleic Acids Res. 18, 6097–6100 (1990).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Crooks, G. E. WebLogo: a sequence logo generator. Genome Res. 14, 1188–1190 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the following alumni and current members of the Gradinaru group for their assistance in this study: K. Y. Chan and R. Challis for performing mouse injections, R. Hurt for performing preliminary characterization of vectors, Y. Lei for assistance with cloning, K. Beadle for vector production, E. Sullivan for tissue sectioning, E. Mackey for tissue sectioning and mouse colony management, N. Flytzanis and N. Goeden for their contributions towards histology, imaging, data analysis and manuscript preparation, P. Anguiano for administrative assistance, and the entire Gradinaru group for discussions. We thank L. V. Sibener at Stanford University for sharing the Matlab scripts used in amino acid clustering analysis. We thank the Biological Imaging Facility at Caltech (supported by Caltech Beckman Institute and the Arnold and Mabel Beckman Foundation). We also thank the Millard and Muriel Jacobs Genetics and Genomics Laboratory at Caltech; and Integrative Genomics Core at City of Hope for providing sequencing service. This work was primarily supported by grants from the National Institutes of Health (NIH) to V.G.: NIH Director’s New Innovator DP2NS087949 and PECASE, NIH BRAIN R01MH117069, NIH Pioneer DP1OD025535 and SPARC 1OT2OD024899. Additional funding includes the Vallee Foundation (V.G.), the Moore Foundation (V.G.), the CZI Neurodegeneration Challenge Network (V.G.), and the NSF NeuroNex Technology Hub grant 1707316 (V.G.), the Heritage Medical Research Institute (V.G.) and the Beckman Institute for CLARITY, Optogenetics and Vector Engineering Research (CLOVER) for technology development and dissemination (V.G.).

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Author notes

  1. Qin Huang & Benjamin E. Deverman

    Present address: Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA

  2. Alon Greenbaum

    Present address: Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC, USA

  3. Alon Greenbaum

    Present address: University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

  4. These authors contributed equally: Timothy F. Miles, Xinhong Chen.

Authors and Affiliations

  1. Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA

    Sripriya Ravindra Kumar, Timothy F. Miles, Xinhong Chen, David Brown, Tatyana Dobreva, Qin Huang, Xiaozhe Ding, Yicheng Luo, Pétur H. Einarsson, Alon Greenbaum, Min J. Jang, Benjamin E. Deverman & Viviana Gradinaru

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Contributions

S.R.K., B.E.D., T.F.M. and V.G. designed the experiments. S.R.K., B.E.D., X.C., T.F.M., Y.L., A.G., Q.H. and M.J.J. performed experiments. X.C. assisted with virus production and characterization of AAV-PHP variants in mice. Q.H. assisted with method optimization, cloning, virus production and tissue harvest. Y.L. assisted with method optimization and processed tissues for deep sequencing for 3-mer-s library. T.F.M. performed the clustering analysis, contributed to experiments related to NGS data validation, variant assessment across mice strains and amino acid bias heat map analysis. A.G. processed and imaged cleared brain hemisphere, and compiled the Supplementary Video 1 with input from S.R.K., and V.G. D.B., T.D. and P.H.E. built the software to process the NGS raw data for analysis with input from B.E.D., T.F.M., V.G. and S.R.K. M.J.J. performed the HCR experiments. X.D. produced structural models for AAV9 and contributed to the data analysis pipeline. S.R.K. prepared the figures with input from all authors. S.R.K., T.F.M., B.E.D. and V.G. wrote the manuscript with input from all authors. V.G. supervised all aspects of the work.

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Correspondence to Viviana Gradinaru.

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The California Institute of Technology has filed and licensed a patent application for the work described in this manuscript with S.R.K., B.E.D., and V.G. listed as inventors (Caltech disclosure reference no. CIT 8198).

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Integrated supplementary information

Supplementary Figure 1 Extended Schematic for Multiplexed-CREATE and Analysis of Round-1 Selection.

a, Diagram of the genetic switch used in M-CREATE. The Acceptor Vector shows the position of the forward and reverse primers between the Lox sites that are used for selective recovery of capsids from the Cre+ cells. The Rep-AAP∆Cap vector shows a deletion of 480 bp in cap gene in addition to the stop codons that are designed to prevent synthesis of VP1, VP2, and VP3 proteins. AAP protein translation is unaffected by these modifications. b, Schematic of the protocol to selectively recover rAAV genomes from the target population using the Cre-Lox flipping strategy and preparation of the sample for deep sequencing. c, The library coverage for R1 DNA and virus libraries obtained from specific sequencing depths. d, The percentage of variant overlap within the sampled DNA and virus, or across different Cre lines within tissues, or across tissues from R1 selection. e, The distributions of AAV capsid read counts for libraries recovered by NGS from brain tissue across different Cre transgenic mice post R1 selection. The dotted line is illustrative only and roughly separates the signal from noise (see Methods for estimation of signal v.s. noise) where signal in this context represents the input for the R2 selection. f, rAAV genome recovery from tissues using different treatments are shown with total rAAV genome recovery from 0.1 g of liver, g, Percentage of rAAV genomes recovered per ng of total extracted DNA, and h, The CT value (cycle threshold from qPCR) of rAAV genome extracted by trizol that were treated with SmaI restriction enzyme or untreated and i, CT value of mitochondrial DNA (internal control for smaller genome recovery, fold change = 10.79 (2ΔCT)) recovered from 1 ng of total DNA from liver tissue. In f-i, n = 4 mice; 2 from GFAP-Cre line and 2 from Tek-Cre line, each data point is drawn from the mean of three technical replicates, error bar is mean ± S.E.M., Mann-Whitney test, two-tailed (exact P-value of 0.0286 (*P ≤ 0.05), in f, g, i, and 0.1143 (n.s., P > 0.05, CI 95%) in h). The data reported f, g, i are from one independent trial, and h, from three independent trials.

Supplementary Figure 2 Analysis of 7-mer-i rAAV Libraries From Round-2 Selections.

a, The vector yields obtained per 10 ng of capsid DNA library across R1 and R2 vector productions. b, Distributions of the DNA and virus libraries produced by the synthetic pool and PCR pool methods by the standard score of NGS read counts. The variants in virus libraries are sorted by the decreasing order of standard score and their scores from respective DNA libraries are mapped onto them. c, Correlations between the standard scores of read counts for the DNA and virus libraries (n = 1 per library) produced by the synthetic pool and PCR pool methods is determined by linear least-squares regression, and the regression line (best fit) and R2 representing the coefficient of determination is shown. d, Distributions of capsid libraries from brain tissue of two mice (purple and green) used in each Cre line selection, as produced by the synthetic pool (left) and PCR pool (right) designs. The distribution of spike-in library introduced in the synthetic pool library design is shown in red (center). e, Correlations of enrichment scores of variants from the brain libraries (n = 2 per Cre line, mean is plotted) produced by synthetic pool and PCR pool methods is determined by the same method described in c.

Supplementary Figure 3 Analysis of Round-2 7-mer-i Tissue Libraries From Synthetic Pool And PCR Pool Methods.

a, Correlation analysis between the enrichment score (log10) of two alternate codon replicates of variants from the GFAP-Cre (left), SNAP-Cre (center), and Syn-Cre (right) brain libraries by linear least-squares regression (n = 2 per Cre line, mean is plotted). The dotted line separates the high-confidence signal from noise. High confidence signal (below) is assessed by a linear regression line (best fit) and R2 represents the coefficient of determination. b, The difference in enrichment score between the two codon replicates of a variant, across different brain libraries, with over 8000 variants recovered in replicates. c, Heatmaps represent the magnitude (log2 fold change) of AA bias in “output” library 1 normalized to “input” library 2 that reach statistical significance (boxed if P-value ≤ 0.0001, two-sided, two-proportion z-test, except in R1 DNA normalized to known NNK template where one-proportion z-test was performed, and P-values corrected for multiple comparisons using Bonferroni correction) is shown. R1 DNA library normalized to NNK template (top left, ~9 million sequences), R1 virus normalized to R1 DNA libraries (bottom left, ~10 million sequences), R2 GFAP library with enrichment score above 1.0 in brain normalized to R2 virus (top right, 20 sequences,) and R2 SNAP library with enrichment score above 1.2 normalized to R2 virus (bottom right, 17 sequences) are shown (n = 1 for DNA, virus, and n = 2 for brain libraries). d, Clustering analysis of positively enriched variants from Tek, GFAP, and combined neuron brain libraries (SNAP and Syn) by PCR pool design, and by synthetic pool design with spike-in library are shown with size of nodes representing their relative enrichment in brain, and the thickness of edges (connecting lines) representing the extent of shared AA identity between nodes. A distinct family is highlighted in yellow with the corresponding AA frequency logo below (AA size reflects prevalence and color coded based on AA properties).

Supplementary Figure 4 AAV-PHP.V1 Efficiently Targets the Brain Vasculature.

a, Expression of AAV9 (above) and AAV-PHP.V1 (below) packaging ssAAV:CAG-mNeonGreen across all organs is shown (n=3, 3x1011 vg dose per adult C57BL/6J mouse, 3 weeks of expression). The background auto fluorescence is represented in magenta. b, Expression in cortical astrocytes (S100+) after IV delivery of AAV-PHP.V1 (left) and AAV-PHP.eB (right) capsids carrying ssAAV:GfABC1D-2xNLS-mTurquoise2 (1x1012 vg dose per adult mouse, 4 weeks of expression). Percentage of cortical S100+ cells that overlapped with mTurquoise2 expression is quantified (n = 2, each data point is mean from 3 images per mouse). c, Expression of AAV9, AAV-PHP.eB (d), AAV-PHP.V1 (e) packaging ssAAV:Ple261-iCre in Ai14-tdTomato reporter adult mouse (n=2–3 per group, 3x1011 vg dose per adult mouse, 3 weeks of expression). f, Expression of AAV-PHP.V1 carrying self-complementary (sc) scAAV:CB6-EGFP (above) and scAAV:CAG-EGFP (below). Magenta represents the lectin DyLight 594 staining (n=2-3, 3x1011 vg dose per adult C57BL/6J mouse, 2 weeks of expression). Experiments in c-e are reported from one independent trial from a fresh batch of viruses, and tittered in the same assay for dosage consistency, e and f validated in two independent trials (n = 2 per group).

Supplementary Figure 5 AAV-PHP.V2 Variant Exhibits Biased Transduction Towards Brain Vascular Cells.

a, Transduction of mouse brain by the AAV-PHP.V2 variant and control AAV9, carrying the ssAAV:CAG-mNeonGreen (n = 3, 3x1011 vg IV dose per C57BL/6J adult mouse, 3 weeks of expression) is shown. The sagittal brain images (left) are imaged under the same settings (also matched to the settings of sagittal brain images in Fig. 3c). Higher-magnification images of AAV-PHP.V2 transduced brain sections stained with αGLUT or αS100 or αOlig2 (magenta) are shown. b, Transduction of brain vasculature by AAV-PHP.V2 carrying ssAAV:CAG-DIO-EYFP (green) in Tek-Cre adult mice (left, 1x1012 vg IV dose per mouse, 4 weeks of expression) is shown, and its efficiency (right) is determined by the overlap of αGLUT1 staining (red) with EYFP expression across different brain areas (n = 2, mean of 3 images per brain region per mouse) c, Transduction of astrocytes by AAV-PHP.V2 in GFAP-Cre adult mouse (1x1012 vg IV dose per mouse, 4 weeks of expression) is shown. Percentage of cortical S100+ cells that overlapped with EYFP expression is quantified (n = 2, mean of 3 images per mouse).

Supplementary Figure 6 Further Validation of Synthetic Pool and PCR Pool Variants Demonstrates Higher Confidence in Synthetic Pool NGS Data.

a, Transduction levels of liver hepatocytes quantified as the percentage of DAPI+ cells that are EGFP+ (n = 3, vectors packaged with ssAAV:CAG-2xNLS-EGFP, 1x1011 vg IV dose/adult C57BL/6J mouse, 3 weeks of expression, mean±S.E.M, 4 images per mouse per group. One-way ANOVA non-parametric Kruskal-Wallis test (approximate P-value of 0.0088), and follow-up multiple comparisons using uncorrected Dunn’s test (P-value of 0.0353 for PHP.eB vs PHP.B, 0.0005 for PHP.eB vs PHP.C1, 0.0025 for PHP.eB vs AAV9, 0.0179 for PHP.B4 vs PHP.C1, 0.0253 for PHP.B5 vs PHP.C1, 0.0414 for PHP.B6 vs PHP.C1) is performed. b, Transduction of brain tissue by AAV-PHP.B4, B7, AAV-PHP.X1 (ARQMDLS), and AAV-PHP.X2 (TNKVGNI) packaging ssAAV:CAG-mNeonGreen genome (n = 3, 1x1011 vg IV dose/adult C57BL/6J mouse, 3 weeks of expression), imaged under the same settings as that of AAV9 and AAV-PHP.V1 sagittal brain images in Fig. 3c. c, Transduction of the brain by AAV-PHP.B8 using the ssAAV:CAG-mRuby2 genome (n = 3, 3x1011 vg IV dose/adult C57BL/6J mouse, 3 weeks of expression). d, Transduction of AAV9 (left), AAV-PHP.X3 (QNVTKGV) (middle) and AAV-PHP.X4 (LNAIKNI) (right) vectors packaging ssAAV:CAG-2xNLS-EGFP (n = 2, 1x1011 vg IV dose/adult C57BL/6J mouse, 3 weeks of expression). a-d data is reported from one independent trial.

Supplementary Figure 7 Evolution of the AAV-PHP.B Capsid by Diversifying Amino Acid Positions 587-597.

a, Distributions of R1 and b, R2 brain libraries (at AA level, standard score (SS) of RCs sorted in decreasing order of scores) is shown. The SS for AAV-PHP.N and AAV-PHP.eB across libraries are mapped on the zoomed-in view of this plot (dotted line box). c, Heatmap of AA distributions across the diversified region of the enriched variants from R2 liver library (top 100 sequences) normalized to the R2 virus (input library). d, Clustering analysis of enriched variants from GFAP and Vglut2 brain libraries are shown with size of nodes representing their relative depletion in liver, and the thickness of edges (connecting lines) representing their relative identity between nodes. e, Expression of AAV-PHP.B (above) and AAV-PHP.N (below) packaged with ssAAV:CAG-mNeonGreen across all organs is shown (n = 3, 3x1011 vg IV dose per adult C57BL/6J mouse, 3 weeks of expression). The background auto fluorescence is in magenta. f, Transduction of mouse brain by the AAV-PHP.N variant, carrying the CAG promoter that drives the expression of mNeonGreen (n = 3, 1x1011 vg IV dose per C57BL/6J adult mouse, 3 weeks of expression) is shown. Fluorescence in situ hybridization chain reaction (FITC-HCR) was used to label excitatory neurons with Vglut1 and inhibitory neurons with Gad1. Few cells where EGFP expression co-localized with specific cell markers are highlighted by asterisks symbol.

Supplementary Figure 8 Investigation of AAV-PHP Variants Across Different Mouse Strains and In Vitro Human Brain Microvascular Endothelial Cells.

a, Transduction of AAV9, AAV-PHP.eB and AAV-PHP.V1 in human brain microvascular endothelial cell culture (HBMEC) is shown. The vectors were packaged with ssAAV:CAG-mNeongreen. The mean fluorescence intensity across the groups were quantified (n=3 tissue culture wells of 0.95 cm2 surface area per group, 3 images per well per group per dose was imaged after three days of expression, doses 1x108 vg and 1x1010 vg per 0.95 cm2 surface area). A two-way ANOVA with correction for multiple comparisons using Tukey’s test gave adjusted P-value of 0.0051 for AAV9 vs PHP.V1, 0.0096 for PHP.eB vs PHP.V1, 0.8222 for AAV9 vs PHP.eB for 1x108 vg, and 0.0052 for AAV9 vs PHP.V1, 0.0049 for PHP.eB vs PHP.V1, 0.9996 for AAV9 vs PHP.eB for 1x1010 vg (**P ≤ 0.01, is shown and P > 0.05 is not shown on the plot; mean ± S.E.M., 95% CI). b, The transduction of cortex brain region by AAV-PHP.B, AAV-PHP.C2 and AAV-PHP.C3 across two different mouse strains: C57BL/6J and BALB/cJ are shown. The vectors were packaged with ssAAV:CAG-mNeongreen (n = 2-3 per group, 1x1011 vg IV dose/ adult mouse, 3 weeks of expression), and imaged under the same settings. The data reported in a,b are from one independent trial where all viruses were freshly prepared and tittered in the same assay for dosage consistency, with additional validation for AAV-PHP.C2 and AAV-PHP.C3 in an independent trial for BALB/cJ.

Supplementary information

Supplementary Information

Supplementary Figs. 1–8, Tables 1–5 and Supplementary Notes 1–29.

Reporting Summary

Supplementary Video 1

Brain-Wide transduction of endothelial cells upon systemic delivery of the AAV-PHP.V1 capsid. ssAAV-PHP.V1:CAG-DIO-EYFP vector was systemically delivered at a dose of 1 × 1012 vg per adult Tek-Cre mouse (n = 2). After 4 weeks of expression, mice were transcardially perfused and fixed with 4% PFA. Fixed brain hemispheres (one per mouse) were subjected to staining with αGFP primary and Alexa Fluor 633 secondary along with tissue clearing as described in the iDISCO protocol (the other fixed hemispheres from the same experiment were sliced sagitally (100-µm thickness), stained with αGLUT1, imaged and quantified to validate expression; data shown in Fig. 3f,g). One of the cleared brain hemisphere was imaged using a commercial light-sheet microscope (Lavision BioTec) with a custom objective lens (×4). The resulting image files were reorganized by a custom MATLAB script to allow stitching with TeraStitcher. For three-dimensional visualization, Imaris (Bitplane) was used. The data are reported from one independent trial.

Supplementary Dataset 1

7-mer-i spike-in library recovery in brain tissue across Cre transgenic lines. Sheet 1 contains the list of peptides included in the 7-mer-i spike-in library, along with their predicted enrichment in brain tissue as per prior study and their validation using the new method, M-CREATE. Sheet 2 includes the enrichment scores of the spike-in library in brain tissue across different Cre transgenic lines.

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Ravindra Kumar, S., Miles, T.F., Chen, X. et al. Multiplexed Cre-dependent selection yields systemic AAVs for targeting distinct brain cell types. Nat Methods 17, 541–550 (2020). https://doi.org/10.1038/s41592-020-0799-7

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