1932

Abstract

Single-cell approaches are quickly changing our view on biological systems by increasing the spatiotemporal resolution of our analyses to the level of the individual cell. The field of plant biology has fully embraced single-cell transcriptomics and is rapidly expanding the portfolio of available technologies and applications. In this review, we give an overview of the main advances in plant single-cell transcriptomics over the past few years and provide the reader with an accessible guideline covering all steps, from sample preparation to data analysis. We end by offering a glimpse of how these technologies will shape and accelerate plant-specific research in the near future.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-arplant-081720-010120
2021-06-17
2024-06-17
Loading full text...

Full text loading...

/deliver/fulltext/arplant/72/1/annurev-arplant-081720-010120.html?itemId=/content/journals/10.1146/annurev-arplant-081720-010120&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Abdelaal T, Michielsen L, Cats D, Hoogduin D, Mei H et al. 2019. A comparison of automatic cell identification methods for single-cell RNA sequencing data. Genome Biol 20:1194
    [Google Scholar]
  2. 2. 
    Arabidopsis Genome Initiat 2000. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408:6814796–815
    [Google Scholar]
  3. 3. 
    Bailey-Serres J. 2013. Microgenomics: genome-scale, cell-specific monitoring of multiple gene regulation tiers. Annu. Rev. Plant Biol. 64:293–325
    [Google Scholar]
  4. 4. 
    Barron M, Li J. 2016. Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data. Sci. Rep. 6:133892
    [Google Scholar]
  5. 5. 
    Becht E, McInnes L, Healy J, Dutertre C-A, Kwok IWH et al. 2019. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37:138–44
    [Google Scholar]
  6. 6. 
    Bergen V, Lange M, Peidli S, Wolf FA, Theis FJ. 2020. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38:1408–14
    [Google Scholar]
  7. 7. 
    Bezrutczyk M, Zöllner NR, Kruse CPS, Hartwig T, Köhrer K et al. 2020. Phloem loading via the abaxial bundle sheath cells in maize leaves. bioRxiv 2020.09.06.284943. https://doi.org/10.1101/2020.09.06.284943
    [Crossref]
  8. 8. 
    Bhosale R, Boudolf V, Cuevas F, Lu R, Eekhout T et al. 2018. A spatiotemporal DNA endoploidy map of the Arabidopsis root reveals roles for the endocycle in root development and stress adaptation. Plant Cell 30:102330–51
    [Google Scholar]
  9. 9. 
    Birnbaum K, Shasha DE, Wang JY, Jung JW, Lambert GM et al. 2003. A gene expression map of the Arabidopsis root. Science 302:56521956–60
    [Google Scholar]
  10. 10. 
    Brady SM, Orlando DA, Lee J-Y, Wang JY, Koch J et al. 2007. A high-resolution root spatiotemporal map reveals dominant expression patterns. Science 318:5851801–6
    [Google Scholar]
  11. 11. 
    Brennecke P, Anders S, Kim JK, Kołodziejczyk AA, Zhang X et al. 2013. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10:111093–95
    [Google Scholar]
  12. 12. 
    Buettner F, Natarajan KN, Casale FP, Proserpio V, Scialdone A et al. 2015. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33:2155–60
    [Google Scholar]
  13. 13. 
    Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. 2018. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36:5411–20
    [Google Scholar]
  14. 14. 
    Cao J, Packer JS, Ramani V, Cusanovich DA, Huynh C et al. 2017. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357:6352661–67
    [Google Scholar]
  15. 15. 
    Chen H, Wang JP, Liu H, Li H, Lin Y-CJ et al. 2019. Hierarchical transcription factor and chromatin binding network for wood formation in Populus trichocarpa. Plant Cell 31:3602–26
    [Google Scholar]
  16. 16. 
    Chen H-IH, Jin Y, Huang Y, Chen Y 2016. Detection of high variability in gene expression from single-cell RNA-seq profiling. BMC Genom 17:S7508
    [Google Scholar]
  17. 17. 
    Chen S, Lake BB, Zhang K. 2019. High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell. Nat. Biotechnol. 37:121452–57
    [Google Scholar]
  18. 18. 
    Coate JE, Farmer AD, Schiefelbein JW, Doyle JJ. 2020. Expression partitioning of duplicate genes at single cell resolution in Arabidopsis roots. Front. Genet. 11:596150
    [Google Scholar]
  19. 19. 
    Denyer T, Ma X, Klesen S, Scacchi E, Nieselt K, Timmermans MCP. 2019. Spatiotemporal developmental trajectories in the Arabidopsis root revealed using high-throughput single-cell RNA sequencing. Dev. Cell 48:6840–852.e5
    [Google Scholar]
  20. 20. 
    Dinneny JR, Long TA, Wang JY, Jung JW, Mace D et al. 2008. Cell identity mediates the response of Arabidopsis roots to abiotic stress. Science 320:5878942–45
    [Google Scholar]
  21. 21. 
    Dorrity MW, Alexandre C, Hamm M, Vigil A-L, Fields S et al. 2020. The regulatory landscape of Arabidopsis thaliana roots at single-cell resolution. bioRxiv 2020.07.17.204792. https://doi.org/10.1101/2020.07.17.204792
    [Crossref]
  22. 22. 
    Duò A, Robinson MD, Soneson C. 2018. A systematic performance evaluation of clustering methods for single-cell RNA-seq data. F1000Research 7:1141
    [Google Scholar]
  23. 23. 
    Efroni I, Ip P-L, Nawy T, Mello A, Birnbaum KD. 2015. Quantification of cell identity from single-cell gene expression profiles. Genome Biol 16:19
    [Google Scholar]
  24. 24. 
    Efroni I, Mello A, Nawy T, Ip P-L, Rahni R et al. 2016. Root regeneration triggers an embryo-like sequence guided by hormonal interactions. Cell 165:71721–33
    [Google Scholar]
  25. 25. 
    Eng C-HL, Lawson M, Zhu Q, Dries R, Koulena N et al. 2019. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+. Nature 568:7751235–39
    [Google Scholar]
  26. 26. 
    Farmer A, Thibivilliers S, Ryu KH, Schiefelbein J, Libault M 2021. Single-nucleus RNA and ATAC sequencing reveals the impact of chromatin accessibility on gene expression in Arabidopsis roots at the single-cell level. Mol. Plant 14:337283
    [Google Scholar]
  27. 27. 
    Freytag S, Tian L, Lönnstedt I, Ng M, Bahlo M. 2018. Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data [version 2; peer review: 3 approved]. F1000Research 7:1297
    [Google Scholar]
  28. 28. 
    Gala HP, Lanctot A, Jean-Baptiste K, Guiziou S, Chu JC et al. 2020. A single cell view of the transcriptome during lateral root initiation in Arabidopsis thaliana. bioRxiv 2020.10.02.324327. https://doi.org/10.1101/2020.10.02.324327
    [Crossref]
  29. 29. 
    Gifford ML, Dean A, Gutierrez RA, Coruzzi GM, Birnbaum KD. 2008. Cell-specific nitrogen responses mediate developmental plasticity. PNAS 105:2803–8
    [Google Scholar]
  30. 30. 
    Gulati GS, Sikandar SS, Wesche DJ, Manjunath A, Bharadwaj A et al. 2020. Single-cell transcriptional diversity is a hallmark of developmental potential. Science 367:6476405–11
    [Google Scholar]
  31. 31. 
    Hagemann-Jensen M, Ziegenhain C, Chen P, Ramsköld D, Hendriks G-J et al. 2020. Single-cell RNA counting at allele and isoform resolution using Smart-seq3. Nat. Biotechnol. 38:6708–14
    [Google Scholar]
  32. 32. 
    Haghverdi L, Buettner F, Theis FJ. 2015. Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics 31:182989–98
    [Google Scholar]
  33. 33. 
    Han Y, Gu Y, Zhang AC, Lo Y-H. 2016. Review: imaging technologies for flow cytometry. Lab Chip 16:244639–47
    [Google Scholar]
  34. 34. 
    Hashimshony T, Senderovich N, Avital G, Klochendler A, de Leeuw Y et al. 2016. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol 17:77
    [Google Scholar]
  35. 35. 
    Hicks SC, Townes FW, Teng M, Irizarry RA. 2018. Missing data and technical variability in single-cell RNA-sequencing experiments. Biostatistics 19:4562–78
    [Google Scholar]
  36. 36. 
    Huo Z, Ding Y, Liu S, Oesterreich S, Tseng G. 2016. Meta-analytic framework for sparse K-means to identify disease subtypes in multiple transcriptomic studies. J. Am. Stat. Assoc. 111:51327–42
    [Google Scholar]
  37. 37. 
    Islam S, Kjällquist U, Moliner A, Zajac P, Fan J-B et al. 2011. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res 21:71160–67
    [Google Scholar]
  38. 38. 
    Islam S, Zeisel A, Joost S, La Manno G, Zajac P et al. 2014. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11:2163–66
    [Google Scholar]
  39. 39. 
    Jean-Baptiste K, McFaline-Figueroa JL, Alexandre CM, Dorrity MW, Saunders L et al. 2019. Dynamics of gene expression in single root cells of Arabidopsis thaliana. Plant Cell 31:5993–1011
    [Google Scholar]
  40. 40. 
    Jiao Y, Peluso P, Shi J, Liang T, Stitzer MC et al. 2017. Improved maize reference genome with single-molecule technologies. Nature 546:7659524–27
    [Google Scholar]
  41. 41. 
    Kim J-Y, Symeonidi E, Pang TY, Denyer T, Weidauer D et al. 2021. Distinct identities of leaf phloem cells revealed by single cell transcriptomics. Plant Cellkoaa060 https://doi.org/10.1093/plcell/koaa060
    [Crossref] [Google Scholar]
  42. 42. 
    Kubo M, Nishiyama T, Tamada Y, Sano R, Ishikawa M et al. 2019. Single-cell transcriptome analysis of Physcomitrella leaf cells during reprogramming using microcapillary manipulation. Nucleic Acids Res 47:94539–53
    [Google Scholar]
  43. 43. 
    Kurimoto K, Yabuta Y, Ohinata Y, Saitou M. 2007. Global single-cell cDNA amplification to provide a template for representative high-density oligonucleotide microarray analysis. Nat. Protoc. 2:3739–52
    [Google Scholar]
  44. 44. 
    Li S, Yamada M, Han X, Ohler U, Benfey PN. 2016. High-resolution expression map of the Arabidopsis root reveals alternative splicing and lincRNA regulation. Dev. Cell 39:4508–22
    [Google Scholar]
  45. 45. 
    Lieckfeldt E, Simon-Rosin U, Kose F, Zoeller D, Schliep M, Fisahn J. 2008. Gene expression profiling of single epidermal, basal and trichome cells of Arabidopsis thaliana. J. Plant Physiol. 165:141530–44
    [Google Scholar]
  46. 46. 
    Lister R, O'Malley RC, Tonti-Filippini J, Gregory BD, Berry CC et al. 2008. Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell 133:3523–36
    [Google Scholar]
  47. 47. 
    Liu Q, Liang Z, Feng D, Jiang S, Wang Y et al. 2021. Transcriptional landscape of rice roots at the single-cell resolution. Mol. Plant 14:38494
    [Google Scholar]
  48. 48. 
    Liu Z, Zhou Y, Guo J, Li J, Tian Z et al. 2020. Global dynamic molecular profiling of stomatal lineage cell development by single-cell RNA Sequencing. Mol. Plant 13:81178–93
    [Google Scholar]
  49. 49. 
    Lopez-Anido CB, Vatén A, Smoot NK, Sharma N, Guo V et al. 2020. Single-cell resolution of lineage trajectories in the Arabidopsis stomatal lineage and developing leaf. bioRxiv. 2020.09.08.288498. https://doi.org/10.1101/2020.09.08.288498
    [Crossref]
  50. 50. 
    Lu C, Koroleva OA, Farrar JF, Gallagher J, Pollock CJ, Tomos AD. 2002. Rubisco small subunit, chlorophyll a/b-binding protein and sucrose:fructan-6-fructosyl transferase gene expression and sugar status in single barley leaf cells in situ. Cell type specificity and induction by light. Plant Physiol 130:31335–48
    [Google Scholar]
  51. 51. 
    Lubeck E, Coskun AF, Zhiyentayev T, Ahmad M, Cai L 2014. Single-cell in situ RNA profiling by sequential hybridization. Nat. Methods 11:4360–61
    [Google Scholar]
  52. 52. 
    Ma S, Zhang B, LaFave LM, Earl AS, Chiang Z et al. 2020. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 183:41103–1116.e20
    [Google Scholar]
  53. 53. 
    Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K et al. 2015. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161:51202–14
    [Google Scholar]
  54. 54. 
    Marand AP, Chen Z, Gallavotti A, Schmitz RJ. 2020. A cis-regulatory atlas in maize at single-cell resolution. bioRxiv 2020.09.27.315499. https://doi.org/10.1101/2020.09.27.315499
    [Crossref]
  55. 55. 
    Martin LBB, Nicolas P, Matas AJ, Shinozaki Y, Catalá C, Rose JKC. 2016. Laser microdissection of tomato fruit cell and tissue types for transcriptome profiling. Nat. Protoc. 11:122376–88
    [Google Scholar]
  56. 56. 
    McGinnis CS, Patterson DM, Winkler J, Conrad DN, Hein MY et al. 2019. MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices. Nat. Methods 16:7619–26
    [Google Scholar]
  57. 57. 
    McInnes L, Healy J, Melville J. 2018. UMAP: Uniform Manifold Approximation and Projection for dimension reduction. arXiv:1802.03426 [stat.ML]
  58. 58. 
    Medaglia C, Giladi A, Stoler-Barak L, De Giovanni M, Salame TM et al. 2017. Spatial reconstruction of immune niches by combining photoactivatable reporters and scRNA-seq. Science 358:63701622–26
    [Google Scholar]
  59. 59. 
    Moffitt JR, Hao J, Wang G, Chen KH, Babcock HP, Zhuang X 2016. High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization. PNAS 113:3911046–51
    [Google Scholar]
  60. 60. 
    Nelms B, Walbot V. 2019. Defining the developmental program leading to meiosis in maize. Science 364:643552–56
    [Google Scholar]
  61. 61. 
    Olsson A, Venkatasubramanian M, Chaudhri VK, Aronow BJ, Salomonis N et al. 2016. Single-cell analysis of mixed-lineage states leading to a binary cell fate choice. Nature 537:7622698–702
    [Google Scholar]
  62. 62. 
    Omary M, Gil-Yarom N, Yahav C, Steiner E, Efroni I. 2020. A conserved superlocus regulates above- and belowground root initiation. bioRxiv 2020.11.11.377937. https://doi.org/10.1101/2020.11.11.377937
    [Crossref]
  63. 63. 
    Preissl S, Fang R, Huang H, Zhao Y, Raviram R et al. 2018. Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation. Nat. Neurosci. 21:3432–39
    [Google Scholar]
  64. 64. 
    Rensing SA, Goffinet B, Meyberg R, Wu SZ, Bezanilla M. 2020. The moss Physcomitrium (Physcomitrella) patens: a model organism for non-seed plants. Plant Cell 32:51361–76
    [Google Scholar]
  65. 65. 
    Rhee SY, Birnbaum KD, Ehrhardt DW. 2019. Towards building a Plant Cell Atlas. Trends Plant Sci 24:4303–10
    [Google Scholar]
  66. 66. 
    Rosenberg AB, Roco CM, Muscat RA, Kuchina A, Sample P et al. 2018. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360:6385176–82
    [Google Scholar]
  67. 67. 
    Roszak P, Heo J-O, Blob B, Toyokura K, de Luis, Balaguer MA et al. 2021. Analysis of phloem trajectory links tissue maturation to cell specialization. bioRxiv 2021.01.18.427084. https://doi.org/10.1101/2021.01.18.427084
    [Crossref]
  68. 68. 
    Ryu KH, Huang L, Kang HM, Schiefelbein J. 2019. Single-cell RNA sequencing resolves molecular relationships among individual plant cells. Plant Physiol 179:41444–56
    [Google Scholar]
  69. 69. 
    Saelens W, Cannoodt R, Todorov H, Saeys Y. 2019. A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37:5547–54
    [Google Scholar]
  70. 70. 
    Saika H, Nonaka S, Osakabe K, Toki S. 2012. Sequential monitoring of transgene expression following agrobacterium-mediated transformation of rice. Plant Cell Physiol 53:111974–83
    [Google Scholar]
  71. 71. 
    Satterlee JW, Strable J, Scanlon MJ 2020. Plant stem-cell organization and differentiation at single-cell resolution. PNAS 117:5233689–99
    [Google Scholar]
  72. 72. 
    Shahan R, Hsu C-W, Nolan TM, Cole BJ, Taylor IW et al. 2020. A single cell Arabidopsis root atlas reveals developmental trajectories in wild type and cell identity mutants. bioRxiv 2020.06.29.178863. https://doi.org/10.1101/2020.06.29.178863
    [Crossref]
  73. 73. 
    Shulse CN, Cole BJ, Ciobanu D, Lin J, Yoshinaga Y et al. 2019. High-throughput single-cell transcriptome profiling of plant cell types. Cell Rep 27:72241–47.e4
    [Google Scholar]
  74. 74. 
    Slane D, Kong J, Berendzen KW, Kilian J, Henschen A et al. 2014. Cell type-specific transcriptome analysis in the early Arabidopsis thaliana embryo. Development 141:244831–40
    [Google Scholar]
  75. 75. 
    Song Q, Ando A, Jiang N, Ikeda Y, Chen ZJ. 2020. Single-cell RNA-seq analysis reveals ploidy-dependent and cell-specific transcriptome changes in Arabidopsis female gametophytes. Genome Biol 21:178
    [Google Scholar]
  76. 76. 
    Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK et al. 2017. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14:9865–68
    [Google Scholar]
  77. 77. 
    Stoeckius M, Zheng S, Houck-Loomis B, Hao S, Yeung BZ et al. 2018. Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biol 19:224
    [Google Scholar]
  78. 78. 
    Street K, Risso D, Fletcher RB, Das D, Ngai J et al. 2018. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genom 19:477
    [Google Scholar]
  79. 79. 
    Streets AM, Zhang X, Cao C, Pang Y, Wu X et al. 2014. Microfluidic single-cell whole-transcriptome sequencing. PNAS 111:197048–53
    [Google Scholar]
  80. 80. 
    Tang F, Barbacioru C, Wang Y, Nordman E, Lee C et al. 2009. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6:5377–82
    [Google Scholar]
  81. 81. 
    Tian C, Du Q, Xu M, Du F, Jiao Y. 2020. Single-nucleus RNA-seq resolves spatiotemporal developmental trajectories in the tomato shoot apex. bioRxiv 2020.09.20.305029. https://doi.org/10.1101/2020.09.20.305029
    [Crossref]
  82. 82. 
    Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S et al. 2014. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32:4381–86
    [Google Scholar]
  83. 83. 
    Turco GM, Rodriguez-Medina J, Siebert S, Han D, Valderrama-Gómez et al. 2019. Molecular mechanisms driving switch behavior in xylem cell differentiation. Cell Rep 28:2342–51.e4
    [Google Scholar]
  84. 84. 
    Vallejo AF, Davies J, Grover A, Tsai C-H, Jepras R et al. 2019. Resolving cellular systems by ultra-sensitive and economical single-cell transcriptome filtering. bioRxiv 800631. https://doi.org/10.1101/800631
    [Crossref]
  85. 85. 
    Vallejos CA, Risso D, Scialdone A, Dudoit S, Marioni JC. 2017. Normalizing single-cell RNA sequencing data: challenges and opportunities. Nat. Methods 14:6565–71
    [Google Scholar]
  86. 86. 
    van der Maaten L, Hinton G. 2008. Visualizing data using t-SNE. J. Mach. Learn. Res. 9:2579–605
    [Google Scholar]
  87. 87. 
    Wang Y, Huan Q, Chu X, Li K, Qian W. 2020. Single-cell transcriptome analyses recapitulate the cellular and developmental responses to abiotic stresses in rice. bioRxiv 2020.01.30.926329. https://doi.org/10.1101/2020.01.30.926329
    [Crossref]
  88. 88. 
    Wendrich JR, Yang B, Vandamme N, Verstaen K, Smet W et al. 2020. Vascular transcription factors guide plant epidermal responses to limiting phosphate conditions. Science 370:6518eaay4970
    [Google Scholar]
  89. 89. 
    White JA, Todd J, Newman T, Focks N, Girke T et al. 2000. A new set of Arabidopsis expressed sequence tags from developing seeds. The metabolic pathway from carbohydrates to seed oil. Plant Physiol 124:41582–94
    [Google Scholar]
  90. 90. 
    Xing QR, El Farran CA, Zeng YY, Yi Y, Warrier T et al. 2020. Parallel bimodal single-cell sequencing of transcriptome and chromatin accessibility. Genome Res 30:71027–39
    [Google Scholar]
  91. 91. 
    Xu X, Crow M, Rice BR, Li F, Harris B et al. 2021. Single-cell RNA sequencing of developing maize ears facilitates functional analysis and trait candidate gene discovery. Dev. Cell 56:55768.e6
    [Google Scholar]
  92. 92. 
    Yan H, Song Q, Lee J, Schiefelbein J, Li S 2020. Identification of cell-type marker genes from plant single-cell RNA-seq data using machine learning. bioRxiv 2020.11.22.393165. https://doi.org/10.1101/2020.11.22.393165
    [Crossref]
  93. 93. 
    Zhang C, Barthelson RA, Lambert GM, Galbraith DW. 2008. Global characterization of cell-specific gene expression through fluorescence-activated sorting of nuclei. Plant Physiol 147:130–40
    [Google Scholar]
  94. 94. 
    Zhang T-Q, Xu Z-G, Shang G-D, Wang J-W. 2019. A single-cell RNA sequencing profiles the developmental landscape of Arabidopsis root. Mol. Plant. 12:5648–60
    [Google Scholar]
  95. 95. 
    Zheng GXY, Terry JM, Belgrader P, Ryvkin P, Bent ZW et al. 2017. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8:14049
    [Google Scholar]
  96. 96. 
    Ziegenhain C, Vieth B, Parekh S, Reinius B, Guillaumet-Adkins A et al. 2017. Comparative analysis of single-cell RNA sequencing methods. Mol. Cell 65:4631–643.e4
    [Google Scholar]
/content/journals/10.1146/annurev-arplant-081720-010120
Loading
/content/journals/10.1146/annurev-arplant-081720-010120
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error