Authors : Sambuddha Talukdar; Satabdi Ghosh

Volume/Issue : Volume 10 - 2025, Issue 12 - December

Google Scholar : https://tinyurl.com/5x4j472r

Scribd : https://tinyurl.com/2vcav77z

DOI : https://doi.org/10.38124/ijisrt/25dec014

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Abstract : Cells form the foundational units of life, yet individual cells within the same organism often differ substantially in their molecular makeup and functional behavior. Understanding these cell-to-cell variations is essential for deciphering complex biological processes, particularly in plants where single-cell studies are still emerging. Single-cell omics technologies enable the characterization of cellular diversity by examining genomes, epigenomes, transcriptomes, proteomes, and metabolomes at the resolution of individual cells. These approaches reveal functional heterogeneity and lineage relationships that cannot be captured through bulk tissue analysis. Recent progress in cell isolation, microfluidics, amplification chemistry, and high-throughput sequencing has transformed the study of unicellular profiles across diverse organisms. This review summarizes the major single-cell omics platforms, discusses plant-specific challenges, and outlines how these technologies contribute to understanding development, stress biology, and cellular specialization. Together, these tools offer an unprecedented window into plant cellular complexity and hold promise for advancing crop improvement strategies.

Keywords : Microfluidic, Enzymolysis, Micromanipulation, Fluorescent Activated Cell Sorting (FACS), Pollen Typing.

References :

1. Barros LF, Martín AS, Hitschfeld TS, Lerchundi R, Moncada IF, Ruminot I, Gutiérrez R, Valdebenito R, Ceballo S, Alegría K, Lehnert FB, Espinoza D. (2013). Small is fast: astrocytic glucose and lactate metabolism at cellular resolution. Frontiers in Cellular Neuroscience, 7: 27. https://doi.org/10.3389/fncel.2013.00027
2. Betancur L, Singh B, Rapp RA, Wendel JF, Marks MD, Roberts AW, Haigler CH. (2010). Phylogenetically distinct cellulose synthase genes support secondary wall thickening in Arabidopsis shoot trichomes and cotton fiber. Journal of Integrative Plant Biology, 52(2): 205–220.
3. Birnbaum KD. (2016). How many ways are there to make a root? Current Opinion in Plant Biology, 34: 61–67.
4. Breakspear A, Liu C, Roy S, Stacey N, Rogers C, Trick M, Morieri G, Mysore KS, Wen J, Oldroyd GED, Downie JA, Murray JD. (2014). The root hair “infectome” of Medicago truncatula uncovers changes in cell cycle genes as a signature of infection. Plant Cell, 26: 4680–4703.
5. Chen C, Xing D, Tan L, Li H, Zhou G, Huang L, et al. (2016). Single-cell whole-genome analyses by Linear Amplification via Transposon Insertion (LIANTI). Science, 356: 407–411.
6. Chen X, Feng B, Li Y, et al. (2008). Detection of QTLs for oil content in soybean using single-pollen genotyping. Theoretical and Applied Genetics, 117: 393–401.
7. Cheng Z, Sun L, Qi T, Zhang B, Peng W, Liu Y, Xie D. (2009). The bHLH transcription factor MYC3 interacts with MYC2 and regulates jasmonate responses in Arabidopsis. Plant Cell, 21: 1063–1080.
8. Coolen S, et al. (2016). Transcriptome profiling of Arabidopsis roots in response to drought stress. Plant Physiology, 171: 2984–3002.
9. Dinesh-Kumar SP, Baker BJ. (2000). Alternatively spliced N resistance gene transcripts. Plant Cell, 12: 443–446.
10. Dreissig S, et al. (2017). Barley pollen sequencing reveals recombination landscape. PNAS, 114: 9823–9828.
11. Duncombe TA, Erdem E, et al. (2016). Microfluidic western blotting for single-cells. Nature Communications, 7: 11407.
12. Efroni I, Birnbaum KD. (2016). The potential of single-cell genomics in plants. Development, 143: 512–520.
13. Efroni I, et al. (2015). Cell identity in Arabidopsis roots revealed by single-cell analysis. Science, 348: 953–956.
14. Efroni I, et al. (2017). Cell lineage and developmental transitions revealed by scRNA-seq. Nature, 543: 95–100.
15. Emmert-Buck MR, et al. (1996). Laser capture microdissection. Science, 274: 998–1001.
16. Filichkin SA, et al. (2010). Genome-wide alternative splicing in Arabidopsis. Plant Physiology, 154: 268–279.
17. Gawad C, Koh W, Quake SR. (2016). Single-cell genome sequencing. Nature Reviews Genetics, 17(3): 175–188.
18. Ghassemian M, et al. (2000). ABA–ethylene crosstalk. Plant Cell, 12: 1117–1126.
19. Gierahn TM, et al. (2017). SeqWell platform for single-cell RNA-seq. Nature Methods, 14: 395–398.
20. Gregory TR. (2005). Genome size diversity in flowering plants. Annals of Botany, 95: 165–175.
21. Haigler CH, et al. (2009). Expression analysis of cotton fibers. Plant Physiology, 150: 1172–1183.
22. Ham RG. (1965). Clonal growth of mammalian cells. Experimental Cell Research, 38: 645–653.
23. Hashimshony T, et al. (2012, 2016). CEL-seq and CEL-seq2. Cell Reports.
24. Hochgerner H, et al. (2017). STRT-seq for full-length transcripts. Nature Communications.
25. Hossain MS, et al. (2015). PCR-based DNA amplification for scDNA. Analytical Chemistry, 87: 10016–10023.
26. Hu Y, et al. (2016). Review of single-cell isolation techniques. Biotechnology Journal, 11: 396–404.
27. Hughes AJ, et al. (2014). Single-cell western blotting. Nature Methods, 11: 749–755.
28. Hulskamp M. (2004). Trichome development. Current Opinion in Plant Biology, 7: 547–552.
29. Ibanez AJ, et al. (2013). Subcellular metabolomics. Analytical Chemistry, 85(20): 10099–10106.
30. Islam S, et al. (2011). STRT-seq. Genome Research.
31. Jean-Baptiste K, et al. (2019). Arabidopsis root atlas. Science, 364: 313–317.
32. Jia H, et al. (2016). Protoplast isolation from wheat leaves. Plant Methods, 12: 30.
33. Kang CC, et al. (2014, 2016). High-resolution single-cell protein detection. Analytical Chemistry.
34. Kaya-Okur HS, et al. (2019). CUT&Tag. Nature Communications, 10: 1930.
35. Kidner CA, et al. (2000). Positional control of cell fate. Development, 127: 3817–3831.
36. Klein AM, et al. (2015). Droplet-based scRNA-seq (InDrop). Cell, 161: 1187–1201.
37. Kragl M, et al. (2009). Lineage tracing. Nature, 460: 60–65.
38. Kwasniewski M, et al. (2010). Single-cell transcriptomics in root hairs. Plant Physiology, 152: 2119–2129.
39. Lan P, et al. (2013). Root hair transcriptomics. Plant Physiology, 161: 1655–1670.
40. Landry ZC, et al. (2013). Optical tweezers in cell isolation. Lab on a Chip, 13: 1967–1974.
41. Laval V, et al. (2002). Isoform allocation. Plant Molecular Biology, 50: 123–137.
42. Libault M, et al. (2010). Soybean root hair transcriptome. Plant Physiology, 152: 541–552.
43. Lin X, et al. (2017). Challenges in scRNA-seq metadata. Genome Biology, 18: 141.
44. Lindstrom NO, et al. (2010). FACS in plant cells. Plant Methods, 6: 17.
45. Macosko EZ, et al. (2015). Drop-seq. Cell, 161: 1202–1214.
46. McCarthy A, et al. (1995). IRS-PCR. Nucleic Acids Research, 23: 5064–5065.
47. Mincarelli L, et al. (2018). Single-cell genomics and proteomics. BMC Biology, 16: 121.
48. Misra A, et al. (2014). Microdissection of plant tissues. Plant Methods, 10: 6.
49. Nabors MW. (2004). Somatic embryogenesis. Plant Cell Tissue Organ Culture, 78: 1–12.
50. Navin N, et al. (2011, 2014). Single-cell DNA sequencing. Nature.
51. Nemes P, et al. (2013). CE–MS for neuronal cells. Analytical Chemistry, 85: 782–790.
52. Nguyen D, et al. (2016). Hormonal crosstalk under stress. Frontiers in Plant Science, 7: 578.
53. O’Malley RC, et al. (2016). Plant transcription factor binding map. Cell, 165: 1280–1292.
54. Picelli S, et al. (2013, 2014). Smart-seq and Smart-seq2. Nature Methods.
55. Rasmussen S, et al. (2013). Disease resistance transcriptomics. Plant Cell, 25: 4781–4796.
56. Regev A, et al. (2017). Human Cell Atlas. eLife, 6: e27041.
57. Saliba AE, et al. (2014). Optical tweezers-assisted microfluidics. PNAS, 111: E6079–E6088.
58. Scangrude FA, et al. (1988). FACS for stem cells. Blood, 71: 1208–1214.
59. Shapiro HM, et al. (2013). FACS overview. Practical Flow Cytometry. Wiley-Liss.
60. Shulse CN, et al. (2019). Arabidopsis root scRNA-seq. Development, 146: dev179749.
61. Sinkala E, Herr AE. (2015). Western blotting in microfluidics. Lab on a Chip, 15: 2466–2475.
62. Song C, et al. (2014). Hormonal regulation under stress. Plant Physiology, 165: 1316–1332.
63. Spangrude GJ, et al. (1988). Separation of stem cells by FACS. Science, 241: 58–62.
64. Sugimoto R, et al. (2011). LA-PCR. Nucleic Acids Research, 39: e15.
65. Tao J, et al. (2015). Ethylene regulation in salt stress. Plant Physiology, 169: 180–199.
66. Wang X, et al. (2015, 2017). Advances in single-cell sequencing. Genome Biology.
67. Wei W, et al. (2013). Single-cell proteomics by mass cytometry. Science, 342: 124–128.
68. Whitesides GM. (2006). Microfluidics in biology. Nature, 442: 368–373.
69. Wolf FA, et al. (2018). Scanpy. Genome Biology, 19: 15.
70. Woodworth MB, et al. (2017). Lineage tracing in neurobiology. Neuron, 93: 747–765.
71. Xia Z, et al. (2012). Flowering gene polymorphisms in soybean. PNAS, 109: 11036–11041.
72. Xu X, et al. (2021). Rice scRNA-seq atlas. Plant Cell, 33: 824–847.
73. Yang S, et al. (2016). Microchip-based proteomics. Nature Communications, 7: 11245.
74. Yuan J, et al. (2018). scDNA sequencing challenges. Nature Reviews Genetics, 19: 551–567.
75. Yu Q, et al. (2017). Cell fate transitions. Plant Physiology, 173: 2257–2267.
76. Zeller G, et al. (2009). Arabidopsis stress transcriptome. PNAS, 106: 16651–16656.
77. Zenobi R. (2013). Single-cell metabolomics. Science, 342: 1243259.