Biotechnology Bulletin ›› 2021, Vol. 37 ›› Issue (1): 60-66.doi: 10.13560/j.cnki.biotech.bull.1985.2020-1011
Previous Articles Next Articles
Received:
2020-08-12
Online:
2021-01-26
Published:
2021-01-15
Contact:
SUN Chao
E-mail:norland1126@163.com;csun@implad.ac.cn
LI Yi, SUN Chao. Research Progress in Single-Cell RNA-Seq of Plant[J]. Biotechnology Bulletin, 2021, 37(1): 60-66.
建库方法 | 分析技术 | 捕获细 胞数目 | 扩增 技术 | 转录本 覆盖率 | 参考文献 |
---|---|---|---|---|---|
多孔板法 | Smartseq2 | 94-384/plate | PCR | 全长 | [10] |
CEL-seq2 | 94-384/plate | IVT | 3'端 | [11] | |
液滴法 | Chromium | 80 000+ | PCR | 3'端 | [13] |
Drop-seq | 50 000+ | PCR | 3'端 | [14] | |
inDrop | 40 000+ | PCR | 3'端 | [15] |
建库方法 | 分析技术 | 捕获细 胞数目 | 扩增 技术 | 转录本 覆盖率 | 参考文献 |
---|---|---|---|---|---|
多孔板法 | Smartseq2 | 94-384/plate | PCR | 全长 | [10] |
CEL-seq2 | 94-384/plate | IVT | 3'端 | [11] | |
液滴法 | Chromium | 80 000+ | PCR | 3'端 | [13] |
Drop-seq | 50 000+ | PCR | 3'端 | [14] | |
inDrop | 40 000+ | PCR | 3'端 | [15] |
建库方法 | 分析技术 | 物种 | 样本类型 | 细胞数 | 总基因数(转录本数) | 基因数(转录本数)/细胞 | 参考文献 |
---|---|---|---|---|---|---|---|
多孔板法 | Smart-seq2 | 拟南芥 | 根 | 238 | —— | —— | [8] |
Cell-seq2 | 玉米 | 生殖细胞 | 144 | 101 245* | —— | [9] | |
Cell-seq2 | 拟南芥 | 静止中心细胞 | 24 | —— | 14 000 | [53] | |
中柱细胞 | 7 | —— | 4 312 | [53] | |||
液滴法 | Chromium | 拟南芥 | 根(3个生物学重复) | 7 522 | >22 000 | 5 000 | [16] |
Chromium | 拟南芥 | 根(2个生物学重复) | 4 727 | 16 975 | 4 276 | [17] | |
Chromium | 拟南芥 | 根 | 3 121 | 22 419 | 2 445 | [18] | |
根(热胁迫) | 1 009 | 21 237 | 1 009 | [18] | |||
根 | 1 079 | 22 971 | 4 079 | [18] | |||
Chromium | 拟南芥 | 根 | 7 695 | 23 161 | —— | [19] | |
Chromium | 拟南芥 | 子叶 | 12 844 | —— | —— | [20] | |
Drop-seq | 拟南芥 | 根 | 6 102 | —— | >1 000* | [54] | |
根(1%蔗糖处理) | 6 096 | —— | —— | [54] | |||
Drop-seq | 拟南芥 | 根 | 374 | —— | —— | [55] |
建库方法 | 分析技术 | 物种 | 样本类型 | 细胞数 | 总基因数(转录本数) | 基因数(转录本数)/细胞 | 参考文献 |
---|---|---|---|---|---|---|---|
多孔板法 | Smart-seq2 | 拟南芥 | 根 | 238 | —— | —— | [8] |
Cell-seq2 | 玉米 | 生殖细胞 | 144 | 101 245* | —— | [9] | |
Cell-seq2 | 拟南芥 | 静止中心细胞 | 24 | —— | 14 000 | [53] | |
中柱细胞 | 7 | —— | 4 312 | [53] | |||
液滴法 | Chromium | 拟南芥 | 根(3个生物学重复) | 7 522 | >22 000 | 5 000 | [16] |
Chromium | 拟南芥 | 根(2个生物学重复) | 4 727 | 16 975 | 4 276 | [17] | |
Chromium | 拟南芥 | 根 | 3 121 | 22 419 | 2 445 | [18] | |
根(热胁迫) | 1 009 | 21 237 | 1 009 | [18] | |||
根 | 1 079 | 22 971 | 4 079 | [18] | |||
Chromium | 拟南芥 | 根 | 7 695 | 23 161 | —— | [19] | |
Chromium | 拟南芥 | 子叶 | 12 844 | —— | —— | [20] | |
Drop-seq | 拟南芥 | 根 | 6 102 | —— | >1 000* | [54] | |
根(1%蔗糖处理) | 6 096 | —— | —— | [54] | |||
Drop-seq | 拟南芥 | 根 | 374 | —— | —— | [55] |
[1] | Libault M, Pingault L, et al. Plant systems biology at the single-cell level[J]. Trends Plant Sci, 2017,22(11):949-960. |
[2] |
文路, 汤富酬. 单细胞转录组高通量测序分析新进展[J]. 遗传, 2014,36(11):1069-1076.
pmid: 25567865 |
Wen L, Tang FC. Recent progress in single-cell RNA-Seq analysis[J]. Hereditas, 2014,36(11):1069-1076.
URL pmid: 25567865 |
|
[3] | Efroni I, Birnbaum KD. The potential of single-cell profiling in plants[J]. Genome Biology, 2016,17(1):65. |
[4] | Kolodziejczyk AA, Kim JK, Svensson V, et al. The technology and biology of single-cell RNA sequencing[J]. Molecular Cell, 2015,58(4):610-620. |
[5] | Baran-Gale J, Chandra T, Kirschner K. Experimental design for single-cell RNA sequencing[J]. Briefings in Functional Genomics, 2017,17(4):233-239. |
[6] |
Jaitin DA, Kenigsberg E, Keren-Shaul H, et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types[J]. Science, 2014,343(6172):776-779.
doi: 10.1126/science.1247651 URL pmid: 24531970 |
[7] |
Andrews TS, Hemberg M. Identifying cell populations with scRNASeq[J]. Mol Aspects Med, 2018,59:114-122.
URL pmid: 28712804 |
[8] | Efroni I, Mello A, Nawy T, et al. Root regeneration triggers an embryo-like sequence guided by hormonal interactions[J]. Cell, 2016,165(7):1721-1733. |
[9] |
Nelms B, Walbot V. Defining the developmental program leading to meiosis in maize[J]. Science, 2019,364(6435):52-56.
doi: 10.1126/science.aav6428 URL pmid: 30948545 |
[10] |
Picelli S, Faridani OR, et al. Full-length RNA-seq from single cells using Smart-seq2[J]. Nature Protocols, 2014,9(1):171-181.
doi: 10.1038/nprot.2014.006 URL |
[11] |
Hashimshony T, Senderovich N, Avital G, et al. CEL-Seq2:sensitive highly-multiplexed single-cell RNA-Seq[J]. Genome Biology, 2016,17(1):77.
doi: 10.1186/s13059-016-0938-8 URL |
[12] |
Zhang X, Li T, Liu F, et al. Comparative analysis of droplet-based ultra-high-throughput single-cell RNA-seq systems[J]. Molecular Cell, 2019,73(1):130-142.
URL pmid: 30472192 |
[13] | Zheng GXY, Terry JM, Belgrader P, et al. Massively parallel digital transcriptional profiling of single cells[J]. Nature Communications, 2017,8(1):14049. |
[14] | Macosko EZ, Basu A, Satija R, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets[J]. Cell, 2015,161(5):1202-1214. |
[15] |
Klein Allon M, Mazutis L, Akartuna I, et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells[J]. Cell, 2015,161(5):1187-1201.
doi: 10.1016/j.cell.2015.04.044 URL pmid: 26000487 |
[16] | Ryu KH, Huang L, Kang HM, et al. Single-cell RNA sequencing resolves molecular relationships among individual plant cells[J]. Plant Physiology, 2019,179(4):1444-1456. |
[17] | Denyer T, Ma X, Klesen S, et al. Spatiotemporal developmental trajectories in the Arabidopsis root revealed using high-throughput single-cell RNA sequencing[J]. Developmental Cell, 2019,48(6):840-852. |
[18] | Jean-Baptiste K, McFaline-Figueroa JL, Alexandre CM, et al. Dynamics of gene expression in single root cells of Arabidopsis thaliana[J]. The Plant Cell, 2019,31(5):993-1011. |
[19] | Zhang TQ, Xu ZG, Shang GD, et al. A single-cell RNA sequencing profiles the developmental landscape of Arabidopsis root[J]. Mol Plant, 2019,12(5):648-660. |
[20] | Liu Z, Zhou Y, Guo J, et al. Global dynamic molecular profiling of stomatal lineage cell development by single-cell RNA sequencing[J]. Mol Plant, 2020,13(8):1178-1193. |
[21] | Prakadan SM, Shalek AK, Weitz DA. Scaling by shrinking:empowering single-cell ‘omics’ with microfluidic devices[J]. Nature Reviews:Genetics, 2017,18(6):345-361. |
[22] | Rich-Griffin C, Stechemesser A, Finch J, et al. Single-cell transcriptomics:A high-resolution avenue for plant functional genomics[J]. Trends Plant Sci, 2020,25(2):186-197. |
[23] | Dal Molin A, Di Camillo B. How to design a single-cell RNA-sequencing experiment:pitfalls, challenges and perspectives[J]. Brief Bioinform, 2018,20(4):1384-1394. |
[24] | Wu Y, Zhang K. Tools for the analysis of high-dimensional single-cell RNA sequencing data[J]. Nat Rev Nephrol, 2020,16(7):408-421. |
[25] | Luecken MD, Theis FJ. Current best practices in single-cell RNA-seq analysis:a tutorial[J]. Molecular Systems Biology, 2019,15(6):e8746. |
[26] | Chen G, Ning B, Shi T. Single-cell RNA-seq technologies and related computational data analysis[J]. Front Genet, 2019,10:317. |
[27] | AlJanahi AA, Danielsen M, Dunbar CE. An Introduction to the analysis of single-cell RNA-sequencing data[J]. Molecular Therapy Methods & Clinical Development, 2018,10:189-196. |
[28] | Weinreb C, Wolock S, Klein AM. SPRING:a kinetic interface for visualizing high dimensional single-cell expression data[J]. Bioinformatics, 2018,34(7):1246-1248. |
[29] | Ziegenhain C, Vieth B, et al. Quantitative single-cell transcriptomics[J]. Brief Funct Genomics, 2018,17(4):220-232. |
[30] | Ding B, Zheng L, Zhu Y, et al. Normalization and noise reduction for single cell RNA-seq experiments[J]. Bioinformatics, 2015,31(13):2225-2227. |
[31] | van den Brink SC, Sage F, Vértesy Á, et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations[J]. Nature Methods, 2017,14(10):935-936. |
[32] | Butler A, Hoffman P, Smibert P, et al. Integrating single-cell transcriptomic data across different conditions, technologies, and species[J]. Nature Biotechnology, 2018,36(5):411-420. |
[33] | Tran HTN, Ang KS, Chevrier M, et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data[J]. Genome Biology, 2020,21(1):12. |
[34] | Brennecke P, Anders S, Kim JK, et al. Accounting for technical noise in single-cell RNA-seq experiments[J]. Nature Methods, 2013,10(11):1093-1095. |
[35] | van der Mauten L, Hinton G. Visualizing data using t-SNE[J]. J Mach Learn Res, 2008,9(2605):2579-2605. |
[36] | Diaz-Papkovich A, Anderson-Trocmé L, et al. UMAP reveals cryptic population structure and phenotype heterogeneity in large genomic cohorts[J]. PLoS Genetics, 2019,15(11):e1008432. |
[37] | Duò A, Robinson MD, Soneson C. A systematic performance evaluation of clustering methods for single-cell RNA-seq data[J]. F1000Research, 2018,7:1141. |
[38] | Abdelaal T, Michielsen L, Cats D, et al. A comparison of automatic cell identification methods for single-cell RNA sequencing data[J]. Genome Biology, 2019,20(1):194. |
[39] | Zhang X, Lan Y, Xu J, et al. CellMarker:a manually curated resource of cell markers in human and mouse[J]. Nucleic Acids Research, 2019,47(D1):D721-D728. |
[40] | Franzén O, Gan LM, Björkegren JLM. PanglaoDB:a web server for exploration of mouse and human single-cell RNA sequencing data[J]. Database(Oxford), 2019(2019)::baz046. |
[41] | Kiselev VY, Yiu A, Hemberg M. scmap:projection of single-cell RNA-seq data across data sets[J]. Nature Methods, 2018,15(5):359-362. |
[42] | Aran D, Looney AP, Liu L, et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage[J]. Nature Immunology, 2019,20(2):163-172. |
[43] | Zhang AW, O’Flanagan C, Chavez EA, et al. Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling[J]. Nature Methods, 2019,16(10):1007-1015. |
[44] | Haghverdi L, Büttner M, Wolf FA, et al. Diffusion pseudotime robustly reconstructs lineage branching[J]. Nature Methods, 2016,13(10):845-848. |
[45] | Saelens W, Cannoodt R, Todorov H, et al. A comparison of single-cell trajectory inference methods[J]. Nature Biotechnology, 2019,37(5):547-554. |
[46] | Trapnell C, Cacchiarelli D, Grimsby J, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells[J]. Nat Biotech, 2014,32(4):381-386. |
[47] | Qiu X, Hill A, Packer J, et al. Single-cell mRNA quantification and differential analysis with Census[J]. Nature Methods, 2017,14(3):309-315. |
[48] | Alessandrì L, Arigoni M, Calogero R, et al. Differential expression analysis in single-cell transcriptomics[J]. Methods in Molecular Biology, 2019,1979:425-432. |
[49] | Wang T, Li B, Nelson CE, et al. Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data[J]. BMC Bioinformatics, 2019,20(1):40. |
[50] | Miao Z, Deng K, Wang X, et al. DEsingle for detecting three types of differential expression in single-cell RNA-seq data[J]. Bioinformatics, 2018,34(18):3223-3224. |
[51] | Wang T, Nabavi S. SigEMD:A powerful method for differential gene expression analysis in single-cell RNA sequencing data[J]. Methods, 2018,145:25-32. |
[52] | Finak G, McDavid A, Yajima M, et al. MAST:a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data[J]. Genome Biology, 2015,16(1):278. |
[53] | Efroni I, Ip PL, Nawy T, et al. Quantification of cell identity from single-cell gene expression profiles[J]. Genome Biology, 2015,16(1):9. |
[54] | Shulse CN, Cole BJ, Ciobanu D, et al. High-throughput single-cell transcriptome profiling of plant cell types[J]. Cell Rep, 2019,27(7):2241-2247. |
[55] | Turco GM, Rodriguez-Medina J, Siebert S, et al. Molecular mechanisms driving switch behavior in xylem cell differentiation[J]. Cell Reports, 2019,28(2):342-351. |
[56] | Yi F, Gu W, Chen J, et al. High temporal-resolution transcriptome landscape of early maize seed development[J]. Plant Cell, 2019,31(5):974-992. |
[57] | Rhee SY, Birnbaum KD, Ehrhardt DW. Towards building a plant cell atlas[J]. Trends Plant Sci, 2019,24(4):303-310. |
[1] | ZHANG Kun, YAN Chang, TIAN Xin-peng. Research Progress in Microbial Single Cell Separation Methods [J]. Biotechnology Bulletin, 2023, 39(9): 1-11. |
[2] | WU Hao, LIU Zi-wei, ZHENG Ying, DAI Ya-wen, SHI Quan. Study on the Heterogeneity of Human Gingival Mesenchymal Stem Cells at Single Cell Level [J]. Biotechnology Bulletin, 2023, 39(7): 325-332. |
[3] | KOU Jia-yi, WANG Yu-ling, ZENG Rui-lin, LAN Dao-liang. Application of Single-cell Transcriptome Sequencing in Mammalian [J]. Biotechnology Bulletin, 2022, 38(11): 41-48. |
[4] | ZHENG Qing-bo, YE Na, ZHANG Xiao-lan, BAO Peng-jia, WANG Fu-bin, REN Wen-wen, LIAO Yue-jiao, YAN Ping, PAN He-ping. Identification of Hair Follicle Cell Subsets and Bioinformatics Analysis of Characteristic Genes in Tianzhu White Yak During Catagen [J]. Biotechnology Bulletin, 2022, 38(10): 262-272. |
[5] | YE Na, ZHANG Xiao-lan, BAO Peng-jia, WANG Xing-dong, YAN Ping, PAN He-ping. Single Cell Sequencing Technology and Its Application in Hair Follicle Development [J]. Biotechnology Bulletin, 2021, 37(10): 245-256. |
[6] | ZHANG Miao, SUN Xiang-rui, XU Chun-ming. Research Progress of Approaches in Single Cell RNA Sequencing Data Analysis [J]. Biotechnology Bulletin, 2021, 37(1): 52-59. |
[7] | CHEN Da-yang,ZHEN He-fu,LIU Ping,QIU Yong,XIE Lin,LIU Hong-tai,CHEN Fang. Application of Low-coverage Whole Genome Sequencing in Detecting Chromosome Micro Variations of Single Cell [J]. Biotechnology Bulletin, 2016, 32(12): 58-64. |
[8] | Pan Xiaoming, Liang Xingguo. Principle of Whole Genome Amplification Technology and Its Progress [J]. Biotechnology Bulletin, 2014, 0(12): 47-54. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||