基因组学已经深刻地改变了生命科学的诸多领域的面貌。目前它的主要内容是新的全基因组碱基序列的测定和在全基因组范围内鉴定那些在不同水平上影响生命活动的基因群的功能和相互作用。为达此要求, 近年出现的第二代测序(深度测序)技术和基因芯片技术发挥了关键作用, 但是两者都需要足够的高质量的核酸样品。所以, 在只有或只能用单细胞或极少量细胞的情况下, 如果没有特殊手段, 上述分析往往不能常规、方便地进行。文章以DNA扩增为主线, 综合阐述了目前在单细胞(特别是微生物)全基因组测序和大基因组的靶向重测序, 以及对单细胞或微量细胞进行的基于深度测序或芯片杂交的功能基因组分析, 如转录组、ChIP和DNA的CpG甲基化分析等的最新策略和技术, 评价了单细胞基因组测序和功能基因组学各技术的特点并对发展前景进行了展望。
The technological progress of the genomics has transformed life science research. The main objectives of genomics are sequencing of new genomes and genome-wide identification of the function and the interaction of genes and their products. The recently developed second generation or next generation sequencing platforms and DNA microarray technology are immensely important and powerful tools for functional genomic analyses. However, their application is limited by the requirement of sufficient amounts of high quality nucleic acid samples. Therefore, when only a single cell or a very small number of cells are available or are preferred, the whole genomic sequencing or functional genomic objectives cannot be achieved conventionally and require a robust amplification method. This review highlights DNA amplification technologies and summarizes the strategies currently utilized for whole genome sequencing of a single cell, with specific focus on studies investigating microorganisms; An outline for targeted re-sequencing enabling the analysis of larger genomes is also provided. Furthermore, the review presents the emerging functional genomic applications using next-generation sequencing or microarray analysis to examine genome-wide transcriptional profile, chromatin modification and other types of protein-DNA binding profile, and CpG methylation mapping in a single cell or a very low quantity of cells. The nature of these technologies and their prospects are also addressed.
[1] Liu ET. Functional genomics of cancer. Curr Opin Genet Dev, 2008, 18(3): 251–256.
[2] Geschwind DH, Konopka G. Neuroscience in the era of functional genomics and systems biology. Nature, 2009, 461(7266): 908–915.
[3] Pieroni E, de la Fuente van Bentem S, Mancosu G, Capobianco E, Hirt H, de la Fuente A. Protein networking: insights into global functional organization of proteomes. Proteomics, 2008, 8(4): 799–816.
[4] Abu-Farha M, Elisma F, Zhou H, Tian R, Zhou H, Asmer MS, Figeys D. Proteomics: from technology developments to biological applications. Anal Chem, 2009, 81(12): 4585–4599.
[5] Wilusz JE, Sunwoo H, Spector DL. Long noncoding RNAs: functional surprises from the RNAworld. Genes Dev, 2009, 23(13): 1494–1504.
[6] Chen PY, Meister G. MicroRNA-guided posttranscriptional gene regulation. Biol Chem, 2005, 386(12): 1205–1218.
[7] Butcher LM, Beck S. Future impact of integrated high-throughput methylome analyses on human health and disease. J Genet Genomics, 2008, 35(7): 391–401.
[8] Beck S, Rakyan VK. The methylome: approaches for global DNA methylation profiling. Trends Genet, 2008, 24(5): 231–237.
[9] Morozova O, Marra MA. Applications of next-generation sequencing technologies in functional genomics. Genomics, 2008, 92(5): 255–264.
[10] Wold B, Myers RM. Sequence census methods for functional genomics. Nat Methods, 2008, 5(1): 19–21.
[11] 谭建新, 孙玉洁. 表观基因组学研究方法进展与评价. 遗传, 2009, 31(1): 3–12.
[12] Suzuki MM, Bird A. DNA methylation landscapes: provocative insights from epigenomics. Nat Rev Genet, 2008, 9(6): 465–476.
[13] Jirtle RL, Skinner MK. Environmental epigenomics and disease susceptibility. Nat Rev Genet, 2007, 8(4): 253–262.
[14] Esteller M. Cancer epigenomics: DNA methylomes and histone-modification maps. Nat Rev Genet, 2007, 8(4): 286–298.
[15] Gondo Y. Trends in large-scale mouse mutagenesis: from genetics to functional genomics. Nat Rev Genet, 2008, 9(10): 803–810.
[16] Gondo Y, Fukumura R, Murata T, Makino S. Next-generation gene targeting in the mouse for functional genomics. BMB Rep, 2009, 42(6): 315–323.
[17] Shendure J, Ji H. Next-generation DNA sequencing. Nat Biotechnol, 2008, 26(10): 1135–1145.
[18] Mardis ER. The impact of next-generation sequencing technology on genetics. Trends Genet, 2008, 24(3): 133–141.
[19] Wang Z, Gerstein M, Snyder M. RNA-seq: a revolutionary tool for transcriptomics. Nat Rev Genet, 2009, 10(1): 57–63.
[20] Metzker ML. Sequencing technologies - the next generation. Nat Rev Genet, 2010, 11(1): 31–46.
[21] Pushkarev D, Neff NF, Quake SR. Single-molecule sequencing of an individual human genome. Nat Biotechnol, 2009, 27(9): 847–850.
[22] Ozsolak F, Platt AR, Jones DR, Reifenberger JG, Sass LE, McInerney P, Thompson JF, Bowers J, Jarosz M, Milos PM. Direct RNA sequencing. Nature, 2009, 461(7265): 814–818.
[23] Ozsolak F, Ting DT, Wittner BS, Brannigan BW, Paul S, Bardeesy N, Ramaswamy S, Milos PM, Haber DA. Amplification-free digital gene expression profiling from minute cell quantities. Nat Methods, 2010, 7(8): 619–621.
[24] Walker A, Parkhill J. Single-cell genomics. Nat Rev Microbiol, 2008, 6(3): 176–177.
[25] Ochman H. Single-cell genomics. Environ Microbiol, 2007, 9(1): 7.
[26] Hamady M, Knight R. Microbial community profiling for human microbiome projects: tools, techniques, and challenges. Genome Res, 2009, 19(7): 1141–1152.
[27] Weckx S, De Vuyst L. Metagenome and metatranscriptome analysis: does the flag always cover the cargo? Int J Food Microbiol, 2009, 133(3): 292–293.
[28] Baveye PC. To sequence or not to sequence the whole-soil metagenome? Nat Rev Mi
