Mutation detection in thousands of acute myeloid leukemia cells using single cell RNA-sequencing
Mutation detection in thousands of acute myeloid leukemia cells using single cell RNA-sequencing
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使用单细胞 RNA 测序对数千个急性髓系白血病细胞进行突变检测
DOI：
10.1101/434746
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
T. Ley
中科院分区：
文献类型：
--
作者：
A. Petti;Stephen R. Williams;Christopher A. Miller;Ian T. Fiddes;S. Srivatsan;David Y. Chen;C. Fronick;R. Fulton;D. Church;T. Ley
事实上，所有肿瘤都具有遗传异质性，包含由不同突变定义的亚克隆细胞群1。亚克隆可能具有影响疾病进展的独特表型2，但这些表型很难表征：亚克隆通常无法进行物理纯化，并且大量基因表达测量掩盖了克隆间差异。单细胞 RNA 测序揭示了多种肿瘤类型内的转录异质性，但尚不清楚这种表达异质性与亚克隆遗传事件有何关系，例如，特定表达簇是否对应于突变定义的亚克隆3,4,5,6-9。为了解决这个问题，我们开发了一种方法，将增强型全基因组测序 (eWGS) 与 10x Genomics Chromium 单细胞 5’ 基因表达工作流程 (scRNA-seq) 相集成，以单细胞分辨率直接将表达的突变与转录谱联系起来。使用来自 5 例原发性人类急性髓性白血病 (AML) 病例的骨髓样本，我们为每个病例生成了 WGS 和 scRNA-seq 数据。从每位患者的骨髓中生成代表每个病例中位数 20,474 个细胞的重复单细胞文库。尽管文库存在 5' 偏向，但我们在距离表达良好的基因 5' 末端最远 10 kbp 处检测到了 cDNA 中的表达突变，这使我们能够在每种情况下识别出数百至数千个具有 AML 特异性体细胞突变的细胞。这些数据使得能够将 AML 细胞（包括正常核型 AML 细胞）与周围正常细胞区分开来，研究肿瘤分化和瘤内表达异质性，识别与亚克隆突变相关的表达特征，并找到可用于纯化亚克隆以供进一步研究的细胞表面标记。数据还揭示了独立于亚克隆突变发生的转录异质性，表明其他因素驱动了表观遗传异质性。这种将 AML 细胞中的基因型与表型联系起来的综合方法广泛适用于任何表型和遗传异质性样本的分析。
Virtually all tumors are genetically heterogeneous, containing subclonal populations of cells that are defined by distinct mutations1. Subclones can have unique phenotypes that influence disease progression2, but these phenotypes are difficult to characterize: subclones usually cannot be physically purified, and bulk gene expression measurements obscure interclonal differences. Single-cell RNA-sequencing has revealed transcriptional heterogeneity within a variety of tumor types, but it is unclear how this expression heterogeneity relates to subclonal genetic events – for example, whether particular expression clusters correspond to mutationally defined subclones3,4,5,6-9. To address this question, we developed an approach that integrates enhanced whole genome sequencing (eWGS) with the 10x Genomics Chromium Single Cell 5’ Gene Expression workflow (scRNA-seq) to directly link expressed mutations with transcriptional profiles at single cell resolution. Using bone marrow samples from five cases of primary human Acute Myeloid Leukemia (AML), we generated WGS and scRNA-seq data for each case. Duplicate single cell libraries representing a median of 20,474 cells per case were generated from the bone marrow of each patient. Although the libraries were 5’ biased, we detected expressed mutations in cDNAs at distances up to 10 kbp from the 5’ ends of well-expressed genes, allowing us to identify hundreds to thousands of cells with AML-specific somatic mutations in every case. This data made it possible to distinguish AML cells (including normal-karyotype AML cells) from surrounding normal cells, to study tumor differentiation and intratumoral expression heterogeneity, to identify expression signatures associated with subclonal mutations, and to find cell surface markers that could be used to purify subclones for further study. The data also revealed transcriptional heterogeneity that occurred independently of subclonal mutations, suggesting that additional factors drive epigenetic heterogeneity. This integrative approach for connecting genotype to phenotype in AML cells is broadly applicable for analysis of any sample that is phenotypically and genetically heterogeneous.
DOI：
10.1056/nejmoa1301689
发表时间：
2013-05-30
期刊：
The New England journal of medicine
影响因子：
--
作者：
Cancer Genome Atlas Research Network;Ley TJ;Miller C;Ding L;Raphael BJ;Mungall AJ;Robertson A;Hoadley K;Triche TJ Jr;Laird PW;Baty JD;Fulton LL;Fulton R;Heath SE;Kalicki-Veizer J;Kandoth C;Klco JM;Koboldt DC;Kanchi KL;Kulkarni S;Lamprecht TL;Larson DE;Lin L;Lu C;McLellan MD;McMichael JF;Payton J;Schmidt H;Spencer DH;Tomasson MH;Wallis JW;Wartman LD;Watson MA;Welch J;Wendl MC;Ally A;Balasundaram M;Birol I;Butterfield Y;Chiu R;Chu A;Chuah E;Chun HJ;Corbett R;Dhalla N;Guin R;He A;Hirst C;Hirst M;Holt RA;Jones S;Karsan A;Lee D;Li HI;Marra MA;Mayo M;Moore RA;Mungall K;Parker J;Pleasance E;Plettner P;Schein J;Stoll D;Swanson L;Tam A;Thiessen N;Varhol R;Wye N;Zhao Y;Gabriel S;Getz G;Sougnez C;Zou L;Leiserson MD;Vandin F;Wu HT;Applebaum F;Baylin SB;Akbani R;Broom BM;Chen K;Motter TC;Nguyen K;Weinstein JN;Zhang N;Ferguson ML;Adams C;Black A;Bowen J;Gastier-Foster J;Grossman T;Lichtenberg T;Wise L;Davidsen T;Demchok JA;Shaw KR;Sheth M;Sofia HJ;Yang L;Downing JR;Eley G
通讯作者：
Eley G
影响因子：
7.5
作者：
Miller, Christopher A.;Tricarico, Christopher;Wartman, Lukas D.
通讯作者：
Wartman, Lukas D.
DOI：
10.1001/jama.2015.9643
发表时间：
2015-08-25
期刊：
JAMA
影响因子：
--
作者：
Klco JM;Miller CA;Griffith M;Petti A;Spencer DH;Ketkar-Kulkarni S;Wartman LD;Christopher M;Lamprecht TL;Helton NM;Duncavage EJ;Payton JE;Baty J;Heath SE;Griffith OL;Shen D;Hundal J;Chang GS;Fulton R;O'Laughlin M;Fronick C;Magrini V;Demeter RT;Larson DE;Kulkarni S;Ozenberger BA;Welch JS;Walter MJ;Graubert TA;Westervelt P;Radich JP;Link DC;Mardis ER;DiPersio JF;Wilson RK;Ley TJ
通讯作者：
Ley TJ