Goldstein Leonard D, Chen Ying-Jiun Jasmine, Dunne Jude, Mir Alain, Hubschle Hermann, Guillory Joseph, Yuan Wenlin, Zhang Jingli, Stinson Jeremy, Jaiswal Bijay, Pahuja Kanika Bajaj, Mann Ishminder, Schaal Thomas, Chan Leo, Anandakrishnan Sangeetha, Lin Chun-Wah, Espinoza Patricio, Husain Syed, Shapiro Harris, Swaminathan Karthikeyan, Wei Sherry, Srinivasan Maithreyan, Seshagiri Somasekar, Modrusan Zora
Molecular Biology Department, Genentech Inc., 1 DNA Way, South San Francisco, CA, 94080, USA.
Wafergen Biosystems Inc., 34700 Campus Drive, Fremont, CA, 94555, USA.
BMC Genomics. 2017 Jul 7;18(1):519. doi: 10.1186/s12864-017-3893-1.
Technological advances have enabled transcriptome characterization of cell types at the single-cell level providing new biological insights. New methods that enable simple yet high-throughput single-cell expression profiling are highly desirable.
Here we report a novel nanowell-based single-cell RNA sequencing system, ICELL8, which enables processing of thousands of cells per sample. The system employs a 5,184-nanowell-containing microchip to capture ~1,300 single cells and process them. Each nanowell contains preprinted oligonucleotides encoding poly-d(T), a unique well barcode, and a unique molecular identifier. The ICELL8 system uses imaging software to identify nanowells containing viable single cells and only wells with single cells are processed into sequencing libraries. Here, we report the performance and utility of ICELL8 using samples of increasing complexity from cultured cells to mouse solid tissue samples. Our assessment of the system to discriminate between mixed human and mouse cells showed that ICELL8 has a low cell multiplet rate (< 3%) and low cross-cell contamination. We characterized single-cell transcriptomes of more than a thousand cultured human and mouse cells as well as 468 mouse pancreatic islets cells. We were able to identify distinct cell types in pancreatic islets, including alpha, beta, delta and gamma cells.
Overall, ICELL8 provides efficient and cost-effective single-cell expression profiling of thousands of cells, allowing researchers to decipher single-cell transcriptomes within complex biological samples.
技术进步已能够在单细胞水平对细胞类型进行转录组表征,从而提供新的生物学见解。非常需要能够实现简单且高通量单细胞表达谱分析的新方法。
在此,我们报告了一种基于新型纳米孔的单细胞RNA测序系统ICELL8,该系统能够对每个样本处理数千个细胞。该系统采用一个包含5184个纳米孔的微芯片来捕获约1300个单细胞并对其进行处理。每个纳米孔都包含预印的寡核苷酸,这些寡核苷酸编码聚d(T)、一个独特的孔条形码和一个独特的分子标识符。ICELL8系统使用成像软件来识别包含活单细胞的纳米孔,并且仅对含有单细胞的孔进行处理以构建测序文库。在此,我们报告了ICELL8在从培养细胞到小鼠实体组织样本等复杂性不断增加的样本中的性能和实用性。我们对该系统区分混合的人源和小鼠细胞的评估表明,ICELL8具有较低的细胞多重率(<3%)和较低的跨细胞污染率。我们对一千多个培养的人源和小鼠细胞以及468个小鼠胰岛细胞的单细胞转录组进行了表征。我们能够在胰岛中识别出不同的细胞类型,包括α细胞、β细胞、δ细胞和γ细胞。
总体而言,ICELL8为数千个细胞提供了高效且经济高效的单细胞表达谱分析,使研究人员能够解读复杂生物样本中的单细胞转录组。