Single-cell transcriptome analysis of endometrial tissue.
Single-cell transcriptome analysis of endometrial tissue.
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DOI：
10.1093/humrep/dew008
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发表时间：
2016-04
期刊：
影响因子：
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通讯作者：
Kere J
中科院分区：
文献类型：
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作者：
Krjutškov K;Katayama S;Saare M;Vera-Rodriguez M;Lubenets D;Samuel K;Laisk-Podar T;Teder H;Einarsdottir E;Salumets A;Kere J
我们如何在单细胞水平上研究子宫内膜间质细胞和上皮细胞的全转录组？通过编译和开发用于活检、组织冷冻保存和解聚、单细胞分选、文库制备、RNA测序（RNA-seq）和统计数据分析的新型分析工具。尽管最近已经发表了来自各种活检组织的单细胞转录组分析，但尚未描述用于人类子宫内膜的相应方案。对冻融的子宫内膜活检组织进行荧光激活细胞分选（FACS），以区分CD 13阳性基质细胞和CD 9阳性上皮细胞，并在不培养细胞的情况下对活检组织进行单细胞转录组分析。我们研究了基因转录，应用现代高效的RNA-seq协议。同时，培养子宫内膜间质细胞，并与未培养细胞的总体表达谱进行比较。为了方法验证，我们使用了两个子宫内膜活检，一个来自分泌中期（第21天，LH+8），另一个来自分泌晚期（第25天）。样品经历单细胞FACS分选、单细胞RNA-seq文库制备和Illumina测序。在这里，我们提出了一个完整的管道单细胞基因表达研究，从临床采样到统计数据分析。组织操作，从解聚和细胞类型特异性标记开始，以单细胞自动分选结束，在低温下在90分钟内进行管理，以最大限度地减少基因表达谱的变化。分别使用CD 13和CD 9特异性抗体分选单个活基质和上皮细胞。在8622个检测到的基因中，2661个在培养的基质细胞中比在活检细胞中更活跃。在活检与培养细胞的比较中，检测到5603个共同表达的基因，其中241个显著差异表达的基因。其中，231个基因在培养细胞中分别上调和10个下调。此外，我们对差异表达基因进行了基因本体论分析，发现这些基因主要与细胞周期、翻译过程和代谢有关。虽然我们的实验室成功建立了CD 9阳性单个上皮细胞分选，但每个上皮细胞的转录组数据量很低，使进一步分析复杂化。这一步骤很可能由于细胞自然过程释放的高剂量RNA酶，或由于快速周转时间或冷冻或单细胞溶液中的凋亡条件而失败。由于只有来自分泌后期的细胞受到更集中的分析，因此需要进一步的研究，包括来自自然月经周期不同时间点的更大样本量。该方法还需要进一步优化，以高质量地检查不同的细胞类型。临床活检与复杂的实验室和生物信息学协议之间的共生关系将临床诊断需求与现代实验室和生物信息学解决方案结合在一起，使我们能够实现精确的分析工具箱，即使在单细胞水平上也可以研究子宫内膜组织。
How can we study the full transcriptome of endometrial stromal and epithelial cells at the single-cell level? By compiling and developing novel analytical tools for biopsy, tissue cryopreservation and disaggregation, single-cell sorting, library preparation, RNA sequencing (RNA-seq) and statistical data analysis. Although single-cell transcriptome analyses from various biopsied tissues have been published recently, corresponding protocols for human endometrium have not been described. The frozen-thawed endometrial biopsies were fluorescence-activated cell sorted (FACS) to distinguish CD13-positive stromal and CD9-positive epithelial cells and single-cell transcriptome analysis performed from biopsied tissues without culturing the cells. We studied gene transcription, applying a modern and efficient RNA-seq protocol. In parallel, endometrial stromal cells were cultured and global expression profiles were compared with uncultured cells. For method validation, we used two endometrial biopsies, one from mid-secretory phase (Day 21, LH+8) and another from late-secretory phase (Day 25). The samples underwent single-cell FACS sorting, single-cell RNA-seq library preparation and Illumina sequencing. Here we present a complete pipeline for single-cell gene-expression studies, from clinical sampling to statistical data analysis. Tissue manipulation, starting from disaggregation and cell-type-specific labelling and ending with single-cell automated sorting, is managed within 90 min at low temperature to minimize changes in the gene expression profile. The single living stromal and epithelial cells were sorted using CD13- and CD9-specific antibodies, respectively. Of the 8622 detected genes, 2661 were more active in cultured stromal cells than in biopsy cells. In the comparison of biopsy versus cultured cells, 5603 commonly expressed genes were detected, with 241 significantly differentially expressed genes. Of these, 231 genes were up- and 10 down-regulated in cultured cells, respectively. In addition, we performed a gene ontology analysis of the differentially expressed genes and found that these genes are mainly related to cell cycle, translational processes and metabolism. Although CD9-positive single epithelial cells sorting was successfully established in our laboratory, the amount of transcriptome data per individual epithelial cell was low, complicating further analysis. This step most likely failed due to the high dose of RNases that are released by the cells' natural processes, or due to rapid turnaround time or the apoptotic conditions in freezing- or single-cell solutions. Since only the cells from the late-secretory phase were subject to more focused analysis, further studies including larger sample size from the different time-points of the natural menstrual cycle are needed. The methodology also needs further optimization to examine different cell types at high quality. The symbiosis between clinical biopsy and the sophisticated laboratory and bioinformatic protocols described here brings together clinical diagnostic needs and modern laboratory and bioinformatic solutions, enabling us to implement a precise analytical toolbox for studying the endometrial tissue even at the single-cell level.