基于单细胞转录组的视网膜母细胞瘤进展特征分析

视网膜母细胞瘤（retinoblastoma,RB）是婴幼儿最常见的眼内恶性肿瘤。在RB进展过程中的关键致病因素目前尚不十分清楚。因此,识别与RB进展密切相关的基因能为病情诊断及基因治疗提供重要信息。然而,肿瘤组织具有很强的细胞异质性,不同病理状态下的细胞,其功能及基因表达都可能呈现显著的差异。本研究从公共基因表达数据库（gene expression omnibus,GEO）下载了1例4个月肿瘤患者和1例2年患者的肿瘤及癌旁组织的单细胞转录组测序数据,从单细胞水平解析不同患病时长的RB肿瘤转录图谱,鉴定与RB进展有潜在关联的细胞亚群及基因集。结果显示,肿瘤组织与癌旁组织在单细胞转录图谱上具有整体的一致性,但视锥前体G1期细胞群、G2期细胞群以及小胶质细胞群在肿瘤与癌旁组织中的分布比例存在明显差异。进一步分析了这3种细胞群在RB肿瘤进展过程中的作用。研究发现,在RB肿瘤的早期阶段,视锥前体细胞在G1期异常增殖,随着RB肿瘤的进展,视锥前体G2期细胞比例显著增加。同时,RB进展过程的小胶质细胞群差异分析结果显示,主要参与免疫应答的关键基因包括RPL23、B2M、HLA家族基因。本研究可为RB发病机制及进展研究提供更多新视角和数据资源。     

Single-cell exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm

Tumor heterogeneity presents a challenge for inferring clonal evolution and driver gene identification. Here, we describe a method for analyzing the cancer genome at a single-cell nucleotide level. To perform our analyses, we first devised and validated a high-throughput whole-genome single-cell sequencing method using two lymphoblastoid cell line single cells. We then carried out whole-exome single-cell sequencing of 90 cells from a JAK2-negative myeloproliferative neoplasm patient. The sequencing data from 58 cells passed our quality control criteria, and these data indicated that this neoplasm represented a monoclonal evolution. We further identified essential thrombocythemia (ET)-related candidate mutations such as SESN2 and NTRK1, which may be involved in neoplasm progression. This pilot study allowed the initial characterization of the disease-related genetic architecture at the single-cell nucleotide level. Further, we established a single-cell sequencing method that opens the way for detailed analyses of a variety of tumor types, including those with high genetic complex between patients.     

Critical roles of mucin-1 in sensitivity of lung cancer cells to tumor necrosis factor-alpha and dexamethasone

Lung cancer is the leading cause of death from cancer. Mucins are glycoproteins with high molecular weight, responsible for cell growth, differentiation, and signaling, and were proposed to be correlated with gene heterogeneity of lung cancer. Here, we report aberrant expression of mucin genes and tumor necrosis factor receptors in lung adenocarcinoma tissues compared with normal tissues in GEO datasets. Mucin-1 (MUC1) gene was selected and considered as the target gene; furthermore, the expression pattern of adenocarcinomic cells (A549, H1650, or H1299 cells) was validated under the stimulation with tumor necrosis factor-alpha (TNFα) or dexamethasone (DEX), separately. MUC1 gene interference was done to A549 cells to show its role in sensitivity of lung cancer cells to TNFα and DEX. Results of our experiments indicate that MUC1 may regulate the influence of inflammatory mediators in effects of glucocorticoids (GCs), as a regulatory target to improve therapeutics. It shows the potential effect of MUC1 and GCs in lung adenocarcinoma (LADC), which may help in LADC treatment in the future.     

单细胞测序技术及其在乳腺癌研究中的应用

乳腺癌是起源于乳腺各级导管和乳腺上皮细胞,由增生到不典型增生而逐步发展成原位癌、早期浸润癌至浸润性癌的一种恶性肿瘤。传统高通量测序技术对乳腺癌的研究主要是鉴定与乳腺癌发生发展相关的"驱动基因",但是对于乳腺癌基因组结构变化以及亚克隆的鉴定等存在一定的局限性,并且忽略了乳腺癌肿瘤细胞之间的异质性。近年来兴起的单细胞测序技术是以单个细胞为研究对象,对基因拷贝和基因表达等数据进行分析,探究乳腺癌的细胞组成、细胞状态和细胞命运,绘制乳腺癌生态系统图谱,对临床患者进行精准分层,为实现个体化治疗提供支持。同时,还可以揭示乳腺癌与T细胞、巨噬细胞等免疫细胞间的相关性,为发现乳腺癌新的治疗靶点、免疫检查点等提供参考。本文对单细胞测序技术及其在乳腺癌研究中的应用和研究进展进行了综述,以期为单细胞测序技术的进一步发展提供参考,同时为理解乳腺癌的发病机制和免疫治疗提供基础支持。     

Essential gene expression pattern of head and neck squamous cell carcinoma revealed by tumor-specific expression rule based on single-cell RNA sequencing

Head and neck squamous cell carcinoma (HNSCC) has been widely reported and considered as one of the most threatening diseases to human health. Derived from complicated tissue subtypes, HNSCC has diverse symptoms and pathogenesis. They make the identification of the core carcinogenic factors of such diseases at the multi-cell level difficult. With the development of single-cell sequencing technologies, the effects of non-malignant cells on traditional bulk sequencing data can be eliminated directly. On the basis of fresh single-cell RNA-seq data, we set up a computational filtering strategy for tumor cell identification in an expression rule manner. This strategy can reveal the accurate expression distinction between tumor cells and adjacent tumor microenvironment, which are all supported by literature reports. Validated by several independent datasets, these rule genes can further group HNSCC patients with significant difference on survival risks. Thus, the establishment of our computational approach may not only provide an efficient tool to identify malignant cells in the tumor ecosystem but also deepen our understanding of tumor heterogeneity and tumorigenesis.     

Niche-Dependent Gene Expression Profile of Intratumoral Heterogeneous Ovarian Cancer Stem Cell Populations

Intratumoral heterogeneity challenges existing paradigms for anti-cancer therapy. We have previously demonstrated that the human embryonic stem cells (hESC)-derived cellular microenvironment in immunocompromised mice, enables functional distinction of heterogeneous tumor cells, including cells which do not grow into a tumor in a conventional direct tumor xenograft platform. We have identified and characterized six cancer cell subpopulations each clonally expanded from a single cell, derived from human ovarian clear cell carcinoma of a single tumor, to demonstrate striking intratumoral phenotypic heterogeneity that is dynamically dependent on the tumor growth microenvironment. These cancer cell subpopulations, characterized as cancer stem cell subpopulations, faithfully recapitulate the full spectrum of histological phenotypic heterogeneity known for human ovarian clear cell carcinoma. Each of the six subpopulations displays a different level of morphologic and tumorigenic differentiation wherein growth in the hESC-derived microenvironment favors growth of CD44+/aldehyde dehydrogenase positive pockets of self-renewing cells that sustain tumor growth through a process of tumorigenic differentiation into CD44-/aldehyde dehydrogenase negative derivatives. Strikingly, these derivative cells display microenvironment-dependent plasticity with the capacity to restore self-renewal markers and CD44 expression. In the current study, we delineate the distinct gene expression and epigenetic profiles of two such subpopulations, representing extremes of phenotypic heterogeneity in terms of niche-dependent self-renewal and tumorigenic differentiation. By combining Gene Set Enrichment, Gene Ontology and Pathway-focused array analyses with methylation status, we propose a suite of robust differences in tumor self-renewal and differentiation pathways that underlie the striking intratumoral phenotypic heterogeneity which characterize this and other solid tumor malignancies.     

肺癌循环肿瘤细胞的单细胞EGFR基因突变检测

循环肿瘤细胞（Circulating tumor cells,CTCs）是从肿瘤原发病灶脱落并侵入外周血循环的肿瘤细胞。由于CTCs存在较大的异质性,其与癌症发展转移密切相关,但目前尚缺乏有效的CTCs单细胞异质性检测方法。鉴于此,本文发展了在单细胞层面对CTCs进行基因突变的检测方法并用于单个肺癌CTC的EGFR（Epidermal growth factor receptor）基因突变检测。首先用集成式微流控系统完成血液中稀有CTCs的捕获,接着将CTCs释放入含有多个微孔的微阵列芯片中,得到含有单个CTC的微孔,通过显微操作将单个CTC转入PCR管内完成单细胞基因组的放大,并进行单细胞的EGFR基因突变检测。以非小细胞肺癌细胞系A549、NCI-H1650和NCI-H1975为样本,通过芯片与毛细管修饰、引物扩增条件（复性温度、循环次数）的优化,结果显示在复性温度59℃、30个循环次数的条件下,引物扩增效果最优。利用该方法成功地对非小细胞肺癌（Non-small cell lung cancer, NSCLC）患者的血液样本进行了测试。从患者2 mL血液中获取5个CTCs,分别对其EGFR基因的第18、19、20、21外显子进行测序,发现该患者CTCs均为EGFR野生型。研究结果证明此检测方法可以灵敏地用于单个CTC基因突变的检测,在临床研究上具有重要的指导意义。     

SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data

The single-cell RNA sequencing (scRNA-seq) technologies obtain gene expression at single-cell resolution and provide a tool for exploring cell heterogeneity and cell types. As the low amount of extracted mRNA copies per cell, scRNA-seq data exhibit a large number of dropouts, which hinders the downstream analysis of the scRNA-seq data. We propose a statistical method, SDImpute (Single-cell RNA-seq Dropout Imputation), to implement block imputation for dropout events in scRNA-seq data. SDImpute automatically identifies the dropout events based on the gene expression levels and the variations of gene expression across similar cells and similar genes, and it implements block imputation for dropouts by utilizing gene expression unaffected by dropouts from similar cells. In the experiments, the results of the simulated datasets and real datasets suggest that SDImpute is an effective tool to recover the data and preserve the heterogeneity of gene expression across cells. Compared with the state-of-the-art imputation methods, SDImpute improves the accuracy of the downstream analysis including clustering, visualization, and differential expression analysis.     

SIDEseq: A Cell Similarity Measure Defined by Shared Identified Differentially Expressed Genes for Single-Cell RNA sequencing Data

One goal of single-cell RNA sequencing (scRNA seq) is to expose possible heterogeneity within cell populations due to meaningful, biological variation. Examining cell-to-cell heterogeneity, and further, identifying subpopulations of cells based on scRNA seq data has been of common interest in life science research. A key component to successfully identifying cell subpopulations (or clustering cells) is the (dis)similarity measure used to group the cells. In this paper, we introduce a novel measure, named SIDEseq, to assess cell-to-cell similarity using scRNA seq data. SIDEseq first identifies a list of putative differentially expressed (DE) genes for each pair of cells. SIDEseq then integrates the information from all the DE gene lists (corresponding to all pairs of cells) to build a similarity measure between two cells. SIDEseq can be implemented in any clustering algorithm that requires a (dis)similarity matrix. This new measure incorporates information from all cells when evaluating the similarity between any two cells, a characteristic not commonly found in existing (dis)similarity measures. This property is advantageous for two reasons: (a) borrowing information from cells of different subpopulations allows for the investigation of pairwise cell relationships from a global perspective and (b) information from other cells of the same subpopulation could help to ensure a robust relationship assessment. We applied SIDEseq to a newly generated human ovarian cancer scRNA seq dataset, a public human embryo scRNA seq dataset, and several simulated datasets. The clustering results suggest that the SIDEseq measure is capable of uncovering important relationships between cells, and outperforms or at least does as well as several popular (dis)similarity measures when used on these datasets.