单细胞PCR体系的建立及其在CRISPR/Cas9靶点活性检测中的应用

单细胞PCR是以单个细胞所含的DNA或RNA为模板进行扩增,因此其模板的制备是影响整个反应成功与否的关键因素。利用三种不同蛋白酶K细胞裂解液(SDS细胞裂解液、NP-40细胞裂解液、Tween-20细胞裂解液)分别对体外培养成熟的单个猪卵母细胞进行裂解,并直接作为模板进行PCR扩增,结果表明:NP-40细胞裂解液制备的模板质量最好,其扩增效率为95%;其次为Tween-20细胞裂解液,其扩增效率为65%;SDS细胞裂解液产物中未检测到阳性条带。结合显微注射技术对sgRNA靶点活性进行检测,结果表明利用单细胞PCR结合显微注射的方法,在受精卵内对CRISPR/Cas9靶点活性进行检测确实可行,检测结果真实可靠。     

染色质可及性分析的研究进展

在细胞核内,染色质可及性模式会随着外部刺激和发育线索的改变而发生动态变化。染色质可及性重构对于基因表达调控至关重要,在建立和维持细胞特性等方面发挥着重要作用。因此开展染色质可及性的研究对染色质功能上的三维解析具有十分重要的意义。近几年,随着高通量测序技术的进步以及测序成本的降低,基于高通量测序技术的染色质可及性分析方法得到了迅速发展。目前观察和分析全基因组染色质开放与否的常见技术主要有脱氧核糖核酸酶I超敏位点测序（DNase-seq）、微球菌核酸酶测序（MNase-seq）、甲醛辅助分离调控元件测序（FAIRE-seq）以及转座酶可及性测序（ATAC-seq）。本文比较了这4种染色质可及性分析技术的优缺点,详细介绍了它们的原理及主要实验流程,并简要讨论了它们的发展及相关技术的应用,期望通过这些互补的方法为染色质分析领域的未来发展提供一些借鉴和思路。     

WormGUIDES: an interactive single cell developmental atlas and tool for collaborative multidimensional data exploration

Background

Imaging and image analysis advances are yielding increasingly complete and complicated records of cellular events in tissues and whole embryos. The ability to follow hundreds to thousands of cells at the individual level demands a spatio-temporal data infrastructure: tools to assemble and collate knowledge about development spatially in a manner analogous to geographic information systems (GIS). Just as GIS indexes items or events based on their spatio-temporal or 4D location on the Earth these tools would organize knowledge based on location within the tissues or embryos. Developmental processes are highly context-specific, but the complexity of the 4D environment in which they unfold is a barrier to assembling an understanding of any particular process from diverse sources of information. In the same way that GIS aids the understanding and use of geo-located large data sets, software can, with a proper frame of reference, allow large biological data sets to be understood spatially. Intuitive tools are needed to navigate the spatial structure of complex tissue, collate large data sets and existing knowledge with this spatial structure and help users derive hypotheses about developmental mechanisms.

Results

Toward this goal we have developed WormGUIDES, a mobile application that presents a 4D developmental atlas for Caenorhabditis elegans. The WormGUIDES mobile app enables users to navigate a 3D model depicting the nuclear positions of all cells in the developing embryo. The identity of each cell can be queried with a tap, and community databases searched for available information about that cell. Information about ancestry, fate and gene expression can be used to label cells and craft customized visualizations that highlight cells as potential players in an event of interest. Scenes are easily saved, shared and published to other WormGUIDES users. The mobile app is available for Android and iOS platforms.

Conclusion

WormGUIDES provides an important tool for examining developmental processes and developing mechanistic hypotheses about their control. Critically, it provides the typical end user with an intuitive interface for developing and sharing custom visualizations of developmental processes. Equally important, because users can select cells based on their position and search for information about them, the app also serves as a spatially organized index into the large body of knowledge available to the C. elegans community online. Moreover, the app can be used to create and publish the result of exploration: interactive content that brings other researchers and students directly to the spatio-temporal point of insight. Ultimately the app will incorporate a detailed time lapse record of cell shape, beginning with neurons. This will add the key ability to navigate and understand the developmental events that result in the coordinated and precise emergence of anatomy, particularly the wiring of the nervous system.     

Visualization of yeast single-cells on fabric surface with a fluorescent glucose and their isolation for culture

An ultra-deep focusing range (UDF) fluorescent microscope system has been combined with a micromanipulation system to develop a viable cell detection-identification system applicable to microbes on environmental surfaces and products. Candida albicans yeast cells on a fabric sample surface were viably stained with a fluorescent glucose derivative, 2-[N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl)amino]-2-deoxy glucose (2-NBDG) and detected with a UDF fluorescent microscope. Visualized single-cells of C. albicans were picked in a glass microcapillary and transferred onto an agar medium. After the culture, the colony was assayed for DNA sequence to identify the isolate. This demonstrates a potential application to the study of unknown environmental microorganisms.     

scDPN for High-throughput Single-cell CNV Detection to Uncover Clonal Evolution During HCC Recurrence

Single-cell genomics provides substantial resources for dissecting cellular heterogeneity and cancer evolution. Unfortunately, classical DNA amplification-based methods have low throughput and introduce coverage bias during sample preamplification. We developed a single-cell DNA library preparation method without preamplification in nanolitre scale (scDPN) to address these issues. The method achieved a throughput of up to 1800 cells per run for copy number variation (CNV) detection. Also, our approach demonstrated a lower level of amplification bias and noise than the multiple displacement amplification (MDA) method and showed high sensitivity and accuracy for cell line and tumor tissue evaluation. We used this approach to profile the tumor clones in paired primary and relapsed tumor samples of hepato-cellular carcinoma (HCC). We identified three clonal subpopulations with a multitude of aneuploid alterations across the genome. Furthermore, we observed that a minor clone of the primary tumor containing additional alterations in chro-mosomes 1q, 10q, and 14q developed into the dominant clone in the recurrent tumor, indicating clonal selection during recurrence in HCC. Overall, this approach provides a comprehensive and scalable solution to understand genome hetero-geneity and evolution.     

Tools used to assay genomic instability in cancers and cancer meiomitosis

Genomic instability is a defining characteristic of cancer and the analysis of DNA damage at the chromosome level is a crucial part of the study of carcinogenesis and genotoxicity. Chromosomal instability (CIN), the most common level of genomic instability in cancers, is defined as the rate of loss or gain of chromosomes through successive divisions. As such, DNA in cancer cells is highly unstable. However, the underlying mechanisms remain elusive. There is a debate as to whether instability succeeds transformation, or if it is a by-product of cancer, and therefore, studying potential molecular and cellular contributors of genomic instability is of high importance. Recent work has suggested an important role for ectopic expression of meiosis genes in driving genomic instability via a process called meiomitosis. Improving understanding of these mechanisms can contribute to the development of targeted therapies that exploit DNA damage and repair mechanisms. Here, we discuss a workflow of novel and established techniques used to assess chromosomal instability as well as the nature of genomic instability such as double strand breaks, micronuclei, and chromatin bridges. For each technique, we discuss their advantages and limitations in a lab setting. Lastly, we provide detailed protocols for the discussed techniques.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12079-021-00661-z.     

Single-cell transcriptomics analysis reveals intratumoral heterogeneity and identifies a gene signature associated with prognosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) tumors exhibit high heterogeneity. However, current understanding of tumor cell heterogeneity of HCC and the association with prognosis remains very limited. In the present study, we collected and examined tumor tissue from one HCC patient by single-cell RNA sequencing (scRNA-seq). We identified 5753 cells and 16 clusters including hepatocytes/cancer cells, T cells, macrophages, endothelial cells, fibroblasts, NK cells, neutrophils, and B cells. In six tumor cell subclusters, we identified a cluster of proliferative tumor cells associated with poor prognosis. We downloaded scRNA-seq data of {"type":"entrez-geo","attrs":{"text":"GSE125449","term_id":"125449"}}GSE125449 from the NCBI-GEO as validation dataset, and found that a cluster of hepatocytes exhibited high proliferation activity in HCC. Furthermore, we identified a gene signature related to the proliferation of HCC cells. This gene signature is efficient to classify HCC patients into two groups with distinct prognosis in both TCGA and ICGC database cohorts. Our results reveal the intratumoral heterogeneity of HCC at single cell level and identify a gene signature associated with HCC prognosis.