MARD: a new method to detect differential gene expression in treatment-control time courses

MOTIVATION: Characterizing the dynamic regulation of gene expression by time course experiments is becoming more and more important. A common problem is to identify differentially expressed genes between the treatment and control time course. It is often difficult to compare expression patterns of a gene between two time courses for the following reasons: (1) the number of sampling time points may be different or hard to be aligned between the treatment and the control time courses; (2) estimation of the function that describes the expression of a gene in a time course is difficult and error-prone due to the limited number of time points. We propose a novel method to identify the differentially expressed genes between two time courses, which avoids direct comparison of gene expression patterns between the two time courses. RESULTS: Instead of attempting to 'align' and compare the two time courses directly, we first convert the treatment and control time courses into neighborhood systems that reflect the underlying relationships between genes. We then identify the differentially expressed genes by comparing the two gene relationship networks. To verify our method, we apply it to two treatment-control time course datasets. The results are consistent with the previous results and also give some new biologically meaningful findings. AVAILABILITY: The algorithm in this paper is coded in C++ and is available from http://leili-lab.cmb.usc.edu/yeastaging/projects/MARD/     

Measurement of bacterial gene expression in vivo by laser capture microdissection and quantitative real-time RT-PCR

Due to the relative small number of bacterial pathogens present in an infected host, exploration of pathogen gene expression in vivo is challenging. This study reports the development of a protocol for quantifying bacterial gene expression in vivo in Actinobacillus pleuropneumoniae using laser capture microdissection and real-time quantitative RT-PCR.     

TSEE: an elastic embedding method to visualize the dynamic gene expression patterns of time series single-cell RNA sequencing data

Background

Time series single-cell RNA sequencing (scRNA-seq) data are emerging. However, the analysis of time series scRNA-seq data could be compromised by 1) distortion created by assorted sources of data collection and generation across time samples and 2) inheritance of cell-to-cell variations by stochastic dynamic patterns of gene expression. This calls for the development of an algorithm able to visualize time series scRNA-seq data in order to reveal latent structures and uncover dynamic transition processes.

Results

In this study, we propose an algorithm, termed time series elastic embedding (TSEE), by incorporating experimental temporal information into the elastic embedding (EE) method, in order to visualize time series scRNA-seq data. TSEE extends the EE algorithm by penalizing the proximal placement of latent points that correspond to data points otherwise separated by experimental time intervals. TSEE is herein used to visualize time series scRNA-seq datasets of embryonic developmental processed in human and zebrafish. We demonstrate that TSEE outperforms existing methods (e.g. PCA, tSNE and EE) in preserving local and global structures as well as enhancing the temporal resolution of samples. Meanwhile, TSEE reveals the dynamic oscillation patterns of gene expression waves during zebrafish embryogenesis.

Conclusions

TSEE can efficiently visualize time series scRNA-seq data by diluting the distortions of assorted sources of data variation across time stages and achieve the temporal resolution enhancement by preserving temporal order and structure. TSEE uncovers the subtle dynamic structures of gene expression patterns, facilitating further downstream dynamic modeling and analysis of gene expression processes. The computational framework of TSEE is generalizable by allowing the incorporation of other sources of information.

     

基于EMA-qPCR的茄科青枯菌活体检测技术的建立

【目的】利用特异性核酸染料叠氮溴乙锭(Ethidium monoazide bromide, EMA)与实时荧光定量PCR技术相结合, 建立一种能有效区分青枯菌死活细胞的检测方法。【方法】样品DNA制备前经EMA渗透预处理, 再进行实时荧光定量PCR特异扩增菌体DNA。【结果】终浓度为2.0 mg/L的EMA能有效排除1.0×107 CFU/mL灭活青枯菌细胞DNA的扩增, 对活细胞和不可培养状态(Viable but non-culturable, VBNC)活菌的DNA扩增均没有影响。当每个定量PCR反应体系中的活细胞在5.0×100?5.0×104 CFU范围内时, 扩增Ct值与定量PCR反应体系中活细胞CFU对数值呈良好的负相关性(R2=0.992 5)。比较EMA-qPCR法和平板计数法对经过不同温度短期保存的青枯菌检测结果发现, 待检样品可在24 °C与4 °C冷藏条件下短期保存。【结论】本研究建立的EMA-qPCR方法能有效检测青枯菌VBNC细胞和有效区分死活菌, 避免或减少青枯菌PCR检测的假阳性和假阴性。     

Small interfering RNA and gene expression analysis using a multiplex branched DNA assay without RNA purification

The authors have developed a novel multiplex detection system that quantitatively measures the expression level of 11 messenger RNAs (mRNAs) directly from cell lysates or tissue homogenates without RNA purification. The system incorporates branched DNA (bDNA) technology from Bayer and a multiplex bead array platform from Luminex. In this study, a 21-nt synthetic small interfering RNA (siRNA; specifically designed to knockdown interleukin-8 [IL-8] expression) was delivered into HeLa cells. Using the multiplex bDNA assay, gene expression levels were measured simultaneously from cell lysates for 11 genes. After treating the HeLa cells for 20 h with phorbol myristate acetate (PMA), IL-8 mRNA levels were induced by almost 50-fold; transfection with 30 nM IL-8-specific siRNA reduced the PMA-induced IL-8 mRNA by 80%. In addition, PMA induced mRNA expression in IL-1alpha (3-fold) and IL-6 (4-fold); however, the IL-8 siRNA did not affect the expression of either of these 2 cytokine genes, indicating that the siRNA was selective for IL-8 mRNA expression. Three housekeeping genes' expression levels were measured under all conditions tested. The multiplex bDNA assay provides a powerful tool for quantitative multiplex gene expression analysis directly from cell lysates, which could be extremely valuable for conservation of rare or difficult-to-obtain samples.     

Optimization of laser capture microdissection and RNA amplification for gene expression profiling of prostate cancer

Background  

To discover prostate cancer biomarkers, we profiled gene expression in benign and malignant cells laser capture microdissected (LCM) from prostate tissues and metastatic prostatic adenocarcinomas. Here we present methods developed, optimized, and validated to obtain high quality gene expression data.     

激光显微切割分离细胞的微量RNA质量鉴定体系的 建立

摘要: 探索一套激光显微切割(Laser capture microdissection, LCM)分离细胞后获得的微量RNA质量鉴定标准操作流程。选取3个低温保存的胃癌旁组织样本, 冰冻切片进行甲酚紫染色和病理学检查, 利用激光显微切割技术分离非癌上皮细胞, 提取RNA并以Agilent 2100生物分析仪鉴定RNA的纯度和完整性。同时, 选择高、中、低3种不同表达丰度的6个基因(EF1A, ACTB, GAPHD, B2M, MED1, CK20), 在每个基因的5′和3′端设计引物, RT-PCR扩增。以3个培养细胞制备的高质量RNA和3个有降解的胃癌旁组织样本RNA作对照, RT-PCR扩增结果与Agilent 2100生物分析仪的结果高度一致。结果显示冻存组织进行冰冻切片结合病理学检查后, LCM获取细胞提取微量RNA采用RT-PCR进行质量鉴定是一种操作简单的稳定方法, 可以作为肿瘤基因组研究的有效和常规方法。     

A method to detect transfected chloramphenicol acetyltransferase gene expression in intact animals

A rapid procedure is described for assaying chloramphenicol acetyltransferase (CAT, EC 2.3.1.28) enzyme activity in intact animals following transfection of the RSV CAT plasmid into mouse bone marrow cells by electroporation. The reconstituted mice were injected with [14C]chloramphenicol and ethyl acetate extracts of 24-h urine samples were analyzed by TLC autoradiography for the excretion of 14C-labeled metabolites. CAT expression in vivo can be detected by the presence of acetylated 14C-labeled metabolites in the urine within 1 week after bone marrow transplantation and, under the conditions described, these metabolites can be detected for at least 3 months. CAT expression in intact mice as monitored by the urine assay correlates with the CAT expression in the hematopoietic tissues assayed in vitro. This method offers a quick mode of screening for introduced CAT gene expression in vivo without sacrificing the mice.