Robust discovery of periodically expressed genes using the laplace periodogram

Background  

Time-course gene expression analysis has become important in recent developments due to the increasingly available experimental data. The detection of genes that are periodically expressed is an important step which allows us to study the regulatory mechanisms associated with the cell cycle.     

PyMix - The Python mixture package - a tool for clustering of heterogeneous biological data

Background  

Cluster analysis is an important technique for the exploratory analysis of biological data. Such data is often high-dimensional, inherently noisy and contains outliers. This makes clustering challenging. Mixtures are versatile and powerful statistical models which perform robustly for clustering in the presence of noise and have been successfully applied in a wide range of applications.     

A comparison of imputation procedures and statistical tests for the analysis of two-dimensional electrophoresis data

Background  

Numerous gel-based softwares exist to detect protein changes potentially associated with disease. The data, however, are abundant with technical and structural complexities, making statistical analysis a difficult task. A particularly important topic is how the various softwares handle missing data. To date, no one has extensively studied the impact that interpolating missing data has on subsequent analysis of protein spots.     

Protein-lipid interactions: correlation of a predictive algorithm for lipid-binding sites with three-dimensional structural data

Background  

Over the past decade our laboratory has focused on understanding how soluble cytoskeleton-associated proteins interact with membranes and other lipid aggregates. Many protein domains mediating specific cell membrane interactions appear by fluorescence microscopy and other precision techniques to be partially inserted into the lipid bilayer. It is unclear whether these protein-lipid-interactions are dependent on shared protein motifs or unique regional physiochemistry, or are due to more global characteristics of the protein.     

Increased entropy of signal transduction in the cancer metastasis phenotype

Background  

The statistical study of biological networks has led to important novel biological insights, such as the presence of hubs and hierarchical modularity. There is also a growing interest in studying the statistical properties of networks in the context of cancer genomics. However, relatively little is known as to what network features differ between the cancer and normal cell physiologies, or between different cancer cell phenotypes.     

Cell cycle arrest by a gradient of Dpp signaling during Drosophila eye development

Background  

The secreted morphogen Dpp plays important roles in spatial regulation of gene expression and cell cycle progression in the developing Drosophila eye. Dpp signaling is required for timely cell cycle arrest ahead of the morphogenetic furrow as a prelude to differentiation, and is also important for eye disc growth. The dpp gene is expressed at multiple locations in the eye imaginal disc, including the morphogenetic furrow that sweeps across the eye disc as differentiation initiates.     

A multistage theory of age-specific acceleration in human mortality

Background

The observation of multiple genetic markers in situ by optical microscopy and their relevance to the study of three-dimensional (3D) chromosomal organization in the nucleus have been greatly developed in the last decade. These methods are important in cancer research because cancer is characterized by multiple alterations that affect the modulation of gene expression and the stability of the genome. It is, therefore, essential to analyze the 3D genome organization of the interphase nucleus in both normal and cancer cells.

Results

We describe a novel approach to study the distribution of all telomeres inside the nucleus of mammalian cells throughout the cell cycle. It is based on 3D telomere fluorescence in situ hybridization followed by quantitative analysis that determines the telomeres' distribution in the nucleus throughout the cell cycle. This method enables us to determine, for the first time, that telomere organization is cell-cycle dependent, with assembly of telomeres into a telomeric disk in the G2 phase. In tumor cells, the 3D telomere organization is distorted and aggregates are formed.

Conclusions

The results emphasize a non-random and dynamic 3D nuclear telomeric organization and its importance to genomic stability. Based on our findings, it appears possible to examine telomeric aggregates suggestive of genomic instability in individual interphase nuclei and tissues without the need to examine metaphases. Such new avenues of monitoring genomic instability could potentially impact on cancer biology, genetics, diagnostic innovations and surveillance of treatment response in medicine.     

Measuring DNA content in live cells by fluorescence microscopy

Background

Live-cell fluorescence microscopy (LCFM) is a powerful tool used to investigate cellular dynamics in real time. However, the capacity to simultaneously measure DNA content in cells being tracked over time remains challenged by dye-associated toxicities. The ability to measure DNA content in single cells by means of LCFM would allow cellular stage and ploidy to be coupled with a variety of imaging directed analyses. Here we describe a widely applicable nontoxic approach for measuring DNA content in live cells by fluorescence microscopy. This method relies on introducing a live-cell membrane-permeant DNA fluorophore, such as Hoechst 33342, into the culture medium of cells at the end of any live-cell imaging experiment and measuring each cell’s integrated nuclear fluorescence to quantify DNA content. Importantly, our method overcomes the toxicity and induction of DNA damage typically caused by live-cell dyes through strategic timing of adding the dye to the cultures; allowing unperturbed cells to be imaged for any interval of time before quantifying their DNA content. We assess the performance of our method empirically and discuss adaptations that can be implemented using this technique.

Results

Presented in conjunction with cells expressing a histone 2B-GFP fusion protein (H2B-GFP), we demonstrated how this method enabled chromosomal segregation errors to be tracked in cells as they progressed through cellular division that were later identified as either diploid or polyploid. We also describe and provide an automated Matlab-derived algorithm that measures the integrated nuclear fluorescence in each cell and subsequently plots these measurements into a cell cycle histogram for each frame imaged. The algorithm’s accurate assessment of DNA content was validated by parallel flow cytometric studies.

Conclusions

This method allows the examination of single-cell dynamics to be correlated with cellular stage and ploidy in a high-throughput fashion. The approach is suitable for any standard epifluorescence microscope equipped with a stable illumination source and either a stage-top incubator or an enclosed live-cell incubation chamber. Collectively, we anticipate that this method will allow high-resolution microscopic analysis of cellular processes involving cell cycle progression, such as checkpoint activation, DNA replication, and cellular division.

     

Using machine learning to speed up manual image annotation: application to a 3D imaging protocol for measuring single cell gene expression in the developing <Emphasis Type="Italic">C. elegans</Emphasis> embryo

Background  

Image analysis is an essential component in many biological experiments that study gene expression, cell cycle progression, and protein localization. A protocol for tracking the expression of individual C. elegans genes was developed that collects image samples of a developing embryo by 3-D time lapse microscopy. In this protocol, a program called StarryNite performs the automatic recognition of fluorescently labeled cells and traces their lineage. However, due to the amount of noise present in the data and due to the challenges introduced by increasing number of cells in later stages of development, this program is not error free. In the current version, the error correction (i.e., editing) is performed manually using a graphical interface tool named AceTree, which is specifically developed for this task. For a single experiment, this manual annotation task takes several hours.     

Stimulation of autophagic activity in human glioma cells by anti-proliferative ardipusilloside I isolated from Ardisia pusilla

Aims

Ardipusilloside I (ADS-I), a triterpenoid saponin isolated from Ardisia pusilla A.DC (Myrsinaceae), has been recently tested for cancer treatment including brain cancer. However, the mechanism of its action remains elusive. The present study was to investigate the role of autophagy activation in the anti-tumor activities of ADS-I in human glioma cells.

Main methods

The tetrazolium dye (MTT) colorimetric assay was used for the measurement of cell proliferation in cultured glioma cells, transmission electron microscopy (TEM) for the examination of autophagic activity, flow cytometric analysis for the determination of cell cycle and apoptotic cells, and immunocytochemistry and Western blot for protein expression of microtubule-associated protein light-chain 3 (LC3) and Beclin 1.

Key findings

ADS-I significantly inhibited the proliferation of both U373 and T98G glioma cells in cultures in a dose-dependent manner. The cytotoxic activity of ADS-I against glioma cell growth was associated not only with the induction of cell cycle arrest at G₂/M phase and cell apoptosis in flow cytometric analysis, but also with the activation of autophagy, indicated by the formation of autophagosomes and up-regulated expression of both autophagic protein Beclin 1 and LC3 in glioma cells. Additionally, the treatment with chloroquine, an autophagy inhibitor, reduced ADS-1-mediated cell death.

Significance

These data suggest that the anti-proliferative activity of ADS-I in human glioma cells is associated with the activation of autophagy in addition to cell cycle arrest and apoptosis, and the antagonistic effect of chloroquine suggests an important role of autophagy in ADS-I-mediated cell death against tumor growth.     

Statistical analysis of real-time PCR data

Background  

Even though real-time PCR has been broadly applied in biomedical sciences, data processing procedures for the analysis of quantitative real-time PCR are still lacking; specifically in the realm of appropriate statistical treatment. Confidence interval and statistical significance considerations are not explicit in many of the current data analysis approaches. Based on the standard curve method and other useful data analysis methods, we present and compare four statistical approaches and models for the analysis of real-time PCR data.     

Comment on and reply to "Analysis of variation of amplitudes in cell cycle gene expression" by Liu,Gaido and Wolfinger: On the analysis of gene expression during the normal,eukaryotic, cell cycle

Background  

The paper of Liu, Gaido and Wolfinger on gene expression during the division cycle of HeLa cells using the data of Whitfield et al. are discussed in order to see whether their analysis is related to gene expression during the division cycle.     

Identification of significant periodic genes in microarray gene expression data

Background  

One frequent application of microarray experiments is in the study of monitoring gene activities in a cell during cell cycle or cell division. A new challenge for analyzing the microarray experiments is to identify genes that are statistically significantly periodically expressed during the cell cycle. Such a challenge occurs due to the large number of genes that are simultaneously measured, a moderate to small number of measurements per gene taken at different time points, and high levels of non-normal random noises inherited in the data.     

Enhanced CellClassifier: a multi-class classification tool for microscopy images

Background  

Light microscopy is of central importance in cell biology. The recent introduction of automated high content screening has expanded this technology towards automation of experiments and performing large scale perturbation assays. Nevertheless, evaluation of microscopy data continues to be a bottleneck in many projects. Currently, among open source software, CellProfiler and its extension Analyst are widely used in automated image processing. Even though revolutionizing image analysis in current biology, some routine and many advanced tasks are either not supported or require programming skills of the researcher. This represents a significant obstacle in many biology laboratories.