Single-cell transcriptome analysis for cancer and biology of the pancreas: A review on recent progress

Single-cell sequencing has become one of the most used techniques across the wide field of biology. It has enabled researchers to investigate the whole transcriptome at the cellular level across tissues, which unlocks numerous potentials for basic and applied studies in future diagnosis and therapy. Here, we review the impact of single-cell RNA sequencing, as the prominent single-cell technique, in pancreatic biology and cancer. We discuss the most recent findings about pancreatic physiology and pathophysiology owing to this technological advancement in the past few years. Using single-cell RNA sequencing, researchers have been able to discover cellular heterogeneity across healthy cell types, as well as cancer tissues of the pancreas. We will discuss the new immunological targets and new molecular mechanisms of progression in the microenvironment of pancreatic cancer studied using single-cell RNA sequencing. The scope is not limited to cancer tissues, and we cover novel developmental, evolutionary, physiological, and heterogenic insights that have also been achieved recently for pancreatic tissues. We cover all biological insights derived from the single-cell RNA sequencing data, discuss the corresponding pros and cons, and finally, conclude how future research can move better by utilizing single-cell analysis for pancreatic biology.

Weighted Gene Co-expression Network Analysis of Endometriosis and Identification of Functional Modules Associated With Its Main Hallmarks

Although many genes have been identified using high throughput technologies in endometriosis (ES), only a small number of individual genes have been analyzed functionally. This is due to the complexity of the disease that has different stages and is affected by various genetic and environmental factors. Many genes are upregulated or downregulated at each stage of the disease, thus making it difficult to identify key genes. In addition, little is known about the differences between the different stages of the disease. We assumed that the study of the identified genes in ES at a system-level can help to better understand the molecular mechanism of the disease at different stages of the development. We used publicly available microarray data containing archived endometrial samples from women with minimal/mild endometriosis (MMES), mild/severe endometriosis (MSES) and without endometriosis. Using weighted gene co-expression analysis (WGCNA), functional modules were derived from normal endometrium (NEM) as the reference sample. Subsequently, we tested whether the topology or connectivity pattern of the modules was preserved in MMES and/or MSES. Common and specific hub genes were identified in non-preserved modules. Accordingly, hub genes were detected in the non-preserved modules at each stage. We identified sixteen co-expression modules. Of the 16 modules, nine were non-preserved in both MMES and MSES whereas five were preserved in NEM, MMES, and MSES. Importantly, two non-preserved modules were found in either MMES or MSES, highlighting differences between the two stages of the disease. Analyzing the hub genes in the non-preserved modules showed that they mostly lost or gained their centrality in NEM after developing the disease into MMES and MSES. The same scenario was observed, when the severeness of the disease switched from MMES to MSES. Interestingly, the expression analysis of the new selected gene candidates including CC2D2A, AEBP1, HOXB6, IER3, and STX18 as well as IGF-1, CYP11A1 and MMP-2 could validate such shifts between different stages. The overrepresented gene ontology (GO) terms were enriched in specific modules, such as genetic disposition, estrogen dependence, progesterone resistance and inflammation, which are known as endometriosis hallmarks. Some modules uncovered novel co-expressed gene clusters that were not previously discovered.

Nonlinear analysis of anesthesia dynamics by Fractal Scaling Exponent

The depth of anesthesia estimation has been one of the most research interests in the field of EEG signal processing in recent decades. In this paper we present a new methodology to quantify the depth of anesthesia by quantifying the dynamic fluctuation of the EEG signal. Extraction of useful information about the nonlinear dynamic of the brain during anesthesia has been proposed with the optimum Fractal Scaling Exponent. This optimum solution is based on the best box sizes in the Detrended Fluctuation Analysis (DFA) algorithm which have meaningful changes at different depth of anesthesia. The Fractal Scaling Exponent (FSE) Index as a new criterion has been proposed. The experimental results confirm that our new Index can clearly discriminate between aware to moderate and deep anesthesia levels. Moreover, it significantly reduces the computational complexity and results in a faster reaction to the transients in patients' consciousness levels in relations with the other algorithms.

Dimensional characterization of anesthesia dynamic in reconstructed embedding space

The depth of anesthesia quantification has been one of the most research interests in the field of EEG signal processing and nonlinear dynamical analysis has emerged as a novel method for the study of complex systems in the past few decades. In this investigation we use the concept of nonlinear time series analysis techniques to reconstruct the attractor of anesthesia from EEG signal which have been obtained from different hypnotic states during surgery to give a characterization of the dimensional complexity of EEG by Correlation Dimension estimation. The dimension of the anesthesia strange attractor can be thought of as a measure of the degrees of freedom or the ;complexity' of the dynamics at different hypnotic levels. The results imply that for awaked state the correlation dimension is high, On the other hand, for light, moderate and deep hypnotic states these values decrease respectively; which means for anesthetized situation we expect lower correlation dimension.