Project description:Understanding how stem cells interact with cardiomyocytes is crucial for cell-based therapies to restore the cardiomyocyte loss that occurs during myocardial infarction and other cardiac diseases. It has been thought that functional myocardial repair and regeneration could be regulated by stem cell-cardiomyocyte contact. However, because various contact modes (junction formation, cell fusion, partial cell fusion, and tunneling nanotube formation) occur randomly in a conventional coculture system, the particular regulation corresponding to a specific contact mode could not be analyzed. In this study, we used laser-patterned biochips to define cell-cell contact modes for systematic study of contact-mediated cellular interactions at the single-cell level. The results showed that the biochip design allows defined stem cell-cardiomyocyte contact-mode formation, which can be used to determine specific cellular interactions, including electrical coupling, mechanical coupling, and mitochondria transfer. The biochips will help us gain knowledge of contact-mediated interactions between stem cells and cardiomyocytes, which are fundamental for formulating a strategy to achieve stem cell-based cardiac tissue regeneration.
Project description:Blind Source Separation (BSS) is a powerful tool for analyzing composite data patterns in many areas, such as computational biology. We introduce a novel BSS method, Convex Analysis of Mixtures (CAM), for separating non-negative well-grounded sources, which learns the mixing matrix by identifying the lateral edges of the convex data scatter plot. We propose and prove a sufficient and necessary condition for identifying the mixing matrix through edge detection in the noise-free case, which enables CAM to identify the mixing matrix not only in the exact-determined and over-determined scenarios, but also in the under-determined scenario. We show the optimality of the edge detection strategy, even for cases where source well-groundedness is not strictly satisfied. The CAM algorithm integrates plug-in noise filtering using sector-based clustering, an efficient geometric convex analysis scheme, and stability-based model order selection. The superior performance of CAM against a panel of benchmark BSS techniques is demonstrated on numerically mixed gene expression data of ovarian cancer subtypes. We apply CAM to dissect dynamic contrast-enhanced magnetic resonance imaging data taken from breast tumors and time-course microarray gene expression data derived from in-vivo muscle regeneration in mice, both producing biologically plausible decomposition results.
Project description:The assessment of inter-rater reliability is a topic that is infrequently addressed in Caenorhabditis elegans research, despite the existence of sophisticated statistical methods and the strong interest in the field in obtaining reliable and accurate data. This study applies statistical modeling as a robust means of analyzing the performance of worm researchers measuring the stage of worm development in terms of the two independent factors that comprise "agreement", which are (1) accuracy, representing trueness, a lack of systematic differences, or lack of bias, and (2) precision, representing reliability or the extent to which random differences are small. In our study, multiple raters assessed the same sample of worms to determine the developmental stage of each animal, and we collected data linking each scorer with their assessment for each worm. To describe the agreement of the raters, we developed a structural equation model with latent variables and thresholds, which assumes that all the raters are jointly scoring each worm. This common factor model separately quantifies the two aspects of agreement. The stage-specific thresholds examine accuracy and characterize the relative biases of each rater during the scoring process. The factor loadings for each rater examine the precision and characterizes the random error of the rater. Within our group, we found that the overall agreement was good, while certain adjustments in particular raters would have decreased systematic differences. Hence, the use of developmental stage as an experimental outcome can be both accurate and precise.
Project description:BackgroundHypoxia suppresses global protein production, yet certain essential proteins are translated through alternative pathways to survive under hypoxic stress. Translation via the internal ribosome entry site (IRES) is a means to produce proteins under stress conditions such as hypoxia; however, the underlying mechanism remains largely uncharacterized.MethodsProteomic and bioinformatic analyses were employed to identify hnRNPM as an IRES interacting factor. Clinical specimens and mouse model of tumorigenesis were used for determining the expression and correlation of hnRNPM and its target gene. Transcriptomic and translatomic analyses were performed to profile target genes regulated by hnRNPM.FindingsHypoxia increases cytosolic hnRNPM binding onto its target mRNAs and promotes translation initiation. Clinical colon cancer specimens and mouse carcinogenesis model showed that hnRNPM is elevated during the development of colorectal cancer, and is associated with poor prognosis. Genome-wide transcriptomics and translatomics analyses revealed a unique set of hnRNPM-targeted genes involved in metabolic processes and cancer neoplasia are selectively translated under hypoxia.InterpretationThese data highlight the critical role of hnRNPM-IRES-mediated translation in transforming hypoxia-induced proteome toward malignancy. FUND: This work was supported by the Ministry of Science and Technology, Taiwan (MOST 104-2320-B-006-042 to HSS and MOST 105-2628-B-001-MY3 to TMC).
Project description:How the human brain evolved has attracted tremendous interests for decades. Motivated by case studies of primate-specific genes implicated in brain function, we examined whether or not the young genes, those emerging genome-wide in the lineages specific to the primates or rodents, showed distinct spatial and temporal patterns of transcription compared to old genes, which had existed before primate and rodent split. We found consistent patterns across different sources of expression data: there is a significantly larger proportion of young genes expressed in the fetal or infant brain of humans than in mouse, and more young genes in humans have expression biased toward early developing brains than old genes. Most of these young genes are expressed in the evolutionarily newest part of human brain, the neocortex. Remarkably, we also identified a number of human-specific genes which are expressed in the prefrontal cortex, which is implicated in complex cognitive behaviors. The young genes upregulated in the early developing human brain play diverse functional roles, with a significant enrichment of transcription factors. Genes originating from different mechanisms show a similar expression bias in the developing brain. Moreover, we found that the young genes upregulated in early brain development showed rapid protein evolution compared to old genes also expressed in the fetal brain. Strikingly, genes expressed in the neocortex arose soon after its morphological origin. These four lines of evidence suggest that positive selection for brain function may have contributed to the origination of young genes expressed in the developing brain. These data demonstrate a striking recruitment of new genes into the early development of the human brain.
Project description:When SARS-CoV-2 emerged at the end of 2019, no approved therapeutics or vaccines were available. An urgent need for countermeasures during this crisis challenges the current paradigm of traditional drug discovery and development, which usually takes years from start to finish. Approaches that accelerate this process need to be considered. Here we propose the minimum data package required to move a compound into clinical development safely. We further define the additional data that should be collected in parallel without impacting the rapid path to clinical development. Accelerated paths for antivirals, immunomodulators, anticoagulants, and other agents have been developed and can serve as "roadmaps" to support prioritization of compounds for clinical testing. These accelerated paths are fueled by a skewed risk-benefit ratio and are necessary to advance therapeutic agents into human trials rapidly and safely for COVID-19. Such paths are adaptable to other potential future pandemics.
Project description:A new silicon chip for protein microarray development, fabrication and validation is proposed. The chip is made of two areas with oxide layers of different thicknesses: an area with a 500 nm SiO2 layer dedicated to interferometric label-free detection and quantification of proteins and an area with 100 nm SiO2 providing enhanced fluorescence. The chip allows, within a single experiment performed on the same surface, label-free imaging of arrayed protein probes coupled with high sensitivity fluorescence detection of the molecular interaction counterparts. Such a combined chip is of high practical utility during assay development process to image arrays, check consistency and quality of the protein array, quantify the amount of immobilized probes and finally detect fluorescence of bioassays.
Project description:Microfluidic biochips hold great potential for liquid analysis in biomedical research and clinical diagnosis. However, the lack of integrated on-chip liquid mixing, bioseparation and signal transduction presents a major challenge in achieving rapid, ultrasensitive bioanalysis in simple microfluidic configurations. Here we report magnetic nanochain integrated microfluidic chip built upon the synergistic functions of the nanochains as nanoscale stir bars for rapid liquid mixing and as capturing agents for specific bioseparation. The use of magnetic nanochains enables a simple planar design of the microchip consisting of flat channels free of common built-in components, such as liquid mixers and surface-anchored sensing elements. The microfluidic assay, using surface-enhanced Raman scattering nanoprobes for signal transduction, allows for streamlined parallel analysis of multiple specimens with greatly improved assay kinetics and delivers ultrasensitive identification and quantification of a panel of cancer protein biomarkers and bacterial species in 1 μl of body fluids within 8 min.
Project description:Electrical stimulation of neural systems is a key tool for understanding neural dynamics and ultimately for developing clinical treatments. Many applications of electrical stimulation affect large populations of neurons. However, computational models of large networks of spiking neurons are inherently hard to simulate and analyze. We evaluate a reduced mean-field model of excitatory and inhibitory adaptive exponential integrate-and-fire (AdEx) neurons which can be used to efficiently study the effects of electrical stimulation on large neural populations. The rich dynamical properties of this basic cortical model are described in detail and validated using large network simulations. Bifurcation diagrams reflecting the network's state reveal asynchronous up- and down-states, bistable regimes, and oscillatory regions corresponding to fast excitation-inhibition and slow excitation-adaptation feedback loops. The biophysical parameters of the AdEx neuron can be coupled to an electric field with realistic field strengths which then can be propagated up to the population description. We show how on the edge of bifurcation, direct electrical inputs cause network state transitions, such as turning on and off oscillations of the population rate. Oscillatory input can frequency-entrain and phase-lock endogenous oscillations. Relatively weak electric field strengths on the order of 1 V/m are able to produce these effects, indicating that field effects are strongly amplified in the network. The effects of time-varying external stimulation are well-predicted by the mean-field model, further underpinning the utility of low-dimensional neural mass models.
Project description:In this study, we used blood samples of nine patients with severe SARS-CoV-2 infection either with or without acute respiratory distress syndrome (ARDS) and analyzed them on the Illumina EPIC methylation microarray.