Studies of fluorescent imaging for mRNA detection in living cells

This dissertation focuses on the study of imaging mRNA in living cells. To achieve this research objective, three approaches have been utilized:: 1) Imaging of a transgenic mRNA tagged by multiple repeats of malachite green: MG) binding aptamer.: 2) Imaging of inducible nitric oxide synthase: iNOS) mRNA by strand-displacement activated Peptide Nucleic Acid: PNA) probes.: 3) Imaging of iNOS mRNA by binary fluorescently labeled PNA probes. The first approach was based on the work of our former lab member Dr. Huafeng Fang, who had constructed a multiple MG binding aptamer tagged transgene: Flag-mβ2AR-GFP-MGVI), which could also express a green fluorescence protein associated with an adrenergic receptor protein. It has been reported that the tagged aptamer sequence can increase the fluorescence of MG up to 2000 fold by binding to MG. Total RNA extract of the transfected MDCK cells has shown up to 22 times increase of fluorescence in the presence of MG. Confocal fluorescence imaging study has shown that in the presence of MG, cells expressing the transgene showed both the fluorescence of GFP and enhanced fluorescence of MG. A flow cytometry study detected that in the presence of MG and transfected cells showed 1.3 fold increase of fluorescence compared to the wild type MDCK cells. The next approach was to use strand-displacement activated PNA probes to detect the iNOS mRNA in living RAW 264.7 mouse macrophage cells. A probe constitutes of an antisense 23-mer fluorescein: FAM) labeled antisense PNA and a 17-mer Dabcylplus labeled complementary DNA was used. The fluorescence of the FAM was quenched when the two strands hybridized to each other. In the presence of target mRNA, the shorter strand was displaced by the mRNA, which has more base pairs complementary to the PNA. The fluorescence of FAM was restored and thus could be used to detect the mRNA. The probe has been shown to be able to detect the target DNA and in vitro transcribed mRNA in solution. Fluorescence in situ hybridization: FISH) showed that the probes showed 3.6: ± 1.8)-fold increase of fluorescence between stimulated cells expressing a high level of iNOS mRNA and non-stimulated cells. Cationic Shell-crosslinked Knedel-like: cSCK) nanoparticles were employed to deliver probes into living cells and the fluorescence of the stimulated cells observed by confocal microscopy increased 16.6: ± 7.9)-fold. RT-PCR was conducted to determine the absolute copy number of the iNOS mRNA in cells. The detected increase of iNOS mRNA after 18 hours of stimulation was around 100 times, and the actual copy number of the mRNA per cell was around 70000. These results reveal that the under our current systems, strand-displacement probes are not sufficient to report quantitatively on the mRNA copy numbers in living cells, but they can be used to achieve mRNA imaging in living cells. The third approach was to use fluorescent labeled binary PNA probes to image the iNOS mRNA in living RAW 264.7 cells. PNAs bearing FAM and Cy5 and targeting the adjacent sites of the iNOS mRNA were synthesized and had shown FRET signal upon binding to the DNA target and in vitro transcribed iNOS mRNA in solution. The probes were delivered into living cells by hybridizing to their partially complementary DNAs and forming complexes with cSCK nanoparticles. Fluorescent images were taken by confocal microscope. The matched probes showed FRET image for stimulated cells while control probes showed almost no FRET signal and non-stimulated cells treated with matched probes showed weak signal. The average FRET intensity detected in stimulated cells was 3.8: ± 0.9) times higher than in non-stimulated cells

Information Complexity versus Corruption and Applications to Orthogonality and Gap-Hamming

Three decades of research in communication complexity have led to the invention of a number of techniques to lower bound randomized communication complexity. The majority of these techniques involve properties of large submatrices (rectangles) of the truth-table matrix defining a communication problem. The only technique that does not quite fit is information complexity, which has been investigated over the last decade. Here, we connect information complexity to one of the most powerful "rectangular" techniques: the recently-introduced smooth corruption (or "smooth rectangle") bound. We show that the former subsumes the latter under rectangular input distributions. We conjecture that this subsumption holds more generally, under arbitrary distributions, which would resolve the long-standing direct sum question for randomized communication. As an application, we obtain an optimal $\Omega(n)$ lower bound on the information complexity---under the {\em uniform distribution}---of the so-called orthogonality problem (ORT), which is in turn closely related to the much-studied Gap-Hamming-Distance (GHD). The proof of this bound is along the lines of recent communication lower bounds for GHD, but we encounter a surprising amount of additional technical detail

Sampled in Pairs and Driven by Text: A New Graph Embedding Framework

In graphs with rich texts, incorporating textual information with structural information would benefit constructing expressive graph embeddings. Among various graph embedding models, random walk (RW)-based is one of the most popular and successful groups. However, it is challenged by two issues when applied on graphs with rich texts: (i) sampling efficiency: deriving from the training objective of RW-based models (e.g., DeepWalk and node2vec), we show that RW-based models are likely to generate large amounts of redundant training samples due to three main drawbacks. (ii) text utilization: these models have difficulty in dealing with zero-shot scenarios where graph embedding models have to infer graph structures directly from texts. To solve these problems, we propose a novel framework, namely Text-driven Graph Embedding with Pairs Sampling (TGE-PS). TGE-PS uses Pairs Sampling (PS) to improve the sampling strategy of RW, being able to reduce ~99% training samples while preserving competitive performance. TGE-PS uses Text-driven Graph Embedding (TGE), an inductive graph embedding approach, to generate node embeddings from texts. Since each node contains rich texts, TGE is able to generate high-quality embeddings and provide reasonable predictions on existence of links to unseen nodes. We evaluate TGE-PS on several real-world datasets, and experiment results demonstrate that TGE-PS produces state-of-the-art results on both traditional and zero-shot link prediction tasks.Comment: Accepted by WWW 2019 (The World Wide Web Conference. ACM, 2019

The Application of Wavelet Neural Network in the Settlement Monitoring of Subway

The settlement monitoring of subway runs through the entire construction stage of subway. It is very important to predict the accurate settlement value for construction safety of subway. In this paper, the wavelet transform is used to denoise the settlement data. The auxiliary wavelet neural network, embedded wavelet neural network and single BP neural network are applied to predict the settlement of Tianjin subway. Compared with single BP neural network and auxiliary wavelet neural network, the embedded wavelet neural network model has a higher accuracy and better prediction effect. The embedded wavelet neural network is more valuable than the BP neural network model so it can be used in the prediction of subway settlemen