Welcome to Fei Lab
Department of Biochemistry and Molecular Biology
What do we study ?
Bacterial small RNA

Small regulatory noncoding RNAs (here broadly defined as sRNAs) play critical roles in regulating genes involved in almost all cellular processes, including development, apoptosis, stress responses, tumorigenesis, infection, and immunity. Due to their specificity and versatility, sRNAs have inspired broad applications including new therapies for human diseases (e.g., small hairpin RNAs) and genome engineering (e.g., CRISPR/Cas systems), among other applications. That said, a precise quantitative description of the fundamental mechanism of sRNA-mediated regulation and interference can largely benefit the further improvement of the efficiency and robustness of these applications by providing critical models and parameters. Our research aims at providing a quantitative description at the molecular, cellular, and the systems levels.Using bacteria as model systems,our missions are to understand the molecular mechanisms by which sRNAsmodulate messenger RNA (mRNA) translation and degradation, as well as physiological response caused by sRNA-mediated regulation in the context of pathogenic bacteria-host interactions.


RNA/protein granule

In eukaryotic cells, RNAs and proteins can self-assemble into various types of non-membranous structures or domains under natural or stressed conditions, including P body and stress granule, etc. in cytoplasm, and nucleolus, nuclear speckle, paraspeckle, and Cajal body, etc. in nucleus. As one of the most conspicuous domains in the nucleus, nuclear speckles are highly heterogeneous in composition, enriched with poly A+ RNAs and numerous proteins involved in mRNA processing.Many cellular functions have been implicated to be associated with nuclear speckles, including the storage and/or assembly sites of pre-mRNA processing factors, as well as the structural domains that control the efficiency and integration of distinct steps in gene expression, ranging from transcription to mRNA export.We are interested in mapping the distribution of components within the speckle using cell imaging techniques, and determining the involvement of such organization in the speckle-ascribed functions.


How do we study ?
Super-resolution imaging

The center of a single fluorophore can be very accurately determined in the diffraction limited spot. Taking the advantage of photophysical properties of certain fluorophores that can stochastically blink between the bright and dark states, in single-molecule localization based super-resolution imaging, only a small fraction of fluorophores are activated and localized each time. By repeating the reactivating-and-imaging cycle many times and combining many of such frames together, a super-resolution image can be reconstructed that usually has ~10-fold enhancement in the resolution compared to diffraction-limited fluorescence microcopy. Super-resolution imaging provides a powerful tool for investigating subcellular localization, higher-order architectures of sub-compartments and inter-molecular interactions as well as tracking the motions of individual molecules inside the cell.


Single-molecule detection

Single-molecule fluorescence microscopies are widely nowadays in biophysical research. By fluorescently labeling different molecules or multiple domains on the same molecule, association/dissociation of factors, critical conformational dynamics, and the temporal order of various events can be simultaneously measured, and heterogeneities in the kinetic pathways can be revealed.In particular, single-molecule fluorescence resonance energy transfer (smFRET) is especially powerful for probing conformational changesfor biomolecules, as the energy transfer efficiency is inverse proportional to the sixth power of the distance between a pair ofthe donor and the acceptor dyes, making FRET a very sensitive microscopic ruler at nanometer scale.


Analysis algorithms
Clustering analysis

We have employed a density based clustering analysis algorithm, DBSCAN, to analyze RNA copy numbers. Spots corresponding to individual localization events in a reconstructed super-resolution image are segregated into clusters based on their spatial density. Clustered data are then superimposed to the DIC image and the boundaries of individual cells were identified using MATLAB code such that clusters are allocated into individual cells. After clustering analysis and cluster allocation, information derived includes: (1) total number of clusters in each cell, which approximates the total number of RNAs in low copy number cases; (2) number of localization spots in each cluster, which we use to build the characteristic distribution of number of spots per RNA; (3) total number of clustered spots in each cell, which is the product of (1) and (2) and is used for estimating the copy number of RNA per cell; (4) average radius of individual clusters; (5) center coordinates of individual cells.



In collaboration with Prof. Chris Wiggins group (Applied Physics and Applied Mathematics, Columbia University), during my graduate research, we have developed a variational Bayesian approach that allows us to generalize the concept of maximum likelihood to determining the most likely model (e.g. the number of conformational states in the biological system) as well as the most likely parameters (e.g. the transition rates between conformational states) that best describe the single-molecule time trajectories. We have coded this algorithm into a widely available opensource software package called vbFRET (http://vbfret.sourceforge.net/).