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Bei liu , PhD

I am a research assistant professor at the University of North Carolina at Chapel Hill. I am a dedicated microscopist with a great passion on engineering biosensors and optogenetic tools. I am also interested in image processing/analysis, particularly with deep learning approaches.

The purpose of this site is to provide useful information related to my research interests. If you are interested in my literatures sharing, please go here.

Microscopy Biotech. Probes Computational Bio. Deep learning
Single molecule imaging Biosensors Fluorescent proteins SM data processing Deep learning
Superresolution imaging Optogenetics Organic dyes, quantum dots FRET biosensor
Light-sheet imaging --- in vivo labeling ---

Microscopy techniques

single molecule imaging

  • Introduction: No two molecules are alike. Single molecule imaging (SMI) also you to follow one molecule at a time, reserving the heterogenous information that may easily ignored by ensemble fluorescence measurement. Meanwhile, SM sensitivity allows researcher to study rare happend events. No over-expression is needed, thus maintaining the system under physilogical situation.

    SM techniques Suitable scenarios
    Single molecule tracking diffusivity, protein-protein interaction
    Single molecule FRET conformational change
    Single molecule bleaching oligomerization states
    PALM/STORM superresolution

    Generally, SMI data processing consists of four steps:

    1. picking molecules from raw movie;
    2. linking correspondent molecules on consecutive frames; 3.
    3. rendering tracking/reconstruction results;
    4. post-analysis.

  • Useful links

  • Refs

superresolution imaging

  • Introduction
  • Useful links
  • Refs

light-sheet imaging

  • Introduction
  • Useful links
  • Refs

Biotech.

biosensors

optogenetic tools

  • Introduction

    I am not a molecular biologist, but I'm always fansnated in controlling singnaling pathway using optogenetic tools. Here, I tried to summarize currently aviliable optogenetic tools based on LOV, Cry2, et. al.

    LOV2 Based

    CRY2 Based

    1. Cry2-mCh: The photolyase homology region (PHR) of Cry2 fused to mCherry (Cry2-mCh) formed distincet fluorescent puncta within 10 s of 405 or 488 nm light illumination. Light-induced Cry2-LRP6c clustering modulates the Wnt/β-catenin pathway. Light-induced clustering activates Rac1 and RhoA. Add a constitutively oligomerizing DIX domain to mCherry-RhoA, the resulting protein was membrane localized.
    2. CRY2olig: Light-Induced Co-clustering (LINC), can be used to interrogate protein interaction dynamics, and to probe protein interactions. CRY2olig bears a E490G mutation, that greatly enhanceds light-induced clustering of CRY2. LINC assay takes CRY2olig-tagged 'bait' protein and a fluorescent-tagged 'prey'. LINK-FRAP is for querying interactions at compact sites, where resident proteins already appear punctate.
    3. optoTrk; optoFGFR;
    4. CLICR: short for Clustering Indirectly using Cryptochrome 2. CLICR can be used to photocontrol of the clustering of diverse transmembrane receptors including FGFR, PDGFR and Integrins, as well as manipulating endogenous receptor tyrosine kinase (RTK); Strategy: Cry2 fused to a binding domain (Cry2-BD) possessing limited affinity for a garget receptor, which would remain largely in the cytoplasm in the absence o light.
    5. CRY2clust: This paper investigated how fusion proteins or tags influence the efficiency of CRY2-based oligomerization. Indentified a short peptide (nine residues), which substantially enhanced light-induced CRY2 clustering.
    6. CRY2high and CRY2low: CRY2 can undergo light-dependent CRY2-CRY2 homo-oligomerization and CRY2-CIB1 hetero-dimerization. N-terminal charges are critical for CRY2-CIB1 interaction, while two C-terminal charges impact CRY2 homo-oligomerization, whith positive charges facilitating oligomerization and negative charges inhibiting it.

  • Useful links

    1. Hahn lab at UNC-Chapel Hill
    2. Optogenetics database

  • Refs

Fluorescent probes

fluorescent proteins

organic dyes

  • Introduction

in vivo labeling

  • self-labeling-enzymes

    SNAP, Halo, Clip-tag

  • unnature-amino-acid

    UAA

Computational Bio.

SM-data-processing

  1. Picking molecule from raw movie
  2. Localization methods
  3. Liking methods
  4. Post-analysis: diffusion
  5. Post-analysis: clustering

FRET-biosensor-data-processing

Deep learning