Concurrent Workshop (Imaging): Spectroscopic Imaging

Abstracts

Systems imaging to reveal the eukaryotic organelle interactome and oral microbiome structure and assembly

Speaker:
Track:

To fully understand the function of any biological system, it is often necessary to know how the different components of the system are arranged in space.  However, the ability to distinguish more than a few different components using fluorescence microscopy is severely limited.  My laboratory has employed spectral imaging and novel computational analyses to study the structure of two biological systems: the eukaryotic cell and the human oral microbial community.

The organization of the eukaryotic cell into discrete membrane-bound organelles allows for the separation of incompatible biochemical processes, but the activities of these organelles must be coordinated.  We have adapted a lattice light sheet microscope for excitation-based spectral imaging and observed the frequency and locality of interactions among six different membrane-bound organelles (endoplasmic reticulum, Golgi, lysosome, peroxisome, mitochondria and lipid droplet) and show how these relationships change over time. We conclude that although the interactions among individual organelles are dynamic and heterogeneous, the sum total of interactions, which we term the organelle interactome, is remarkably stable over time.

Authors:
  • Alex Valm
    Author Email
    avalm@albany.edu
    Institution
    University at Albany, State University of New York

Spectral Phenotyping

Track:

 

SPECTRAL PHENOTYPING.

The goal of the proposal is to develop spectral phenotyping, a non-destructive, label-free method to identify cell types and altered call states based on their spectral properties. Spectral signatures are acquired by scanning a cell with infrared (IR) light to computationally “learn” what features identify a cell type, a method conceptually akin to “face recognition” software. The technology integrates Fourier transform infrared (FTIR) spectromicroscopy and deep learning to distinguish among cell types (e.g., neuron versus astrocyte) or cell states (e.g., disease versus normal). FTIR signatures alone rapidly classify living unlabeled human disease cells.We propose that identification of cell subtypes by spectral phenotyping will be possible with a precision and reliability that has not previously been achieved using conventional approaches.

 

 

Authors:
  • Cynthia McMurray
    Author Email
    ctmcmurray@lbl.gov
    Institution
    Lawrence Berkeley Laboratory