Driving the imaging revolution in biology

July 16, 2018

BIDS Data Science Fellow Joh Schöneberg uses advanced lattice light-sheet microscopy (LLSM) to image cells and tissues in 4D, and develops novel image analytics tools - such as the pyLattice library - to make new biological discoveries possible. 

Clathrin coated vesicles, about 200nm in size, are formed on the cell membrane.LLSM, developed by Nobel Laureate Eric Betzig, enables unprecedented cell and tissue imaging. One example is the study of clathrin mediated endocytosis (CME), a critical pathway that is responsible for the uptake of metabolites, hormones, proteins and receptors into the cell. In the pathway, clathrin-coated vesicles, about 200nm in size (see Figure 1), are formed on the cell membrane. You can picture them as containers that carry the absorbed molecules into the cell’s interior. To study the pathway - in health and in disease - these vesicles have to be detected and tracked over time. 

While most methods rely on imaging CME in a small plane (2D), LLSM allows researchers to take movies of the process in all three dimensions (3D), over a field of view that is millions of cubic microns in size. Figure 2 shows such a field of view: critical molecules in the CME pathway (clathrin, red; dynamin, green) have been genetically engineered in stem cells to carry a tag that makes them fluorescent. The stem cells were then differentiated into intestinal epithelial organoids and imaged with the LLSM (for more details, see Drubin Lab, Hockemeyer Lab, Betzig Lab and [1]).

Molecular details of endocytosis

The resulting imaging datasets, with tens to hundreds of gigabytes per movie, easily reach terabytes in size. Analyzing these movies involves 1) detecting the punctate clathrin vesicles, 2) tracking them 3) matching the tracks from both channels (green, red) to each other and 4) overall statistical analysis. 

While tools have been developed for detection and tracking in hundreds of megabytes and in 2D, the detection and tracking in LLSM data in hundreds of gigabytes and in 3D poses a novel challenge. The pyLattice library aims to enable and facilitate this data analytics task for LLSM data. Figure 3 illustrates raw AO-LLSM imaging data (left) and how individual puncta can be detected and tracked using pyLattice (right). 

tracking algorithm

Figure 4 depicts thousands of vesicle tracks throughout the tissue that were captured with the LLSM and revealed using data analytics

thousands of vesicle tracks

Endocytosis is only one in many thousand cellular processes. In this example, the ground breaking LLSM technology holds the promise of revealing a more detailed understanding and new biological insights. The key to unlock these insights is - and increasingly will be - appropriate high performance imaging data analytics that can tame the terabytes pouring from the scopes.

For more information, please contact Joh directly.


[1] Liu and Upadhyayula et al. Observing the cell in its native state: Imaging subcellular dynamics in multicellular organisms

Featured Fellows

Johannes Schöneberg

Molecular and Cell Biology, UC Berkeley
Alumni - BIDS Data Science Fellow