Skip to main content
 

Markerless Detection and Tracking of Subcellular Structures for Fluorescent Time-Lapse Microscopy

Friday, September 16, 2016 — Poster Session IV

12:00 p.m. – 1:30 p.m.
FAES Terrace
NIA
COMPBIO-13

Authors

  • HP Patel
  • MH Sung

Abstract

In recent decades, fluorescence live-cell imaging has played an integral role in modern cell biology by allowing us to visualize many biological processes in real time. Advances in the technique have dramatically accelerated the progress in the biological sciences, as well as highlighted the need for interdisciplinary collaborations. Innovative microscopy approaches can now be used to acquire high-resolution digital images in an increasing efficiency. However, for reproducibility and extraction of relevant information, aesthetically pleasing images have little value in the scientific community without accompanying quantification. Therefore, it is important for software-integrated analysis tools to be developed and made widely available, such that researchers can objectively extract the wealth of information from imaging data. CellProfiler, one such open-source tool, measures properties of interest, such as morphology, intensity, and texture, for any cell type in a high-throughput manner, thus attempting to alleviate the existing bottleneck at the image analysis stage. However, for this and many other existing tools, the sample needs to be labeled with organelle markers or treated with dyes for segmentation of subcellular structures of interest. This is a major limitation for long-term, live-cell imaging due to toxicity concerns, as well as reducing the dimension of color channels. We are developing a new algorithm that semi-automatically segments, tracks, and quantifies the protein levels of unmarked subcellular structures, such as nuclei. This algorithm will facilitate analyses using live-cell microscopy, including translocation assays of shuttling transcription factors, such as NF-kappaB, for which the single-cell signaling dynamics can be complex and asynchronous.

Category: Computational Biology