Abstract
Determining vesicle localization and association in live microscopy may be challenging due to non-simultaneous imaging of rapidly moving objects with two excitation channels. Besides errors due to movement of objects, imaging may also introduce shifting between the image channels, and traditional colocalization methods cannot handle such situations. Our approach to quantifying the association between tagged proteins is to use an object-based method where the exact match of object locations is not assumed. Point-pattern matching provides a measure of correspondence between two point-sets under various changes between the sets. Thus, it can be used for robust quantitative analysis of vesicle association between image channels. Results for a large set of synthetic images shows that the novel association method based on point-pattern matching demonstrates robust capability to detect association of closely located vesicles in live cell-microscopy where traditional colocalization methods fail to produce results. In addition, the method outperforms compared Iterated Closest Points registration method. Results for fixed and live experimental data shows the association method to perform comparably to traditional methods in colocalization studies for fixed cells and to perform favorably in association studies for live cells.
Highlights
Live cell-imaging in subcellular scale has revolutionized the way cells are studied in molecular cell biology
We have presented a computational method for estimating protein association between two image channels using an object-based point-pattern matching approach
The advantage of the proposed method stems from the inherited robustness of pointpattern matching (PPM) against directed movement between the image channels, which could be potentially caused by misaligned image channels
Summary
Live cell-imaging in subcellular scale has revolutionized the way cells are studied in molecular cell biology. Tagging proteins with specific fluorescent stains enables studying various cell functions through detection of protein-specific cell organelles, provided that the fluorescence-signal captured in digital images can be accurately analyzed. The spatial pattern and location [1,2] of the detected signal may reveal the cell function or role of proteins, and colocalization of tagged proteins is in particular of interest [3]. There are several tools to study colocalization, represented by different-colored voxels occupying the same spatial location. If differently colored objects are frequently associated they may be considered to keep near each other over time and follow each other in the cell cytoplasm. Analysis of closely associated objects in fixed cells allows accurate analysis, without errors caused by the movement of the objects between subsequent imaging of different channels – provided that the channels are aligned. In a live-imaging setup the quantification of sudden and transient events is challenging [4], and live imaging is prone to such errors that depend on the speed of the imaging setup
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