bioRxivBiophysicsPreprint
Detection of Fortunate Molecules Induce PArticle Resolution Shift (PAR-shift) towards Single-molecule Limit in SMLM: A Technique for Resolving Molecular Clusters in Cellular System
Abstract
Molecules capable of emitting a large number of photons (also known as fortunate molecules) are crucial for achieving resolution close to a single molecule limit (the actual size of a single molecule). We propose a long-exposure single molecule localization microscopy (leSMLM) technique that enables detection of fortunate molecules, which is based on the fact that detecting a relatively small subset of molecules with large photon emission increases its localization [Formula]. Fortunate molecules have the ability to emit a large burst of photons over a prolonged time (> average triplet-state lifetime). So, a long exposure time allows the time window necessary to detect these elite molecules. The technique involves the detection of fortunate molecules to generate enough statistics for a quality reconstruction of the target protein distribution in a cellular system. Studies show a significant PArticle Resolution Shift (PAR-shift) of about 6 nm and 11 nm towards Single-molecule-limit (away from diffraction-limit) for an exposure time window of 60 ms and 90 ms, respectively. In addition, a significant decrease in the fraction of fortunate molecules (single molecules with small localization precision) is observed. Specifically, 8.33% and 3.43% molecules are found to emit in 30 - 60 ms and 60 - 90 ms, respectively, when compared to SMLM. The long exposure has enabled better visualization of Dendra2HA molecular cluster, with sub-clusters within a large cluster. Thus, the proposed technique (leSMLM) facilitates a better study of cluster formation in fixed samples. Overall, the method enables better spatial resolution at the cost of relatively poor temporal resolution.
- assay: fluorescence / live-cell microscopy
- research focus: cell type annotation
- research focus: clustering
- research focus: spatial domain detection
- approach: deep learning
- approach: statistical model
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