Unbiased choice of global clustering parameters for single-molecule localization microscopy

Abstract

Single-molecule localization microscopy resolves objects below the diffraction limit of light via sparse, stochastic detection of target molecules. Single molecules appear as clustered detection events after image reconstruction. However, identification of clusters of localizations is often complicated by the spatial proximity of target molecules and by background noise. Clustering results of existing algorithms often depend on user-generated training data or user-selected parameters, which can lead to unintentional clustering errors. Here we suggest an unbiased algorithm (FINDER) based on adaptive global parameter selection and demonstrate that the algorithm is robust to noise inclusion and target molecule density. We benchmark the most common density based clustering algorithms in test scenarios which we designed based on experimental datasets. We show that FINDER can keep the number of false positive inclusions low while also maintaining a low number of false negative detections in densely populated regions.

The prevalence and specificity of local protein synthesis during neuronal synaptic plasticity

“…Synaptic nascent protein localizations were further identified by a custom-written, noise-tolerant cluster identification algorithm (Density-based spatial clustering of applications with Noise based) (42), the spatial-distance parameter (r) of which was determined using the measured localization precisions for each dendrite. In particular, any cluster with <3 localizations within r was excluded from clustering, which is more selective than DBSCAN.…”