Bayesian optimized clustering for unbiased, reproducible analysis of single molecule imaging
Presented by Joseph Hammer
Abstract:
Super resolution microscopy has led to a greater understanding of protein organization. In cell membranes, transmembrane proteins form nanoclusters via a variety of mechanisms, which may include adapter protein crosslinking, organization by lipid domains, and/or activation through mechanical forces. Regardless of mechanism, evaluation of underlying cluster structure gives important insights into cellular function and the coordination of these molecules. While density-based clustering approaches have proven to be valuable tools for extracting clustering metrics across a diverse range of datasets, the parameters for such algorithms are often sensitive and unintuitive. Furthermore, little guidance is available for evaluating the accuracy of data clustering. Thus, we developed an unbiased optimization method to guide protein clustering in cases where ground truth information is unavailable. To do this, we couple density-based clustering algorithms with a fast implementation of a density-based cluster validation (DBCV) score, along with Bayesian optimization techniques to efficiently explore the parameter space. This technique allows for unbiased cluster identification with a scoring metric that facilitates comparisons across datasets and between individual clusters. With this method, we demonstrate a significant improvement in clustering performance that should improve both the integrity and reproducibility of clustering analyses across various biological fields of study.
Rapid single-particle chemical imaging of nanoplastics by SRS microscopy
Presented by Niaxin Qian
Abstract:
Plastics are now omnipresent in our daily lives. The existence of microplastics (1 µm to 5 mm in length) and possibly even nanoplastics (<1 μm) has recently raised health concerns. In particular, nanoplastics are believed to be more toxic since their smaller size renders them much more amenable, compared to microplastics, to enter the human body. However, detecting nanoplastics imposes tremendous analytical challenges on both the nano-level sensitivity and the plastic-identifying specificity, leading to a knowledge gap in this mysterious nanoworld surrounding us. To address these challenges, we developed a hyperspectral stimulated Raman scattering (SRS) imaging platform with an automated plastic identification algorithm that allows micro-nano plastic analysis at the single-particle level with high chemical specificity and throughput. We first validated the sensitivity enhancement of the narrow band of SRS to enable high-speed single nanoplastic detection below 100 nm. We then devised a data-driven spectral matching algorithm to address spectral identification challenges imposed by sensitive narrow-band hyperspectral imaging and achieve robust determination of common plastic polymers. With the established technique, we studied the micro-nano plastics from bottled water as a model system. We successfully detected and identified nanoplastics from major plastic types. Micro-nano plastics concentrations were estimated to be about 2.4 ± 1.3 × 105 particles per liter of bottled water, about 90% of which are nanoplastics. This is orders of magnitude more than the microplastic abundance reported previously in bottled water. High-throughput single-particle counting revealed extraordinary particle heterogeneity and nonorthogonality between plastic composition and morphologies; the resulting multidimensional profiling sheds light on the science of nanoplastics.
