The popularity of artificial intelligence (AI) and its applications has been steadily growing across various disciplines, including the field of science. In life sciences, AI has been increasingly applied for imaging flow cytometry towards automated management and classification of biological cell image data. However, when considering imaging flow cytometry for cell sorting, the need for employing AI over more facile, traditional image analysis methods are not as pronounced. This is primarily due to the time restrictions between image acquisition and sorting actuation that inevitably come with cell sorting. AI-enabled image-activated cell sorting (IACS) methods remain limited, and recent advancements in IACS still find success while relying on traditional feature gating strategies. Here, we compare the performance of feature gating, classical machine learning (ML), and deep learning (DL) in the differentiation of Saccharomyces cerevisiae mutant images taken by our intelligent IACS system to assess the advantages of AI for image classification in IACS. We show that while both classical ML and DL impart an increase in enrichment capability over feature gating, employing classical ML resulted in a smaller improvement at a larger cost of longer processing time than DL. We further performed IACS on mixed mutant populations and quantified target enrichment via downstream DNA sequencing to confirm the applicability of DL for the proposed study. Our findings validate the benefit and practicability of employing DL in IACS for microscopy-based genetic screening of S. cerevisiae, encouraging its inclusion for future advancements in this field.
We present a computational method, termed Wasserstein-induced flux (WIF), to robustly quantify the accuracy of individual localizations within a single-molecule localization microscopy (SMLM) dataset without ground- truth knowledge of the sample. WIF relies on the observation that accurate localizations are stable with respect to an arbitrary computational perturbation. Inspired by optimal transport theory, we measure the stability of individual localizations and develop an efficient optimization algorithm to compute WIF. We demonstrate the advantage of WIF in accurately quantifying imaging artifacts in high-density reconstruction of a tubulin network. WIF represents an advance in quantifying systematic errors with unknown and complex distributions, which could improve a variety of downstream quantitative analyses that rely upon accurate and precise imaging. Furthermore, thanks to its formulation as layers of simple analytical operations, WIF can be used as a loss function for optimizing various computational imaging models and algorithms even without training data.
Modulating the polarization of excitation light, resolving the polarization of emitted fluorescence, and point spread function (PSF) engineering have been widely leveraged for measuring the orientation of single molecules. Typically, the performance of these techniques is optimized and quantified using the Cramér-Rao bound (CRB), which describes the best possible measurement variance of an unbiased estimator. However, CRB is a local measure and requires exhaustive sampling across the measurement space to fully characterize measurement precision. We develop a global variance upper bound (VUB) for fast quantification and comparison of orientation measurement techniques. Our VUB tightly bounds the diagonal elements of the CRB matrix from above; VUB overestimates the mean CRB by ~34%. However, compared to directly calculating the mean CRB over orientation space, we are able to calculate VUB ~1000 times faster.
Amyloid fibrils and tangles are signatures of Alzheimer disease, but nanometer-sized aggregation intermediates are hypothesized to be the structures most toxic to neurons. The structures of these oligomers are too small to be resolved by conventional light microscopy. We have developed a simple and versatile method, called transient amyloid binding (TAB), to image amyloid structures with nanoscale resolution using amyloidophilic dyes, such as Thioflavin T, without the need for covalent labeling or immunostaining of the amyloid protein. Transient binding of ThT molecules to amyloid structures over time generates photon bursts that are used to localize single fluorophores with nanometer precision. Continuous replenishment of fluorophores from the surrounding solution minimizes photobleaching, allowing us to visualize a single amyloid structure for hours to days. We show that TAB microscopy can image both the oligomeric and fibrillar stages of amyloid-β aggregation. We also demonstrate that TAB microscopy can image the structural remodeling of amyloid fibrils by epi-gallocatechin gallate. Finally, we utilize TAB imaging to observe the non-linear growth of amyloid fibrils.
We present a method to measure the molecular orientation and rotational mobility of single-molecule emitters by designing and implementing a Tri-spot point spread function. It can measure all degrees of freedom related to molecular orientation and rotational mobility. Its design is optimized by maximizing the theoretical limit of its measurement precision. We evaluate the precision and accuracy of the Tri-spot PSF by measuring the orientation and effective rotational mobility of single fluorescent molecules embedded in a polymer matrix.
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