When using fluorescent microscopy to study cellular dynamics, trade-offs typically have to be made between light exposure and quality of recorded image to balance the phototoxicity and image signal-to-noise ratio. Image denoising is an important tool for retrieving information from dim cell images. Recently, deep learning based image denoising is becoming the leading method because of its promising denoising performance, achieved by leveraging available prior knowledge about the noise model and samples at hand. We demonstrate that incorporating temporal information in the model can further improve the results. However, the practical application of this method has seen challenges because of the requirement of large, task-specific training datasets. In this work, we addressed this challenge by combining self-supervised learning with transfer learning, which eliminated the demand of task-matched training data while maintaining denoising performance. We demonstrate its application in fluorescent imaging of different subcellular structures.
Predicting the cellular activities of proteins from their primary amino acid sequences is a highly desirable capability that could greatly enhance our functional understanding of the proteome. Here, we demonstrate CELL-E, a text-to-image transformer architecture, which given a protein sequence and a reference image for cell (or nucleus) morphology, can generate a 2D probability density map of the protein distribution within cells. Unlike previous in silico methods, which rely on existing, discrete class annotation of protein localization to predefined subcellular compartments, CELL-E uses imaging data directly, thus relying on a native description of protein localization relative to the cellular context.
Accurately predicting cellular activities of proteins based on their primary amino acid sequences would greatly improve our understanding of the proteome. In this paper, we present CELL-E, a text-to-image transformer model that generates 2D probability density images describing the spatial distribution of proteins within cells. Given an amino acid sequence and a reference image for cell or nucleus morphology, CELL-E predicts a more refined representation of protein localization, as opposed to previous in silico methods that rely on pre-defined, discrete class annotations of protein localization to subcellular compartments.
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