AUTHOR=Lu Guolan , Baertsch Marc A. , Hickey John W. , Goltsev Yury , Rech Andrew J. , Mani Lucas , Forgó Erna , Kong Christina , Jiang Sizun , Nolan Garry P. , Rosenthal Eben L. TITLE=A real-time GPU-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data JOURNAL=Frontiers in Immunology VOLUME=13 YEAR=2022 URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2022.981825 DOI=10.3389/fimmu.2022.981825 ISSN=1664-3224 ABSTRACT=
Highly multiplexed, single-cell imaging has revolutionized our understanding of spatial cellular interactions associated with health and disease. With ever-increasing numbers of antigens, region sizes, and sample sizes, multiplexed fluorescence imaging experiments routinely produce terabytes of data. Fast and accurate processing of these large-scale, high-dimensional imaging data is essential to ensure reliable segmentation and identification of cell types and for characterization of cellular neighborhoods and inference of mechanistic insights. Here, we describe RAPID, a Real-time, GPU-Accelerated Parallelized Image processing software for large-scale multiplexed fluorescence microscopy Data. RAPID deconvolves large-scale, high-dimensional fluorescence imaging data, stitches and registers images with axial and lateral drift correction, and minimizes tissue autofluorescence such as that introduced by erythrocytes. Incorporation of an open source CUDA-driven, GPU-assisted deconvolution produced results similar to fee-based commercial software. RAPID reduces data processing time and artifacts and improves image contrast and signal-to-noise compared to our previous image processing pipeline, thus providing a useful tool for accurate and robust analysis of large-scale, multiplexed, fluorescence imaging data.