Table_1_A real-time GPU-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data.xlsx

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.

高度复杂数据集的单细胞成像技术已彻底革新了我们对于健康与疾病相关空间细胞相互作用的认知。随着抗原数量、区域大小和样本规模的持续增加，复杂数字荧光成像实验通常会产生数以太字节的数据。对大规模、高维成像数据进行快速且精确的处理对于确保可靠的细胞类型分割和识别，以及细胞邻域的特征描述和机制洞察的推断至关重要。在此，我们描述了RAPID，这是一种用于大规模复杂数字荧光显微镜数据的实时、GPU加速并行图像处理软件。RAPID能够解卷积大规模、高维荧光成像数据，通过轴向和横向漂移校正拼接和注册图像，并最小化如红细胞引入的组织自荧光。采用开源CUDA驱动、GPU辅助的解卷积技术，其产生的结果与付费商业软件相当。与我们的先前图像处理流程相比，RAPID降低了数据处理时间和伪影，并提高了图像对比度和信噪比，因此为大规模、复杂数字荧光成像数据的精确且稳健分析提供了一种有价值的工具。