RASP: A new method for single puncta detection in complex cellular backgrounds

Super-resolution and single-molecule microscopy are increasingly applied to complex biological systems. A major challenge of this approach is that fluorescent puncta must be detected in the low signal, high noise, heterogeneous background environments of cells and tissue. We present RASP, Radiality Analysis of Single Puncta, a bioimaging-segmentation method that solves this problem. RASP removes false positive puncta that other analysis methods detect, and detects features over a broad range of spatial scales: from single proteins to complex cell phenotypes. RASP outperforms the state-of-the-art in precision and speed, using image gradients to separate Gaussian-shaped objects from background. We demonstrate RASP's power by showing it can extract spatial correlations between microglia, neurons, and alpha-synuclein oligomers in the human brain. This sensitive, computationally efficient approach enables fluorescent puncta and cellular features to be distinguished in cellular and tissue environments with a sensitivity down to the level of the single protein.超分辨率显微镜与单分子显微镜技术正日益应用于复杂生物系统的研究中。此类技术面临的一项核心挑战是，需在细胞与组织的低信号、高噪声且异质性的背景环境中，实现荧光斑点（fluorescent puncta）的检测。我们提出了单斑点径向分析（Radiality Analysis of Single Puncta，缩写RASP）这一生物成像分割方法，可有效解决上述难题。RASP能够剔除其他分析方法误检的假阳性荧光斑点，且可在宽广的空间尺度范围内识别特征——从单个蛋白质到复杂的细胞表型。RASP通过图像梯度将高斯形目标与背景分离，在精度与速度上均优于当前顶尖方法。我们通过展示其可提取人类大脑中小胶质细胞、神经元与α-突触核蛋白寡聚体之间的空间相关性，验证了RASP的卓越性能。这种高灵敏度且计算高效的方法，能够在细胞与组织环境中区分荧光斑点与细胞特征，其检测灵敏度可达到单个蛋白质级别。