From genomics to transcriptomics to proteomics, microfluidic tools underpin recent advances in single-cell biology. Detection of specific proteoforms—with single-cell resolution—presents challenges in detection specificity and sensitivity. Miniaturization of protein immunoblots to single-cell resolution mitigates these challenges. For example, in microfluidic western blotting, protein targets are separated by electrophoresis and subsequently detected using fluorescently labeled antibody probes. To quantify the expression level of each protein target, the fluorescent protein bands are fit to Gaussians; yet, this method is difficult to use with noisy, low-abundance, or low-SNR protein bands, and with significant band skew or dispersion. In this study, we investigate segmentation-based approaches to robustly quantify protein bands from single-cell protein immunoblots. As compared to a Gaussian fitting pipeline, the segmentation pipeline detects >1.5× more protein bands for downstream quantification as well as more of the low-abundance protein bands (i.e., with SNR ∼3). Utilizing deep learning-based segmentation approaches increases the recovery of low-SNR protein bands by an additional 50%. However, we find that segmentation-based approaches are less robust at quantifying poorly resolved protein bands (separation resolution, R_s < 0.6). With burgeoning needs for more single-cell protein analysis tools, we see microfluidic separations as benefitting substantially from segmentation-based analysis approaches.

中文翻译：

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基于分割的单细胞免疫印迹分析

从基因组学到转录组学再到蛋白质组学，微流体工具支撑着单细胞生物学的最新进展。以单细胞分辨率检测特定蛋白形式在检测特异性和灵敏度方面提出了挑战。将蛋白质免疫印迹小型化为单细胞分辨率可以缓解这些挑战。例如，在微流控蛋白质印迹中，蛋白质靶标通过电泳分离，随后使用荧光标记的抗体探针进行检测。为了量化每个蛋白质靶标的表达水平，荧光蛋白条带适合高斯；然而，这种方法难以用于嘈杂、低丰度或低 SNR 的蛋白质条带，并且条带偏斜或分散显着。在这项研究中，我们研究了基于分割的方法来稳健地量化来自单细胞蛋白质免疫印迹的蛋白质条带。与高斯拟合管道相比，分割管道检测到 >1.5 倍的蛋白质条带用于下游定量以及更多的低丰度蛋白质条带（即 SNR ∼3）。利用基于深度学习的分割方法可将低 SNR 蛋白条带的恢复率提高 50%。然而，我们发现基于分割的方法在量化解析不佳的蛋白质条带（分离分辨率，利用基于深度学习的分割方法可将低 SNR 蛋白条带的恢复率提高 50%。然而，我们发现基于分割的方法在量化解析不佳的蛋白质条带（分离分辨率，利用基于深度学习的分割方法可将低 SNR 蛋白条带的恢复率提高 50%。然而，我们发现基于分割的方法在量化解析不佳的蛋白质条带（分离分辨率，R _s < 0.6)。随着对更多单细胞蛋白质分析工具的需求不断增长，我们认为微流体分离从基于分割的分析方法中受益匪浅。