Alexandria Gabrielly, Valerio Hellen P, Massafera Mariana P, Reis Lorenna R, Coelho Fernando R, Di Mascio Paolo, Ronsein Graziella E
Department of Biochemistry, Institute of Chemistry, University of São Paulo, Avenida Professor Lineu Prestes, 748, São Paulo 05508-000, Brazil.
J Leukoc Biol. 2025 Mar 14;117(3). doi: 10.1093/jleuko/qiae224.
Neutrophils are the innate immune system's first line of defense, and their storage organelles are essential to their function. The storage organelles are divided into 3 different granule types named azurophilic, specific, and gelatinase granules, besides a fourth component called secretory vesicles. The isolation of neutrophil's granules is challenging, and the existing procedures rely on large sample volumes, about 400 mL of peripheral blood, precluding the use of multiple biological and technical replicates. Therefore, the aim of this study was to develop a miniaturized isolation of neutrophil granules method, using biochemical assays, mass spectrometry-based proteomics and a machine learning approach to investigate the protein content of these organelles. Neutrophils were isolated from 40 mL of blood collected from 3 apparently healthy volunteers and disrupted using nitrogen cavitation; the organelles were fractionated with a discontinuous 3-layer Percoll density gradient. The method was proven successful and allowed for a reasonable separation and enrichment of neutrophil's storage organelles using a gradient approximately 37 times smaller than the methods described in the literature. Moreover, mass spectrometry-based proteomics identified 368 proteins in at least 3 of the 5 analyzed samples, and using a machine learning strategy aligned with markers from the literature, the localization of 50 proteins was predicted with confidence. When using markers determined within our dataset by a clusterization tool, the localization of 348 proteins was confidently determined. Importantly, this study was the first to investigate the proteome of neutrophil granules using technical and biological replicates, creating a reliable database for further studies.
中性粒细胞是固有免疫系统的第一道防线，其储存细胞器对其功能至关重要。储存细胞器分为三种不同类型的颗粒，即嗜天青颗粒、特异性颗粒和明胶酶颗粒，此外还有第四种成分称为分泌小泡。分离中性粒细胞颗粒具有挑战性，现有方法依赖大量样本量，约400毫升外周血，这使得无法使用多个生物学和技术重复样本。因此，本研究的目的是开发一种小型化的中性粒细胞颗粒分离方法，利用生化分析、基于质谱的蛋白质组学和机器学习方法来研究这些细胞器的蛋白质含量。从3名明显健康的志愿者采集的40毫升血液中分离出中性粒细胞，并用氮空化法使其破裂；细胞器用不连续的三层Percoll密度梯度进行分级分离。该方法被证明是成功的，使用的梯度比文献中描述的方法小约37倍，能够合理地分离和富集中性粒细胞的储存细胞器。此外，基于质谱的蛋白质组学在5个分析样本中的至少3个中鉴定出368种蛋白质，并使用与文献中的标记物一致的机器学习策略，自信地预测了50种蛋白质的定位。当使用聚类工具在我们的数据集中确定的标记物时，348种蛋白质的定位得到了可靠确定。重要的是，本研究首次使用技术和生物学重复样本研究中性粒细胞颗粒的蛋白质组，为进一步研究创建了一个可靠的数据库。
