CN114858907A - 用于诊断新冠肺炎的质谱模型的构建方法 - Google Patents
用于诊断新冠肺炎的质谱模型的构建方法 Download PDFInfo
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Abstract
本发明提供一种检测新冠病毒肺炎的特征多肽组合物,包括29种具有特定质荷比的特征多肽,通过分析所述特征多肽的表达情况,可判断该样本是否为新冠肺炎患者。本发明还提供根据该特征多肽组合物所制备的质谱模型、诊断新冠肺炎的产品等用途。本发明首次提出根据新冠肺炎患者/正常人、肺结核患者、具有新冠肺炎类型症状对照中寻找具有差异的多个特征蛋白组合,突破了传统的仅限于正常人和新冠肺炎患者中寻找特征多肽的研究思路,有效地避免与新冠肺炎症状相似的假阳性结果的感染,并具有操作简单,检测成本低,准确率高,有望用于新冠肺炎的大规模筛查。
Description
技术领域
本发明属于检测领域,涉及一种利用飞行时间质谱技术快速检测新型冠状病毒肺炎的技术。
背景技术
冠状病毒是一类主要引起呼吸道、肠道疾病的病原体。这类病毒颗粒的表面有许多规则排列的突 起,整个病毒颗粒就像一顶帝王的皇冠,因此得名“冠状病毒”。冠状病毒除人类以外,还可感染猪、 牛、猫、犬、貂、骆驼、蝙蝠、老鼠、刺猬等多种哺乳动物以及多种鸟类。新型冠状病毒COVID-19 是以前从未在人体中发现的新型冠状病毒新毒株,其传播规律、感染机制、以及进化和变异规律仍然 不清晰,为防治带来了困难。
为了预防新型冠状病毒(COVID-19)肺炎的发生和流行,迅速采取措施,有效控制疫情的发展蔓延, 新型冠状病毒肺炎的快速检测尤为重要。长期以来,对冠状病毒的鉴定都采用传统的微生物学检测方 法,即形态学、生理生化特征及血清学鉴定。此方法虽然准确度高,但所需时间太长,最快也要十几 个小时才能完成,难以适应快速检测的要求。以多重PCR为基础的核酸检测方法,对冠状病毒的早期 诊断和传染源的发现具有重要意义。并且多重PCR检测针对多个基因,假阴性率比单重PCR降低, 然而,PCR检测方法也存在检测过程繁琐、成本较高、检测高通量有限。
基质辅助激光解吸电离飞行时间质谱(matrix-assisted laser desorption/ionization time-of-flight massspectrometry,简称MALDI-TOF MS)技术,是20世纪80年代末问世并迅速发展起来的一种质 谱分析技术。其质量分析器是一个离子漂移管(iondirft tube),由离子源产生的离子首先被收集,在 收集器中所有离子速度变为0,使用一个脉冲电场加速后进入无场漂移管,并以恒定速度飞向离子接 收器,离子质量越大,到达接收器所用时间越长;离子质量越小,到达接收器所用时间越短。根据这 一原理,可以把不同质量的离子按质荷比大小进行分离,准确检测多肽、蛋白质、核酸、多糖等生物 大分子的分子质量和纯度,具有准确性高、灵活性强、通量大、检测周期短、性价比高的优点。
近年来,已经出现质谱技术来检测致病微生物或病毒的特征多肽或多肽的质谱技术。例如,中国 专利申请CN102337223A,“产黄青霉抗真菌蛋白Pc-Arctin及其制备方法”,公开了一种检测产黄青霉 抗真菌蛋白Pc-Arctin的MALDI-TOF鉴定方法,其中从平板上挑取产黄青霉A096孢子接种于SGY液体 培养基培养,预处理得到粗蛋白溶液在色谱柱上分离纯化,并在羧甲基阳离子交换色谱柱上分离纯化, 收集各洗脱组分,各组分离心超滤浓缩至所需体积,以宛氏拟青霉为敏感受试指示菌,追踪抗真菌活 性组分,确定的活性成分判断获得蛋白的纯度;割取SDS-PAGE电泳图上的单一条带,进行MALDI-TOF 鉴定。该方法仅适用于特定微生物,且需要多重蛋白纯化过程,最终用MALDI-TOF鉴定特征多肽 Pc-Arctin,其过程繁琐,适用面窄,不能实现质谱检测病毒的目的。
中国专利申请201110154723、“MALDI TOF MS辅助鉴定单增李斯特氏菌的方法”和201110154469、 “MALDI TOF MS辅助鉴定霍乱弧菌的方法”公开了一种利用MALDI TOF MS技术辅助鉴定细菌的方法, 包括:预处理细菌培养物,采集所有菌株样品的MALDI TOF MS图谱,根据软件制备细菌标准图谱, 使用相同的方法检测并采集待测细菌的图谱,以及比较二者图谱,根据匹配分数进行判定。由于该方 法使用常规的处理(通过无水乙醇、甲酸和乙腈处理,并辅以离心,最后吸取上清液进行检测),尽 管其在一定程度上能表征该细菌的特征图谱,但由于其待测物中含有蛋白质、脂类、脂多糖和脂寡糖、 DNA、多肽及其它能被离子化的分子,其得到的图谱实质上是上述各种分子的图谱集合,因此既需要 处理和比对的图谱信息量过大,并且因待检分子过于庞大而导致其图谱特征性偏低,只适用于某具体 细菌而无法推广到其他大量的病毒检测中。
中国专利申请200880121570、发明名称“用于诊断和监测精神疾病的方法和生物标志物”报道了 可以通过MALDI-TOF质谱技术,检测包括流感病毒在内的近百种与精神疾病相关的生物肽。然而,该 方法仅仅简单概括了各种可能的技术,其既没有报道具体方案,也没有报道冠状病毒的特定靶点,因 此难以教导研究者通过MALDI-TOF质谱技术来检测流感病毒。
因此,目前需要一种通过基质辅助激光解析电离飞行时间质谱(MALDI-TOF-MS)来检测新型冠状病 毒肺炎的特征多肽质谱模型以及用途。
发明内容
本发明第一个目的提供一组基于血清肽组学(peptidome)特征多肽的组合物,该特征多肽可以 通过MALDI-TOF质谱检测新冠病毒(COVID-19),其中该特征多肽组合物包括具有如下质荷比的25种 特征多肽:5158m/z、5366m/z、5893m/z、6357m/z、6654m/z、6939m/z、7364m/z、7614m/z、8034m/z、 8043m/z、8226m/z、8425m/z、8560m/z、8986m/z、9626m/z、13719m/z、13765m/z、13886m/z、14049m/z、14095m/z、14102m/z、15123m/z、15867m/z、28091m/z、28232m/z,或者包括具有如下质荷比的29 种特征多肽:5158m/z、5366m/z、5893m/z、6357m/z、6654m/z、6939m/z、7364m/z、7614m/z、8034m/z、 8043m/z、8226m/z、8425m/z、8560m/z、8986m/z、9626m/z、11435m/z、11495m/z、11523m/z、 11680m/z、13719m/z、13765m/z、13886m/z、14049m/z、14095m/z、14102m/z、15123m/z、15867m/z、 28091m/z、28232m/z。
在上述任意一个实施方案中,当特征多肽8986m/z、28091m/z的峰上调,同时特征多肽6939m/z、 13886m/z、14049m/z、14102m/z的峰下调表达时,表示该血清样本为阳性样本,即判定该患者为新 冠肺炎患者,十折交叉验证准确率约为91%。在一个优选实施方案中,所述特征多肽的组合物仅包含 质合比分别为8986m/z、28091m/z,和6939m/z、13886m/z、14049m/z、14102m/z的特征多肽。
在另一任意实施方案中,当特征多肽7614m/z、8034m/z、8226m/z、8986m/z、9626m/z、11435 m/z、11495m/z、11523m/z、11680m/z、15123m/z、15867m/z、28091m/z的峰上调,同时特征多 肽6939m/z、13719m/z、13765m/z、13886m/z、14049m/z、14095m/z、14102m/z的峰下调表达时, 表示该血清样本为阳性样本,即该患者为新冠肺炎患者,十折交叉验证准确率约为93.31%。在一个 优选实施方案中,所述特征多肽的组合物仅包含质合比分别为7614m/z、8034m/z、8226m/z、8986m/z、 9626m/z、11435m/z、11495m/z、11523m/z、11680m/z、15123m/z、15867m/z、28091m/z,和6939m/z、 13719m/z、13765m/z、13886m/z、14049m/z、14095m/z、14102m/z的特征多肽。
在其他实施方案中,当特征多肽5158m/z、5366m/z、5893m/z、7364m/z、7614m/z、8034m/z、 8043m/z、8226m/z、8425m/z、8560m/z、8986m/z、9626m/z、11435m/z、11495m/z、11523m/z、 11680m/z、15123m/z、15867m/z、28091m/z的峰上调,同时特征多肽6357m/z、6654m/z、6939m/z、 13719m/z、13765m/z、13886m/z、14049m/z、14095m/z、14102m/z、28232m/z的峰下调表达时,表 示该血清样本为阳性样本,即该患者为新冠肺炎患者,十折交叉验证准确率约为98.69%。
本发明的第二个发明目的是提供一种用于检测新冠肺炎的质谱模型,该质谱模型由具有上述任一 方案的质荷比峰值的特征多肽组合物所制备而成。
在一个实施方案中,所述质谱模型由特征多肽5158m/z、5366m/z、5893m/z、6357m/z、6654m/z、 6939m/z、7364m/z、7614m/z、8034m/z、8043m/z、8226m/z、8425m/z、8560m/z、8986m/z、9626m/z、 13719m/z、13765m/z、13886m/z、14049m/z、14095m/z、14102m/z、15123m/z、15867m/z、28091m/z、 28232m/z所制备而成,其中当特征多肽5158m/z、5366m/z、5893m/z、7364m/z、7614m/z、8034m/z、 8043m/z、8226m/z、8425m/z、8560m/z、8986m/z、9626m/z、15123m/z、15867m/z、28091m/z的峰 上调,同时特征多肽6357m/z、6654m/z、6939m/z、13719m/z、13765m/z、13886m/z、14049m/z、14095m/z、 14102m/z、28232m/z的峰下调表达时,表示该血清样本为阳性样本,即该患者为新冠肺炎患者,十 折交叉验证准确率约为97.96%。
或者,在上述另一实施方案中,所述质谱模型由特征多肽5158m/z、5366m/z、5893m/z、6357m/z、 6654m/z、6939m/z、7364m/z、7614m/z、8034m/z、8043m/z、8226m/z、8425m/z、8560m/z、8986m/z、 9626m/z、11435m/z、11495m/z、11523m/z、11680m/z、13719m/z、13765m/z、13886m/z、14049m/z、 14095m/z、14102m/z、15123m/z、15867m/z、28091m/z、28232m/z所制备而成,其中当特征多肽 5158m/z、5366m/z、5893m/z、7364m/z、7614m/z、8034m/z、8043m/z、8226m/z、8425m/z、8560m/z、 8986m/z、9626m/z、11435m/z、11495m/z、11523m/z、11680m/z、15123m/z、15867m/z、28091m/z 的峰上调,同时特征多肽6357m/z、6654m/z、6939m/z、13719m/z、13765m/z、13886m/z、14049m/z、 14095m/z、14102m/z、28232m/z的峰下调表达时,表示该血清样本为阳性样本,即该患者为新冠肺 炎患者,十折交叉验证准确率约为98.69%。
在另一实施方案中,所述质谱模型仅由以下质合比分别为7614m/z、8034m/z、8226m/z、8986m/z、 9626m/z、11435m/z、11495m/z、11523m/z、11680m/z、15123m/z、15867m/z、28091m/z,和6939m/z、 13719m/z、13765m/z、13886m/z、14049m/z、14095m/z、14102m/z的特征多肽组合物所制备而成, 其中当特征多肽7614m/z、8034m/z、8226m/z、8986m/z、9626m/z、11435m/z、11495m/z、11523m/z、 11680m/z、15123m/z、15867m/z、28091m/z的峰上调,同时特征多肽6939m/z、13719m/z、13765m/z、 13886m/z、14049m/z、14095m/z、14102m/z的峰下调表达时,表示该血清样本为阳性样本,即该患 者为新冠肺炎患者,十折交叉验证准确率约为93.31%。
在其他实施方案中,所述质谱模型仅由以下质合比分别为8986m/z、28091m/z、6939m/z、13886m/z、 14049m/z、14102m/z的特征多肽组合物所制备而成,其中当特征多肽8986m/z、28091m/z的峰上调, 同时特征多肽6939m/z、13886m/z、14049m/z、14102m/z的峰下调表达时,表示该血清样本为阳性 样本,即判定该患者为新冠肺炎患者,十折交叉验证准确率约为91%。
本发明的第三个发明目的是提供一种用于检测新冠肺炎的试剂盒,其包含上述的特征多肽组合物, 或包含上述的质谱模型。
在一个实施方案中,所述多肽组合物或质谱模型由特征多肽5158m/z、5366m/z、5893m/z、6357m/z、 6654m/z、6939m/z、7364m/z、7614m/z、8034m/z、8043m/z、8226m/z、8425m/z、8560m/z、8986m/z、 9626m/z、13719m/z、13765m/z、13886m/z、14049m/z、14095m/z、14102m/z、15123m/z、15867m/z、 28091m/z、28232m/z所制备而成,其中当特征多肽5158m/z、5366m/z、5893m/z、7364m/z、7614m/z、 8034m/z、8043m/z、8226m/z、8425m/z、8560m/z、8986m/z、9626m/z、15123m/z、15867m/z、28091m/z 的峰上调,同时特征多肽6357m/z、6654m/z、6939m/z、13719m/z、13765m/z、13886m/z、14049m/z、 14095m/z、14102m/z、28232m/z的峰下调表达时,表示该血清样本为阳性样本,即该患者为新冠肺 炎患者,十折交叉验证准确率约为97.96%。
或者,在另一个实施方案中,所述多肽组合物或质谱模型由特征多肽5158m/z、5366m/z、5893m/z、 6357m/z、6654m/z、6939m/z、7364m/z、7614m/z、8034m/z、8043m/z、8226m/z、8425m/z、8560m/z、 8986m/z、9626m/z、11435m/z、11495m/z、11523m/z、11680m/z、13719m/z、13765m/z、13886m/z、 14049m/z、14095m/z、14102m/z、15123m/z、15867m/z、28091m/z、28232m/z所制备而成,其中当 特征多肽5158m/z、5366m/z、5893m/z、7364m/z、7614m/z、8034m/z、8043m/z、8226m/z、8425m/z、 8560m/z、8986m/z、9626m/z、11435m/z、11495m/z、11523m/z、11680m/z、15123m/z、15867m/z、 28091m/z的峰上调,同时特征多肽6357m/z、6654m/z、6939m/z、13719m/z、13765m/z、13886m/z、 14049m/z、14095m/z、14102m/z、28232m/z的峰下调表达时,表示该血清样本为阳性样本,即该患 者为新冠肺炎患者,十折交叉验证准确率约为98.69%。
在另一实施方案中,所述多肽组合物或质谱模型仅由以下质荷比分别为7614m/z、8034m/z、 8226m/z、8986m/z、9626m/z、11435m/z、11495m/z、11523m/z、11680m/z、15123m/z、15867m/z、 28091m/z,和6939m/z、13719m/z、13765m/z、13886m/z、14049m/z、14095m/z、14102m/z的特征 多肽所制备而成,其中当特征多肽7614m/z、8034m/z、8226m/z、8986m/z、9626m/z、11435m/z、 11495m/z、11523m/z、11680m/z、15123m/z、15867m/z、28091m/z的峰上调,同时特征多肽6939m/z、 13719m/z、13765m/z、13886m/z、14049m/z、14095m/z、14102m/z的峰下调表达时,表示该血清样 本为阳性样本,即该患者为新冠肺炎患者,十折交叉验证准确率约为93.31%。
在其他实施方案中,所述多肽组合物或质谱模型仅由以下质荷比分别为8986m/z、28091m/z、 6939m/z、13886m/z、14049m/z、14102m/z的特征多肽所制备而成,其中当特征多肽8986m/z、 28091m/z的峰上调,同时特征多肽6939m/z、13886m/z、14049m/z、14102m/z的峰下调表达时,表 示该血清样本为阳性样本,即判定该患者为新冠肺炎患者,十折交叉验证准确率约为91%。
在一个实施方案中,该试剂盒包括样本处理液,该处理液由北京毅新博创生物科技有限公司研制。
在另一实施方案中,该试剂盒还包括保证质谱仪所测分子量准确的标准质谱样品管,该样品管既 可以是含有单一特征多肽的多种样品管,也可以是含有多种特征多肽的一种样品管,所述标准样品管 中的样品用于与待测样品进行质谱时进行平行质谱测试,以判断待测样品分子量信息是否准确可靠。
在另一个实施方案中,该试剂盒可含有上述特征多肽的标准数据库的软件或芯片,可用于待测样 品进行质谱时提供标准数据或曲线的比对,以判断待测样品中特征多肽的表达状况。
本发明的第四个发明目的是提供所述特征多肽组合物,或所述的质谱模型,在制备诊断新冠肺炎 的产品中的用途。
在一个实施方案中,所述多肽组合物或质谱模型由特征多肽5158m/z、5366m/z、5893m/z、6357m/z、 6654m/z、6939m/z、7364m/z、7614m/z、8034m/z、8043m/z、8226m/z、8425m/z、8560m/z、8986m/z、 9626m/z、13719m/z、13765m/z、13886m/z、14049m/z、14095m/z、14102m/z、15123m/z、15867m/z、 28091m/z、28232m/z所制备而成,其中当特征多肽5158m/z、5366m/z、5893m/z、7364m/z、7614m/z、 8034m/z、8043m/z、8226m/z、8425m/z、8560m/z、8986m/z、9626m/z、15123m/z、15867m/z、28091m/z 的峰上调,同时特征多肽6357m/z、6654m/z、6939m/z、13719m/z、13765m/z、13886m/z、14049m/z、 14095m/z、14102m/z、28232m/z的峰下调表达时,表示该血清样本为阳性样本,即该患者为新冠肺 炎患者,十折交叉验证准确率约为97.96%。
或者,在另一个实施方案中,所述多肽组合物或质谱模型由特征多肽5158m/z、5366m/z、5893m/z、 6357m/z、6654m/z、6939m/z、7364m/z、7614m/z、8034m/z、8043m/z、8226m/z、8425m/z、8560m/z、 8986m/z、9626m/z、11435m/z、11495m/z、11523m/z、11680m/z、13719m/z、13765m/z、13886m/z、 14049m/z、14095m/z、14102m/z、15123m/z、15867m/z、28091m/z、28232m/z所制备而成,其中当 特征多肽5158m/z、5366m/z、5893m/z、7364m/z、7614m/z、8034m/z、8043m/z、8226m/z、8425m/z、 8560m/z、8986m/z、9626m/z、11435m/z、11495m/z、11523m/z、11680m/z、15123m/z、15867m/z、 28091m/z的峰上调,同时特征多肽6357m/z、6654m/z、6939m/z、13719m/z、13765m/z、13886m/z、 14049m/z、14095m/z、14102m/z、28232m/z的峰下调表达时,表示该血清样本为阳性样本,即该患 者为新冠肺炎患者,十折交叉验证准确率约为98.69%。
在另一实施方案中,所述多肽组合物或质谱模型仅由以下质合比分别为7614m/z、8034m/z、 8226m/z、8986m/z、9626m/z、11435m/z、11495m/z、11523m/z、11680m/z、15123m/z、15867m/z、 28091m/z,和6939m/z、13719m/z、13765m/z、13886m/z、14049m/z、14095m/z、14102m/z的特征 多肽所制备而成,其中当特征多肽7614m/z、8034m/z、8226m/z、8986m/z、9626m/z、11435m/z、 11495m/z、11523m/z、11680m/z、15123m/z、15867m/z、28091m/z的峰上调,同时特征多肽6939m/z、 13719m/z、13765m/z、13886m/z、14049m/z、14095m/z、14102m/z的峰下调表达时,表示该血清样 本为阳性样本,即该患者为新冠肺炎患者,十折交叉验证准确率约为93.31%。
在其他实施方案中,所述多肽组合物或质谱模型仅由以下质合比分别为8986m/z、28091m/z、 6939m/z、13886m/z、14049m/z、14102m/z的特征多肽所制备而成,其中当特征多肽8986m/z、 28091m/z的峰上调,同时特征多肽6939m/z、13886m/z、14049m/z、14102m/z的峰下调表达时,表 示该血清样本为阳性样本,即判定该患者为新冠肺炎患者,十折交叉验证准确率约为91%。
在上述任一实施方案中,所述诊断新冠肺炎的产品指用于诊断新冠肺炎的任何常规产品,包括: 检测试剂、检测芯片、检测载体,以及检测试剂盒等。
本发明的第五个发明目的是提供制备所述的质谱模型的构建方法,包括:
1)收集多例临床确诊的新冠肺炎人员和非新冠肺炎对照人员(包括肺结核患者、发热咳嗽的症状 类似患者和健康人群)的血清样本,进行低温冷冻备用;
2)对血清蛋白进行质谱前预处理;
3)对预处理过的两组血清蛋白进行质谱检测读取,获得两组血清多肽的指纹图谱;
4)对所有的患者和正常人血清多肽的指纹图谱进行标准化处理,并收集数据;
5)对所得数据进行质控处理,筛选出具有下列质荷比峰的特征多肽:5158m/z、5366m/z、5893m/z、 6357m/z、6654m/z、6939m/z、7364m/z、7614m/z、8034m/z、8043m/z、8226m/z、8425m/z、8560m/z、 8986m/z、9626m/z、13719m/z、13765m/z、13886m/z、14049m/z、14095m/z、14102m/z、15123m/z、 15867m/z、28091m/z、28232m/z,对所述特征多肽进行二级质谱鉴定,并根据这些质荷比峰建立检 测新冠肺炎的质谱模型。
在一个实施方案中,其中步骤5)对所得数据进行质控处理,筛选出具有下列质荷比峰的特征多 肽:5158m/z、5366m/z、5893m/z、6357m/z、6654m/z、6939m/z、7364m/z、7614m/z、8034m/z、 8043m/z、8226m/z、8425m/z、8560m/z、8986m/z、9626m/z、11435m/z、11495m/z、11523m/z、 11680m/z、13719m/z、13765m/z、13886m/z、14049m/z、14095m/z、14102m/z、15123m/z、15867m/z、 28091m/z、28232m/z,对所述特征多肽进行二级质谱鉴定,并根据这些质荷比峰建立检测新冠肺炎 的质谱模型。
在一个优选实施方案中,其中步骤5)的质谱模型仅由以下质合比分别为7614m/z、8034m/z、 8226m/z、8986m/z、9626m/z、11435m/z、11495m/z、11523m/z、11680m/z、15123m/z、15867m/z、 28091m/z,和6939m/z、13719m/z、13765m/z、13886m/z、14049m/z、14095m/z、14102m/z的特征 多肽所制备而成,其中当特征多肽7614m/z、8034m/z、8226m/z、8986m/z、9626m/z、11435m/z、 11495m/z、11523m/z、11680m/z、15123m/z、15867m/z、28091m/z的峰上调,同时特征多肽6939m/z、 13719m/z、13765m/z、13886m/z、14049m/z、14095m/z、14102m/z的峰下调表达时,表示该血清样 本为阳性样本,即该患者为新冠肺炎患者,十折交叉验证准确率约为93.31%。
在另一实施方案中,其中步骤5)的质谱模型仅由以下质合比分别为8986m/z、28091m/z、6939m/z、 13886m/z、14049m/z、14102m/z的特征多肽所制备而成,其中当特征多肽8986m/z、28091m/z的峰 上调,同时特征多肽6939m/z、13886m/z、14049m/z、14102m/z的峰下调表达时,表示该血清样本 为阳性样本,即判定该患者为新冠肺炎患者,十折交叉验证准确率约为91%。
此外,在上述任意一个发明目的中任意一种实施方案中,所述特征多肽组合物、质谱模型、检测 产品、用途、构建方法中,可涉及仅仅包含具有如下质荷比和多肽序列的19种特征多肽:
质荷比为6939m/z的特征多肽,其多肽序列选自如SEQ ID No.1所示的序列;
质荷比为7614m/z的特征多肽,其多肽序列选自如SEQ ID No.2所示的序列;
质荷比为8034m/z的特征多肽,其多肽序列选自如SEQ ID No.3所示的序列;
质荷比为8226m/z的特征多肽,其多肽序列选自如SEQ ID No.4所示的序列;
质荷比为8986m/z的特征多肽,其多肽序列选自如SEQ ID No.5所示的序列;
质荷比为9626m/z的特征多肽,其多肽序列选自如SEQ ID No.6所示的序列;
质荷比为13719m/z的特征多肽,其多肽序列选自如SEQ ID No.7所示的序列;
质荷比为13765m/z的特征多肽,其多肽序列选自如SEQ ID No.8所示的序列;
质荷比为13886m/z的特征多肽,其多肽序列选自如SEQ ID No.9所示的序列;
质荷比为14049m/z的特征多肽,其多肽序列选自如SEQ ID No.10所示的序列;
质荷比为14095m/z的特征多肽,其多肽序列选自如SEQ ID No.11所示的序列;
质荷比为14102m/z的特征多肽,其多肽序列选自如SEQ ID No.12所示的序列;
质荷比为15123m/z的特征多肽,其多肽序列选自如SEQ ID No.13所示的序列;
质荷比为15867m/z的特征多肽,其多肽序列选自如SEQ ID No.14所示的序列;
质荷比为28091m/z的特征多肽,其多肽序列选自如SEQ ID No.15所示的序列;
质荷比为11435m/z的特征多肽,其多肽序列选自如SEQ ID No.16所示的序列;
质荷比为11495m/z的特征多肽,其多肽序列选自如SEQ ID No.17所示的序列;
质荷比为11523m/z的特征多肽,其多肽序列选自如SEQ ID No.18所示的序列;
质荷比为11680m/z的特征多肽,其多肽序列选自如SEQ ID No.19所示的序列。
在上述任意的实施方案中,其中步骤2)预处理的方法包括使用样本处理液稀释稳定样品中的血清 蛋白或多肽。
在上述任意的实施方案中,其中所述步骤3)采用多肽质谱通用前处理试剂盒对两组血清蛋白进行 稀释和读取,获得两组血清多肽的指纹图谱。
在上述任意的实施方案中,所述步骤5)所述的质控处理,对于空白基质,用相同的质谱参数检测 空白基质结晶点,若出现明显质谱峰则认为基质溶液质量不合格。
在上述任意一个实施方案中,其中所述步骤5)所述的质控处理,选取如下8个特征峰作为质控峰: 6426m/z、6623m/z、8753m/z、8785m/z、8904m/z、9118m/z、9409m/z、9700m/z。
在飞行时间质谱检测生物样本的过程中,质谱谱图的质量受个体差异、样本质量、环境温湿度变 化、样本与基质的结晶状态等多个条件影响。为避免异常谱图对分析结果的影响,引入人血清中共有 的上述8个特征峰作为质控峰,而质控峰的出现与患者是否患有新型冠状病毒肺炎无关。在采集到的 843张谱图中,有683张谱图可以检测到全部的8个质控峰(占谱图总数的81.0%),有156张谱图可 以检测到其中的7个质控峰(占谱图总数的18.5%)。其中,设定如下谱图质量控制条件:在单个样 本的谱图中,质控峰出现数量6~8个且内标峰分子量偏移偏差小于0.002时(或偏移范围不超过2‰) 视为质控合格。不合格的谱图需要重新检测。
本发明结合生物信息学方法筛选出相应的新冠肺炎标志物并建立检测模型进行分析检测,所述的 生物信息学方法包括对指纹图谱进行标准化处理、对所得数据进实验质控处理、筛选期望的血清特征 多肽并建立质谱模型,以及可选择地包括使用LR算法建立并验证质谱模型等。其中,所述的实验质 控处理,保留内标峰出峰数量不低于6个的质谱图谱数据,并用内标峰进行谱图的二次校准。
术语和定义
十折交叉验证,英文名叫做10-fold cross-validation,用来测试算法准确性。是常用的测试方法。 将数据集分成十份,轮流将其中9份作为训练数据,1份作为测试数据,进行试验。每次试验都会得 出相应的正确率(或差错率)。10次的结果的正确率(或差错率)的平均值作为对算法精度的估计,一般 还需要进行多次10折交叉验证(例如10次10折交叉验证),再求其均值,作为对算法准确性的估计。 应当指出的是,十折交叉验证准确率与实际检测的准确率(或敏感性)存在相关性但并非等同。在本 发明评价测试算法的效果的过程中,效果符合置信区间的十折交叉验证准确率,如果随着特征多肽的 数量而呈现相关性变化,并达到临床诊断可行的数值,则表明由这些多肽所构建的质谱模型符合临床 诊断的要求。
SAA蛋白(Serum amyloid A protein)是血清淀粉样蛋白A家族,是一种急性时相反应蛋白,属于 载脂蛋白家族中的异质类蛋白质。在人体中存在4种血清淀粉样蛋白A基因分别为SAA1-SAA4,其中 SAA1和SAA2是急性期(acute phase)的两种蛋白称为A-SAA。
技术效果
与现有技术相比,本发明具有以下优点:
1、本发明采用新冠肺炎患者与正常人、肺结核患者以及具有新冠肺炎类型症状的对照患者具有 差异的多个特征蛋白组合进行对血清样本的检测,并采用了传统统计学与现代生物信息学方法相结合 的方法进行数据处理,从而得到肺炎患者和健康人以及其他对照患者的多肽指纹图谱检测模型,并且 所发现的一系列蛋白质质荷比峰为寻找新的更理想的标志物提供了基础和资源。
2、与以往的检测方法比较具有较高的敏感性和特异性,操作简单,检测成本低,通量高,有望 用于新冠肺炎的大规模筛查。
3、本发明模型的构建方法设计合理可行,为提供新冠肺炎的临床治愈率提供了新的筛查方法, 同时也为探索新冠肺炎发生发展的机制提供了新的思路。
4、本发明首次提出根据146例新冠肺炎确诊患者与46例正常人、33例肺结核患者对照以及具 有新冠肺炎类型症状的73例对照中寻找具有差异的多个特征蛋白组合,突破了传统的仅限于正常人 和新冠肺炎患者中寻找特征多肽的研究思路,有效地避免与新冠肺炎症状相似的假阳性结果的感染。
5、通过本发明的质谱模型,检测准确率达到99%,敏感性为98%,特异性为100%,该结果表明 本发明的血清肽组学特征多肽模型能快速用于筛查人群中新冠肺炎患者。
6、相对于25种特征多肽构建的组合物、质谱模型,新引入的4种特征特征多肽(即SEQ ID NO:16-19)属于SAA蛋白标志物家族,其可作为生物标志物常用于临床上通过ELASA、免疫比浊法、 胶体金法、免疫荧光层析法等方法来诊断病菌性、病毒性感染。然而,在完成的25种特种多肽的质 谱模型的基础上,本发明首次提出使用SAA蛋白标志物用于激光飞行质谱检测病毒,并首次精准鉴定 具体的SAA蛋白序列(即SEQ ID NO:16-19),能有效避免正常样本在临床上出现误诊的情形。结果显 示,相比于25种特征多肽质谱模型的十折交叉验证准确率约为97.96%,引入4种SAA多肽标志物的 29种特征多肽质谱模型,其十折交叉验证准确率约为98.69%。
附图说明
图1:不同组(健康人组、肺结核组、类似症状组、新冠患者组)血清多肽指纹图谱对比,其中由上 至下分别为阴性健康人图谱、阴性肺结核图谱、阴性类似症状、阳性新冠患者
图2-1:LASSO中重复频率最高的20个峰。图2-2:为PLS-DA中VIP变化重要度最高的20个峰。
图2-3:在RFECV中交叉验证准确度最高的10个峰。
图3:各特征峰强度,其中左栏为阴性对照组,右栏为阳性对照组。
图4-1:各种机器学习方法,训练集ROC曲线对比。图4-2:测试集ROC曲线对比。
图5:真实分组的测试集混淆矩阵的预测结果。
图6:用于建立快速筛选新冠肺炎(COVID-19)患者的特征多肽质谱模型的流程。
图7:特征多肽m/z 5157.6的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图8:特征多肽m/z 5366.2的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图9:特征多肽m/z 5892.9的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图10:特征多肽m/z 6357.4的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图11:特征多肽m/z 6654.0的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图12:特征多肽m/z 6939.1的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图13:特征多肽m/z 7364.2的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图14:特征多肽m/z 7614.2的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图15:特征多肽m/z 8034.3的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图16:特征多肽m/z 8042.7的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图17:特征多肽m/z 8226.4的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图18:特征多肽m/z 8424.9的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图19:特征多肽m/z 8559.8的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图20:特征多肽m/z 8986.1的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图21:特征多肽m/z 9626.4的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图22:特征多肽m/z 13719.2的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图23:特征多肽m/z 13765.2的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图24:特征多肽m/z 13886.1的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图25:特征多肽m/z 14049.4的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图26:特征多肽m/z 14094.7的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图27:特征多肽m/z 14101.8的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图28:特征多肽m/z 15123.4的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图29:特征多肽m/z 15866.5的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图30:特征多肽m/z 28091.4的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图31:特征多肽m/z 28231.5的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图32:特征多肽m/z 11435.1的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图33:特征多肽m/z 11495.3的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图34:特征多肽m/z 11522.8的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
图35:特征多肽m/z 11680.3的质谱峰图谱,上图为非新冠对照的质谱图,下图为COVID-19质谱图。
具体实施方式
以下实施例用于说明本发明,但不用来限制本发明的范围。
实施例1样本处理
146例确诊患者的血清样本来自2020年2月的重庆某医院,所有患者均为核酸检测阳性,并按照指 南标准进行严格分类。
根据以下标准进行分类:
(1)轻型:临床症状轻微,影像学未见肺炎表现;
(2)普通型:具有发热、呼吸道症状,影像学可见肺炎表现;
(3)重型:呼吸困难,呼吸率≥30次/分钟,静态下氧饱和度≤93%,动脉血分压(PaO2)/氧浓度 (FiO2)≤300mmHg;
(4)危重型:呼吸衰竭,需要呼吸机,出现休克,出现其他器官衰竭应送至ICU抢救。
作为对照的非新冠肺炎的152例血清样本来自2020年3月的重庆某医院,包括46例正常人、33例 肺结核患者对照以及具有新冠肺炎类型症状的73例对照。
所有样本均在清晨未进食前空腹下抽取,装入未含添加剂的真空血清采集管中,2,264g离心10min, 并在56℃温育30min,然后将血清样本分装冷冻在-80℃。
血清样品的质谱前处理:在进行质谱检测实验前,从低温冰箱提取分装的血清样品各1管,放于湿 冰上。化冻60-90分钟。吸取5uL血清样本,加入45uL样本处理液,1200rpm涡旋30s;吸取处理后的样 本溶液10uL加入配置好的基质溶液10uL,1200rpm涡旋30s;将1uL混合液点到靶板上,每个样本需要点 三个实验重复,自然晾干,即可进行质谱检测。
实施例2、建立MALDI-TOF-MS的质谱模型
(一)样品准备
将每份样本的5ul血清稀释在45ul样本处理液(Bioyong Technologies Inc.)。然后取出10ul已稀释的血 清,与10ul基质溶液(Bioyong Technologies Inc.)进行混合。
取出2ul混合液滴加至不锈钢靶板。室温干燥后,将样品进样MALDI-TOF MS质谱仪(Clin-TOF-II;Bioyong Technologies Inc.)。每个样品平行测试3次。
基质辅助激光解吸飞行时间质谱Clin-TOF及实验用的多肽质谱通用前处理试剂盒由中国Bioyong公司 研制。使用MALDIquant程序做数据的预处理,对处理后的数据进行平方根变换,使用滤波拟合法进行平滑 处理,并进行基线校正。质谱仪用分子量已知的多肽蛋白混合物来进行校准。校准品的质量漂移应该在500 ppm以内。每个样品点采集500张谱图。分子量采集范围是m/z 3000~30000。
不同组样本质谱谱图参见图1(图1:不同组血清多肽指纹图谱对比,其中由上至下分别为阴性健康人 图谱、阴性肺结核图谱、阴性类似症状、阳性新冠患者)。在阴性健康人谱图中,5158m/z、5366m/z、5893m/z、 7364m/z、7614m/z、8034m/z、8043m/z、8425m/z、8560m/z、8986m/z、9626m/z、11435m/z、11523m/z、 15123m/z、15867m/z、28091m/z的峰强度较低,而6357m/z、6654m/z、6939m/z、13719m/z、13765m/z、 13886m/z、14049m/z、14095m/z、14102m/z、28232m/z的峰强度较高。在阴性肺结核谱图中,5158m/z、 5366m/z、5893m/z、7364m/z、7614m/z、8034m/z、8043m/z、8226m/z、8425m/z、8560m/z、8986m/z、 9626m/z、11435m/z、11495m/z、11523m/z、11680m/z、15123m/z、15867m/z、28091m/z的峰强度较低,而6654m/z、6939m/z、13719m/z、13765m/z、13886m/z、14049m/z、14095m/z、14102m/z、28232m/z 的峰强度较高。在类似阴性症状组谱图中,5158m/z、5366m/z、7364m/z、7614m/z、8034m/z、8043m/z、 8425m/z、8560m/z、8986m/z、9626m/z、15123m/z、15867m/z、28091m/z的峰强度较低,而6357m/z、 6654m/z、6939m/z、13719m/z、13765m/z、13886m/z、14049m/z、14095m/z、14102m/z、28232m/z的峰 强度较高。在阳性新冠患者谱图中,5158m/z、5366m/z、5893m/z、7364m/z、7614m/z、8034m/z、8043m/z、 8226m/z、8425m/z、8560m/z、8986m/z、9626m/z、11435m/z、11495m/z、11523m/z、11680m/z、15123m/z、 15867m/z、28091m/z的峰强度较高,而6357m/z、6654m/z、6939m/z、13719m/z、13765m/z、13886m/z、 14049m/z、14095m/z、14102m/z、28232m/z的峰强度较低。
(二)质谱数据采集
应用Clin-TOF质谱仪。设置合适的激光能量采集样品结晶点的某一个点。每个样品点选取50个激光 轰击位置,每个位置轰击10次,即对每个样品结晶点进行500次激光轰击,收集谱图。激光频率:30Hz。 数据收集范围:3~30KDa。在每次采集样品结晶点前用标准品进行外标校准,平均分子量偏差小于500ppm。
实验质控:
(1)用相同的质谱参数检测空白基质结晶点,若出现明显质谱峰则认为基质溶液质量不合格,需更换 一支新的基质。
(2)用标准品进行外标校准时需保证不同校准品点的质量偏移不超过500ppm,5个校准品峰必须同 时满足要求。
(3)选取8个血清中故有的多肽峰作为内标质控峰。若有6~8个内标峰可以被检测到,且内标峰分子 量偏移范围不超过2‰则认为谱图合格。否则需重新采集谱图。内标峰m/z如下:6426m/z、6623m/z、8753m/z、 8785m/z、8904m/z、9118m/z、9409m/z、9700m/z。
(三)原始数据预处理
MALDI-TOF原始数据用内标校准软件进行内标二次校准,并保存为txt格式文件。所用内标峰m/z 为:6426m/z、6623m/z、8753m/z、8785m/z、8904m/z、9118m/z、9409m/z、9700m/z。然后用MALDIquant 程序对谱图进行处理。谱图处理内容包括平滑、基线校正以及分子量校准。在信噪比为3的情况下进行峰 值检测。使用binPeaks命令对峰值进行bin处理,其容错为0.002。保留组内出峰频率不低于25%的峰。最 后,将得到的矩阵用于下面的分析。
经log2变换后,将峰强度矩阵与R包limma进行分位数归一化。在所有样本中,缺失值用最小值来 填充。将COVID-19患者数据和对照样本数据随机分为训练组和测试组,分配比例为2:1。
(四)特征蛋白的选择
经过强度归一化和缺失值归一化后,训练组的峰值用如下三种机器学习方法进行分析:套索算法 (LASSO)、偏最小二乘回归分析法(PLS-DA)和带交叉验证的递归特征消除(RFECV)。LASSO全称Least absolute shrinkage and selection operator,是一种压缩估计。它通过构造一个惩罚函数得到一个较为精炼的 模型,使得它压缩一些回归系数,即强制系数绝对值之和小于某个固定值;同时设定一些回归系数为零。 因此保留了子集收缩的优点,是一种处理具有复共线性数据的有偏估计。
图2-1展示了LASSO中重复频率最高的20个峰。其中纵轴为各优选特征峰的质核比。偏最小二乘法 判别分析(PLS-DA)是一种用于判别分析的多变量统计分析方法。判别分析是一种根据观察或测量到的若 干变量值,来判断研究对象如何分类的常用统计分析方法。其原理是对不同处理样本(如观测样本、对照 样本)的特性分别进行训练,产生训练集,并检验训练集的可信度。
图2-2展示了PLS-DA中VIP变化重要度最高的20个峰。其中纵轴为各优选特征峰的质核比。RFECV 指通过交叉验证来找到最优的特征数量。其中RFE(Recursivefeatureelimination)指递归特征消除,用来对 特征进行重要性评级。CV(Cross Validation)指交叉验证,即在特征评级后,通过交叉验证,选择最佳数量 的特征。图2-3展示了在RFECV中交叉验证准确度最高的10个峰。其中纵轴为各优选特征峰的质核比。
通过对所选峰的原始谱图的经验检验,筛选出质量控制合格的29个峰作为特征。各特征峰强度如图 3。图中每一行代表一个特征峰,每一列代表一个谱图数据,图中颜色深浅代表峰的强度。其中左栏为阴性 对照组,右栏为阳性组。可以看到特征多肽6357m/z、6654m/z、6939m/z、13719m/z、13765m/z、13886m/z、 14049m/z、14095m/z、14102m/z、28232m/z的峰在阴性组中表达量普遍高于阳性组,同时特征多肽5158m/z、 5366m/z、5893m/z、7364m/z、7614m/z、8034m/z、8043m/z、8226m/z、8425m/z、8560m/z、8986m/z、9626m/z、11435m/z、11495m/z、11523m/z、11680m/z、15123m/z、15867m/z、28091m/z的峰在阳性组 中表达量普遍高于阴性组。这些峰值的强度在COVID-19和对照组之间有显著差异。
(五)模型算法
我们尝试用8种机器学习方法用训练组数据的29个特征峰建立模型,通过交叉验证准确率评估模型结 果。分析的8中机器学习方法如下:逻辑回归(LR),支持向量机(SVM),随机森林(RF),朴素贝叶斯法(NB), 梯度下降树(GBDT),K临近算法(KNN),决策树(DT)和自适应增强算法(Adaboost)。
图4-1和图4-2分别用ROC曲线的形式展示了训练组和测试组的模型结果。ROC曲线是根据一系列不同 的二分类方式(分界值或决定阈),以真阳性率(灵敏度)为纵坐标,假阳性率(1-特异度)为横坐标绘制的曲线。 通过分别计算各个试验的ROC曲线下的面积(AUC)进行比较,哪一种试验的AUC最大,则哪一种试验的诊 断价值最佳。在本研究中,训练组所有模型的ROC曲线下面积AUC皆大于0.99,其中LR、SVM、GBDT、 DT和Adaboost的AUC为1(图4-1)。在对验证组数据的ROC曲线分析中发现,8种机器学习方法得到的8 个模型在测试集的AUC都超过了0.94,对于LR模型,AUC为1(图4-2)。在评估了8个模型的准确率、召 回率、精密度、F1、灵敏度和特异度后发现LR模型具有最好的分类性能(AUC=1,灵敏度=98%,特异性=100%, 准确率=99%,精密度=99%,召回率=99%,F1=99%),可进一步应用于COVID-19的检测。
测试集中LR模型的混淆矩阵如图5所示,图中纵轴代表样本真实分组情况,上面一行表示阴性样本数 量,下面一行表示阳性样本数量;横轴代表模型预测结果,左面一列表示被模型判断为阴性的样本数量, 右面一列表示被模型判断结果为阳性的样本数量。在51个阴性样本中,全部判断为阴性,阴性样本判断准 确率(即模型特异度)为100%;在49例阳性样本中,有1例被误判断为阴性,48例被判断为阳性,阳性 样本判断准确率(即模型灵敏度)为98.0%。
表1.训练集中29个特征多肽在各组中的均值及四分位距
具体的用于建立快速筛选新冠肺炎(COVID-19)患者的特征多肽质谱模型的流程,参见图6。该流程 包括:(1)分别搜集新冠肺炎患者和阴性对照人群并采集血清样本;(2)用试剂盒对血清样本进行质谱前 处理;(3)MALDI-TOF MS质谱检测,得到谱图信息;(4)谱图处理并获得峰列表;(5)生物信息学分析; (6)确定质谱模型。
实施例3、新冠肺炎患者筛选模型的建立
选择298例血清样本(146例来自确诊的新冠肺炎患者,另外46例正常人、33例肺结核患者对照 以及具有新冠肺炎类似症状(发热咳嗽)的73例对照)中的198例作为训练样本,用于模型的建立,其中 97例来自新冠肺炎患者,以及34例来自正常人、19例来自肺结核患者对照以及48例来自具有新冠肺炎类 似症状。所有的血清样本均在清晨空腹下抽取,分离血清并灭活病毒后储存在-80℃低温冰箱中。
剩余样本(49例新冠肺炎患者、12例正常人、14例肺结核、25例新冠肺炎类似症状)作为验证样 本,用于盲选测试。处理方式同上。
用实施例1-2筛选出的新冠肺炎患者血清特征多肽峰建立新冠肺炎多肽的质谱模型。该模型定为采 用29个特征峰,分别是:5158m/z、5366m/z、5893m/z、6357m/z、6654m/z、6939m/z、7364m/z、7614m/z、 8034m/z、8043m/z、8226m/z、8425m/z、8560m/z、8986m/z、9626m/z、11435m/z、11495m/z、11523m/z、 11680m/z、13719m/z、13765m/z、13886m/z、14049m/z、14095m/z、14102m/z、15123m/z、15867m/z、 28091m/z、28232m/z。
上述特征多肽的特征质谱峰谱图,参见图7-35所示。
LR模型的训练集和验证集AUC均大于0.99。测试集准确率99%,灵敏度98%,特异度100%。模型 具有很好的预测能力。
表2.模型训练结果
样本 | 例数 | 预测为新冠肺炎 | 预测为非新冠肺炎 | 预测准确率% |
患者组 | 97 | 97 | 0 | 100.00 |
正常组 | 34 | 0 | 34 | 100.00 |
肺结核组 | 19 | 0 | 19 | 100.00 |
症状类似组 | 48 | 0 | 48 | 100.00 |
总计 | 198 | 100.00 |
从上表中可以看出对训练组样本的结果为:34例正常组中的34例判断正确,特异性100.00%;97 例患者中的97例判断正确,敏感性100.00%;19例肺结核患者中的19例判断正确,敏感性为100.00%; 48例症状类似患者中的48例判断正确,敏感性为100.00%。
实施例4、新冠肺炎特征多肽的鉴定
据实施例2和3中确定待鉴定峰后,查找前期处理样本中待鉴定峰值强度高低不同的7个血清样本。 样本经DTT还原后,超滤离心除去分子量大于50kDa的蛋白。滤出的小分子蛋白/多肽用tricine-SDS-PAGE 分离。各条带经胶内酶切后进行二级质谱鉴定。
采用nano-LC-MS/MS平台进行多肽序列鉴定,包括nanoflow HPLC(Thermo FisherScientific,USA)和 Q-Exactive mass spectrometer(Thermo Fisher Scientific,USA)。离子模式为正离子模式,扫描范围 为300-1400m/z。一级质谱分辨率为70000,二级质谱分辨率为17500。
液相分析柱:型号:Exsil Pure 120 C18(Dr.Maisch GmbH,USA);规格:360μm×12cm;内径:150μm; 粒:1.9um。洗脱方式:流动相从7%B液(80%乙腈,0.1%甲酸)到45%B液,线性洗脱。流速:600nl/min; 总时间38分钟。鉴定结果见表3和表4。
表3.特征峰多肽鉴定结果
m/z | 基因名称 | 蛋白名称 |
5158 | H2AJ | Histone H2A.J |
6357 | S100A7 | Protein S100-A7 |
6654 | IGLL5 | Immunoglobulin lambda-like polypeptide 5 |
6939 | UBB | Polyubiquitin-B |
7364 | IGKV3-7 | Probable non-functional immunoglobulin kappa variable 3-7 |
7614 | PF4V1 | Platelet factor 4 variant |
8034 | IGKV3-15 | Immunoglobulin kappa variable 3-15 |
8226 | CFI | Complement factor I |
8986 | RAB7A | Ras-related protein Rab-7a |
9626 | ELANE | Neutrophil elastase |
13719 | B2M | Beta-2-microglobulin |
13765 | TTR | Transthyretin |
13886 | PPBP | Platelet basic protein |
14049 | DUSP14 | Dual specificity protein phosphatase 14 |
14095 | H2AC11 | Histone H2A type 1 |
14102 | H2AC6 | Histone H2A type 1-C |
15123 | HBA1 | Hemoglobin subunit alpha |
15867 | HBB | Hemoglobin subunit beta |
28091 | WRAP73 | WD repeat-containing protein WRAP73 |
11435 | SAA1 | Serum amyloid A-1 protein |
11495 | SAA2 | Serum amyloid A-2 protein |
11523 | SAA1 | Serum amyloid A-1 protein |
11680 | SAA1 | Serum amyloid A-1 protein |
表4.多肽鉴定序列
实施例5、新冠肺炎患者筛选模型的盲选测试
模型训练完成后,建立起了一个涉及SEQ ID NO:1-15的25个特征多肽片段的输入变量的模型,以及 涉及SEQ ID NO:1-19的29个特征多肽片段的输入变量的模型,另外建立完成测序的19个特征多肽片段(即 序列SEQ 1-19)的输入变量的模型。
根据实施例3的方法,用上述三种模型对49例新冠肺炎患者、12例正常人、14例肺结核、21例类 型症状的样本来盲选预测,并判断出样本的类别,方法同以上实施例所述。结果分别如表5-1、表5-2、表 5-3所示。
表5-1.通过25个变量对测试样本预测结果
从表5-1中可以看出对测试组样本的结果为:12例正常组中的12例判断正确,特异性100.00%;49 例患者中的48例判断正确,敏感性97.96%;14例肺结核患者中的14例判断正确,敏感性为100.00%;25 例症状类似患者中的25例判断正确,敏感性为100.00%。
表5-2.通过29个变量对测试样本预测结果
样本 | 例数 | 预测为新冠肺炎 | 预测为非新冠肺炎 | 预测准确率% |
患者组 | 49 | 48 | 1 | 97.96 |
正常组 | 12 | 0 | 12 | 100.00 |
肺结核组 | 14 | 0 | 14 | 100.00 |
症状类似组 | 25 | 0 | 25 | 100.00 |
总计 | 100 | 99.00 |
从表5-2中可以看出对测试组样本的结果为:12例正常组中的12例判断正确,特异性100.00%;49 例患者中的48例判断正确,敏感性97.96%;14例肺结核患者中的14例判断正确,敏感性为100.00%;25 例症状类似患者中的25例判断正确,敏感性为100.00%。
由表5-1和表5-2所知,二者对于100个相同样本的预测准确率均符合临床诊断的标准。虽然准确 率均相同,可能原因是国内待检患者例数过少,导致未显示区分度。但根据十折交叉验证的准确率可知, 可以预测随着待检患者例数的增加,使用29种特征多肽的质谱诊断模型将呈现出更高的准确率。
表5-3.通过19个变量对测试样本预测结果
样本 | 例数 | 预测为新冠肺炎 | 预测为非新冠肺炎 | 预测准确率% |
患者组 | 49 | 46 | 3 | 93.88 |
正常组 | 12 | 0 | 12 | 100.00 |
肺结核组 | 14 | 0 | 14 | 100.00 |
症状类似组 | 25 | 4 | 21 | 84.00 |
总计 | 100 | 93.00 |
从表5-3中可以看出对测试组样本的结果为:49例新冠患者中的46例判断正确,敏感性93.88%; 12例正常组中的12例判断正确,特异性100.00%;14例肺结核患者中的14例判断正确,特异性为100.00%; 25例症状类似患者中的21例判断正确,敏感性为84.00%。这说明,由19种特征多肽的输入变量所组成的 模型,对于健康人和肺结核患者,特异性与完全变量的检测结果相同,其它两组出现极少数的错判。该模 型已经符合临床上快速筛查确诊患者的需求。
另外,从上表中可以看出:本发明使用29种特征多肽的完全变量对于新冠肺炎组的盲选检测准确率 基本相同于模型训练,但对于非新冠组的预测结果达到100%,说明在模型训练后的结果上,实验人员通过 细微优化,即可完全排除假阳性结果,这说明其对于阳性结果的诊断结果真实可信,最大程度上避免了漏 诊和/或误诊,因此具有积极意义。
以上所述仅是本发明的优选实施方式,应当指出,对于本技术领域的普通技术人员来说,在不脱离本 发明技术原理的前提下,还可以做出若干改进和润饰,这些改进和修饰也应视为本发明的保护范围。
序列表
<110> 北京毅新博创生物科技有限公司
<120> 用于诊断新冠肺炎的质谱模型的构建方法
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Claims (10)
1.一种用于诊断新冠肺炎的质谱模型的构建方法,步骤包括:
1)收集多例临床确诊的新冠肺炎人员和非新冠肺炎对照人员(包括肺结核患者、发热咳嗽的症状类似患者和健康人群)的血清样本,进行低温冷冻备用;
2)对血清蛋白进行质谱前预处理;
3)对预处理过的两组血清蛋白进行质谱检测读取,获得两组血清多肽的指纹图谱;
4)对所有的患者和正常人血清多肽的指纹图谱进行标准化处理,并收集数据;
5)对所得数据进行质控处理,筛选出具有下列质荷比峰的特征多肽:5158m/z、5366m/z、5893m/z、6357m/z、6654m/z、6939m/z、7364m/z、7614m/z、8034m/z、8043m/z、8226m/z、8425m/z、8560m/z、8986m/z、9626m/z、13719m/z、13765m/z、13886m/z、14049m/z、14095m/z、14102m/z、15123m/z、15867m/z、28091m/z、28232m/z,或者筛选出具有下列质荷比峰的特征多肽:5158m/z、5366m/z、5893m/z、6357m/z、6654m/z、6939m/z、7364m/z、7614m/z、8034m/z、8043m/z、8226m/z、8425m/z、8560m/z、8986m/z、9626m/z、11435m/z、11495m/z、11523m/z、11680m/z、13719m/z、13765m/z、13886m/z、14049m/z、14095m/z、14102m/z、15123m/z、15867m/z、28091m/z、28232m/z,对所述特征多肽进行二级质谱鉴定,并根据这些质荷比峰建立检测新冠肺炎的质谱模型。
2.权利要求1所述的构建方法,其中所述特征多肽分别为:
质荷比为6939m/z的特征多肽,其多肽序列选自如SEQ ID No.1所示的序列;
质荷比为7614m/z的特征多肽,其多肽序列选自如SEQ ID No.2所示的序列;
质荷比为8034m/z的特征多肽,其多肽序列选自如SEQ ID No.3所示的序列;
质荷比为8226m/z的特征多肽,其多肽序列选自如SEQ ID No.4所示的序列;
质荷比为8986m/z的特征多肽,其多肽序列选自如SEQ ID No.5所示的序列;
质荷比为9626m/z的特征多肽,其多肽序列选自如SEQ ID No.6所示的序列;
质荷比为13719m/z的特征多肽,其多肽序列选自如SEQ ID No.7所示的序列;
质荷比为13765m/z的特征多肽,其多肽序列选自如SEQ ID No.8所示的序列;
质荷比为13886m/z的特征多肽,其多肽序列选自如SEQ ID No.9所示的序列;
质荷比为14049m/z的特征多肽,其多肽序列选自如SEQ ID No.10所示的序列;
质荷比为14095m/z的特征多肽,其多肽序列选自如SEQ ID No.11所示的序列;
质荷比为14102m/z的特征多肽,其多肽序列选自如SEQ ID No.12所示的序列;
质荷比为15123m/z的特征多肽,其多肽序列选自如SEQ ID No.13所示的序列;
质荷比为15867m/z的特征多肽,其多肽序列选自如SEQ ID No.14所示的序列;
质荷比为28091m/z的特征多肽,其多肽序列选自如SEQ ID No.15所示的序列;
质荷比为11435m/z的特征多肽,其多肽序列选自如SEQ ID No.16所示的序列;
质荷比为11495m/z的特征多肽,其多肽序列选自如SEQ ID No.17所示的序列;
质荷比为11523m/z的特征多肽,其多肽序列选自如SEQ ID No.18所示的序列;
质荷比为11680m/z的特征多肽,其多肽序列选自如SEQ ID No.19所示的序列。
3.权利要求1或2所述的构建方法,其中步骤5)的质谱模型仅由以下质合比分别为7614m/z、8034m/z、8226m/z、8986m/z、9626m/z、11435m/z、11495m/z、11523m/z、11680m/z、15123m/z、15867m/z、28091m/z,和6939m/z、13719m/z、13765m/z、13886m/z、14049m/z、14095m/z、14102m/z的特征多肽所制备而成。
4.权利要求3所述的构建方法,其中当特征多肽7614m/z、8034m/z、8226m/z、8986m/z、9626m/z、11435m/z、11495m/z、11523m/z、11680m/z、15123m/z、15867m/z、28091m/z的峰上调,同时特征多肽6939m/z、13719m/z、13765m/z、13886m/z、14049m/z、14095m/z、14102m/z的峰下调表达时,表示该血清样本为阳性样本,即该患者为新冠肺炎患者,十折交叉验证准确率约为93.31%。
5.权利要求1或2所述的构建方法,其中其中步骤5)的质谱模型仅由以下质合比分别为8986m/z、28091m/z、6939m/z、13886m/z、14049m/z、14102m/z的特征多肽所制备而成。
6.权利要求5所述的构建方法,其中当特征多肽8986m/z、28091m/z的峰上调,同时特征多肽6939m/z、13886m/z、14049m/z、14102m/z的峰下调表达时,表示该血清样本为阳性样本,即判定该患者为新冠肺炎患者,十折交叉验证准确率约为91%。
7.权利要求1-6任一所述的构建方法,其中步骤2)预处理的方法包括使用样本处理液稀释稳定样品中的血清蛋白或多肽。
8.权利要求1-6任一所述的构建方法,其中所述步骤3)采用多肽质谱通用前处理试剂盒对两组血清蛋白进行稀释和读取,获得两组血清多肽的指纹图谱。
9.权利要求1-6任一所述的构建方法,其中所述步骤5)所述的质控处理,对于空白基质,用相同的质谱参数检测空白基质结晶点,若出现明显质谱峰则认为基质溶液质量不合格。
10.权利要求9所述的构建方法,其中所述步骤5)所述的质控处理,选取如下8个特征峰作为质控峰:6426m/z、6623m/z、8753m/z、8785m/z、8904m/z、9118m/z、9409m/z、9700m/z。
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