蛋白质组学研究

Proteomics Research蛋白质组学研究

研究生物体、组织或细胞在特定时间、特定条件下表达的所有蛋白质及其结构、功能、相互作用和动态变化的科学，探究疾病、药物等对生命过程的影响，解释基因表达调控的机制。旨在揭示生命活动机制和疾病相关的分子基础。

TMT-定量蛋白组学

利用同位素标记的多肽探针，通过质谱技术对不同样本中的蛋白质进行标记、分离和定量分析的高通量方法，能够精确比较不同生物条件下蛋白质的表达水平差异。TMT采用6、10、16、18种同位素标签，可同时比较18种样品之间的蛋白质表达量。核序生物的TMT蛋白组学具有定量准确、极高的重复性以及鉴定深度。

A high-throughput method that uses isotope-labeled peptide probes to label, separate and quantify proteins in different samples through mass spectrometry technology can accurately compare the differences in protein expression levels under different biological conditions. TMT uses 6, 10, 16, and 18 isotope labels to simultaneously compare protein expression levels between 18 samples. TMT proteomics of NucleoSequence Biology has accurate quantitative analysis, extremely high repeatability, and identification depth.

Label-Free-定量蛋白组学

无需化学标记的的蛋白质定量技术。通过液质联用的方法检测蛋白质的解肽段从而大规模分析鉴定蛋白质时所产生的质谱数据。适用于临床大样本的检测，绝对的检测样本中蛋白的“有或者无”。核序生物独有的Label-free定量蛋白组学离子利用率和准确度更高，能以较少的上样量实现高灵敏度、高通量、高精确度的蛋白检测。

Protein quantification technology that does not require chemical labeling. The mass spectrometry data generated when the peptide fragments of proteins are detected by liquid chromatography-mass spectrometry to analyze and identify proteins on a large scale. It is suitable for the detection of large clinical samples and can absolutely detect the "presence or absence" of proteins in the samples. The unique Label-free quantitative proteomics of NucleoSequence Biotechnology has higher ion utilization and accuracy, and can achieve high-sensitivity, high-throughput, and high-precision protein detection with less sample loading.

DIA-定量蛋白组学

采用数据非依赖性扫描模式（DIA），通过系统性分段扫描所有肽段离子，从而无遗漏、无差异地获得样本中所有离子的信息。避免了数据依赖性采集（DDA）遗漏的一些低丰度的蛋白，从而大大提高了蛋白的检出率。降低了样本检测的缺失值，同时提高了定量准确性和重复性，实现大样本队列中高稳定，高精准的蛋白质组定量分析。广泛应用于生物标志物发现和疾病机制研究。

The data-independent scanning mode (DIA) is used to systematically scan all peptide ions in segments, so as to obtain information on all ions in the sample without omission or difference. It avoids some low-abundance proteins missed by data-dependent acquisition (DDA), thereby greatly improving the detection rate of proteins. It reduces the missing values of sample detection, improves quantitative accuracy and repeatability, and realizes highly stable and accurate proteome quantitative analysis in large sample cohorts. It is widely used in biomarker discovery and disease mechanism research.