@article{TEXTUAL,
      recid = {6251},
      author = {Tiruvayipati, Suma and Wolfgeher, Don and Yue, Ming and  Duan, FangFang and Andrade, Jorge and Jiang, Hui and  Schuger, Lucia},
      title = {Variability in protein cargo detection in technical and  biological replicates of exosome-enriched extracellular  vesicles},
      journal = {PLOS ONE},
      address = {2020-03-02},
      number = {TEXTUAL},
      abstract = {Exosomes are extracellular vesicles (EVs) of ~20–200 nm  diameter that shuttle DNAs, RNAs, proteins and other  biomolecules between cells. The large number of  biomolecules present in exosomes demands the frequent use  of high-throughput analysis. This, in turn, requires  technical replicates (TRs), and biological replicates (BRs)  to produce accurate results. As the number and abundance of  identified biomolecules varies between replicates (Rs),  establishing the replicate variability predicted for the  event under study is essential in determining the number of  Rs required. Although there have been few reports of  replicate variability in high throughput biological data,  none of them focused on exosomes. Herein, we determined the  replicate variability in protein profiles found in exosomes  released from 3 lung adenocarcinoma cell lines, H1993, A549  and H1975. Since exosome isolates are invariably  contaminated by a small percentage of ~200–300 nm  microvesicles, we refer to our samples as exosome-enriched  EVs (EE-EVs). We generated BRs of EE-EVs from each cell  line, and divided each group into 3 TRs. All Rs were  analyzed by liquid chromatography/mass spectrometry  (LC/MS/MS) and customized bioinformatics and biostatistical  workflows (raw data available via ProteomeXchange:  PXD012798). We found that the variability among TRs as well  as BRs, was largely qualitative (protein present or absent)  and higher among BRs. By contrast, the quantitative  (protein abundance) variability was low, save for the H1975  cell line where the quantitative variability was  significant. Importantly, our replicate strategy identified  90% of the most abundant proteins, thereby establishing the  utility of our approach.},
      url = {http://knowledge.uchicago.edu/record/6251},
}