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EV-origin: Enumerating the tissue-cellular origin of circulating extracellular vesicles using exLR profile

Li, Yuchen, He, Xigan, Li, Qin, Lai, Hongyan, Zhang, Hena, Hu, Zhixiang, Li, Yan, Huang, Shenglin·2020·Computational and Structural Biotechnology Journal
DOI 10.1016/j.csbj.2020.10.002PMC PMC7588739
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Graphical abstract CC BY · PubMed Central
Fig. 1 Schematics of estimating EVs origin from exLR-seq expression profile. (a) An overview of the computational model of EV-origin. (b) The expression pattern and frequency distribution of 810 TSGs among all healthy exLR-seq expression profiles. (c) The circular figure of 95 candidate TSGs represented 16 types of tissue/visceral organs in EV-origin approach. The circularized diagram was developed by five tracks. The first track integrally identifies the official gene ID of all tissue-specific targets with their represented tissues. The second and third tracks represent the expression level of these TSGs in plasma EVs and tissue respectively. The expression frequency and TSS value of each target are illustrated in the last two panels.
Fig. 1 Schematics of estimating EVs origin from exLR-seq expression profile. (a) An overview of the computational model of EV-origin. (b) The expression pattern and frequency distribution of 810 TSGs among all healthy exLR-seq expression profiles. (c) The circular figure of 95 candidate TSGs represented 16 types of tissue/visceral organs in EV-origin approach. The circularized diagram was developed by five tracks. The first track integrally identifies the official gene ID of all tissue-specific targets with their represented tissues. The second and third tracks represent the expression level of these TSGs in plasma EVs and tissue respectively. The expression frequency and TSS value of each target are illustrated in the last two panels.CC BY · PubMed Central
Fig. 2 Comparison of the performance of six deconvolution models using different EV transcriptomic datasets. (a-b) Root mean square error (RMSE) and Pearson’s correlation coefficient (PCC) between the predicted matrices and input profiles were used to evaluate the performance of the six models on blood and tissue origin of EVs. The higher PCC and lower RMSE of assessed results for each model were respectively indicated to a higher concordance and lower differences between known and estimated tissue/cell-type proportions. (c) The estimated proportion of blood cell components generated from xCell and the six deconvolution models were compared among all normal samples. (d) Evaluation of frequency distribution of relative enrichment results from the six models. (e) Comparison of the stability and relative enrichment distribution predicted by the different models. (f) The comparison of relative prediction results of brain components from each model from normal CSF exLR-seq profiles.
Fig. 2 Comparison of the performance of six deconvolution models using different EV transcriptomic datasets. (a-b) Root mean square error (RMSE) and Pearson’s correlation coefficient (PCC) between the predicted matrices and input profiles were used to evaluate the performance of the six models on blood and tissue origin of EVs. The higher PCC and lower RMSE of assessed results for each model were respectively indicated to a higher concordance and lower differences between known and estimated tissue/cell-type proportions. (c) The estimated proportion of blood cell components generated from xCell and the six deconvolution models were compared among all normal samples. (d) Evaluation of frequency distribution of relative enrichment results from the six models. (e) Comparison of the stability and relative enrichment distribution predicted by the different models. (f) The comparison of relative prediction results of brain components from each model from normal CSF exLR-seq profiles.CC BY · PubMed Central
Fig. 3 The landscape of tissue/blood cell origins by the EV-origin approach among normal plasma samples. (a) The relative distribution of all blood cell components from EV-origin. (b) The pie plot shows the relative comparison of and 16 types of tissue fractions from EV origin. (c and d) The estimation of age-related (c) and gender-related (d) components from EV-origin. (e) Pearson’s correlation between the platelet component with other tissue fractions from the absolute results of EV-origin.
Fig. 3 The landscape of tissue/blood cell origins by the EV-origin approach among normal plasma samples. (a) The relative distribution of all blood cell components from EV-origin. (b) The pie plot shows the relative comparison of and 16 types of tissue fractions from EV origin. (c and d) The estimation of age-related (c) and gender-related (d) components from EV-origin. (e) Pearson’s correlation between the platelet component with other tissue fractions from the absolute results of EV-origin.CC BY · PubMed Central
Fig. 4 Heterogeneous origins of plasma EVs from different disease conditions. (a) Boxplots show the comparison of the absolute liver fraction of EV-origin in diverse hepatic diseases. (b) Three-dimensional scatterplot visualization displaying the fold-change comparisons of EV-origin absolute results for HCC and other types of non-tumor samples. (c) Comparison of relative liver fraction of EV-origin from all types of exLR-seq samples. The results are shown as the mean ± SEM. *P < 0.05; ***P < 0.001. (d) ROC plot unravels the diagnostic performance of EV-origin in distinguishing HCC and hepatic disease samples from healthy individuals. The high diagnostic performance of EV-origin for separating carcinoma from non-carcinoma individuals is also indicated. HD: hepatic disorders. (e) ROC diagram shows the diagnostic efficiency of absolute hepatic fraction derived from EV-origin in validation cohort.
Fig. 4 Heterogeneous origins of plasma EVs from different disease conditions. (a) Boxplots show the comparison of the absolute liver fraction of EV-origin in diverse hepatic diseases. (b) Three-dimensional scatterplot visualization displaying the fold-change comparisons of EV-origin absolute results for HCC and other types of non-tumor samples. (c) Comparison of relative liver fraction of EV-origin from all types of exLR-seq samples. The results are shown as the mean ± SEM. *P < 0.05; ***P < 0.001. (d) ROC plot unravels the diagnostic performance of EV-origin in distinguishing HCC and hepatic disease samples from healthy individuals. The high diagnostic performance of EV-origin for separating carcinoma from non-carcinoma individuals is also indicated. HD: hepatic disorders. (e) ROC diagram shows the diagnostic efficiency of absolute hepatic fraction derived from EV-origin in validation cohort.CC BY · PubMed Central

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Metadata

Authors
Li, Y., He, X., Li, Q., Lai, H., Zhang, H., Hu, Z., Li, Y., & Huang, S.
Affiliations
  • Department of Integrative Oncology, Fudan University Shanghai Cancer Center, and the Shanghai Key Laboratory of Medical Epigenetics, the International Co-laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
  • Department of Hepatic Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai 200032, China
Venue
Computational and Structural Biotechnology Journal
Publisher
American Association for the Advancement of Science (AAAS)
Published
2020-01
Volume
18
Pages
2851-2859
ISSN
2001-0370
DOI
10.1016/j.csbj.2020.10.002
PMC
PMC7588739
Cited by
197
References
4
Language
en
Licence
Funders
  • Science and Technology Innovation Plan Of Shanghai Science and Technology Commission (20JC1419000)
  • National Natural Science Foundation of China (81672779, 81871939, 81872294)

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