Comparison of proteomic landscape of extracellular vesicles in pleural effusions isolated by three strategies

Extracellular vesicles (EVs) derived from pleural effusion (PE) is emerging as disease biomarkers. However, the methods for isolation of EVs from PE (pEVs) were rarely studied. In our study, three methods for isolating pEVs of lung cancer patients were compared, including ultracentrifugation (UC), a combination of UC and size exclusion chromatography (UC-SEC) and a combination of UC and density gradient ultracentrifugation (UC-DGU). The subpopulation of pEVs was identified by nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), Western blotting (WB) and nano-flow cytometry (nFCM). Additionally, the proteomic landscape of pEVs was analyzed by Label-free proteomics. The results showed that, compared with UC and UC-DGU, the UC-SEC method separated pEVs with the highest purity. In the proteomic analysis, on average, 1595 proteins were identified in the pEVs isolated by UC-SEC, much more than pEVs isolated by UC (1222) or UC-DGU (807). Furthermore, approximately 90% of identified proteins in each method were found in the EVs public database ExoCarta. Consistent with this, GO annotation indicated that the core proteins identified in each method were mainly enriched in “extracellular exosome.” Many of the top 100 proteins with high expression in each method were suggested as protein markers to validate the presence of EVs in the MISEV2018 guidelines. In addition, combined with lung tissue-specific proteins and vesicular membrane proteins, we screened out and validated several novel protein markers (CD11C, HLA DPA1 and HLA DRB1), which were enriched in pEVs rather than in plasma EVs. In conclusion, our study shows that the method of UC-SEC could significantly improve the purity of EVs and the performance of mass spectrometry-based proteomic profiling in analyzing pEVs. The exosomal proteins CD11C, HLA DPA1 and HLA DRB1 may act as potential markers of pEVs. The proteomic analysis of pEVs provides important information and new ideas for studying diseases complicated with PE

Modulating RhoA Effectors Induces Transitions to Oscillatory and More Wavelike RhoA Dynamics in Caenorhabditis elegans Zygotes

The biological world is full of patterns, from macroscopic patterns such as leaf veins to microscopic patterns that underlie processes such as cell polarization and migration. The implementation of such biological functions requires spatiotemporal control of cellular activities. A key challenge in biology is to understand the mechanisms of pattern formation. Mathematical theories on biological pattern formation were first introduced in the 1950s. Since then mathematical biologists have been trying to explain biological patterns with simple models made up of a few physical interactions. Advances in imaging, computation and the identification of many key molecular components of biological circuits in the last decade made it possible to reduce the gap between simple models and biological reality in many contexts. However, the knowledge gap is still far from closed for most biological patterns.In this thesis, I study the mechanisms that govern the spatiotemporal patterns of pulsatile active RhoA dynamics in the Caenorhabditis elegans (C. elegans) zygote. RhoA is a small GTPase whose activation drives the assembly of actomyosin networks. Pulsatile RhoA activation underlies a wide range of cell and tissue behaviors. The circuits that produce these dynamics in different cells share common architectures based on fast positive and delayed negative feedback through F-actin, but they can produce very different spatiotemporal patterns of RhoA activity. However, the underlying causes of this variation remain poorly understood. Here I asked whether and how this variation could arise through modulation of actin network dynamics downstream of active RhoA in early C. elegans embryos. I am particularly interested in tuning the temporal dynamics from excitable to oscillatory and tuning the spatial spread from focal pulses to traveling waves. I combined genetic perturbations of actomyosin regulators with live multicolor microscopy and developed an image analysis pipeline to characterize and quantify patterns of RhoA activation and measure the correlation between RhoA activation and the level of inhibition from F-actin. I found that perturbing two RhoA effectors—formin and anillin—induce transitions from nonrecurrent focal pulses to either large noisy oscillatory pulses (formin depletion) or noisy oscillatory waves (anillin depletion). The observed dynamics in anillin or formin-depleted embryos could in principle be explained by simple models based on fast- positive and delayed negative feedback in which F-actin serves as the inhibitor. The underlying mechanisms for F-actin depletion are distinct, with different dependencies on myosin II activity. The transition from wildtype to anillin or formin-depleted dynamics could be explained by reduction in the overall level of inhibition from F-actin. However, wildtype dynamics cannot be explained by the simplest models, suggesting that additional feedback mechanisms are required to generate focal pulses. Thus, the same circuit can generate different spatiotemporal dynamics of RhoA activation associated with different physiological or morphogenetic functions, which can be accessed by modulating actomyosin network dynamics

Image1_Comparison of proteomic landscape of extracellular vesicles in pleural effusions isolated by three strategies.TIF

Extracellular vesicles (EVs) derived from pleural effusion (PE) is emerging as disease biomarkers. However, the methods for isolation of EVs from PE (pEVs) were rarely studied. In our study, three methods for isolating pEVs of lung cancer patients were compared, including ultracentrifugation (UC), a combination of UC and size exclusion chromatography (UC-SEC) and a combination of UC and density gradient ultracentrifugation (UC-DGU). The subpopulation of pEVs was identified by nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), Western blotting (WB) and nano-flow cytometry (nFCM). Additionally, the proteomic landscape of pEVs was analyzed by Label-free proteomics. The results showed that, compared with UC and UC-DGU, the UC-SEC method separated pEVs with the highest purity. In the proteomic analysis, on average, 1595 proteins were identified in the pEVs isolated by UC-SEC, much more than pEVs isolated by UC (1222) or UC-DGU (807). Furthermore, approximately 90% of identified proteins in each method were found in the EVs public database ExoCarta. Consistent with this, GO annotation indicated that the core proteins identified in each method were mainly enriched in “extracellular exosome.” Many of the top 100 proteins with high expression in each method were suggested as protein markers to validate the presence of EVs in the MISEV2018 guidelines. In addition, combined with lung tissue-specific proteins and vesicular membrane proteins, we screened out and validated several novel protein markers (CD11C, HLA DPA1 and HLA DRB1), which were enriched in pEVs rather than in plasma EVs. In conclusion, our study shows that the method of UC-SEC could significantly improve the purity of EVs and the performance of mass spectrometry-based proteomic profiling in analyzing pEVs. The exosomal proteins CD11C, HLA DPA1 and HLA DRB1 may act as potential markers of pEVs. The proteomic analysis of pEVs provides important information and new ideas for studying diseases complicated with PE.</p

Image2_Comparison of proteomic landscape of extracellular vesicles in pleural effusions isolated by three strategies.TIF

Extracellular vesicles (EVs) derived from pleural effusion (PE) is emerging as disease biomarkers. However, the methods for isolation of EVs from PE (pEVs) were rarely studied. In our study, three methods for isolating pEVs of lung cancer patients were compared, including ultracentrifugation (UC), a combination of UC and size exclusion chromatography (UC-SEC) and a combination of UC and density gradient ultracentrifugation (UC-DGU). The subpopulation of pEVs was identified by nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), Western blotting (WB) and nano-flow cytometry (nFCM). Additionally, the proteomic landscape of pEVs was analyzed by Label-free proteomics. The results showed that, compared with UC and UC-DGU, the UC-SEC method separated pEVs with the highest purity. In the proteomic analysis, on average, 1595 proteins were identified in the pEVs isolated by UC-SEC, much more than pEVs isolated by UC (1222) or UC-DGU (807). Furthermore, approximately 90% of identified proteins in each method were found in the EVs public database ExoCarta. Consistent with this, GO annotation indicated that the core proteins identified in each method were mainly enriched in “extracellular exosome.” Many of the top 100 proteins with high expression in each method were suggested as protein markers to validate the presence of EVs in the MISEV2018 guidelines. In addition, combined with lung tissue-specific proteins and vesicular membrane proteins, we screened out and validated several novel protein markers (CD11C, HLA DPA1 and HLA DRB1), which were enriched in pEVs rather than in plasma EVs. In conclusion, our study shows that the method of UC-SEC could significantly improve the purity of EVs and the performance of mass spectrometry-based proteomic profiling in analyzing pEVs. The exosomal proteins CD11C, HLA DPA1 and HLA DRB1 may act as potential markers of pEVs. The proteomic analysis of pEVs provides important information and new ideas for studying diseases complicated with PE.</p

DataSheet1_Comparison of proteomic landscape of extracellular vesicles in pleural effusions isolated by three strategies.ZIP

Extracellular vesicles (EVs) derived from pleural effusion (PE) is emerging as disease biomarkers. However, the methods for isolation of EVs from PE (pEVs) were rarely studied. In our study, three methods for isolating pEVs of lung cancer patients were compared, including ultracentrifugation (UC), a combination of UC and size exclusion chromatography (UC-SEC) and a combination of UC and density gradient ultracentrifugation (UC-DGU). The subpopulation of pEVs was identified by nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), Western blotting (WB) and nano-flow cytometry (nFCM). Additionally, the proteomic landscape of pEVs was analyzed by Label-free proteomics. The results showed that, compared with UC and UC-DGU, the UC-SEC method separated pEVs with the highest purity. In the proteomic analysis, on average, 1595 proteins were identified in the pEVs isolated by UC-SEC, much more than pEVs isolated by UC (1222) or UC-DGU (807). Furthermore, approximately 90% of identified proteins in each method were found in the EVs public database ExoCarta. Consistent with this, GO annotation indicated that the core proteins identified in each method were mainly enriched in “extracellular exosome.” Many of the top 100 proteins with high expression in each method were suggested as protein markers to validate the presence of EVs in the MISEV2018 guidelines. In addition, combined with lung tissue-specific proteins and vesicular membrane proteins, we screened out and validated several novel protein markers (CD11C, HLA DPA1 and HLA DRB1), which were enriched in pEVs rather than in plasma EVs. In conclusion, our study shows that the method of UC-SEC could significantly improve the purity of EVs and the performance of mass spectrometry-based proteomic profiling in analyzing pEVs. The exosomal proteins CD11C, HLA DPA1 and HLA DRB1 may act as potential markers of pEVs. The proteomic analysis of pEVs provides important information and new ideas for studying diseases complicated with PE.</p

Image3_Comparison of proteomic landscape of extracellular vesicles in pleural effusions isolated by three strategies.TIF

Extracellular vesicles (EVs) derived from pleural effusion (PE) is emerging as disease biomarkers. However, the methods for isolation of EVs from PE (pEVs) were rarely studied. In our study, three methods for isolating pEVs of lung cancer patients were compared, including ultracentrifugation (UC), a combination of UC and size exclusion chromatography (UC-SEC) and a combination of UC and density gradient ultracentrifugation (UC-DGU). The subpopulation of pEVs was identified by nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), Western blotting (WB) and nano-flow cytometry (nFCM). Additionally, the proteomic landscape of pEVs was analyzed by Label-free proteomics. The results showed that, compared with UC and UC-DGU, the UC-SEC method separated pEVs with the highest purity. In the proteomic analysis, on average, 1595 proteins were identified in the pEVs isolated by UC-SEC, much more than pEVs isolated by UC (1222) or UC-DGU (807). Furthermore, approximately 90% of identified proteins in each method were found in the EVs public database ExoCarta. Consistent with this, GO annotation indicated that the core proteins identified in each method were mainly enriched in “extracellular exosome.” Many of the top 100 proteins with high expression in each method were suggested as protein markers to validate the presence of EVs in the MISEV2018 guidelines. In addition, combined with lung tissue-specific proteins and vesicular membrane proteins, we screened out and validated several novel protein markers (CD11C, HLA DPA1 and HLA DRB1), which were enriched in pEVs rather than in plasma EVs. In conclusion, our study shows that the method of UC-SEC could significantly improve the purity of EVs and the performance of mass spectrometry-based proteomic profiling in analyzing pEVs. The exosomal proteins CD11C, HLA DPA1 and HLA DRB1 may act as potential markers of pEVs. The proteomic analysis of pEVs provides important information and new ideas for studying diseases complicated with PE.</p

Table1_Comparison of proteomic landscape of extracellular vesicles in pleural effusions isolated by three strategies.XLSX

Extracellular vesicles (EVs) derived from pleural effusion (PE) is emerging as disease biomarkers. However, the methods for isolation of EVs from PE (pEVs) were rarely studied. In our study, three methods for isolating pEVs of lung cancer patients were compared, including ultracentrifugation (UC), a combination of UC and size exclusion chromatography (UC-SEC) and a combination of UC and density gradient ultracentrifugation (UC-DGU). The subpopulation of pEVs was identified by nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), Western blotting (WB) and nano-flow cytometry (nFCM). Additionally, the proteomic landscape of pEVs was analyzed by Label-free proteomics. The results showed that, compared with UC and UC-DGU, the UC-SEC method separated pEVs with the highest purity. In the proteomic analysis, on average, 1595 proteins were identified in the pEVs isolated by UC-SEC, much more than pEVs isolated by UC (1222) or UC-DGU (807). Furthermore, approximately 90% of identified proteins in each method were found in the EVs public database ExoCarta. Consistent with this, GO annotation indicated that the core proteins identified in each method were mainly enriched in “extracellular exosome.” Many of the top 100 proteins with high expression in each method were suggested as protein markers to validate the presence of EVs in the MISEV2018 guidelines. In addition, combined with lung tissue-specific proteins and vesicular membrane proteins, we screened out and validated several novel protein markers (CD11C, HLA DPA1 and HLA DRB1), which were enriched in pEVs rather than in plasma EVs. In conclusion, our study shows that the method of UC-SEC could significantly improve the purity of EVs and the performance of mass spectrometry-based proteomic profiling in analyzing pEVs. The exosomal proteins CD11C, HLA DPA1 and HLA DRB1 may act as potential markers of pEVs. The proteomic analysis of pEVs provides important information and new ideas for studying diseases complicated with PE.</p