Recommended
More Related Content
What's hot
What's hot (20)
Similar to exRNA Atlas and deconvolution tools at the transition from ERCC1 to ERCC2
Similar to exRNA Atlas and deconvolution tools at the transition from ERCC1 to ERCC2 (20)
Recently uploaded
Recently uploaded (20)
exRNA Atlas and deconvolution tools at the transition from ERCC1 to ERCC2
- 1. ExRNA Atlas and deconvolution tools at the transition from ERCC1 to ERCC2 Emily LaPlante Graduate assistant Bioinformatics Research Laboratory at BCM ERCC Data Management and Resource Repository (DMRR)
- 2. Overview 1. ExRNA Atlas a) What is the atlas? b) Submissions to the atlas and private analysis 2. Deconvolution a) What is it and why do we need it? b) Results of deconvoluting the atlas (v4.13.6) c) Application of deconvolution to identify differentially expressed miRNA 3. Deconvolution for ERCC2 a) How can deconvolution check validity of enrichment or purification?
- 3. Data Coordination Center Aleksandar Milosavljevic Matthew E. Roth Oscar D. Murillo William Thistlethwaite Sai Lakshmi Subramanian Rocco Lucero Neethu Shah Andrew R. Jackson Data Integration and Analysis Mark B. Gerstein Joel Rozowsky Robert R. Kitchen Timur Galeev Jonathan Warrell James A. Diao Kei-Hoi Cheung The DMRR was tasked with the integration of exRNA profiling data using metadata, biomedical ontologies, and linked data technologies 3
- 4. The ERCC created the exRNA Atlas resource for data sharing, accessibility, and analysis • 7,505 samples • 17 different conditions • 13 biofluids • RNA sources • 8 RNA isolation methods 4exrna-atlas.org
- 5. Rozowsky et al. 2019 Cell Systems 5 small RNA-seq studies submitted to the exRNA Atlas are uniformly processed using exceRpt tool
- 6. Murillo et al. 2019 Cell 6 Data analysis can be utilized by submitting to the public exRNA Atlas or private Genboree group Analysis Analysis
- 7. A suite of analysis and visualization tools are integrated within the exRNA Atlas Murillo et al. 2019 Cell 7 Deconvolution
- 8. Deconvolution: What is it and why do we need it?
- 9. Murillo et al. 2019 Cell 9 miRNA expression profiles cluster primarily by exRNA study despite uniform exceRpt processing
- 10. Variability may be due to a combination of (1) heterogeneity of exRNA carriers and (2) experimental variation in carrier sampling
- 11. Vesicular and non-vesicular carriers have been shown to have differential abundance of miRNAs “exosomal” RNA isolation methods biofluid sample exoRNeasy miRCURY ultracentrifugation (UC) expression profiles miR-W miR-X miR-Y miR-Z miR-W miR-X miR-Y miR-Z miR-W miR-X miR-Y miR-Z miR-W miR-X miR-Y miR-Z miR-W miR-X miR-Y miR-Z11
- 12. 12 Deconvolution assigns RNA cargo to each carrier and estimates per sample proportion biofluid sample miR-W miR-X miR-Y miR-Z miR-W miR-X miR-Y miR-Z miR-W miR-X miR-Y miR-Z miR-W miR-X miR-Y miR-Z proportion of carrier miRNA profile 20% 30% 15% 35% deconvolution
- 13. Computational deconvolution of complex biofluids may identify cargo profiles of constituent carriers Murillo et al. 2019 Cell 13 Deconvolution: decomposition of a dataset into its constituent components
- 14. Deconvolution was applied to the RNA-seq profiles of biofluid samples compiled by the exRNA Atlas Compiled from 19 studies from the NIH Common Fund exRNA Communication Consortium 14
- 15. Constituent cargo profiles show similarities across studies and can be grouped into six cargo types Murillo et al. 2019 Cell 15 CT = Cargo Type
- 16. Physical fractionation of biofluids was performed to validate deconvoluted cargo types Cushioned-Density Gradient Ultracentrifugation (C-DGUC) 𝞪-CD9 𝞪-Flotillin 𝞪-ApoA1 EV markers LPP marker RNPs EVs HDLs Li et al. 2018a Methods in Molecular Biology
- 17. Independent isolation of additional carriers was performed to validate remaining cargo profiles Sequential density UC + fast-protein liquid chromatography AGO-2 positive immunoprecipitation HDL LDL Chylomicron VLDL diameter (nm) density(g/mL) 𝞪-Ago2 Li et al. 2018b Methods in Molecular Biology
- 18. Correlation analysis of RNA-seq profiles was used to associate carriers to deconvoluted cargo types Murillo et al. 2019 Cell 18
- 19. Correlation analysis of RNA-seq profiles was used to associate carriers to deconvoluted cargo types Murillo et al. 2019 Cell 19
- 20. 30 Cargo types show distinct RNA Biotype composition
- 21. Example dataset submitted to the exRNA Atlas: changes in plasma exRNA in response to exercise Shah et al. 2017 American Journal of Physiology Plasma exRNA from healthy males and females: baseline and peak-exercise Large sample-sample variability Differentially expressed miRNA are involved in immune pathways 21
- 22. Deconvolution increases power to detect biological signal by accounting for cargo type variance Deconvolution estimates 3 cargo profiles Increased proportion of EVs in peak-exercise samples Plasma exRNA-seq of healthy males and females Baseline (n = 26) Peak-exercise (n = 26)
- 23. Assign miRNA changes to specific carriers Detect physiologically relevant pathways not previously observed Deconvolution increases power to detect biological signal by accounting for cargo type variance 3 & 4 miRNAs involved in pathways are carried by EVs and LPPs, respectively Shah et al. 2017 American Journal of Physiology
- 24. Deconvolution as a tool for ERCC2 Improved Isolation and Analysis of exRNA-Carrier Subclasses Towards Single Extracellular Vesicle (EV) Sorting, Isolation, and Analysis of Cargo
- 25. Current RNA isolation methods show cargo type bias that may contribute to study-study variation Murillo et al. 2019 Cell
- 26. biofluid sample Isolation Method Deconvolution Deconvolution can be used to test how well isolation methods isolate carrier subclasses Control
- 27. An example workflow: Isolation Method: Purification methods or physical separation Ex: IP pull down Correlate profiles to cargo types Control Experimental
- 28. An example workflow: Deconvolution of results in a case control manner Identify constituent profiles Check for enrichment of profiles Control Experimental
- 29. Future Directions: CT5 • Deconvolution can be used to validate and optimize isolation methods during ERCC2 • As samples are added to the atlas datasets can be added to the deconvolution • Better separation of samples may identify new cargo types LDL
- 30. For Questions about Deconvolution, Submitting to the Atlas, or questions about using the tools please contact me at Emily.LaPlante@bcm.edu
- 31. Milosavljevic Lab Aleksandar Milosavljevic Matthew E. Roth William Thistlethwaite (former) Oscar Murillo Sai Lakshmi Subramanian Neethu Shah Andrew Jackson Rocco Lucero Eugene Lurie (former) Lillian Thistlethwaite Varduhi Petrosyan Funding NIH Common Fund: U54DA036134 (PI: Milosavljevic, PhD) Collaborators Srimeenakshi Srinivasan Allen Chung Clara D. Laurent Robert R. Kitchen Timur Galeev Jonathan Warrell James A. Diao Joshua A. Welsh Kristina Hanspers Anders Riutta Sebastian Burgstaller- Muehlbacher Ravi Shah Ashish Yeri Lisa M. Jenkins Mehmet E. Ahsen Carlos Cordon-Cardo Navneet Dogra Stacey M. Gifford Joshua T. Smith Gustavo Stolovitzky Ashutosh K. Tewari Benjamin H. Wunsch Kamlesh K. Yadav Kirsty M. Danielson Justyna Filant Courtney Moeller Parham Nejad Anu Paul Bridget Simonson David K. Wong Xuan Zhang Leonora Balaj Roopali Gandhi Anil K. Sood Roger P. Alexander Liang Wang Chunlei Wu David Wong David J. Galas Kendall Van Keuren- Jensen Tushar Patel Jennifer J. Jones Saumya Das Kei-Hoi Cheung Alexander Pico Andrew I. Su Robert L. Raffai Louise C. Laurent Mark B. Gerstein Acknowledgements 31
Editor's Notes
- Data Management And Resource Repository – GOAL: integrate the efforts of the ERCC Data Coordination Center Administrative Core Scientific Outreach Data Integration and Analysis
- Condition, Biofluid, RNA source (EV, etc.), RNA isolation kit
- Post-processing
- Other lab to characterize our cargo types Robert Raffai
- What are each of the colors, columns are samples
- Optimization and validation