2nd Westlake Symposium for Clinical Mass Spectrometry

2nd Westlake Symposium for Clinical Mass Spectrometry

Progresses and Perspectives of Mass Spectrometry-based Clinical Proteomics

基于质谱的临床蛋白质组学的研究进展与展望

Co-chairs: Stephen Pennington, Fuchu He and Tiannan Guo

Supported by Westlake University, CNHUPO, HUPO

When: 04 Dec, 2020, 09:00-23:00 (UTC+8)

Where: Playbacks

WSMS20 Discussion Group

Summary

The rapidly developing mass spectrometry-based proteomics is indispensable for precision medicine. However, its penetration into clinic has yet to prevail. One technical bottleneck is high throughput proteomic analysis of clinical specimens from (multi-center) cohort studies, whereas integrative analysis of proteomics data with clinical and other omics data poses another technical challenge. Moreover, the comprehension of the unmet clinical needs by the mass spectrometrists, and the understanding of the progresses and limitations of cutting-edge proteomic techniques by the clinicians are non-trivial. This symposium gathers experts from clinical medicine, proteomics and beyond, with a goal of building a complete picture of the MS-based clinical proteomics: the current status and the perspective for future development.

28

Speakers

10

Coordinators

2671

Online Participants

14.5

Hours

Speakers and coordinators

Opening

08:50-09:00 Beijing

19:50-20:00(-1) Boston

0:50-01:00 London

11:50-12:00 Sydney

01:50-02:00 Zürich

Introduction and Overview

Stephen Pennington (University College Dublin)
Fuchu He 贺福初 (National Center for Protein Sciences, Beijing)
Tiannan Guo 郭天南 (Westlake University)

Video

Welcome from the president of Westlake University

09:00-09:10 Beijing

20:00-20:10(-1) Boston

01:00-01:10 London

12:00-12:10 Sydney

02:00-02:10 Zürich

Yigong Shi 施一公 (Westlake University)

Section A1: Proteomics for biomedical research and clinical medicine

Coordinators:

Ping Xu 徐平 ( National Center for Protein Sciences, Beijing)
Stan Z. Li 李子青 ( Westlake University)
Tiannan Guo 郭天南 (Westlake University)

09:10-09:26 Beijing

20:10-20:26(-1) Boston

01:10-01:26 London

12:10-12:26 Sydney

02:10-02:26 Zürich

Proteomics and biomarkers (colorectal cancer)

Shu Zheng 郑树 (Zhejiang University)

Video

 

Cancer Research and proteomics: By means of genomics and proteomics studies, they investigated tissue samples, plasma samples and exosome samples from colorectal cancer patients, and revealed the expression changes of genome and proteome in the development of colorectal cancer. Several biomarkers for colorectal cancer were discovered and validated, providing promising methods for early diagnosis and precise treatment.

09:26-09:45 Beijing

20:26-20:45(-1) Boston

01:26-01:45 London

12:26-12:45 Sydney

02:26-02:45 Zürich

Proteomics and infectious disease: clinical and basic research

Lanjuan Li 李兰娟 (Zhejiang University)

09:45-10:08 Beijing

20:45-21:08(-1) Boston

01:45-02:08 London

12:45-13:08 Sydney

02:45-03:08 Zürich

PDPM: proteomics-driven precision medicine

Fuchu He 贺福初
Talking language: Chinese
Slide language: English

(National Center for Protein Sciences, Beijing)

10:08-10:23 Beijing

21:08-21:23(-1) Boston

02:08-02:23 London

13:08-13:23 Sydney

03:08-03:23 Zürich

How should prognostic and predictive biomarkers be evaluated?

Ruiping Xiao 肖瑞平
Talking language: English
Slide language: English

(Peking University & Associate Editor, NEJM)

Video

 

Dr. Ruiping Xiao briefly introduce the history, the focuses and review processes of NEJM. Recently the they prefers creative medical breakthrough, and take 21-gene as a prognostic biomarker in breast cancer as an example. Finally, he summarized the talents and limits of the application of proteomics study in clinical researches, and also gave some corresponding solutions.

10:23-10:30 Beijing

21:23-21:30(-1) Boston

02:23-02:30 London

13:23-13:30 Sydney

03:23-03:30 Zürich

Questions, Recommendations and Conclusions

Section A2: Proteomics for biomedical research and clinical medicine

Coordinators:

Jimin Shao 邵吉民 (Zhejiang University)
Tiannan Guo 郭天南 (Westlake University)

10:30-10:46 Beijing

21:30-21:46(-1) Boston

02:30-02:46 London

13:30-13:46 Sydney

03:30-03:46 Zürich

Drug discovery and proteomics

Hong Shen 沈宏
Talking language: English
Slide language: English

(Roche Innovation Center Shanghai)

Video

 

Mass spectrometry-based proteomics is a powerful technology to profile proteome and monitor protein communities in drug discovery. The applications in drug discovery demonstrate the impact of mass spectrometry-based proteomics in target and off-target identification, the understanding of PPI & biological pathways, time-, space- and dose-dependent signaling events mediated by PTMs, etc. Future directions could be to evaluate protein communities more often in vivo, to measure stoichiometry of proteins, and to provide methods for clinical samples.

10:46-11:05 Beijing

21:46-22:05(-1) Boston

02:46-03:05 London

13:46-14:05 Sydney

03:46-04:05 Zürich

Proteomics in clinical research

Xiujun Cai 蔡秀军
Talking language: Chinese
Slide language: Chinese

(Sir Run Run Shaw Hospital, Zhejiang University)

 

11:05-11:25 Beijing

22:05-22:25(-1) Boston

03:05-03:25 London

14:05-14:25 Sydney

04:05-04:25 Zürich

Potential roles of proteomics in a City Brain

Xiansheng Hua 华先胜
Talking language: Chinese
Slide language: English

(City Brain & VP, Artificial Intelligence Center, Alibaba)

Video

 

城市大脑对城市进行4维时空建模,需要Video, GPS,Coil,Bus等数据。城市大脑可以为医疗健康领域赋能。城市大脑和人体的循环系统(人体的运作)等有异曲同工之妙。可以利用大数据的技术对药物靶点进行优化(疫苗,抗原设计)。

11:25-11:45 Beijing

22:25-22:45(-1) Boston

03:25-03:45 London

14:25-14:45 Sydney

04:25-04:45 Zürich

COVID-19 and cardiac injury

Daowen Wang 汪道文
Talking language: English
Slide language: English

(Tongji Medical College, Huazhong University for Science & Technology)

Video

 

In severe COVID-19 patients, the levels of heart biomarkers upregulate. Cardiac injure might cause by cytokine storm. RAAS inhibitors will increase ACE2 expression leading to the occur of Cardiac Injure?

11:45-11:57 Beijing

22:45-22:57(-1) Boston

03:45-03:57 London

14:45-14:57 Sydney

04:45-04:57 Zürich

Standardization and harmonization of multi-center proteotype analysis supporting translational studies

Min Huang 黄敏
Talking language: English
Slide language: English

(Thermo Fisher Scientific)

Video

 

High resolution MS1-DIA workflow-digital biobank: the standardization method includes defined QC, optimized LC separation and efficient data acquisition. Tow kinds samples: QC samples (Commercial HeLa peptides) and controlled samples (mixture of HeLa, yeast and E.coli). several QC criteria for LC and MS performance: media LC peak, number of MS1, MS2 data points, precursor ID, proteins ID and inter-injection median CV of precursors.QC-standard: Three injections per day were needed. Inter-day and inter-lab reproducibility: over 80% of the total quantification proteins were quantified on each day or locally. Quantification accuracy and precision: the median values were lower than 10% deviation for human and yeast proteins and lower than 20% deviation for E.coli proteins. Median CV were lower 5% for the human and yeast proteins and 10% for E.coli. Application: Archival ovarian cancer tissue-clear cell and high grade serous, 5721 unique protein groups were identified from all tumor samples, 394 significantly dysregulation proteins between the two types of ovarian cancer samples. Use a streamlined HRMS1-DIA workflow to acquire data and analyze.Clinical Study: 5712 proteins were identified from all tumor samples, 394 significantly altered proteins between those two types of ovarian cancer samples.

11:57-12:20 Beijing

22:57-23:20(-1) Boston

03:57-04:20 London

14:57-15:20 Sydney

04:57-05:20 Zürich

Questions, Recommendations and Conclusions

12:20-13:00 Beijing

23:20-00:00(-1) Boston

04:20-05:00 London

15:20-16:00 Sydney

05:20-06:00 Zürich

Lunch Break

Section B: Proteomics and beyond

Coordinators:

Edouard Nice (Monash University)
Catherine Wong 黄超兰 (Peking University)
Tiannan Guo 郭天南 (Westlake University)

13:00-13:20 Beijing

00:00-00:20 Boston

05:00-05:20 London

16:00-16:20 Sydney

06:00-06:20 Zürich

Proteogenomics of non-smoking lung cancer in East Asia delineates molecular signatures of pathogenesis and progression

Yu-Ju Chen 
Talking language: English
Slide language: English

(Academia Sinica)

Video

 

Proteogenomics of non-smoking lung cancer in East Asia delineates molecular signatures of pathogenesis and progression Proteogenomics reveals signaling network of driver gene, such as EGFR, KRAS, which have been as effective drug targets.  
In the genome level, East Asia is mainly induced by EGFR, while USA is mainly induced by KRAS mutation. The unmet clinical needs in late stage patients focus on seeking effective drug targets. 
High quality specimens and analytical pipeline as the support. 
In the early stage, higher APOBEC mutation signature in female with short survival but better response to immunotherapy. 
The downstream of MAPK signaling cascade in EGFR-Mt/WT patient Protein network of proteomics for drug target 

13:20-13:40 Beijing

00:20-00:40 Boston

05:20-05:40 London

16:20-16:40 Sydney

06:20-06:40 Zürich

Exploring novel metabolic markers for early diagnosis of colorectal cancer based on metabolomics study

Maode Lai 来茂德
Talking language: Chinese
Slide language: Chinese

(China Pharmaceutical University)

Video

 

来茂德教授通过利用代谢组学手段,对健康/代谢综合征/结直肠癌人群的血清样本进行非靶向代谢组学检测,找到30个候选代谢物,进一步在1594个样本中进行进一步验证,最终得到7个潜在代谢物,并对其与临床参数相关性进行描绘。最终从7个代谢物简化到2个代谢物,并在独立队列中得以验证。本研究对结直肠癌诊断提供新方法新思路

13:40-14:00 Beijing

00:40-01:00 Boston

05:40-06:00 London

16:40-17:00 Sydney

06:40-07:00 Zürich

Metabolomics and precision medicine

Huiru Tang 唐惠儒

(Fudan University)

14:00-14:20 Beijing

01:00-01:20 Boston

06:00-06:20 London

17:00-17:20 Sydney

07:00-07:20 Zürich

AI for life sciences

Stan Z. Li 李子青 
Talking language: English
Slide language: English

(Westlake University)

Video

 

Prof. Li introduces how to study omic researches by AI based strategy. Firstly, introduce AI and proteomic strategies assist cancer diagnosis, then give an example: differential diagnosis of benign and malignant thyroid nodules. The type of thyroid nodules is able to diagnose by artificial neural network, meanwhile provide another method for classifying cancer type by proteomic profiling. Secondly, introduce a novel high-dimensional data analysis method, which is called Deep Manifold Transformation (DMT). Compared with t-SNE and UMAP, the results demonstrated DMT is much better in many aspects, such as dimensionality reduction, clustering, visualization and so on. As far as possible, the global structure and serial characteristics of data are guaranteed in the low dimensional space and have good application results in single cell data.

14:20-14:30 Beijing

01:20-01:30 Boston

06:20-06:30 London

17:20-17:30 Sydney

07:20-07:30 Zürich

Questions, Recommendations and Conclusions

14:30-15:00 Beijing

01:30-02:00 Boston

06:30-07:00 London

17:30-18:00 Sydney

07:30-08:00 Zürich

Tea break

Section C1: Emerging proteomics technologies

Coordinators:

Stephen Pennington (University College Dublin)
Yu-Ju Chen (Academia Sinica)
Edouard Nice (Monash University)
Tiannan Guo (Westlake University)

15:00-15:45 Beijing

02:00-02:45 Boston

07:00-07:45 London

18:00-18:45 Sydney

08:00-08:45 Zürich

Technology and applications of MS-based proteomics to body fluid and tissue analysis

Matthias Mann (Max Planck Institute)

Video

 

These years they focus more on clinical applications than cellular biology, and that’s what the following 5 parts (technology, PTM, plasma proteome profiling,single cell proteomics and deep visual proteomics) will be related with.

Technology (Introduction of two software and one hardware)

AlphaPept Framework: --a super-fast framework for the analysis of MS-based proteomics--open source, codebase by python and UI solution for non-experts--Python combined with Numba package make the analysis ultrafast the Clinical Knowledge Graph (CKG)--integrates proteomics data into clinical decision-making--Open source tool for data integration, analysis (the latest statistical and machine learning algorithms included) and interpretation (based on relevant experimental data, public databases and the literature)

Trapped ion mobility spectrometry (TIMS)--Ion mobility separation Parallel Accumulation – Serial Fragmentation (PASEF)

--Increased signal-to-noise by signal compression
--Full precursor mass resolution
--Multiplied sensitivity and sequencing speed
--diaPASEF could be more robust for future clinical proteomics

Post-translational modifications

(LRRK2 in Parkinson’s disease)

Method: Phosphoproteomics simplified protocol EasyPhos
Application: hunt for substrates of multidomain Leucine-rich repeat kinase 2 (LRRK2)
--genetic cause of Parkinson’s disease: pathogenic mutations of LRRK2.
--G2019S substitution on LRRK2 protein activates the kinase
--genetics (cells from genetically modified mice), pharmacology (selective LRRK2 inhibitors), phosphoproteomics (EasyPhos, 27,000 phosphosites on ~6000 proteins)
--Rab10 and Rab12 are phosphorylated by LRRK2

Plasma proteome profiling (liver disease)

Application: diagnostic tool for early detection of liver disease

Study 1: plasma proteomics identified novel proteins associated with non-alcoholic fatty liver disease (NAFLD)

--plasma proteome of 3 matched sub-cohorts by BoxCar acquisition
--in cirrhotic liver, 77% downregulated plasma proteins are “liver-specific”
--six proteins significantly associated with NAFLD
--global correlation of plasma proteins and clinical parameters (liver enzymes)
--In HFD-induced NAFLD mouse model, identified maker candidates in human cohort recapitulated similar changes, providing evidence that these proteins may be good biomarkers.

Study 2: proteomics identified circulating protein markers for alcohol-related liver disease (ALD)

--a paired plasma (N=596) and liver tissue (N=79) proteome profiling
--of the 407 overlapped proteins between liver and plasma, 91 had significant correlations
--52 co-regulated proteins in the plasma and liver display distinct temporal patterns across fibrosis stages.
--machine learning models based on plasma proteome to predict early-stage of fibrosis, inflammation and steatosis, which outperform existing tests

Single cell proteomics

In EvoTip single cell processing and Evosep liquid chromatography for ultra high-sensitivity single cell proteomics.

~1000 protein groups identified with high quantitative reproducibility at the single cell level (Hela cell line).

Deep visual proteomics

Workflow: High-parametric images with subcellular resolution of archived patient tissue samples – image segmentation with deep learning training – machine learning algorithms to predict cellular phenotypes – single-cell isolation (including with subcellular spatial resolution or other arbitrary structures) using laser capture microdissection – cell type specific proteomes – Clinical Knowledge Graph – resource for researchers and clinicians

15:45-16:30 Beijing

02:45-03:30 Boston

07:45-08:30 London

18:45-19:30 Sydney

08:45-09:30 Zürich

The modular proteome and its clinical significance

Ruedi Aebersold (ETH Zurich)

Video

 

 

Ruedi tried to find out how alterations in the molecular constellation determines alterations in function/phenotype from three aspects.
1. How does genomic variability affect proteotype composition and phenotype? Method:Multi-layer molecular profiles for Hela cells with different phenotypes (e.g. doubling time and the ability to be infected)Effect of genomic variability on molecular profiles and phenotypes was calculated. Conclusion: Genotypic variability propagates along the axis of the central dogma in poorly predictable ways.Organization of proteins into complexes acts as a significant buffer of genotype variability.
2. How does genomic variability affect proteotype organization and deterministic functions? Method: Size exclusion chromatography mass spectrometry (SEC-MS) and size exclusion chromatography algorithmic toolkit (SECAT) Compute effect of genotypic variation on protein complexes and protein interaction networks. Results: Genomic variability affects the proteotype organization at the level of complexes and PPIs. Conclusion: New MS method and algorithms enable us to relate function to phenotype.
3.How can we predict altered proteotype modules and deterministic functions from transcript or protein profiles? Method: Proteome abundance profiles by SWATH/DIA Evaluation of changes in modularity based on changes in co-expression covariance and observing different deterministic functions, they tried to find out the relationship between these features. Conclusion: Proteins in the same complex (e.g. ATP synthase subunit alpha) tend to correlate across different samples. Different stage of prostate cancers exhibit altered quantitative relationships in many protein pairs. Genomic variants can alter specific protein modules and thus their function.

16:30-16:45 Beijing

03:30-03:45 Boston

08:30-08:45 London

19:30-19:45 Sydney

09:30-09:45 Zürich

Mattias Q&A

16:45-17:00 Beijing

03:45-04:00 Boston

08:45-09:00 London

19:45-20:00 Sydney

09:45-10:00 Zürich

Ruedi Q&A

Section C2: Emerging proteomics technologies

Coordinators:

Yu-Ju Chen (Academia Sinica)
Edouard Nice (Monash University)
Tiannan Guo (Westlake University)

17:25-17:50 Beijing

04:25-04:50 Boston

09:25-09:50 London

20:25-20:50 Sydney

10:25-10:50 Zürich

Technologies for high-throughput clinical proteomics

Bernhard Küster (Technische Universität Muenchen)

Video

 

Benhard develops MS-based technologies for high-throughput at high quality.

1, analysis of body fluids and whole proteomes by micro-LD MS/MS.The 50 uL/min LC flow generates stable performances while high quality. Data from 1500 consecutive injections showed high retention time consistence. It was also applied to analyzed the phosphoproteomes of 377 cancer cell line and quantified 12,700 proteins in total and 12000 p-sites.

2, analysis of FFPE material by single-shot LC-FAIMS-MS/MS.The optimized workflow also generates high ids (~30,000 peptides) per shot with low CVs.

3, Prosit prediction of spectral libraries and re-scoring of LC-MS/MS data.The great effort made on Prosit establishment can predict fragment patterns and retention time with high precision, and is currently serving the scientific community very well.

17:50-18:15 Beijing

04:50-05:15 Boston

09:50-10:15 London

20:50-21:15 Sydney

10:50-11:15 Zürich

Clinical proteomics in interesting times

Roman Fischer (University of Oxford)

18:40-19:02 Beijing

05:40-06:02 Boston

10:40-11:02 London

21:40-22:02 Sydney

11:40-12:02 Zürich

Single cell proteomics (SCP): glass-Oil-Air-Droplet (gOAD) pico chip

Catherine Wong (Peking University)

19:02-19:10 Beijing

06:02-06:10 Boston

11:02-11:10 London

22:02-22:10 Sydney

12:02-12:10 Zürich

Questions, Recommendations and Conclusions

19:10-20:00 Beijing

06:10-07:00 Boston

11:10-12:00 London

22:10-23:00 Sydney

12:10-13:00 Zürich

Dinner break

Section D: Industrialized proteomics for the clinic

Coordinators:

Stephen Pennington (University College Dublin)
Yu-Ju Chen (Academia Sinica)
Andrea Urbani (Università Cattolica del "Sacro Cuore")
Edouard Nice (Monash University)
Tiannan Guo (Westlake University)

20:00-20:17 Beijing

07:00-07:17 Boston

12:00-12:17 London

23:00-23:17 Sydney

13:00-13:17 Zürich

Development of a pipeline for biomarker discovery using proteomics

Anthony Whetton (The University of Manchester)

Video

 

Introduce the concept of precision medicine and genomic/proteomic applications in precision medicine nowadays. Further explain why we need to discover biomarkers; the reason is that individuals have differential responses for the same drugs or therapies. Briefly introduce Stoller Biomarker Discovery Center in Manchester and industrializing clinical proteomic strategies. Anthony focuses on SWATH-MS based plasma proteomics and gives some examples used industrializing clinical proteomic strategies: 1) grey platelet syndrome 2) chronic kidney disease. UK Biobank has had genomic data and its proteomic analysis of plasma/urine/mixed saliva is planned now.

20:17-20:40 Beijing

07:17-08:40 Boston

12:17-12:40 London

23:17-00:40(+1) Sydney

13:17-13:40 Zürich

Procan-adapting proteomics for the cancer clinic

Roger Reddel (The University of Sydney)

20:40-21:00 Beijing

07:40-09:00 Boston

12:40-13:00 London

23:40-01:00(+1) Sydney

13:40-14:00 Zürich

Break

Section E: Translational proteomics

Coordinators:

Stephen Pennington (University College Dublin)
Yu-Ju Chen (Academia Sinica)
Jun Qin (National Center for Protein Sciences, Beijing)
Edouard Nice (Monash University)
Tiannan Guo (Westlake University)

21:00-21:30 Beijing

08:00-08:30 Boston

13:00-13:30 London

00:00-00:30(+1) Sydney

14:00-14:30 Zürich

Translation of protein biomarkers: It’s simple

Stephen Pennington (University College Dublin)

Video

 

New protein biomarkers will be complimentary to genomics to promote precision medicine. The protein biomarker pipeline includes discovery, confirmation assay development, evaluation/validation and approval/adoption. The sample amounts and detection methods for each stage are different. The authors suggested the application of MRM in the confirmation and evaluation stages.

Dr. Pennington presented one of his study about developing blood biomarkers for discriminate patients with psoriatic arthritis (PsA) and Rheumatoid Arthritis (RA). Before that, there were no criteria to distinguish PsA form RA or other arthropathies. After comparing the serum proteomic profiles between PsA and RA patients using MS-based labeled proteomics, SOMAscan and Luminex, they found some unbiased biomarker candidates. They further supplement the biomarker candidates with existing panel and literature to make a panel called PAPRICA containing ~200 proteins and >400 peptides for the following MRM analysis. They have tested the panel on a new cohort containing 169 patients and achieved an AUC of 0.9 using normalized MS data. They ‘ll further evaluate the panel in a larger cohort containing 1000 patients

21:30-21:48 Beijing

08:30-08:48 Boston

13:30-13:48 London

00:30-00:48(+1) Sydney

14:30-14:48 Zürich

Psychiatric disorder pathway illumination using omics

Chris W. Turck (Max Planck Institute of Psychiatry)

Video

 

Psychiatric disorders have a great effect on the physical health of modern people. The diagnosis of psychiatric disorders is difficult using barely the clinical determined factors or indicators. People have been applying omics technologies to study psychiatric disorders, and comparing with the results of genomic characterization, the results of proteome/metabolome/microbiome characterization vary from day to day, herein could better reflect the psychiatric conditions, feasible for this type of study. In Psychiatric disorder biomarker study, animal models and body fluid from patients can be the substitutes for the human brain tissue because of their easy access.

To study the biosignatures of anxiety disorder, Dr. Turck and his team created the stress susceptible DBA/2NCrl and C57BL/6NCrl mice and performed transcriptomic proteomic analysis of the brain tissue of susceptible mice. They found:

1) opposite regulation of the same mitochondria related genes in DBA/2NCrl and C57BL/6NCrl stress susceptible mice, which indicated the gene background affects the stress-induced behaviors
2) blood gene expression of mitochondria related genes in panic disorder patients resembles DBA/2NCrl stress susceptible mice but in lower intensity

Dr. Turck demonstrated the alterations of mitochondria related genes in anxiety and panic disorder, and further stated that these studies are still far from clinical insights.

22:15-22:40 Beijing

09:15-09:40 Boston

14:15-14:40 London

01:15-01:40(+1) Sydney

15:15-15:40 Zürich

Questions, Recommendations and Conclusions

Concluding remarks

22:40-23:00 Beijing

09:40-10:00 Boston

14:40-15:00 London

01:40-02:00(+1) Sydney

15:40-16:00, Zürich

Tiannan Guo 郭天南 (Westlake University)
Matthias Mann (Max Planck Institute)
Jun Qin 秦钧 (National Center for Protein Sciences, Beijing)
Edouard Nice (Monash University)
Stephen Pennington (University College Dublin)

Acknowledgements

Progresses and Perspectives of Mass Spectrometry-based Clinical Proteomics

基于质谱的临床蛋白质组学的研究进展与展望

Co-chairs: Stephen Pennington, Fuchu He and Tiannan Guo

Supported by Westlake University, CNHUPO, HUPO

When: 04 Dec, 2020, 09:00-23:00 (UTC+8)

Where: Playbacks

WSMS20 Discussion Group

Summary

The rapidly developing mass spectrometry-based proteomics is indispensable for precision medicine. However, its penetration into clinic has yet to prevail. One technical bottleneck is high throughput proteomic analysis of clinical specimens from (multi-center) cohort studies, whereas integrative analysis of proteomics data with clinical and other omics data poses another technical challenge. Moreover, the comprehension of the unmet clinical needs by the mass spectrometrists, and the understanding of the progresses and limitations of cutting-edge proteomic techniques by the clinicians are non-trivial. This symposium gathers experts from clinical medicine, proteomics and beyond, with a goal of building a complete picture of the MS-based clinical proteomics: the current status and the perspective for future development.

28

Speakers

10

Coordinators

2671

Online Participants

14.5

Hours

Speakers and coordinators

Opening

08:50-09:00 Beijing

19:50-20:00(-1) Boston

0:50-01:00 London

11:50-12:00 Sydney

01:50-02:00 Zürich

Introduction and Overview

Stephen Pennington (University College Dublin)
Fuchu He 贺福初 (National Center for Protein Sciences, Beijing)
Tiannan Guo 郭天南 (Westlake University)

Video

Welcome from the president of Westlake University

09:00-09:10 Beijing

20:00-20:10(-1) Boston

01:00-01:10 London

12:00-12:10 Sydney

02:00-02:10 Zürich

Yigong Shi 施一公 (Westlake University)

Section A1: Proteomics for biomedical research and clinical medicine

Coordinators:

Ping Xu 徐平 ( National Center for Protein Sciences, Beijing)
Stan Z. Li 李子青 ( Westlake University)
Tiannan Guo 郭天南 (Westlake University)

09:10-09:26 Beijing

20:10-20:26(-1) Boston

01:10-01:26 London

12:10-12:26 Sydney

02:10-02:26 Zürich

Proteomics and biomarkers (colorectal cancer)

Shu Zheng 郑树 (Zhejiang University)

Video

 

Cancer Research and proteomics: By means of genomics and proteomics studies, they investigated tissue samples, plasma samples and exosome samples from colorectal cancer patients, and revealed the expression changes of genome and proteome in the development of colorectal cancer. Several biomarkers for colorectal cancer were discovered and validated, providing promising methods for early diagnosis and precise treatment.

09:26-09:45 Beijing

20:26-20:45(-1) Boston

01:26-01:45 London

12:26-12:45 Sydney

02:26-02:45 Zürich

Proteomics and infectious disease: clinical and basic research

Lanjuan Li 李兰娟 (Zhejiang University)

09:45-10:08 Beijing

20:45-21:08(-1) Boston

01:45-02:08 London

12:45-13:08 Sydney

02:45-03:08 Zürich

PDPM: proteomics-driven precision medicine

Fuchu He 贺福初
Talking language: Chinese
Slide language: English

(National Center for Protein Sciences, Beijing)

10:08-10:23 Beijing

21:08-21:23(-1) Boston

02:08-02:23 London

13:08-13:23 Sydney

03:08-03:23 Zürich

How should prognostic and predictive biomarkers be evaluated?

Ruiping Xiao 肖瑞平
Talking language: English
Slide language: English

(Peking University & Associate Editor, NEJM)

Video

 

Dr. Ruiping Xiao briefly introduce the history, the focuses and review processes of NEJM. Recently the they prefers creative medical breakthrough, and take 21-gene as a prognostic biomarker in breast cancer as an example. Finally, he summarized the talents and limits of the application of proteomics study in clinical researches, and also gave some corresponding solutions.

10:23-10:30 Beijing

21:23-21:30(-1) Boston

02:23-02:30 London

13:23-13:30 Sydney

03:23-03:30 Zürich

Questions, Recommendations and Conclusions

Section A2: Proteomics for biomedical research and clinical medicine

Coordinators:

Jimin Shao 邵吉民 (Zhejiang University)
Tiannan Guo 郭天南 (Westlake University)

10:30-10:46 Beijing

21:30-21:46(-1) Boston

02:30-02:46 London

13:30-13:46 Sydney

03:30-03:46 Zürich

Drug discovery and proteomics

Hong Shen 沈宏
Talking language: English
Slide language: English

(Roche Innovation Center Shanghai)

Video

 

Mass spectrometry-based proteomics is a powerful technology to profile proteome and monitor protein communities in drug discovery. The applications in drug discovery demonstrate the impact of mass spectrometry-based proteomics in target and off-target identification, the understanding of PPI & biological pathways, time-, space- and dose-dependent signaling events mediated by PTMs, etc. Future directions could be to evaluate protein communities more often in vivo, to measure stoichiometry of proteins, and to provide methods for clinical samples.

10:46-11:05 Beijing

21:46-22:05(-1) Boston

02:46-03:05 London

13:46-14:05 Sydney

03:46-04:05 Zürich

Proteomics in clinical research

Xiujun Cai 蔡秀军
Talking language: Chinese
Slide language: Chinese

(Sir Run Run Shaw Hospital, Zhejiang University)

 

11:05-11:25 Beijing

22:05-22:25(-1) Boston

03:05-03:25 London

14:05-14:25 Sydney

04:05-04:25 Zürich

Potential roles of proteomics in a City Brain

Xiansheng Hua 华先胜
Talking language: Chinese
Slide language: English

(City Brain & VP, Artificial Intelligence Center, Alibaba)

Video

 

城市大脑对城市进行4维时空建模,需要Video, GPS,Coil,Bus等数据。城市大脑可以为医疗健康领域赋能。城市大脑和人体的循环系统(人体的运作)等有异曲同工之妙。可以利用大数据的技术对药物靶点进行优化(疫苗,抗原设计)。

11:25-11:45 Beijing

22:25-22:45(-1) Boston

03:25-03:45 London

14:25-14:45 Sydney

04:25-04:45 Zürich

COVID-19 and cardiac injury

Daowen Wang 汪道文
Talking language: English
Slide language: English

(Tongji Medical College, Huazhong University for Science & Technology)

Video

 

In severe COVID-19 patients, the levels of heart biomarkers upregulate. Cardiac injure might cause by cytokine storm. RAAS inhibitors will increase ACE2 expression leading to the occur of Cardiac Injure?

11:45-11:57 Beijing

22:45-22:57(-1) Boston

03:45-03:57 London

14:45-14:57 Sydney

04:45-04:57 Zürich

Standardization and harmonization of multi-center proteotype analysis supporting translational studies

Min Huang 黄敏
Talking language: English
Slide language: English

(Thermo Fisher Scientific)

Video

 

High resolution MS1-DIA workflow-digital biobank: the standardization method includes defined QC, optimized LC separation and efficient data acquisition. Tow kinds samples: QC samples (Commercial HeLa peptides) and controlled samples (mixture of HeLa, yeast and E.coli). several QC criteria for LC and MS performance: media LC peak, number of MS1, MS2 data points, precursor ID, proteins ID and inter-injection median CV of precursors.QC-standard: Three injections per day were needed. Inter-day and inter-lab reproducibility: over 80% of the total quantification proteins were quantified on each day or locally. Quantification accuracy and precision: the median values were lower than 10% deviation for human and yeast proteins and lower than 20% deviation for E.coli proteins. Median CV were lower 5% for the human and yeast proteins and 10% for E.coli. Application: Archival ovarian cancer tissue-clear cell and high grade serous, 5721 unique protein groups were identified from all tumor samples, 394 significantly dysregulation proteins between the two types of ovarian cancer samples. Use a streamlined HRMS1-DIA workflow to acquire data and analyze.Clinical Study: 5712 proteins were identified from all tumor samples, 394 significantly altered proteins between those two types of ovarian cancer samples.

11:57-12:20 Beijing

22:57-23:20(-1) Boston

03:57-04:20 London

14:57-15:20 Sydney

04:57-05:20 Zürich

Questions, Recommendations and Conclusions

12:20-13:00 Beijing

23:20-00:00(-1) Boston

04:20-05:00 London

15:20-16:00 Sydney

05:20-06:00 Zürich

Lunch Break

Section B: Proteomics and beyond

Coordinators:

Edouard Nice (Monash University)
Catherine Wong 黄超兰 (Peking University)
Tiannan Guo 郭天南 (Westlake University)

13:00-13:20 Beijing

00:00-00:20 Boston

05:00-05:20 London

16:00-16:20 Sydney

06:00-06:20 Zürich

Proteogenomics of non-smoking lung cancer in East Asia delineates molecular signatures of pathogenesis and progression

Yu-Ju Chen 
Talking language: English
Slide language: English

(Academia Sinica)

Video

 

Proteogenomics of non-smoking lung cancer in East Asia delineates molecular signatures of pathogenesis and progression Proteogenomics reveals signaling network of driver gene, such as EGFR, KRAS, which have been as effective drug targets.  
In the genome level, East Asia is mainly induced by EGFR, while USA is mainly induced by KRAS mutation. The unmet clinical needs in late stage patients focus on seeking effective drug targets. 
High quality specimens and analytical pipeline as the support. 
In the early stage, higher APOBEC mutation signature in female with short survival but better response to immunotherapy. 
The downstream of MAPK signaling cascade in EGFR-Mt/WT patient Protein network of proteomics for drug target 

13:20-13:40 Beijing

00:20-00:40 Boston

05:20-05:40 London

16:20-16:40 Sydney

06:20-06:40 Zürich

Exploring novel metabolic markers for early diagnosis of colorectal cancer based on metabolomics study

Maode Lai 来茂德
Talking language: Chinese
Slide language: Chinese

(China Pharmaceutical University)

Video

 

来茂德教授通过利用代谢组学手段,对健康/代谢综合征/结直肠癌人群的血清样本进行非靶向代谢组学检测,找到30个候选代谢物,进一步在1594个样本中进行进一步验证,最终得到7个潜在代谢物,并对其与临床参数相关性进行描绘。最终从7个代谢物简化到2个代谢物,并在独立队列中得以验证。本研究对结直肠癌诊断提供新方法新思路

13:40-14:00 Beijing

00:40-01:00 Boston

05:40-06:00 London

16:40-17:00 Sydney

06:40-07:00 Zürich

Metabolomics and precision medicine

Huiru Tang 唐惠儒

(Fudan University)

14:00-14:20 Beijing

01:00-01:20 Boston

06:00-06:20 London

17:00-17:20 Sydney

07:00-07:20 Zürich

AI for life sciences

Stan Z. Li 李子青 
Talking language: English
Slide language: English

(Westlake University)

Video

 

Prof. Li introduces how to study omic researches by AI based strategy. Firstly, introduce AI and proteomic strategies assist cancer diagnosis, then give an example: differential diagnosis of benign and malignant thyroid nodules. The type of thyroid nodules is able to diagnose by artificial neural network, meanwhile provide another method for classifying cancer type by proteomic profiling. Secondly, introduce a novel high-dimensional data analysis method, which is called Deep Manifold Transformation (DMT). Compared with t-SNE and UMAP, the results demonstrated DMT is much better in many aspects, such as dimensionality reduction, clustering, visualization and so on. As far as possible, the global structure and serial characteristics of data are guaranteed in the low dimensional space and have good application results in single cell data.

14:20-14:30 Beijing

01:20-01:30 Boston

06:20-06:30 London

17:20-17:30 Sydney

07:20-07:30 Zürich

Questions, Recommendations and Conclusions

14:30-15:00 Beijing

01:30-02:00 Boston

06:30-07:00 London

17:30-18:00 Sydney

07:30-08:00 Zürich

Tea break

Section C1: Emerging proteomics technologies

Coordinators:

Stephen Pennington (University College Dublin)
Yu-Ju Chen (Academia Sinica)
Edouard Nice (Monash University)
Tiannan Guo (Westlake University)

15:00-15:45 Beijing

02:00-02:45 Boston

07:00-07:45 London

18:00-18:45 Sydney

08:00-08:45 Zürich

Technology and applications of MS-based proteomics to body fluid and tissue analysis

Matthias Mann (Max Planck Institute)

Video

 

These years they focus more on clinical applications than cellular biology, and that’s what the following 5 parts (technology, PTM, plasma proteome profiling,single cell proteomics and deep visual proteomics) will be related with.

Technology (Introduction of two software and one hardware)

AlphaPept Framework: --a super-fast framework for the analysis of MS-based proteomics--open source, codebase by python and UI solution for non-experts--Python combined with Numba package make the analysis ultrafast the Clinical Knowledge Graph (CKG)--integrates proteomics data into clinical decision-making--Open source tool for data integration, analysis (the latest statistical and machine learning algorithms included) and interpretation (based on relevant experimental data, public databases and the literature)

Trapped ion mobility spectrometry (TIMS)--Ion mobility separation Parallel Accumulation – Serial Fragmentation (PASEF)

--Increased signal-to-noise by signal compression
--Full precursor mass resolution
--Multiplied sensitivity and sequencing speed
--diaPASEF could be more robust for future clinical proteomics

Post-translational modifications

(LRRK2 in Parkinson’s disease)

Method: Phosphoproteomics simplified protocol EasyPhos
Application: hunt for substrates of multidomain Leucine-rich repeat kinase 2 (LRRK2)
--genetic cause of Parkinson’s disease: pathogenic mutations of LRRK2.
--G2019S substitution on LRRK2 protein activates the kinase
--genetics (cells from genetically modified mice), pharmacology (selective LRRK2 inhibitors), phosphoproteomics (EasyPhos, 27,000 phosphosites on ~6000 proteins)
--Rab10 and Rab12 are phosphorylated by LRRK2

Plasma proteome profiling (liver disease)

Application: diagnostic tool for early detection of liver disease

Study 1: plasma proteomics identified novel proteins associated with non-alcoholic fatty liver disease (NAFLD)

--plasma proteome of 3 matched sub-cohorts by BoxCar acquisition
--in cirrhotic liver, 77% downregulated plasma proteins are “liver-specific”
--six proteins significantly associated with NAFLD
--global correlation of plasma proteins and clinical parameters (liver enzymes)
--In HFD-induced NAFLD mouse model, identified maker candidates in human cohort recapitulated similar changes, providing evidence that these proteins may be good biomarkers.

Study 2: proteomics identified circulating protein markers for alcohol-related liver disease (ALD)

--a paired plasma (N=596) and liver tissue (N=79) proteome profiling
--of the 407 overlapped proteins between liver and plasma, 91 had significant correlations
--52 co-regulated proteins in the plasma and liver display distinct temporal patterns across fibrosis stages.
--machine learning models based on plasma proteome to predict early-stage of fibrosis, inflammation and steatosis, which outperform existing tests

Single cell proteomics

In EvoTip single cell processing and Evosep liquid chromatography for ultra high-sensitivity single cell proteomics.

~1000 protein groups identified with high quantitative reproducibility at the single cell level (Hela cell line).

Deep visual proteomics

Workflow: High-parametric images with subcellular resolution of archived patient tissue samples – image segmentation with deep learning training – machine learning algorithms to predict cellular phenotypes – single-cell isolation (including with subcellular spatial resolution or other arbitrary structures) using laser capture microdissection – cell type specific proteomes – Clinical Knowledge Graph – resource for researchers and clinicians

15:45-16:30 Beijing

02:45-03:30 Boston

07:45-08:30 London

18:45-19:30 Sydney

08:45-09:30 Zürich

The modular proteome and its clinical significance

Ruedi Aebersold (ETH Zurich)

Video

 

 

Ruedi tried to find out how alterations in the molecular constellation determines alterations in function/phenotype from three aspects.
1. How does genomic variability affect proteotype composition and phenotype? Method:Multi-layer molecular profiles for Hela cells with different phenotypes (e.g. doubling time and the ability to be infected)Effect of genomic variability on molecular profiles and phenotypes was calculated. Conclusion: Genotypic variability propagates along the axis of the central dogma in poorly predictable ways.Organization of proteins into complexes acts as a significant buffer of genotype variability.
2. How does genomic variability affect proteotype organization and deterministic functions? Method: Size exclusion chromatography mass spectrometry (SEC-MS) and size exclusion chromatography algorithmic toolkit (SECAT) Compute effect of genotypic variation on protein complexes and protein interaction networks. Results: Genomic variability affects the proteotype organization at the level of complexes and PPIs. Conclusion: New MS method and algorithms enable us to relate function to phenotype.
3.How can we predict altered proteotype modules and deterministic functions from transcript or protein profiles? Method: Proteome abundance profiles by SWATH/DIA Evaluation of changes in modularity based on changes in co-expression covariance and observing different deterministic functions, they tried to find out the relationship between these features. Conclusion: Proteins in the same complex (e.g. ATP synthase subunit alpha) tend to correlate across different samples. Different stage of prostate cancers exhibit altered quantitative relationships in many protein pairs. Genomic variants can alter specific protein modules and thus their function.

16:30-16:45 Beijing

03:30-03:45 Boston

08:30-08:45 London

19:30-19:45 Sydney

09:30-09:45 Zürich

Mattias Q&A

16:45-17:00 Beijing

03:45-04:00 Boston

08:45-09:00 London

19:45-20:00 Sydney

09:45-10:00 Zürich

Ruedi Q&A

Section C2: Emerging proteomics technologies

Coordinators:

Yu-Ju Chen (Academia Sinica)
Edouard Nice (Monash University)
Tiannan Guo (Westlake University)

17:25-17:50 Beijing

04:25-04:50 Boston

09:25-09:50 London

20:25-20:50 Sydney

10:25-10:50 Zürich

Technologies for high-throughput clinical proteomics

Bernhard Küster (Technische Universität Muenchen)

Video

 

Benhard develops MS-based technologies for high-throughput at high quality.

1, analysis of body fluids and whole proteomes by micro-LD MS/MS.The 50 uL/min LC flow generates stable performances while high quality. Data from 1500 consecutive injections showed high retention time consistence. It was also applied to analyzed the phosphoproteomes of 377 cancer cell line and quantified 12,700 proteins in total and 12000 p-sites.

2, analysis of FFPE material by single-shot LC-FAIMS-MS/MS.The optimized workflow also generates high ids (~30,000 peptides) per shot with low CVs.

3, Prosit prediction of spectral libraries and re-scoring of LC-MS/MS data.The great effort made on Prosit establishment can predict fragment patterns and retention time with high precision, and is currently serving the scientific community very well.

17:50-18:15 Beijing

04:50-05:15 Boston

09:50-10:15 London

20:50-21:15 Sydney

10:50-11:15 Zürich

Clinical proteomics in interesting times

Roman Fischer (University of Oxford)

18:40-19:02 Beijing

05:40-06:02 Boston

10:40-11:02 London

21:40-22:02 Sydney

11:40-12:02 Zürich

Single cell proteomics (SCP): glass-Oil-Air-Droplet (gOAD) pico chip

Catherine Wong (Peking University)

19:02-19:10 Beijing

06:02-06:10 Boston

11:02-11:10 London

22:02-22:10 Sydney

12:02-12:10 Zürich

Questions, Recommendations and Conclusions

19:10-20:00 Beijing

06:10-07:00 Boston

11:10-12:00 London

22:10-23:00 Sydney

12:10-13:00 Zürich

Dinner break

Section D: Industrialized proteomics for the clinic

Coordinators:

Stephen Pennington (University College Dublin)
Yu-Ju Chen (Academia Sinica)
Andrea Urbani (Università Cattolica del "Sacro Cuore")
Edouard Nice (Monash University)
Tiannan Guo (Westlake University)

20:00-20:17 Beijing

07:00-07:17 Boston

12:00-12:17 London

23:00-23:17 Sydney

13:00-13:17 Zürich

Development of a pipeline for biomarker discovery using proteomics

Anthony Whetton (The University of Manchester)

Video

 

Introduce the concept of precision medicine and genomic/proteomic applications in precision medicine nowadays. Further explain why we need to discover biomarkers; the reason is that individuals have differential responses for the same drugs or therapies. Briefly introduce Stoller Biomarker Discovery Center in Manchester and industrializing clinical proteomic strategies. Anthony focuses on SWATH-MS based plasma proteomics and gives some examples used industrializing clinical proteomic strategies: 1) grey platelet syndrome 2) chronic kidney disease. UK Biobank has had genomic data and its proteomic analysis of plasma/urine/mixed saliva is planned now.

20:17-20:40 Beijing

07:17-08:40 Boston

12:17-12:40 London

23:17-00:40(+1) Sydney

13:17-13:40 Zürich

Procan-adapting proteomics for the cancer clinic

Roger Reddel (The University of Sydney)

20:40-21:00 Beijing

07:40-09:00 Boston

12:40-13:00 London

23:40-01:00(+1) Sydney

13:40-14:00 Zürich

Break

Section E: Translational proteomics

Coordinators:

Stephen Pennington (University College Dublin)
Yu-Ju Chen (Academia Sinica)
Jun Qin (National Center for Protein Sciences, Beijing)
Edouard Nice (Monash University)
Tiannan Guo (Westlake University)

21:00-21:30 Beijing

08:00-08:30 Boston

13:00-13:30 London

00:00-00:30(+1) Sydney

14:00-14:30 Zürich

Translation of protein biomarkers: It’s simple

Stephen Pennington (University College Dublin)

Video

 

New protein biomarkers will be complimentary to genomics to promote precision medicine. The protein biomarker pipeline includes discovery, confirmation assay development, evaluation/validation and approval/adoption. The sample amounts and detection methods for each stage are different. The authors suggested the application of MRM in the confirmation and evaluation stages.

Dr. Pennington presented one of his study about developing blood biomarkers for discriminate patients with psoriatic arthritis (PsA) and Rheumatoid Arthritis (RA). Before that, there were no criteria to distinguish PsA form RA or other arthropathies. After comparing the serum proteomic profiles between PsA and RA patients using MS-based labeled proteomics, SOMAscan and Luminex, they found some unbiased biomarker candidates. They further supplement the biomarker candidates with existing panel and literature to make a panel called PAPRICA containing ~200 proteins and >400 peptides for the following MRM analysis. They have tested the panel on a new cohort containing 169 patients and achieved an AUC of 0.9 using normalized MS data. They ‘ll further evaluate the panel in a larger cohort containing 1000 patients

21:30-21:48 Beijing

08:30-08:48 Boston

13:30-13:48 London

00:30-00:48(+1) Sydney

14:30-14:48 Zürich

Psychiatric disorder pathway illumination using omics

Chris W. Turck (Max Planck Institute of Psychiatry)

Video

 

Psychiatric disorders have a great effect on the physical health of modern people. The diagnosis of psychiatric disorders is difficult using barely the clinical determined factors or indicators. People have been applying omics technologies to study psychiatric disorders, and comparing with the results of genomic characterization, the results of proteome/metabolome/microbiome characterization vary from day to day, herein could better reflect the psychiatric conditions, feasible for this type of study. In Psychiatric disorder biomarker study, animal models and body fluid from patients can be the substitutes for the human brain tissue because of their easy access.

To study the biosignatures of anxiety disorder, Dr. Turck and his team created the stress susceptible DBA/2NCrl and C57BL/6NCrl mice and performed transcriptomic proteomic analysis of the brain tissue of susceptible mice. They found:

1) opposite regulation of the same mitochondria related genes in DBA/2NCrl and C57BL/6NCrl stress susceptible mice, which indicated the gene background affects the stress-induced behaviors
2) blood gene expression of mitochondria related genes in panic disorder patients resembles DBA/2NCrl stress susceptible mice but in lower intensity

Dr. Turck demonstrated the alterations of mitochondria related genes in anxiety and panic disorder, and further stated that these studies are still far from clinical insights.

22:15-22:40 Beijing

09:15-09:40 Boston

14:15-14:40 London

01:15-01:40(+1) Sydney

15:15-15:40 Zürich

Questions, Recommendations and Conclusions

Concluding remarks

22:40-23:00 Beijing

09:40-10:00 Boston

14:40-15:00 London

01:40-02:00(+1) Sydney

15:40-16:00, Zürich

Tiannan Guo 郭天南 (Westlake University)
Matthias Mann (Max Planck Institute)
Jun Qin 秦钧 (National Center for Protein Sciences, Beijing)
Edouard Nice (Monash University)
Stephen Pennington (University College Dublin)

Acknowledgements