Course syllabus

Course syllabus

BBT020 Advanced analytical chemistry: metabolomics and proteomics lp4 VT22 (7.5 hp).

The course is offered by the department of Biology and Biological Engineering

Contact details

Examiner, course responsible, lecturers

Examiner and course organizer:

Professor Rikard Landberg, Division of Food and Nutrition Science, Department of Biology and Biological Engineering, Chalmers, 412 96 Gothenburg.

Phone: +46 72 350 93 86

Email: Rikard.landberg@chalmers.se

Table 1: Contact details to teachers and lab. Supervisors

Function	Person	Contact details
Teacher	Dr. Otto Savolainen	Otto.savolainen@chalmers.se
Lab. Supervisor	PhD-student Viktor Skantze	viktor.skantze@fcc.chalmers.se
Lab. Supervisor	PhD-student Olle Hartvigsson	ollehar@chalmers.se
Lab. Supervisor	Dr. Marina Armein	Marina.armeni@chalmers.se
Lab. Supervisor	Dr. Karin Larsson	Karin.larsson@chalmers.se
Teacher	Dr. Anders Pedersen	Anders.pedersen@nmr.gu.se
Teacher	Dr. Annika Thorsell	Annika.thorsell@gu.se
Teacher	Dr. Marcus Henriksson	Marcus.henricsson@wlab.gu.se
Teacher	Dr. Jörg Hanrieder	Jorg.hanrieder@neuro.g.se
Teacher	Dr. Carl Brunius	Carl.brunius@chalmers.se
Teacher	Dr. Aleksej Zelezniak	Aleksej.zelezniak@chalmers.se
Teacher	Dr. Ville Koistinen	ville.m.koistinen@uef.fi

 

Course purpose

The aim of this course is to give an overview of metabolomics and proteomics, and how they can be applied to advance understanding of biology and biological effects.

Learning objectives (after completion of the course the student shall be able to):

• Design a metabolomics or proteomics experiment to help solve a biological question
• Critically evaluate the use of different methods for metabolomics and proteomics
• Critically assess different data analysis tools
• Understand integration of metabolomics and proteomics data with other types of data

Schedule

TimeEdit

BBT020 Metabolomics and Proteomics –Schedule (updated 2022-03-17)

Date	Time	Place	Lecture	Lecturer
Monday 21 March	10.00-12.00	KS1	Welcome and Introduction to Proteomics and Metabolomics & preparations for lab.	Rikard Landberg
Wedn. 23 March	13.00-14.45	Zoom	MS-based metabolomics	Otto Savolainen
Wedn. 23 March	15.00-17.00	Utsikten*	Data analysis for metabolomics and proteomics I + preparation for lab 1	Carl Brunius
Monday 28 March	10.00-12.00	KS1	Study design, pre-analytical sample handling + introduction of seminar task	Rikard Landberg
Wedn. 30 March	10.00-12.00	KS1	Lab: Sampling strategy for metabolomics experiment and visit to CMSI	Rikard Landberg + Marina Armeni
Wedn.30 March	13.00-16.00	Utsikten*	Lab: Data analysis I	Carl Brunius
Monday 4 April	09.00-12.00	Zoom	Identification of unknown compounds- a bottle neck step in untargeted metabolomics	Ville Koistinen
Wedn. 6 April	10.00-12.00	KS1	Introduction to proteomics	Annika Thorsell
Wedn.6 April	13.00-15.00	D41	Quantitative Mass Spectrometry in Proteomics	Annika Thorsell
Wedn. 20 April	10:00-12.00	KS1	Data analysis for metabolomics and proteomics II	Carl Brunius
Wedn. 20 April	13.00-16.00	Fältarbete	Lab: Analysis of samples at CMSI	Marina Armeni and Cecilia Martinez- Escobedo
Monday 25 April	10.00-12.00	KS1	Lipidomics	Markus Henriksson
Wedn. 27 April	13.00-16.00	KS1	Imaging mass spectrometry for metabolomics and proteomics	Jörg Hanrieder
Monday 2 May	10.00-12.00	KS1	NMR in Metabolomics	Anders Pedersen
Wedn. 4 May	10.00-12.00	KS1	Lab: Data analysis II	Carl Brunius
Monday 9 May	10.00-12.00	KS1	Reserve time	TBA
Monday 11 May	13.00-16.00	D41	Journal club	Rikard Landberg
Monday 16 May	9.00-12.00	KS1	Visit to the NMR centre	Göran Karlsson
Wedn. 18 May	13.00-16.00	D41	Integration of different types of OMICs- data	Aleksej Zelezniak
Monday 23 May	10.00-12.00	KS1	Seminar: Results from lab.	Rikard Landberg
June	TBA	Zoom	EXAM

*Utsikten- The conference-room at floor 7 at the Division of Food and Nutrition Science.

 

Course literature

In this course, we do not use any specific book as course literature. Students are encouraged to search for scientific original articles and reviews with the field. Below is a list of recommended reading which is related to the different lectures.

Pre-analytical sample handling

Yin P, Lehmann R, Xu G. Effects of pre-analytical processes on blood samples used in metabolomics studies. Anal Bioanal Chem. 2015 Jul;407(17):4879-92. doi: 10.1007/s00216-015-8565-x

Anton G, Wilson R, Yu ZH, Prehn C, Zukunft S, Adamski J, Heier M, Meisinger C, Römisch-Margl W, Wang-Sattler R, Hveem K, Wolfenbuttel B, Peters A, Kastenmüller G, Waldenberger M. Pre-analytical sample quality: metabolite ratios as an intrinsic marker for prolonged room temperature exposure of serum samples. PLoS One. 2015 Mar 30;10(3):e0121495. doi: 10.1371/journal.pone.0121495

Brunius C, Pedersen A, Malmodin D, Karlsson BG, Andersson LI, Tybring G, Landberg R. Prediction and modeling of pre-analytical sampling errors as a strategy to improve plasma NMR metabolomics data. Bioinformatics. 2017 Nov 15;33(22):3567-3574. doi: https://doi.org/10.1093/bioinformatics/btx442

MS-based metabolomics

Savolainen OI, Sandberg AS, Ross AB. A Simultaneous Metabolic Profiling and Quantitative Multimetabolite Metabolomic Method for Human Plasma Using Gas-Chromatography Tandem Mass Spectrometry. J Proteome Res. 2016 Jan 4;15(1):259-65. doi: https://pubs.acs.org/doi/10.1021/acs.jproteome.5b00790

Patti GJ, Yanes O, Siuzdak G. Innovation: Metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol. 2012 Mar 22;13(4):263-9. doi: 10.1038/nrm3314. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3682684/

Scalbert A, Brennan L, Manach C, Andres-Lacueva C, Dragsted LO, Draper J, Rappaport SM, van der Hooft JJ, Wishart DS. The food metabolome: a window over dietary exposure. Am J Clin Nutr. 2014 Jun;99(6):1286-308. https://academic.oup.com/ajcn/article/99/6/1286/4577352

Lei Z, Huhman DV, Sumner LW. Mass spectrometry strategies in metabolomics. J Biol Chem. 2011 Jul 22;286(29):25435-42. doi: 10.1074/jbc.R111.238691. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3138266/

Johnson CH, Ivanisevic J, Siuzdak G. Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol. 2016 Jul;17(7):451-9. doi: 10.1038/nrm.2016.25. https://www.ncbi.nlm.nih.gov/pubmed/26979502

 

 

NMR-based metabolomics

Larive, C. K., Barding, G. A., Jr., & Dinges, M. M. (2015). NMR Spectroscopy and Metabolomics and Metabolic Profiling. Analytical Chemistry, 87(1), 133–146. http://doi.org/10.1021/ac504075g

Gowda, G. A. N., & Raftery, D. (2016). Recent Advances in NMR-Based Metabolomics. Analytical Chemistry, acs.analchem.6b04420. http://doi.org/10.1021/acs.analchem.6b04420

Markley, J. L., Brüschweiler, R., Edison, A. S., Eghbalnia, H. R., Powers, R., Raftery, D., & Wishart, D. S. (2017). The future of NMR-based metabolomics. Current Opinion in Biotechnology, 43, 34–40. http://doi.org/10.1016/j.copbio.2016.08.001

Proteomics

Savitski M.M. et al Multiplexed Proteome Dynamics Profiling Reveals Mechanisms Controlling Protein Homeostasis. Cell 2018, 173(1), 260-274.e25 DOI: 10.1016/j.cell.2018.02.030

Robles M.S. et al Phosphorylation Is a Central Mechanism for Circadian Control of Metabolism and Physiology. Cell Metabolism 2017, 25(1), 118-127 DOI: 10.1016/j.cmet.2016.10.004

Mohammed H. et al Rapid immunoprecipitation mass spectrometry of endogenous proteins (RIME) for analysis of chromatin complexes. Nature Protocols 2016, 11(2), 316-326 DOI: 10.1038/nprot.2016.020

Keshishian H. et al Quantitative, multiplexed workflow for deep analysis of human blood plasma and biomarker discovery by mass spectrometry. Nature Protocols 2017, 12(8), 1683- 1701. DOI: 10.0.4.14/nprot.2017.054

Song B. et al Quantitative Proteomics for Cardiac Biomarker Discovery Using Isoproterenol-Treated Nonhuman Primates. Journal of Proteome Research 2014, 13(12), 5909-5917 DOI: 10.1021/pr500835w

Huang F.-K. et al Deep Coverage of Global Protein Expression and Phosphorylation in Breast Tumor Cell Lines Using TMT 10-plex Isobaric Labeling. Journal of Proteome Research 2017,16(3), 1121-1132. DOI: 10.1021/acs.jproteome.6b00374

Imaging mass spectrometry for metabolomics and proteomics

Hanrieder J, Malmberg P, Ewing AG. Spatial neuroproteomics using imaging mass spectrometry. Biochemica et Biophysica Acta. 2018. In Press. https://www.ncbi.nlm.nih.gov/pubmed/25582083 (Links to an external site.)

Statos A. Mass spectrometric imaging of small molecules. Trends in Biotechnology 2010:28:425-434. https://www.ncbi.nlm.nih.gov/pubmed/20580110 (Links to an external site.)

Zemski Berry KA, Hankin JA, Barkley RM, Spraggins JM, Caprioli RM, Murphy RC. MALDI Imaging of Lipid Biochemistry in Tissues by Mass Spectrometry. Chem Rev. (Links to an external site.) 2011 Oct 12;111(10):6491-512. doi: 10.1021/cr200280p.

Data-fusion

Haas R, Zelezniak A, Iacovacci J, Kamrad S, Townsend St J, Ralser M. Designing and interpreting ‘multi omic’ experiments that may change our understanding of biology. Current Opinion in Systems Biology, Volume 6, Pages 37-45. https://doi.org/10.1016/j.coisb.2017.08.009

Mülleder M, Calvani E, Alam MT, Wang RK, Eckerstorfer F, Zelezniak A, Ralser M. Functional Metabolomics Describes the Yeast Biosynthetic Regulome. Cell. 2016 Oct 6;167(2):553-565.e12. doi: 10.1016/j.cell.2016.09.007. http://www.sciencedirect.com/science/article/pii/S0092867416312375

Hackett SR, Zanotelli VR, Xu W, Goya J, Park JO, Perlman DH, Gibney PA, Botstein D, Storey JD, Rabinowitz JD. Systems-level analysis of mechanisms regulating yeast metabolic flux. Science. 2016 Oct 28;354(6311). pii: aaf2786. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5414049/

Stefely JA, Kwiecien NW, Freiberger EC, Richards AL, Jochem A, Rush MJP Ulbrich A, Robinson KP, Hutchins PD, Veling MT, Guo X, Kemmerer ZA, Connors KJ, Trujillo EA, Sokol J, Marx H, Westphall MS, Hebert AS, Pagliarini DJ, Coon JJ. Mitochondrial protein functions elucidated by multi-omic mass spectrometry profiling. Nat Biotechnol. 2016 Nov;34(11):1191-1197. doi: 10.1038/nbt.3683. https://www.nature.com/articles/nbt.3683

Alam MT, Zelezniak A, Mülleder M, Shliaha P, Schwarz R, Capuano F, Vowinckel J, Radmanesfahar E, Krüger A, Calvani E, Michel S, Börno S, Christen S, Patil KR, Timmermann B, Lilley KS, Ralser M. The metabolic background is a global player in Saccharomyces gene expression epistasis. Nat Microbiol. 2016

https://www.nature.com/articles/nmicrobiol201530?WT.feed_name=subjects_fungalphysiology

Data analysis

Trygg J, Holmes E, Lundstedt T. Chemometrics in metabonomics. J Proteome Res. 2007 Feb;6(2):469-79. https://pubs.acs.org/doi/abs/10.1021/pr060594q (Links to an external site.)

Sheng Ren, Anna A. Hinzman, Emily L. Kang, Rhonda D. Szczesniak, Long Jason Lu.Computational and statistical analysis of metabolomics data. Metabolomics (2015) 11:1492–1513. https://www.semanticscholar.org/paper/Computational-and-statistical-analysis-of-data-

Ren-Hinzman/20c4e298f0675a8f072cae36f98e45c35d0f9ba7

Johan A. Westerhuis Æ Huub C. J. Hoefsloot Æ Suzanne Smit Æ Daniel J. Vis Æ Age K. Smilde Æ Ewoud J. J. van Velzen Æ John P. M. van Duijnhoven Æ Ferdi A. van Dorsten. Assessment of PLSDA cross validation. Metabolomics (2008) 4:81–89. https://core.ac.uk/download/pdf/81868223.pdf

Edoardo Saccenti, Huub C. J.,Hoefsloot, Age K. Smilde, Johan A. Westerhuis, Margriet M. W. B. Hendriks. Reflections on univariate and multivariate analysis of metabolomics data. Metabolomics (2014) 10:361–374.

https://link.springer.com/article/10.1007/s11306-013-0598-6

Gromski PS, Muhamadali H, Ellis DI, Xu Y, Correa E, Turner ML, Goodacre R. A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding. Anal Chim Acta. 2015 Jun 16;879:10-23. https://www.sciencedirect.com/science/article/pii/S0003267015001889?via%3Dihub

 

Course design

The course contains lectures, seminars, a laboratory exercise, and study visits. These activities will together provide support to reach the learning objectives of the course.

The course provides a broad overview of what can be done with metabolomics and proteomics, and how this can be applied in life science. Through laboratory exercises, students will get the opportunity to generate their own lab-based data, which will be the basis of practical work on data analysis and the course project. Through the journal club, students will have the chance to analyze and discuss scientific data presented in peer-reviewed journals.

The course covers the following main topics:

• Metabolomics using NMR and mass spectrometry
• Proteomics
• Study design and sample preparation
• Applications of metabolomics and proteomics in industry
• Imaging mass spectrometry metabolomics
• Multivariate data analysis for metabolomics and proteomics
• Integrated analysis of metabolomics and proteomics data

Lectures

The lectures are not mandatory, but students are encouraged to attend all lectures to maximize chance to learn as much as possible.

Laboratory exercises

This course contains a wet lab exercise along with computer lab exercises. These are interconnected. All laboratory (wet/computer) exercises are mandatory. Instructions are provided in a separate folder.

Two mandatory seminars will be arranged. The first seminar is a journal club where students will be divided into groups and provided one scientific paper to read and present and one paper they will act as opponents on. Instructions to this assignment are provided in a separate folder.

At the second seminar, results from the laboratory project will be presented and opposed. Instructions to the are provided in a separate folder.

Study visit

We will have two study visits during the course. One internal visit to Chalmers Mass Spectrometry Infrastructure and one external visit to the Swedish NMR Centre. Study visits are not mandatory, but we kindly ask students to attend them.

About 25h scheduled lectures, 4h computer- and 12 h lab exercises as well as 2 study visits will be provided.

Throughout the course, we use Canvas to share and update information as well as contacting students.

Changes made since the last occasion

The course leader will take the following measures to ensure a better outcome of the course to be given during 2022:

• For the 2022 edition, the whole course will be given at campus, including laboratory exercise. Expected outcome: Presence will facilitate day to day contact and communication with the students. 
• Different measures will be taken to facilitate good communication and working climate. First, the examiner will ensure to respond quickly to emails (typically the case) with students. To avoid confusion and potential contradicting messages, he will include all relevant actors in the conversation. Secondly, the examiner will have a meeting with relevant teachers before the course starts to go through all instruction material to ensure all interpret the instructions in the same way and that they are clearly communicated. Expected outcome: A good communication and working climate.
• Course objectives will be presented and clearly exemplified, concretized and linked to the learning activities at the course introduction. Each lecturer will be asked to highlight to what course objectives their lecture addresses and in what way. Expected outcome: Student will clearly understand the objectives.
• Avoiding overlap. The course leader will have a detailed chat with the teachers to ensure it is clear what they need to include in their lectures to avoid overlap and that they need to emphasize the learning objectives of their lectures. In some cases, this has not helped, and in those cases the teachers will now be replaced. Expected outcome: Less overlap and clearer to the student what they are expected to learn from each lecture/learning activity.
• The course leader will ensure he is available at Chalmers during the time course is given (lectures, labs, seminar tasks etc.). If he cannot be present at Chalmers, he will appoint a back-up person who can take the role as stand in and solve urgent issues if needed. Expected outcome: Improved handling of urgent upcoming issues, such as arranging stand in for sick teachers, changing schedule etc.
• Canvas will be structured better to ensure it is simple for all to find the material. Expected outcome: will reduce the risk that information is not put at the right place.

Learning objectives

Learning objectives:

• Design a metabolomics or proteomics experiment to help solve a biological question
• Critically evaluate the use of different methods for metabolomics and proteomics
• Critically assess different data analysis tools
• Understand integration of metabolomics and proteomics data with other types of data

The course leader will provide more detailed explanations to the general learning objectives. Learning objectives of the separate learning occasions (lectures, exercises and seminars) will be presented as well as their link to the overall course objectives.

Link to the syllabus on Studieportalen:

Study plan

 

Examination

The course will be examined on 1) theoretical knowledge acquired during the course through a written exam 2) translation of theoretical knowledge into practice through a lab project.

The written exam (1) will correspond to 3.5 HP. The second (2) part will be based on:

A) participation in obligatory laboratory- and computer exercise

B) a written report and a poster

C) an oral presentation of the lab project

D) completing the journal club assignment and participation at journal club.

The second part will correspond to 4.5 HP. All activities will follow the published schedule. Laboratory exercises may be adapted time-wise depending student group size (i.e. split into several occasions). If so, we will be flexible to enable participation of all students and avoiding collisions with other on-going courses.

Laboratory exercises, study visits, journal club and the project seminar are mandatory. If a student cannot attend an obligatory session, alternative solutions can be put up in dialogue with the course leader/examiner, for example set up new time to perform the obligatory task. Please contact the course leader immediately under such circumstances.

 

Grading

The grade will be based on assessment at several levels (Table 2). Attendance to obligatory sessions is mandatory for grade 3 and above. The written report and journal club assignment must be handed in in time to allow any grade above 3. An uncomplete written report can be complemented up to grade 3, but never beyond that.

  

Table 2. Grading

Component	% contribution to the total grade
Written exam	50
Laboratory exercise/project work Of which: Written report Oral presentation	50   30 20

The written report shall be prepared according to the instructions provided and will be evaluated based on the following criteria:

• Overall impression (20%)
• Content and understanding (40%)
• Structure (20%)
• Language (20%)

The oral presentation will be assessed according to the following criteria:

• Content (40%)
• Structure/disposition (20%)
• Presentation technique (20%)
• Handling of questions and the time (20%)

The written exam will be on questions where short answers are expected (1-3 points/question) and on assay questions (6-9 points/question). The short questions will stand for 40% of the total credits and the assay questions for the remaining 60%.

For exam grade 3: >60% of the answers must be correct,

For exam grade 4: >70-80% of the answers must be correct,

For exam grade 5: >80% of the answers must be correct,

Conducted journal club assignment will be pre-requisite for grade 3 and above.