Developing Algorithms for the Determination of Relative Abundances of Peptides from LC/MS Data

Transcription

1 Developing Algorithms for the Determination of Relative Abundances of Peptides from LC/MS Data RIPS Team Jake Marcus (Project Manager) Anne Eaton Melanie Kanter Aru Ray Faculty Mentors Shawn Cokus Matteo Pellegrini Industry Sponsors Parag Mallick Roland Luethy

2 Key terms Protein: a large biomolecule carrying out various functions of a cell Peptide: a fragment of a protein Digestion Protein Peptides

3 What is proteomics? Proteome: all the proteins expressed in an individual at a given time Proteomics: the study of proteins, their structure and function Replication Transcription Translation DNA RNA Protein Metabolic and bodily functions thousand genes Millions of proteins

4 Why study proteomics? Diagnosis of disease Personalized medicine Analysis

5 Our sponsor Cedars-Sinai Health System Spielberg Family Center for Applied Proteomics dedicated to developing proteomic technologies to guide doctors in patient management decisions focus on identifying and quantifying proteins using liquid chromatography/mass spectrometry (LC/MS)

6 Liquid chromatography A method to separate substances based on their affinity to water Retention time (RT): amount of time a substance takes to pass through the chromatography column RT=1 RT=2 RT=3

7 Intensity Mass spectrometry A method to separate the components of a mixture according to molecular mass Molecules are ionized, separated according to mass/charge, and detected Sample Ionization and Acceleration Electromagnet Mass/Charge Mass/Charge

8 Intensity Intensity LC/MS: combining liquid chromatography and mass spectrometry Retention time 1 Sample Mass/Charge Retention time 2 Retention Time Separated by Retention Time Mass/Charge

9 Retention Time The data Mass/Charge List of Identifications Retention Time Mass/charge Peptide Protein Confidence K.ACSQRPR.W ADH 86% R.IGYADIK.W EPO 12% K.LGANAILK.W HB 99.45%

10 Retention Time Intensity The problem Determine the relative abundance of peptides in the original samples based on LC/MS data Mass/Charge

11 Intensity Challenges Locate isotopes Identifications not centered Unknown spread along retention time Noise Isotopes Point of Identification

12 Peptide quantification modules Extract 2D Neighborhood Squish Isotopes Limit Retention Time Axis Fit Curve Quantify

13 Intensity (x10 5 ) Extract 2D neighborhood Pick 2D neighborhood around identified location Must be large enough to include entire feature Point of Identification Mass/Charge Extract 2D Neighborhood Squish Isotopes Limit Retention Time Fit Curve Quantify

14 Intensity (x10 5 ) Squish isotopes Isotopes have similar retention times Select relevant mass/charge values, extract corresponding data Mass/Charge Extract 2D Neighborhood Squish Isotopes Limit Retention Time Fit Curve Quantify

15 Retention Time Squish isotopes: quantize Mass/Charge Signal Noise Actual mass/charges Extract 2D Neighborhood Squish Isotopes Limit Retention Time Fit Curve Quantify

16 Intensity Squish isotopes: combine Mass/Charge Extract 2D Neighborhood Squish Isotopes Limit Retention Time Fit Curve Quantify

17 Intensity (x10 5 ) Limit retention time Find highest peak Search along retention time until 4 out of 5 consecutive data points are below threshold Threshold Retention Time (seconds) Extract 2D Neighborhood Squish Isotopes Limit Retention Time Fit Curve Quantify

18 Intensity (scaled) Fit curve Gamma curve fit to data by nonlinear regression Gamma, R2= Gamma, R 2 = Gamma, R 2 = 0.98 Right-skewed 1 Limited by liquid chromatography flow-rate Retention Time (scaled) Extract 2D Neighborhood Squish Isotopes Limit Retention Time Fit Curve Quantify

19 Intensity (scaled) Quantify Area under curve corresponds to peptide abundance Retention Time (scaled) Extract 2D Neighborhood Squish Isotopes Limit Retention Time Fit Curve Quantify

20 Evaluation 6 protein mix (~150 peptides) Same amount in every sample 5 protein mix (~250 peptides) SILAC Different amounts in each sample Controlled proportion of two isotopes of each protein in a sample 1x 1x 1x 1x 2x 3x 1:2

21 Intensity Intensity Data filtering Remove peptides: Not derived from sample protein mix Identified with confidence < 0.99 Difference in retention time > 100 seconds Retention Time Retention Time

22 Obs/Exp IQR Optimizing the algorithm Developed different versions of each module Evaluated combinations of different versions of modules Module combinations Modules Final Version Final Version Module Modules combinations

23 6 protein mix evaluations ~7 outliers per run not shown

24 5 protein mix evaluations ~3 outliers per run not shown

25 Median observed ratio Concentration dependence Expected ratio

26 Intensity SILAC introduction SILAC: Stable Isotope Labeling of Amino Acids in Cell Culture Peptides labeled with isotopes Light Isotope Medium x:y Protein Isolation LC/MS Mass/Charge Heavy Isotope Medium A single retention time slice

27 Preliminary SILAC evaluations ~1 outlier per run excluded ~6 outliers per run excluded

28 Intensity Intensity Future directions: better data filtering User inputs a pair of matched features If mismatched, ratio is meaningless Potential to predict when features are mismatched Retention Time Retention Time

29 Future directions: better data filtering Report match confidence for every ratio Possible diagnostic variables: Confidence of identifications Difference in retention time Difference in maximum intensity

30 Future directions: peptides to proteins Combine data from peptides to estimate quantity of mutual parent protein Sample 1 x 50 Protein Sample 2 digestion Peptides 5:1 5:1 5:1 algorithm Output 5.2:1 4.9:1 5.1:1 x 10 Protein Expected ratio 5:1 Mean ratio: 5.07:1 estimation

31 Observed/Expected Future directions: peptides to proteins Preliminary results /10

32 Acknowledgements Faculty Mentors Shawn Cokus Matteo Pellegrini Industry Sponsors Parag Mallick Roland Luethy Jake Thank You! Aru Everyone at the Spielberg Center IPAM Anne Melanie