Cross_Integration_Poster
1
Re-integration across Samples in Sample Set for Better Accuracy in Metabolite Analysis Hongping Dai, Corey DeHaven, Anne Evans • Metabolon, Inc 800 Capitola Drive Suite 1, Durham, NC 27713 • www.metabolon.com Introduction Due to their high throughput and sensivity, GC/MS and UHPLC/MS/MS2 are widely used in metabolomic studies. Such high throughput analyses produce a large amount of raw scan data that need to be automacally processed from sample to sample. The quality of the results is compromised with the inherently exisng inconsistency in peak detecon and peak integraon from sample to sample, partly due to incomplete separaon of compounds or overloading commonly occurred in complex biological samples. New techniques are needed to detect and overcome such inconsistency in order to achieve high accuracy. Ion peak re-integraon across all samples in the sample set is a novel technique capable of detecng and correcng such inconsistency and therefore achieving beer accuracy in metabolite analysis. Cross-Integration Strategy Chromatograms of Peaks represenng the quantave mass from all the samples are evaluated to see • If majority of the sample peaks are on the trailing edge of another peak, • If majority of the sample peaks are on the leading edge of another peak, • If the majority are peaks that encompass two peaks in other samples. Peak integraon ranges are evaluated with alignment by retenon index and stascs of peak limits across the sample set. Accordingly, correcons in consistency and re-integraon are suggested and presented for review and approval, in addion to user specified manual correcon. Workflow in Metabolomics Data Processing at Metabolon • GC/MS, LC(NEG)/MS n and LC(POS)/MS n measurement of metabolite samples. • Automac Ion Peak Detecon and Peak Integraon • Automac Ion Peak Componenzaon • Automac Library Matches to Idenfy Metabolites • Manual Curaon of metabolites • Cross-Integraon for Consistency and Accuracy • Stascs (Historical Stascs and Stascs in the sample set), Quality Control and Elucidaon of Metabolism and Pathway. CrossIntegration TM Interface Fig. 1. CrossIntegraonTM Interface: Upper Leſt : Idenfied metabolites (200~600) in the specified sample set; Middle Leſt: quant peaks for selected metabolite in the samples in the sample set; Lower Leſt: Type of samples and Informaon about the sample peaks Upper Right: Peak chromatograms Lower Right: Sample peak area (blue for original integraon and red for re-integrated Combining Peaks When a metabolite in a sample is at a high level, it can overload the column and therefore distort the chromatographic peak. Even through it may be out of the linear range, a consistent integraon of the peak is sll needed to characterize the group of samples. Distorted peaks produce wrong pick of the quant mass peak. In Figure 2, the peak for glucose was inaccurately split by the automated peak integrator. Cross-reintegraon would correct this. The example in Figure 2 improves the relave standard deviaon from 20.1 to 7.4. Conclusion CrossIntegraon TM soſtware can detect inconsistency in peak integraon across samples in a sample set and improve the accuracy in integraon of detected metabolites, thereby improving stascs and quality control, which will contribute significantly to the elucidaon of metabolism and metabolite pathway. Fig. 1. CrossIntegration TM Interface: Functionalities • Automac merging of approved peaks from the sample that match to the same lib compound. • Detecon of Shoulder Peaks Based on RI-aligned peak start or peak end distribuon across the samples. • Manual Integraon • Manual Peak Spling • Show peak chromatograms in overlay mode or tabular mode to easy review/manual re-integraon. • Update peak integraons, peak recovery and lib re-match Fig.2. Combining Peaks 0.0 2.0 4.0 6.0 0.0 2.0 4.0 6.0 0.0 2.0 4.0 6.0 0.0 2.0 4.0 6.0 0.0 2.0 4.0 6.0 0.0 2.0 4.0 6.0 0.0 2.0 4.0 6.0 0.0 2.0 4.0 6.0 0.0 2.0 4.0 6.0 0.0 2.0 4.0 6.0 Intensity/10,000,000 1850.0 1855.0 1860.0 1865.0 1870.0 1875.0 1850.0 1855.0 1860.0 1865.0 1870.0 1875.0 1850.0 1855.0 1860.0 1865.0 1870.0 1875.0 1880.0 RI 1246500 1246512 1246524 1246534 1246545 1246557 1246580 1246592 1246604 1246616 1246628 1246640 1246652 1246676 1246688 1246700 1246712 1246724 1246736 1246748 1246770 1246778 1246786 1246793 1246800 1246808 1246816 1246828 1246832 1246836 Task ID 0.0 0.4 0.8 1.2 1.6 2.0 Area/100,000,000 Inconsistency in Small Shoulder Peaks As seen in Figure 3 and 4, small peaks on the leading or tailing side of a larger peak are oſten integrated inconsistently: • Somemes small shoulder peaks are detected • Somemes small shoulder peaks are not detected • Small shoulder peaks are combined into the main peak • New soſtware shows user the inconsistency and permits the peaks to be consistently and accurately integrated 5420 5440 5460 5480 5500 5520 5540 5560 5580 5600 5620 RI 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 Intensity/1,000,000 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 Intensity/1,000,000 5420 5460 5500 5540 5580 5420 5460 5500 5540 5580 5420 5460 5500 5540 5580 5620 RI 5420 5460 5500 5540 5580 5420 5460 5500 5540 5580 5420 5460 5500 5540 5580 5620 RI Fig. 4. Examples in inconsistent Shoulder Peaks In Figure 3, the major peak on the leſt is idenfied as cysteine, whereas the shoulder on the leſt side is from threonate. In one sample, the small peak from threonate was inaccurately combined into the main peak for cysteine when it was automacally integrated, thus inadvertently increasing the response for cysteine in that sample. Aſter re-integraon the erroneous integraon was corrected thereby restoring the correct integraon for cysteine and perming the detecon of threonate in the sample as well. Fig. 3. Examples in inconsistent Shoulder Peaks. Upper: Spling of shoulder; Lower: Area change aſter re-integraon (Blue for automac integraon and red for cross re-integraon. In Figure 4, the major peak on the right is idenfied as 1-docosahexaenoylglycerophosphocholine (1-DHGPC), whereas the shoulder on the leſt side is idenfied as 2-docosahexaenoylglycerophosphocholine (2-DHGPC). In one sample, the peak for 2-DHGPC was inaccurately combined into the peak for 1-DHGPC when it was automacally integrated. In another sample, the baseline was not calculated consistently. The curves at the lower right shows the correcon. Aſter re-integraon the erroneous integraon was corrected and the small peak for 2-DHGPC recovered. 10446_META_Poster-R3.indd 1 5/19/10 9:26 AM
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Transcript of Cross_Integration_Poster
Re-integration across Samples in Sample Set for Better Accuracy in Metabolite AnalysisHongping Dai, Corey DeHaven, Anne Evans • Metabolon, Inc 800 Capitola Drive Suite 1, Durham, NC 27713 • www.metabolon.com
IntroductionDue to their high throughput and sensitivity, GC/MS and UHPLC/MS/MS2 are widely used in metabolomic
studies. Such high throughput analyses produce a large amount of raw scan data that need to be automatically
processed from sample to sample. The quality of the results is compromised with the inherently existing
inconsistency in peak detection and peak integration from sample to sample, partly due to incomplete
separation of compounds or overloading commonly occurred in complex biological samples. New techniques
are needed to detect and overcome such inconsistency in order to achieve high accuracy. Ion peak re-integration
across all samples in the sample set is a novel technique capable of detecting and correcting such inconsistency
and therefore achieving better accuracy in metabolite analysis.
Cross-Integration Strategy Chromatograms of Peaks representing the quantitative mass from all the samples are evaluated to see
• If majority of the sample peaks are on the trailing edge of another peak,
• If majority of the sample peaks are on the leading edge of another peak,
• If the majority are peaks that encompass two peaks in other samples. Peak integration ranges are evaluated
with alignment by retention index and statistics of peak limits across the sample set. Accordingly, corrections
in consistency and re-integration are suggested and presented for review and approval, in addition to user
specified manual correction.
Workflow in Metabolomics Data Processing at Metabolon• GC/MS, LC(NEG)/MSn and LC(POS)/MSn measurement of metabolite samples.
• Automatic Ion Peak Detection and Peak Integration
• Automatic Ion Peak Componentization
• Automatic Library Matches to Identify Metabolites
• Manual Curation of metabolites
• Cross-Integration for Consistency and Accuracy
• Statistics (Historical Statistics and Statistics in the sample set), Quality Control and Elucidation of Metabolism
and Pathway.
CrossIntegrationTM Interface
Fig. 1. CrossIntegrationTM Interface: Upper Left : Identified metabolites (200~600) in the specified sample set; Middle Left: quant peaks for selected metabolite in the samples in the sample set; Lower Left: Type of samples and Information about the sample peaks Upper Right: Peak chromatograms Lower Right: Sample peak area (blue for original integration and red for re-integrated
Combining PeaksWhen a metabolite in a sample is at a high level, it can overload the column and therefore distort the
chromatographic peak. Even through it may be out of the linear range, a consistent integration of the peak is
still needed to characterize the group of samples. Distorted peaks produce wrong pick of the quant mass peak.
In Figure 2, the peak for glucose was inaccurately split by the automated peak integrator. Cross-reintegration
would correct this. The example in Figure 2 improves the relative standard deviation from 20.1 to 7.4.
Conclusion CrossIntegrationTM software can detect inconsistency in peak integration across samples in a sample set and improve the accuracy in integration of detected metabolites, thereby improving statistics and quality control, which will contribute significantly to the elucidation of metabolism and metabolite pathway.
Fig. 1. CrossIntegrationTM Interface: Upper Left : Identified metabolites (200~600) in the specified sample set;Middle Left: quant peaks for selected metabolite in the samples in the sample set;Lower Left: Type of samples and Information about the sample peaksUpper Right: Peak chromatogramsLower Right: Sample peak area (blue for original integration and red for re-integrated
CrossIntegrationTM Interface
Functionalities• Automatic merging of approved peaks from the sample that match to the same lib compound.• Detection of Shoulder Peaks Based on RI-aligned peak start or peak end distribution across the samples.• Manual Integration• Manual Peak Splitting• Show peak chromatograms in overlay mode or tabular mode to easy review/manual re-integration.• Update peak integrations, peak recovery and lib re-match
Fig.2. Combining Peaks
Fig.2. Combining Peaks
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Inconsistency in Small Shoulder PeaksAs seen in Figure 3 and 4, small peaks on the leading or tailing side of a larger peak are often integrated inconsistently:• Sometimes small shoulder peaks are detected• Sometimes small shoulder peaks are not detected • Small shoulder peaks are combined into the main peak• New software shows user the inconsistency and permits the peaks to be consistently and accurately integrated
Fig. 4. Examples in inconsistent Shoulder Peaks
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Fig. 4. Examples in inconsistent Shoulder Peaks
Fig. 3. Examples in inconsistent Shoulder Peaks. Upper: Splitting of shoulder; Lower: Area change after re-integration (Blue for automatic
integration and red for cross re-integration.
In Figure 3, the major peak on the left is identified as cysteine, whereas the shoulder on the left side is from threonate. In one sample, the small peak from threonate was inaccurately combined into the main peak for cysteine when it was automatically integrated, thus inadvertently increasing the response for cysteine in that sample. After re-integration the erroneous integration was corrected thereby restoring the correct integration for cysteine and permitting the detection of threonate in the sample as well.
Fig. 3. Examples in inconsistent Shoulder Peaks. Upper: Splitting of shoulder; Lower: Area change after re-integration (Blue for automatic integration and red for cross re-integration.
In Figure 4, the major peak on the right is identified as 1-docosahexaenoylglycerophosphocholine (1-DHGPC), whereas the shoulder on the left side is identified as 2-docosahexaenoylglycerophosphocholine (2-DHGPC). In one sample, the peak for 2-DHGPC was inaccurately combined into the peak for 1-DHGPC when it was automatically integrated. In another sample, the baseline was not calculated consistently. The curves at the lower right shows the correction. After re-integration the erroneous integration was corrected and the small peak for 2-DHGPC recovered.
10446_META_Poster-R3.indd 1 5/19/10 9:26 AM
