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plasma proteomic data 7k somascan assay  (SomaLogic)

 
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    SomaLogic plasma proteomic data 7k somascan assay
    a, An in vitro model of senescent monocytes was developed by exposing THP-1 cells to IR and measuring senescence markers. Further quantitative mass spectrometry proteomics was performed on the secretome of these cells using the automated nanoparticle processing and digestion platform, Proteograph. Age association of the differentially secreted proteins was evaluated using proteomic and phenotypic data from the <t>BLSA</t> and InCHIANTI aging cohorts. b, The analysis pipeline used DIA-NN to identify monocyte secretome followed by filtering out the bovine and shared peptides. Peptide quantities from each nanoparticle was rolled up to proteins to determine the differentially expressed proteins.
    Plasma Proteomic Data 7k Somascan Assay, supplied by SomaLogic, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/plasma+7k+assay/plasma+proteomic+data+7k+somascan+assay/med_rxiv__2024__08__01__24311368-56-9-19
    Average 90 stars, based on 1 article reviews
    plasma proteomic data 7k somascan assay - by Bioz Stars, 2026-10
    90/100 stars

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    1) Product Images from "A plasma proteomic signature links secretome of senescent monocytes to aging- and obesity-related clinical outcomes in humans"

    Article Title: A plasma proteomic signature links secretome of senescent monocytes to aging- and obesity-related clinical outcomes in humans

    Journal: medRxiv

    doi: 10.1101/2024.08.01.24311368

    a, An in vitro model of senescent monocytes was developed by exposing THP-1 cells to IR and measuring senescence markers. Further quantitative mass spectrometry proteomics was performed on the secretome of these cells using the automated nanoparticle processing and digestion platform, Proteograph. Age association of the differentially secreted proteins was evaluated using proteomic and phenotypic data from the BLSA and InCHIANTI aging cohorts. b, The analysis pipeline used DIA-NN to identify monocyte secretome followed by filtering out the bovine and shared peptides. Peptide quantities from each nanoparticle was rolled up to proteins to determine the differentially expressed proteins.
    Figure Legend Snippet: a, An in vitro model of senescent monocytes was developed by exposing THP-1 cells to IR and measuring senescence markers. Further quantitative mass spectrometry proteomics was performed on the secretome of these cells using the automated nanoparticle processing and digestion platform, Proteograph. Age association of the differentially secreted proteins was evaluated using proteomic and phenotypic data from the BLSA and InCHIANTI aging cohorts. b, The analysis pipeline used DIA-NN to identify monocyte secretome followed by filtering out the bovine and shared peptides. Peptide quantities from each nanoparticle was rolled up to proteins to determine the differentially expressed proteins.

    Techniques Used: In Vitro, Mass Spectrometry


    Figure Legend Snippet: Demographic and clinical traits in the Baltimore Longitudinal Study of Aging. Data reported as mean (standard deviation).

    Techniques Used: Standard Deviation

    a, 1550 Monocyte SASP are detected in the BLSA 7k SomaScan. b, Elastic Net models were trained on 80% of the BLSA cohort and used to predict clinical traits of the remaining 20%. Spearman correlations are shown between the predicted and observed values in the test set for each clinical trait. c, Elastic Net modeling was used for feature selection, and the number of Elastic Net Selected Proteins (ENSPs) implicated in each clinical trait are shown. c, ROC plot comparing the predictive potential (80% train, 20% test) of ENSPs positively associated with BMI to predict obesity with control-only models, showing that ENSPs seem to provide additional predictive potential beyond age and other controls alone.
    Figure Legend Snippet: a, 1550 Monocyte SASP are detected in the BLSA 7k SomaScan. b, Elastic Net models were trained on 80% of the BLSA cohort and used to predict clinical traits of the remaining 20%. Spearman correlations are shown between the predicted and observed values in the test set for each clinical trait. c, Elastic Net modeling was used for feature selection, and the number of Elastic Net Selected Proteins (ENSPs) implicated in each clinical trait are shown. c, ROC plot comparing the predictive potential (80% train, 20% test) of ENSPs positively associated with BMI to predict obesity with control-only models, showing that ENSPs seem to provide additional predictive potential beyond age and other controls alone.

    Techniques Used: Selection, Control

    a, Elastic Net models were trained on 80% of the BLSA cohort and used to predict clinical traits of the remaining 20%. Spearman correlations are shown between the predicted and observed values of the test set for each clinical trait. b, Elastic Net modeling was used for feature selection, and the number of Elastic Net Selected Proteins (ENSPs) implicated in each clinical trait are shown. c, ROC plot comparing the predictive potential (80% train, 20% test) of ENSPs positively associated with BMI to predict obesity with control-only models, showing that ENSPs seem to provide additional predictive potential beyond age and other controls alone. d, The correlation between observed waist size and that predicted by Elastic Net Modeling (80% train, 20% test).
    Figure Legend Snippet: a, Elastic Net models were trained on 80% of the BLSA cohort and used to predict clinical traits of the remaining 20%. Spearman correlations are shown between the predicted and observed values of the test set for each clinical trait. b, Elastic Net modeling was used for feature selection, and the number of Elastic Net Selected Proteins (ENSPs) implicated in each clinical trait are shown. c, ROC plot comparing the predictive potential (80% train, 20% test) of ENSPs positively associated with BMI to predict obesity with control-only models, showing that ENSPs seem to provide additional predictive potential beyond age and other controls alone. d, The correlation between observed waist size and that predicted by Elastic Net Modeling (80% train, 20% test).

    Techniques Used: Selection, Control

    a, 220 monocyte SASP were detected in both the BLSA (7k SomaScan) and InCHIANTI (1.3k SomaScan). Elastic Net modeling was used for feature selection in both Inchianti and BLSA, and linear models were constructed using only proteins selected in both studies for each trait. Spearman’s correlation of predicted values of linear models trained on the BLSA and observed values in InCHIANTI are shown on the x-axis, and Spearman’s correlation of predicted values of linear models trained on InCHIANTI and observed values in the BLSA are shown on the y-axis b, Binomial models were trained either using controls (age, sex) or controls + ENSPs in BLSA, then used to predict obesity in Inchianti.
    Figure Legend Snippet: a, 220 monocyte SASP were detected in both the BLSA (7k SomaScan) and InCHIANTI (1.3k SomaScan). Elastic Net modeling was used for feature selection in both Inchianti and BLSA, and linear models were constructed using only proteins selected in both studies for each trait. Spearman’s correlation of predicted values of linear models trained on the BLSA and observed values in InCHIANTI are shown on the x-axis, and Spearman’s correlation of predicted values of linear models trained on InCHIANTI and observed values in the BLSA are shown on the y-axis b, Binomial models were trained either using controls (age, sex) or controls + ENSPs in BLSA, then used to predict obesity in Inchianti.

    Techniques Used: Selection, Construct

    For a 14-trait panel, proteins were ranked by the number of features for which they were selected via Elastic Net in the BLSA, and the most frequently selected proteins are shown with their cross-trait importance on the x-axis. Only proteins that were positively associated with negative traits such as BMI and CRP, and those that were inversely associated with positive traits such as mobility were selected. Stars indicate those that were also detected in InCHIANTI. b, Linear models were trained on 80% of the BLSA cohort and used to predict clinical traits in the remaining 20%. Spearman’s correlation between the predicted and observed test values are shown. c, Linear models were trained on 80% of the InCHIANTI cohort and used to predict clinical traits in the remaining 20%. Spearman’s correlation between the predicted and observed test values are shown. d, Principal Component Analysis was used to condense the high-impact panel into a composite senescence burden score in the BLSA. Principal Component 1 was used to represent an eigengene for the high impact panel. With the BLSA cohort ranked from low to moderate to high senescence burden, linear trait trends reveal that positive traits HDL and Walking Pace show a negative trend, while negative traits BMI and CRP show a positive trend.
    Figure Legend Snippet: For a 14-trait panel, proteins were ranked by the number of features for which they were selected via Elastic Net in the BLSA, and the most frequently selected proteins are shown with their cross-trait importance on the x-axis. Only proteins that were positively associated with negative traits such as BMI and CRP, and those that were inversely associated with positive traits such as mobility were selected. Stars indicate those that were also detected in InCHIANTI. b, Linear models were trained on 80% of the BLSA cohort and used to predict clinical traits in the remaining 20%. Spearman’s correlation between the predicted and observed test values are shown. c, Linear models were trained on 80% of the InCHIANTI cohort and used to predict clinical traits in the remaining 20%. Spearman’s correlation between the predicted and observed test values are shown. d, Principal Component Analysis was used to condense the high-impact panel into a composite senescence burden score in the BLSA. Principal Component 1 was used to represent an eigengene for the high impact panel. With the BLSA cohort ranked from low to moderate to high senescence burden, linear trait trends reveal that positive traits HDL and Walking Pace show a negative trend, while negative traits BMI and CRP show a positive trend.

    Techniques Used:

    Related Articles

    Clinical Proteomics:

    Article Title: SomaScan Bioinformatics: Normalization, Quality Control, and Assessment of Pre-Analytical Variation
    Article Snippet: .. For each human protein SOMAmer in the plasma 7K assay, we calculated the Spearman’s correlation between the fully normalized RFU values from the adat file provided by SomaLogic using external references (a file designated with the “hybNorm.medNormInt.plateScale.calibrate.anmlQC.qcCheck.anmlSMP” suffix) and the full normalization described here using internal references (“hyb.msnCal.ps.cal.msnAll”). ..



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    a, An in vitro model of senescent monocytes was developed by exposing THP-1 cells to IR and measuring senescence markers. Further quantitative mass spectrometry proteomics was performed on the secretome of these cells using the automated nanoparticle processing and digestion platform, Proteograph. Age association of the differentially secreted proteins was evaluated using proteomic and phenotypic data from the <t>BLSA</t> and InCHIANTI aging cohorts. b, The analysis pipeline used DIA-NN to identify monocyte secretome followed by filtering out the bovine and shared peptides. Peptide quantities from each nanoparticle was rolled up to proteins to determine the differentially expressed proteins.
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    plasma 7k assay  (SomaLogic)
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    Venn diagram showing <t>the</t> <t>SOMAmer</t> overlap (based on “SeqId” identifiers) between the 1.3K, 5K, <t>7K,</t> and 11K SomaScan assays.
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    a, An in vitro model of senescent monocytes was developed by exposing THP-1 cells to IR and measuring senescence markers. Further quantitative mass spectrometry proteomics was performed on the secretome of these cells using the automated nanoparticle processing and digestion platform, Proteograph. Age association of the differentially secreted proteins was evaluated using proteomic and phenotypic data from the BLSA and InCHIANTI aging cohorts. b, The analysis pipeline used DIA-NN to identify monocyte secretome followed by filtering out the bovine and shared peptides. Peptide quantities from each nanoparticle was rolled up to proteins to determine the differentially expressed proteins.

    Journal: medRxiv

    Article Title: A plasma proteomic signature links secretome of senescent monocytes to aging- and obesity-related clinical outcomes in humans

    doi: 10.1101/2024.08.01.24311368

    Figure Lengend Snippet: a, An in vitro model of senescent monocytes was developed by exposing THP-1 cells to IR and measuring senescence markers. Further quantitative mass spectrometry proteomics was performed on the secretome of these cells using the automated nanoparticle processing and digestion platform, Proteograph. Age association of the differentially secreted proteins was evaluated using proteomic and phenotypic data from the BLSA and InCHIANTI aging cohorts. b, The analysis pipeline used DIA-NN to identify monocyte secretome followed by filtering out the bovine and shared peptides. Peptide quantities from each nanoparticle was rolled up to proteins to determine the differentially expressed proteins.

    Article Snippet: This analysis utilized the plasma proteomic data from the BLSA that had been acquired using the 7K SOMAscan Assay (Somalogic Inc., Boulder, CO), which performs 7,288 protein measurements , .

    Techniques: In Vitro, Mass Spectrometry

    Journal: medRxiv

    Article Title: A plasma proteomic signature links secretome of senescent monocytes to aging- and obesity-related clinical outcomes in humans

    doi: 10.1101/2024.08.01.24311368

    Figure Lengend Snippet: Demographic and clinical traits in the Baltimore Longitudinal Study of Aging. Data reported as mean (standard deviation).

    Article Snippet: This analysis utilized the plasma proteomic data from the BLSA that had been acquired using the 7K SOMAscan Assay (Somalogic Inc., Boulder, CO), which performs 7,288 protein measurements , .

    Techniques: Standard Deviation

    a, 1550 Monocyte SASP are detected in the BLSA 7k SomaScan. b, Elastic Net models were trained on 80% of the BLSA cohort and used to predict clinical traits of the remaining 20%. Spearman correlations are shown between the predicted and observed values in the test set for each clinical trait. c, Elastic Net modeling was used for feature selection, and the number of Elastic Net Selected Proteins (ENSPs) implicated in each clinical trait are shown. c, ROC plot comparing the predictive potential (80% train, 20% test) of ENSPs positively associated with BMI to predict obesity with control-only models, showing that ENSPs seem to provide additional predictive potential beyond age and other controls alone.

    Journal: medRxiv

    Article Title: A plasma proteomic signature links secretome of senescent monocytes to aging- and obesity-related clinical outcomes in humans

    doi: 10.1101/2024.08.01.24311368

    Figure Lengend Snippet: a, 1550 Monocyte SASP are detected in the BLSA 7k SomaScan. b, Elastic Net models were trained on 80% of the BLSA cohort and used to predict clinical traits of the remaining 20%. Spearman correlations are shown between the predicted and observed values in the test set for each clinical trait. c, Elastic Net modeling was used for feature selection, and the number of Elastic Net Selected Proteins (ENSPs) implicated in each clinical trait are shown. c, ROC plot comparing the predictive potential (80% train, 20% test) of ENSPs positively associated with BMI to predict obesity with control-only models, showing that ENSPs seem to provide additional predictive potential beyond age and other controls alone.

    Article Snippet: This analysis utilized the plasma proteomic data from the BLSA that had been acquired using the 7K SOMAscan Assay (Somalogic Inc., Boulder, CO), which performs 7,288 protein measurements , .

    Techniques: Selection, Control

    a, Elastic Net models were trained on 80% of the BLSA cohort and used to predict clinical traits of the remaining 20%. Spearman correlations are shown between the predicted and observed values of the test set for each clinical trait. b, Elastic Net modeling was used for feature selection, and the number of Elastic Net Selected Proteins (ENSPs) implicated in each clinical trait are shown. c, ROC plot comparing the predictive potential (80% train, 20% test) of ENSPs positively associated with BMI to predict obesity with control-only models, showing that ENSPs seem to provide additional predictive potential beyond age and other controls alone. d, The correlation between observed waist size and that predicted by Elastic Net Modeling (80% train, 20% test).

    Journal: medRxiv

    Article Title: A plasma proteomic signature links secretome of senescent monocytes to aging- and obesity-related clinical outcomes in humans

    doi: 10.1101/2024.08.01.24311368

    Figure Lengend Snippet: a, Elastic Net models were trained on 80% of the BLSA cohort and used to predict clinical traits of the remaining 20%. Spearman correlations are shown between the predicted and observed values of the test set for each clinical trait. b, Elastic Net modeling was used for feature selection, and the number of Elastic Net Selected Proteins (ENSPs) implicated in each clinical trait are shown. c, ROC plot comparing the predictive potential (80% train, 20% test) of ENSPs positively associated with BMI to predict obesity with control-only models, showing that ENSPs seem to provide additional predictive potential beyond age and other controls alone. d, The correlation between observed waist size and that predicted by Elastic Net Modeling (80% train, 20% test).

    Article Snippet: This analysis utilized the plasma proteomic data from the BLSA that had been acquired using the 7K SOMAscan Assay (Somalogic Inc., Boulder, CO), which performs 7,288 protein measurements , .

    Techniques: Selection, Control

    a, 220 monocyte SASP were detected in both the BLSA (7k SomaScan) and InCHIANTI (1.3k SomaScan). Elastic Net modeling was used for feature selection in both Inchianti and BLSA, and linear models were constructed using only proteins selected in both studies for each trait. Spearman’s correlation of predicted values of linear models trained on the BLSA and observed values in InCHIANTI are shown on the x-axis, and Spearman’s correlation of predicted values of linear models trained on InCHIANTI and observed values in the BLSA are shown on the y-axis b, Binomial models were trained either using controls (age, sex) or controls + ENSPs in BLSA, then used to predict obesity in Inchianti.

    Journal: medRxiv

    Article Title: A plasma proteomic signature links secretome of senescent monocytes to aging- and obesity-related clinical outcomes in humans

    doi: 10.1101/2024.08.01.24311368

    Figure Lengend Snippet: a, 220 monocyte SASP were detected in both the BLSA (7k SomaScan) and InCHIANTI (1.3k SomaScan). Elastic Net modeling was used for feature selection in both Inchianti and BLSA, and linear models were constructed using only proteins selected in both studies for each trait. Spearman’s correlation of predicted values of linear models trained on the BLSA and observed values in InCHIANTI are shown on the x-axis, and Spearman’s correlation of predicted values of linear models trained on InCHIANTI and observed values in the BLSA are shown on the y-axis b, Binomial models were trained either using controls (age, sex) or controls + ENSPs in BLSA, then used to predict obesity in Inchianti.

    Article Snippet: This analysis utilized the plasma proteomic data from the BLSA that had been acquired using the 7K SOMAscan Assay (Somalogic Inc., Boulder, CO), which performs 7,288 protein measurements , .

    Techniques: Selection, Construct

    For a 14-trait panel, proteins were ranked by the number of features for which they were selected via Elastic Net in the BLSA, and the most frequently selected proteins are shown with their cross-trait importance on the x-axis. Only proteins that were positively associated with negative traits such as BMI and CRP, and those that were inversely associated with positive traits such as mobility were selected. Stars indicate those that were also detected in InCHIANTI. b, Linear models were trained on 80% of the BLSA cohort and used to predict clinical traits in the remaining 20%. Spearman’s correlation between the predicted and observed test values are shown. c, Linear models were trained on 80% of the InCHIANTI cohort and used to predict clinical traits in the remaining 20%. Spearman’s correlation between the predicted and observed test values are shown. d, Principal Component Analysis was used to condense the high-impact panel into a composite senescence burden score in the BLSA. Principal Component 1 was used to represent an eigengene for the high impact panel. With the BLSA cohort ranked from low to moderate to high senescence burden, linear trait trends reveal that positive traits HDL and Walking Pace show a negative trend, while negative traits BMI and CRP show a positive trend.

    Journal: medRxiv

    Article Title: A plasma proteomic signature links secretome of senescent monocytes to aging- and obesity-related clinical outcomes in humans

    doi: 10.1101/2024.08.01.24311368

    Figure Lengend Snippet: For a 14-trait panel, proteins were ranked by the number of features for which they were selected via Elastic Net in the BLSA, and the most frequently selected proteins are shown with their cross-trait importance on the x-axis. Only proteins that were positively associated with negative traits such as BMI and CRP, and those that were inversely associated with positive traits such as mobility were selected. Stars indicate those that were also detected in InCHIANTI. b, Linear models were trained on 80% of the BLSA cohort and used to predict clinical traits in the remaining 20%. Spearman’s correlation between the predicted and observed test values are shown. c, Linear models were trained on 80% of the InCHIANTI cohort and used to predict clinical traits in the remaining 20%. Spearman’s correlation between the predicted and observed test values are shown. d, Principal Component Analysis was used to condense the high-impact panel into a composite senescence burden score in the BLSA. Principal Component 1 was used to represent an eigengene for the high impact panel. With the BLSA cohort ranked from low to moderate to high senescence burden, linear trait trends reveal that positive traits HDL and Walking Pace show a negative trend, while negative traits BMI and CRP show a positive trend.

    Article Snippet: This analysis utilized the plasma proteomic data from the BLSA that had been acquired using the 7K SOMAscan Assay (Somalogic Inc., Boulder, CO), which performs 7,288 protein measurements , .

    Techniques:

    Venn diagram showing the SOMAmer overlap (based on “SeqId” identifiers) between the 1.3K, 5K, 7K, and 11K SomaScan assays.

    Journal: bioRxiv

    Article Title: SomaScan Bioinformatics: Normalization, Quality Control, and Assessment of Pre-Analytical Variation

    doi: 10.1101/2024.02.09.579724

    Figure Lengend Snippet: Venn diagram showing the SOMAmer overlap (based on “SeqId” identifiers) between the 1.3K, 5K, 7K, and 11K SomaScan assays.

    Article Snippet: For each human protein SOMAmer in the plasma 7K assay, we calculated the Spearman’s correlation between the fully normalized RFU values from the adat file provided by SomaLogic using external references (a file designated with the “hybNorm.medNormInt.plateScale.calibrate.anmlQC.qcCheck.anmlSMP” suffix) and the full normalization described here using internal references (“hyb.msnCal.ps.cal.msnAll”).

    Techniques:

    Distribution of Spearman’s correlation estimates for the 7,289 human protein SOMAmers in the 7K plasma SomaScan assay, calculated over nearly 1,800 human donor samples from the BLSA . For each SOMAmer, the correlation was calculated between the fully normalized RFU values from SomaLogic’s pipeline using external references and the full normalization described here using internal references.

    Journal: bioRxiv

    Article Title: SomaScan Bioinformatics: Normalization, Quality Control, and Assessment of Pre-Analytical Variation

    doi: 10.1101/2024.02.09.579724

    Figure Lengend Snippet: Distribution of Spearman’s correlation estimates for the 7,289 human protein SOMAmers in the 7K plasma SomaScan assay, calculated over nearly 1,800 human donor samples from the BLSA . For each SOMAmer, the correlation was calculated between the fully normalized RFU values from SomaLogic’s pipeline using external references and the full normalization described here using internal references.

    Article Snippet: For each human protein SOMAmer in the plasma 7K assay, we calculated the Spearman’s correlation between the fully normalized RFU values from the adat file provided by SomaLogic using external references (a file designated with the “hybNorm.medNormInt.plateScale.calibrate.anmlQC.qcCheck.anmlSMP” suffix) and the full normalization described here using internal references (“hyb.msnCal.ps.cal.msnAll”).

    Techniques: Clinical Proteomics