Grant Details
Grant Number: |
5U01CA235482-03 Interpret this number |
Primary Investigator: |
Patti, Gary |
Organization: |
Washington University |
Project Title: |
A Comprehensive Platform for High-Throughput Profiling of the Human Reference Metabolome |
Fiscal Year: |
2020 |
Abstract
Project Summary
The last decade has seen two complementary trends: (i) technology to perform untargeted metabolomics
with liquid chromatography/mass spectrometry (LC/MS) has become readily available to most investigators,
and (ii) interest in metabolism has continued to heighten in many disparate research fields ranging from cancer
and immunology to neuroscience and aging. Accordingly, the number of investigators who are acquiring
untargeted metabolomic data with LC/MS is dramatically increasing. Yet, informatic tools to analyze the
acquired data have lagged far behind and interpretation of the results remains a serious challenge, even for
experienced users. Thus, there is a substantial number of investigators performing untargeted metabolomics
with LC/MS who either cannot interpret the data generated or, even worse, are interpreting it incorrectly.
When untargeted metabolomics is performed on a typical biological sample, it is common to detect
thousands to tens of thousands of signals (aka features). Translating these signals into metabolite names is
the biggest informatic barrier limiting biomedical applications of the technology. The process is arduous,
particularly for inexperienced investigators, because the majority of signals detected do not correspond to non-
redundant metabolites originating from the biological sample. Rather, most signals (up to 95% in some of our
experiments) are due to complicating factors such as contaminants, artifacts, fragments, etc. Because many of
these complicating signals are not currently in metabolomic databases such as METLIN, they can be
challenging to annotate for inexperienced users. While there are software programs available to annotate the
signals within the data, these tools are beyond the reach of most clinical and biological investigators because
(i) they are not automated with a graphical user interface, and (ii) they rely on a costly experimental design
involving isotopes to find contaminants and artifacts.
We propose to develop an automated solution to name and quantify most of the metabolites detected in
untargeted metabolomic LC/MS experiments. Our strategy is to assume the computational burden of
completely annotating all detected metabolites in untargeted metabolomic data, which only has to be
performed once for a given sample type, so that less-experienced investigators do not have to in their future
experiments. We will completely annotate untargeted metabolomic data sets from different biological samples
using the mz.unity software and credentialing technology developed by the Patti lab. Based on experiments
that we have already performed, we expect to find ~5,000 unique bonafide metabolites per sample. We will
then use these endogenous signals to develop targeted LC/MS methods that enable automated analysis of all
detectable metabolites (i.e., the “reference metabolome”). This will allow investigators with minimal expertise in
metabolomics to profile the unique and bonafide metabolites in their samples at an untargeted scale, but
without informatic barriers that have historically limited progress in the field.
Publications
Chemical Discovery in the Era of Metabolomics.
Authors: Sindelar M.
, Patti G.J.
.
Source: Journal of the American Chemical Society, 2020-05-20; 142(20), p. 9097-9105.
EPub date: 2020-05-11.
PMID: 32275430
Related Citations
The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution.
Authors: Rozenblatt-Rosen O.
, Regev A.
, Oberdoerffer P.
, Nawy T.
, Hupalowska A.
, Rood J.E.
, Ashenberg O.
, Cerami E.
, Coffey R.J.
, Demir E.
, et al.
.
Source: Cell, 2020-04-16; 181(2), p. 236-249.
PMID: 32302568
Related Citations
Leveraging insights into cancer metabolism-a symposium report.
Authors: Cable J.
, Finley L.
, Tu B.P.
, Patti G.J.
, Oliver T.G.
, Vardhana S.
, Mana M.
, Ericksen R.
, Khare S.
, DeBerardinis R.
, et al.
.
Source: Annals of the New York Academy of Sciences, 2020 02; 1462(1), p. 5-13.
EPub date: 2019-12-02.
PMID: 31792987
Related Citations
Dose-Response Metabolomics To Understand Biochemical Mechanisms and Off-Target Drug Effects with the TOXcms Software.
Authors: Yao C.H.
, Wang L.
, Stancliffe E.
, Sindelar M.
, Cho K.
, Yin W.
, Wang Y.
, Patti G.J.
.
Source: Analytical chemistry, 2020-01-21; 92(2), p. 1856-1864.
EPub date: 2020-01-07.
PMID: 31804057
Related Citations
International Ring Trial of a High Resolution Targeted Metabolomics and Lipidomics Platform for Serum and Plasma Analysis.
Authors: Thompson J.W.
, Adams K.J.
, Adamski J.
, Asad Y.
, Borts D.
, Bowden J.A.
, Byram G.
, Dang V.
, Dunn W.B.
, Fernandez F.
, et al.
.
Source: Analytical chemistry, 2019-11-19; 91(22), p. 14407-14416.
EPub date: 2019-11-08.
PMID: 31638379
Related Citations
Perspectives on Data Analysis in Metabolomics: Points of Agreement and Disagreement from the 2018 ASMS Fall Workshop.
Authors: Baker E.S.
, Patti G.J.
.
Source: Journal of the American Society for Mass Spectrometry, 2019 Oct; 30(10), p. 2031-2036.
EPub date: 2019-08-22.
PMID: 31440979
Related Citations
Systems-level analysis of isotopic labeling in untargeted metabolomic data by X13CMS.
Authors: Llufrio E.M.
, Cho K.
, Patti G.J.
.
Source: Nature protocols, 2019 07; 14(7), p. 1970-1990.
EPub date: 2019-06-05.
PMID: 31168088
Related Citations
Hepatocyte-Macrophage Acetoacetate Shuttle Protects against Tissue Fibrosis.
Authors: Puchalska P.
, Martin S.E.
, Huang X.
, Lengfeld J.E.
, Daniel B.
, Graham M.J.
, Han X.
, Nagy L.
, Patti G.J.
, Crawford P.A.
.
Source: Cell metabolism, 2019-02-05; 29(2), p. 383-398.e7.
EPub date: 2018-11-15.
PMID: 30449686
Related Citations
Mitochondrial fusion supports increased oxidative phosphorylation during cell proliferation.
Authors: Yao C.H.
, Wang R.
, Wang Y.
, Kung C.P.
, Weber J.D.
, Patti G.J.
.
Source: eLife, 2019-01-29; 8, .
EPub date: 2019-01-29.
PMID: 30694178
Related Citations
A Protocol to Compare Methods for Untargeted Metabolomics.
Authors: Wang L.
, Naser F.J.
, Spalding J.L.
, Patti G.J.
.
Source: Methods in molecular biology (Clifton, N.J.), 2019; 1862, p. 1-15.
PMID: 30315456
Related Citations
Isotope Tracing Untargeted Metabolomics Reveals Macrophage Polarization-State-Specific Metabolic Coordination across Intracellular Compartments.
Authors: Puchalska P.
, Huang X.
, Martin S.E.
, Han X.
, Patti G.J.
, Crawford P.A.
.
Source: iScience, 2018-11-30; 9, p. 298-313.
EPub date: 2018-11-02.
PMID: 30448730
Related Citations
Metabolic and Transcriptional Modules Independently Diversify Plasma Cell Lifespan and Function.
Authors: Lam W.Y.
, Jash A.
, Yao C.H.
, D'Souza L.
, Wong R.
, Nunley R.M.
, Meares G.P.
, Patti G.J.
, Bhattacharya D.
.
Source: Cell reports, 2018-08-28; 24(9), p. 2479-2492.e6.
PMID: 30157439
Related Citations
Transport-exclusion pharmacology to localize lactate dehydrogenase activity within cells.
Authors: Niu X.
, Chen Y.J.
, Crawford P.A.
, Patti G.J.
.
Source: Cancer & metabolism, 2018; 6, p. 19.
EPub date: 2018-12-12.
PMID: 30559963
Related Citations