MITO-Tag Mice enable rapid isolation and multimodal profiling of mitochondria from specific cell types in vivo
Authors: Caroline A Lewis
Kƒ±van√ß Birsoy
Sze Ham Chan
Monther Abu-Remaileh
Erol C Bayraktar
Andrew L Cangelosi
Tenzin Kunchok
David M Sabatini
Ceren √ñzerdem
Lou Baudrier
Walter W Chen
Journal: Proceedings of the National Academy of Sciences of the United States of America (2019)
Discipline: Multidisciplinary
Keywords: MITO-Tag Mice; lipidomics; metabolomics; mitochondria; proteomics.
Affiliations: Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY 10065., Whitehead Institute for Biomedical Research, Cambridge, MA 02142., Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02139., Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139., Koch Institute for Integrative Cancer Research, Cambridge, MA 02139., Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142., Whitehead Institute for Biomedical Research, Cambridge, MA 02142;   ., Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY 10065;   .

Abstract:  Mitochondria are metabolic organelles that are essential for mammalian life, but the dynamics of mitochondrial metabolism within mammalian tissues in vivo remains incompletely understood. While whole-tissue metabolite profiling has been useful for studying metabolism in vivo, such an approach lacks resolution at the cellular and subcellular level. In vivo methods for interrogating organellar metabolites in specific cell types within mammalian tissues have been limited. To address this, we built on prior work in which we exploited a mitochondrially localized 3XHA epitope tag (MITO-Tag) for the fast isolation of mitochondria from cultured cells to generate MITO-Tag Mice. Affording spatiotemporal control over MITO-Tag expression, these transgenic animals enable the rapid, cell-type-specific immunoisolation of mitochondria from tissues, which we verified using a combination of proteomic and metabolomic approaches. Using MITO-Tag Mice and targeted and untargeted metabolite profiling, we identified changes during fasted and refed conditions in a diverse array of mitochondrial metabolites in hepatocytes and found metabolites that behaved differently at the mitochondrial versus whole-tissue level. MITO-Tag Mice should have utility for studying mitochondrial physiology, and our strategy should be generally applicable for studying other mammalian organelles in specific cell types in vivo. 

Retracted: No

PubPeer comments:
 There are a number of duplicated values across metabolites and treatments, as well as a number of near-duplicated values. I am posting some examples below. This is not an exhaustive list, I could find many others duplicate values between metabolites. Given that the data represents different metabolites, it s not clear why these patterns exist. Could the authors please explain?    , 
 Dear Paphiopedilum Venustum, Regarding your comments about the metabolomics data, we have examined our original data and found that everything is consistent with the metabolomic workflow that was used for this study and our reply is presented below: The lipidomic interrogation of mitochondria done in this work relies on automated peak calling and analysis by a program known as LipidSearch. As described in our Materials and Methods: u201cHigh-throughput identification and relative quantification of lipids was performed separately for positive and negative ionization mode data, using LipidSearch software (Thermo Fisher Scientific Mitsui Knowledge Industries)(32, 33) with the default parameters for QExactive product search and alignment.u201d LipidSearch uses an automated peak-picking and alignment algorithm and searches against an in-silico spectral database for lipid identification. This automatic process is known in the metabolomics field to be imperfect, such as when there are lipid isomers that are co-eluting closely-eluting, which is the case for the metabolites you have highlighted. As a result, when using this automatic process, it can lead to the results you have commented on and instances in which different metabolites are reported to have the same integrated peak values across certain samples. This is why it is important to manually inspect, in the data that the software outputs, any metabolites that one may want to draw conclusions from. Indeed, for all lipids that we drew conclusions from, namely the cardiolipins as shown in Fig. 2B, we not only manually inspected these cardiolipins but also only chose the cardiolipins that received the top grade u201cA.u201d All of this is detailed in the Materials and Methods: u201cCardiolipins were validated by manually checking the peak alignment and matching the MS MS spectra to the characteristic fragmentation patterns found in the LIPIDMAPS database (http:www.lipidmaps.org), as well as those described previously (34). Validated cardiolipin species were reanalyzed using Xcalibur Quanbrowser 4.1 (Thermo Fisher Scientific) and graded based on the quality of MS MS spectra collected: Grade Au2014CL annotation is correct, and all four acyl chains are correctly annotated; Grade Bu20143 4 acyl chains are correctly annotated; Grade Cu20142 4 acyl chains are correctly annotated; and Grade Du20141 4 acyl chains are correctly annotated or CL species were confirmed through the presence of head group, but acyl chain identification is ambiguous.u201d We provided these metabolomics data in the spirit of transparency, but, as is worth emphasizing once more, for either polar or lipidomic analysis, there needs to be manual inspection of peaks for any metabolites of interest and this was done for all such metabolites in this work. We believe this reply addresses your comments. Sincerely, Walter Chen ,