4 research outputs found
Complementary RNA and Protein Profiling Identifies Iron as a Key Regulator of Mitochondrial Biogenesis
Mitochondria are centers of metabolism and signaling whose content and function must adapt to changing cellular environments. The biological signals that initiate mitochondrial restructuring and the cellular processes that drive this adaptive response are largely obscure. To better define these systems, we performed matched quantitative genomic and proteomic analyses of mouse muscle cells as they performed mitochondrial biogenesis. We find that proteins involved in cellular iron homeostasis are highly coordinated with this process and that depletion of cellular iron results in a rapid, dose-dependent decrease of select mitochondrial protein levels and oxidative capacity. We further show that this process is universal across a broad range of cell types and fully reversed when iron is reintroduced. Collectively, our work reveals that cellular iron is a key regulator of mitochondrial biogenesis, and provides quantitative data sets that can be leveraged to explore posttranscriptional and posttranslational processes that are essential for mitochondrial adaptation
Automated Gas-Phase Purification for Accurate, Multiplexed Quantification on a Stand-Alone Ion-Trap Mass Spectrometer
Isobaric tagging enables the acquisition of highly multiplexed
proteome quantification, but it is hindered by the pervasive problem
of precursor interference. The elimination of coisolated contaminants
prior to reporter tag generation can be achieved through the use of
gas-phase purification via proton transfer ion/ion reactions (QuantMode);
however, the original QuantMode technique was implemented on the high-resolution
linear ion-trapāOrbitrap hybrid mass spectrometer enabled with
electron transfer dissociation (ETD). Here we extend this technology
to stand-alone linear ion-trap systems (trapQuantMode, trapQM). Facilitated
by the use of inlet beam-type activation (i.e., trapHCD) for production
and observation of the low mass-to-charge reporter region, this scan
sequence comprises three separate events to maximize peptide identifications,
minimize duty cycle requirements, and increase quantitative accuracy,
precision, and dynamic range. Significant improvements in quantitative
accuracy were attained over standard methods when using trapQM to
analyze an interference model system comprising tryptic peptides of
yeast that we contaminated with human peptides. Finally, we demonstrate
practical benefits of this method by analysis of the proteomic changes
that occur during mouse skeletal muscle myoblast differentiation.
While the reduced duty cycle of trapQM led to the identification of
fewer proteins than conventional operation (4050 vs 2964), trapQM
identified more significant differences (>1.5 fold, 1362 vs 1132,
respectively; <i>p</i> < 0.05) between the proteomes
of undifferentiated myoblasts and differentiated myotubes and nearly
10-fold more differences with changes greater than 5-fold (96 vs 12).
We further show that our trapQM dataset is superior for identifying
changes in protein abundance that are consistent with the metabolic
and structural changes known to accompany myotube formation
Multiplexed Quantification for Data-Independent Acquisition
Data-independent
acquisition (DIA) strategies provide a sensitive
and reproducible alternative to data-dependent acquisition (DDA) methods
for large-scale quantitative proteomic analyses. Unfortunately, DIA
methods suffer from incompatibility with common multiplexed quantification
methods, specifically stable isotope labeling approaches such as isobaric
tags and stable isotope labeling of amino acids in cell culture (SILAC).
Here we expand the use of neutron-encoded (NeuCode) SILAC to DIA applications
(NeuCoDIA), producing a strategy that enables multiplexing within
DIA scans without further convoluting the already complex MS<sup>2</sup> spectra. We demonstrate duplex NeuCoDIA analysis of both mixed-ratio
(1:1 and 10:1) yeast and mouse embryo myogenesis proteomes. Analysis
of the mixed-ratio yeast samples revealed the strong accuracy and
precision of our NeuCoDIA method, both of which were comparable to
our established MS<sup>1</sup>-based quantification approach. NeuCoDIA
also uncovered the dynamic protein changes that occur during myogenic
differentiation, demonstrating the feasibility of this methodology
for biological applications. We consequently establish DIA quantification
of NeuCode SILAC as a useful and practical alternative to DDA-based
approaches