10 research outputs found
Binding Site Characterization of AM1336, a Novel Covalent Inverse Agonist at Human Cannabinoid 2 Receptor, Using Mass Spectrometric Analysis
Cannabinoid 2 receptor (CB2R), a Class-A G-protein coupled receptor
(GPCR), is a promising drug target under a wide array of pathological
conditions. Rational drug design has been hindered due to our poor
understanding of the structural features involved in ligand binding.
Binding of a high-affinity biarylpyrazole inverse agonist AM1336 to
a library of the human CB2 receptor (hCB2R) cysteine-substituted mutants
provided indirect evidence that two cysteines in transmembrane helix-7
(H7) were critical for the covalent attachment. We used proteomics
analysis of the hCB2R with bound AM1336 to directly identify peptides
with covalently attached ligand and applied in silico modeling for
visualization of the ligand–receptor interactions. The hCB2R,
with affinity tags (FlaghCB2His6), was produced in a baculovirus–insect
cell expression system and purified as a functional receptor using
immunoaffinity chromatography. Using mass spectrometry-based bottom-up
proteomic analysis of the hCB2R-AM1336, we identified a peptide with
AM1336 attached to the cysteine C284(7.38) in H7. The hCB2R homology
model in lipid bilayer accommodated covalent attachment of AM1336
to C284(7.38), supporting both biochemical and mass spectrometric
data. This work consolidates proteomics data and in silico modeling
and integrates with our ligand-assisted protein structure (LAPS) experimental
paradigm to assist in structure-based design of cannabinoid antagonist/inverse
agonists
Advanced Precursor Ion Selection Algorithms for Increased Depth of Bottom-Up Proteomic Profiling
Conventional TopN data-dependent
acquisition (DDA) LC–MS/MS
analysis identifies only a limited fraction of all detectable precursors
because the ion-sampling rate of contemporary mass spectrometers is
insufficient to target each precursor in a complex sample. TopN DDA
preferentially targets high-abundance precursors with limited sampling
of low-abundance precursors and repeated analyses only marginally
improve sample coverage due to redundant precursor sampling. In this
work, advanced precursor ion selection algorithms were developed and
applied in the bottom-up analysis of HeLa cell lysate to overcome
the above deficiencies. Precursors fragmented in previous runs were
efficiently excluded using an automatically aligned exclusion list,
which reduced overlap of identified peptides to ∼10% between
replicates. Exclusion of previously fragmented high-abundance peptides
allowed deeper probing of the HeLa proteome over replicate LC–MS
runs, resulting in the identification of 29% more peptides beyond
the saturation level achievable using conventional TopN DDA. The gain
in peptide identifications using the developed approach translated
to the identification of several hundred low-abundance protein groups,
which were not detected by conventional TopN DDA. Exclusion of only
identified peptides compared with the exclusion of all previously
fragmented precursors resulted in an increase of 1000 (∼10%)
additional peptide identifications over four runs, suggesting the
potential for further improvement in the depth of proteomic profiling
using advanced precursor ion selection algorithms
Host Cell Protein Profiling by Targeted and Untargeted Analysis of Data Independent Acquisition Mass Spectrometry Data with Parallel Reaction Monitoring Verification
Host
cell proteins (HCPs) are process-related impurities of biopharmaceuticals
that remain at trace levels despite multiple stages of downstream
purification. Currently, there is interest in implementing LC-MS in
biopharmaceutical HCP profiling alongside conventional ELISA, because
individual species can be identified and quantitated. Conventional
data dependent LC-MS is hampered by the low concentration of HCP-derived
peptides, which are 5–6 orders of magnitude less abundant than
the biopharmaceutical-derived peptides. In this paper, we present
a novel data independent acquisition (DIA)-MS workflow to identify
HCP peptides using automatically combined targeted and untargeted
data processing, followed by verification and quantitation using parallel
reaction monitoring (PRM). Untargeted data processing with DIA-Umpire
provided a means of identifying HCPs not represented in the assay
library used for targeted, peptide-centric, data analysis. An IgG1
monoclonal antibody (mAb) purified by Protein A column elution, cation
exchange chromatography, and ultrafiltration was analyzed using the
workflow with 1D-LC. Five protein standards added at 0.5 to 100 ppm
concentrations were detected in the background of the purified mAb,
demonstrating sensitivity to low ppm levels. A calibration curve was
constructed on the basis of the summed peak areas of the three highest
intensity fragment ions from the highest intensity peptide of each
protein standard. Sixteen HCPs were identified and quantitated on
the basis of the calibration curve over the range of low ppm to over
100 ppm in the purified mAb sample. The developed approach achieves
rapid HCP profiling using 1D-LC and specific identification exploiting
the high mass accuracy and resolution of the mass spectrometer
A Complete Workflow for High Throughput Human Single Skeletal Muscle Fiber Proteomics
Skeletal muscle is a major regulatory tissue of whole-body
metabolism
and is composed of a diverse mixture of cell (fiber) types. Aging
and several diseases differentially affect the various fiber types,
and therefore, investigating the changes in the proteome in a fiber-type
specific manner is essential. Recent breakthroughs in isolated single
muscle fiber proteomics have started to reveal heterogeneity among
fibers. However, existing procedures are slow and laborious, requiring
2 h of mass spectrometry time per single muscle fiber; 50 fibers would
take approximately 4 days to analyze. Thus, to capture the high variability
in fibers both within and between individuals requires advancements
in high throughput single muscle fiber proteomics. Here we use a single
cell proteomics method to enable quantification of single muscle fiber
proteomes in 15 min total instrument time. As proof of concept, we
present data from 53 isolated skeletal muscle fibers obtained from
two healthy individuals analyzed in 13.25 h. Adapting single cell
data analysis techniques to integrate the data, we can reliably separate
type 1 and 2A fibers. Ninety-four proteins were statistically different
between clusters indicating alteration of proteins involved in fatty
acid oxidation, oxidative phosphorylation, and muscle structure and
contractile function. Our results indicate that this method is significantly
faster than prior single fiber methods in both data collection and
sample preparation while maintaining sufficient proteome depth. We
anticipate this assay will enable future studies of single muscle
fibers across hundreds of individuals, which has not been possible
previously due to limitations in throughput
A Complete Workflow for High Throughput Human Single Skeletal Muscle Fiber Proteomics
Skeletal muscle is a major regulatory tissue of whole-body
metabolism
and is composed of a diverse mixture of cell (fiber) types. Aging
and several diseases differentially affect the various fiber types,
and therefore, investigating the changes in the proteome in a fiber-type
specific manner is essential. Recent breakthroughs in isolated single
muscle fiber proteomics have started to reveal heterogeneity among
fibers. However, existing procedures are slow and laborious, requiring
2 h of mass spectrometry time per single muscle fiber; 50 fibers would
take approximately 4 days to analyze. Thus, to capture the high variability
in fibers both within and between individuals requires advancements
in high throughput single muscle fiber proteomics. Here we use a single
cell proteomics method to enable quantification of single muscle fiber
proteomes in 15 min total instrument time. As proof of concept, we
present data from 53 isolated skeletal muscle fibers obtained from
two healthy individuals analyzed in 13.25 h. Adapting single cell
data analysis techniques to integrate the data, we can reliably separate
type 1 and 2A fibers. Ninety-four proteins were statistically different
between clusters indicating alteration of proteins involved in fatty
acid oxidation, oxidative phosphorylation, and muscle structure and
contractile function. Our results indicate that this method is significantly
faster than prior single fiber methods in both data collection and
sample preparation while maintaining sufficient proteome depth. We
anticipate this assay will enable future studies of single muscle
fibers across hundreds of individuals, which has not been possible
previously due to limitations in throughput
A Complete Workflow for High Throughput Human Single Skeletal Muscle Fiber Proteomics
Skeletal muscle is a major regulatory tissue of whole-body
metabolism
and is composed of a diverse mixture of cell (fiber) types. Aging
and several diseases differentially affect the various fiber types,
and therefore, investigating the changes in the proteome in a fiber-type
specific manner is essential. Recent breakthroughs in isolated single
muscle fiber proteomics have started to reveal heterogeneity among
fibers. However, existing procedures are slow and laborious, requiring
2 h of mass spectrometry time per single muscle fiber; 50 fibers would
take approximately 4 days to analyze. Thus, to capture the high variability
in fibers both within and between individuals requires advancements
in high throughput single muscle fiber proteomics. Here we use a single
cell proteomics method to enable quantification of single muscle fiber
proteomes in 15 min total instrument time. As proof of concept, we
present data from 53 isolated skeletal muscle fibers obtained from
two healthy individuals analyzed in 13.25 h. Adapting single cell
data analysis techniques to integrate the data, we can reliably separate
type 1 and 2A fibers. Ninety-four proteins were statistically different
between clusters indicating alteration of proteins involved in fatty
acid oxidation, oxidative phosphorylation, and muscle structure and
contractile function. Our results indicate that this method is significantly
faster than prior single fiber methods in both data collection and
sample preparation while maintaining sufficient proteome depth. We
anticipate this assay will enable future studies of single muscle
fibers across hundreds of individuals, which has not been possible
previously due to limitations in throughput
A Complete Workflow for High Throughput Human Single Skeletal Muscle Fiber Proteomics
Skeletal muscle is a major regulatory tissue of whole-body
metabolism
and is composed of a diverse mixture of cell (fiber) types. Aging
and several diseases differentially affect the various fiber types,
and therefore, investigating the changes in the proteome in a fiber-type
specific manner is essential. Recent breakthroughs in isolated single
muscle fiber proteomics have started to reveal heterogeneity among
fibers. However, existing procedures are slow and laborious, requiring
2 h of mass spectrometry time per single muscle fiber; 50 fibers would
take approximately 4 days to analyze. Thus, to capture the high variability
in fibers both within and between individuals requires advancements
in high throughput single muscle fiber proteomics. Here we use a single
cell proteomics method to enable quantification of single muscle fiber
proteomes in 15 min total instrument time. As proof of concept, we
present data from 53 isolated skeletal muscle fibers obtained from
two healthy individuals analyzed in 13.25 h. Adapting single cell
data analysis techniques to integrate the data, we can reliably separate
type 1 and 2A fibers. Ninety-four proteins were statistically different
between clusters indicating alteration of proteins involved in fatty
acid oxidation, oxidative phosphorylation, and muscle structure and
contractile function. Our results indicate that this method is significantly
faster than prior single fiber methods in both data collection and
sample preparation while maintaining sufficient proteome depth. We
anticipate this assay will enable future studies of single muscle
fibers across hundreds of individuals, which has not been possible
previously due to limitations in throughput
A Complete Workflow for High Throughput Human Single Skeletal Muscle Fiber Proteomics
Skeletal muscle is a major regulatory tissue of whole-body
metabolism
and is composed of a diverse mixture of cell (fiber) types. Aging
and several diseases differentially affect the various fiber types,
and therefore, investigating the changes in the proteome in a fiber-type
specific manner is essential. Recent breakthroughs in isolated single
muscle fiber proteomics have started to reveal heterogeneity among
fibers. However, existing procedures are slow and laborious, requiring
2 h of mass spectrometry time per single muscle fiber; 50 fibers would
take approximately 4 days to analyze. Thus, to capture the high variability
in fibers both within and between individuals requires advancements
in high throughput single muscle fiber proteomics. Here we use a single
cell proteomics method to enable quantification of single muscle fiber
proteomes in 15 min total instrument time. As proof of concept, we
present data from 53 isolated skeletal muscle fibers obtained from
two healthy individuals analyzed in 13.25 h. Adapting single cell
data analysis techniques to integrate the data, we can reliably separate
type 1 and 2A fibers. Ninety-four proteins were statistically different
between clusters indicating alteration of proteins involved in fatty
acid oxidation, oxidative phosphorylation, and muscle structure and
contractile function. Our results indicate that this method is significantly
faster than prior single fiber methods in both data collection and
sample preparation while maintaining sufficient proteome depth. We
anticipate this assay will enable future studies of single muscle
fibers across hundreds of individuals, which has not been possible
previously due to limitations in throughput
EVs detected in plasma from a healthy donor.
<p>Plasma from 5 mL of blood was collected, centrifuged to remove cellular debris (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0144678#sec002" target="_blank">methods</a>), and imaged following a series of dilutions in PBS (A) to test for ‘swarming’. An EV population (based on positioning of 100 nm liposomes and size distribution of plasma samples) from the plasma of a healthy donor was sorted (gate R2; B) and imaged using atomic force microscopy (C). The size distribution of sorted EVs was analyzed by qNano and is represented in D. The gating strategy for these experiments is detailed in the methods section.</p
NanoFCM allows identification of beads and liposomes down to 100 nm.
<p>Separation of a mixture containing 200 and 500 nm latex beads by LSRII (A), and NanoView (B) instruments show more distinct separation with the NanoView Instrument. The NanoView is capable of separating a mixture of 100–500 nm beads into distinct populations (C) and can detect 100 nm liposomes (D). The gating strategy for these experiments to determine instrument and background noise are described in the methods section.</p