6 research outputs found
Extending the Compatibility of the SP3 Paramagnetic Bead Processing Approach for Proteomics
The diversity in
protein and peptide biochemistry necessitates
robust protocols and reagents for efficiently handling and enriching
these molecules prior to analysis with mass spectrometry (MS) or other
techniques. Further exploration of the paramagnetic bead-based approach,
single-pot solid-phase-enhanced sample preparation (SP3), is carried
out toward updating and extending previously described conditions
and experimental workflows. The SP3 approach was tested in a wide
range of experimental scenarios, including (1) binding solvents (acetonitrile,
ethanol, isopropanol, acetone), (2) binding pH (acidic vs neutral),
(3) solvent/lysate ratios (50–200%, v/v), (4) mixing and rinsing
conditions (on-rack vs off-rack rinsing), (5) Enrichment of nondenatured
proteins, and (6) capture of individual proteins from noncomplex mixtures.
These results highlight the robust handling of proteins in a broad
set of scenarios while also enabling the development of a modified
SP3 workflow that offers extended compatibility. The modified SP3
approach is used in quantitative in-depth proteome analyses to compare
it with commercial paramagnetic bead-based HILIC methods (MagReSyn)
and across multiple binding conditions (e.g., pH and solvent during
binding). Together, these data reveal the extensive quantitative coverage
of the proteome possible with SP3 independent of the binding approach
utilized. The results further establish the utility of SP3 for the
unbiased handling of peptides and proteins for proteomic applications
Extending the Compatibility of the SP3 Paramagnetic Bead Processing Approach for Proteomics
The diversity in
protein and peptide biochemistry necessitates
robust protocols and reagents for efficiently handling and enriching
these molecules prior to analysis with mass spectrometry (MS) or other
techniques. Further exploration of the paramagnetic bead-based approach,
single-pot solid-phase-enhanced sample preparation (SP3), is carried
out toward updating and extending previously described conditions
and experimental workflows. The SP3 approach was tested in a wide
range of experimental scenarios, including (1) binding solvents (acetonitrile,
ethanol, isopropanol, acetone), (2) binding pH (acidic vs neutral),
(3) solvent/lysate ratios (50–200%, v/v), (4) mixing and rinsing
conditions (on-rack vs off-rack rinsing), (5) Enrichment of nondenatured
proteins, and (6) capture of individual proteins from noncomplex mixtures.
These results highlight the robust handling of proteins in a broad
set of scenarios while also enabling the development of a modified
SP3 workflow that offers extended compatibility. The modified SP3
approach is used in quantitative in-depth proteome analyses to compare
it with commercial paramagnetic bead-based HILIC methods (MagReSyn)
and across multiple binding conditions (e.g., pH and solvent during
binding). Together, these data reveal the extensive quantitative coverage
of the proteome possible with SP3 independent of the binding approach
utilized. The results further establish the utility of SP3 for the
unbiased handling of peptides and proteins for proteomic applications
Parsing and Quantification of Raw Orbitrap Mass Spectrometer Data Using RawQuant
Effective
analysis of protein samples by mass spectrometry (MS)
requires careful selection and optimization of a range of experimental
parameters. As the output from the primary detection device, the “raw”
MS data file can be used to gauge the success of a given sample analysis.
However, the closed-source nature of the standard raw MS file can
complicate effective parsing of the data contained within. To ease
and increase the range of analyses possible, the RawQuant tool was
developed to enable parsing of raw MS files derived from Thermo Orbitrap
instruments to yield meta and scan data in an openly readable text
format. RawQuant can be commanded to export user-friendly files containing
MS<sup>1</sup>, MS<sup>2</sup>, and MS<sup>3</sup> metadata as well
as matrices of quantification values based on isobaric tagging approaches.
In this study, the utility of RawQuant is demonstrated in several
scenarios: (1) reanalysis of shotgun proteomics data for the identification
of the human proteome, (2) reanalysis of experiments utilizing isobaric
tagging for whole-proteome quantification, and (3) analysis of a novel
bacterial proteome and synthetic peptide mixture for assessing quantification
accuracy when using isobaric tags. Together, these analyses successfully
demonstrate RawQuant for the efficient parsing and quantification
of data from raw Thermo Orbitrap MS files acquired in a range of common
proteomics experiments. In addition, the individual analyses using
RawQuant highlights parametric considerations in the different experimental
sets and suggests targetable areas to improve depth of coverage in
identification-focused studies and quantification accuracy when using
isobaric tags
Parsing and Quantification of Raw Orbitrap Mass Spectrometer Data Using RawQuant
Effective
analysis of protein samples by mass spectrometry (MS)
requires careful selection and optimization of a range of experimental
parameters. As the output from the primary detection device, the “raw”
MS data file can be used to gauge the success of a given sample analysis.
However, the closed-source nature of the standard raw MS file can
complicate effective parsing of the data contained within. To ease
and increase the range of analyses possible, the RawQuant tool was
developed to enable parsing of raw MS files derived from Thermo Orbitrap
instruments to yield meta and scan data in an openly readable text
format. RawQuant can be commanded to export user-friendly files containing
MS<sup>1</sup>, MS<sup>2</sup>, and MS<sup>3</sup> metadata as well
as matrices of quantification values based on isobaric tagging approaches.
In this study, the utility of RawQuant is demonstrated in several
scenarios: (1) reanalysis of shotgun proteomics data for the identification
of the human proteome, (2) reanalysis of experiments utilizing isobaric
tagging for whole-proteome quantification, and (3) analysis of a novel
bacterial proteome and synthetic peptide mixture for assessing quantification
accuracy when using isobaric tags. Together, these analyses successfully
demonstrate RawQuant for the efficient parsing and quantification
of data from raw Thermo Orbitrap MS files acquired in a range of common
proteomics experiments. In addition, the individual analyses using
RawQuant highlights parametric considerations in the different experimental
sets and suggests targetable areas to improve depth of coverage in
identification-focused studies and quantification accuracy when using
isobaric tags