bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Апрель 26, 2024
Abstract
Recent
advances
have
allowed
for
the
detection
of
10,000
proteins
from
cultured
human
cell
samples,
such
as
HeLa
and
HEK293T
cells
in
a
single-shot
proteome
analysis.
However,
deeper
analysis
remains
challenging.
Therefore,
this
study,
we
aimed
to
perform
deep
proteomic
using
timsTOF
HT.
To
achieve
proteomics,
developed
Thin-diaPASEF,
parallel
accumulation-serial
fragmentation
(PASEF)
technology
featuring
thinly
divided
m/z
axis
only
regions
high
ion
density.
Furthermore,
60-cm
long
C18
column
with
particle
size
1.7
µm,
an
average
11,698,
11,615
11,019
unique
were
successfully
detected
500
ng
HEK293T,
K562
digests,
respectively,
100
min
active
gradient.
The
same
system
was
used
analyze
Lycopersicon
esculentum
lectin
(LEL)
enriched
plasma
serum.
LEL
method
identified
8,613
4,078
proteins,
serum,
respectively.
Our
ultra-deep
will
be
helpful
in-depth
comparison
medical
biological
research
because
it
enables
highly
coverage
single-shot.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2023,
Номер
unknown
Опубликована: Июнь 3, 2023
We
evaluate
the
quantitative
performance
of
newly
released
Asymmetric
Track
Lossless
(Astral)
analyzer.
Using
data
independent
acquisition,
Thermo
Scientific™
Orbitrap™
Astral™
mass
spectrometer
quantifies
5
times
more
peptides
per
unit
time
than
state-of-the-art
spectrometers,
which
have
long
been
gold
standard
for
high
resolution
proteomics.
Our
results
demonstrate
that
Orbitrap
Astral
can
produce
quality
measurements
across
a
wide
dynamic
range.
also
use
developed
extra-cellular
vesicle
enrichment
protocol
to
reach
new
depths
coverage
in
plasma
proteome,
quantifying
over
5,000
proteins
60-minute
gradient
with
spectrometer.
Nature Communications,
Год журнала:
2023,
Номер
14(1)
Опубликована: Ноя. 18, 2023
Abstract
Trapped
ion
mobility
spectrometry
(TIMS)
adds
an
additional
separation
dimension
to
mass
(MS)
imaging,
however,
the
lack
of
fragmentation
spectra
(MS
2
)
impedes
confident
compound
annotation
in
spatial
metabolomics.
Here,
we
describe
mobility-scheduled
exhaustive
(SIMSEF),
a
dataset-dependent
acquisition
strategy
that
augments
TIMS-MS
imaging
datasets
with
MS
spectra.
The
experiments
are
systematically
distributed
across
sample
and
scheduled
for
multiple
collision
energies
per
precursor
ion.
Extendable
data
processing
evaluation
workflows
implemented
into
open
source
software
MZmine.
workflow
capabilities
demonstrated
on
rat
brain
tissue
thin
sections,
measured
by
matrix-assisted
laser
desorption/ionisation
(MALDI)-TIMS-MS,
where
SIMSEF
enables
on-tissue
through
spectral
library
matching
rule-based
lipid
within
MZmine
maps
(un)known
chemical
space
molecular
networking.
algorithm
analysis
pipelines
modular
provide
community
resource.
Journal of Proteome Research,
Год журнала:
2024,
Номер
23(6), С. 2078 - 2089
Опубликована: Апрель 26, 2024
Data-independent
acquisition
(DIA)
has
become
a
well-established
method
for
MS-based
proteomics.
However,
the
list
of
options
to
analyze
this
type
data
is
quite
extensive,
and
use
spectral
libraries
an
important
factor
in
DIA
analysis.
More
specifically
silico
predicted
gaining
more
interest.
By
working
with
differential
spike-in
human
standard
proteins
(UPS2)
constant
yeast
tryptic
digest
background,
we
evaluated
sensitivity,
precision,
accuracy
analysis
workflows
compared
established
workflows.
Three
commonly
used
software
tools,
DIA-NN,
EncyclopeDIA,
Spectronaut,
were
each
tested
library
mode
library-free
mode.
In
mode,
independent
prediction
tools
PROSIT
MS2PIP
together
DeepLC,
next
classical
data-dependent
(DDA)-based
libraries.
total,
benchmarked
12
computational
DIA.
Our
comparison
showed
that
DIA-NN
reached
highest
sensitivity
while
maintaining
good
compromise
on
reproducibility
levels
either
or
using
pointing
general
benefit
Journal of Proteome Research,
Год журнала:
2024,
Номер
23(6), С. 2306 - 2314
Опубликована: Апрель 29, 2024
With
the
increased
usage
and
diversity
of
methods
instruments
being
applied
to
analyze
Data-Independent
Acquisition
(DIA)
data,
visualization
is
becoming
increasingly
important
validate
automated
software
results.
Here
we
present
MassDash,
a
cross-platform
DIA
mass
spectrometry
validation
for
comparing
features
results
across
popular
tools.
MassDash
provides
web-based
interface
Python
package
interactive
feature
visualizations
summary
report
plots
multiple
detection
tools,
including
OpenSwath,
DIA-NN,
dreamDIA.
Furthermore,
processes
peptides
on
fly,
enabling
dozens
runs
simultaneously
personal
computer.
supports
various
multidimensional
retention
time,
ion
mobility,
m/z,
intensity,
providing
additional
insights
into
data.
The
modular
framework
easily
extendable,
rapid
algorithm
development
novel
peak-picker
techniques,
such
as
deep-learning-based
approaches
refinement
existing
open-source
under
BSD
3-Clause
license
freely
available
at
https://github.com/Roestlab/massdash,
demo
version
can
be
accessed
https://massdash.streamlit.app.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Апрель 26, 2024
Abstract
Recent
advances
have
allowed
for
the
detection
of
10,000
proteins
from
cultured
human
cell
samples,
such
as
HeLa
and
HEK293T
cells
in
a
single-shot
proteome
analysis.
However,
deeper
analysis
remains
challenging.
Therefore,
this
study,
we
aimed
to
perform
deep
proteomic
using
timsTOF
HT.
To
achieve
proteomics,
developed
Thin-diaPASEF,
parallel
accumulation-serial
fragmentation
(PASEF)
technology
featuring
thinly
divided
m/z
axis
only
regions
high
ion
density.
Furthermore,
60-cm
long
C18
column
with
particle
size
1.7
µm,
an
average
11,698,
11,615
11,019
unique
were
successfully
detected
500
ng
HEK293T,
K562
digests,
respectively,
100
min
active
gradient.
The
same
system
was
used
analyze
Lycopersicon
esculentum
lectin
(LEL)
enriched
plasma
serum.
LEL
method
identified
8,613
4,078
proteins,
serum,
respectively.
Our
ultra-deep
will
be
helpful
in-depth
comparison
medical
biological
research
because
it
enables
highly
coverage
single-shot.
