Single-nucleus proteomics identifies regulators of protein transport
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Июнь 18, 2024
The
physiological
response
of
a
cell
to
stimulation
depends
on
its
proteome
configuration.
Therefore,
the
abundance
variation
regulatory
proteins
across
unstimulated
single
cells
can
be
associatively
linked
with
their
stimulation.
Here
we
developed
an
approach
that
leverages
this
association
individual
and
nuclei
systematically
identify
potential
regulators
biological
processes,
followed
by
targeted
validation.
Specifically,
applied
nucleocytoplasmic
protein
transport
in
macrophages
stimulated
lipopolysaccharide
(LPS).
To
end,
quantified
proteomes
3,412
nuclei,
sampling
dynamic
LPS
treatment,
linking
functional
variability
proteomic
variability.
Minutes
after
stimulation,
correlated
strongly
known
regulators,
thus
revealing
impact
natural
cellular
response.
We
found
simple
biophysical
constraints,
such
as
quantity
nuclear
pores,
partially
explain
LPS-induced
transport.
Among
many
newly
identified
associated
response,
selected
16
for
validation
knockdown.
knockdown
phenotypes
confirmed
inferences
derived
from
demonstrating
(sub-)single-cell
proteomics
infer
regulation.
expect
generalize
broad
applications
enhance
interpretability
single-cell
omics
data.
Язык: Английский
Revealing the dynamics of fungal disease with proteomics
Molecular Omics,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 1, 2025
The
occurrence
and
distribution
of
new
re-emerging
fungal
pathogens,
along
with
rates
antifungal
resistance,
are
rising
across
the
globe,
correspondingly,
so
our
awareness
call
for
action
to
address
this
public
health
concern.
To
effectively
detect,
monitor,
treat
infections,
biological
insights
into
mechanisms
that
regulate
pathogenesis,
influence
survival,
promote
resistance
urgently
needed.
Mass
spectrometry-based
proteomics
is
a
high-resolution
technique
enables
identification
quantification
proteins
diverse
systems
better
understand
biology
driving
phenotypes.
In
review,
we
highlight
dynamic
innovative
applications
characterize
three
critical
pathogens
(i.e.,
Candida
spp.,
Cryptococcus
Aspergillus
spp.)
causing
disease
in
humans.
We
present
strategies
investigate
host-pathogen
interface,
virulence
factor
production,
protein-level
drivers
resistance.
Through
these
studies,
opportunities
biomarker
development,
drug
target
discovery,
immune
system
remodeling
discussed,
supporting
use
combat
plethora
diseases
threatening
global
health.
Язык: Английский
Global analysis of protein turnover dynamics in single cells
Cell,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 1, 2025
Single-cell
proteomics
(SCPs)
has
advanced
significantly,
yet
it
remains
largely
unidimensional,
focusing
primarily
on
protein
abundances.
In
this
study,
we
employed
a
pulsed
stable
isotope
labeling
by
amino
acids
in
cell
culture
(pSILAC)
approach
to
simultaneously
analyze
abundance
and
turnover
single
cells
(SC-pSILAC).
Using
state-of-the-art
SCP
workflow,
demonstrated
that
two
SILAC
labels
are
detectable
from
∼4,000
proteins
HeLa
recapitulating
known
biology.
We
performed
large-scale
time-series
SC-pSILAC
analysis
of
undirected
differentiation
human
induced
pluripotent
stem
(iPSCs)
encompassing
6
sampling
times
over
2
months
analyzed
>1,000
cells.
Protein
dynamics
highlighted
differentiation-specific
co-regulation
complexes
with
core
histone
turnover,
discriminating
dividing
non-dividing
Lastly,
correlating
diameter
the
individual
showed
histones
some
cell-cycle
do
not
scale
size.
The
method
provides
multidimensional
view
single-cell
Язык: Английский
The plant proteome delivers from discovery to innovation
Trends in Plant Science,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 1, 2025
Язык: Английский
A Comprehensive and Robust Multiplex-DIA Workflow Profiles Protein Turnover Regulations Associated with Cisplatin Resistance
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 31, 2024
Summary
Measuring
protein
turnover
is
essential
for
understanding
cellular
biological
processes
and
advancing
drug
discovery.
The
multiplex
DIA
mass
spectrometry
(DIA-MS)
approach,
combined
with
dynamic
SILAC
labeling
(pulse-SILAC,
or
pSILAC),
has
proven
to
be
a
reliable
method
analyzing
degradation
kinetics.
Previous
DIA-MS
workflows
have
employed
various
strategies,
including
leveraging
the
highest
isotopic
channels
of
peptides
enhance
detection
MS
signal
pairs
clusters.
In
this
study,
we
introduce
an
improved
robust
workflow
that
integrates
novel
machine
learning
strategy
channel-specific
statistical
filtering,
enabling
adaptation
systematic
temporal
variations
in
channel
ratios.
This
allows
comprehensive
profiling
throughout
pSILAC
experiment
without
relying
solely
on
signals.
Additionally,
developed
KdeggeR
,
data
processing
analysis
package
optimized
pSILAC-DIA
experiments,
which
estimates
visualizes
peptide
rates
profiles.
Our
integrative
was
benchmarked
both
2-channel
3-channel
standard
datasets
aneuploid
cancer
cell
model
before
after
cisplatin
resistance
development
demonstrated
strong
negative
correlation
between
transcript
regulation
major
complex
subunits.
We
also
identified
specific
signatures
associated
resistance.
Язык: Английский