Nature Communications,
Год журнала:
2023,
Номер
14(1)
Опубликована: Ноя. 24, 2023
Abstract
The
spatial
organisation
of
cellular
protein
expression
profiles
within
tissue
determines
function
and
is
key
to
understanding
disease
pathology.
To
define
molecular
phenotypes
in
the
context
tissue,
there
a
need
for
unbiased,
quantitative
technology
capable
mapping
proteomes
structures.
Here,
we
present
workflow
spatially-resolved,
proteomics
that
generates
maps
abundance
across
slices
derived
from
human
atypical
teratoid-rhabdoid
tumour
at
three
resolutions,
highest
being
40
µm,
reveal
distinct
patterns
thousands
proteins.
We
employ
spatially-aware
algorithms
do
not
require
prior
knowledge
fine
structure
detect
proteins
pathways
with
correlate
heterogeneity
features
such
as
extracellular
matrix
or
proximity
blood
vessels.
identify
PYGL,
ASPH
CD45
markers
boundary
immune
response-driven,
spatially-organised
networks
matrix.
Overall,
demonstrate
deep
proteo-phenotyping
heterogeneity,
re-define
biology
pathology
level.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Июнь 2, 2024
Abstract
Mass
spectrometry
(MS)-based
proteomics
continues
to
evolve
rapidly,
opening
more
and
application
areas.
The
scale
of
data
generated
on
novel
instrumentation
acquisition
strategies
pose
a
challenge
bioinformatic
analysis.
Search
engines
need
make
optimal
use
the
for
biological
discoveries
while
remaining
statistically
rigorous,
transparent
performant.
Here
we
present
alphaDIA,
modular
open-source
search
framework
independent
(DIA)
proteomics.
We
developed
feature-free
identification
algorithm
particularly
suited
detecting
patterns
in
produced
by
sensitive
time-of-flight
instruments.
It
naturally
adapts
novel,
eTicient
scan
modes
that
are
not
yet
accessible
previous
algorithms.
Rigorous
benchmarking
demonstrates
competitive
quantification
performance.
While
supporting
empirical
spectral
libraries,
propose
new
strategy
named
end-to-end
transfer
learning
using
fully
predicted
libraries.
This
entails
continuously
optimizing
deep
neural
network
predicting
machine
experiment
specific
properties,
enabling
generic
DIA
analysis
any
post-translational
modification
(PTM).
AlphaDIA
provides
high
performance
running
locally
or
cloud,
community.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Июнь 2, 2024
Abstract
Even
with
recent
improvements
in
sample
preparation
and
instrumentation,
single-cell
proteomics
(SCP)
analyses
mostly
measure
protein
abundances,
making
the
field
unidimensional.
In
this
study,
we
employ
a
pulsed
stable
isotope
labeling
by
amino
acids
cell
culture
(SILAC)
approach
to
simultaneously
evaluate
abundance
turnover
single
cells
(SC-pSILAC).
Using
state-of-the-art
SCP
workflow,
demonstrated
that
two
SILAC
labels
are
detectable
from
∼4000
proteins
HeLa
recapitulating
known
biology.
We
investigated
drug
effects
on
global
specific
performed
large-scale
time-series
SC-pSILAC
analysis
of
undirected
differentiation
human
induced
pluripotent
stem
(iPSC)
encompassing
six
sampling
times
over
months
analyzed
>1000
cells.
Abundance
measurements
highlighted
cell-specific
markers
various
organ-specific
types.
Protein
dynamics
differentiation-specific
co-regulation
core
members
complexes
histone
discriminating
dividing
non-dividing
potential
cancer
research.
Our
study
represents
most
comprehensive
date,
offering
new
insights
into
cellular
diversity
pioneering
functional
beyond
abundance.
This
method
distinguishes
other
omics
approaches
enhances
its
scientific
relevance
biological
research
multidimensional
manner.
Cell Genomics,
Год журнала:
2024,
Номер
4(11), С. 100691 - 100691
Опубликована: Ноя. 1, 2024
SummaryThe
insufficient
availability
of
comprehensive
protein-level
perturbation
data
is
impeding
the
widespread
adoption
systems
biology.
In
this
perspective,
we
introduce
rationale,
essentiality,
and
practicality
proteomics.
Biological
are
perturbed
with
diverse
biological,
chemical,
and/or
physical
factors,
followed
by
proteomic
measurements
at
various
levels,
including
changes
in
protein
expression
turnover,
post-translational
modifications,
interactions,
transport,
localization,
along
phenotypic
data.
Computational
models,
employing
traditional
machine
learning
or
deep
learning,
identify
predict
responses,
mechanisms
action,
functions,
aiding
therapy
selection,
compound
design,
efficient
experiment
design.
We
propose
to
outline
a
generic
PMMP
(perturbation,
measurement,
modeling
prediction)
pipeline
build
foundation
models
other
suitable
mathematical
based
on
large-scale
Finally,
contrast
between
artificially
naturally
highlight
importance
proteomics
for
advancing
our
understanding
predictive
biological
systems.
Nature Communications,
Год журнала:
2023,
Номер
14(1)
Опубликована: Ноя. 24, 2023
Abstract
The
spatial
organisation
of
cellular
protein
expression
profiles
within
tissue
determines
function
and
is
key
to
understanding
disease
pathology.
To
define
molecular
phenotypes
in
the
context
tissue,
there
a
need
for
unbiased,
quantitative
technology
capable
mapping
proteomes
structures.
Here,
we
present
workflow
spatially-resolved,
proteomics
that
generates
maps
abundance
across
slices
derived
from
human
atypical
teratoid-rhabdoid
tumour
at
three
resolutions,
highest
being
40
µm,
reveal
distinct
patterns
thousands
proteins.
We
employ
spatially-aware
algorithms
do
not
require
prior
knowledge
fine
structure
detect
proteins
pathways
with
correlate
heterogeneity
features
such
as
extracellular
matrix
or
proximity
blood
vessels.
identify
PYGL,
ASPH
CD45
markers
boundary
immune
response-driven,
spatially-organised
networks
matrix.
Overall,
demonstrate
deep
proteo-phenotyping
heterogeneity,
re-define
biology
pathology
level.
