Frontiers in Genetics,
Journal Year:
2024,
Volume and Issue:
15
Published: Dec. 24, 2024
Background
IgA
nephropathy
(IgAN)
is
a
leading
cause
of
renal
failure,
but
its
pathogenesis
remains
unclear,
complicating
diagnosis
and
treatment.
The
invasive
nature
biopsy
highlights
the
need
for
non-invasive
diagnostic
biomarkers.
Bulk
RNA
sequencing
(RNA-seq)
urine
offers
promising
approach
identifying
molecular
changes
relevant
to
IgAN.
Methods
We
performed
bulk
RNA-seq
on
53
samples
from
11
untreated
IgAN
patients
healthy
controls,
integrating
these
data
with
public
RNA-seq,
microarray,
scRNA-seq
datasets.
Machine
learning
was
used
identify
key
differentially
expressed
genes,
protein
expression
validated
by
immunohistochemistry
(IHC)
drug-target
interactions
explored
via
docking.
Results
Urine
analysis
revealed
differential
profiles,
which
TYROBP
HCK
were
identified
as
biomarkers
using
machine
learning.
These
in
both
test
cohort
an
external
validation
cohort,
demonstrating
strong
predictive
accuracy.
confirmed
their
cell-specific
patterns,
correlating
function
metrics
such
GFR
serum
creatinine.
IHC
further
expression,
docking
suggested
potential
therapeutic
treatments.
Conclusion
are
urinary
Their
accuracy,
through
learning,
along
confirmation
insights,
supports
applications
Advanced Science,
Journal Year:
2024,
Volume and Issue:
12(2)
Published: Nov. 21, 2024
Abstract
Proteomic
communications
in
neighboring
microenvironments
during
early
organ
development
is
a
dynamic
process
that
continuously
reshapes
human
embryonic
stem
cells
(hESCs)
developmental
fate.
Such
proteomic
alteration
the
microenvironment
consists
of
both
freely
secreted
proteome
and
exosome‐encapsulated
proteome.
Simultaneous
monitoring
time‐lapse
shift
proteomes
with
live
organoids
remains
technically
challenging.
Here,
c
ontinuous
o
rganoid
s
ecretion/
e
ncapsulation
p
roteome
tandem
LC‐MS/MS
(COSEP‐LCM)
introduced,
which
permits
alterations
free
secretion
form
exosome
encapsulated
at
organoids’
microenvironment.
Continuous
growth
cerebral
(COs)
free‐secretion/exosome‐encapsulation
proteomics
acquisition
COSEP‐LCM
for
60
days
demonstrated.
SERPINF1,
F5,
EFNB1
are
initially
enriched
inside
exosomes
as
excretion
then
gradually
outside
excretion,
while
C3
excretion.
pattern
paradigm
may
imply
critical
strategy
evolution
development.
offers
platform
technique
continuous
inside/outside
co‐analysis
Analytical Chemistry,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 23, 2024
The
increasing
focus
of
small
extracellular
vesicles
(sEVs)
in
liquid
biopsy
has
created
a
significant
demand
for
streamlined
improvements
sEV
isolation
methods,
efficient
collection
high-quality
data,
and
powerful
rapid
analysis
large
data
sets.
Herein,
we
develop
high-throughput
dual-use
mass
spectroscopic
chip
array
(DUMSCA)
the
detection
plasma
sEVs.
DUMSCA
realizes
more
than
50%
increase
speed
compared
to
traditional
method
confirms
proficiency
robust
storage,
reuse,
high-efficiency
desorption/ionization,
metabolite
quantification.
With
collected
metabolic
matrix
sEVs,
deep
learning
model
achieves
high-performance
diagnosis
Crohn's
disease.
Furthermore,
discovered
biomarkers
by
feature
sparsification
tandem
spectrometry
experiments
also
exhibited
remarkable
performance
diagnosis.
This
work
demonstrates
rapidity
validity
disease
diagnosis,
enabling
diseases
without
necessity
prior
knowledge
providing
technology
sEV-based
that
will
empower
its
vigorous
development.
Frontiers in Genetics,
Journal Year:
2024,
Volume and Issue:
15
Published: Dec. 24, 2024
Background
IgA
nephropathy
(IgAN)
is
a
leading
cause
of
renal
failure,
but
its
pathogenesis
remains
unclear,
complicating
diagnosis
and
treatment.
The
invasive
nature
biopsy
highlights
the
need
for
non-invasive
diagnostic
biomarkers.
Bulk
RNA
sequencing
(RNA-seq)
urine
offers
promising
approach
identifying
molecular
changes
relevant
to
IgAN.
Methods
We
performed
bulk
RNA-seq
on
53
samples
from
11
untreated
IgAN
patients
healthy
controls,
integrating
these
data
with
public
RNA-seq,
microarray,
scRNA-seq
datasets.
Machine
learning
was
used
identify
key
differentially
expressed
genes,
protein
expression
validated
by
immunohistochemistry
(IHC)
drug-target
interactions
explored
via
docking.
Results
Urine
analysis
revealed
differential
profiles,
which
TYROBP
HCK
were
identified
as
biomarkers
using
machine
learning.
These
in
both
test
cohort
an
external
validation
cohort,
demonstrating
strong
predictive
accuracy.
confirmed
their
cell-specific
patterns,
correlating
function
metrics
such
GFR
serum
creatinine.
IHC
further
expression,
docking
suggested
potential
therapeutic
treatments.
Conclusion
are
urinary
Their
accuracy,
through
learning,
along
confirmation
insights,
supports
applications
