Briefings in Bioinformatics,
Journal Year:
2024,
Volume and Issue:
25(6)
Published: Sept. 23, 2024
The
spatial
reconstruction
of
single-cell
RNA
sequencing
(scRNA-seq)
data
into
transcriptomics
(ST)
is
a
rapidly
evolving
field
that
addresses
the
significant
challenge
aligning
gene
expression
profiles
to
their
origins
within
tissues.
This
task
complicated
by
inherent
batch
effects
and
need
for
precise
characterization
accurately
reflect
information.
To
address
these
challenges,
we
developed
SELF-Former,
transformer-based
framework
utilizes
multi-scale
structures
learn
representations,
while
designing
correlation
constraints
corresponding
ST
data.
SELF-Former
excels
in
recovering
information
effectively
mitigates
between
scRNA-seq
A
novel
aspect
introduction
filtration
module,
which
significantly
enhances
selecting
genes
are
crucial
accurate
positioning
reconstruction.
superior
performance
effectiveness
SELF-Former's
modules
have
been
validated
across
four
benchmark
datasets,
establishing
it
as
robust
effective
method
tasks.
demonstrates
its
capability
extract
meaningful
from
map
context
real
Our
represents
advancement
field,
offering
reliable
approach
Journal of Clinical Oncology,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 13, 2025
Colorectal
cancer
(CRC)
remains
a
major
global
health
burden,
being
one
of
the
most
prevalent
cancers
with
high
mortality
rates.
Despite
advances
in
conventional
treatment
modalities,
patients
metastatic
CRC
often
face
limited
options
and
poor
outcomes.
Chimeric
antigen
receptor-T
(CAR-T)
cell
therapy,
initially
successful
hematologic
malignancies,
presents
promising
avenue
for
treating
solid
tumors,
including
CRC.
This
review
explores
potential
CAR-T
therapy
by
analyzing
clinical
trials
highlighting
prominent
CRC-specific
targets.
We
discuss
challenges
such
as
immunosuppressive
microenvironment,
tumor
heterogeneity,
physical
barriers
that
limit
efficacy.
Emerging
strategies,
logic-gated
dual-targeting
cells,
offer
practical
solutions
to
overcome
these
hurdles.
Furthermore,
we
explore
combination
immune
checkpoint
inhibitors
enhance
T-cell
persistence
infiltration.
As
field
continues
evolve,
therapies
hold
significant
revolutionizing
landscape
Journal of Cerebral Blood Flow & Metabolism,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 9, 2024
Single-cell
RNA
sequencing
(scRNA-seq)
is
a
high-throughput
transcriptomic
approach
with
the
power
to
identify
rare
cells,
discover
new
cellular
subclusters,
and
describe
novel
genes.
scRNA-seq
can
simultaneously
reveal
dynamic
shifts
in
phenotypes
heterogeneities
subtypes.
Since
publication
of
first
protocol
on
2009,
this
evolving
technology
has
continued
improve,
through
use
cell-specific
barcodes,
adoption
droplet-based
systems,
development
advanced
computational
methods.
Despite
induction
stress
response
during
tissue
dissociation
process,
remains
popular
technology,
commercially
available
methods
have
been
applied
brain.
Recent
advances
spatial
transcriptomics
now
allow
researcher
capture
positional
context
transcriptional
activity,
strengthening
our
knowledge
organization
cell-cell
interactions
spatially
intact
tissues.
A
combination
data
proteomic,
metabolomic,
or
chromatin
accessibility
promising
direction
for
future
research.
Herein,
we
provide
an
overview
workflow,
analyses
methods,
pros
cons
technology.
We
also
summarize
latest
achievements
stroke
acute
traumatic
brain
injury,
applications
transcriptomics.
npj Systems Biology and Applications,
Journal Year:
2025,
Volume and Issue:
11(1)
Published: March 20, 2025
Single-cell
RNA
sequencing
(scRNA-seq)
has
revolutionized
our
understanding
of
cellular
variability
by
capturing
gene
expression
profiles
individual
cells.
The
importance
cell-to-cell
in
determining
and
shaping
cell
function
been
widely
appreciated.
Nevertheless,
differential
(DE)
analysis
remains
a
cornerstone
method
analytical
practice.
Current
computational
analyses
overlook
the
rich
information
encoded
within
single-cell
data
focusing
exclusively
on
mean
expression.
To
offer
deeper
systems,
there
is
need
for
approaches
to
assess
rather
than
just
mean.
Here
we
present
spline-DV,
statistical
framework
(DV)
using
scRNA-seq
data.
spline-DV
identifies
genes
exhibiting
significantly
increased
or
decreased
among
cells
derived
from
two
experimental
conditions.
Case
studies
show
that
DV
identified
are
representative
functionally
relevant
tested
conditions,
including
obesity,
fibrosis,
cancer.
Frontiers in Immunology,
Journal Year:
2025,
Volume and Issue:
16
Published: April 8, 2025
Fibroblasts
can
regulate
tumour
development
by
secreting
various
factors.
For
COAD
survival
prediction
and
CAFs-based
treatment
recommendations,
it
is
critical
to
comprehend
the
heterogeneity
of
CAFs
find
biomarkers.
We
identified
fibroblast-associated
specific
marker
genes
in
colon
adenocarcinoma
single-cell
sequencing
analysis.
A
fibroblasts-related
gene
signature
was
developed,
patients
were
classified
into
high-risk
low-risk
cohorts
based
on
median
risk
score.
Additionally,
impact
these
categories
tumor
microenvironment
evaluated.
The
ability
CAFGs
assess
prognosis
guide
validated
using
external
cohorts.
Ultimately,
we
verified
MAN1B1
expression
function
through
vitro
assays.
Relying
bulk
RNA-seq
scRNA-seq
data
study,
created
a
predictive
profile
with
11
CAFGs.
effectively
differentiated
differences
among
patients.
nomogram
further
predicted
patients,
having
better
prognosis.
higher
immune
infiltration
rate
lower
IC50
values
anticancer
drugs
significant
group.
In
cellular
experiments,
Following
knockdown,
cell
assays,
colony
formation,
migration,
invasion
HCT116
HT29
lines
decreased.
Our
CAFG
provides
important
insights
role
CAF
cells
influencing
It
may
also
serve
as
for
selecting
immunotherapy
options
predicting
chemotherapy
responses
