Small Science,
Journal Year:
2024,
Volume and Issue:
unknown
Published: June 26, 2024
Accurate
mapping
between
single‐cell
RNA
sequencing
(scRNA‐seq)
and
low‐resolution
spatial
transcriptomics
(ST)
data
compensates
for
both
limited
resolution
of
ST
missing
information
scRNA‐seq.
Celloc,
a
method
developed
this
purpose,
incorporates
graph
attention
autoencoder
comprehensive
loss
functions
to
facilitate
flexible
single
cell‐to‐spot
mapping.
This
enables
either
the
dissection
cell
composition
within
each
spot
or
assignment
locations
every
in
scRNA‐seq
data.
Celloc's
performance
is
benchmarked
on
simulated
data,
demonstrating
superior
accuracy
robustness
compared
state‐of‐the‐art
methods.
Evaluations
real
datasets
suggest
that
Celloc
can
reconstruct
cellular
structures
with
various
types
across
different
tissues
histological
regions.
Theranostics,
Journal Year:
2024,
Volume and Issue:
14(7), P. 2946 - 2968
Published: Jan. 1, 2024
Recent
advancements
in
modern
science
have
provided
robust
tools
for
drug
discovery.
The
rapid
development
of
transcriptome
sequencing
technologies
has
given
rise
to
single-cell
transcriptomics
and
single-nucleus
transcriptomics,
increasing
the
accuracy
accelerating
discovery
process.
With
evolution
spatial
(ST)
technology
emerged
as
a
derivative
approach.
Spatial
hot
topic
field
omics
research
recent
years;
it
not
only
provides
information
on
gene
expression
levels
but
also
offers
expression.
This
shown
tremendous
potential
disease
understanding
In
this
article,
we
introduce
analytical
strategies
review
its
applications
novel
target
mechanism
unravelling.
Moreover,
discuss
current
challenges
issues
that
need
be
addressed.
conclusion,
new
perspective
Advanced Science,
Journal Year:
2023,
Volume and Issue:
10(16)
Published: April 7, 2023
Spatial
transcriptomics
is
a
newly
emerging
field
that
enables
high-throughput
investigation
of
the
spatial
localization
transcripts
and
related
analyses
in
various
applications
for
biological
systems.
By
transitioning
from
conventional
studies
to
"in
situ"
biology,
can
provide
transcriptome-scale
information.
Currently,
ability
simultaneously
characterize
gene
expression
profiles
cells
relevant
cellular
environment
paradigm
shift
studies.
In
this
review,
recent
progress
its
neuroscience
cancer
are
highlighted.
Technical
aspects
existing
technologies
future
directions
new
developments
(as
March
2023),
computational
analysis
transcriptome
data,
application
notes
studies,
discussions
regarding
multi-omics
their
expanding
roles
biomedical
emphasized.
Journal of Hematology & Oncology,
Journal Year:
2024,
Volume and Issue:
17(1)
Published: Aug. 24, 2024
The
emergence
of
spatial
multi-omics
has
helped
address
the
limitations
single-cell
sequencing,
which
often
leads
to
loss
context
among
cell
populations.
Integrated
analysis
genome,
transcriptome,
proteome,
metabolome,
and
epigenome
enhanced
our
understanding
biology
molecular
basis
human
diseases.
Moreover,
this
approach
offers
profound
insights
into
interactions
between
intracellular
intercellular
mechanisms
involved
in
development,
physiology,
pathogenesis
In
comprehensive
review,
we
examine
current
advancements
technologies,
focusing
on
their
evolution
refinement
over
past
decade,
including
improvements
throughput
resolution,
modality
integration,
accuracy.
We
also
discuss
pivotal
contributions
revealing
heterogeneity,
constructing
detailed
atlases,
deciphering
crosstalk
tumor
immunology,
advancing
translational
research
cancer
therapy
through
precise
mapping.
Advanced Science,
Journal Year:
2024,
Volume and Issue:
11(31)
Published: June 19, 2024
Abstract
Small
cell
lung
cancer
(SCLC)
is
a
highly
aggressive
malignancy
characterized
by
rapid
growth
and
early
metastasis
susceptible
to
treatment
resistance
recurrence.
Understanding
the
intra‐tumoral
spatial
heterogeneity
in
SCLC
crucial
for
improving
patient
outcomes
clinically
relevant
subtyping.
In
this
study,
whole
transcriptome‐wide
analysis
of
25
patients
at
sub‐histological
resolution
using
GeoMx
Digital
Spatial
Profiling
technology
performed.
This
deciphered
multi‐regional
heterogeneity,
distinct
molecular
profiles,
biological
functions,
immune
features,
subtypes
within
spatially
localized
histological
regions.
Connections
between
different
transcript‐defined
phenotypes
their
impact
on
survival
therapeutic
response
are
also
established.
Finally,
gene
signature,
termed
ITHtyper,
based
prevalence
levels,
which
enables
risk
stratification
from
bulk
RNA‐seq
profiles
identified.
The
prognostic
value
ITHtyper
rigorously
validated
independent
multicenter
cohorts.
study
introduces
preliminary
tumor‐centric,
regionally
targeted
transcriptome
resource
that
sheds
light
previously
unexplored
SCLC.
These
findings
hold
promise
improve
tumor
reclassification
facilitate
development
personalized
treatments
patients.
Small Methods,
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 18, 2025
Abstract
Rapidly
developing
spatial
omics
technologies
provide
us
with
new
approaches
to
deeply
understanding
the
diversity
and
functions
of
cell
types
within
organisms.
Unlike
traditional
approaches,
enable
researchers
dissect
complex
relationships
between
tissue
structure
function
at
cellular
or
even
subcellular
level.
The
application
provides
perspectives
on
key
biological
processes
such
as
nervous
system
development,
organ
tumor
microenvironment.
This
review
focuses
advancements
strategies
technologies,
summarizes
their
applications
in
biomedical
research,
highlights
power
advancing
life
sciences
related
development
disease.
Military Medical Research,
Journal Year:
2023,
Volume and Issue:
10(1)
Published: Aug. 17, 2023
Abstract
The
respiratory
system’s
complex
cellular
heterogeneity
presents
unique
challenges
to
researchers
in
this
field.
Although
bulk
RNA
sequencing
and
single-cell
(scRNA-seq)
have
provided
insights
into
cell
types
the
system,
relevant
specific
spatial
localization
interactions
not
been
clearly
elucidated.
Spatial
transcriptomics
(ST)
has
filled
gap
widely
used
studies.
This
review
focuses
on
latest
iterative
technology
of
ST
recent
years,
summarizing
how
can
be
applied
physiological
pathological
processes
with
emphasis
lungs.
Finally,
current
potential
development
directions
are
proposed,
including
high-throughput
full-length
transcriptome,
integration
multi-omics,
temporal
omics,
bioinformatics
analysis,
etc.
These
viewpoints
expected
advance
study
systematic
mechanisms,
Abstract
Spatially
resolved
transcriptomics
has
been
dramatically
transforming
biological
and
medical
research
in
various
fields.
It
enables
transcriptome
profiling
at
single‐cell,
multi‐cellular,
or
sub‐cellular
resolution,
while
retaining
the
information
of
geometric
localizations
cells
complex
tissues.
The
coupling
cell
spatial
its
molecular
characteristics
generates
a
novel
multi‐modal
high‐throughput
data
source,
which
poses
new
challenges
for
development
analytical
methods
data‐mining.
Spatial
transcriptomic
are
often
highly
complex,
noisy,
biased,
presenting
series
difficulties,
many
unresolved,
analysis
generation
insights.
In
addition,
to
keep
pace
with
ever‐evolving
experimental
technologies,
existing
theories
tools
need
be
updated
reformed
accordingly.
this
review,
we
provide
an
overview
discussion
current
computational
approaches
mining
data.
Future
directions
perspectives
methodology
design
proposed
stimulate
further
discussions
advances
models
algorithms.
This
article
is
categorized
under:
RNA
Methods
>
Analyses
Cells
Evolution
Genomics
Computational
Export
Localization
Clinical Cancer Bulletin,
Journal Year:
2024,
Volume and Issue:
3(1)
Published: June 4, 2024
Abstract
Tumor
research
is
a
fundamental
focus
of
medical
science,
yet
the
intrinsic
heterogeneity
and
complexity
tumors
present
challenges
in
understanding
their
biological
mechanisms
initiation,
progression,
metastasis.
Recent
advancements
single-cell
transcriptomic
sequencing
have
revolutionized
way
researchers
explore
tumor
biology
by
providing
unprecedented
resolution.
However,
key
limitation
loss
spatial
information
during
preparation.
Spatial
transcriptomics
(ST)
emerges
as
cutting-edge
technology
that
preserves
RNA
transcripts,
thereby
facilitating
deeper
heterogeneity,
intricate
interplay
between
cells
microenvironment.
This
review
systematically
introduces
ST
technologies
summarizes
latest
applications
research.
Furthermore,
we
provide
thorough
overview
bioinformatics
analysis
workflow
for
data
offer
an
online
tutorial
(
https://github.com/SiyuanHuang1/ST_Analysis_Handbook
).
Lastly,
discuss
potential
future
directions
ST.
We
believe
will
become
powerful
tool
unraveling
new
insights
effective
treatment
precision
medicine
oncology.
