Genome biology,
Journal Year:
2025,
Volume and Issue:
26(1)
Published: April 29, 2025
Normalization
of
spatial
transcriptomics
data
is
challenging
due
to
association
between
region-specific
library
size
and
biology.
We
develop
SpaNorm,
the
first
spatially-aware
normalization
method
that
concurrently
models
effects
underlying
biology,
segregates
these
effects,
thereby
removes
without
removing
biological
information.
Using
27
tissue
samples
from
6
datasets
spanning
4
technological
platforms,
SpaNorm
outperforms
commonly
used
single-cell
approaches
while
retaining
domain
information
detecting
spatially
variable
genes.
versatile
works
equally
well
for
multicellular
subcellular
with
relatively
robust
performance
under
different
segmentation
methods.
Cardiovascular
diseases
constitute
a
marked
threat
to
global
health,
and
the
emergence
of
spatial
omics
technologies
has
revolutionized
cardiovascular
research.
This
review
explores
application
omics,
including
transcriptomics,
proteomics,
metabolomics,
genomics,
epigenomics,
providing
more
insight
into
molecular
cellular
foundations
disease
highlighting
critical
contributions
science,
discusses
future
prospects,
technological
advancements,
integration
multi-omics,
clinical
applications.
These
developments
should
contribute
understanding
guide
progress
precision
medicine,
targeted
therapies,
personalized
treatments.
Genome biology,
Journal Year:
2025,
Volume and Issue:
26(1)
Published: March 28, 2025
Spatial
transcriptomics
allows
gene
expression
to
be
measured
within
complex
tissue
contexts.
Among
the
array
of
spatial
capture
technologies
available
is
10x
Genomics'
Visium
platform,
a
popular
method
which
enables
transcriptome-wide
profiling
sections.
offers
range
sample
handling
and
library
construction
methods
introduces
need
for
benchmarking
compare
data
quality
assess
how
well
technology
can
recover
expected
features
biological
signatures.
Here
we
present
SpatialBenchVisium,
unique
reference
dataset
generated
from
spleen
mice
responding
malaria
infection
spanning
several
preparation
protocols
(both
fresh
frozen
FFPE,
with
either
manual
or
CytAssist
placement).
We
note
better
control
metrics
in
samples
prepared
using
probe-based
methods,
particularly
those
processed
CytAssist,
validating
improvement
produced
platform.
Our
analysis
replicate
extends
explore
spatially
variable
detection,
outcomes
clustering
cell
deconvolution
matched
single-cell
RNA-sequencing
publicly
identify
types
regions
spleen.
Multi-sample
differential
recovered
known
signatures
related
sex
knockout.
Genome biology,
Journal Year:
2025,
Volume and Issue:
26(1)
Published: April 29, 2025
Normalization
of
spatial
transcriptomics
data
is
challenging
due
to
association
between
region-specific
library
size
and
biology.
We
develop
SpaNorm,
the
first
spatially-aware
normalization
method
that
concurrently
models
effects
underlying
biology,
segregates
these
effects,
thereby
removes
without
removing
biological
information.
Using
27
tissue
samples
from
6
datasets
spanning
4
technological
platforms,
SpaNorm
outperforms
commonly
used
single-cell
approaches
while
retaining
domain
information
detecting
spatially
variable
genes.
versatile
works
equally
well
for
multicellular
subcellular
with
relatively
robust
performance
under
different
segmentation
methods.
