Journal of Translational Medicine,
Год журнала:
2023,
Номер
21(1)
Опубликована: Май 18, 2023
Spatial
transcriptomics
technologies
developed
in
recent
years
can
provide
various
information
including
tissue
heterogeneity,
which
is
fundamental
biological
and
medical
research,
have
been
making
significant
breakthroughs.
Single-cell
RNA
sequencing
(scRNA-seq)
cannot
spatial
information,
while
allow
gene
expression
to
be
obtained
from
intact
sections
the
original
physiological
context
at
a
resolution.
Various
insights
generated
into
architecture
further
elucidation
of
interaction
between
cells
microenvironment.
Thus,
we
gain
general
understanding
histogenesis
processes
disease
pathogenesis,
etc.
Furthermore,
silico
methods
involving
widely
distributed
R
Python
packages
for
data
analysis
play
essential
roles
deriving
indispensable
bioinformation
eliminating
technological
limitations.
In
this
review,
summarize
available
transcriptomics,
probe
several
applications,
discuss
computational
strategies
raise
future
perspectives,
highlighting
developmental
potential.
Science,
Год журнала:
2019,
Номер
363(6434), С. 1463 - 1467
Опубликована: Март 28, 2019
Spatial
positions
of
cells
in
tissues
strongly
influence
function,
yet
a
high-throughput,
genome-wide
readout
gene
expression
with
cellular
resolution
is
lacking.
We
developed
Slide-seq,
method
for
transferring
RNA
from
tissue
sections
onto
surface
covered
DNA-barcoded
beads
known
positions,
allowing
the
locations
to
be
inferred
by
sequencing.
Using
we
localized
cell
types
identified
single-cell
sequencing
datasets
within
cerebellum
and
hippocampus,
characterized
spatial
patterns
Purkinje
layer
mouse
cerebellum,
defined
temporal
evolution
type-specific
responses
model
traumatic
brain
injury.
These
studies
highlight
how
Slide-seq
provides
scalable
obtaining
spatially
resolved
data
at
resolutions
comparable
sizes
individual
cells.
Nature Communications,
Год журнала:
2022,
Номер
13(1)
Опубликована: Апрель 1, 2022
Recent
advances
in
spatially
resolved
transcriptomics
have
enabled
comprehensive
measurements
of
gene
expression
patterns
while
retaining
the
spatial
context
tissue
microenvironment.
Deciphering
spots
a
needs
to
use
their
information
carefully.
To
this
end,
we
develop
graph
attention
auto-encoder
framework
STAGATE
accurately
identify
domains
by
learning
low-dimensional
latent
embeddings
via
integrating
and
profiles.
better
characterize
similarity
at
boundary
domains,
adopts
an
mechanism
adaptively
learn
neighboring
spots,
optional
cell
type-aware
module
through
pre-clustering
expressions.
We
validate
on
diverse
datasets
generated
different
platforms
with
resolutions.
could
substantially
improve
identification
accuracy
denoise
data
preserving
patterns.
Importantly,
be
extended
multiple
consecutive
sections
reduce
batch
effects
between
extracting
three-dimensional
(3D)
from
reconstructed
3D
effectively.
Spatial
omics
has
been
widely
heralded
as
the
new
frontier
in
life
sciences.
This
term
encompasses
a
wide
range
of
techniques
that
promise
to
transform
many
areas
biology
and
eventually
revolutionize
pathology
by
measuring
physical
tissue
structure
molecular
characteristics
at
same
time.
Although
field
came
age
past
5
years,
it
still
suffers
from
some
growing
pains:
barriers
entry,
robustness,
unclear
best
practices
for
experimental
design
analysis,
lack
standardization.
In
this
Review,
we
present
systematic
catalog
different
families
spatial
technologies;
highlight
their
principles,
power,
limitations;
give
perspective
suggestions
on
biggest
challenges
lay
ahead
incredibly
powerful-but
hard
navigate-landscape.
The
molecular
mechanism
underlying
brain
regeneration
in
vertebrates
remains
elusive.
We
performed
spatial
enhanced
resolution
omics
sequencing
(Stereo-seq)
to
capture
spatially
resolved
single-cell
transcriptomes
of
axolotl
telencephalon
sections
during
development
and
regeneration.
Annotated
cell
types
exhibited
distinct
distribution,
features,
functions.
identified
an
injury-induced
ependymoglial
cluster
at
the
wound
site
as
a
progenitor
population
for
potential
replenishment
lost
neurons,
through
state
transition
process
resembling
neurogenesis
development.
Transcriptome
comparisons
indicated
that
these
induced
cells
may
originate
from
local
resident
cells.
further
uncovered
defined
neurons
lesion
regress
immature
neuron-like
state.
Our
work
establishes
transcriptome
profiles
anamniote
tetrapod
decodes
regeneration,
thus
providing
mechanistic
insights
into
vertebrate
