Nature Biotechnology,
Journal Year:
2022,
Volume and Issue:
unknown
Published: Sept. 22, 2022
Methods
to
spatially
profile
the
transcriptome
are
dominated
by
a
trade-off
between
resolution
and
throughput.
Here
we
develop
method
named
Enhanced
ELectric
Fluorescence
in
situ
Hybridization
(EEL
FISH)
that
can
rapidly
process
large
tissue
samples
without
compromising
spatial
resolution.
By
electrophoretically
transferring
RNA
from
section
onto
capture
surface,
EEL
speeds
up
data
acquisition
reducing
amount
of
imaging
needed,
while
ensuring
molecules
move
straight
down
toward
preserving
single-cell
We
apply
on
eight
entire
sagittal
sections
mouse
brain
measure
expression
patterns
440
genes
reveal
complex
organization.
Moreover,
be
used
study
challenging
human
removing
autofluorescent
lipofuscin,
enabling
visual
cortex
visualized.
provide
full
hardware
specifications,
all
protocols
complete
software
for
instrument
control,
image
processing,
analysis
visualization.
Cell,
Journal Year:
2022,
Volume and Issue:
185(10), P. 1777 - 1792.e21
Published: May 1, 2022
Spatially
resolved
transcriptomic
technologies
are
promising
tools
to
study
complex
biological
processes
such
as
mammalian
embryogenesis.
However,
the
imbalance
between
resolution,
gene
capture,
and
field
of
view
current
methodologies
precludes
their
systematic
application
analyze
relatively
large
three-dimensional
mid-
late-gestation
embryos.
Here,
we
combined
DNA
nanoball
(DNB)-patterned
arrays
in
situ
RNA
capture
create
spatial
enhanced
resolution
omics-sequencing
(Stereo-seq).
We
applied
Stereo-seq
generate
mouse
organogenesis
spatiotemporal
atlas
(MOSTA),
which
maps
with
single-cell
high
sensitivity
kinetics
directionality
transcriptional
variation
during
organogenesis.
used
this
information
gain
insight
into
molecular
basis
cell
heterogeneity
fate
specification
developing
tissues
dorsal
midbrain.
Our
panoramic
will
facilitate
in-depth
investigation
longstanding
questions
concerning
normal
abnormal
development.
Nature Methods,
Journal Year:
2022,
Volume and Issue:
19(2), P. 171 - 178
Published: Jan. 31, 2022
Spatial
omics
data
are
advancing
the
study
of
tissue
organization
and
cellular
communication
at
an
unprecedented
scale.
Flexible
tools
required
to
store,
integrate
visualize
large
diversity
spatial
data.
Here,
we
present
Squidpy,
a
Python
framework
that
brings
together
from
image
analysis
enable
scalable
description
molecular
data,
such
as
transcriptome
or
multivariate
proteins.
Squidpy
provides
efficient
infrastructure
numerous
methods
allow
efficiently
manipulate
interactively
is
extensible
can
be
interfaced
with
variety
already
existing
libraries
for
Nature Communications,
Journal Year:
2023,
Volume and Issue:
14(1)
Published: March 1, 2023
Abstract
Spatial
transcriptomics
technologies
generate
gene
expression
profiles
with
spatial
context,
requiring
spatially
informed
analysis
tools
for
three
key
tasks,
clustering,
multisample
integration,
and
cell-type
deconvolution.
We
present
GraphST,
a
graph
self-supervised
contrastive
learning
method
that
fully
exploits
data
to
outperform
existing
methods.
It
combines
neural
networks
learn
informative
discriminative
spot
representations
by
minimizing
the
embedding
distance
between
adjacent
spots
vice
versa.
demonstrated
GraphST
on
multiple
tissue
types
technology
platforms.
achieved
10%
higher
clustering
accuracy
better
delineated
fine-grained
structures
in
brain
embryo
tissues.
is
also
only
can
jointly
analyze
slices
vertical
or
horizontal
integration
while
correcting
batch
effects.
Lastly,
superior
deconvolution
capture
niches
like
lymph
node
germinal
centers
exhausted
tumor
infiltrating
T
cells
breast
tissue.
