Genome biology,
Journal Year:
2022,
Volume and Issue:
23(1)
Published: Oct. 17, 2022
Abstract
Background
Cell-cell
interactions
are
important
for
information
exchange
between
different
cells,
which
the
fundamental
basis
of
many
biological
processes.
Recent
advances
in
single-cell
RNA
sequencing
(scRNA-seq)
enable
characterization
cell-cell
using
computational
methods.
However,
it
is
hard
to
evaluate
these
methods
since
no
ground
truth
provided.
Spatial
transcriptomics
(ST)
data
profiles
relative
position
cells.
We
propose
that
spatial
distance
suggests
interaction
tendency
cell
types,
thus
could
be
used
evaluating
tools.
Results
benchmark
16
by
integrating
scRNA-seq
with
ST
data.
characterize
into
short-range
and
long-range
distributions
ligands
receptors.
Based
on
this
classification,
we
define
enrichment
score
apply
an
evaluation
workflow
tools
15
simulated
5
real
datasets.
also
compare
consistency
results
from
single
commonly
identified
interactions.
Our
suggest
predicted
highly
dynamic,
statistical-based
show
overall
better
performance
than
network-based
ST-based
Conclusions
study
presents
a
comprehensive
scRNA-seq.
CellChat,
CellPhoneDB,
NicheNet,
ICELLNET
other
terms
software
scalability.
recommend
at
least
two
ensure
accuracy
have
packaged
detailed
documentation
GitHub
(
https://github.com/wanglabtongji/CCI
).
Genome Medicine,
Journal Year:
2022,
Volume and Issue:
14(1)
Published: June 27, 2022
Abstract
Single-cell
transcriptomics
(scRNA-seq)
has
become
essential
for
biomedical
research
over
the
past
decade,
particularly
in
developmental
biology,
cancer,
immunology,
and
neuroscience.
Most
commercially
available
scRNA-seq
protocols
require
cells
to
be
recovered
intact
viable
from
tissue.
This
precluded
many
cell
types
study
largely
destroys
spatial
context
that
could
otherwise
inform
analyses
of
identity
function.
An
increasing
number
platforms
now
facilitate
spatially
resolved,
high-dimensional
assessment
gene
transcription,
known
as
‘spatial
transcriptomics’.
Here,
we
introduce
different
classes
method,
which
either
record
locations
hybridized
mRNA
molecules
tissue,
image
positions
themselves
prior
assessment,
or
employ
arrays
probes
pre-determined
location.
We
review
sizes
tissue
area
can
assessed,
their
resolution,
genes
profiled.
discuss
if
preservation
influences
choice
platform,
provide
guidance
on
whether
specific
may
better
suited
discovery
screens
hypothesis
testing.
Finally,
bioinformatic
methods
analysing
transcriptomic
data,
including
pre-processing,
integration
with
existing
inference
cell-cell
interactions.
Spatial
-omics
are
already
improving
our
understanding
human
tissues
research,
diagnostic,
therapeutic
settings.
To
build
upon
these
recent
advancements,
entry-level
those
seeking
own
research.
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.
Science,
Journal Year:
2022,
Volume and Issue:
376(6597)
Published: May 12, 2022
Single-cell
genomics
studies
have
decoded
the
immune
cell
composition
of
several
human
prenatal
organs
but
were
limited
in
describing
developing
system
as
a
distributed
network
across
tissues.
We
profiled
nine
tissues
combining
single-cell
RNA
sequencing,
antigen-receptor
and
spatial
transcriptomics
to
reconstruct
system.
This
revealed
late
acquisition
immune-effector
functions
by
myeloid
lymphoid
subsets
maturation
monocytes
T
cells
before
peripheral
tissue
seeding.
Moreover,
we
uncovered
system-wide
blood
development
beyond
primary
hematopoietic
organs,
characterized
B1
cells,
shed
light
on
origin
unconventional
cells.
Our
atlas
provides
both
valuable
data
resources
biological
insights
that
will
facilitate
engineering,
regenerative
medicine,
disease
understanding.
