Nucleic Acids Research,
Год журнала:
2023,
Номер
51(W1), С. W560 - W568
Опубликована: Май 24, 2023
Single-cell
RNA
sequencing
(scRNA-seq)
provides
insights
into
gene
expression
heterogeneities
in
diverse
cell
types
underlying
homeostasis,
development
and
pathological
states.
However,
the
loss
of
spatial
information
hinders
its
applications
deciphering
spatially
related
features,
such
as
cell-cell
interactions
a
context.
Here,
we
present
STellaris
(https://spatial.rhesusbase.com),
web
server
aimed
to
rapidly
assign
scRNA-seq
data
based
on
their
transcriptomic
similarity
with
public
transcriptomics
(ST)
data.
is
founded
101
manually
curated
ST
datasets
comprising
823
sections
across
different
organs,
developmental
stages
states
from
humans
mice.
accepts
raw
count
matrix
type
annotation
input,
maps
single
cells
locations
tissue
architecture
properly
matched
section.
Spatially
resolved
for
intercellular
communications,
distance
ligand-receptor
(LRIs),
are
further
characterized
between
annotated
types.
Moreover,
also
expanded
application
multiple
regulatory
levels
single-cell
multiomics
data,
using
transcriptome
bridge.
was
applied
several
case
studies
showcase
utility
adding
value
ever-growing
perspective.
Nature Communications,
Год журнала:
2023,
Номер
14(1)
Опубликована: Март 21, 2023
Abstract
Spatial
transcriptomics
technologies
are
used
to
profile
transcriptomes
while
preserving
spatial
information,
which
enables
high-resolution
characterization
of
transcriptional
patterns
and
reconstruction
tissue
architecture.
Due
the
existence
low-resolution
spots
in
recent
technologies,
uncovering
cellular
heterogeneity
is
crucial
for
disentangling
cell
types,
many
related
methods
have
been
proposed.
Here,
we
benchmark
18
existing
resolving
a
deconvolution
task
with
50
real-world
simulated
datasets
by
evaluating
accuracy,
robustness,
usability
methods.
We
compare
these
comprehensively
using
different
metrics,
resolutions,
spot
numbers,
gene
numbers.
In
terms
performance,
CARD,
Cell2location,
Tangram
best
conducting
task.
To
refine
our
comparative
results,
provide
decision-tree-style
guidelines
recommendations
method
selection
their
additional
features,
will
help
users
easily
choose
fulfilling
concerns.
Nature Communications,
Год журнала:
2023,
Номер
14(1)
Опубликована: Ноя. 29, 2023
The
rapid
emergence
of
spatial
transcriptomics
(ST)
technologies
is
revolutionizing
our
understanding
tissue
architecture
and
biology.
Although
current
ST
methods,
whether
based
on
next-generation
sequencing
(seq-based
approaches)
or
fluorescence
in
situ
hybridization
(image-based
approaches),
offer
valuable
insights,
they
face
limitations
either
cellular
resolution
transcriptome-wide
profiling.
To
address
these
limitations,
we
present
SpatialScope,
a
unified
approach
integrating
scRNA-seq
reference
data
using
deep
generative
models.
With
innovation
model
algorithm
designs,
SpatialScope
not
only
enhances
seq-based
to
achieve
single-cell
resolution,
but
also
accurately
infers
expression
levels
for
image-based
data.
We
demonstrate
SpatialScope's
utility
through
simulation
studies
real
analysis
from
both
approaches.
provides
characterization
structures
at
facilitating
downstream
analysis,
including
detecting
communication
ligand-receptor
interactions,
localizing
subtypes,
identifying
spatially
differentially
expressed
genes.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2023,
Номер
unknown
Опубликована: Фев. 14, 2023
Abstract
The
Xenium
In
Situ
platform
is
a
new
spatial
transcriptomics
product
commercialized
by
10X
Genomics
capable
of
mapping
hundreds
transcripts
in
situ
at
subcellular
resolution.
Given
the
multitude
commercially
available
technologies,
recommendations
choice
and
analysis
guidelines
are
increasingly
important.
Herein,
we
explore
eight
preview
datasets
mouse
brain
two
human
breast
cancer
comparing
scalability,
resolution,
data
quality,
capacities
limitations
with
other
spatially
resolved
technologies.
addition,
benchmarked
performance
multiple
open
source
computational
tools
when
applied
to
tasks
including
cell
segmentation,
segmentation-free
analysis,
selection
variable
genes
domain
identification,
among
others.
This
study
serves
as
first
independent
Xenium,
provides
best-practices
for
such
datasets.
Cell,
Год журнала:
2024,
Номер
187(8), С. 1990 - 2009.e19
Опубликована: Март 20, 2024
Multiple
sclerosis
(MS)
is
a
neurological
disease
characterized
by
multifocal
lesions
and
smoldering
pathology.
Although
single-cell
analyses
provided
insights
into
cytopathology,
evolving
cellular
processes
underlying
MS
remain
poorly
understood.
We
investigated
the
dynamics
of
modeling
temporal
regional
rates
progression
in
mouse
experimental
autoimmune
encephalomyelitis
(EAE).
By
performing
spatial
expression
profiling
using
situ
sequencing
(ISS),
we
annotated
neighborhoods
found
centrifugal
evolution
active
lesions.
demonstrated
that
disease-associated
(DA)-glia
arise
independently
are
dynamically
induced
resolved
over
course.
Single-cell
mapping
human
archival
spinal
cords
confirmed
differential
distribution
homeostatic
DA-glia,
enabled
deconvolution
inactive
sub-compartments,
identified
new
lesion
areas.
establishing
resource
neuropathology
at
resolution,
our
study
unveils
intricate
MS.
Nature Biotechnology,
Год журнала:
2023,
Номер
41(10), С. 1465 - 1473
Опубликована: Фев. 16, 2023
Abstract
Transferring
annotations
of
single-cell-,
spatial-
and
multi-omics
data
is
often
challenging
owing
both
to
technical
limitations,
such
as
low
spatial
resolution
or
high
dropout
fraction,
biological
variations,
continuous
spectra
cell
states.
Based
on
the
concept
that
these
are
best
described
mixtures
cells
molecules,
we
present
a
computational
framework
for
transfer
their
combinations
(TACCO),
which
consists
an
optimal
transport
model
extended
with
different
wrappers
annotate
wide
variety
data.
We
apply
TACCO
identify
types
states,
decipher
spatiomolecular
tissue
structure
at
molecular
level
resolve
differentiation
trajectories
using
synthetic
datasets.
While
matching
exceeding
accuracy
specialized
tools
individual
tasks,
reduces
requirements
by
up
order
magnitude
scales
larger
datasets
(for
example,
considering
runtime
annotation
1
M
simulated
observations).
