Benchmark of cellular deconvolution methods using a multi-assay dataset from postmortem human prefrontal cortex
Genome biology,
Journal Year:
2025,
Volume and Issue:
26(1)
Published: April 7, 2025
Language: Английский
Transcriptomic characterization of human lateral septum neurons reveals conserved and divergent marker genes across species
iScience,
Journal Year:
2025,
Volume and Issue:
28(2), P. 111820 - 111820
Published: Jan. 18, 2025
The
lateral
septum
(LS)
is
a
midline,
subcortical
structure
that
critical
regulator
of
social
behaviors.
Mouse
studies
have
identified
molecularly
distinct
neuronal
populations
within
the
LS,
which
control
specific
facets
behavior.
Despite
its
known
molecular
heterogeneity
in
mouse
and
role
regulating
behavior,
comprehensive
profiling
human
LS
has
not
been
performed.
Here,
we
conducted
single-nucleus
RNA
sequencing
(snRNA-seq)
to
generate
transcriptomic
profiles
compared
recently
collected
snRNA-seq
datasets.
Our
analyses
TRPC4
as
conserved
marker
while
FREM2
enriched
only
LS.
We
also
identify
cell
type
marked
by
OPRM1,
gene
encoding
μ-opioid
receptor.
Together,
these
results
highlight
transcriptional
robust
genes
for
Language: Английский
An integrated single-nucleus and spatial transcriptomics atlas reveals the molecular landscape of the human hippocampus
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2024,
Volume and Issue:
unknown
Published: April 28, 2024
Abstract
The
hippocampus
contains
many
unique
cell
types,
which
serve
the
structure’s
specialized
functions,
including
learning,
memory
and
cognition.
These
cells
have
distinct
spatial
topography,
morphology,
physiology,
connectivity,
highlighting
need
for
transcriptome-wide
profiling
strategies
that
retain
cytoarchitectural
organization.
Here,
we
generated
spatially-resolved
transcriptomics
(SRT)
single-nucleus
RNA-sequencing
(snRNA-seq)
data
from
adjacent
tissue
sections
of
anterior
human
across
ten
adult
neurotypical
donors.
We
defined
molecular
profiles
hippocampal
types
domains.
Using
non-negative
matrix
factorization
transfer
integrated
these
to
define
gene
expression
patterns
within
snRNA-seq
infer
in
SRT
data.
With
this
approach,
leveraged
existing
rodent
datasets
feature
information
on
circuit
connectivity
neural
activity
induction
make
predictions
about
axonal
projection
targets
likelihood
ensemble
recruitment
spatially-defined
cellular
populations
hippocampus.
Finally,
genome-wide
association
studies
with
transcriptomic
identify
enrichment
genetic
components
neurodevelopmental,
neuropsychiatric,
neurodegenerative
disorders
domains,
To
comprehensive
atlas
accessible
scientific
community,
both
raw
processed
are
freely
available,
through
interactive
web
applications.
Language: Английский
SMART: spatial transcriptomics deconvolution using marker-gene-assisted topic model
Genome biology,
Journal Year:
2024,
Volume and Issue:
25(1)
Published: Dec. 2, 2024
Abstract
While
spatial
transcriptomics
offer
valuable
insights
into
gene
expression
patterns
within
the
context
of
tissue,
many
technologies
do
not
have
a
single-cell
resolution.
Here,
we
present
SMART,
marker
gene-assisted
deconvolution
method
that
simultaneously
infers
cell
type-specific
profile
and
cellular
composition
at
each
spot.
Using
multiple
datasets,
show
SMART
outperforms
existing
methods
in
realistic
settings.
It
also
provides
two-stage
approach
to
enhance
its
performance
on
subtypes.
The
covariate
model
enables
identification
differentially
expressed
genes
across
conditions,
elucidating
biological
changes
single-cell-type
Language: Английский
lute: estimating the cell composition of heterogeneous tissue with varying cell sizes using gene expression
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2024,
Volume and Issue:
unknown
Published: April 6, 2024
Relative
cell
type
fraction
estimates
in
bulk
RNA-sequencing
data
are
important
to
control
for
composition
differences
across
heterogenous
tissue
samples.
Current
computational
tools
estimate
relative
RNA
abundances
rather
than
proportions
tissues
with
varying
sizes,
leading
biased
estimates.
We
present
lute,
a
tool
accurately
deconvolute
types
sizes.
Our
software
wraps
existing
deconvolution
algorithms
standardized
framework.
Using
simulated
and
real
datasets,
we
demonstrate
how
lute
adjusts
sizes
improve
the
accuracy
of
composition.
Software
is
available
from
https://bioconductor.org/packages/lute.
Language: Английский
BLEND: Probabilistic Cellular Deconvolution with Automated Reference Selection
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2024,
Volume and Issue:
unknown
Published: Aug. 6, 2024
Cellular
deconvolution
aims
to
estimate
cell
type
fractions
from
bulk
transcriptomic
and
other
omics
data.
Most
existing
methods
fail
account
for
the
heterogeneity
in
type-specific
(CTS)
expression
across
samples,
ignore
discrepancies
between
CTS
reference
data,
provide
no
guidance
on
selection
or
integration.
To
address
these
issues,
we
introduce
BLEND,
a
hierarchical
Bayesian
method
that
leverages
multiple
datasets.
BLEND
learns
most
suitable
references
each
sample
by
exploring
convex
hulls
of
employs
"bag-of-words"
representation
count
data
deconvolution.
speed
up
computation,
an
efficient
EM
algorithm
parameter
estimation.
Notably,
requires
transformation,
normalization,
marker
gene
selection,
quality
evaluation.
Benchmarking
studies
both
simulated
real
human
brain
highlight
BLEND's
superior
performance
various
scenarios.
The
analysis
Alzheimer's
disease
illustrates
application
resource
Language: Английский