Nature Biotechnology,
Год журнала:
2023,
Номер
42(9), С. 1384 - 1393
Опубликована: Ноя. 20, 2023
The
interactions
of
microorganisms
among
themselves
and
with
their
multicellular
host
take
place
at
the
microscale,
forming
complex
networks
spatial
patterns.
Existing
technology
does
not
allow
simultaneous
investigation
between
a
multitude
its
colonizing
microorganisms,
which
limits
our
understanding
host-microorganism
within
plant
or
animal
tissue.
Here
we
present
metatranscriptomics
(SmT),
sequencing-based
approach
that
leverages
16S/18S/ITS/poly-d(T)
multimodal
arrays
for
transcriptome-
microbiome-wide
characterization
tissues
55-µm
resolution.
We
showcase
SmT
in
outdoor-grown
Arabidopsis
thaliana
leaves
as
model
system,
find
tissue-scale
bacterial
fungal
hotspots.
By
network
analysis,
study
inter-
intrakingdom
well
response
to
microbial
provides
an
answering
fundamental
questions
on
host-microbiome
interplay.
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.
Cell Reports Methods,
Год журнала:
2023,
Номер
3(6), С. 100498 - 100498
Опубликована: Июнь 1, 2023
Biological
systems
are
immensely
complex,
organized
into
a
multi-scale
hierarchy
of
functional
units
based
on
tightly
regulated
interactions
between
distinct
molecules,
cells,
organs,
and
organisms.
While
experimental
methods
enable
transcriptome-wide
measurements
across
millions
popular
bioinformatic
tools
do
not
support
systems-level
analysis.
Here
we
present
hdWGCNA,
comprehensive
framework
for
analyzing
co-expression
networks
in
high-dimensional
transcriptomics
data
such
as
single-cell
spatial
RNA
sequencing
(RNA-seq).
hdWGCNA
provides
functions
network
inference,
gene
module
identification,
enrichment
analysis,
statistical
tests,
visualization.
Beyond
conventional
RNA-seq,
is
capable
performing
isoform-level
analysis
using
long-read
data.
We
showcase
from
autism
spectrum
disorder
Alzheimer's
disease
brain
samples,
identifying
disease-relevant
modules.
directly
compatible
with
Seurat,
widely
used
R
package
demonstrate
the
scalability
by
dataset
containing
nearly
1
million
cells.
Abstract
The
recent
advancement
in
spatial
transcriptomics
technology
has
enabled
multiplexed
profiling
of
cellular
transcriptomes
and
locations.
As
the
capacity
efficiency
experimental
technologies
continue
to
improve,
there
is
an
emerging
need
for
development
analytical
approaches.
Furthermore,
with
continuous
evolution
sequencing
protocols,
underlying
assumptions
current
methods
be
re-evaluated
adjusted
harness
increasing
data
complexity.
To
motivate
aid
future
model
development,
we
herein
review
statistical
machine
learning
transcriptomics,
summarize
useful
resources,
highlight
challenges
opportunities
ahead.
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.
PLoS Computational Biology,
Год журнала:
2022,
Номер
18(9), С. e1010492 - e1010492
Опубликована: Сен. 12, 2022
We
perform
a
thorough
analysis
of
RNA
velocity
methods,
with
view
towards
understanding
the
suitability
various
assumptions
underlying
popular
implementations.
In
addition
to
providing
self-contained
exposition
mathematics,
we
undertake
simulations
and
controlled
experiments
on
biological
datasets
assess
workflow
sensitivity
parameter
choices
biology.
Finally,
argue
for
more
rigorous
approach
velocity,
present
framework
Markovian
that
points
directions
improvement
mitigation
current
problems.
