International Journal of Molecular Sciences,
Год журнала:
2025,
Номер
26(4), С. 1466 - 1466
Опубликована: Фев. 10, 2025
The
advancement
of
multi-omics
tools
has
revolutionized
the
study
complex
biological
systems,
providing
comprehensive
insights
into
molecular
mechanisms
underlying
critical
traits
across
various
organisms.
By
integrating
data
from
genomics,
transcriptomics,
metabolomics,
and
other
omics
platforms,
researchers
can
systematically
identify
characterize
elements
that
contribute
to
phenotypic
traits.
This
review
delves
recent
progress
in
applying
approaches
elucidate
genetic,
epigenetic,
metabolic
networks
associated
with
key
plants.
We
emphasize
potential
these
integrative
strategies
enhance
crop
improvement,
optimize
agricultural
practices,
promote
sustainable
environmental
management.
Furthermore,
we
explore
future
prospects
field,
underscoring
importance
cutting-edge
technological
advancements
need
for
interdisciplinary
collaboration
address
ongoing
challenges.
bridging
this
aims
provide
a
holistic
framework
advancing
research
plant
biology
agriculture.
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.
Cell Research,
Год журнала:
2023,
Номер
33(8), С. 585 - 603
Опубликована: Июнь 19, 2023
Abstract
Dissecting
and
understanding
the
cancer
ecosystem,
especially
that
around
tumor
margins,
which
have
strong
implications
for
cell
infiltration
invasion,
are
essential
exploring
mechanisms
of
metastasis
developing
effective
new
treatments.
Using
a
novel
border
scanning
digitization
model
enabled
by
nanoscale
resolution-SpaTial
Enhanced
REsolution
Omics-sequencing
(Stereo-seq),
we
identified
500
µm-wide
zone
centered
in
patients
with
liver
cancer,
referred
to
as
“the
invasive
zone”.
We
detected
immunosuppression,
metabolic
reprogramming,
severely
damaged
hepatocytes
this
zone.
also
subpopulation
increased
expression
serum
amyloid
A1
A2
(referred
collectively
SAAs)
located
close
on
paratumor
side.
Overexpression
CXCL6
adjacent
malignant
cells
could
induce
activation
JAK-STAT3
pathway
nearby
hepatocytes,
subsequently
caused
SAAs’
overexpression
these
hepatocytes.
Furthermore,
secretion
SAAs
lead
recruitment
macrophages
M2
polarization,
further
promoting
local
potentially
resulting
progression.
Clinical
association
analysis
additional
five
independent
cohorts
primary
secondary
(
n
=
423)
showed
had
worse
prognosis.
Further
vivo
experiments
using
mouse
models
situ
confirmed
knockdown
genes
encoding
decreased
macrophage
accumulation
delayed
growth.
The
identification
characterization
human
not
only
add
an
important
layer
regarding
invasion
metastasis,
but
may
pave
way
therapeutic
strategies
advanced
other
solid
tumors.
Computational and Structural Biotechnology Journal,
Год журнала:
2022,
Номер
20, С. 4870 - 4884
Опубликована: Янв. 1, 2022
Transcriptome
level
expression
data
connected
to
the
spatial
organization
of
cells
and
molecules
would
allow
a
comprehensive
understanding
how
gene
is
structure
function
in
biological
systems.
The
transcriptomics
platforms
may
soon
provide
such
information.
However,
current
still
lack
resolution,
capture
only
fraction
transcriptome
heterogeneity,
or
throughput
for
large
scale
studies.
strengths
weaknesses
ST
computational
solutions
need
be
taken
into
account
when
planning
basis
analysis
developed
single-cell
RNA-sequencing
data,
with
advancements
taking
connectedness
transcriptomes.
scRNA-seq
tools
are
modified
new
like
deep
learning-based
joint
expression,
spatial,
image
extract
information
spatially
resolved
can
reveal
remarkable
insights
patterns
cell
signaling,
type
variations
connection
type-specific
signaling
complex
tissues.
This
review
covers
topics
that
help
choosing
platform
research.
We
focus
on
currently
available
methods
their
limitations.
Of
solutions,
we
an
overview
steps
used
analysis.
compatibility
types
provided
by
frameworks
summarized.
Genomics,
Год журнала:
2023,
Номер
115(5), С. 110671 - 110671
Опубликована: Июнь 21, 2023
The
diverse
cell
types
of
an
organ
have
a
highly
structured
organization
to
enable
their
efficient
and
correct
function.
To
fully
appreciate
gene
functions
in
given
type,
one
needs
understand
how
much,
when
where
the
is
expressed.
Classic
bulk
RNA
sequencing
popular
single
destroy
structural
fail
provide
spatial
information.
However,
location
expression
or
complex
tissue
provides
key
clues
comprehend
neighboring
genes
cells
cross
talk,
transduce
signals
work
together
as
team
complete
job.
functional
requirement
for
content
has
been
driving
force
rapid
development
transcriptomics
technologies
past
few
years.
Here,
we
present
overview
current
with
special
focus
on
commercially
available
currently
being
commercialized
technologies,
highlight
applications
by
category
discuss
experimental
considerations
first
experiment.
Journal of genetics and genomics/Journal of Genetics and Genomics,
Год журнала:
2023,
Номер
50(9), С. 625 - 640
Опубликована: Март 27, 2023
The
ability
to
explore
life
kingdoms
is
largely
driven
by
innovations
and
breakthroughs
in
technology,
from
the
invention
of
microscope
350
years
ago
recent
emergence
single-cell
sequencing,
which
scientific
community
has
been
able
visualize
at
an
unprecedented
resolution.
Most
recently,
Spatially
Resolved
Transcriptomics
(SRT)
technologies
have
filled
gap
probing
spatial
or
even
three-dimensional
organization
molecular
foundation
behind
mysteries
life,
including
origin
different
cellular
populations
developed
totipotent
cells
human
diseases.
In
this
review,
we
introduce
progress
challenges
on
SRT
perspectives
bioinformatic
tools,
as
well
representative
applications.
With
currently
fast-moving
promising
results
early
adopted
research
projects,
can
foresee
bright
future
such
new
tools
understanding
most
profound
analytical
level.
Nucleic Acids Research,
Год журнала:
2023,
Номер
52(D1), С. D1053 - D1061
Опубликована: Ноя. 11, 2023
Abstract
Recent
technological
developments
in
spatial
transcriptomics
allow
researchers
to
measure
gene
expression
of
cells
and
their
locations
at
the
single-cell
level,
generating
detailed
biological
insight
into
processes.
A
comprehensive
database
could
facilitate
sharing
transcriptomic
data
streamline
acquisition
process
for
researchers.
Here,
we
present
Spatial
TranscriptOmics
DataBase
(STOmicsDB),
a
that
serves
as
one-stop
hub
transcriptomics.
STOmicsDB
integrates
218
manually
curated
datasets
representing
17
species.
We
annotated
cell
types,
identified
regions
genes,
performed
cell-cell
interaction
analysis
these
datasets.
features
user-friendly
interface
rapid
visualization
millions
cells.
To
further
reusability
interoperability
data,
developed
standards
archiving
constructed
system.
Additionally,
offer
distinctive
capability
customizing
dedicated
sub-databases
researchers,
assisting
them
visualizing
analyses.
believe
contribute
research
insights
field,
including
archiving,
sharing,
analysis.
is
freely
accessible
https://db.cngb.org/stomics/.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2023,
Номер
unknown
Опубликована: Май 11, 2023
Abstract
Single-cell
genomics
technologies
enable
multimodal
profiling
of
millions
cells
across
temporal
and
spatial
dimensions.
Experimental
limitations
prevent
the
measurement
all-encompassing
cellular
states
in
their
native
dynamics
or
tissue
niche.
Optimal
transport
theory
has
emerged
as
a
powerful
tool
to
overcome
such
constraints,
enabling
recovery
original
context.
However,
most
algorithmic
implementations
currently
available
have
not
kept
up
pace
with
increasing
dataset
complexity,
so
that
current
methods
are
unable
incorporate
information
scale
single-cell
atlases.
Here,
we
introduce
multi-omics
optimal
(moscot),
general
scalable
framework
for
applications
genomics,
supporting
multimodality
all
applications.
We
demonstrate
moscot’s
ability
efficiently
reconstruct
developmental
trajectories
1.7
million
mouse
embryos
20
time
points
identify
driver
genes
first
heart
field
formation.
The
moscot
formulation
can
be
used
dimensions
well:
To
this,
enrich
transcriptomics
datasets
by
mapping
from
profiles
liver
sample,
align
multiple
coronal
sections
brain.
then
present
moscot.spatiotemporal,
new
approach
leverages
gene
expression
uncover
spatiotemporal
embryogenesis.
Finally,
disentangle
lineage
relationships
novel
murine,
time-resolved
pancreas
development
using
paired
measurements
chromatin
accessibility,
finding
evidence
shared
ancestry
between
delta
epsilon
cells.
Moscot
is
an
easy-to-use,
open-source
python
package
extensive
documentation
at
https://moscot-tools.org
.
