Trends in biotechnology,
Journal Year:
2021,
Volume and Issue:
40(6), P. 647 - 676
Published: Dec. 28, 2021
Tumors
are
unique
and
complex
ecosystems,
in
which
heterogeneous
cell
subpopulations
with
variable
molecular
profiles,
aggressiveness,
proliferation
potential
coexist
interact.
Understanding
how
heterogeneity
influences
tumor
progression
has
important
clinical
implications
for
improving
diagnosis,
prognosis,
treatment
response
prediction.
Several
recent
innovations
data
acquisition
methods
computational
metrics
have
enabled
the
quantification
of
spatiotemporal
across
different
scales
organization.
Here,
we
summarize
most
promising
efforts
from
a
common
experimental
perspective,
discussing
their
advantages,
shortcomings,
challenges.
With
personalized
medicine
entering
new
era
unprecedented
opportunities,
our
vision
is
that
future
workflows
integrating
modalities,
scales,
dimensions
to
capture
intricate
aspects
ecosystem
open
avenues
improved
patient
care.
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.
Journal of Hematology & Oncology,
Journal Year:
2021,
Volume and Issue:
14(1)
Published: June 9, 2021
Single-cell
sequencing,
including
genomics,
transcriptomics,
epigenomics,
proteomics
and
metabolomics
is
a
powerful
tool
to
decipher
the
cellular
molecular
landscape
at
single-cell
resolution,
unlike
bulk
which
provides
averaged
data.
The
use
of
sequencing
in
cancer
research
has
revolutionized
our
understanding
biological
characteristics
dynamics
within
lesions.
In
this
review,
we
summarize
emerging
technologies
recent
progress
obtained
by
information
related
landscapes
malignant
cells
immune
cells,
tumor
heterogeneity,
circulating
underlying
mechanisms
behaviors.
Overall,
prospects
facilitating
diagnosis,
targeted
therapy
prognostic
prediction
among
spectrum
tumors
are
bright.
near
future,
advances
will
undoubtedly
improve
highlight
potential
precise
therapeutic
targets
for
patients.
Science,
Journal Year:
2023,
Volume and Issue:
381(6657)
Published: Aug. 3, 2023
Spatial
omics
has
been
widely
heralded
as
the
new
frontier
in
life
sciences.
This
term
encompasses
a
wide
range
of
techniques
that
promise
to
transform
many
areas
biology
and
eventually
revolutionize
pathology
by
measuring
physical
tissue
structure
molecular
characteristics
at
same
time.
Although
field
came
age
past
5
years,
it
still
suffers
from
some
growing
pains:
barriers
entry,
robustness,
unclear
best
practices
for
experimental
design
analysis,
lack
standardization.
In
this
Review,
we
present
systematic
catalog
different
families
spatial
technologies;
highlight
their
principles,
power,
limitations;
give
perspective
suggestions
on
biggest
challenges
lay
ahead
incredibly
powerful-but
hard
navigate-landscape.
