bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 8, 2024
Abstract
Advancing
our
understanding
of
tissue
organization
and
its
disruptions
in
disease
remains
a
key
focus
biomedical
research.
Histological
slides
stained
with
Hematoxylin
Eosin
(H&E)
provide
an
abundant
source
morphological
information,
while
Spatial
Transcriptomics
(ST)
enables
detailed,
spatiallyresolved
gene
expression
(GE)
analysis,
though
at
high
cost
limited
clinical
accessibility.
Predicting
GE
directly
from
H&E
images
using
ST
as
reference
has
thus
become
attractive
objective;
however,
current
patch-based
approaches
lack
single-cell
resolution.
Here,
we
present
sCellST,
multipleinstance
learning
model
that
predicts
by
leveraging
cell
morphology
alone,
achieving
remarkable
predictive
accuracy.
When
tested
on
pancreatic
ductal
adenocarcinoma
dataset,
sCellST
outperformed
traditional
methods,
underscoring
the
value
basing
predictions
rather
than
patches.
Additionally,
demonstrate
can
detect
subtle
differences
among
types
utilizing
marker
genes
ovarian
cancer
samples.
Our
findings
suggest
this
approach
could
enable
level
across
large
cohorts
H&E-stained
slides,
providing
innovative
means
to
valorize
resource
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Июнь 3, 2024
Abstract
The
interconnection
between
wound
healing
and
cancer
has
long
been
recognized,
as
epitomized
by
the
expression
“cancer
is
a
that
does
not
heal”.
However,
impact
of
inducing
wound,
such
through
biopsy
collection,
on
progression
established
tumors
remains
largely
unknown.
In
this
study,
we
apply
single-cell
spatial
transcriptomics
to
characterize
heterogeneity
human
basal
cell
carcinoma
(BCC)
identify
response
gene
program
most
prominent
feature
highly
invasive
BCC.
To
explore
causal
relationship
wounding
progression,
perform
longitudinal
experiment
compare
at
baseline
one
week
post-biopsy.
Our
results
demonstrate
collection
triggers,
in
proximity
same
transcriptional
state
observed
This
switch
coupled
with
morphological
changes
reprogramming
cancer-associated
fibroblasts
(CAFs).
study
provides
evidence
triggers
warrants
further
research
potentially
harmful
effects
biopsies
wound-inducing
treatments.
Summary
Single-cell
metabolomics
promises
to
resolve
metabolic
cellular
heterogeneity,
yet
current
methods
struggle
with
detecting
small
molecules,
throughput,
and
reproducibility.
Addressing
these
gaps,
we
developed
HT
SpaceM,
a
high-throughput
single-cell
method
novel
cell
preparation,
custom
glass
slides,
small-molecule
MALDI
imaging
mass
spectrometry
protocol,
batch
processing.
We
propose
unified
framework
covering
essential
data
analysis
steps
including
quality
control,
characterization,
differential
analysis,
structural
validation
functional
analysis.
Interrogating
human
HeLa
mouse
NIH3T3
cells,
detected
73
diverse
metabolites
validated
by
bulk
LC-MS/MS,
achieving
high
reproducibility
across
wells
slides.
nine
NCI-60
cancer
cells
HeLa,
identified
cell-type
markers
in
subpopulations.
Functional
revealed
overrepresented
pathways,
co-abundant
metabolites,
hubs.
demonstrate
the
ability
of
SCM
analyze
over
120,000
from
112
samples,
provide
guidance
interpret
revealing
insights
beyond
population
averages.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 8, 2024
Abstract
Advancing
our
understanding
of
tissue
organization
and
its
disruptions
in
disease
remains
a
key
focus
biomedical
research.
Histological
slides
stained
with
Hematoxylin
Eosin
(H&E)
provide
an
abundant
source
morphological
information,
while
Spatial
Transcriptomics
(ST)
enables
detailed,
spatiallyresolved
gene
expression
(GE)
analysis,
though
at
high
cost
limited
clinical
accessibility.
Predicting
GE
directly
from
H&E
images
using
ST
as
reference
has
thus
become
attractive
objective;
however,
current
patch-based
approaches
lack
single-cell
resolution.
Here,
we
present
sCellST,
multipleinstance
learning
model
that
predicts
by
leveraging
cell
morphology
alone,
achieving
remarkable
predictive
accuracy.
When
tested
on
pancreatic
ductal
adenocarcinoma
dataset,
sCellST
outperformed
traditional
methods,
underscoring
the
value
basing
predictions
rather
than
patches.
Additionally,
demonstrate
can
detect
subtle
differences
among
types
utilizing
marker
genes
ovarian
cancer
samples.
Our
findings
suggest
this
approach
could
enable
level
across
large
cohorts
H&E-stained
slides,
providing
innovative
means
to
valorize
resource
