bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Авг. 13, 2024
ABSTRACT
Recent
advances
in
barcoding
technologies
have
significantly
enhanced
the
scalability
of
single-cell
genomic
experiments.
However,
large-scale
experiments
are
still
rare
due
to
high
costs,
complex
logistics,
and
laborintensive
procedures.
To
facilitate
routine
application
largest
scalability,
it
is
critical
simplify
production
use
reagents.
Here,
we
introduce
AmpliDrop,
a
technology
that
initiates
process
using
pool
inexpensive
single-copy
barcodes
integrates
barcode
multiplicity
generation
with
tagging
cellular
content
into
single
reaction
driven
by
DNA
polymerase
during
library
preparation.
The
reactions
compartmentalized
an
electronic
pipette
or
robotic
standalone
liquid
handling
system.
These
innovations
eliminate
need
for
barcoded
beads
combinatorial
indexing
workflows
provide
flexibility
wide
range
scales
tube
formats,
as
well
compatibility
automation.
We
show
AmpliDrop
capable
capturing
transcriptomes
chromatin
accessibility,
can
also
be
adapted
user-customized
applications,
including
antibody-based
protein
detection,
bacterial
viral
CRISPR
perturbations
without
dual
guide
RNA-expression
vectors.
validated
investigating
influence
short-term
static
culturing
on
cell
composition
human
forebrain
organoids,
revealing
metabolic
reprogramming
lineage
progenitors.
Clinical and Translational Medicine,
Год журнала:
2024,
Номер
14(9)
Опубликована: Сен. 1, 2024
Abstract
With
rapid
development
and
mature
of
single‐cell
measurements,
biology
pathology
become
an
emerging
discipline
to
understand
the
disease.
However,
it
is
important
address
concerns
raised
by
clinicians
as
how
apply
measurements
for
clinical
practice,
translate
signals
systems
into
determination
phenotype,
predict
patient
response
therapies.
The
present
Perspective
proposes
a
new
system
coined
artificial
intelligent
(caiSC)
with
dynamic
generator
informatics,
analyzers,
molecular
multimodal
reference
boxes,
inputs
outs,
AI‐based
computerization.
This
provides
reliable
information
impacting
diagnoses,
monitoring,
prediction
disease
at
level.
caiSC
represents
step
milestone
measurement
application,
assist
clinicians’
decision‐making,
improve
quality
medical
services.
There
increasing
evidence
support
possibility
proposal,
since
corresponding
biotechnologies
associated
caiSCs
are
rapidly
developed.
Therefore,
we
call
special
attention
efforts
from
various
scientists
on
believe
that
appearance
can
shed
light
future
medicine.
Advances
in
spatially-resolved
transcriptomics
(SRT)
technologies
have
propelled
the
development
of
new
computational
analysis
methods
to
unlock
biological
insights.
The
lowering
cost
SRT
data
generation
presents
an
unprecedented
opportunity
create
large-scale
spatial
atlases
and
enable
population-level
investigation,
integrating
across
multiple
tissues,
individuals,
species,
or
phenotypes.
Here,
unique
challenges
are
described
integration,
where
analytic
impact
varying
resolutions
is
characterized
explored.
A
succinct
review
spatially-aware
integration
strategies
provided.
Exciting
opportunities
advance
algorithms
amenable
atlas-scale
datasets
along
with
standardized
preprocessing
methods,
leading
improved
sensitivity
reproducibility
future
further
highlighted.
Nucleic Acids Research,
Год журнала:
2025,
Номер
53(1)
Опубликована: Янв. 7, 2025
Abstract
Cross-species
single-cell
RNA-seq
data
hold
immense
potential
for
unraveling
cell
type
evolution
and
transferring
knowledge
between
well-explored
less-studied
species.
However,
challenges
arise
from
interspecific
genetic
variation,
batch
effects
stemming
experimental
discrepancies
inherent
individual
biological
differences.
Here,
we
benchmarked
nine
data-integration
methods
across
20
species,
encompassing
4.7
million
cells,
spanning
eight
phyla
the
entire
animal
taxonomic
hierarchy.
Our
evaluation
reveals
notable
differences
in
removing
preserving
variance
distances.
Methods
that
effectively
leverage
gene
sequence
information
capture
underlying
variances,
while
generative
model-based
approaches
excel
effect
removal.
SATURN
demonstrates
robust
performance
diverse
levels,
cross-genus
to
cross-phylum,
emphasizing
its
versatility.
SAMap
excels
integrating
species
beyond
cross-family
level,
especially
atlas-level
cross-species
integration,
scGen
shines
within
or
below
cross-class
As
a
result,
our
analysis
offers
recommendations
guidelines
selecting
suitable
integration
methods,
enhancing
analyses
advancing
algorithm
development.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 29, 2025
Abstract
Detecting
cell-cell
communications
(CCCs)
in
single-cell
transcriptomics
studies
is
fundamental
for
understanding
the
function
of
multicellular
organisms.
Here,
we
introduce
FastCCC,
a
permutation-free
framework
that
enables
scalable,
robust,
and
reference-based
analysis
identifying
critical
CCCs
uncovering
biological
insights.
FastCCC
relies
on
fast
Fourier
transformation-based
convolution
to
compute
p
-values
analytically
without
permutations,
introduces
modular
algebraic
operation
capture
broad
spectrum
CCC
patterns,
can
leverage
atlas-scale
single
cell
references
enhance
user-collected
datasets.
To
support
routine
analysis,
constructed
first
human
reference
panel,
encompassing
19
distinct
tissue
types,
over
450
unique
approximately
16
million
cells.
We
demonstrate
advantages
across
multiple
datasets,
most
which
exceed
analytical
capabilities
existing
methods.
In
real
reliably
captures
biologically
meaningful
CCCs,
even
highly
complex
environments,
including
differential
interactions
between
endothelial
immune
cells
linked
COVID-19
severity,
dynamic
thymic
during
T-cell
development,
as
well
analysis.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 7, 2025
The
integration
of
genetic
and
chemical
perturbations
has
driven
transformative
advances
in
elucidating
cellular
mechanisms
accelerating
drug
discovery.
However,
the
lack
a
unified
representation
for
diverse
perturbagen
types
limits
comprehensive
analysis
joint
modeling
multi-domain
perturbation
agents
(molecular
cause
space)
their
resulting
phenotypes
(phenotypic
effect
spaces).
Here,
we
present
UniPert,
hybrid
deep
learning
framework
that
encodes
perturbagens
into
shared
semantic
space.
UniPert
employs
tailored
encoders
to
address
inherent
molecular-scale
differences
across
leverages
contrastive
with
experiment-driven
compound-target
interactions
bridge
these
domains.
Extensive
experiments
validate
UniPert’s
versatility
application.
generated
representations
effectively
capture
hierarchical
pharmacological
relationships
perturbagens,
facilitating
annotations
understudied
targets
compounds.
can
be
plugged
advanced
frameworks
enhance
performance
both
outcome
prediction
tasks.
Notably,
paves
way
cross-domain
modeling,
driving
novel
genetic-to-chemical
transfer
paradigm,
boosting
context-specific
silico
screening
efficiency
development
personalized
therapies.
