Nucleic Acids Research,
Journal Year:
2024,
Volume and Issue:
53(D1), P. D1173 - D1185
Published: Oct. 29, 2024
Abstract
Single-cell
lineage
tracing
(scLT)
is
a
powerful
technique
that
integrates
cellular
barcoding
with
single-cell
sequencing
technologies.
This
new
approach
enables
the
simultaneous
measurement
of
cell
fate
and
molecular
profiles
at
resolution,
uncovering
gene
regulatory
program
determination.
However,
comprehensive
scLT
database
not
yet
available.
Here,
we
present
(scLTdb,
https://scltdb.com)
containing
109
datasets
are
manually
curated
analyzed
through
standard
pipeline.
The
scLTdb
provides
interactive
analysis
modules
for
visualizing
re-analyzing
datasets,
especially
relationship
analysis.
Importantly,
also
allows
users
to
identify
fate-related
signatures.
In
conclusion,
an
interface
data
exploration
analysis,
will
facilitate
understanding
decision
commitment
in
development
diseases.
Science,
Journal Year:
2024,
Volume and Issue:
385(6714)
Published: Sept. 12, 2024
Macrophages
maintain
hematopoietic
stem
cell
(HSC)
quality
by
assessing
surface
Calreticulin
(Calr),
an
“eat-me”
signal
induced
reactive
oxygen
species
(ROS).
Using
zebrafish
genetics,
we
identified
Beta-2-microglobulin
(B2m)
as
a
crucial
“don’t
eat-me”
on
blood
cells.
A
chemical
screen
revealed
inducers
of
Calr
that
promoted
HSC
proliferation
without
triggering
ROS
or
macrophage
clearance.
Whole-genome
CRISPR-Cas9
screening
showed
Toll-like
receptor
3
(Tlr3)
signaling
regulated
b2m
expression.
Targeting
tlr3
reduced
the
clonality.
Elevated
B2m
levels
correlated
with
high
expression
repetitive
element
(RE)
transcripts.
Overall,
our
data
suggest
RE-associated
double-stranded
RNA
could
interact
TLR3
to
stimulate
and
progenitor
These
findings
balance
regulates
macrophage-HSC
interactions
defines
The Journal of Experimental Medicine,
Journal Year:
2025,
Volume and Issue:
222(6)
Published: March 12, 2025
Leukemia-driving
mutations
are
thought
to
arise
in
hematopoietic
stem
cells
(HSC),
yet
the
natural
history
of
their
spread
is
poorly
understood.
We
genetically
induced
within
endogenous
murine
HSC
and
traced
them
unmanipulated
animals.
In
contrast
associated
with
clonal
hematopoiesis
(such
as
Tet2
deletion),
leukemogenic
KrasG12D
mutation
dramatically
accelerated
contribution
all
lineages.
The
acceleration
was
mediated
by
KrasG12D-expressing
multipotent
progenitors
(MPP)
that
lacked
self-renewal
but
showed
increased
proliferation
aberrant
transcriptome.
deletion
osteopontin,
a
secreted
negative
regulator
stem/progenitor
cells,
delayed
early
expansion
mutant
progenitors.
KrasG12D-carrying
CXCR4-driven
motility
bone
marrow,
blockade
CXCR4
reduced
MPP
vivo.
Finally,
therapeutic
KRASG12D
spared
mature
progeny.
Thus,
transforming
facilitate
own
from
reprogramming
MPP,
creating
preleukemic
state
via
two-component
circuit.
Cureus,
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 14, 2025
Lineage
tracing
is
an
essential
tool
for
understanding
cellular
development
and
tissue
dynamics.
This
review
examines
retrospective
lineage
as
optimal
approach
studying
contrasts
with
prospective
methods.
Retrospective
approaches
leverage
naturally
occurring
genetic
barcodes,
such
single
nucleotide
polymorphisms
(SNPs),
copy
number
variants
(CNVs),
mitochondrial
DNA
mutations,
which
enables
the
detailed
reconstruction
of
cell
lineages
without
prior
manipulation.
Researchers
can
ultimately
infer
developmental
trajectories
clonal
relationships
across
hematopoiesis
tumorigenesis
by
analyzing
these
endogenous
markers.
paper
considers
how
methods
circumvent
limitations
approaches,
need
exogenous
labeling,
valuable
human
hematopoiesis.
Annual Review of Immunology,
Journal Year:
2025,
Volume and Issue:
43(1), P. 693 - 722
Published: April 25, 2025
The
immune
system,
critical
for
human
health
and
implicated
in
many
diseases,
defends
against
pathogens,
monitors
physiological
stress,
maintains
tissue
organismal
homeostasis.
It
exhibits
substantial
variability
both
within
across
individuals
populations.
Recent
technological
conceptual
progress
systems
immunology
has
provided
predictive
insights
that
link
personal
states
to
intervention
responses
disease
susceptibilities.
Artificial
intelligence
(AI),
particularly
machine
learning
(ML),
emerged
as
a
powerful
tool
analyzing
complex
data
sets,
revealing
hidden
patterns
biological
scales,
enabling
models
individualistic
potentially
personalized
interventions.
This
review
highlights
recent
advances
deciphering
variation
predicting
outcomes,
through
the
concepts
of
setpoint,
health,
use
system
window
measuring
health.
We
also
provide
brief
history
AI;
ML
modeling
approaches,
including
their
applications
immunology;
explore
potential
AI
develop
state
embeddings
detect
early
signs
disease,
forecast
interventions,
guide
strategies.
