Developmental Cell,
Journal Year:
2018,
Volume and Issue:
46(4), P. 504 - 517.e7
Published: Aug. 1, 2018
Pdgfra+
oligodendrocyte
precursor
cells
(OPCs)
arise
in
distinct
specification
waves
during
embryogenesis
the
central
nervous
system
(CNS).
It
is
unclear
whether
there
a
correlation
between
these
and
different
(OL)
states
at
adult
stages.
Here,
we
present
bulk
single-cell
transcriptomics
resources
providing
insights
on
how
transitions
occur.
We
found
that
post-natal
OPCs
from
brain
spinal
cord
similar
transcriptional
signatures.
Moreover,
OPC
progeny
of
E13.5
electrophysiological
profiles
to
derived
subsequent
waves,
indicating
pre-OPCs
rewire
their
network
development.
Single-cell
RNA-seq
lineage
tracing
indicates
subset
originates
pericyte
lineage.
Thus,
our
results
indicate
embryonic
CNS
give
rise
cell
lineages,
including
with
convergent
regions.
Genome biology,
Journal Year:
2017,
Volume and Issue:
18(1)
Published: May 5, 2017
High-throughput
technologies
have
revolutionized
medical
research.
The
advent
of
genotyping
arrays
enabled
large-scale
genome-wide
association
studies
and
methods
for
examining
global
transcript
levels,
which
gave
rise
to
the
field
"integrative
genetics".
Other
omics
technologies,
such
as
proteomics
metabolomics,
are
now
often
incorporated
into
everyday
methodology
biological
researchers.
In
this
review,
we
provide
an
overview
focus
on
their
integration
across
multiple
layers.
As
compared
a
single
type,
multi-omics
offers
opportunity
understand
flow
information
that
underlies
disease.
Briefings in Bioinformatics,
Journal Year:
2016,
Volume and Issue:
unknown, P. bbw139 - bbw139
Published: Dec. 8, 2016
Gene
co-expression
networks
can
be
used
to
associate
genes
of
unknown
function
with
biological
processes,
prioritize
candidate
disease
or
discern
transcriptional
regulatory
programmes.
With
recent
advances
in
transcriptomics
and
next-generation
sequencing,
constructed
from
RNA
sequencing
data
also
enable
the
inference
functions
associations
for
non-coding
splice
variants.
Although
gene
typically
do
not
provide
information
about
causality,
emerging
methods
differential
analysis
are
enabling
identification
underlying
various
phenotypes.
Here,
we
introduce
guide
researchers
through
a
(differential)
analysis.
We
an
overview
tools
create
analyse
expression
data,
explain
how
these
identify
role
disease.
Furthermore,
discuss
integration
other
types
offer
future
perspectives
PLoS Computational Biology,
Journal Year:
2017,
Volume and Issue:
13(5), P. e1005457 - e1005457
Published: May 18, 2017
Transcriptomics
technologies
are
the
techniques
used
to
study
an
organism's
transcriptome,
sum
of
all
its
RNA
transcripts.
The
information
content
organism
is
recorded
in
DNA
genome
and
expressed
through
transcription.
Here,
mRNA
serves
as
a
transient
intermediary
molecule
network,
whilst
noncoding
RNAs
perform
additional
diverse
functions.
A
transcriptome
captures
snapshot
time
total
transcripts
present
cell.
first
attempts
whole
began
early
1990s,
technological
advances
since
late
1990s
have
made
transcriptomics
widespread
discipline.
has
been
defined
by
repeated
innovations
that
transform
field.
There
two
key
contemporary
field:
microarrays,
which
quantify
set
predetermined
sequences,
sequencing
(RNA-Seq),
uses
high-throughput
capture
sequences.
Measuring
expression
genes
different
tissues,
conditions,
or
points
gives
on
how
regulated
reveals
details
biology.
It
can
also
help
infer
functions
previously
unannotated
genes.
Transcriptomic
analysis
enabled
gene
changes
organisms
instrumental
understanding
human
disease.
An
entirety
allows
detection
broad
coordinated
trends
cannot
be
discerned
more
targeted
assays.
