The
genomics
of
human
development
Understanding
the
trajectory
a
developing
requires
an
understanding
how
genes
are
regulated
and
expressed.
Two
papers
now
present
pooled
approach
using
three
levels
combinatorial
indexing
to
examine
single-cell
gene
expression
chromatin
landscapes
from
15
organs
in
fetal
samples.
Cao
et
al.
focus
on
measurements
RNA
broadly
distributed
cell
types
provide
insights
into
organ
specificity.
Domcke
examined
accessibility
cells
these
identify
regulatory
elements
that
regulate
expression.
Together,
analyses
generate
comprehensive
atlases
early
development.
Science
,
this
issue
p.
eaba7721
eaba7612
Nature Communications,
Год журнала:
2018,
Номер
9(1)
Опубликована: Дек. 11, 2018
The
assay
for
transposase-accessible
chromatin
using
sequencing
(ATAC-seq)
is
widely
used
to
identify
regulatory
regions
throughout
the
genome.
However,
very
few
studies
have
been
performed
at
single
cell
level
(scATAC-seq)
due
technical
challenges.
Here
we
developed
a
simple
and
robust
plate-based
scATAC-seq
method,
combining
upfront
bulk
Tn5
tagging
with
single-nuclei
sorting.
We
demonstrate
that
our
method
works
robustly
across
various
systems,
including
fresh
cryopreserved
cells
from
primary
tissues.
By
profiling
over
3000
splenocytes,
distinct
immune
types
reveal
type-specific
related
transcription
factors.
Cancer
chromatin
accessibility
landscape
The
Genome
Atlas
(TCGA)
provides
a
high-quality
resource
of
molecular
data
on
large
variety
human
cancers.
Corces
et
al.
used
recently
modified
assay
to
profile
determine
the
accessible
in
410
TCGA
samples
from
23
cancer
types
(see
Perspective
by
Taipale).
When
were
integrated
with
other
omics
available
for
same
tumor
samples,
inherited
risk
loci
predisposition
revealed,
transcription
factors
and
enhancers
driving
subtypes
patient
survival
differences
identified,
noncoding
mutations
associated
clinical
prognosis
discovered.
Science
,
this
issue
p.
eaav1898
;
see
also
401
Nature Genetics,
Год журнала:
2021,
Номер
53(3), С. 403 - 411
Опубликована: Фев. 25, 2021
Abstract
The
advent
of
single-cell
chromatin
accessibility
profiling
has
accelerated
the
ability
to
map
gene
regulatory
landscapes
but
outpaced
development
scalable
software
rapidly
extract
biological
meaning
from
these
data.
Here
we
present
a
suite
for
analysis
in
R
(ArchR;
https://www.archrproject.com/
)
that
enables
fast
and
comprehensive
ArchR
provides
an
intuitive,
user-focused
interface
complex
analyses,
including
doublet
removal,
clustering
cell
type
identification,
unified
peak
set
generation,
cellular
trajectory
DNA
element-to-gene
linkage,
transcription
factor
footprinting,
mRNA
expression
level
prediction
multi-omic
integration
with
RNA
sequencing
(scRNA-seq).
Enabling
over
1.2
million
single
cells
within
8
h
on
standard
Unix
laptop,
is
end-to-end
will
accelerate
understanding
regulation
at
resolution
individual
cells.
