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From YouTube: DevoWorm #31: Digital Bacillaria, GSoC Updates, Cell types, development, and regeneration in Axolotl
Description
Contributions to Digital Bacillaria and progress on Digital Microspheres. Cell types and next-gen sequencing, developmental and regeneration in Axolotl, amniotes, and organoids. Attendees: Alon Samuel, Richard Gordon, Bradly Alicea, Susan Crawford-Young, Morgan Hough, Harikrishna Pillai, and Karan Lohaan
A
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A
D
Yes,
so
this
is
a
video
before
that.
There's
a
video
that
thomas
shared
with
me
and
it's
really
good,
because
it's
kind
of
the
resolutions
quite
well.
It's
a,
I
think,
a
thousand
two
hundred
and
eighty
or
seven
hundred
that's
a
hundred
frame
per
second,
but
it's
kinda
like
maybe
not
just
doesn't
matter,
I
think
maybe
so
much.
D
Maybe
it's
kind
of
just
contributes
to
smoothness,
but
it's
also
like
with
just
like
a
single
that
the
camera
doesn't
move
around
so
much
that's
kind
of
good
and
over
that
what
I
did.
I
took
the
optical
flow
from
the
website
and
what
I
did
it
kind
of
find.
It
found
some
points
on
the
screen
or
on
the
basilar
to
kind
of
good
to
track,
and
then
we
you
can
see
that
it.
D
It
shows
the
kind
of
trajectory
of
the
of
these
points
and
I
just
was
kind
of
trying
it
and
kind
of
like
happily
out
of
the
box.
It's
just
like
produced
like
pretty
good
results,
so
that
was
really
good
to
have
you
see,
there's
like
a
bit
of
car
like
the
the
video
is
moving
a
bit,
so
you
can
see
these
kind
of
on
the
tracks
that
it
kind
of
moves
which
is
a
bit
weird,
but
other
than
that.
D
D
Like
from
the
tutorial
kind
of
like
for
opencv,
okay,
yeah
yeah,
this
one
and
the
code
yeah
it's
just
this
one
that
it
took
and
then
just
made
it
work
on
the
on
the
kind
of
video
itself.
I
also
like
added
another
component
that
kind
of
saves
kind
of
like
everything,
just
kind
of
like
the
printing
on
the
on
the
images
and
yeah.
Just
how
that's
how
I
produced
in
the
video.
A
B
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A
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D
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A
D
D
A
Let
me
indicate
one
thing,
which
would
be
fun
to
probably
may
or
may
not
show
up.
We
have
a
hypothesis
that
the
synchronization
occurs
at
the
moment
that
the
two
cells
are
aligned
when
it
occurs
yeah
if
two
cells
are
perfectly
locked
like
this,
you
know
these
they're
on
top
of
each
other.
At
that
point
there
might
be
a
light
piping
effect,
so
the
light
goes
from
one
to
the
other
and
they
can
synchronize
that
way.
A
Maybe
that's
a
place
for
optical
queen's
tomography.
Maybe
it
would.
Might
I
don't
know?
Maybe
the
detector
would
detect
the
light.
Maybe
you
need
to
do
it.
Maybe
you
know
for
stationary
diatoms
there's
a
possibility
to
change
diatoms.
If
you
have
a
colonial
chain,
the
light
goes
from
one
end
to
the
other.
A
A
Oh,
it's
just
a
a
light
pipe.
The
simplest
light.
Pipe
is
simply
a
long,
narrow
piece
of
plastic
or
glass
and
if
you
put
light
in
one
end,
it
comes
out
the
other
okay,
a
fiber
optic
cable
is
a
more
comfortable
optic.
Cable.
All
your
optical
tables
work.
This
way
it's
based
on
the
principle
of
total
internal
reflection,
yeah,
okay,
and
if
you
have
a
clean
enough
glass,
you
can
have
kilometers
of
the
stuff
and
it
works.
A
D
A
D
D
D
Okay,
all
right!
Another
thing
I
did
do
some
more.
I
did
look
at
the
digital
basilaria
and
there
are
some
issues
open
there
from
different
warm.
I
don't
know
who
opened
them,
because
I
don't
see
any
explanation
of
kind
of
like
what
to
do
exactly
there.
Okay,
so
I
don't
know
if
there's
a
maintainer,
can
you
show
us
what
you
mean?
A
D
B
B
D
B
B
B
B
D
Yes,
I
think
I'll
check
if
someone
if
he
wants
to
download
and
run
it
because
I
don't
know
if
I
shared
like
the
requirements
and
an
explanation
to
what
to
do.
There
is
a
devlog,
a
development
blog
connect
there,
okay
in
the
files,
so
that's
also
like
with
images
and
kind
of
like
maybe
some
of
my
notes
of
kinda,
like
what
kind
of
my
research
kind
of
going
on,
and
you
can
see
that
the
yeah
the
last
files
there
like
at
the
bottom
dev
vlog.
A
D
A
D
A
Yeah,
well,
what
you
can
do
is
use
a
microscope,
for
example,
that
tracks
the
depth
of
focus
and
then
automatically
focus.
That
brings
the
image
into
focus
yeah.
There
are
ways
of
handling
it,
but
not
by
image-
processing,
yes
yeah,
but
the
movies
that
thomas
said
they
are
like
really
good
in
focus
it's
what
you
said:
yeah,
he
probably
just
picked
the
segment.
That's
in
focus
see
the
whole
colony
often
well
sometimes
sometimes
appears
to
be
a
spiral,
in
which
case
it
is
three-dimensional.
A
A
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A
A
A
A
A
A
A
D
B
I
don't
know
I
mean
you
know
we
could
do
replicates
on
it
and
see
if
it's
stable
across
the
different
videos.
I
don't
think
the
condition
there.
I
think
there
may
be
a
couple
where
the
conditions
are
a
little
bit
different,
but
I
don't
know
I
haven't
been
through
those
videos
in
a
while,
but
you
have
the
access
to
that
folder
that
I
think
you
have
access
to
the
folder
that
I
think
has
sent
you.
D
B
B
D
B
B
C
Yeah
last
week
I
reckoned
because
I
had
this
mid-same
exam.
You
know
that
was
going
on,
so
I
was
expecting
to
complete
most
of
the
things
before
before
yeah
that
got
delayed
so
yeah.
As
far
as
the
submission
goes,
I
think
it's
starting.
It
will
start
today
by
11
30
p.m.
It's
eight
o'clock
right
now
yeah.
So
I
was
going
through
the
you
know,
work
submission
guidelines,
so
they
have
some.
You
know
it's
just
some
things
that
I've
kind
of
adding
on
okay.
C
C
C
C
B
B
Yeah,
so
there
him
and
harry
krishnan
are
building
these
models,
they're
3d
models,
and-
and
you
know
we
have
this
data
set
of
images
that
were
collected
from
the
outside
of
the
embryo
and
they're
being
stitched
onto
this
3d
volume
and
so
they're
going
to
be,
like
you
know,
some
sort
of
atlas.
You
know
you
can
explore
yeah
and
so
yeah
nice.
A
D
B
Connectome
data
sets
out
there
on
c
elegans
and
you
know
you
have
the
and
then
like
we've
done
some
work
on
development.
The
development
of
the
connectome
sort
of
the
timing
of
when
different
cells
emerge
from
their
developmental
precursors-
and
you
know,
then
you
know
they
get
connected
in
development.
B
A
B
C
Yeah
yeah
so
I'll
be
dividing
it
into
two
versions.
The
second
version
still
has
work
to
be
done.
You
know
on
the
projection
part,
so
I'll
you
know
write
that
this.
This
much
has
already
been
done.
You
know
the
documentation
itself
as
far
as
improvements
go
and
but
since
the
three
goals
were
there,
you
know
keeping
them
in
mind.
C
C
Yeah
the
project
with
the
model
is
the
one
that
I
showed
you
yeah
right
now,
so
that
that
will
be
like
version
one.
You
know
I'll
explain
in
the
documentation.
How
to
you
know,
convert
your
images
into
3d
model
using
the
steps
we
had
done
with
the
earlier
mockup.
For
the
second
part,
I'll
mentioned
what
all
improvements
have
taken
place.
You
know
in
step
one
and
step
two
step.
C
So
testing
was
there
that
you
know
I
was
kind
of
not
thought
of
before,
but
if
somebody
uses
different
images,
you
know
different
sizes
different,
you
know
formats
and
all
those
things
or
if
they
have
images
which
have
slightly
more
embryos
that
have
that
are
slightly
more
offset.
From
the
I
mean
there
are
a
lot
of
ways.
You
know
a
lot
of
outliers
that
would
not
arrive
at
the
same
results.
So
that
is
that
so
this
testing
thing,
that
is
there
that
is
kind
of
that
will
probably
do
by
tomorrow
and
updated.
B
Okay,
good
yeah,
well
yeah.
I
just
wanna
make
sure
we're
on
track
for
that
and
yeah.
Please
follow
the
guidelines
and
yeah
good
luck
with
the
last
week,
and
so
that
that
you
know
this
is
kind
of
like
the
home
stretch,
just
making
sure
everything's
in
place
and
yeah.
It
looks
like
it's.
You
know
at
some
point
we'll
you
know.
B
B
So
yeah,
just
let
us
know,
and
then
the
next
step
after
this
would
be
to
like
see
where
we
are
with
the
projects,
so
hari,
krishna
and
koran
are
both
working
on
the
same
thing
but
doing
different
doing
it
in
different
ways,
and
so
now
you
know
if
someone
wants
to
use
this
as
a
something
you
know
something
they
can
use
for
some
data
set
that
they're
collecting
you
know
they
can
actually
use
it
and
have
a
couple
of.
I
think
I
have
some
options
for
what
they
want
to
do
with
it.
B
B
B
This
is
okay,
yeah,
guided
graph
spectral,
embedding
application
of
the
c
elegans
connectome.
So
this
is
graph
spectral
analysis,
so
there's
a
lot
of
stuff.
Of
course
we
talk
about
the
connectome,
we're
talking
about
networks
and
networks
of
neurons
and
c.
Elegans
is
nice
because
it
has
like
302
neurons.
So
we
know
every
sort
of
connection.
We
know
the
gap,
junction
connections
or
the
electrical
connections,
and
we
have
a
pretty
good
handle
on
the
synaptic
connections
or
the
chemical
connections.
The
chemical
connections
have
in
development.
B
It's
really
interesting
because
there's
a
lot
of
variation
with
respect
to
you
know
which
connections
are
made
and
which
are
sort
of
pruned
back
in
development.
So
there's
a
paper
that
it
came
out
like
I
think,
two
years
ago
now
or
maybe
last
year,
where
they
did
this
study
of
different
larval
stages
of
c
elegans
and
they
looked
at
the
synapses,
the
synaptic
connections
over
these
different
larval
stages
and
they
show
there's
actually
a
lot
of
plasticity
in
the
synaptic
connections.
B
B
A
B
Neuron
thing
in
c
elegans
you
have
a
neuron
by
neuron
characterization
and
then
there's
some
also
for
drosophila
and
for
zebrafish.
There
are
also
good
connectome
datasets.
There
too.
This
looks
like
they're
doing
some
things
with
mathematical
processing
graph
signal
processing,
which
is
an
area
that
I've
heard
of,
but
I'm
not
really
familiar
with,
and
then
this
is
the
sort
of
the
way.
This
is
their
work
on
that
for
the
c
elegans
connecto
a
lot
of
times,
you'll
see
people
use
the
c
elegans
connect
them
as
a
benchmark
too
for
different
problems.
B
B
B
Which
we
usually
like
in
neuroscience,
because
we
can
map
that
to
a
behavior
in
insects
and
in
c
elegans
and
other
organ
drosophila,
I
think
as
well.
You
actually
have
some
really
nice
established
circuits
for
like
movement
and
for
feeding
and
for
olfaction
things
like
that,
and
you
know
you
have
the
cells
that
are
active
and
you
just
say:
well,
these
cells
are
connected
this
way
and
if
you
activate
them,
then
they
give
you
this
behavior,
and
so
it's
very
nice,
but
you
know
they're.
We.
B
The
entire
connectome,
it's
really
kind
of
difficult
to
know
what
you
know,
what
it's
doing
in
terms
of
what
kinds
of
for
maybe
more
complex,
behaviors
or
other
things.
So
it's
a
nice
set
of
techniques,
so
yeah
morgan.
If
you
could
get
that
person
to
give
a
talk,
it'd
be
great
because
it's
definitely
a
area
that
we
talk
about.
B
I
hope
that
you
know
we
get
our
projects
in
on
time
and
it
looks
like
we'll
and
we're
also
going
to
be
working
on
that
paper
for
that
learning
on
graphs
conference.
But
that's
that's
going
to
be
dependent
on
us
getting
everything
in
on
time,
but
I
think
it'll
be
fine,
okay,
so
yeah!
It's!
Let's
see!
I'm
gonna
talk
about
some
papers
now,
and
I
know
that
morgan
saw
this
and
he
was
excited
about
it.
B
But
there
was
a
bunch
of
there's
a
issue
of
science
this
week
that
focuses
on
axolotl
development
and,
like
I
think,
brain
development
and
some
more
general
development
and
so
they're
just
a
whole
host
of
papers
on
this
topic.
Now
just
came
out
this
week
in
science,
so
I'm
gonna
share
some
papers
on
that.
B
Let's
see
we'll
talk
about
that
too,
but
yeah.
So
I
think
this
is
the
cover
here
yeah.
So
this
is
distant.
Mirror
includes
mammalian
brain
evolution
from
salamander
and
laser
neurons,
so
they're,
looking
at
salamander
and
lizard
neurons
and
they're,
looking
at
mammalian
brain
evolution
or
sort
of
the.
B
A
B
Denoted
by
a
mouse,
but
the
humans
out
here,
so
the
tree
of
life
shows
that
there's
an
inner
relationship
between
these
species
that
the
brain
is.
You
know
that
there's
a
a
refinement
of
the
of
this
sort
of
brain
that
we
all
share,
and
so
this
is
a
figure
referencing
that
this
is
a
figure
from
the
paper,
so
they
did
some
interesting
work
with
cell
cell
cell
type
identity,
and
so
this
is
just
a
taste
of
it
where
they
put
this
into
a
identification
matrix
and
they're.
B
B
A
single
cell
stereo
seek
reveals
induced
progenitor
cells
involved
in
axolotl
brain
regeneration,
so
this
is
a
paper
on
brain
regeneration
in
axolotl
and
they're.
Looking
at
some
progenitor
cells,
so
they're
using
this
technique
called
stereo
seek
and
I'm
not
really
familiar
with
it.
But
I
assume
it's
some
sort
of
microscopy
technique,
coupled
with
identifying
gene
expression
that
identify
like
different
cell
types,
so
we
then
used
high
definition.
Large
field
stereo,
seek
spatial,
enhanced
resolution,
omics
sequencing,
so
they're
doing
this.
B
A
B
Their
graph
one
of
their
take
home
graphs
here,
where
you
have
the
brain
from
a
certain
local
plane
that
they're
looking
at
this
is
the
spatial
landscape
of
the
adult
axolotl
encephalin.
So
this
is
a
structure
in
the
axolotl
brain.
This
is
where
they
have
the
cells
here
and
they
have
this
color
coded,
and
these
are
color
coded
by
the
cell
type.
So
they
have
some
markers
that
tell
them
about
cell
type
or
maybe
it's
been
identified-
I'm
not
really
sure
what
their
methods
are
here.
B
I
don't
really
have
the
methods
up,
but
this
shows
the
difference
between
development
and
regeneration
on
the
right.
So
if
you
look
at
this
graph,
you
have
250
micron
resolution
here.
So
that's
pretty
good
resolution
for
looking
at
this
in
development.
You
have
in
stage
57
as
they
refer
to
it.
You
have
this
sort
of
structure
here
and
you
have
these
different
cell
types,
neural,
progenitor,
neuroblast,
immature
neuron.
You
can
see
that
they're
different
areas
where
these
cells
are
and
then
in
regeneration.
B
You
have
these
diff
other
cell
types
in
a
similar
structure.
I
think
this
is
15
days
post
injury.
So
you
have
a
reactive,
neural,
progenitor,
an
intermediate
progenitor,
an
immature
neuron
and
a
mature
neuron,
and
it
looks
like
they
recapitulate:
the
sort
of
layering
where
you
have
the
neural
progenitors
and
you
move
out
towards
mature
neurons.
B
B
I
I
guess
so
yeah
I
mean
they
just
like
yeah
yeah,
some
sort
of
brain
injury,
but
yeah.
The
point
being
is
that
in
development,
this
area
sort
of
differentiates
as
it
gets
put
into
place
and
then
in
regeneration.
I
guess
they
get
rid
of
it
or
they
they
injure
it
somehow
and
it
regenerates,
and
the
idea
is
that
it
has
the
same
layering
from
reactive,
which
is
like
sort
of
a
neural
progenitor
type
moving
out.
B
B
B
Decode
complex
cellular
molecular
responses.
It
also
seemed
to
us
that
a
comparison
between
brain
regeneration
and
developmental
processes
would
help
to
provide
new
insights
into
the
nature
of
brain
regeneration.
Accordingly,
we
removed
a
small
portion
of
the
lateral
pallium
region
of
the
axillary
left
and
elencephalon,
which
is
this
area
here,
and
I
think
the
conventions
flipped.
So
this
is
the
left
side.
This
is
the
right
side
and
then
and
collected
tissue
samples
at
multiple
stages
during
regeneration.
B
So
that's
one
paper,
there's
another
paper
here
where
they
did
this
single
cell
analysis
of
axolotl,
telling
cephalon
organization,
neurogenesis
and
regeneration,
and
this
is
a
different
group
of
people.
So
this
is
really
looking
at
cell
diversity
in
the
toencephalon
looking
at
conservation
across
species,
so
you
have
a
comparison
with
mouse,
which
is
of
course,
a
mammal
analog
and
then
turtles,
which
is
another
type
of
lizard
and
amphibian,
and
then
this
homeostatic
and
regenerative
neurogenesis.
B
C
B
Technique,
but
this
gives
you
so
their
conclusions
say.
Our
findings
indicate
that
cell
types
and
gene
expression
patterns
associated
with
mammalian
talent
suffering
regions
are
also
evident
in
the
amphibian
brain.
So
the
mammalian
and
amphibian-
and
you
know,
synapomorphy-
exists
with
this,
which
means
it's
a
shared
derived
characteristic.
So
in
the
mammalian.
A
B
You
have,
or
in
the
in
the
vertebrate
brain
you
have
these
shared
mechanisms
and
they're
derived
in
different
species,
but
you
can
see
evidence
of
their
shared
nature
in
this
in
this
study.
So
they
look
at
the
way
that
these
tone
cell
one
regions
are
shaped
and
how
they
develop,
and
they
can
see
parallels
with
mammalian
telecephalon
regions.
B
The
evolutionary
history
of
cell
types
clarified
the
larger
divergence
of
glutaminergic
glutamatergic
compared
with
gabaergic
neurons,
which
are
just
different
types
of
neurotransmitters.
We
observe
the
axolotl,
as
was
also
seen
in
reptiles.
We
conclude
that
in
post-embryonic,
axolotl,
double
encephalone
neurogenesis
progresses
through
diverse
neuroblast
progenitors,
which
are
associated
with
specific
neuron
types
and
dependent
on
shared,
as
well
as
specific
regulatory
programs.
B
B
B
So
this
is
again
making
that
connection
between
the
salamander,
and
so
this
is
not
we're
no
longer
in
axolotl
or
in
salamanders,
and
we're
looking
at
things
that
teaches
us
about
vertebrae
for
marine
evolution
and
the
connections
between
the
two.
So
you
can
see
in
this
graphical
abstract
that
in
the
salamander
forebrain
atlas,
which
they
have
drawn
from,
they
have
these
cell
types,
which
are
these
different
types
of
neural
cell
they're.
Looking
at
connectivity
and
then
they're
looking
at
these
different
innovations
versus
what
we
call
homology.
B
So
homology
is
like
this
idea
of
well,
I
talked
about
synapomorphies
earlier
I
threw
out
a
term
that
was
from
logistics,
but
this
is
the
larger
category
of
homology
and
homology.
Just
simply
means
things
that
have
a
common
evolutionary
origin
that
you
see
in
other
species.
So
there
are
things
that
are
conserved.
B
There
are
things
that
are
derived
and
shared
or
synapomorphic,
and
so
you
can
see
that
some
of
those
things
exist
here.
You
also
have
innovations
which
are
unique
to
a
certain
lineage,
so
in
mammals,
for
example,
they're
things
that
are
innovations
there,
like
neocortex
in
reptiles.
You
have
this
dvr
region
in
amphibians.
You
have
this
vp
region
and
they're
all
like,
basically
from
the
same
sort
of
basic
tissue
type
but
they're.
B
B
B
This
next
paper
is
molecular
diversity
and
evolution
of
neuron
types
in
the
amniobrain,
so
amniotes
are
organisms
that
have
like
placental
births
and
things
like
that.
So
vertebrate
evolution
took
an
important
turn
before
the
onset
of
the
permian
320
million
years
ago,
with
the
transition
of
early
tetrapods
from
water
to
land,
the
appearance
of
amniotes
and
soon
thereafter,
they're
bifurcation
into
saropsins.
B
So
these
are
all
groups
that
are
like
you
know:
you
have
this
transition
to
living
on
land,
this
transition
from
eggs
to
like
live
births,
and
then
this
these
other
groups,
where
you
have
reptiles
and
birds
in
them.
What
would
become
mammals?
And
so
you
have
all
these
different
changes
in
evolution
and
despite
this
branched
history,
the
brains
of
all
tetrapods
share
the
same
ancestral
architecture
defined
by
brain
regions
established
during
embryonic
development.
So
there
are
these
different.
There's
basic.
A
A
B
A
very
diverse
group
of
organisms
now,
but
back
then
they
had
a
common
ancestry
and
they
had
this
basic
architecture
of
a
brain.
So
brain
regions,
however,
do
not
operate
in
isolation,
raising
the
possibility
that
the
evolution
of
interconnected
neurons
might
be
correlated,
and
so
here
they
look
at
this
brain
atlas.
They
look
at
the
amniote
ancestor
here
at
320
million
years
ago.
There
are
mammals,
reptiles
and
birds
that
have
all
descended
from
this
common
ancestor
and
they're,
looking
at
lizards,
specifically
and
they've,
taken
one
lizard
here
and
they're.
B
Looking
at
the
cell
type
atlas,
which
is
where
you
take
these
different
markers,
you
use
a
dimensionality
reduction.
You
map
and
you
plot
it
out
on
this
bivariate
graph,
they've,
also
integrated
lizard
data
and
mouse
data.
So
mouse
is
mice,
are
here
in
mammals
and
they're,
making
that
comparison
between
the
two
and
then
they're.
B
And
so
you
can
see
that
there's
transcriptome
by
in
terms
of
transcriptomic
similarity.
There
are
different
neurons
that
are
very
highly
similar
and
some
that
are
not,
and
so
you
can
see
the
brains
changed
quite
a
bit
in
terms
of
shape,
but
there's
still
some
similarities
in
different
regions.
So
this
is
an
interesting
approach
and
then
there's
this
final
paper,
and
this
is
actually
really
kind
of
the
introduction.
B
C
B
B
B
They
also
use
whole
cell
brain
atlases
and
they're
able
to
get
a
handle
on
some
of
this
cell
diversity,
and
so
you
know
these
a
lot
of
different
new
techniques,
a
lot
of
spatial
transcriptomics
together.
These
studies
reveal
that,
rather
than
being
a
set
of
old
and
new
regions,
vertebrate
brains
are
formed
from
a
mosaic.
A
B
B
Then
they
also
look
at
marker
gene
expression,
which
are
like
where,
if
a
gene
is
expressed,
you
have
you
can
put
a
marker
with
that
genes
and
when
it's
expressed,
you
seem
like
a
fluorescent
marker
or
something,
and
that
shows
you
that's
distribution
and
its
amount
so
with
which
is
something.
That's
you
know
imperfect.
You
can't
really
do
that
living
specimens,
but
it's
it's
informative
in
terms
of
this
sort
of
alternative
to
cyto
architecture
and
then.
B
Origin,
thus,
the
brain
is
often
considered
a
collection
of
old
and
new
brain
regions.
The
traditional
approaches
to
region
for
region
comparison
do
not
resolve
cell
types,
however,
but
region
level
conservation
might
generalize
to
the
cell
type
level,
which
is
why
we
want
to
know
more
about
how
to
define
these
different
cell
types.
So
these
single
cell
ohmics
methods
really
can
get
us
get
to
some
of
these.
You
know
distinctions
between
cell
types,
so
some
of
the
cell
type
distinctions
are
rather
subtle
and
they
have.
I
you
know
digging
into
these.
B
B
You
have
you
know
different
functional
types
that
are
hard
to
distinguish
between
so
making.
That
estimate
has
been
very
hard.
It's
been
like
an
order
of
magnitude
or
more
in
terms
of
making
a
pr.
The
proper
estimate,
based
on
like
some
sample
of
you,
know
a
sample
of
the
brain,
or
you
know
just
kind
of
thinking
about
the
different
cells
in
the
in
the
body.
It's
been
it's.
B
Is
it
that
one
gene
out
of
a
thousand
is,
is
differentially
expressed,
or
is
it
some
sort
of
morphological
difference
in
the
cell?
You
know
what
what
is
our
definition
of
a
cell
type?
So
this
is
the
thing.
That's
it's
still
kind
of
a
thing:
that's
up
in
the
air,
but
so
they're,
using
inhane
at
all
they're,
looking
at
cell
type
hypothesis
by
producing
a
cell
type
atlas
of
the
brain
of
the
bearded
dragon,
which
is
a
species,
and
this.
B
And
then
they
compare
the
wizard
and
mouse
data
and
they
find
that
of
course,
cells
that,
from
broadly
defined
regions
of
both
species
correspond
to
each
other
indicating
conserved
regions,
which
means
that
those
cells
don't
really
change
in
terms
of
their
identity
across
species.
So
that
means
that
you
know
you
don't
have
a
lot
of
generation,
at
least
in
that
region
of
you
know,
have
a
lot
of
proliferation
of
different
cell
types.
B
D
B
Yeah
there's
some
some
of
that
like
so.
In
some
cases,
you'll
have
like
shared
structures
that
are
between
species
and
because
you
know
you
think
about
like
the
wizard
brain
and
the
amphibian
brain
and
the
mammalian
brain.
You
have
structures
that
are
shared,
but
they
look
different
and
they
have
different
functions
and,
to
some
extent,
and
so
they're
going
to
have
different
cell
types
that
are
diversified.
B
A
B
B
So
and
then,
like
there's
yeah,
so
there's
one
last
thing
we're
going
to
be
interested
in
which
is
this
bio
archives,
pre-print
in
interference,
inferring
and
perturbing,
sulfate
reguloms
in
the
human
cerebral
organ.
So
this
is
where
they
go
to
organoids,
which
are
these.
They
grow
these
in
in
culture
and
they're,
basically
from
neural
cells.
So
they're
growing,
like
these
analogs
of
brains
or
brain
structures,
and
so
they're
able
to
grow
these
from
pluripotent
stem
cells.
So
they
differentiate
into
these
different
cell
types,
and
but
they
can
look
they're
very
controlled.
B
A
B
B
This
is
one
of
the
same
authors
as
some
of
the
papers
or
one
of
the
papers.
That
was
in
the
special
collection
in
science,
and
this
paper
kind
of
goes
over
some
of
the
comparisons
between
organoids
and
some
of
these
counterparts
and
mouse
and
human
brain,
which
is,
of
course
different,
but
they
give
they
do
the
same
thing.
They
look
at
the
transcriptomic
profile.
They
look
at
like
the
different
cell
types
and
they
are
able
to
make
some
connections
between
those
two
things
so.
D
B
B
B
Instead
of
from
a
brain
and
so
they're
able
to
do
this
as
well
and
yeah
so
actually
in
organoids,
you
tend
to
get
some
patterning,
so
that's
actually
good
for
looking
at
cell
type
diversity
you
get,
but
you
don't
get
the
entire
brain
structure.
You
get
like
this
sort
of
amalgam
of
brain,
it's
kind
of
a
it's
still
a
developing
area,
but
it's
it's
somewhat
useful
because
you
can
control
the
process
more
of
neurogenesis.
A
A
Dna
in
each
cell-
and
this
is
done
by
heat,
shocking-
the
very
early
embryos,
so
copies
of
the
genome
get
made
without
celebration.
Okay,
so
it's
been
done
up
to
seven
employed.
In
other
words,
you
can.
You
can
then
grow
an
axolotl
with
seven
times
as
much
genetic
information
herself,
okay,
yeah
now
the
result
of
this
was
curious.
The
size
of
the
adult
animals
was
the
same,
which
means
they
had
fewer
cells.
A
B
Yeah
yeah,
they
didn't
do
anything
with
behavior
here
it
was
just
kind
of
like
looking
at
yeah
yeah,
but
it's
it's
kind
of
interesting
because
brain
you
know
why
do
we
have
different
types
of
cells?
Is
it,
for
you
know
different
types
of
inputs
different
for
generating
different
types
of
behaviors
it?
You
know.