next talk is a team talk from the Allen
Institute and it's looking at some
incredible data from electron microscopy
I'm looking at the chandelier cells
there the title is enlightening the
chandelier and it's presented by Agnes
bode or Casey Schneider Mizell and in
Andy and Jung Swan so we asked the
question what is the logic of the
inhibition of in the neocortex and when
and how inhibition can affect a cortical
circuits to answer this question he used
a very particular type the chandelier
saw which has very distinct morphology
and also connectivity and because of
these features of this cell it's a very
good candidate to examine this type
through many pipeline in the Institute
since the connectivities is very unique
it's possible to know every synopsis of
this cell type which make it possible to
to test the effect on pyramidal cells
the target cell of this subtype in a
model and then in the genetical profile
also unique we are able to label this
cell and test the hypothesis regarding
the effect of the chandeliers on
pyramidal cell in a live animal so here
you can see the cell
- and ELISA the population we examined
it in mouse primary visual cortex and
it's lays in a top of layer 2 actually
between the border in there 1 and 2 and
you can see the dendrites go up to layer
1 and all the axons below the soma
occupying layer 2 3 and when you look
detailed you can see this vertical line
which reminded the early discovery to
the candles of the chandelier and when
you look this structure carefully these
are small chains of Sinope
Amina's and as you can see they are
always vertical so it such as that it
might target a vertical structure in the
cortex here so actually every
morphological most of the morphological
side type was discovered early but this
attacks under ESL is a is an exception
it was discovered only the early
seventies by Sant'Agata and Jones in a
same time but independently this is an
original drawings from Santa gotta be
honest integrity the Hungarian
neuroanatomist and they try to draw the
whole the structure of the neocortex you
can see the the layers also the
pyramidal cells and you can see the
inhibitory cells within and you can see
this type here labeled s HC which is a
common abbreviation for SH under ESL and
you can see the structure I described
you can see the soma the ox on below and
targeting on the drawing some vertical
structure which is the apical dendrite
of the pyramid aza
but if you look the drawing more
carefully you see another cell which
look exactly i described - under ESL and
that level is ox ox or excel the only
difference is here the candles targets
drop some initial segment little studies
shows these two subtypes are the same
and it only target exclusively the axon
initial segment of the pyramidal cells
so in this talk Casey and I will use
electron microscopy to to examine the
logic of the chandelier connectivity
then Annie will use modeling to to
examine the effect of based on our am
data on the on the target cell the
parameters are function and
and June will use live animal to test
the hypothesis based on the modeling in
life animals I introduced here the
electron microscopy data set we have you
can see we carefully mapped the visual
cortex then sectioned the tissue to 40
nanometer electron microscopic sections
and what is really unique about this
dataset
theoretically we can see anything which
is visible in electron microscope or the
all the cell all the accidents over the
synapses and here you can see pedometer
from this dataset and also we have a
tool to automatically detect every
synapses and you can see here the
incoming synapses with greens on the
dendrites and the soma and on the axon
initial segment and through the talk we
will use this form - just for clarity we
only show the so mind ox initial segment
with the incoming synapses here I show
for nearby pyramid ASSA from our dataset
and you can see the roshanda reacts on
target each of these axon initial
segment
differently some didn't receive any and
some may see more and some receive less
input from this chandelier socks on on
average each boot on mostly form just
one synapses and you can see here
another axon it's doing the same thing
its target another set with another
different amount of synaptic terminals
and here you can see every single Shanda
reaction in this dataset which target
this forty ramadasa and you can see each
axon initial segment receive a different
amount of input in a different
combination of these axons and of course
we don't have only four cells but we
have almost hundred and fifty good
initial segment of the pyramid arson in
this data set and also we have all the
chandelier acts on target DISA so we can
see theoretically the entire
connectivity of the chandelier cell and
in the next case we will talk about it
is there any logic any any particular
system how these oxen targets
Thank You Angus and thankfully the
answer is yes there is a bit of logic so
despite this amazing specificity with
what chandelier sells innervate the the
axon initial segment pyramidal neurons
not very much is known either about what
function they're performing in visual
processing or cortical processing in
general nor is much known about the
network structure of how populations of
chandelier cells talk to populations of
pyramidal neurons and the e/m datasets a
really good place to start looking at
this because we get it just exquisite
knowledge of where cells are in space in
relation to each other in the same
nervous system the same biological
tissue as well as beautiful precise
connectivity maps where we know for
every one of the axon Pregnant's that we
have reconstructed in a.m. we can tell
you exactly how many synapse is on it
makes onto specific parameter neurons so
the question is can we put this together
to understand something more about the
population architecture of how
chandelier cells talk to pyramidal
neurons and the first question that we
decided to ask was if you're a pyramidal
neuron how much chandelier inhibition do
you get anyway when you look at a
histogram of this of this measure you
see something that looks maybe
surprisingly wide to you that there are
some cells that are receiving zero one
maybe two synapses of Shandi input
they're basically escaping any
inhibition from chandelier cells whereas
other cells are receiving maybe two or
even up to three times the average
amount of Chilean foot so some cells are
getting slammed by inhibition
potentially and others are escaping it
and this is extremely different from the
other major source of inhibition on to
these neurons the synapses that
pyramidal cells receive on their somas
here if we take the exact same
sells and we count the synapses on the
soma you see that not only do all
pyramidal neurons receive plenty of
somatic inhibition the sort of most
structurally inhibited cells as it were
are maybe getting 30% more than average
so there's something different about the
amount of variability and shantu the
inhibition compared to other sources of
inhibition and cortex so we want to
understand if there's any structural
properties that could help understand
what's going on here and so for every
pyramidal cell I computationally cut out
the soma and a constant part of the axon
initial segment and for these bits we
could measure a number of properties
about the location of the cell the size
of the cell the number of synapses it's
receiving and then as well the AIS we
can get information about the the size
of the axon initial segment and then
also inputs from some non channel dia
types that are sort of rare but
interesting if you take a data-driven
approach to the measurements of all of
these properties across the 150 or so
pyramidal neurons you can actually get
these sort of data-driven mixtures sort
of doing sort of something like
principal components analysis that seem
to be very interpretable that there's a
1:1 component that corresponds to how
much inhibition the cell gets from other
sources of cell types other cell types
than Chandeleur cells one component that
corresponds to how big the cell is and
one component that corresponds to how
deep it is within layer 2 3 so for every
pyramidal neuron we can use this
components analysis to basically make
three numbers one giving an indication
of how big the cell is in size one
giving how much how much inhibition that
cells receiving and one where the cell
is in depth within layer two or three so
now the question is do these three
structural measures have anything to do
with the amount of change the inhibition
the cell is receiving so the simplest
approach to this is just ordinary
least-squares right is there a linear
relationship between these values so I
can fit this to the data and I can
express these coefficients so I'm going
to express the coefficients in this
horizontal plot so a positive
coefficient here means that there's a
positive correlation between this value
in this case some inhibition and the
shengelia cells sorry and early synapses
that a pyramidal neuron receives so what
this means for example is that the more
input a pyramidal cell gets from other
inhibitory cell types the more chain of
the a cell input the same cell receives
size shows a weak but statistically
significant positive correlation and
depth is a strong correlation that's
quite negative so the deeper you are in
layer 2 3 the less chandelier but it's
less shantu the ei input you receive so
great there's some interesting structure
here but synapses alone are pretty
aggregate measure here for example are
two different ways that you could
receive eight synapses if you're a
pyramidal neuron and they might be very
biologically distinct one involves a
single axon that puts eight distinct
synapses on you and the other involves
four different axons so for different
connections each putting two synapses
and these might be biologically
meaningfully different because the data
lets us actually trace the identity of
every one of these axons independently
we can measure these two values
separately for every axon initial
segment and get both the synapses per
connection and the number of connections
for every pyramidal neuron now if we run
the same analysis we see no significant
correlation with the structural
properties and the average number of
synapses per connection but we see
extremely strong relationships with the
number of distinct connections
suggesting that the these structural
heterogeneity in shanna the input is
driven much more by a number of distinct
axons targeting the pyramidal neurons
and less by the synapses per connection
so what's interesting about this is that
once you start thinking about
connections you have to start thinking
about geometry because if I showed you
these two parameter cells in gray and
asked you which one is more likely to
talk to this chandelier axon fragment
here in red you would be pretty quickly
saying well this one's close enough to
target it and this one's not so you know
this is a very different realm of
connectivity than here so to take this
intuition and formalize it we went back
to this you know exquisite Anatomy data
and for every axon initial segment
segment could look around a given radius
of in this case 7.5
microns and ask how many chandeliers ons
are within that radius and that will be
what we're going to say is our number of
potential connections and then AM of
those potential connections what
fraction of them actually form synapses
on to the axon initial segment and that
will be the connectivity fraction it's
the product of these two values that's
the number of connections but we can
measure them independently so when we do
the same regression analysis we start
seeing something really interesting if
you look at the number of potential
connections you no longer see any
relationship with somatic inhibition but
you still see this negative correlation
with depth so that suggests as you go
deeper in layer two three
there's just fewer chandelier axons
around in contrast if you look at the
connectivity fraction the number of
nearby cells that are actually recruited
to form synapses then you no longer see
any modulation with depth but you see
this positive correlation of somatic
inhibition suggesting that the more
inhibition of pyramidal cell receives at
the soma the more it recruits the nearby
chandelier cells to connect to it now
this model that I just described
implicitly assumed randomness that a
schandle a cell and a pyramidal neuron
didn't actually care who was who when
they form connections and so we wanted
to test that you know is there is this
connectivity completely intermixed or
are there some chandelier cells that
prefer to talk to some pyramidal neurons
and other chandelier cells that prefer
to other talk to other pyramidal neurons
so to measure this we basically asked
how often is this particular motif where
two chandelier neurons both try to talk
to two parabola neurons how often is
that last edge connected in the case
that this is a geometrically allowed
connection in the sense that I just
described and we could compare the
likelihood of this connection be
existing with a shuffled Network where
we maintained the same geometric
structure of proximity between X between
chandelier axons and pyramidal cells and
what we saw is that there's actually no
real difference between the observed
value and the shuffled Network
suggesting that there's no evidence of
target selectivity beyond the geometric
proximity between
yawns so we were able to put together
some facts about the logic of chandelier
connectivity at the structural level but
to interpret this in in terms of
processing and what the animal is doing
we needed to know more about function
and so for that we turn to our
colleagues who make amazing biophysical
models of how neurons work and Andy's
going to talk about what he found there
thanks Casey
so we wanted to understand how this
chandelier projections affect the layer
2/3 pyramidal cell in Mouse visual
cortex and for us to probe that question
we needed to simulate this pyramidal
cell with synapses and for that we
needed biophysically detailed models of
this pyramidal cells and before I go
into modeling I just wanted to point out
that this would not have been possible
because of unless we had this in-house
pipeline project namely IV SCC which
involves morphological reconstruction
wholesale patch clamp recording and then
packaging all this amazing data into an
API and all of this data is publicly
available at this website so going back
to the modeling what I mean by single
neuron modeling is distributing synapses
distributing conductances along the
morphology of this neuron such that the
model defined by this conductances and
the morphology replicates the
experimental recordings shown here and
we adopt an evolutionary algorithm to
fit this conductances here I'm showing
the algorithm in action what it does is
in an automated framework generates a
massive number of models by sampling
from the parameter space shown here and
then evaluates this model against the
experimental recording until a suitable
match is detected and for a before and
after shot for this modelling here is
how the model matches
the experiment at the first generation
and here is at the end of the
optimization so on the top left I am
showing a snapshot of how this model
performs against the experiment so here
you can see the fight curve this
sub-threshold and spiking responses and
as well as the AP shape so now that we
have the model we are in a position to
distribute synapses on this on the
morphology of the of the neuron
especially excited synapses on the
apical dendrite and inhibitory synapses
mimicking the chandelier projection on
the axon initial segment of the
pyramidal cell and just wanted to also
point out this is a collaborative work
with my colleague Tom Chartrand within
the modeling group so now that we want
to distribute this chandelier synapses
on the axon initial segment we don't
have to make assumptions anymore we have
the e/m data to inform us about how this
synapses are being distributed for
example if there are eight synapses I'm
showing here that there are three
different presynaptic chandelier
terminals that are projecting this eight
synapses and we call this am driven
because this is the mostly common
scenario we see in the data that is each
pyramidal cell getting inhibited from
number of different presynaptic
chandelier cells and we compared this
particular configuration with two other
configuration namely synchronous with
this same eight synapses coming from one
particular presynaptic shunt layered
terminal and asynchronous where this
eight synapses come from eight different
presynaptic Shanley terminals and here
I'm showing in the movie that how the
presynaptic Shan layer spike trains are
inhibiting the pyramidal cells
dynamically so I have now laid out the
three different configurations
here the e/m driven synchronous and
asynchronous configuration and also for
comparison I have this green case where
there is no inhibition so just for an
example as you can see when there is no
inhibition versus when there is
inhibition the spiking output of the
pyramidal set goes down so when I
compare this three different cases with
both excitation and inhibition what I
see is across different level of
inhibition regime there is synchronous
case that is when the synapses are
coming from different cells is the most
effective form of inhibition in
inhibiting the pyramidal cells spiking
the synchronous case is the least
effective form of inhibition and the e/m
driven or the biological case is
mediating between this two cases and so
this is providing some sort of a
trade-off between the most effective
form of inhibition represented by the
asynchronous form and the least
effective form of inhibition represented
by the synchronous form because the
synchronous case only offers one
connections so next we we saw in the e/m
data that this shandler projections are
acts acts on ik
so naturally we wanted to understand
whether there is any difference between
the external projections of the
chandeliers or and the somatic
projection so what we did is repeated
the same simulations but now instead
like along with the axonal inhibitory
projections we also have somatic
inhibitory projections and Here I am
showing on top the excitation and
inhibition excitation in black and
inhibition in red and here in the two
rows I am showing the raster plot for
the pyramidal cell on top is the axonal
and bottom is a somatic and here you can
see that when the excitation and
inhibition are offset by some time the
pyramidal cells are spiking output with
in the two cases on top and bottom are
comparable here but when these are when
the excitation and inhibition are Co
active that's when the axonal emission
is much more affecting effective in
reducing the spiking of the pyramidal
cell than the somatic inhibition so if
we focus on this particular case and
look at the statistics of in our
simulation we see that in the more in a
moderate inhibition regime this axonal
and inhibition is at least 20% more
effective than the somatic inhibition
and this moderate in inhibition regime
is also representative of the Chandeleur
synapse distribution that casey showed
earlier in his data so from my
simulation study the prediction was
prediction is that the chandelier
projection is most effective
shine layer acts acts only projections
are most effective when the excitation
and inhibitory Burridge's are co active
but what are this chandelier cells doing
in vivo and for that we turn to Jun
thank you honey
my name is Jing I'm a physiologist so
after seeing this beautiful
reconstruction the quantitative analysis
of their connections and the nice
modeling work to to predict the
effectiveness of inhibition you
understand why I'm so dying to see there
there are activities in vivo but because
this is almost purely morphologically
defines your type and there are
extremely sparse in cortex so
historically it is very very hard to
target those cells in a live animal and
sex to the previous team talks and I
think you have been already convinced
that we can target Genelia cells in vivo
but here I'm just going to show you once
more like as a use case of like how to
use Ellen Institute data data product
to guide your own study so our
large-scale transcriptomic study have
indicated there's a one-gene may be
related to Chanel yourself which is
called VIP receptor - I'll called Viper
- gene and if you go to our data portal
and type type in this gene USC okay this
G among all this more than 100
transcriptomic redefine stereotypes VIP
Viper - gene only expressed very tightly
concentrate in one of the cell type
which is a subtype of albumin positive
cells and others really chandelier cells
we go to another data set IV SEC which
has been introduced by other team talks
and Ani this time we are talking we are
looking at Viper - positive cells so
here is an example of the fully
morphologically reconstructed such a
cell and those are there in vitro
physiology you can see the nice
chandelier morphology of their Excel and
indeed faster spiking and therefore the
other Viper - positive cells are 92
percent of them are Chanel yourself or
for all the morphologically fully
reconstructed 92 arsenal yourself and
32% in the visual cortex and earlier so
basically almost all the fully
reconstructed a Viper - positive cells a
chandelier except for just one cell so
hopefully I can convince you that now we
can target specifically the chandelier
cells in vivo and we made this
mousseline so it suddenly opens the door
for all sorts of in vivo experiments you
can do so the most easy strategy where
we you just inject create dependence
whatever target gene into cortex and
then you can into the Viper - cream
Mouse and then you can label those cells
into whatever you want so first we just
inject the GFP a structure label and
here is in vivo two-photon z-stack so
you can see that they enjoy the cell
body and the pillow the silver you can
see this little punk has
everywhere representing the candles and
if you do histology you can see almost
this textbook like morphology of general
yourselves and the same thing is true
for the if you inject a communicator
gcamp and you can see the cell body
slightly between layer one and layer two
and all the candles are in there two
three just resemble the morphology so
since we have the chasm indicator in the
chandelier see us of course we want to
image their activity so we go our
leverage another large-scale pipeline
which you were here tomorrow and so I'm
not going to introduce grab this in
detail so these pipelines called spring
Observatory allows us to do in vivo
two-photon calcium imaging and the one
thing I just want to point out is these
setup allows us to record the pupil and
the running speed of the mouse at the
same time and you will see later why
this is important okay so there are
still one thing left it is like look if
you look at the cell body distribution
in the cortex you can see there are
extremely sparse if you just do a
regular two photon imaging a single
single playing you probably will see two
austria's for imaging searching which is
a very low throughput so here thanks to
the Institute another effort to develop
state-of-the-art imaging tools in this
case deep scope which uses a special
mode light modulator very quickly to
switch the depths of your focal plane
allows you to do simultaneously almost
simultaneously multiplayer imaging so we
can in this case we can image three
different planes at the same time
significantly increase all through food
ok so finally we have all of our tools
together so we put a GQ m6f
into the chandelier cells and we use a
volumetric imaging with simultaneous
recording three planes with 60 microns
apart and we reach the volume rate of 12
Hertz and this is just to my knowledge
will be the very first in vivo
recordings of Genelia cells so
here it goes
okay I don't know if you have noticed it
or not to me everything just slides off
at the same time right and you can not
only just see the cell body but also
this candles just like lights it's
almost the same time but this is a
Chinese speak 20 times speed of the
real-time so those are actually pretty
slow events
okay and the good thing of this pipeline
is just after every imaging session I
can package all the data into a standard
data format namely neural data without
borders and uh babies so file format so
in there I can with this file format I
can just very easily share this data
with other people so in this case I just
send this data to another scientist
Brian with very small amount of
extermination I he understand the data
he can start to analyze it so here is
the calcium traces you have just a scene
from this movie from this in total only
five cells you can see they are really
highly corrective corrective and to
quantify this we did Brian did the
pairwise cross correlation you can see
the correlation coefficient is about 0.5
which meaning which means they are very
really very high highly called Co
activated and what about the other cell
types we compare we use the same data
analysis and compare the other
inhibitory cell types PVC our VIP server
and SS ISA and chandelier cells
represent a cell type which has the
highest correlation even if it is just a
subset of the PV cell okay so now you
wonder like during these highly
activated
period what is the most - I'm not
showing anything on the screen it's just
dark and they are just sitting in the
dark and given that their cell body is
in between the layer 1 and layer 2 and
the superficial layers which receives a
lot of input from the near Lord
neuromodulatory external inputs so my
hypothesis these activity may be a brain
state related and and hopefully we have
the recordings of the mouse fee
and running so we can just plot them
together
thanks these are the running and the PLO
traces so really everything just
happened at the same time and if you if
you zoom into this little period and
when the mouse people are already
dilated but it hasn't started to run it
the the chandelier activity already
catches up meaning that this is not just
motor related activity but indeed it is
indeed as a chandelier is representing a
activity related to the innate brain
States and quantitative analysis showing
you indeed a a chandelier activation is
more correlated to the pupil than the
running so the Eastcote is consistent
very well with modeling work of any
meaning if the chandelier sells
inhibition needs to be more effective it
needs to be happened during the period
which you have a lot of excitation which
is exactly what's happening during
arousal
so these conclude the in vivo
part and now to wrap up our hands a mic
to Casey so just like to take a couple
of minutes the very end here to pull
these threads together and think about
how what we found about chandeliers
might affect visual processing or in
cortical processing in general so at
jeune just showed was that this pupil
response which is thought to be a strong
correlate of an internal arousal state
that this arousal state drives
collectively the chandelier neurons and
it's as Jim just said it's known from
the work of a David McCormick under it
Elias Jessica Cardin and probably others
that it's exactly during this period
that there's widespread depolarization
on the excitatory neurons in this
network olney showed that the inhibition
from chandelier is onto the layer 2 3
pyramidal neurons is particularly strong
and is really well tuned to shape their
activity except and then what we showed
is that this inhibition isn't just
distributed uniformly through the
population but is actually highly
structured
first forming a stronger to weaker
gradient across the depth of layer 2 3
possibly creating a sort of less
excitable and more excitable subgroup
subpopulations within that within the
lair and also respecting cell specific
inhibition amplitudes that might
potentially reflect some sort of
inhibitory homeostatic balance and so
taken together the Schenley cells really
seem to be a key piece in reshaping the
inhibitory structure of cortical
processing a layer 2 3 during these
particular periods of arousal and of
course potentially other roles as well
and you know looking forward we pulled
function Anatomy and modeling all
together to analyze one particular cell
type two cell type interaction and we're
on the cusp of getting data that's
really going to let us take this
integrated approach and interrogate
deeply the rich menagerie of cell types
and their interactions and we're looking
forward to that in the years to come
here so with that on behalf of everyone
I'd like to thank really a small army of
people working on am on modeling on
computation on in vivo imaging and all
of the teams here of course our founder
Paul Allen and with that we would love
to take any questions you may have
[Applause]
Thank You team there's a question of the
top and a microphone is on its way what
what do you know about about I mean two
questions eight do you know anything
about gap Junction I realize it's a like
quite a path but given the correlated
activity you know anything about gap
Junction among they are gap junction
they connected yes chandelier cell
dendrites are gap junction I connected
with one another it's known in the
literature and we also see it within
even the very small um volume we have so
how much of the cost correlation can be
explained by by the coupling I have no
idea about that I don't even know how
you get into it does it matter if the
functional responses are if the if the
if the spikes are it's important to
understand how much is given by by
common input yeah you know yeah that
would be great to study in the future
but we don't we don't know that right
now and then in the I guess it's the
question for Neil in them in the
modeling do you assume that the axonal
initial segment has a different
distribution as a high density of of
sodium channels or their shifted towards
lower potential lower thresholds
compared to the years somatic fare
sodium channels yeah the we made sure to
constrain the spiking nation initiation
at the axon and spike the sodium density
is higher at the axon also the appian
ish threshold is lower compared to so
maybe we can do one more question
a wonderful data it's very interesting
one thing I'm curious about is whether
you have looked at whether the the
pyramids that get inhibited by these
chandelier studies do these pyramids
excite these chandeliers the very same
ones where you're able to look at that
some do some don't it's known so the
volume that we're studying is a little
too small to get full axon collaterals
on the on the pyramidal neurons so for
the some of them we absolutely do have
this this feedback there's data from
Josh pong paper a couple years ago that
does show actually that there's
interesting local pyramidal cell de tu
chandelier feedback we can't
unfortunately the our datasets a little
too small to give their result but we
can show certainly the existence proof
of excitatory feedback yeah again we can
do one more question
so so given that their arousal
modulation and you know the gap
junctions I mean maybe this is seeming
like it's sort of a you know more
globally oriented population but along
the lines of the last question it might
be hard to look for you know direct
connectivity because of axons moving in
and out of the volume but is it would it
be easier to look at at common input
like axons that are local synapsing on
you know the same or connected pyramidal
and Chandeleur cells that's absolutely a
thing we could do I think so this pinky
volume that was described now is kind of
our phase one dataset and we are moving
kind of quickly in the next coming
months towards millimeter scale um and
so I think some of these questions would
be better suited to just waiting until
the data is there to really answer the
question right just just one quick thing
to add onto crystals question about gap
junctions there are some blockers like
mefloquine that are not perfect but that
that can do some modulation of gap
junctions you could hypothetically do
the physiology experiment but inject
meth liquid into the animal and look to
see if the synchrony changes I would say
not weighs the calcium imaging because
calcium imaging is too slow to be able
to tell for sure if they are gap
Junction connected and then since they
are very sparse so only thing I can
think of is you do slice work you do you
label the chandelier cells and the patch
them and patch both of them to see if
they are I'm alright okay we can do
another round of applause for the team
[Applause]