Report

In Vivo Imaging of Dentate

Gyrus Mossy Cells inBehaving Mice

Highlights

d Ca2+ imaging indicates that MCs are significantly more active

than GCs in vivo

d MCs exhibit spatially organized firing during navigation

d Spatially tunedMCs strongly remap in response to contextual

manipulation

Danielson et al., 2017, Neuron 93, 552–559February 8, 2017 ª 2016 Elsevier Inc.http://dx.doi.org/10.1016/j.neuron.2016.12.019

Authors

Nathan B. Danielson, Gergely F. Turi,

Max Ladow, Spyridon Chavlis,

Panagiotis C. Petrantonakis,

Panayiota Poirazi, Attila Losonczy

Correspondencepoirazi@imbb.forth.gr (P.P.),al2856@cumc.columbia.edu (A.L.)

In Brief

Danielson et al. monitored the activity of

hippocampal mossy cells in vivo. They

show that as a population, mossy cells

are an active place coding population

engaged in contextual encoding.

Neuron

Report

In Vivo Imaging of Dentate GyrusMossy Cells in Behaving MiceNathan B. Danielson,1,2,7 Gergely F. Turi,1,7 Max Ladow,1 Spyridon Chavlis,3,4 Panagiotis C. Petrantonakis,3

Panayiota Poirazi,3,* and Attila Losonczy1,5,6,8,*1Department of Neuroscience2Doctoral Program in Neurobiology and BehaviorColumbia University, New York, NY 10032, USA3Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology – Hellas (FORTH), 700 13 Heraklion,

Crete, Greece4Department of Biology, School of Sciences and Engineering, University of Crete, 741 00 Heraklion, Crete, Greece5Kavli Institute for Brain Science6Mortimer B. Zuckerman Mind Brain Behavior Institute

Columbia University, New York, NY 10032, USA7Co-first author8Lead Contact

*Correspondence: poirazi@imbb.forth.gr (P.P.), al2856@cumc.columbia.edu (A.L.)

http://dx.doi.org/10.1016/j.neuron.2016.12.019

SUMMARY

Mossy cells in the hilus of the dentate gyrusconstitute a major excitatory principal cell type inthe mammalian hippocampus; however, it remainsunknown how these cells behave in vivo. Here, wehave used two-photon Ca2+ imaging to monitorthe activity of mossy cells in awake, behaving mice.We find that mossy cells are significantly more activethan dentate granule cells in vivo, exhibit spatialtuning during head-fixed spatial navigation, andundergo robust remapping of their spatial represen-tations in response to contextual manipulation.Our results provide a functional characterization ofmossy cells in the behaving animal and demonstratetheir active participation in spatial coding andcontextual representation.

INTRODUCTION

The mammalian hippocampus supports spatial information pro-

cessing and episodic memory by routing information through its

canonical trisynaptic circuit: from the dentate gyrus (DG) input

node, to area CA3, and finally to the CA1 output node, with

each subfield carrying out specialized computational operations

(Eichenbaum, 2000; Knierim and Neunuebel, 2016; Treves and

Rolls, 1994). In particular, the DG has been widely implicated

in pattern separation, a computational process of segregating

and storing similar events in a non-overlapping fashion. This de-

correlation function of the DG is commonly attributed to granule

cells (GCs) (Knierim and Neunuebel, 2016; Treves and Rolls,

1994), the most numerous principal excitatory cell type in

the hippocampus. The polymorphic hilar region of the DG, how-

ever, also contains another major glutamatergic principal cell

type, mossy cells (MCs) (Amaral, 1978; Scharfman, 2016), which

552 Neuron 93, 552–559, February 8, 2017 ª 2016 Elsevier Inc.

receive their major excitatory input from a small number of GCs

via massive synaptic boutons onto large spine complexes along

their proximal dendrites (Amaral, 1978; Frotscher et al., 1991;

Ribak et al., 1985). MCs provide widespread feedback mono-

synaptic excitatory and disynaptic inhibitory inputs to GCs

(Scharfman, 1995, 2016), and therefore may play an important

role in pattern separation (Jinde et al., 2012; Scharfman, 2016).

However, at present there are no in vivo data available on the

firing patterns of MCs in behaving animals, and thus physiolog-

ical support for this hypothesized role of MCs in behavioral

pattern separation is lacking. Two-photon (2p) Ca2+ imaging

has recently become available for optical recordings of the DG

in vivo, including its hilar region, during head-fixed behaviors in

mice (Danielson et al., 2016a). In this study we therefore em-

ployed this approach to monitor Ca2+ activity in MCs, providing

a functional characterization of this enigmatic cell type during

spatial navigation and behavior.

RESULTS

In Vivo Imaging of Identified Mossy CellsWe first sought to selectively label MCs with the genetically en-

coded Ca2+ indicator GCaMP6f by taking advantage of two

characteristic anatomical features: MCs are immunoreactive

for glutamate receptor type 2 or 3 (GluR2/3) (Ratzliff et al.,

2004); and MCs comprise the predominant hilar cell population

projecting to the contralateral DG (Frotscher et al., 1991; Ribak

et al., 1985) (Figure 1). Therefore, we used a retrograde variant

of recombinant adeno-associated virus (rAAV2-retro) (Tervo

et al., 2016) expressing Cre-recombinase injected into the

contralateral DG, in combination with a Cre-dependent rAAV ex-

pressing GCaMP6f injected ipsilaterally into the hilar imaging

site, to label contralaterally projecting hilar neurons (Figures 1A

and 1B). This strategy labeled hilar but not CA3 neurons (Fig-

ure 1B), and in agreement with previous reports (Ratzliff et al.,

2004), the vast majority of retrogradely labeled hilar neurons

A

Viral injection &window / headpost implant

1w

Behavioraltraining

2w

Imaging

rAAV2-retro(Cre)

rAAV(GCaMP6f)Cre

150 µm

300 µm

15 µm 15 µm 15 µm

GCaMP6f GluR2/3 Merge

Glu

R2/

3+, G

CaM

P6f

+

/ GC

aMP

6f+ (

%)

Glu

R2/

3+, G

CaM

P6f

+

/ Glu

R2/

3+ (

%)

0

50

100

0

50

100

B

C

D

waterreward

CA1

CA3

DG

15 µm

2p in vivoE F

500%

ΔF

/F

2 min.

DG

CA1

CA3

1

2

3

1

2

3

0

2m

Figure 1. Optical Imaging of Identified Mossy Cells

(A) Viral labeling strategy for mossy cells.

(B) Top: confocal image of a horizontal section through the hilus of the dorsal DG. GCaMP6f-expressing and GluR2/3-expressing cells are labeled green and

magenta, respectively. The area indicated is shown at high resolution at right. Bottom left: low-magnification image through the hilus shows the lack of CA3

pyramidal cell labeling. Bottom right: nearly all GCaMP6f+ cells were also GluR2/3+ (99.2%± 0.2%,mean ± SD, n = 11 slices from 3mice), and approximately half

of MCs were labeled with GCaMP6f (48% ± 35%, mean ± SD, n = 11 slices from 3 mice).

(C) Experimental timeline.

(D) Schematic of the experimental apparatus. Head-fixed mice explored multisensory contexts comprised of the treadmill belt and other sensory stimuli (light,

odor, sound).

(E) Max Z-projection image of a volume acquired in vivo of three GCaMP6f-expressing MCs. Example regions of interest in red.

(F) DF/F traces of three simultaneously recorded MCs. Significant transients in red (p < 0.05). The mouse’s position on the treadmill is indicated below.

were immunopositive for GluR2/3; we therefore identified these

cells as GluR2/3+, contralaterally projecting MCs (Figure 1B,

identified MCs hereinafter denoted as iMCs).

Following viral injection, mice were implanted with a chronic

imaging window above the dorsal DG to provide the optical ac-

cess necessary for visualizing the hilus (Figure 1C). To record

Ca2+ activity from iMCs, we performed head-fixed 2p imaging

as the animals (n = 6) performed a random foraging task by

running for water reward on a treadmill across three sessions

in different linear environments (Figures 1D–1F; see STAR

Methods; Danielson et al., 2016a, 2016b). In total, we recorded

from 57 iMCs in 6 animals (n = 10 ± 4 cells per mouse, mean ±

SD). Correction of motion artifacts was performed using a 2D

Hidden Markov Model (Dombeck et al., 2007) implemented in

the SIMA package (Kaifosh et al., 2014). Regions of interest

(ROIs) were drawn over MC somata, and registration of ROIs

across imaging sessions was performed using the ROI Buddy

GUI. Signals were extracted from the resulting binary masks,

and significant Ca2+ transients were identified as described in

the STAR Methods.

Mossy Cells Are More Active than GCs In VivoWe first quantified the activity of iMCs during head-fixed naviga-

tion by measuring the rate of area under significant Ca2+ tran-

sients (AUC rate, Figure 2). In each session, nearly every cell fired

at least one transient (and most fired many more), resulting in a

high non-silent fraction (Figure 2B) across both recordings and

behavioral states (running and non-running). Within this active

population, the frequency of transients across all frames was

1.6 ± 1.2 transients/min, (mean ± SD, n = 57 cells) and of large

amplitude (79%± 46%DF/F, mean ± SD, n = 57 cells; Figure S2),

resulting in a high AUC rate within the non-silent population (Fig-

ure 2C). This finding was in stark contrast to the activity levels

we previously reported for GCs in our recent study (identical

Neuron 93, 552–559, February 8, 2017 553

250%

ΔF

/F

20 s

Granulecells

Mossycells

BA***

***iMC

iMC

pMC

All framesRunningNon-running

iMC

C***

Figure 2. Mossy Cells Are More Active than GCs In Vivo

(A) Schematic of GC andMCactivity. Only a small fraction of GCs are active (red shading) relative toMCs, and activeMCs exhibit a significantly higher rate of Ca2+

activity than active GCs. Ca2+ traces from three example active GCs (right) and three example MCs (top). Significant Ca2+ transients (p < 0.05) are red.

(B) Activity was assessed by the rate of area under significant transients. The fraction of cells exhibiting non-zero activity is shown. Each circle represents a single

recording session (with a minimum of three recorded cells) and is colored by the frames included in the analysis (non-running, running, and all frames). iMCs were

significantly more active than GCs (F(1, 144) = 1,510, p < 0.001, two-way ANOVA). pMCs were not included in this analysis.

(C) The activity of non-silent cells within each condition (non-running, running, all) is shown. Non-silent iMCs were significantly more active than non-silent GCs

(F(1, 12,588) = 740, p < 0.001, two-way ANOVA). Similarly, the activity of pMCs was significantly higher than GCs (F(1, 12,453) = 345, p < 0.001, two-way ANOVA).

behavioral paradigm, Danielson et al., 2016a), and we therefore

directly compared the activity of iMCs and GCs (pooling our

adult-born and mature recordings). GCs fired transients at a

very low frequency (0.08 ± 0.14 transients/min, mean ± SD, n =

8,396 cells), and iMCs were thus considerably more active

than GCs (Figures 2B and 2C). To estimate activity in both pop-

ulations in a manner independent of our transient detection

procedure, we also estimated the spike trains underlying the

fluorescence signals (Deneux et al., 2016). This analysis further

supported a highly significant difference in spiking activity be-

tween iMCs and GCs (Figure S1). In summary, iMCs are more

active than GCs at both the population and single-cell levels.

Because our GC and iMC recordings were acquired in

different animals, we attempted to control for any possible differ-

ences between preparations by retrospectively analyzing our

previous DG imaging dataset (Danielson et al., 2016a) to look

for the presence of putative MCs (pMCs) in those recordings.

The high spatial resolution of 2p imaging allows for the unam-

biguous separation of GCs from hilar cells based on their loca-

tion and distinct morphology. In our previous imaging study,

GCaMP6f imaging was performed with broad labeling of all

neuron types in the DG. In four of our fields of views (n = 4 ani-

mals), we could identify large, multipolar cells in the hilus (Fig-

ure S1 in Danielson et al., 2016a), which were excluded from

the original GC imaging dataset. In a subset of these hilar cells

(n = 11 out of 20), we were able to detect large-amplitude tran-

sients with fast onset and exponential decay similar to the

pattern observed in iMCs (Figure 2A, bottom). When we directly

554 Neuron 93, 552–559, February 8, 2017

compared the activity rates between these simultaneously re-

corded hilar pMCs and non-silent GCs, we again found a highly

significant difference in activity between these populations (Fig-

ures 2B and 2C).

Mossy Cells Exhibit More Diffuse Spatial Tuning ProfilesCompared to Granule CellsBecause principal cells throughout the hippocampus are known

to exhibit spatially selective, ‘‘place cell’’ firing (Hartley et al.,

2013), we next sought to determine whether this feature extends

toMCs. Thus, for eachMCwe calculated a spatial firing ratemap

and identified those cells whose distribution of Ca2+ transients

contained statistically significant spatial information (Skaggs

et al., 1993; see STAR Methods, Figure 3A). In contrast to the

very low place cell fraction among GCs (1.4% ± 1.0%, mean ±

SD, n = 32 recording sessions), we found a significant spatial

coding population among MCs: 16% ± 20% (mean ± SD, n =

18 recording sessions, Figure 3B) of our iMCs were classified

as spatially tuned.

Previous extracellular electrophysiological recordings sug-

gested that GCs (Leutgeb et al., 2007), perhaps with preference

toward adult-born GCs located closer to the hilar border

(Neunuebel and Knierim, 2012), exhibit multiple firing fields.

While the sparse activity in our imaging dataset precluded

precise quantification of the number of place fields, we attemp-

ted to address this question by comparing the tuning specificity

(Danielson et al., 2016a, 2016b) between the subpopulations

of spatially tuned GCs and combined MCs (cMCs), in which we

GC

GC

GC

iMC

iMC

pMC

Context

A BB

1-1.5 hr 1-1.5 hr

AB1

B2

GC MC

cMCGC

iMC GC cMC GC

cMC GCAB BB

*

***

****

A B C

D E F

Figure 3. Spatial Coding Features of Mossy Cells

(A) Spatial tuning plots for three example GCs andMCs identified as spatially tuned. Each running-related transient is represented as a vector (direction, position

at onset; magnitude, inverse of occupancy), and the complex sum of these vectors forms the tuning vector (black).

(B) The fraction of cells identified as spatially tuned (R4 Ca2+ transients, spatial information p value < 0.05) within GC and iMC recording sessions. A higher

fraction of MCs were spatially tuned than of GCs across recording sessions (U(48) = 183, p = 0.017, Mann Whitney U).

(C) Among cells identified as spatially tuned, cMCs exhibited a significantly lower tuning specificity than GCs (U(282) = 836, p < 0.001, Mann Whitney U).

(D) Top: experimental schematic. Mice ran on a treadmill for randomly administered water reward during three 12 min sessions in multisensory contexts ‘‘A’’ and

‘‘B.’’ Contexts differed in auditory, visual, olfactory, and tactile cues, and sessions were separated by 60–90 min. Bottom: spatial tuning curves are shown for an

example GC and MC.

(E) The 1D correlation in spatial rate maps for GCs and cMCs were correlated in the ‘‘A-B’’ and ‘‘B-B’’ conditions. For a cell to be included, it was required to be

active and spatially tuned in at least one of the two sessions being compared. Both populations exhibited a higher correlation in ‘‘B-B’’ than ‘‘A-B’’ (F(1, 355) = 40.3,

p < 0.001, two-way ANOVA).

(F) To calculate theDstability, we examined the subset of cells satisfying the inclusion criterion for both conditions. TheDstability was significantly higher for cMCs

than GCs (U(88) = 211, p = 0.016, Mann Whitney U).

pooled the spatially tuned iMCs and pMCs to increase our statis-

tical power. Finding both significant spatial information and low

tuning specificity would indicate the presence of multiple firing

fields. We found that the tuning specificity for spatially tuned

cMCs was significantly lower than for spatially tuned GCs (Fig-

ure 3C). To guard against any biases resulting from our selection

criteria, we expanded the analysis to include all cells firing at

least four transients, and again we found that MCs exhibited a

lower tuning specificity than GCs (data not shown, n = 1,203

active GC recordings, n = 195 active cMC recordings, p <

0.001). These results indicate that although MCs are more likely

to exhibit spatially tuned firing than GCs, MCs exhibit more

distributed spatial firing patterns than GCs.

Mossy Cells Robustly Discriminate ContextsGCs in the hippocampus are thought to implement a ‘‘pattern

separation’’ computation by remapping their firing fields in

response to a contextual manipulation (Knierim and Neunuebel,

Neuron 93, 552–559, February 8, 2017 555

GC

MC

BC

Output

PP

A B C D

*** ***NS

*** ***NS

ControlDeletion

HIPP

Figure 4. The Role of Mossy Cells in Pattern Separation: A Computational Approach

(A) Schematic of the DG computational model. Granule cells (GC, green) receive excitatory input from the perforant path (PP) and mossy cells (MC) in addition to

feedforward inhibition from hilar perforant path-associated (HIPP) cells and feedback inhibition from basket cells (BC). MCs also provide excitation to BCs.

(B–D) Top: the fraction of recruitedGCs in response to PP input. Green denotes themodel under control conditions, while purple reflects the fraction of activeGCs

under various deletion conditions: complete MC deletion (B), deletion of the direct excitatory (C), or deletion of the disynaptic inhibition (D). Deletion of MCs (B)

increased the fraction of GCs recruited (KS Stat = 0.49, n = 100 control, 100 deletion simulations, p < 0.001, two-sample KS Test). This effect was mediated by

disynaptic inhibition (D; KS Stat = 0.45, n = 100 control, 100 deletion simulations, p < 0.001, two-sample KS Test). Bottom: pattern separation efficiency is

measured as the output distance between the GC populations responding to two highly overlapping PP inputs for the three aforementioned cases. MC deletion

(B) results in a reduction in pattern separation efficiency (U(98) = 395, p < 0.001, Mann Whitney U), and this effect again was mediated by the disynaptic inhibitory

pathway (D; U(98) = 349, p < 0.001, Mann Whitney U).

2016; Leutgeb et al., 2007). We asked whether this feature is

unique to GCs or whether we could find evidence for pattern

separationwithinMCsaswell. To this end,wecompared the sim-

ilarity of single cell spatial tuning profiles between two sequential

exposures to either different (‘‘A-B,’’ sessions 1 and 2) or identical

(‘‘B-B,’’ sessions 2 and 3) multisensory contexts (Figure 3D). To

quantify remapping in each of the two conditions (‘‘A-B’’ and

‘‘B-B’’), we required that the cell be spatially tuned in at least

one of the two sessions being compared. This was the same

paradigmused inDanielson et al. (2016a), and thereforewe could

directly compare the remapping of cMCs with that of GCs.

For both cMCs and GCs, we found that placemapsweremore

stable in the ‘‘B-B’’ than in the ‘‘A-B’’ conditions, as assessed by

the tuning curve correlation (Figure 3E). By looking at the subset

of cells included in both comparisons, we calculated Dstability

as the difference in stability between the ‘‘B-B’’ and ‘‘A-B’’ con-

ditions (Figure 3F). We found that cMCs had a significantly higher

Dstability than GCs. These data indicate that MCs robustly

discriminate contexts on the basis of their spatial tuning profiles.

Local Dentate Network Model: MCs Contribute toSparseness in GC CodingThe efficacy of pattern separation is thought to depend on the

sparseness of the GC representation (Alme et al., 2010; Treves

and Rolls, 1994). To investigate whether and how MCs might

shape the sparseness, and thus pattern separation efficacy, of

556 Neuron 93, 552–559, February 8, 2017

GCs, we developed a simple, yet biologically relevant, computa-

tional model of the DG (Figure 4A) as per Chavlis et al. (2016),

albeit with GCs modeled as point neurons. The model was cali-

brated to reflect an approximate control sparseness of �5%

(Figures 4B–4D, top, green), reflecting the fraction of GCs we

found to reliably code for an environment by regularly firing tran-

sients. We then simulated the effect on GC sparseness resulting

from the deletion of MCs (Figure 4B). Deletion of MCs resulted in

the recruitment of significantly more GCs in response to simu-

lated entorhinal input (Figure 4B, top, purple), and this in turn

led to a reduction in the pattern separation efficacy of GCs (Fig-

ure 4B, bottom), as measured by the distance between GC pop-

ulations responding to two overlapping input patterns (see STAR

Methods). To investigate whether this effect was primarily medi-

ated through the direct excitatory or indirect inhibitory pathways

between MCs and GCs, we deleted each of these connections

individually. While deletion of the direct excitatory connection

had little effect on GC excitability and pattern separation (Fig-

ure 4C), we found that deletion of the MC-basket cell connection

significantly increased GC excitability at the expense of pattern

separation performance (Figure 4D).

DISCUSSION

In summary, we performed cellular-resolution 2p Ca2+ imag-

ing of MCs during head-fixed navigation in addition to

computational modeling of the local DG network. We charac-

terized the in vivo activity levels, spatial coding fractions,

context specificity, and remapping dynamics of MCs, and we

identified prominent differences in these properties between

MCs and GCs. We found that MCs were more active and

more likely to have detected place fields than GCs, and there-

fore, MCs represent an active population of spatially tuned

hippocampal principal neurons during behavior. In addition to

exhibiting a higher place coding fraction, our results also sug-

gest that these cells are more likely to exhibit multiple firing

fields than GCs. While we cannot exclude the possibility that

in vivo GCaMP Ca2+ imaging is biased toward the detection

of burst events over single spikes in GCs, burst spiking

is thought to be the most efficient mode of transmission

from granule cells to their downstream targets (Bischofberger

et al., 2006; Henze et al., 2002), and our most active GCs fired

transients at a rate of 1–2/min, comparable to the burst rate

reported in a previous in vivo intracellular recording study of

GCs (Pernıa-Andrade and Jonas, 2014). Moreover, although

we cannot completely exclude the possibility that differences

in intracellular Ca2+ buffering contributed to the observed differ-

ences in activity between GCs and MCs, the highly significant

differences in activity, as reflected by both the transient AUC

rate and the frequency of sharply peaked transients, suggest

that differences in Ca2+ buffer protein expression are unlikely

to fully explain our results.

Because the primary excitatory inputs onto MCs originate

from large, ‘‘detonator’’ mossy terminal synapses from the

mossy fibers of GCs (Amaral, 1978; Henze et al., 2002; Scharf-

man, 2016), it is possible that the spatially tuned firing pattern

of MCs we observed is conveyed to MCs by GCs. However,

given both the very sparse firing of GCs (Danielson et al.,

2016a) and the low convergence of GCs to MCs (Amaral,

1978; Frotscher et al., 1991; Ribak et al., 1985; Scharfman,

2016), our results suggest that GC activity alone is unlikely to fully

account for the high activity level and distributed spatially tuning

of MCs we observed. Relatedly, it is possible that MCs are pref-

erentially innervated by young adult-born GCs, which exhibit

higher activity and more diffuse firing compared to their mature

counterparts (Danielson et al., 2016a). Excitation from entorhinal

inputs (Scharfman, 1991), backprojecting CA3 pyramidal cells

(Ishizuka et al., 1990), or various subcortical neuromodulatory in-

puts (Scharfman, 2016) might also contribute to the activity and

tuning profiles of MCs.

We also found that MCs exhibit strong remapping of their

firing fields during random foraging in distinct contexts, sug-

gesting that MCs may contribute to pattern separation on the

behavioral level. Our computational model also suggests that

MCs contribute to the sparseness of GC representations and

may thus support pattern separation in this manner as well.

Furthermore, we found that this effect is primarily mediated

through disynaptic inhibition, in agreement with previously pub-

lished experimental data (Jinde et al., 2012; Scharfman, 1995,

2016). Indeed, an active role for MCs in pattern separation

and a disinhibitory influence of MCs on GCs are supported

by previous experiments in which MC loss caused transient

GC hyperexcitability and impaired contextual fear learning

(Jinde et al., 2012). The enhanced remapping of firing fields

in MCs compared to GCs we observed also suggests that

context-specific inputs by sources other than GCs are

conveyed to MCs. Taken together, our results support a view

of hippocampal DG function in which pattern separation is im-

plemented by the joint action of both MCs and GCs. Future

in vivo studies combining recording with cell-type-specific

manipulations will help to delineate the precise mechanisms

by which granule cells, mossy cells, and other excitatory and

inhibitory cell types of the dentate gyrus (Hosp et al., 2014; Wil-

liams et al., 2007) interact to support hippocampal-dependent

behaviors.

STAR+METHODS

Detailed methods are provided in the online version of this paper

and include the following:

d KEY RESOURCES TABLE

d CONTACT FOR REAGENT AND RESOURCE SHARING

d EXPERIMENTAL MODEL AND SUBJECT DETAILS

d METHOD DETAILS

B Stereotactic viral injection and imagingwindow implant

B Histology and Immunohistochemistry

B In vivo 2-photon imaging

B Training

B Contexts

B Stimulus presentation and behavioral readout

B Calcium data processing

B Calcium transient detection

B Spike estimation

B Spatial tuning vectors

B Spatial information p value analysis (place cell classifi-

cation)

B Remapping analysis

B Computational modeling

d QUANTIFICATION AND STATISTICAL ANALYSIS

d DATA AND SOFTWARE AVAILABILITY

SUPPLEMENTAL INFORMATION

Supplemental Information includes two figures, two tables, and onemovie and

can be found with this article online at http://dx.doi.org/10.1016/j.neuron.

2016.12.019.

AUTHOR CONTRIBUTIONS

N.B.D., G.F.T., M.L., and A.L. conceived the project. N.B.D., G.F.T., and M.L.

performed experiments. S.C., P.C.P., and P.P. performed the computational

modeling. All authors wrote the manuscript.

ACKNOWLEDGMENTS

We are indebted to Alla Karpova and the GENIE Project of the Howard

Hughes Medical Institute for the rAAV2-retro. N.B.D. is supported by NINDS

F30NS090819. A.L. is supported by NIMH 1R01MH100631, 1U01NS090583,

1R01NS094668, Zegar Family Foundation and the Kavli Institute for Brain

Science at Columbia University. S.C., P.C.P., and P.P. are supported by the

European Research Council Starting Grant dEMORY (STG 311435). P.P.

acknowledges support from the FENS-Kavli Network of Excellence. While

our experiments were underway, similar results and conclusions were reached

by the laboratories of G. Buzsaki and J.J. Knierim.

Neuron 93, 552–559, February 8, 2017 557

Received: September 11, 2016

Revised: October 20, 2016

Accepted: November 26, 2016

Published: January 26, 2017

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Neuron 93, 552–559, February 8, 2017 559

STAR+METHODS

KEY RESOURCES TABLE

REAGENT or RESOURCE SOURCE IDENTIFIER

Antibodies

Rabbit anti-GluR2/3 Millipore AB1506, RRID: AB_90710

chicken anti-GFP Abcam ab6556, RRID: AB_305564

Alexa 488 conjugated anti-chicken IgY Jackson ImmunoResearch 703-545-155, RRID: AB_2340375

Dylight 649 conjugated anti-rabbit IgG Jackson ImmunoResearch 211-492-171, RRID: AB_2339164

Experimental Models: Organisms/Strains

Mouse: C57Bl6 Jackson Laboratories 000664

Recombinant DNA

rAAV2-retro.CAG.Cre.WPRE.SV40 Tervo et al., 2016 N/A

rAAV1.Syn.Flex.GCaMP6f.WPRE.SV40 Penn Vector Core AV-1-PV2819

Software and Algorithms

SIMA Kaifosh, et al., 2014 http://www.losonczylab.org/sima

BRIAN neural network simulator Brette and Goodman, 2011 v1.4.3

CONTACT FOR REAGENT AND RESOURCE SHARING

Further information and requests for resources and reagents should be directed to the Lead Contact Attila Losonczy

(al2856@cumc.columbia.edu).

EXPERIMENTAL MODEL AND SUBJECT DETAILS

Here we describe in brief the procedures pertaining to experiments with retrogradely-labeled MCs. Experimental procedures

for simultaneous imaging of putative MCs and GCs are described in (Danielson et al., 2016a). All experiments were conducted in

accordance with the USNational Institutes of Health guidelines andwith the approval of the Columbia University Institute Institutional

Animal Care and Use Committees.

Six 4-month-old wild-type male and female C57Bl6 mice were used for mossy cell imaging experiments. Mice were kept in

the vivarium on a reversed 12-h light/dark cycle and were housed 3-5 mice in each cage. Experiments were performed during the

dark portion of the cycle.

METHOD DETAILS

Stereotactic viral injection and imaging window implantFor experiments with retrogradely-labeled mossy cells, a retrograde variant of recombinant adeno associated virus (rAAV2-

retro, Tervo et al., 2016; gift from Dr. A. Karpova, Janelia Research Campus), expressing Cre-recombinase (rAAV2-

retro.CAG.Cre.WPRE.SV40) with titer of 4x1012 was stereotactically injected into the right dorsal dentate gyrus using a Nanoject

syringe with injection coordinates �2.1, �2.3, �2.5 mm AP, �1.5 ML, and �1.7 DV; 60 nL of virus was injected per location

in 20 nL increments. We also stereotactically injected Cre-dependent rAAV carrying the GCaMP6f with titer of 2-4x1012

(rAAV1.Syn.Flex.GCaMP6f.WPRE.SV40, Penn Vector Core) into the left dorsal dentate gyrus with the same injection coordinates

and 80 nL of virus per DV location in 20 nL increments.

Mice were surgically implanted with an imaging window over the left dorsal dentate gyrus and implanted with a stainless-steel

headpost for head fixation during imaging experiments. Imaging cannulas were constructed by adhering (Norland optical adhesive)

a 1.5 mm glass coverslip (Tower Optical Corporation) to a cylindrical steel cannula (1.5-mm diameter x 2.3-mm height). We used a

headpost in which the posterior bar was thinned to accommodate the full 3.5mmworking distance of the objective. The imaging win-

dow was implanted 100-200 mm above the hippocampal fissure, providing optical access to the dorsal blade of the DG and the hilus

below. Following induction of anesthesia (Isoflurane: 3% induction, 1.5 - 2.0%maintenance; 1.0 L/min O2) and administration of anal-

gesia (buprenorphine 0.05-0.1mg/kg), the scalp was removed, and a 1.5 mm diameter craniotomy was performed with a fine-tipped

dental drill (V00033, Henry-Schein). The dura and the underlying cortex were aspirated to reveal the capsular fibers. Following this, a

30 g blunt syringe (B30-50, SAI) was used to gently aspirate CA1 directly superior to the dentate until the loose fibers and vasculature

e1 Neuron 93, 552–559.e1–e4, February 8, 2017

of stratum moleculare were visible. Bleeding was controlled with a collagen gel sponge (A00063, Avitene) and continuous irrigation

with sterile aCSF. We then gently fit the cannula into the craniotomy and affixed the headpost to the skull using dental cement

(675572, Dentsply). Mice were active within 15 min of surgery, and analgesia was continued for three days post-operatively.

Histology and ImmunohistochemistryFollowing the last imaging session, mice were deeply anaesthetized with isoflurane and transcardially perfused with 4% paraformal-

dehyde solution (in 0.1mMPBS). The brains were removed from the skull, left in the fixative for overnight post-fixation, and slicedwith

a vibratome in horizontal or coronal planes. The slices were incubated for two days in a cocktail of the following primary antibodies:

1:1000 dilution of chicken anti-GFP (Abcam, ab6556) and 1:100 dilution of rabbit anti-GluR2/3 antibody (EMD Millipore, AB1506).

Following several washes in PBS, the slices were incubated for two days in the secondary antibody cocktail: alexa488 conjugated

anti-chicken IgY and Dylight649 conjugated anti-rabbit IgG (Jackson Immunoresearch Laboratories;1:250 dilution) and mounted on

microscope slides. Two channel confocal image stacks were captured with a Leica SP5 microscope (Leica Microsystems) with the

suitable excitation and emission filter sets. The single and double labeled cells in the hilus were quantified by two investigators

independently.

In vivo 2-photon imagingWe used the same imaging system as described previously (Kaifosh et al., 2013; Lovett-Barron et al., 2014) with an 8kHz resonant

galvanometer (Bruker). Approximately 150-250 mW of laser power was used during imaging (measured under the objective), with

adjustments in power levels to accommodate varying window clarity. To optimize light transmission, we adjusted the angle of the

mouse’s head using two goniometers (Edmund Optics, ± 10 degree range) such that the imaging window was parallel to the objec-

tive. All images were acquired with a Nikon 40X NIR water-immersion objective (0.8 NA, 3.5 mmWD). Green (GCaMP6f) fluorescence

was acquired using an emission cube set (green, HQ525/70 m-2p; red, HQ607/45 m-2p; 575dcxr, Chroma Technology) taking im-

ages (512 3 512 pixels) at �30 Hz covering 160 mm x 160 mm area. The emitted photons were captured with a photomultiplier tube

(GaAsP PMT, Hamamatsu Model 7422P-40), and a dual stage preamp (1.4 3 105 dB, Bruker) was used to amplify signals prior to

digitization.

TrainingMice were water-restricted (>90% pre-deprivation weight) and trained to run on a cue-deplete burlap treadmill belt for water reward

over the course of 1-2 weeks. We applied a progressively restrictive water reward schedule, with mice initially receiving 20-25

randomly placed reward per lap and ultimately receiving 3 randomly placed reward per lap. Mice were trained for 20 min daily until

they regularly ran at least one lap per minute. Mice were also habituated to the optical instrumentation (presence of objective, laser,

shutter sounds) prior to imaging experiments.

ContextsAs in our previous work (Danielson et al., 2016a, 2016b; Kaifosh et al., 2013), each context (A and B) consisted of the same treadmill

belt (the same sequence of 3 joined fabric ribbons), but distinct in their visual, auditory, tactile, and olfactory stimuli. During imaging

sessions, mice received three randomly placed water reward per lap, with reward positions changing randomly each lap. To allow for

comparison of activity between similar contexts, the same three fabrics were used in the same order, but the locations of all of the

tactile cues were shuffled around between the two belts.

Stimulus presentation and behavioral readoutVisual, auditory, and olfactory stimuli were presented and behavioral data were recorded as described previously (Danielson et al.,

2016a, 2016b; Kaifosh et al., 2013; Lovett-Barron et al., 2014). In order to reliably track the position of the treadmill belt, we estab-

lished registration anchors at known positions along the belts and interpolated between themusing a quadrature encodedmovement

signal tied to the rotation of the treadmill wheels. Registration anchors were marked by radio-frequency identification (RFID) buttons

(16mm, 125kHz, SparkFun Electronics) at evenly spaced positions along the belt, andwere read off as they passed over a fixed RFID

reader (ID-12LA, SparkFun). The rotational quadrature signal was produced by marking treadmill wheels with offset tick marks, and

this signal was encoded by a pair of photodiodes (SEN-0024, SparkFun) aligned to the wheels (<0.5 cm resolution).

Calcium data processingAll imaging data were analyzed using the SIMA software package (Kaifosh et al., 2014). Motion correction was performed using a 2D

Hidden Markov Model (Dombeck et al., 2010; Kaifosh et al., 2013), with modifications to accommodate the specific features of data

acquired with resonant galvanometers. In cases where motion artifacts were not adequately corrected, the affected data was dis-

carded from further analysis. We used the SIMA project’s ROI Buddy graphical user interface (Kaifosh et al., 2014) to draw regions

of interest (ROIs) overMC somata visible in the time-averaged image of themotion-corrected green/GCaMP6f channel. We also used

this software to determine the correspondence of ROIs across datasets from different trials in which the same FOV was imaged.

Dynamic GCaMP6f fluorescence signals were extracted fromROIs using SIMA (Kaifosh et al., 2014) then converted to relative fluo-

rescence changes (DF/F) as described in Jia et al. (2011), with uniform smoothing window t1 = 3 s and baseline size t2 = 60 s. After

Neuron 93, 552–559.e1–e4, February 8, 2017 e2

performing Ca2+ transient detection (see below), we recomputed the baseline excluding detected transients, after which we recom-

puted DF/F. This iterative procedure was repeated three times and effectively removed the transient contamination from the calcu-

lated baseline.

Calcium transient detectionIn order to identify significant Ca2+ transients, we implemented an approach first described by Dombeck et al. (2007). This technique

has been used in a number of subsequent hippocampal in vivo Ca2+ imaging publications from both our lab (Danielson et al., 2016a,

2016b; Lovett-Barron et al., 2014) and others (Dombeck et al., 2010; Rajasethupathy et al., 2015; Sheffield and Dombeck, 2015).

There are two primary assumptions underlying this algorithm: 1. All negative deflections in the DF/F trace are attributable to motion

out of the focal plane; 2. Motion-induced deflections in the DF/F trace occur with equal likelihood in the positive and negative

directions.

Combining these assumptions, for each positive deflection of the DF/F trace, we can estimate the probability that the observed

event was motion-induced rather than activity-related by calculating the ratio of negative- to positive-going events. Because the

signal-to-noise ratio can vary across cells,DF/F is expressed in units of s (the standard deviation of the baseline fluorescence). Event

onsets are defined as the frame at which fluorescence exceeds 2s, and offset is defined as the frame at which it falls below 0.5s. For

an event of a given magnitude, this technique thus allows us to determine the minimum duration necessary to claim with confidence

that the event was not attributable to motion. Put another way, this technique allows us to determine a duration threshold beyond

which we can reject the null hypothesis that the deflection was not attributable to neuronal activity. In contrast to simple

threshold-based techniques, this approach has the important advantage of being able to assign a statistical confidence level to

each event.

One minor difference between our implementation of the algorithm and that described in Dombeck et al. (2007) concerns the defi-

nition ofs. Dombeck et al. ‘‘.manually selected regions of the fluorescence time series that did not contain large transients.’’ In order

automate this procedure, which was necessary given the large number of recordings we obtained (>20,000), we performed an iter-

ative process in whichswas initially estimated as the standard deviation of the entireDF/F trace. This is certainly an overestimate ofs

(though not an unreasonable one in sparsely firing cells), but it allowed us to conservatively identify transients at a p < 0.01 threshold.

We then re-calculated s after excluding these epochs and re-identified transients at a p < 0.01 threshold. After re-estimating s a third

and final time, we obtained our final identified transients. Empirically we have found that running the algorithm for additional iterations

has little effect.

Spike estimationWe used the ML spike package (Deneux et al., 2016) to perform a maximum likelihood estimation of the spike trains underlying each

fluorescence trace in our iMC, pMC, andGCdatasets. We used the default GCaMP6f nonlinearity parameter ([0.85,�0.006]) and drift

parameter (0.015, multiplicative). The only parameter we varied between GCs andMCswas the ‘spike-rate’ parameter. Deneux et al.

empirically recommend a default value of 1, which performed well for our MCs. However, this value led to a number of visually

apparent false positives for our GCs; setting this value to 0.01 resulted in satisfactory performance for our GCs. Auto-calibration

was performed for all remaining parameters.

Spatial tuning vectorsWhen evaluating the spatial tuning of MCs, we restricted our analysis to running-related epochs, defined as consecutive frames of

forward locomotion (defined as an imaging frame in which at least one forward pair of beam breaks occurred on the quadrant reader)

at least 1 s in duration andwith aminimumpeak speed of 5 cm/sec. Consecutive epochs separated by < 0.5 sweremerged. Running-

related transients were defined as those that were initiated during a running-related epoch. Transient start was defined as the

first imaging frame with mean fluorescence > = 2s, with s equal to the standard deviation of the baseline frames. Offset was defined

as the first frame with mean fluorescence % 0.5s (Dombeck et al., 2010). The spatial tuning vector was calculated asP

jðeiqj=oðqjÞÞ,where qj is the position of the mouse at the onset time of the j-th running-related transient, and oj is the fraction of running frames

acquired at position qj.

Spatial information p value analysis (place cell classification)For each cell we first computed the spatial information content (Skaggs et al., 1993) as IN =

PNi = 1li ln ðli=lÞpi where li and pi are the

transient rate and fraction of time spent in the i th bin, l is the overall firing rate, and N is the number of bins. We computed IN for

multiple values of N = 2, 4, 5, 8, 10, 20, 25, and 100. We then created 100,000 random reassignments of the transient onset times

within the running-related epochs and re-computed the values of IsN, where s is the index of the shuffle. In calculated spatial informa-

tion using binned data, it is important to note that this measure is biased by both the number of bins chosen and by the number of

events fired by the cell. Performing shuffles on a per-cell basis addresses the latter bias. To roughly correct from the bias associated

with binning, we subtracted the mean of this null distribution from all estimates to obtain values bIN = IN � ð1=100; 000ÞP100;000s= 1 IsN.

Finally, we computed a single estimate of the information content for the true transient onset times, bI =maxN bIN, and for the shuffles,bIs =maxN bI s

N . Note that by maximizing bI s

N over bin sizes we have allowed each iteration of the shuffle to maximize its value across bin

e3 Neuron 93, 552–559.e1–e4, February 8, 2017

sizes (an alternate less conservative choice would have been to enforce that each shuffle be calculated using the same bin size as

was optimal for bI). The spatial tuning p value was taken as the fraction of values of s for which bI exceeded bIs.

Remapping analysisRate maps were formed by dividing the number of transients starting in each bin by the occupancy of that bin. We calculated rate

maps with 100 position bins and smoothed with a Gaussian kernel s = 3 bins). The tuning curve correlation for each cell was defined

as the Pearson correlation coefficient between tuning curves for a cell in the two sequential context exposures.

Computational modelingThemodel was inspired by the structure and connectivity features described byMyers and Scharfman (Myers and Scharfman, 2009)

and was derived from a more detailed DG network model detailed in (Chavlis et al., 2016). All simulations were performed using the

BRIAN (BRIAN v1.4.3) spiking network simulator (Brette and Goodman, 2011; Goodman and Brette, 2009) running on a High-Perfor-

mance Computing Cluster (HPCC) with 312 cores under a 64-bit CentOS 6.8 Linux operating system. The network model consists of

2000 simulated granule cells (GCs), a scale that represents 1/500 of the one million GCs found in the rat brain (West et al., 1991); the

remaining neuronal cell types in the model are scaled down proportionally. The model population of GCs was organized into non-

overlapping clusters, with each cluster containing 20 GCs; this organization roughly corresponds to the lamellar organization along

the septotemporal extent of the DG (Sloviter and Lømo, 2012). Apart from the excitatory GCs andMCs, themodel included two types

of inhibitory interneurons: basket cells (BCs), which provide GCswith feedback inhibition; and the HIPP cells, which receive perforant

path (PP) input, thus delivering feed-forward inhibition to GCs.

We included one BC per cluster of GCs, which in turn corresponds to 100 simulated BCs in the model. This is a form of ‘‘winner-

take-all’’ competition (Coultrip et al., 1992), in which all but the most strongly activated GCs in a cluster are silenced. Given 100 clus-

ters in the model, approximately 5% of GCs were active for a given stimulus under control conditions; this is in agreement with the

theoretical and experimental estimates of �5% regular activity in the substrate (Danielson et al., 2016a; Treves et al., 2008). All cells

included in the network were simulated as adaptive exponential integrate-and-fire models (Brette and Gerstner, 2005). External input

to the networkmodel was provided by 400 afferents representing themajor input the DG receives from Entorhinal Cortex (EC) Layer II

cells via the PP. For simplicity, the input cells were simulated as independent Poisson spike trains with a frequency of 45 Hz. The

simulations reported here assumed that a given stimulus is represented by 10% of the PP afferents, which is in line with experimental

evidence (McNaughton et al., 1991). Connectivity matrices were constructed randomly (uniform random distribution) before the start

of the simulations and remained fixed across all simulations (no rewiring).

A GC model cell was considered active if it fired at least one spike during stimulus presentation. In order to quantify pattern

separation efficiency, we used a metric f (‘‘population distance’’):

f =HD

2ð1� sÞNwhere s denotes the sparsity (i.e., the ratio of silent neurons to all neurons), N the number of neurons, andHD is the hamming distance

between two binary patterns (Hamming, 1950). The factor of 2 in the denominator is used to limit the metric at zero. Our network is

said to perform pattern separation if the input distance is smaller than the output distance, i.e., when fðinputÞ < fðoutputÞ.For each simulation experiment, we constructed two overlapping input patterns. Each input pattern consisted of 40 PP afferents,

16 of which were common to both inputs, corresponding to fðinputÞ = 0:2. The distance of the input remained constant across all trials

and different conditions. For each condition we performed 50 trials, using a new pair of inputs each time, and for each trial the network

was simulated for 1310 ms. PP inputs were delivered at 300 ms and lasted for 1000ms. The first 300 ms and the final 10 ms were

excluded from the analysis to assess the network during stimulus presentation. The time step for all simulations was set to 0.1 ms.

QUANTIFICATION AND STATISTICAL ANALYSIS

All statistical tests are described in the corresponding figure legends and in Tables S1 and S2. All comparisons were two sided. Non-

parametric Mann-Whitney U Test or ANOVA were used throughout; two-sample KS Test was used in Figures 4B–4D to compare

distributions. Summary data are presented in boxplots, where horizontal line is the median, boxes are 25th-75th percentiles, and

whiskers are 1.5*IQR (interquartile range).

DATA AND SOFTWARE AVAILABILITY

The SIMA package for Ca2+ imaging analysis is freely available online (http://www.losonczylab.org/sima). The computational model

is available for download from ModelDB (accession #: 206397) and the Poirazi lab web site: http://www.dendrites.gr.

Neuron 93, 552–559.e1–e4, February 8, 2017 e4