Temporal evolution of cortical ensembles promoting remote memory retrieval

DeNardo, Laura A.; Liu, Cindy D.; Allen, William E.; Adams, Eliza L.; Friedmann, Drew; Fu, Lisa; Guenthner, Casey J.; Tessier-Lavigne, Marc; Luo, Liqun

doi:10.1038/s41593-018-0318-7

Article
Published: 28 January 2019

Temporal evolution of cortical ensembles promoting remote memory retrieval

Laura A. DeNardo ORCID: orcid.org/0000-0002-7607-4773^1,2^nAff4,
Cindy D. Liu ORCID: orcid.org/0000-0002-6473-3284^1,2,
William E. Allen^1,2,3,
Eliza L. Adams^1,3,
Drew Friedmann^1,2,
Lisa Fu^1,2,
Casey J. Guenthner^1,2,3,
Marc Tessier-Lavigne^1,3 &
…
Liqun Luo ORCID: orcid.org/0000-0001-5467-9264^1,2,3

Nature Neuroscience volume 22, pages 460–469 (2019)Cite this article

37k Accesses
227 Citations
50 Altmetric
Metrics details

Abstract

Memories of fearful events can last a lifetime. The prelimbic (PL) cortex, a subregion of prefrontal cortex, plays a critical role in fear memory retrieval over time. Most studies have focused on acquisition, consolidation, and retrieval of recent memories, but much less is known about the neural mechanisms of remote memory. Using a new knock-in mouse for activity-dependent genetic labeling (TRAP2), we demonstrate that neuronal ensembles in the PL cortex are dynamic. PL neurons TRAPed during later memory retrievals are more likely to be reactivated and make larger behavioral contributions to remote memory retrieval compared to those TRAPed during learning or early memory retrieval. PL activity during learning is required to initiate this time-dependent reorganization in PL ensembles underlying memory retrieval. Finally, while neurons TRAPed during earlier and later retrievals have similar broad projections throughout the brain, PL neurons TRAPed later have a stronger functional recruitment of cortical targets.

You have full access to this article via your institution.

Download PDF

Reactivation of encoding ensembles in the prelimbic cortex supports temporal associations

Article Open access 07 March 2024

Prefrontal cortex neuronal ensembles encoding fear drive fear expression during long-term memory retrieval

Article Open access 24 July 2019

Memory strength gates the involvement of a CREB-dependent cortical fear engram in remote memory

Article Open access 24 May 2019

Main

Consolidation of remote memories reflects dynamic neural processes spanning time and space. Fear conditioning is a well-established model of learning and memory, whose neural basis has been studied extensively both at the time of learning and during memory retrieval occurring hours or days later. However, the mechanisms of remote memory are less well-understood. In the hours after fear learning, a cascade of molecular changes leads to synaptic strengthening in regions including the amygdala and hippocampus¹. Over weeks, new brain regions and neural pathways are recruited to support memory retrieval^2,3. In line with the systems consolidation hypothesis, dependence on the hippocampus for retrieval of contextual fear memories degrades with time and cortical structures become more important². Recent work also supports time-dependent reorganization of circuits underlying memories of cued fear conditioning³. For instance, while the amygdala remains required for cued memory retrieval⁴, there is time-dependent turnover of participating amygdala ensembles, and high order sensory and association cortices are selectively recruited at remote timepoints^5,6.

The prelimbic (PL) cortex, a subregion of the medial prefrontal cortex, is required for the expression of cued and contextual fear memories from hours after learning until weeks later^7,8,9,10,11. However, while some studies suggested that prefrontal cortical neurons active during learning are required for memory retrieval^11,12,13, others provided evidence for ongoing circuit reorganization during memory consolidation from a day to a month after learning^9,14,15. It has been difficult to causally relate the activity of cortical neurons during learning or recent memory retrieval to their function in remote memory, in part due to a lack of tools¹⁶. Furthermore, although the PL cortex projects broadly to many cortical and subcortical brain regions involved in fear learning and memory^17,18, most studies have focused on interactions between the PL cortex, the amygdala, and the hippocampus during fear memory consolidation and retrieval^19,20,21,22. Therefore, it remains unclear how the PL cortex influences downstream activity in many of its target regions during memory retrieval.

To interrogate the connectivity and function of neurons activated during behavior, we recently developed a new version of ‘targeted recombination in active populations’²³ (TRAP2)²⁴. Here we report a detailed characterization of TRAP2. We demonstrate that it has enhanced efficiency over the previous version, providing brain-wide access to neurons activated by a particular experience. We use TRAP2 to ask: to what extent do PL neurons activated during learning or recent memory retrieval contribute to remote memory? How specific is their function for the learned association? How do PL neurons coordinate activity in their many downstream target regions to contribute to memory-guided behavior?

Results

Characterization of TRAP2

TRAP allows permanent genetic access to neurons activated by a specific experience²³. The TRAP system uses an immediate early gene locus to drive the expression of tamoxifen-inducible Cre recombinase (CreER), along with a transgenic or virally-delivered Cre-dependent effector. When a neuron is active in the presence of tamoxifen, CreER can enter the nucleus to catalyze recombination, resulting in permanent expression of the effector (Fig. 1a). Because the original FosTRAP (TRAP1) disrupts endogenous Fos²³ and does not efficiently access many brain regions, we developed a new mouse line, TRAP2²⁴, that preserves endogenous Fos, including the highly conserved first intron²⁵ and the 3′ untranslated region critical for mRNA destabilization²⁶ (Fig. 1b and Supplementary Fig. 1). Furthermore, we replaced the original Cre with a codon-optimized iCre for improved expression²⁷.

**Fig. 1: TRAP2 design and characterization.**

To characterize TRAP2, we first determined the time course of TRAPing and sensitivity of TRAP2 using the tdTomato Cre reporter Ai14²⁸. We dark adapted TRAP1;Ai14 and TRAP2;Ai14 double transgenic mice and then exposed them to 1 hour of light at different times relative to injection of 4-hydroxytamoxifen (4-OHT) (Supplementary Fig. 2a). Resultant patterns of tdTomato expression revealed that the majority of TRAPing occurred within a 6-hour window centered around the 4-OHT injection. At peak, there was a ~12-fold induction in TRAPed cells above dark controls in primary visual cortex for TRAP2, an improvement over a ~5-fold induction for TRAP1 (Supplementary Fig. 2b–g). To examine the ability of TRAP2 to capture activity in different brain regions, we injected TRAP2;Ai14 and TRAP1;Ai14 mice with 4-OHT while they explored a novel environment (Supplementary Fig. 3a). TRAP2 labeled many more cells than TRAP1 throughout the brain (Supplementary Fig. 3b–d) in a manner more consistent with endogenous Fos expression²⁹. TRAP2;Ai14 mice that received sham injections had very few tdTomato⁺ cells, indicating minimal Cre-mediated recombination in the absence of 4-OHT (Supplementary Fig. 3b–d).

To test the utility of TRAP2 in interrogating neural circuits for fear learning and memory, we injected TRAP2;Ai14 mice with 4-OHT immediately after a differential auditory fear conditioning (FC) protocol in which a conditioned tone (CS⁺) that co-terminated with a footshock was interleaved with an unreinforced non-conditioned tone (CS^–) (Supplementary Fig. 4a). Subsequent iDISCO+-based whole-brain immunostaining³⁰ revealed significant increases in the numbers of TRAPed cells above non-shocked (NS) controls in expected brain regions³¹, including parabrachial nucleus, periacqueductal grey, and subregions of the amygdala and hypothalamus (Fig. 1c–e and Supplementary Table 1).

PL neurons TRAPed during memory retrieval change over time

While the PL cortex is required for fear memory retrieval, it remains unclear as to what extent PL ensembles supporting memory are stable or dynamic over time (Fig. 2a). We used TRAP2 and Fos immunostaining to ask what proportion of PL neurons TRAPed during an earlier memory experience were reactivated during remote retrieval. To ensure that the targeting allele in TRAP2 mice did not disrupt endogenous Fos, we first compared Fos expression following remote memory retrieval in wild-type and TRAP2 mice. We observed no differences between groups when we quantified Fos in six relevant brain regions (Supplementary Fig. 5).

**Fig. 2: PL activation patterns during fear learning and memory retrieval over time.**

We subjected four groups of TRAP2;Ai14 mice to the auditory fear conditioning protocol described above. TRAPing occurred immediately after FC, or after memory retrieval 1 day (1d), 7 days (7d), or 14 days (14d) after learning, respectively. Control animals were not shocked (NS), and thus did not undergo associative learning. Twenty-eight days after fear conditioning, all groups underwent a remote memory retrieval session and were sacrificed one hour later for Fos immunostaining (Fig. 2b,c). We quantified freezing behavior as an expression of fear. Mice froze preferentially during presentations of the conditioned tone (CS⁺) (Supplementary Fig. 4a). Furthermore, all fear-conditioned groups exhibited comparable levels of conditioned freezing, while NS animals did not freeze (Supplementary Fig. 4b).

We found that 7d- and 14d-TRAPed PL neurons were significantly more likely to be reactivated (TRAPed and Fos⁺) during remote memory retrieval compared to NS, FC, and 1d conditions, when measured as a fraction of total Fos⁺ neurons (Fig. 2d) or total TRAPed neurons (Fig. 2e). This is despite the fact that the numbers of TRAPed and Fos⁺ neurons were mostly similar across groups (Supplementary Fig. 4c,d). Most TRAPed PL neurons were located in deep layers, and activated neurons in later retrievals had a larger proportion of TRAPed neurons in layer 6 at the expense of layers 2/3 compared with the 1d group (Supplementary Fig. 6). Together, these data suggest that new PL neurons are recruited to the remote memory trace over time after initial learning. As controls, we observed no time-dependent changes in Fos expression in piriform cortex (Supplementary Fig. 7) or in non-TRAPed PL neurons (Supplementary Fig. 4e). However, in all groups, TRAPed cells were more likely to be reactivated than non-TRAPed cells. Thus, a proportion of TRAPed PL neurons may stably encode features of the environment, while another subset undergoes memory-dependent reorganization.

Using the same approach, we examined time-dependent changes in dentate gyrus (DG) and basolateral amygdala (BLA), which have established roles in contextual and cued fear memory consolidation³². We did not observe time-dependent changes in TRAP/Fos overlap in DG (Fig. 2f), consistent with studies showing that the hippocampus is involved in recent but not remote memory³³. We observed a trending time-dependent increase in the fraction of TRAPed BLA neurons that were Fos⁺ following remote memory retrieval (Fig. 2g, Double⁺/Fos⁺: P = 0.06; Double⁺/TRAPed: P = 0.05, student’s t-test), consistent with work showing that BLA plays a continual role in cued fear memory⁴ and that memory-related BLA populations turn over throughout at least a week after learning⁵.

TRAPed PL neurons promote remote memory in a time-dependent manner

To test the behavioral function of TRAPed neurons, we expressed channelrhodopsin (ChR2)³⁴ in PL neurons TRAPed at different timepoints and optogenetically stimulated them during remote memory retrieval (Fig. 3a). In the absence of tones, reactivating TRAPed PL cells increased freezing above baseline levels in all fear conditioned groups (Fig. 3b). However, despite having similar numbers of TRAPed neurons in most groups (Supplementary Fig. 4c), the extent to which TRAPed neurons drove freezing was time-dependent, such that stimulating PL ensembles TRAPed later produced more freezing (Fig. 3c; F_Interaction(3,57) = 3.55, P = 0.019, 2-way repeated measures ANOVA). These data indicate that reactivating PL neurons TRAPed during earlier memory events promotes freezing in the conditioning context at remote times, and that the functional contribution of TRAPed PL neurons to remote memory retrieval increases during the first two weeks after learning.

**Fig. 3: Temporal changes in the causal role of TRAPed PL neurons in remote fear memory retrieval.**

To further elucidate the functional contributions of TRAPed PL neurons during remote memory, we performed additional optogenetic experiments to investigate their specificity for the conditioned tone and context. Reactivating TRAPed PL cells during presentations of the CS⁺ was not sufficient to increase freezing above the level of the tones (Fig. 3d,h), suggesting that their function may be occluded by the tone. Furthermore, reactivating TRAPed PL neurons did not impact freezing during the CS^– (Fig. 3e), nor the behavioral discrimination between CS⁺ and CS^– (Fig. 3f). Reactivating TRAPed neurons in an altered context in the absence of tones (Day 29, Fig. 3a) caused only a modest increase in freezing (Fig. 3g), suggesting that contextual information facilitates the ability of TRAPed PL neurons to enhance fear memory³⁵.

In further support of the behavioral specificity of fear memory-TRAPed neurons, photoactivating NS-TRAPed ensembles did not cause freezing (Fig. 3b), even though similar numbers of neurons were TRAPed (Supplementary Fig. 4c). To test whether the NS-TRAPed ensembles, which likely represent the neutral tone and context (Supplementary Fig. 4e), could contribute to a newly formed fear memory, we fear-conditioned the NS mice on Day 32 to generate the NS/FC group. The following day, we performed a memory retrieval session during which we photostimulated the NS PL ensembles that had been TRAPed on Day 0 (Supplementary Fig. 8a). Although ChR2 was highly expressed (Supplementary Fig. 9), reactivating NS-TRAPed cells did not reliably drive contextual or tone-evoked freezing (Supplementary Fig. 8b,c), suggesting that TRAPed PL neurons must be linked to the fearconditioning event to participate in the memory trace. While we cannot definitively rule out the possibility of nonspecific effects on freezing induced by light, optogenetic stimulation did not significantly impact contextual freezing levels in mice that were fear conditioned after TRAPing (Supplementary Fig. 8b), suggesting that light alone is not sufficient to produce the previously observed behavioral effects. Finally, we observed no significant aversion to photoactivation of TRAPed neurons in a real-time place aversion task (Supplementary Fig. 10), suggesting that the observed effects on freezing reflect a modulation of responses to conditioned stimuli rather than general aversion.

Testing the requirement of PL activity in remote memory retrieval

To test whether activity in TRAPed neurons is required for remote memory retrieval, we injected an AAV expressing a Cre-dependent light-activated chloride channel iC++³⁶ into the PL cortex. We injected 4-OHT after 1d or 14d memory retrieval, and photoinhibited TRAPed PL neurons during presentations of the conditioned tone on Day 28 (Fig. 4a and Supplementary Fig. 11). Inhibiting 1d-TRAPed cells did not reliably impair remote memory retrieval (Fig. 4b), whereas inhibiting 14d-TRAPed cells significantly reduced freezing to the conditioned tone (Fig. 4c). Interestingly, neither manipulation directly impacted CS^–-evoked freezing, but inhibiting 14d-TRAPed cells specifically impaired discrimination between the CS⁺ and CS^– (Fig. 4c). In addition, neither manipulation impacted contextual freezing (Fig. 4b,c), which may be due to the floor effects of already low levels of contextual freezing in the absence of photostimulation. Thus, 14d-TRAPed cells but not 1d-TRAPed cells were required for the full tone fear memory and tone discrimination during remote memory retrieval. These results support dynamic changes in PL ensembles that promote remote memory retrieval.

**Fig. 4: Effects of inhibiting PL neurons on remote memory retrieval.**

Despite making a small contribution to the remote memory trace, PL neurons activated during fear conditioning could nevertheless play a critical role in initiating a dynamic process that recruits new PL neurons to the memory trace over time^11,12,37. To test this hypothesis, we injected AAVs (adeno-associated viruses) expressing non-conditional chemogenetic silencer hM4D³⁸ bilaterally, and Cre-conditional ChR2-eYFP unilaterally into the PL cortex of the same animal, observing substantial overlap between ChR2- and hM4D-expressing cells in the PL cortex (Fig. 4e). Mice received clozapine-N-oxide (CNO) 30 minutes before fear conditioning on Day 0, were TRAPed during 14d-memory retrieval, and were tested on Day 28 as before (Fig. 4d,e). In control animals lacking hM4D, photoactivating TRAPed PL neurons significantly increased freezing in the conditioning context as before (Fig. 3b); however, photoactivating TRAPed cells in hM4D⁺ mice no longer increased freezing levels in the majority of the animals tested (Fig. 4f). Furthermore, reducing PL activity during FC had no impact on memory strength in the absence of photoactivation (Fig. 4f,g), consistent with previous results⁹. Thus, while other regions may compensate for PL inhibition during learning to support remote memory formation, PL activity during learning or early stages of consolidation is essential to establish the ensemble of PL neurons that supports remote memory retrieval.

Brain regions whose TRAPing patterns co-vary with the PL

In a final set of experiments, we interfaced TRAP2 with whole-brain analyses to generate hypotheses about how PL interacts with its many downstream targets to influence memory retrieval over time. First, we leveraged variability in TRAPing patterns of individual animals to identify brain regions that co-vary with PL cortex (Fig. 5a,b). Using iDISCO+ and ClearMap³⁰, we counted brain-wide TRAPed cells in seven 1d- and nine 14d-TRAPed mice. We performed unbiased clustering of brain regions based on numbers of TRAPed cells and visualized their relationships with t-distributed stochastic neighbor embedding (tSNE)³⁹. In both cohorts, brain regions segregated into three clusters (Fig. 5c). For both 1d- and 14d-TRAPed animals, the PL cortex was housed in a cluster with many other cortical areas including anterior cingulate, temporal association, ectorhinal, auditory, and entorhinal areas, all of which have known roles in remote memory^40,41,42 (Fig. 5c,d and Supplementary Table 2). We also observed several time-dependent changes in cluster membership. Notably, basolateral amygdala, basomedial amygdala, central amygdala, midline thalamic nuclei, and some high-order visual areas clustered with the PL cortex at 1d but not 14d memory retrieval, indicating that the relationship between the PL cortex and these regions may change with time.

**Fig. 5: Relating whole-brain TRAPing patterns to memory-guided behavior.**

To understand how activity in these regions relates to memory retrieval, we examined correlations between TRAP patterns and behavioral characteristics of individual animals. For each brain region, we correlated numbers of TRAPed cells with tone discrimination (differential freezing between CS⁺ and CS^–; Fig. 5e–h), CS⁺-evoked freezing, and contextual freezing (Supplementary Fig. 12). In general, areas with high correlation with freezing measures tended to fall within the same cluster, suggesting TRAPing patterns in the PL and its associated regions are linked to memory-guided behavior. In 1d-TRAPed brains, correlations with contextual and CS⁺-evoked freezing were higher in hippocampus and central amygdala, while in 14d brains, correlations with these metrics were higher in the PL cortex (Supplementary Fig. 12). Interestingly, 14d-TRAPed brains had the highest correlations with tone discrimination, particularly in cortical association areas including PL, anterior cingulate, ventral auditory, temporal association, ectorhinal and entorhinal areas that also clustered together (Fig. 5e–h). Together, these data suggest that the PL cortex interacts with distinct sets of regions to contribute to memory retrieval at different points in time and, furthermore, that cortical association areas likely contribute more to memory specificity during remote memory retrieval.

PL ensembles TRAPed later preferentially recruit cortical targets

To determine which relationships identified in the tSNE analysis likely reflect direct PL cortex targets, we examined the projections of TRAPed PL neurons. The PL cortex is connected with many regions critical for fear learning and memory^17,18,43,44, but the specific projections of neurons activated during memory retrieval have not been globally mapped. We used iDISCO+ and a custom axon analysis pipeline (Methods) to quantify the brain-wide axonal projections of 1d- and 14d-TRAPed PL neurons expressing membrane-tagged GFP (Fig. 6a,b). TRAPed PL neurons projected broadly, with particularly dense innervation in cortical association areas, amygdala, and hypothalamus, and some innervation in ventral striatum and pallidum (Fig. 6c,d and Supplementary Table 3). Importantly, regions identified in the PL-containing cluster (Fig. 5c) were some of the most densely innervated, suggesting that co-variance in TRAPing patterns does indeed reflect direct connectivity. However, the global projections of 1d- and 14d-TRAPed neurons were indistinguishable at this level (Fig. 6b–d). This could be because neurons participating in memory retrieval across time derive from the same broad class of projection neurons, or because TRAPed neurons also include those that stably encode features of the environment as discussed earlier.

**Fig. 6: Whole-brain analyses of network involving TRAPed PL neurons.**

Although the structural projections of 1d- and 14d-TRAPed neurons were similar, they could still elicit different functional effects in target regions through time-dependent changes in connection strength or target cell response properties. To test this hypothesis at the whole-brain level, we used the same 1d- and 14d-TRAPed animals from our behavioral analyses (Fig. 3). We photostimulated TRAPed PL neurons expressing ChR2 while animals were in the home-cage, and sacrificed the mice one hour later to examine resultant Fos induction throughout the brain using iDISCO+ and ClearMap³⁰ (Fig. 6e). Principal components analysis (PCA) on the Fos⁺ cell counts in 1d- and 14d-TRAPed groups revealed that mice from the two groups segregated along PC2 (Fig. 6f). Examining the PC loadings indicated that sensory and association cortical areas, central amygdala, and tuberal nucleus largely explained the variance along PC2, with cortical areas higher in the 14d condition, and subcortical regions contributing more strongly towards the 1d condition (Fig. 6g). To further explore group-level differences in Fos induction, we analyzed normalized Fos levels by region and observed 21 regions with differential Fos expression between 1d- and 14d-TRAPed animals (Supplementary Table 4). Again, regions with higher Fos in the 14d condition tended to be in the neocortex, including high-order auditory, visual, and somatosensory areas, while regions with higher Fos in the 1d condition were largely subcortical, including nuclei in the hypothalamus, thalamus, striatum, and pallidum (Fig. 6h). Interestingly, several cortical areas higher in the 14d condition are highly innervated by TRAPed PL axons, belong to PL’s cluster in our co-variation analysis above, and are highly correlated with memory-guided behaviors. Together, these data suggest that dynamic changes in PL manifest as increasing functional recruitment of cortical targets with time (Fig. 6i).

Discussion

Using TRAP2, we accessed PL neurons activated during fear conditioning or 1-, 7-, or 14-day memory retrieval, and assessed their contributions to 28-day remote memory. We found that PL neurons TRAPed at later retrieval times were more likely to be reactivated during remote memory retrieval, more effectively promoted remote memory retrieval, and were required for the full remote cued fear memory. Furthermore, reducing PL activity during learning blunted the ability of TRAPed PL neurons to promote remote memory retrieval. Finally, unbiased whole-brain analyses identified a set of cortical regions whose activity co-varied with the PL cortex and correlated with memory specificity. Many of these regions were densely innervated by and preferentially activated by PL neurons TRAPed during 14-day retrieval. These findings support a model in which PL ensembles underlying remote memory undergo dynamic changes during the first two weeks after learning, which manifest as increased functional recruitment of cortical targets (Fig. 6i).

Characterization of TRAP2

We have provided a detailed characterization of TRAP2, demonstrating that it has several advantages over the previous version. While both versions have a similar TRAPing window of ~6 hours surrounding a 4-OHT injection, TRAP2 has enhanced labeling efficiency and provides improved genetic access throughout the brain, particularly in subcortical regions including the striatum, amygdala, hypothalamus, and midbrain structures. By using a bicistronic cassette in the targeting allele, we preserved expression of endogenous Fos so that TRAP2 animals are viable as homozygotes. To validate that the targeting allele did not interfere with endogenous Fos expression, we showed that Fos induction in TRAP2 mice is indistinguishable from wild-type mice following remote fear memory retrieval. With improved access throughout the brain, permanent labeling, a short labeling window, compatibility with whole brain analysis tools, and a modular design that meshes with existing Cre-dependent effectors, TRAP2 offers many advantages over existing activity-dependent tools¹⁶. Here we leveraged its unique features to identify dynamic changes in cortical circuits that promote remote fear memory retrieval. Permanent labeling gave us the ability to track the same neurons across a month so we could causally relate their activity during learning or recent memory to their function during remote memory. We recently reported that TRAP2 could efficiently access dehydration-activated hypothalamic neurons to investigate neural basis of thirst motivation²⁴. We expect that TRAP2 can be used for whole-brain mapping of the circuits and activity patterns underlying diverse behaviors.

Dynamic changes in PL ensembles underlying memory retrieval over time

While memories reorganize over time at the systems level^2,3, the precise nature of this reorganization at the level of individual cortical neurons was unclear. Focusing on the PL cortex, a prefrontal subregion required for fear memory retrieval over time, we provide compelling evidence that the PL ensembles that support remote memory undergo dynamic changes during consolidation. A recent study demonstrated that PL neurons activated during contextual fear conditioning are required for memory retrieval two weeks later, that hippocampal activity supports their functional maturation, and that BLA-PL interactions have a selective role in remote memory¹¹. Using a cued fear conditioning paradigm to examine PL memory circuits a month after learning, we extend these findings in several ways. By TRAPing at different times after conditioning, we demonstrate that while FC-TRAPed neurons make a small contribution, neurons TRAPed during later memory retrieval have greater influence over remote memory. We interpret these dynamic changes to reflect different neurons being recruited to the PL memory trace with time, in line with recent work showing dynamic turnover in task-relevant neural ensembles in the hippocampus⁴⁵, amygdala⁵, and parietal cortex⁴⁶. However, our observations are also consistent with changes in activity patterns^10,47 that could push neurons above or below the TRAPing threshold. Either way, these changes may reflect shifting demands on PL circuits during memory retrieval over time^7,11,48.

What drives these dynamic changes in PL? We provide evidence that activity of PL neurons during learning influences recruitment of neurons to the memory trace. Previous work showed that pharmacologically silencing PL cortex during fear conditioning did not impair memory⁹, but these studies did not examine the impact on the causal function of PL during subsequent memory retrieval. Here we find that while reducing PL activity during FC did not impact memory retrieval at the behavioral level, it impaired the ability of 14d-TRAPed PL neurons to enhance remote memory retrieval. We propose that PL neurons activated during learning, along with long-range input⁴⁴ from amygdala³², hippocampus⁴⁹ and entorhinal cortex¹¹, initiate a process of local changes within PL circuits during memory consolidation, which underlies the temporal evolution of PL ensembles for remote memory retrieval we observed (Fig. 6i).

Whole-brain analyses of PL circuits underlying memory retrieval over time

The systems consolidation hypothesis posits that remote memories are stored in a distributed cortical network², although only a few studies have examined the brain-wide memory network at the cellular level^15,48, and the role of the PL cortex in shaping downstream activity patterns remained unclear. Our analyses revealed that even though 1d- and 14d-TRAPed neurons had similar structural projections, a set of high-order sensory and association cortices had preferentially increased Fos induction following photoactivation of 14d-TRAPed PL neurons. Thus, TRAPed populations may contain distinct subclasses of projection neurons that are enriched at different times. Alternatively, members of the same large projection class with differential target-dependent plasticity may be recruited to the memory trace over time. Additional analyses of whole-brain TRAPing patterns highlighted a set of frontal, temporal, and parietal cortical association areas that reliably clustered with the PL cortex and correlated with cued freezing behavior, while several subcortical regions only clustered with the PL cortex during 1d memory retrieval, and their correlations with freezing behavior degraded with time. Thus, though systems consolidation has been better-studied for contextual memory, our data support its central tenet in our paradigm that includes contextual and cued fear memory. As PL and other cortical association areas from the same tSNE cluster were highly correlated with tone discrimination specifically in 14d TRAPed brains, and silencing 14d- (but not 1d-) TRAPed PL neurons reduced tone discrimination during remote memory retrieval, PL cortex and associated cortical regions recruited at remote timepoints might contribute to memory specificity. Furthermore, regions in other clusters became anti-correlated with tone discrimination over time, suggesting that bidirectional changes in coordinated activity patterns may underlie memory retrieval over time.

Overall, our whole-brain analyses converged on a partially overlapping set of cortical areas, including auditory, temporal association, ectorhinal, and entorhinal areas (Fig. 6i), which have demonstrated roles in remote fear memory retrieval^6,41,42. Together, our data indicate that changes in PL ensembles promoting remote memory may reflect a time-dependent recruitment of cortical targets whose function could underlie the specificity of the retrieved memory. These studies will guide future work examining the dynamics of interactions between PL cortex and identified target regions during memory retrieval.

Methods

All animal procedures followed animal care guidelines approved by Stanford University’s Administrative Panel on Laboratory Animal Care (APLAC). TRAP2 mice were generated in a 129Sv/SvJ background. For behavior experiments, they were backcrossed to C57Bl6/J for 3 generations. See accompanying Life Sciences Reporting Summary for additional details about experimental design and reagents.

Mouse genetics

Generation of the Fos^2A-iCreER/+ (TRAP2) mice²⁴ and FosTRAP (TRAP1)²³ were previously described. We have deposited TRAP2 mice to Jackson Labs (stock #030323). Homozygous Fos^{2A-iCreER/2A-iCreER} mice are viable. R26^AI14/+ (AI14) mice²⁸ were obtained from Jackson Labs (stock #007914). TRAP2 mice were crossed to AI14 mice to obtain the double heterozygous (TRAP2;Ai14) mice used in many experiments described in this study. Genotyping for AI14 was performed using the standard PCR protocol provided by Jackson Labs. Genotyping for the Fos^2A-iCreER alleles was performed using iCre primers (Fwd: GTGCAAGCTGAACAACAGGA, Rev: ATCAGCATTCTCCCACCATC) that produce a 420 bp band.

Drug preparation

4-hydroxytamoxifen (4-OHT; Sigma, Cat# H6278) was dissolved at 20 mg mL^–1 in ethanol by shaking at 37°C for 15 min and was then aliquoted and stored at –20°C for up to several weeks. Before use, 4-OHT was redissolved in ethanol by shaking at 37°C for 15 min, a 1:4 mixture of castor oil:sunflower seed oil (Sigma, Cat #s 259853 and S5007) was added to give a final concentration of 10 mg mL^–1 4-OHT, and the ethanol was evaporated by vacuum under centrifugation. The final 10 mg mL^–1 4-OHT solutions were always used on the day they were prepared. All injections were delivered intraperitoneally (i.p.).

Visual stimulation

TRAP1 or TRAP2 mice were singly housed in a light-proof box for 48 h. On the TRAPing day, mice were exposed to 1 h of light inside the box and i.p. injected with 50 mg kg^–1 4-OHT under infrared light either 6 (TRAP2 group only) or 3 h before light exposure, or 0 or 3 h after light exposure. Mice were returned to the dark box for an additional 2 days and then returned to their home-cage until the time of sacrifice 7 days after TRAPing. Animals were randomly assigned to experimental groups, and animals assigned to different experimental conditions were run in parallel.

Novel environment

TRAP1 or TRAP2 mice were either placed in a novel environment (a clean rat cage with tunnels and a running wheel), or in their home-cage in the same room, for 2 h. Halfway through the 2-h period, mice were i.p. injected with 50 mg kg^–1 4-OHT. Mice then returned to their home-cage until the time of sacrifice 7 days after TRAPing. Animals were randomly assigned to experimental groups, and animals assigned to different experimental conditions were run in parallel.

Fear conditioning

TRAP2;Ai14 mice were habituated to the conditioning chamber and tones for 15 min per day for 3 days. On the fourth day (Day 0), they were either fear conditioned (FC, 1d, 7d, 14d groups) or presented with the same number of tones but no shocks in the conditioning chamber (NS group). The fear-conditioning chamber consisted of a square cage (18 × 18 × 30 cm) with a grid floor wired to a shock generator and a scrambler, surrounded by an acoustic chamber (Coulbourn Instruments). We used two tones in a differential auditory fear conditioning protocol (CS⁺: 4 kHz, 30 s, ~75 dB and CS^–: white noise, 30 s, ~75 dB). Our fear conditioning protocol consisted of 4 baseline tones (2CS⁺, 2CS^–, interleaved), followed by interleaved presentations of 8 × CS⁺, which co-terminated with a 1 s, 0.5 mA footshock, and 5× CS^–, which were not paired with a shock. During a 1d memory retrieval session, FC and 1d animals returned to the conditioning chamber and after a 1-min contextual baseline were presented with interleaved 8× CS⁺ and 4× CS^–. 7d or 14d after training, the 7d or 14d group returned to the conditioning chamber for an identical retrieval session. NS controls were balanced across groups, with the 1^st retrieval occurring on day 1, 7, or 14. Twenty-eight days after fear conditioning, all 5 groups returned to the conditioning chamber for an identical remote memory retrieval session. In optogenetic experiments, after a baseline period of 1 min, mice were presented with two photstimulation periods in the absence of tones, and 6× CS⁺ and 6× CS^–, half of which were paired with photostimulation. These mice underwent a third retrieval session on day 29 in the same chamber, except the shock floor was replaced with a thin wire grid floor. Following a 1-min baseline and two photostimulation periods in the absence of tones, mice were presented with 6 × CS⁺, half of which were paired with photostimulation. Freezing was automatically quantified using FreezeFrame software, except for optogenetic stimulation experiments during which the patch-cable interfered with automatic detection of freezing. These videos were scored manually by a blind observer. Groups represent pooled results from multiple, independently-run behavioral cohorts (NS: 8, FC: 5, 1d: 7, 7d: 8, 14d: 4 cohorts). Four animals were excluded from the study due to mistargeted optical fibers. Animals were randomly assigned to experimental groups, animals assigned to different experimental conditions were run in parallel, and there was no randomization in the organization of stimulus presentations.

Histology and immunostaining

Animals were perfused transcardially with phosphate buffered saline (PBS) followed by 4% paraformaldehyde (PFA). Brains were dissected, post-fixed in 4% PFA for 12–24 h and placed in 30% sucrose for 24–48 h. They were then embedded in Optimum Cutting Temperature (OCT, Tissue Tek) and stored at −80°C until sectioning. 60-μm floating sections were collected into PBS. For Fos immunostaining, sections were incubated in 0.3% PBST and 10% donkey serum for 1 h and then stained with rabbit anti-Fos (Synaptic Systems 226-003, 1:10,000) and chicken anti-GFP (for brains that received AAV-DIO-ChR2-eYFP, AVES Labs GFP 1020, 1:2000) for five nights at 4°C in 0.3% PBST and 3% donkey serum. All sections were washed 3 × 10 min in PBS and additionally stained with Donkey anti-Rabbit Alexa 647 (Jackson Immunoresearch 711-605-152, 1:1000) and Donkey anti-Chicken Alexa 488 (Jackson Immunoresearch 702-545-155, 1:1000) in 0.3% PBST and 5% donkey serum for 2 h at room temperature and then washed once 1 × 10 min in PBS, then with PBS containing DAPI (1:10,000 of 5 mg mL^–1, Sigma-Aldrich) in PBS for 10–15 min, and then washed once more with PBS prior to mounting onto Superfrost Plus slides and coverslipping with Fluorogel (Electron Microscopy Sciences). Confocal images were obtained with a Zeiss LSM 780 by a blind experimenter and Fos⁺ nuclei were quantified in a semi-automated fashion using a custom ImageJ macro. Layer analysis was done using custom MatLab software as described previously⁴⁴.

Virus injections and fiber implants

For optogenetic activation experiments, we used an AAV-DJ containing EF1a-DIO-ChR2-eYFP³⁴ (2 × 10¹¹ genomic copies (GC) mL^–1) produced by the Stanford Viral Vector Core. During surgery, animals were anesthetized with 1–2% isoflourane (VetOne). To target PL, the needle was placed 1.8 mm anterior, 0.45 mm lateral, and 2.3 mm ventral to bregma⁵⁰. 0.4 μl of ChR2 virus was injected into the left hemisphere of 5–6 week old mice using a stereotactic apparatus (KOPF). After injecting the ChR2 virus, a chronic fiber (ThorLabs CFMLC22L01 Fiber Optic Cannula, Ø1.25 mm Ceramic Ferrule, Ø200 µm Core, 0.22 NA, L = 2 mm) was implanted directly above the injection site and secured with Metabond (Parkell, S371, S398, S398). For optogenetic inhibition experiments, we used an AAV-DJ vector containing EF1a-DIO-iC + + -eYFP³⁶ (7.2 × 10¹³ GC mL^–1) produced by the Stanford Viral Vector Core. After injecting the iC++virus, a chronic fiber (Bifurcated Fiber Bundle, Ø200 µm Core, 0.22 NA, FC/PC to Ø2.5 mm Ferrules, L = 2 mm) was implanted bilaterally above the injection sites and secured with Metabond. For axon tracing, we used an AAV vector containing CAG-FLEx-mGFP-2A-Synaptophysin-mRuby⁵¹ (5 × 10¹² GC mL^–1) produced by the UNC Viral Vector Core. For hM4D experiments, animals were injected bilaterally into PL with 0.4 μl AAV-DJ containing either CaMKIIa-mCherry (1.1 × 10¹³ GC/mL) or CaMKIIa-hM4D(Gi)-mCherry (1.5 × 10¹² GC mL^–1), and then unilaterally with AAV-DJ-EF1a-DIO-ChR2-eYFP as described above. After recovery, animals were housed in a regular 12 h dark/light cycle with food and water ad libitum.

Optogenetic stimulation during behavior

Optical stimulation through the fiber-optic connector was administered by delivering light through a patch-cord connected to a 473-nm laser. Stimulation was delivered at 5-Hz, 15-ms pulses (ChR2) with 8–10 mW power at the fiber tip, or continuously (iC++) with 2 mW power at the fiber tip. During fear retrieval, mice received 40-s bouts of photostimulation. Two bouts occurred in a period prior to the onset of the first tone for contextual fear memory. During the test for auditory fear memory, half of the tones coincided with photostimulation that began 10 s before the 30-s tone started. During real-time place aversion, mice were placed in a two-chambered box (25 cm by 50 cm) with behavior monitored by a webcam (Logitech). On day 1, mice were habituated to the chamber for 5 min and then a 15-min baseline was collected with the patch cord attached. The following day, mice returned to the chamber and preferred side was paired with photostimulation with the 473-nm laser (5 Hz, 15 ms, 8–10 mW). Video was acquired and the time spent in each chamber was automatically quantified using BioviewerIII software.

Chemogenetic manipulation during behavior

Clozapine-N-Oxide (CNO, ApexBio A3317) was dissolved in DMSO at a concentration of 0.1 mg µL^–1 and stored at –20°C. Immediately before the experiment, the stock was dissolved in 0.9% NaCl to generate a working solution of 0.5 mg mL^–1. Each animal received an intraperitoneal injection of CNO at 5 mg kg^–1 30 min before fear conditioning^52,53.

iDISCO+ sample processing

Modifications and continuous updates to the protocol can be found at http://www.idisco.info. Animals were perfused transcardially with PBS followed by 4% PFA. All harvested samples were post-fixed overnight at 4°C in 4% PFA in PBS, and processed with the iDISCO+ immunolabeling protocol, as detailed previously³⁰. Samples were stained with the following primary antibodies: Fos (Synaptic Systems 226 003) at 1:500, RFP (Rockland 600-401-379) at 1:300, GFP (AVES Labs GFP 1020) at 1:2000. Alexafluor 647 or 568 secondary antibodies (ThermoFisher Scientific) were used at the same concentrations as the primary antibodies in each case.

iDISCO+ imaging

At least one day after clearing, iDISCO+ samples were imaged by a blind experimenter on a light-sheet microscope (Ultramicroscope II, LaVision Biotec) equipped with a sCMOS camera (Andor Neo) and a 2×/0.5 NA objective lens (MVPLAPO 2×) equipped with a 6 mm working distance dipping cap. Version v285 of the Imspector Microscope controller software was used. We imaged using 488-nm, 561-nm, and 640-nm lasers. The samples were scanned with a step-size of 3 µm using the continuous light-sheet scanning method with the included contrast adaptive algorithm for the 640-nm channel (20 acquisitions per plane), and without horizontal scanning for the 488-nm autofluorescence and 561-nm channels.

Image processing and analysis

Cell and axon counts were calculated blind to experimental conditions. iDISCO+ samples immunostained for Fos⁺ and tdTomato⁺ cells (in Ai14 mice) were quantified using the ClearMap cell detection module³⁰, with cell detection parameters optimized and validated by two expert users based on the intensity and shape parameters of each antibody’s immunolabeling profile (specific values used for ClearMap’s Image Processing Modules available upon request). To analyze GFP⁺ axons, a two-dimensional pixel classifier was trained in Ilastik (www.Ilastik.org) using ~15 images from each of four brains. Autofluorescent fiber tracts were separated from labeled axons with a second pixel classifier. The image stack of autofluorescence in the 488 nm channel was aligned to the Allen Institute’s Common Coordinate Framework (CCF) using the Elastix toolbox and subsequently, the processed stack of axons was transformed to the same coordinates. Voxels classified as axons were equally thresholded in all brains and counted by regions as described in the 2017 CCF. Within the Allen’s hierarchy of brain areas, regions distinguished solely by layers or anatomical location were collapsed into their ‘parent’ region (or example, layers 1–6 of both dorsal and ventral anterior cingulate area are labeled as ‘anterior cingulate area’). These decisions were made prior to analysis. Resultant innervation probability maps were binarized and axon-positive voxels were then aligned using ClearMap registration toolbox³⁰ and analyzed using custom MatLab software. Reported values of axonal labeling density for individual brain regions are normalized to region volumes.

Statistical methods

No statistical methods were used to pre-determine sample sizes, but our sample sizes are similar to those reported in previous publications (for example, ref. ^7,11,44). Data distribution was assumed to be normal when we performed ANOVA and t-tests, but these were not formally tested.

Analyses of the TRAP and Fos whole-brain counts were performed in Python. In Fig. 5c–d, TRAPed cells were quantified per brain area using ClearMap³⁰, and then visualized using tSNE³⁹ applied to the per-mouse count data for each area. To cluster the TRAP brains, the shared-nearest-neighbor algorithm with multilevel community detection, using Jaccard similarity as a metric, was applied⁵⁴. This algorithm attempted to cluster brain areas based on the amount of TRAPing within that area per mouse, and automatically determined that the brain areas were divided into three clusters. For correlation with behavioral characteristics (Fig. 5e–h and Supplementary Fig. 12), the tSNE plots were colored by Pearson correlation between counts per area and the tone discrimination index, the CS⁺ freezing time, and the context freezing time.

To further analyze Fos data, the number of Fos⁺ cells per brain area was first normalized by the volume of that area in the Allen Brain Atlas. Statistical tests between counts in 1-day and 14-day conditions were computed using a t-test, and then were false discovery rate (FDR) corrected (Fig. 6h and Supplementary Table 4). To visual the Fos data in two-dimensional space, principal components analysis was applied to these normalized, per area counts to find the axes that would capture the most variance across different brain areas. The per-animal data across all brain areas was then projected onto the first two principal components (Fig. 6f,g). All computations were performed using scikit-learn and NumPy.

Exclusion criteria are reported in Methods. Summary graphs represent mean ±SEM. The statistical tests, including post-hoc tests for multiple comparisons, are reported in the figure legends along with the definition of n. Significance was defined as alpha = 0.05 or FDR = 0.1, and statistical tests were performed in Prism (GraphPad). Paired t-tests were two-tailed.

Statistics and reproducibility

Characterization of whole-brain TRAPing patterns in FC and NS animals (Fig. 1) are based on n = 4 biologically independent animals per condition, run in parallel through iDISCO+ immunolabeling and light sheet imaging protocols, as recommended by the authors of the iDISCO+ manuscript³⁰. Similar results for TRAP–Fos overlap were obtained in 7, 4, 3, 2, and 2 independent cohorts of animals for NS, FC, 1d, 7d, and 14d conditions, respectively (Fig. 2). Similar results for optogenetic activation with ChR2 were obtained in 5, 5, 6, and 3 independent cohorts of animals for FC, 1d, 7d, and 14d conditions, respectively (Fig. 3, Supplementary Fig. 8). Similar results for optogenetic inhibition were obtained in 2 independent cohorts of animals per condition (Fig. 4 and Supplementary Fig. 11). Similar results for correlating behavioral data with whole-brain TRAPing patterns for 4 and 3 cohorts of 1d- and 14d-TRAPed animals, respectively (Fig. 5 and Supplementary Fig. 12). Characterization of whole-brain axon innervation patterns in 1d and 14d-TRAPed animals (Fig. 6) are based on n = 5 and n = 3 biologically independent animals, respectively, run in parallel through iDISCO+ immunolabeling and light sheet imaging protocols.

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Code availability

Custom code for axon quantification, tSNE, and PCA analyses are available from the authors upon reasonable request.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

Maren, S. Neurobiology of Pavlovian fear conditioning. Annu. Rev. Neurosci. 24, 897–931 (2001).
Article CAS Google Scholar
Frankland, P. W. & Bontempi, B. The organization of recent and remote memories. Nat. Rev. Neurosci. 6, 119–130 (2005).
Article CAS Google Scholar
Do Monte, F. H., Quirk, G. J., Li, B. & Penzo, M. A. Retrieving fear memories, as time goes by…. Mol. Psychiatry 21, 1027–1036 (2016).
Article Google Scholar
Gale, G. D. et al. Role of the basolateral amygdala in the storage of fear memories across the adult lifetime of rats. J. Neurosci. 24, 3810–3815 (2004).
Article CAS Google Scholar
Grewe, B. F. et al. Neural ensemble dynamics underlying a long-term associative memory. Nature 543, 670–675 (2017).
Article CAS Google Scholar
Todd, T. P., Mehlman, M. L., Keene, C. S., DeAngeli, N. E. & Bucci, D. J. Retrosplenial cortex is required for the retrieval of remote memory for auditory cues. Learn. Mem. 23, 278–288 (2016).
Article Google Scholar
Do-Monte, F. H., Quiñones-Laracuente, K. & Quirk, G. J. A temporal shift in the circuits mediating retrieval of fear memory. Nature 519, 460–463 (2015).
Article CAS Google Scholar
Sierra-Mercado, D., Padilla-Coreano, N. & Quirk, G. J. Dissociable roles of prelimbic and infralimbic cortices, ventral hippocampus, and basolateral amygdala in the expression and extinction of conditioned fear. Neuropsychopharmacology 36, 529–538 (2011).
Article Google Scholar
Corcoran, K. A. & Quirk, G. J. Activity in prelimbic cortex is necessary for the expression of learned, but not innate, fears. J. Neurosci. 27, 840–844 (2007).
Article CAS Google Scholar
Courtin, J. et al. Prefrontal parvalbumin interneurons shape neuronal activity to drive fear expression. Nature 505, 92–96 (2014).
Article Google Scholar
Kitamura, T. et al. Engrams and circuits crucial for systems consolidation of a memory. Science 356, 73–78 (2017).
Article CAS Google Scholar
Lesburguères, E. et al. Early tagging of cortical networks is required for the formation of enduring associative memory. Science 331, 924–928 (2011).
Article Google Scholar
Tse, D. et al. Schema-dependent gene activation and memory encoding in neocortex. Science 333, 891–895 (2011).
Article CAS Google Scholar
Maviel, T., Durkin, T. P., Menzaghi, F. & Bontempi, B. Sites of neocortical reorganization critical for remote spatial memory. Science 305, 96–99 (2004).
Article CAS Google Scholar
Wheeler, A. L. et al. Identification of a functional connectome for long-term fear memory in mice. PLoS Comput. Biol. 9, e1002853 (2013).
Article CAS Google Scholar
DeNardo, L. & Luo, L. Genetic strategies to access activated neurons. Curr. Opin. Neurobiol. 45, 121–129 (2017).
Article CAS Google Scholar
Vertes, R. P. Differential projections of the infralimbic and prelimbic cortex in the rat. Synapse 51, 32–58 (2004).
Article CAS Google Scholar
Gabbott, P. L. A., Warner, T. A., Jays, P. R. L., Salway, P. & Busby, S. J. Prefrontal cortex in the rat: projections to subcortical autonomic, motor, and limbic centers. J. Comp. Neurol. 492, 145–177 (2005).
Article Google Scholar
Karalis, N. et al. 4-Hz oscillations synchronize prefrontal-amygdala circuits during fear behavior. Nat. Neurosci. 19, 605–612 (2016).
Article CAS Google Scholar
Senn, V. et al. Long-range connectivity defines behavioral specificity of amygdala neurons. Neuron 81, 428–437 (2014).
Article CAS Google Scholar
Sotres-Bayon, F., Sierra-Mercado, D., Pardilla-Delgado, E. & Quirk, G. J. Gating of fear in prelimbic cortex by hippocampal and amygdala inputs. Neuron 76, 804–812 (2012).
Article CAS Google Scholar
Popa, D., Duvarci, S., Popescu, A. T., Léna, C. & Paré, D. Coherent amygdalocortical theta promotes fear memory consolidation during paradoxical sleep. Proc. Natl. Acad. Sci. USA. 107, 6516–6519 (2010).
Article CAS Google Scholar
Guenthner, C. J., Miyamichi, K., Yang, H. H., Heller, H. C. & Luo, L. Permanent genetic access to transiently active neurons via TRAP: targeted recombination in active populations. Neuron 78, 773–784 (2013).
Article CAS Google Scholar
Allen, W. E. et al. Thirst-associated preoptic neurons encode an aversive motivational drive. Science 357, 1149–1155 (2017).
Article CAS Google Scholar
Coulon, V., Chebli, K., Cavelier, P. & Blanchard, J. M. A novel mouse c-fos intronic promoter that responds to CREB and AP-1 is developmentally regulated in vivo. PLoS One 5, e11235 (2010).
Article Google Scholar
Wilson, T. & Treisman, R. Removal of poly(A) and consequent degradation of c-fos mRNA facilitated by 3′ AU-rich sequences. Nature 336, 396–399 (1988).
Article CAS Google Scholar
Shimshek, D. R. et al. Codon-improved Cre recombinase (iCre) expression in the mouse. Genesis 32, 19–26 (2002).
Article CAS Google Scholar
Madisen, L. et al. A robust and high-throughput Cre reporting and characterization system for the whole mouse brain. Nat. Neurosci. 13, 133–140 (2010).
Article CAS Google Scholar
Hess, U. S., Lynch, G. & Gall, C. M. Regional patterns of c-fos mRNA expression in rat hippocampus following exploration of a novel environment versus performance of a well-learned discrimination. J. Neurosci. 15, 7796–7809 (1995).
Article CAS Google Scholar
Renier, N. et al. Mapping of brain activity by automated volume analysis of immediate early genes. Cell 165, 1789–1802 (2016).
Article CAS Google Scholar
Tovote, P., Fadok, J. P. & Lüthi, A. Neuronal circuits for fear and anxiety. Nat. Rev. Neurosci. 16, 317–331 (2015).
Article CAS Google Scholar
LeDoux, J. E. Emotion circuits in the brain. Annu. Rev. Neurosci. 23, 155–184 (2000).
Article CAS Google Scholar
Anagnostaras, S. G., Maren, S. & Fanselow, M. S. Temporally graded retrograde amnesia of contextual fear after hippocampal damage in rats: within-subjects examination. J. Neurosci. 19, 1106–1114 (1999).
Article CAS Google Scholar
Lee, J. H. et al. Global and local fMRI signals driven by neurons defined optogenetically by type and wiring. Nature 465, 788–792 (2010).
Article CAS Google Scholar
Maren, S., Phan, K. L. & Liberzon, I. The contextual brain: implications for fear conditioning, extinction and psychopathology. Nat. Rev. Neurosci. 14, 417–428 (2013).
Article CAS Google Scholar
Berndt, A. et al. Structural foundations of optogenetics: determinants of channelrhodopsin ion selectivity. Proc. Natl Acad. Sci. USA. 113, 822–829 (2016).
Article CAS Google Scholar
Bero, A. W. et al. Early remodeling of the neocortex upon episodic memory encoding. Proc. Natl Acad. Sci. USA. 111, 11852–11857 (2014).
Article CAS Google Scholar
Armbruster, B., Li, X., Pausch, M. H., Herlitze, S. & Roth, B. L. Evolving the lock to fit the key to create a family of G protein-coupled receptors potently activated by an inert ligand. Proc. Natl Acad. Sci. USA. 104, 5163–5168 (2007).
Article Google Scholar
van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
Google Scholar
Frankland, P. W., Bontempi, B., Talton, L. E., Kaczmarek, L. & Silva, A. J. The involvement of the anterior cingulate cortex in remote contextual fear memory. Science 304, 881–883 (2004).
Article CAS Google Scholar
Sacco, T. & Sacchetti, B. Role of secondary sensory cortices in emotional memory storage and retrieval in rats. Science 329, 649–656 (2010).
Article CAS Google Scholar
Takehara-Nishiuchi, K. Entorhinal cortex and consolidated memory. Neurosci. Res. 84, 27–33 (2014).
Article Google Scholar
Hoover, W. B. & Vertes, R. P. Anatomical analysis of afferent projections to the medial prefrontal cortex in the rat. Brain. Struct. Funct. 212, 149–179 (2007).
Article Google Scholar
DeNardo, L. A., Berns, D. S., DeLoach, K. & Luo, L. Connectivity of mouse somatosensory and prefrontal cortex examined with trans-synaptic tracing. Nat. Neurosci. 18, 1687–1697 (2015).
Article CAS Google Scholar
Ziv, Y. et al. Long-term dynamics of CA1 hippocampal place codes. Nat. Neurosci. 16, 264–266 (2013).
Article CAS Google Scholar
Driscoll, L. N., Pettit, N. L., Minderer, M., Chettih, S. N. & Harvey, C. D. Dynamic reorganization of neuronal activity patterns in parietal cortex. Cell 170, 986–999.e16 (2017).
Article CAS Google Scholar
Burgos-Robles, A., Vidal-Gonzalez, I. & Quirk, G. J. Sustained conditioned responses in prelimbic prefrontal neurons are correlated with fear expression and extinction failure. J. Neurosci. 29, 8474–8482 (2009).
Article CAS Google Scholar
Vetere, G. et al. Chemogenetic interrogation of a brain-wide fear memory network in mice. Neuron 94, 363–374.e4 (2017).
Article CAS Google Scholar
Preston, A. R. & Eichenbaum, H. Interplay of hippocampus and prefrontal cortex in memory. Curr. Biol. 23, R764–R773 (2013).
Article CAS Google Scholar
Paxinos, G. & Franklin, K. B. J. The Mouse Brain in Stereotaxic Coordinates. 4th edn, (Academic Press, Cambridge, MA, USA, 2001).
Google Scholar
Beier, K. T. et al. Circuit Architecture of VTA dopamine neurons revealed by systematic input-output mapping. Cell 162, 622–634 (2015).
Article CAS Google Scholar
Zhou, T. et al. History of winning remodels thalamo-PFC circuit to reinforce social dominance. Science 357, 162–168 (2017).
Article CAS Google Scholar
Wayman, W. N. & Woodward, J. J. Chemogenetic excitation of accumbens-projecting infralimbic cortical neurons blocks toluene-induced conditioned place preference. J. Neurosci. 38, 1462–1471 (2018).
Article CAS Google Scholar
Shekhar, K. et al. Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics. Cell 166, 1308–1323.e30 (2016).
Article CAS Google Scholar

Download references

Acknowledgements

We thank C. Malanac for mouse genotyping, the Stanford Transgenic Facility for help in producing TRAP2 knock-in mice and Stanford Viral Core, K. Deisseroth and C. Ramakrishnan for AAV vectors, T. Bonhoeffer, P. Goltstein, and members of the Luo Lab for comments on the manuscript. L.A.D. was supported by an NIH postdoctoral fellowship (F32NS087860) and a Mentored Research Scientist Development Award (K01MH11626401). W.E.A. was supported by a Fannie & John Hertz Foundation Fellowship and a National Science Foundation Graduate Research Fellowship (grant DGE-114747). E.L.A. was supported by the Standford Bio-X Ph.D Fellowship program and the William K. Bowles Jr. Foundation. L.L. is an HHMI investigator. This work was supported by grants from National Science Foundation, National Institutes of Health, and a Hughes Collaborative Innovation Award.

Author information

Laura A. DeNardo
Present address: Department of Physiology, University of California Los Angeles, Los Angeles, CA, USA

Authors and Affiliations

Department of Biology, Stanford University, Stanford, CA, USA
Laura A. DeNardo, Cindy D. Liu, William E. Allen, Eliza L. Adams, Drew Friedmann, Lisa Fu, Casey J. Guenthner, Marc Tessier-Lavigne & Liqun Luo
Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
Laura A. DeNardo, Cindy D. Liu, William E. Allen, Drew Friedmann, Lisa Fu, Casey J. Guenthner & Liqun Luo
Neurosciences Program, Stanford University, Stanford, CA, USA
William E. Allen, Eliza L. Adams, Casey J. Guenthner, Marc Tessier-Lavigne & Liqun Luo

Authors

Laura A. DeNardo
View author publications
You can also search for this author in PubMed Google Scholar
Cindy D. Liu
View author publications
You can also search for this author in PubMed Google Scholar
William E. Allen
View author publications
You can also search for this author in PubMed Google Scholar
Eliza L. Adams
View author publications
You can also search for this author in PubMed Google Scholar
Drew Friedmann
View author publications
You can also search for this author in PubMed Google Scholar
Lisa Fu
View author publications
You can also search for this author in PubMed Google Scholar
Casey J. Guenthner
View author publications
You can also search for this author in PubMed Google Scholar
Marc Tessier-Lavigne
View author publications
You can also search for this author in PubMed Google Scholar
Liqun Luo
View author publications
You can also search for this author in PubMed Google Scholar

Contributions

L.A.D. and L.L. designed experiments. L.A.D. and C.J.G. generated the TRAP2 targeting construct. L.A.D. characterized the TRAP2 mouse. L.A.D. and C.D.L. performed behavior assays. L.A.D. and L.F. performed histology and confocal imaging. L.A.D., C.D.L., and L.F. analyzed the data, W.E.A. analyzed iDISCO+-generated data sets. E.L.A. (with support from M.T.-L.) advised and provided training in the iDISCO+ and ClearMap methods. D.F. wrote software and advised for quantitative whole-brain axon analysis. L.A.D. and L.L. wrote the manuscript.

Corresponding authors

Correspondence to Laura A. DeNardo or Liqun Luo.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1

Targeting strategy for generating TRAP2 mice.

Supplementary Figure 2 Time course of TRAPing.

a, Timeline of visual stimulation experiment to determine effective TRAPing window. b, Example images of TRAPed cells in primary visual cortex (V1) and primary somatosensory cortex (S1) of TRAP2 mice that underwent the visual stimulation experiment. Scale bars, 100 µm. c, Quantification of TRAPed cell density in TRAP2 mice (V1, F(4,16) = 10.07, P = 0.0003, n = 4, 4, 4, 5, 4 for dark, –6h, –3h, 0h, and 3h conditions, respectively); S1, F(3,6) = 1.786, P = 0.249, n = 4, 3, 3, 4, 3 for dark, –6h, –3h, 0h, and 3h conditions, respectively). d, Quantification of fold change in TRAPed cells in TRAP2 mice (V1, F(4,17) = 10.85, P = 0.0001, n = 4, 4, 4, 5, 4 for dark, –6h, –3h, 0h, and 3h conditions, respectively; S1, F(4,12) = 2.590, P = 0.0903 n = 4, 3, 3, 4, 3 for dark, –6h, –3h, 0h, and 3h conditions, respectively; for both c and d, one-way ANOVA with Dunnett post-hoc test). e–g, Analogous experiments and analyses as b–d, but using TRAP1 mice. Statistics for f, V1, F(3,6) = 12.93, P = 0.0005; S1, F(3,6) = 1.786, P = 0.2497; , = 3 per condition. For g, V1, F(3,7) = 11.16, P = 0.0047; S1, F(3,6) = 1.786, P = 0.2497, n = 3 per condition. In all plots and statistical tests, n represents biologically independent animals. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 and summary graphs show mean±SEM. Scale bars, 100 µm.

Supplementary Figure 3 Sensitivity of TRAP2.

a, Schematic for novel environment (NE) experiment to determine sensitivity of TRAP2. b, Example coronal hemisections from TRAP2 (left), TRAP1 (middle), and TRAP2 mice injected with solvent instead of 4-OHT (right) from NE condition. Numbers beside the atlas diagrams represent anterior-posterior positions with respect to bregma. c, Quantification of TRAPed cells in motor cortex (MOp), primary somatosensory cortex (SSp), dorsal striatum (STRd), nucleus accumbens (ACB), lateral septum (LS), bed nucleus of the stria terminalis (BST), lateral hypothalamic area (LHA), basolateral amygdala (BLA), hippocampal field CA1, and periaqueductal grey (PAG) (F(2,58) = 101.7, P < 0.0001, n = 3 per condition, 2-way ANOVA with Dunnett post-hoc test). d, Quantification of TRAPed cell density in TRAP2 home-cage compared to TRAP1 home-cage conditions (F(1,40) = 13.33, P = 0.0007, n = 3 per condition, 2-way ANOVA with Sidak post-hoc test). Scale bars, 500 µm. In all plots and statistical tests, n represents biologically independent animals. Summary graphs show mean±SEM. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Supplementary Figure 4 Summary of behavior and TRAP/Fos activation in PL.

a, Summary of freezing behavior for all time points used throughout the study [FC, fear conditioning; Ret1, retrieval 1 (1d, 7d or 14d), Ret2, retrieval 2 (28d)]; Pre: before conditioning, CS⁺: conditioned tone; CS^–: unreinforced tone; ctx: context). (FC: F = 61.31, P < 0.0001, Ret 1: F = 32.23, P < 0.0001, Ret 2: F = 37.3, P < 0.0001, One-way ANOVA with Tukey post-hoc test, n = 29). b, Quantification of freezing to CS⁺ for FC (n = 8), 1d (n = 8), 7d (n = 8), 14d (n = 4), and NS groups (n = 15). (FC: F = 69.58, P < 0.0001, Ret1: F = 36.66, P < 0.0001, Ret2: F = 7, P < 0.0001, One-way ANOVA with Tukey post-hoc test). c, Quantification of % TRAPed cells by group (F = 7.06, P = 0.0002, One-way ANOVA with Tukey post-hoc test). d, Quantification of % Fos⁺ cells by group (F = 1.59, P = 0.196, One-way ANOVA with Tukey post-hoc test). e, Comparison of Fos expression normalized to chance levels in TRAPed (colored bars, (Double⁺/DAPI)/((Fos⁺/DAPI)x(TRAP⁺/DAPI))) and non-TRAPed (black bars, (Double⁺/DAPI)/((Fos⁺/DAPI)x(TRAP^–/DAPI))) PL neurons; stars represents results of multiple t-tests with Holm-sidak posthoc correction for multiple comparisons). For c–e, n = 15, 8, 8, 8, 8 for NS, FC, 1d, 7d, and 14d conditions, respectively. In all plots and statistical tests, n represents biologically independent animals. In b, boxes represent 25–75^th percentile, whiskers represent 10–90^th percentile, lines represent median. All other summary graphs show mean±SEM. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Supplementary Figure 5 Comparing Fos expression following remote fear memory retrieval in WT and TRAP2 animals.

a,d,g, Schematics showing locations of confocal imaging (blue). Example confocal stacks and summary graphs for b, prelimbic (PL) cortex [P = 0.36; WT, n = 3, TRAP2, n = 5], c, piriform cortex [P = 0.82; WT, n = 3, TRAP2, n = 6], e, paraventricular thalamus (PVT) [P = 0.82; WT, n = 3, TRAP2, n = 4], f, paraventricular hypothalamus (PVH) [P = 0.79, WT, n = 3, TRAP2 n = 3], h, basolateral amygdala (BLA) [P = 0.13, WT, n = 3, TRAP2, n = 10], i, dentate gyrus (DG) [P = 0.38; WT, n = 3, TRAP2, n = 7], two-sided unpaired t-tests. Scale bars, 100 µm. In all plots and statistical tests, n represents biologically independent animals. Summary graphs show mean±SEM. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Supplementary Figure 6 Analysis of activated neurons in PL by cortical layer.

a, Example images of TRAPed and Fos⁺ neurons in PL from each experimental group. Insets show high-magnifications of example reactivated cells. Scale bars, 100 µm. b, Quantification of cortical layer thickness in each experimental condition [F_Group(3,24) = 0.777, P = 0.518, 2-way ANOVA with Tukey post-hoc test]. c, Quantification of active neurons in PL cortical layers in each experimental group, expressed as a fraction of total active neurons in PL per brain analyzed [F_Group(3,32) = 50.73, P < 0.0001, 2-way repeated measures ANOVA with Tukey post-hoc test]. d, Quantification of TRAPed neurons in PL cortical layers that are reactivated during 28d memory retrieval. Reactivated (Double⁺/TRAPed) cells per layer presented as a fraction of total reactivated cells counted in PL for each brain [F_Group(3,28) = 1.3, P = 0.294, 2-way ANOVA with Tukey post-hoc test]. For b–d, n = 14, 7, 7, 4, 4, 18 for FC, 1d, 7d, 14d, and 28d (Fos) conditions, respectively. In all plots and statistical tests, n represents biologically independent animals. Summary graphs show mean±SEM. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Supplementary Figure 7 TRAP/Fos activation in piriform cortex.

a, Example confocal images of TRAPed and Fos⁺ neurons in piriform cortex from each experimental group as in Fig. 2. Scale bars, 100 µm. b, Quantification of TRAPed cells per section (F = = 6.588, P = 0.0149). c, Quantification of Fos⁺ cells / section (F = 0.3839, P = 0.7676). d, Quantification of Double⁺ / Fos⁺ cells (F = 2.772, P = 0.1106). e, Quantification of Double⁺/TRAPed cells per section (F = 0.3604, P = 0.7834). One-way ANOVA with Tukey post-hoc test, n = 3 for all conditions. In all plots and statistical tests, n represents biologically independent animals. Summary graphs show mean±SEM. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Supplementary Figure 8 Additional optogenetics and behavior analyses.

a, Experimental protocol for fear conditioning and testing the photoactivation effect of NS-TRAPed animals. b,c, Quantification of contextual (b, P = 0.103, n = 9, two-sided paired t-test) and CS⁺-evoked (c, P = 0.938, n = 9, two-sided paired t-test) freezing in NS animals that were subsequently fear conditioned in response to photoactivation of NS-TRAPed cells. d–h, Freezing levels with or without (±) ChR2 activation for individual animals summarized in Fig. 3. d, freezing to CS⁺ (FC, P = 0.012, n = 18; 1d, P = 0.410, n = 19; 7d, P = 0.182, n = 10; 14d, P = 0.250, n = 10; two-sided paired t-tests. e, freezing to CS^– (FC, P = 0.731, n = 11; 1d, P = 0.710, n = 13; 7d, P = 0.078, n = 10; 14d, P = 0.947, n = 10; two-sided paired t-tests). f, tone discrimination index (FC, P = 0.597, n = 11; 1d, P = 0.478, n = 13; 7d, P = 0.233, n = 10; 14d, P = 0.858, n = 10; two-sided paired t-tests). g, freezing to altered context (FC, P = 0.093, n = 11; 1d, P = 0.039, n = 13; 7d, P = 0.021, n = 12; 14d, P = 0.100, n = 8; two-sided paired t-tests). h, freezing to CS⁺ in altered context (FC, P = 0.466, n = 11; 1d, P = 0.268, n = 13; 7d, P = 0.705, n = 11; 14d, P = 0.039, n = 8; two-sided paired t-tests). In all plots and statistical tests, n represents biologically independent animals. Summary graphs show mean±SEM. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Supplementary Figure 9 Histological analysis of fiber placement in PL in optogenetic activation experiments.

a, Representative slide scanner images of PL showing optical fiber placement and ChR2-eYFP injections across experimental groups. b, Cre-dependent virus did not express in solvent-injected animals (no 4-OHT). c, Mapping ChR2 optical fiber locations to a standard mouse brain atlas (Paxinos, G. & Franklin, K.B.J. The Mouse Brain in Stereotaxic Coordinates, 2^nd ed. Acadmic Press, 2001). Each circle represents the position of optic fiber termination site of one mouse. Colors correspond to TRAPing group. Scale bars, 500 µm.

Supplementary Figure 10 No effect of stimulating TRAPed PL neurons in real-time place-aversion assay.

a, Representative animal tracks from five TRAP2 groups during 15 min stimulation session (Day 2). b, Quantification of aversive behavior [% time on unstimulated side during photostimulation session (Day 2) – % time on unstimulated side during baseline session (Day 1)] (F(4,51) = 1.702, P = 0.1639, One-way ANOVA with post-hoc Tukey test, n = 9, 8, 17, 12, 9 for NS, FC, 1d, 7d, 14, respectively). n represents biologically independent animals. Summary graph shows mean±SEM. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Supplementary Figure 11 Histological analysis of fiber placements in opto- and chemogenetic inhibition experiments.

a, Example image of iC++ expression and dual fiber implant in 1d-TRAPed brain. b, Fiber placement for 1d- and 14d-TRAPed iC++ brains. c, Fiber placement for hM4D experiments.

Supplementary Figure 12 Correlations between whole-brain TRAPing patterns and behavior.

Pearson correlations of regional TRAP counts with context freezing (a,b) or CS⁺-evoked freezing (c,d) color mapped onto tSNE clusters (as shown in Fig. 5c), or onto coronal sections from a standard brain for 1d-TRPAed (a,c) and 14d-TRAPed (b,d) brains (1d, n = 7; 14d, n = 9 biologically independent animals, see also Supplementary Table 2).

Supplementary information

Supplementary text and figures

Supplementary Figs. 1–12 and Supplementary Tables 1–4

Reporting Summary

Rights and permissions

Reprints and permissions

About this article

Cite this article

DeNardo, L.A., Liu, C.D., Allen, W.E. et al. Temporal evolution of cortical ensembles promoting remote memory retrieval. Nat Neurosci 22, 460–469 (2019). https://doi.org/10.1038/s41593-018-0318-7

Download citation

Received: 05 June 2018
Accepted: 05 December 2018
Published: 28 January 2019
Issue Date: March 2019
DOI: https://doi.org/10.1038/s41593-018-0318-7

This article is cited by

Dynamic and selective engrams emerge with memory consolidation
- Douglas Feitosa Tomé
- Ying Zhang
- Claudia Clopath
Nature Neuroscience (2024)
Context memory formed in medial prefrontal cortex during infancy enhances learning in adulthood
- María P. Contreras
- Marta Mendez
- Marion Inostroza
Nature Communications (2024)
Spatial transcriptomics reveal neuron–astrocyte synergy in long-term memory
- Wenfei Sun
- Zhihui Liu
- Stephen R. Quake
Nature (2024)
Reactivation of encoding ensembles in the prelimbic cortex supports temporal associations
- Thays Brenner Santos
- Cesar Augusto de Oliveira Coelho
- Maria Gabriela Menezes Oliveira
Neuropsychopharmacology (2024)
Dorsal peduncular cortex activity modulates affective behavior and fear extinction in mice
- Justin J. Botterill
- Abdessattar Khlaifia
- Maithe Arruda-Carvalho
Neuropsychopharmacology (2024)

Abstract

Similar content being viewed by others

Main

Results

Characterization of TRAP2

PL neurons TRAPed during memory retrieval change over time

TRAPed PL neurons promote remote memory in a time-dependent manner

Testing the requirement of PL activity in remote memory retrieval

Brain regions whose TRAPing patterns co-vary with the PL

PL ensembles TRAPed later preferentially recruit cortical targets

Discussion

Characterization of TRAP2

Dynamic changes in PL ensembles underlying memory retrieval over time

Whole-brain analyses of PL circuits underlying memory retrieval over time

Methods

Mouse genetics

Drug preparation

Visual stimulation

Novel environment

Fear conditioning

Histology and immunostaining

Virus injections and fiber implants

Optogenetic stimulation during behavior

Chemogenetic manipulation during behavior

iDISCO+ sample processing

iDISCO+ imaging

Image processing and analysis

Statistical methods

Statistics and reproducibility

Reporting Summary

Code availability

Data availability

References

Acknowledgements

Author information

Authors and Affiliations

Contributions

Corresponding authors

Ethics declarations

Competing interests

Additional information

Integrated supplementary information

Supplementary information

Rights and permissions

About this article

Cite this article

Share this article

This article is cited by

Search

Quick links