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Nature | Vol 587 | 19 November 2020 | 437
Article
Persistent transcriptional programmes are associated with remote memory
Michelle B. Chen1,5, Xian Jiang2,3,5, Stephen R. Quake1,4 ✉ & Thomas C. Südhof2,3 ✉
The role of gene expression during learning and in short-term memories has been studied extensively1–3, but less is known about remote memories, which can persist for a lifetime4. Here we used long-term contextual fear memory as a paradigm to probe the single-cell gene expression landscape that underlies remote memory storage in the medial prefrontal cortex. We found persistent activity-specific transcriptional alterations in diverse populations of neurons that lasted for weeks after fear learning. Out of a vast plasticity-coding space, we identified genes associated with membrane fusion that could have important roles in the maintenance of remote memory. Unexpectedly, astrocytes and microglia also acquired persistent gene expression signatures that were associated with remote memory, suggesting that they actively contribute to memory circuits. The discovery of gene expression programmes associated with remote memory engrams adds an important dimension of activity-dependent cellular states to existing brain taxonomy atlases and sheds light on the elusive mechanisms of remote memory storage.
Long-term memories do not form immediately after learning, but are consolidated over time4. Previous studies have identified important contributions of molecular and cellular processes to learning and mem-ory, such as gene expression changes, cAMP signalling and synaptic plasticity1, and identified a central role for RNA synthesis and protein translation in memory consolidation2. Despite these discoveries, the molecular underpinnings of memory consolidation remain elusive. For instance, while changes in gene expression are found in the first 24 h of learning, it is unclear whether these changes are maintained or whether new changes are acquired to consolidate a long-term memory trace3. Moreover, the dependence on the hippocampus for long-term memory is known to degrade over time, with cortical structures such as the medial prefrontal cortex (mPFC) becoming increasingly impor-tant5. Recently, the development of activity-dependent genetic label-ling tools have allowed identification of sparsely activated neuronal ensembles, enabling access to the molecular mechanisms that underlie experience-dependent connectivity and plasticity6.
Neuron subtypes in remote memory engramsTo identify and study the transcriptional programmes of neurons involved in remote memory, we used TRAP2; Ai14 mice expressing iCre-ERT2 recombinase in an activity-dependent manner along with a iCre-dependent tdTomato (tdT) reporter allele (Extended Data Fig. 1a), enabling us to label memory recall-activated neurons. To genetically label the engrams that are associated with remote mem-ory, we trained mice in a conditioning chamber with three pairs of tone–foot shocks on day 0, and induced fear memory recall (FR) on day 16 by returning mice to the conditioning chamber. Memory recall activates the consolidated memory engrams, thereby labelling the
neuronal ensemble that encodes the consolidated remote memory7, while also inducing memory reconsolidation8. Because the molecular and cellular substrates of consolidation and reconsolidation were indistinguishable in this paradigm, we denote them here collectively as memory consolidation. We used control mice that were not fear conditioned but exposed to the recall context (no fear (NF)), fear conditioned but not subjected to recall (no recall (NR)), and neither fear conditioned nor exposed to the recall condition (homecage (HC)) (Fig. 1a–c). All mice were injected with 4-hydroxytamoxifen (4-OHT) before the FR procedure (or at the equivalent time) to allow activity-dependent production of tdT, thus enabling the distinc-tion between the molecular programmes that are specific to remote memory versus background activation.
Nine days after FR9, single neuronal and non-neuronal cells were col-lected from the mPFC (Extended Data Fig. 1b) via fluorescence-activated cell sorting (FACS) and gating on the tdT signal, followed by plate-based single-cell mRNA sequencing (Fig. 1d). The percentage of TRAPed cells collected via FACS was significantly higher in FR (about 1.5% of all cells) than in other conditions (Extended Data Fig. 2a), further con-firming that the TRAP2 system captured increased neuronal activity during the FR process. We sequenced 3,691 neurons (Snap25 +/tdT + or Snap25 +/tdT − mRNA) and 2,672 non-neuronal cells with high quality and depth (Extended Data Fig. 1c, d). Unbiased transcriptome clustering of cells from all four training conditions allowed the identification of major cell types and confirmed the dominance of neurons among tdT + cells, whereas tdT − cells comprised both neurons and non-neuronal cells (Cldn5 + endothelial, Pdgfra + oligodendrocyte progenitor cells (OPCs), Cx3cr1 + microglia and Aqp4 + astrocytes) (Fig. 1d, e, Extended Data Fig. 1e). Both tdT + and tdT − cells from FR and control groups were represented in all clusters, which suggests that neither the neuronal
https://doi.org/10.1038/s41586-020-2905-5
Received: 26 September 2019
Accepted: 17 August 2020
Published online: 11 November 2020
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1Department of Bioengineering, Stanford University, Stanford, CA, USA. 2Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA, USA. 3Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA. 4Chan Zuckerberg Biohub, Stanford, CA, USA. 5These authors contributed equally: Michelle B. Chen, Xian Jiang. ✉e-mail: steve@quake-lab.org; tcs1@stanford.edu
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Article
activation state nor the training paradigm significantly altered funda-mental cell-type identities (Fig. 1e).
Sub-clustering of 3,691 Snap25 + neurons using the top 2,000 highly variable genes revealed seven putative neuron sub-populations—four glutamatergic (C0, C1, C3 and C5) and three GABAergic neuron populations (C2, C4 and C6)—all of which were consistently observed throughout four biological replicates (Fig. 2a–c, Extended Data Fig. 2b). These are molecularly distinct populations (Fig. 2d), with each subtype expressing at least one distinctive marker gene (see Methods; Fig. 2e). All subtypes contained tdT + cells, which indicated activation of all neu-ron subtypes regardless of the training state (Extended Data Fig. 2c). A comparison of key layer-specific marker genes (C0-Dkkl1, C1-Rprm, C2-Calb2, C3-Tesc, C4-Tnfaip8l3, C5-Tshz2 and C6-Lhx6) to existing cortical single-cell expression databases10 confirmed their presence in the mPFC (Extended Data Fig. 2d).
Surprisingly, no significant differences were found in the neuron subtype composition of TRAPed populations between the FR and NF groups (Fig. 2f, Extended Data Fig. 2e), which suggests a lack of training-dependent recruitment of neuron types during consolidation compared to baseline active populations in a NF memory scenario. Both excitatory and inhibitory neuron types were found in active FR popula-tions, with glutamatergic cells comprising about 60–70%. Within the same FR brains, active and inactive populations had roughly similar compositions of neuron subtypes, with the exception of C2-Calb2 and C3-Tesc, suggesting only slight shifts in the recruitment or retirement of neuron subtypes due to activity.
Memory-associated gene expressionTo determine whether remote-memory-associated transcriptional changes occur in recall-activated neurons, we looked for differentially expressed genes (DEGs; log2 fold change (log2FC) > 0.3 and false discov-ery rate (FDR) < 0.01) in TRAPed FR versus NF cells (Fig. 3a). Single-cell resolution enables a comparison of neurons within the same subtype and the identification of genes that are specifically associated with memory consolidation and recall. Of 23,355 genes, 1,292 were found
to be consolidation-dependent. Expression patterns indicated an overall transcriptional activation, with more genes upregulated than downregulated. Interestingly, DEGs were heterogenous across neuron subtypes, which suggests that remote memory consolidation involves subtype-specific transcriptional programmes (Fig. 3b).
We applied a set of strict criteria to identify possible effector genes. First, each DEG had to be differentially expressed in at least three-quarters of biological replicates, enforcing reproducibility. The removal of DEGs that are also differentially expressed between the inactive populations in FR versus NF mice allowed the identification of changes that were specific to active populations (Extended Data Fig. 3a). Next, DEGs must be differentially expressed when FR cells are compared to NR and HC controls, ensuring that DEGs are not just a con-sequence of a fear experience. Last, DEGs had to pass a permutation test with shuffled labels (Extended Data Fig. 3b). These criteria produced a set of 99 ‘remote-memory-associated DEGs’ (Fig. 3c; see Methods). Several genes encoded proteins with regulatory roles, including known regulators of transcription (Hmg20a, Hnrnpk and Zfp706) and transla-tion (Nck2, Alpl1 and Eif2ak1). Interestingly, even among the condensed list of remote-memory-associated DEGs, we found strong enrichments in genes encoding proteins involved in vesicle exocytosis (Vamp2, Gdi2, Rab15, Rab5a, Rab24, Atp6v0c, Syt13, Stx1b and Nsf), transmembrane transport (Slc30a9, Slc25a46, Mfsd14a, Tmem50a, Gpm6a, Mfsd14b and Abcf3), dendritic spine organization (Strip1, Pls3 and Gsk3b) and long-range intracellular transport (Timm29, Atad1, Pak1, Plehkb2, Sarnp, Rtn3, Dmtn, Sar1a and Hid1) (Fig. 3c, Extended Data Fig. 3b, c). More than half of the remote-memory-associated DEGs are associated with neuronal diseases, suggesting links between the functional role of these genes to various memory-affecting neuronal disorders, in addition to the regulation of remote memory.
We further investigated the specificity of our findings by analysing TRAPed neurons that were activated by a salient experience unrelated to fear memory (Extended Data Fig. 4a). Using food deprivation as a sali-ence signal, we identified TRAPed neuronal ensembles that contained the same neuronal subtypes as in TRAPed FR ensembles, but with dif-ferences in the subtype composition ratios (Extended Data Fig. 4b–d).
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Fig. 1 | Labelling and collection of single memory engram cells via the TRAP2; Ai14 line. a, The experimental paradigm includes generating remote fear-memory traces via contextual fear conditioning, isolating TRAP+-activated neurons via flow cytometry and plate-based single-cell RNA sequencing (scRNA-seq). b, Representative image of tdT + TRAPed (red) cells in the anterior cingulate cortex (ACC) region of the mPFC 9 days after an injection of 4-OHT (at the time of remote memory recall). Scale bars, 1 mm (0.5 mm for zoomed in images). c, Degree of freezing upon return to the novel context 16 days after fear conditioning (FR) or no conditioning (NF) (n = 4 mice per condition,
***P = 4.89 × 10−9; mean ± s.d.). d, Representative flow gating for tdT + TRAPed cells (about 1.5% of events in FR) in one mouse from the FR condition (left). Post-sequencing analysis of 722 example cells from a representative FR brain shows enrichment of tdT mRNA (pos) in the positive sort gate (orange violin) (middle). Enrichment of neuronal cells in the positive gate and neuronal and non-neuronal cell types in the negative gate in all brains (right). AU, arbitrary units; cpm, counts per million. e, All sorted cells, with neuronal cells identified via the expression of Snap25 mRNA. All training and control conditions are represented in all cell clusters.
Nature | Vol 587 | 19 November 2020 | 439
While we found a total of 143 DEGs (FDR < 0.01) when comparing sali-ence to no salience groups, there was almost no intersection of these DEGs with those found in FR mice (Extended Data Figs. 4e, 5). Thus, while new transcriptional programmes are activated in salience ensem-bles, the nature of these molecular changes is experience-specific and probably modulated by the particular valence of and/or functional requirements arising from the experience.
Hierarchical clustering of TRAPed FR neurons by the expression levels for each remote-memory-associated DEG allowed distinct populations of ‘highly activated’ and ‘lowly activated’ cells to emerge, suggesting that different transcriptional modules are concertedly regulated during memory consolidation in each neuronal subtype (Fig. 3d). To determine the subtype specificity of these modules, we found that the fraction of cells activated with the subtype-specific DEGs was generally highest in the corresponding subtype when compared to the activation levels in other subtypes or in the inactive populations (Fig. 3e, Extended Data Fig. 6a). Together, this could indicate the presence of subtype-specific common regulatory elements.
To address this possibility, we analysed our DEGs using hypergeomet-ric optimization of motif enrichment (HOMER) to search for common regulatory motifs in an unbiased manner (see Methods; Extended Data Fig. 6b). We found 12 putative de novo and two known motifs enriched within our target DEG set (P < 0.01). While we did not find significant enrichment of motifs within subtype-specific DEGs, the Hif1b binding motif was found in >40% of total DEGs, including the synaptic transmis-sion and plasticity-related genes Rab5a, Rab24, Vamp2, Gdi2, Gpm6a, Strip1, Ptp4a1, Trim32, Mfsd14a, Mfsd14b and Slc25a46. Interestingly, these findings agree with recent studies indicating a potential dual role for HIF1 transcription factors during hippocampal-dependent spatial learning and consolidation under normoxic conditions11. Interestingly, motifs associated with Creb, Nfkb, Cbp and C/ebp—canonical transcrip-tional regulators of neuronal activity and plasticity12,13—were absent near the transcription start site (−400 to +100 bp).
Vesicle exocytosis signatures in memoryTo further elucidate the significance of these remote-memory-dependent transcriptional programmes, we used STRING to look for known and predicted protein–protein interactions. K-means clustering of the gene nodes revealed a significantly connected network (P = 1.75 × 10−6) that was centred around a large cluster of genes related to vesicle-mediated transport, exocytosis and neurotransmitter secretion, all of which were highly connected (confidence = 0.4; Extended Data Fig. 6c). Remark-ably, 20 out of 99 remote-memory-associated DEGs fell within these functional categories, including Stx1b, Syt13, Vamp2, the SNAP receptor (SNARE) ATPase (Nsf) and the GTPase Rab5a, all of which are function-ally linked to the SNARE complex and to vesicle exocytosis (Fig. 4a). Interestingly, the two most highly and ubiquitously upregulated genes across subtypes were Serinc1 and Serinc3, which are thought to be serine incorporators14. Notably, phosphatidylserine phospholipids are calcium-dependent binding partners for synaptotagmins15, sug-gesting that Serinc1 and Serinc3 may have important roles in enhanc-ing phosphatidylserine levels and vesicle membrane fusion during memory consolidation. Finally, in situ hybridization confirmed the endogenous proportions of neuronal subtypes in TRAPed populations (Extended Data Fig. 7a, b), as well as the upregulated expression of key remote-memory-associated DEGs, including Serinc3, Syt13, Vamp2 and Stx1b in respective neuronal subtypes (Fig. 4b, c, Extended Data Fig. 7c).
Non-neuronal gene expression changesRemarkably, we discovered that non-neuronal cells also exhibited tran-scriptional changes associated with remote memory consolidation (FR compared to NF mice; Fig. 5a, b, Extended Data Fig. 8a, b). These signa-tures were distinct from those of neurons, indicating that non-neuronal programmes may support maintenance of the remote fear-memory trace. Surprisingly, >95% of these DEGs were upregulated, which sug-gests an overall transcriptional activation during consolidation. Not only was this response detectable weeks after the initial learning but it was observed even without enrichment of the non-neuronal cells directly associated with the TRAPed engram cells (that is, the TRAP method is neuron-specific).
Astrocytes and microglia showed the greatest number of tran-scriptional changes, with 181 and 308 genes perturbed, respectively (log2FC > 1 and FDR < 0.01) (Fig. 5c). Most of these DEGs represent largely diverging pathways (Fig. 5d). In particular, upregulated astro-cytic genes were enriched in lipid, cholesterol and steroid metabolic functions (Gja1, Hmgcr, Dhcr7, Insig1, Acsl3, Idi1, Acsbg1, 10Asah1 and Hacd3) as well as glucose transport (Abcc5, Slc39a1, Slc6a1, Slc27a1, Slco1c1, Gnb1 and Ttyh1). Enhanced metabolic support from astrocytes may be required during memory consolidation since astrocyte–neu-ron metabolic coupling is elevated during neuronal activity16. Moreo-ver, 95 out of 181 astrocyte DEGs were reproduced when comparing
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Fig. 2 | Molecular identification of active neurons during remote memory consolidation. a, Dimensional reduction of all Snap25 + neurons (n = 3,691 cells) reveals seven distinct neuronal subtypes (C0–C6). b, Neuronal subtypes fall into two distinct categories: excitatory (glutamatergic) and inhibitory (GABAergic). The Glut–GABA signature is calculated based on the difference of the scaled expression level of Gad1 and Slc17a7. c, Expression levels of the top marker genes for each neuron subtype (C0–C6). d, Differences in the composition of neuron subtypes of TRAPed populations in FR and NF conditions, as well as inactive populations in FR mice (1 mouse per condition per replicate, n = 5 replicates, two-sided t-test) in the composition of TRAPed populations between FR and NF conditions are found. FR TRAPed populations are composed of significantly more C2 (GABAergic) neurons and less C3 (glutamatergic) neurons than the inactive population (error bars indicate s.e.m.).
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FR to NR mice, suggesting that a large portion of DEGs is specific to the recall experience itself and not merely a remnant of the fear experience.
By contrast, DEGs from microglial cells were enriched in innate immunity (Il6r, Stat6, Csf3r, Il1a, Irf5, Cd86, Tnfrsf1b, Ywhaz, Litaf, Ptgs1, Gdi2 and Rnf13) and cytoskeletal reorganization/focal adhe-sion maintenance pathways (Cdc42, Rhoa, Rhoh, Prkcd, Vasp, Arf6,
Vav1 and Actr2), suggesting that upregulation of specific inflammatory molecules and enhancement of cell migration may be involved in the maintenance of memory. While less is known regarding the immu-nomodulatory roles of microglia in memory and learning, previous studies have shown that low levels of inflammatory cytokines (such as IL-1, IL-6 and tumour necrosis factor) can regulate neuronal circuit remodelling and long-term potentiation17.
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Hid1Kctd10Pdha1PigqSkiv2l2Abcf3Miga2MmdSar1aEif2ak1Acsf3Cdc42se2Fam134aVamp2Gsk3bHnrnph2Pja2SdhaGdi2Rab15Fam131aGfra2Pip4k2cNcdnUsp5Nell2Tmem151aDmtnRtn3Pcsk2PfkmTrim32Mfsd14bRab5aTdgEmc1Gpm6aElmod1CycsSarnpRab24Erp29GhitmZfp706Sult4a1AppCckEmc4Psmb6Atp6v0bHint1Guk1Rtn1ClppCrip2Mpc1Atp6v0cAtp5g3Tmem50aPlekhb2Syt13Garnl3Dpysl4Aplp1HnrnpkNsfMfsd14aMir124-2hgNck2Stx1bPak1Slc25a46Itfg1Lmbrd1Tmx1DnerAtad1Ankrd45Timm29Vopp1Pls3Hmg20aCtbp1Strip1Cdv3Inpp5fPrkar1bSlc30a9Alg2Trim35Hacd3Serinc1Serinc3Ptp4a1
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Fig. 3 | Transcriptional programmes activated by consolidation of remote memories are distinct across neuron subtypes. a, DEGs in FR versus NF in the C0 neuron subtype (n = 126 cells (NF), n = 289 cells (FR)) with all replicated pooled. Differential expression is defined by FDR < 0.01 and abs(log2FC) > 0.3 (red points) via a two-sided Mann–Whitney test. Remote-memory-associated DEGs (which remain differentially expressed in three-quarters or more of replicates and when FR is compared to the NR and HC groups (see Extended Data Fig. 3)) are labelled in black. b, The number of DEGs per neuron subtype (left) and the number of shared DEGs between each neuron subtype (using pooled cells) (right). c, log2FC of remote-memory-associated DEGs (FR versus
NF) per neuron subtype. Each gene is further annotated with potential biological functions. DE, differentially expressed; EXH, excitatory; GO, Gene Ontology; INH, inhibitory; NS, not significant. Skiv2l2 is also known as Mtrex, and Fam143a is also known as Retreq2. d, The percentile in which a TRAPed neuron (from C0) lies in the distribution of expression of a C0 DEG for all TRAPed C0 cells. The box plots (median ± s.d.) show the log2cpm distribution for each C0 DEG. Hierarchical clustering reveals one common C0 transcriptional programme that is concertedly upregulated. e, The fraction of cells in each neuron subtype that is activated with the transcriptional programme (DEGs) from C0.
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In addition to neuron–neuron coupling, communication pro-grammes between neurons and non-neuronal cells may support the memory trace over long periods. We looked for the expression
of receptors or ligands in non-neuronal cells whose known binding partner18 is perturbed in TRAPed FR neurons (Extended Fig. 8c–e). We focused on genes that were differentially expressed in both the ligand-bound and the receptor-bound cell type (Extended Data Fig. 8c). In FR mice, we found upregulation of neuronal neuroligin-1 and neuroligin-3 (encoded by Nlgn1 and Nlgn3, respectively) and its binding partner neurexin-1 (encoded by Nrxn1) on astrocytes, com-plexes that may enhance neuron–glia adhesions and modulate synap-tic function19. Thus, the concerted upregulation of these binding pairs in FR mice strongly suggests a role for astrocyte–neurexin–neuroligin
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Fig. 4 | Remote memory consolidation is associated with specific markers for vesicle exocytosis. a, A subset of remote-memory-associated DEGs that are known to regulate vesicle membrane fusion and exocytosis at the presynaptic terminal. The violin plots are overlayed with a drumstick plot indicating the average expression per mouse. The red points represent the median. The bubble plots depict the log2FC and the degree of significance (FDR) for each replicate and are coloured by such. In addition to the FR/NF log2FC (first column), DEGs were also confirmed to be differentially expressed when compared to NR (second column), and their activation is specific only to the active (inactive (In)) (last column). The red dots in coloured panels indicate which neuronal subtype these particular genes are upregulated in. b, Representative in situ images of Serinc3 expression (purple) in Dkkl1+/tdT+/DAPI+ cells in the mPFC. Scale bars, 100 μm. c, In situ validation of key genes involved in vesicle exocytosis in various neuron subtypes, including Serinc3 (in the Dkkl1+ subtype, n = 268 (FR), n = 62 (NF) cells), Stx1b (in the Rprm+ subtype, n = 342 (FR), n = 144 (NF) cells), Syt13 (in the Tnfai8lp+ subtype, n = 244 (FR), n = 44 (NF) cells) and Vamp2 (in the Tesc+ subtype, n = 326 (FR), n = 292 (NF) cells). Each point represents the normalized integrated intensity of the probe per cell analysed.
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Fig. 5 | Transcriptomic changes in non-neuronal cells associated with remote memory consolidation. a, Sub-clustering of non-neuronal cells reveals five non-neuronal cell types (astrocyte, endothelial (EC), microglia, OPC and oligodendrocyte (oligo)) that were collected in an unbiased manner through tdT-negative flow cytometry gates. b, DEGs in non-neuronal cells (FR versus NF) (left). DEGs are defined as log2FC > 1 and FDR < 0.01. The number of DEGs that satisfy these criteria in each non-neuronal cell type is also shown (right). The top DEGs (FR versus NF) for glial cells (astrocytes and microglia)
that are also differentially expressed in FR versus NR are labelled. c, DEGs (determined by two-sided Mann–Whitney test) that are upregulated and downregulated in astrocytes (left) and microglia (right) in FR versus NF mice. DEGs (FDR < 0.01, log2FC > 1) are indicated by red dots, and the top DEGs are labelled in black. d, Pathway analysis of the DEGs (FR versus NF) in microglia and astrocytes. The score is defined as the –log2(P value) using the GeneAnalytics software.
442 | Nature | Vol 587 | 19 November 2020
Articleinteractions in the maintenance of synaptic strength during fear memory storage.
DiscussionWhile high-resolution gene expression atlases of the brain have provided invaluable information about cellular taxonomy10,20, char-acterization of activity-dependent states within these cell types is necessary to understand how experience modulates gene expres-sion, synaptic plasticity and neuronal circuitry. The ability to form and maintain unique synaptic connections that encode a particular memory out of a vast pool of other experiences requires a complex coding space. By using a combination of activity-dependent label-ling of neurons and single-cell transcriptomics, we discovered that (1) all mPFC neuron types can be activated during consolidation of remote memory via heterogenous transcriptional programmes; (2) enhanced membrane fusion and vesicle exocytosis may be a critical mode of synaptic strengthening during memory consolidation; (3) a specific set of exocytosis-related genes out of a vast coding space may be involved in allowing highly unique, experience-specific con-nections to be made; (4) these particular transcriptional programmes are detectable at remote time points and thus are probably involved in maintaining the memory trace weeks after learning; and (5) consoli-dation of remote memory also induces a persistent transcriptional programme in astrocytes and microglia.
Deciphering the temporal evolution of engram populations and their associated gene programmes through the various stages of initial learning, recent memory and remote memory is crucial for understanding the basis of conversion of short-term memo-ries to long-term memories. We found that the majority of gene programmes affected in activated neurons during early stages of learning21–23 and recent memory24 do not intersect with our remote-memory-associated DEGs (Extended Data Fig. 9a), nor with genes enriched in TRAPed FR populations over inactive ones (Extended Data Fig. 9b). This suggests that remote memory could be governed by temporally unique transcriptional programmes. However, future experiments using unified technologies to decon-volve the neuronal compositions of recent and remote engrams and identify the immediate transcriptional changes in recent memory will be of great importance. Relevant to this point, TRAPed neu-rons in NR mice also exhibited continuous transcriptional changes at moderate levels (when compared to NF mice) (Extended Data Fig. 10). However, these DEGs are largely non-intersecting with remote-memory-associated DEGs, which suggests that the experi-ence of fear itself can induce long-lasting changes in gene expression programmes and that the process of recall induces new transcrip-tional programmes in a different set of neurons. The current data therefore provide the first step towards deciphering the transcrip-tional coding landscape that is specifically associated with remote memory consolidation.
Online contentAny methods, additional references, Nature Research reporting sum-maries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author con-tributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s41586-020-2905-5.
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11. O’Sullivan, C. N., Sheridan, G. & Murphy, K. in Transcription Factors CREB and NF-κB: Involvement in Synaptic Plasticity and Memory Formation 43–65 (Bentham Science, 2012).
12. Suzuki, A. et al. Upregulation of CREB-mediated transcription enhances both short- and long-term memory. J. Neurosci. 31, 8786–8802 (2011).
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© The Author(s), under exclusive licence to Springer Nature Limited 2020
Methods
MiceAll animal experiments were conducted following protocols approved by the Administrative Panel on Laboratory Animal Care at Stanford University. The TRAP2; Ai14 mouse line was kindly gifted by the Luo laboratory at Stanford. TRAP2 mice were heterozygous for the Fos2A-iCreER allele, and homozygous for Ai14, and were bred with Ai14 homozygous mice in the C57BL/6 background. Mice were group-housed (maximum five mice per cage) on a 12 h light–dark cycle (07:00 to 19:00, light) with food and water freely available. Male mice 42–49 days of age were used for all the experiments. Mice were handled daily for 3 days before their first behavioural experiment.
GenotypingThe following primers: GAG GGA CTA CCT CCT GTA CC (forward) and TGC CCA GAG TCA TCC TTG GC (reverse) were used for genotyping of the Fos2A-iCreER allele.
Fear conditioningThe fear conditioning training was performed as previously described25. Briefly, mice were individually placed in the fear conditioning chamber (Coulbourn Instruments) located in the centre of a sound attenuating cubicle, which was cleaned with 10% ethanol to provide a background odour. A ventilation fan provided a background noise at approxi-mately 55 dB. After a 2-min exploration period, three tone–foot shock pairings separated by 1-min intervals were provided. The 85 dB 2-kHz tone lasted for 30 s, and the foot shocks were at 0.75 mA and lasted for 2 s. The foot shocks were co-terminated with the tone. The mice remained in the training chamber for another 60 s before being returned to the home cages. For the context recall, mice were placed back into the original conditioning chamber for 5 min 16 days after the training. 4-OHT injections were performed immediately (within 30 min) before the recall experiments. For the HC and the NR groups, 4-OHT was injected at a similar time when the other two groups were subjected to recall. The behaviour of the mice was recorded and ana-lysed with the FreezeFrame software (v.4; Coulbourn Instruments). Motionless bouts that lasted more than 1 s were considered as freeze. Data were analysed with the tracking software Viewer III (Biobserve).
Food deprivationMice were deprived of food for 16 h, and then 4-OHT was injected to the animals and food was returned to one group (salience group) imme-diately afterwards (within 30 min), while no food was returned to the other group (no salience group) until 10 h later.
tdT florescence examinationMice were deep anaesthetized with tribromoethanol and perfused with PBS followed by fixative (4% paraformaldehyde diluted in PBS). The brains were then removed and postfixed in 4 °C overnight and immersed in 30% sucrose solution for 2 days before being sectioned at a thickness of 50 μm on a cryostat (CM3050 S, Leica Biosystems). Imag-ing was performed with a scanning microscope (BX61VS, Olympus).
Single-cell dissociation and flow cytometrymPFC regions were microdissected from live vibratome sections (300 μm thick) of the prefrontal cortex. Tissue pieces were enzymatically disso-ciated via a papain-based digestion system (LK003150, Worthington). Briefly, tissue chunks were incubated in 1 ml of papain (containing l-cysteine and EDTA), DNase and kynurenic acid for 1 h at 37 °C and 5% CO2. After 10 min of incubation, tissues were triturated briefly with a P1000 wide bore pipette tip and returned. Cells were triturated another four times (around 30 each) with a P200 pipette tip over the rest of the remaining incubation time. At room temperature, cell suspensions were centrifuged at 350g for 10 min, resuspended in 1 ml EBSS with 10% v/v
ovomucoid inhibitor, 4.5% v/v DNase and 0.1% v/v kynurenic acid, and centrifuged again. Supernatant was removed and 1 ml ACSF was added to cells. ACSF was composed of: 1 mM KCl, 7 mM MgCl2, 0.5 mM CaCl2, 1.3 mM NaH2PO4, 110 mM choline chloride, 24 mM NaHCO3, 1.3 mM Na ascorbate, 20 mM glucose and 0.6 mM sodium pyruvate. Cells were passed through a 70-μm cell strainer to remove debris. Hoechst stain was added (1:2,000; H3570, Life Technologies) and incubated in the dark at 4 °C for 10 min. Samples were centrifuged (350g for 8 min at 4 °C) and resuspended in 0.5 ml of ACSF and kept on ice for flow cytometry.
Cells were sorted via the Sony SH800 into 96-well or 384-well plates (Bio-Rad) directly into lysis buffer26 with oligodT, and immediately snap frozen until processing. A positive ‘TRAP’ gate was set for cells that were both Hoechst+ and tdT +. A negative ‘TRAP’ gate was set for all Hoechst+ and tdT − cells in general. No gating on forward or backscatter was used to avoid size biases that might be present in a heterogenous neuronal population. Each plate was kept on the sorter for <25 min to prevent evaporation.
SequencingWhole-cell lysis, first-strand synthesis and cDNA synthesis were per-formed using the Smart-seq-2 protocol as described previously26 in both 96-well and 384-well formats, with some modifications. After cDNA amplification (23 cycles), cDNA concentrations were determined via capillary electrophoresis (96-well format) or the PicoGreen quantita-tion assay (384-well format), and wells were chosen to improve quality and reduce cost of sequencing. Only wells with >0.2 ng μl−1 of cDNA were selected and cDNA concentrations were subsequently normalized to ~0.2 ng μl−1 per sample, using the TPPLabtech Mosquito HTS and Mantis (Formulatrix) robotic platforms. Libraries were prepared, pooled and cleaned using the Illumina Nextera XT kits or in-house Tn5, following the manufacturer’s instructions. Libraries were then sequenced on Nextseq or Novaseq (Illumina) using 2 × 75-bp paired-end reads and 2 × 8-bp index reads with a 200 cycle kit (20012861, Illumina). Samples were sequenced at an average of 1.5 million reads per cell.
RNAscopeThe RNAscope experiment was performed following the manufac-turer’s instructions using the RNAscope multiplex fluorescent reagent kit v2 (323100, ACD). All probes were purchased from existing stocks or custom designed from ACD.
Bioinformatics and data analysisMapping to the genome. Sequences from Nextseq or Novaseq were demultiplexed using bcl2fastq, and reads were aligned to the mm10 genome augmented with ERCC (External RNA Controls Consortium) sequences, using STAR version 2.5.2b. Gene counts were made using HTSEQ version 0.6.1p1. All packages were called and run through a custom Snakemake pipeline. We applied standard algorithms for cell filtration, feature selection and dimensionality reduction. Briefly, genes that appeared in fewer than five cells, samples with fewer than 100 genes and samples with less than 50,000 reads were excluded from the analy-sis. Out of these cells, those with more than 30% of reads as ERCC, and more than 10% mitochondrial or 10% ribosomal were also excluded from analysis. Counts were log-normalized and then scaled where appropriate.
Next, the ‘canonical correlation analysis’ function from the Seurat package27 was used to align raw data from multiple experiments. Only the first ten canonical components were used. After alignment, relevant features were selected by filtering expressed genes to a set of ~2,500 with the highest positive and negative pairwise correlations. Genes were then projected into principal component space using the robust principal component analysis. Single-cell principal component scores and gene loadings for the first 20 principal components were used as inputs into Seurat’s (v2) FindClusters and RunTsne functions to cal-culate 2D tSNE coordinates and search for distinct cell populations.
ArticleBriefly, a shared-nearest-neighbour graph was constructed based on the Euclidean distance metric in the principal component space, and cells were clustered using the Louvain method. Cells and clusters were then visualized using 3D tSNE embedding on the same distance metric. A neuron was characterized as ‘TRAPed’ trapped if it satisfied two conditions: (1) from the tdT + sort gate (tdT protein positive) and (2) tdT mRNA raw count > 0. Neuron subtype marker genes were found by using the FindAllMarkers function in Seurat (min.pct = 0.3, thresh.use = 0.25, min.diff.pct = 0.2). DEG analysis was done by applying the Mann–Whitney U-test on various cell populations. Raw P values were adjusted to an FDR. Permutation tests were then performed on all genes of interest. All graphs and analyses were generated and performed in R. GeneAnalytics and GeneCards packages offered by the gene set enrichment analysis tool were used for GO/KEGG/REACTOME pathway analysis and classification of enriched genes in each subpopulation.
Finding remote-memory-associated DEGs. To reduce our list of DEGs (FR TRAP versus NF TRAP results in 1,291 DEGs, cells from 4 biological replicates pooled, logFC > 0.3, FDR < 0.01) to only the most recall-specific, 4 steps were taken. Analysis was limited to C0–C4 neu-ron subtypes due to insufficient numbers of cells in C5 and C6 across all experimental conditions to make meaningful comparisons. First, DEGs are recalculated by assessing each experiment individually us-ing the whole transcriptome, and only DEGs (via the same criteria as pooled) that intersect in three-quarters of replicates are kept. Three out of four criteria were chosen as a compromise due to the high strict-ness of four out of four, which yielded only a maximum of seven DEGs (for a neuron subtype). All resulting DEGs are found in the initial DEG list (all replicates pooled), indicating that no additional DEGs were found as a result of analysing replicates separately. Second, ‘inactive’ (tdT-negative) populations were also compared (FR inactive versus NF inactive) and any DEGs that intersected with the DEGs left after the first criteria, were removed. This ensures that DEGs are activity-dependent, and not merely an overall upregulation in all cells due to the experience. This routinely removed genes such as Hsp90aa1 and Pcna-ps2. Third, the remaining DEGs had to be differentially expressed when FR TRAP was compared to either NR TRAP or HC TRAP. This ensures that the DEGs are specific to only neuronal ensembles that labelled by memory recall, and not due to forms of baseline activity (HC) or activity that remained from the initial fear learning (NR). Last, the remaining DEGs must pass a permutation test in which the training labels are shuffled and a distribution of log2FC is computed based on these labels. The true observed logFC must be above the 95th percentile of the distribution of the shuffled distribution. After placing these constraints, 99 genes remain from the original list of 1,291.
Assessment of activation score. A TRAPed (or inactive) cell is consid-ered to be ‘activated’ by the remote-memory DEG programme if 25%, 50% or 75% of the subtype-specific DEGs (remote-memory-associated DEGs only) is expressed above the 90th percentile of the distribution of that gene in NF TRAP controls from the same subtype. This calculation is then repeated with DEG programmes that are specific to each neu-ronal subtype. The fraction of cells activated with the subtype-specific signature is calculated as the number of activated cells divided by all cells in the subtype/activity group.
Regulatory motif analysis. Enrichment of known and de novo motifs was found using HOMER by inputting the list of 99 remote- memory-associated DEGs and using the function findMotifs.pl and
the criteria ‘–start -400 -end 100 -len 8,10 -p 2’. The locations of the motifs in specific DEGs were found using the -find < motif file > option of findMotifs.pl.
RNAscope image analysis. Images were taken using a Nikon Confocal Microscope (at ×10 or ×20, NA = 0.45) and images were processed in ImageJ to only obtain the mPFC regions. The resulting images were fed into a custom image analysis pipeline on CellProfiler (using a com-bination of the functions IdentifyPrimaryObjects, RelateObjects, FilterObjects, MeasureObjectIntensity, ClassifyObjects and Calcu-lateMath. The custom pipeline can be found in Supplementary Meth-ods). Briefly, images were corrected with control slides (unstained sample and negative control probes) and cells were segmented using the DAPI signal. Those harbouring a signal (above a set threshold level) for both the subtype marker and the tdT probe were retained. The integrated fluorescence intensity of the DEG probe was calculated for each DAPI+/subtype+/tdT + cell. Cells that were not double-positive were not considered. The integrated fluorescent intensity was then normalized to the integrated DAPI signal per cell and results were plotted with custom scripts in R.
Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this paper.
Data availabilityThe accession number for the single-cell RNA sequencing data reported in this paper is GSE152632.
Code availabilityCustom scripts can be found at https://github.com/mbchen-424/memory-sc-rnaseq.
25. Zhou, M. et al. A central amygdala to zona incerta projection is required for acquisition and remote recall of conditioned fear memory. Nat. Neurosci. 21, 1515–1519 (2018).
26. Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).
27. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
Acknowledgements We thank L. Chen, M. Zhou and J. Li for discussion of the experimental design; S. Kolluru and D. Henderson for assistance in library preparation; N. Neff and J. Okamoto for assistance with sequencing; J. Lui for advice on brain dissociation; L. Denardo, J. Lui and L. Luo for the gift and help with the TRAP2 line; and W. Wang, G. Stanley and F. Horns for helpful discussions and computational assistance. S.R.Q. is a Chan Zuckerberg Investigator. This work is supported by a grant from the NIH (MH115999 to T.C.S.)
Author contributions X.J. and T.C.S. designed the animal experiments. M.B.C. and S.R.Q. designed the single-cell RNA sequencing experiments. X.J. performed the animal experiments, brain dissection, and in situ hybridization and imaging. M.B.C. performed the brain dissociation, flow cytometry, single-cell library preparation and sequencing pipelines. M.B.C. performed all single-cell RNA sequencing data and image analysis, with input from X.J., S.R.Q. and T.C.S. M.B.C. wrote the manuscript with substantial contributions from X.J., S.R.Q. and T.C.S. T.C.S. and S.R.Q. oversaw the project.
Competing interests The authors declare no competing interests.
Additional informationSupplementary information is available for this paper at https://doi.org/10.1038/s41586-020-2905-5.Correspondence and requests for materials should be addressed to S.R.Q. or T.C.S.Peer review information Nature thanks Steve Ramirez and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.Reprints and permissions information is available at http://www.nature.com/reprints.
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the mPFC analysed in this study (anterior cingulate cortex (ACC), the prelimbic (PrL) and the infralimbic cortex (IL)). Scale bars are 1mm (and 0.5 mm in the insets). c, Violin plots of the number of reads and number of genes per biological replicates (m1–m4), per cell. d, Scatterplot depicting the lack of strong relationship between number of genes detected and number of reads obtained per cell. e, Scaled expression of canonical markers in non-neuronal cells (Cldn5-BECs, Pdg fra-OPCs, Cx3cr1-microglia, Aqp4-astrocytes).
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d, Number of TRAPed and Inactive cells (as defined by non-zero expression of tdT mRNA) collected per neuron subtype, in either fear-recall (FR) or no-fear (NF) mice. e, Representative images of subtype marker genes (C0–C6) in the Allen Brain ISH atlas. f, Neuron subtype composition of FR and NF populations. Number of TRAPed (tdT mRNA+) cells collected in each experimental condition that fall in one of 7 neuronal subtype categories.
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Sdha
Tmem151aPfkm
Pip4k2c
Pja2
Gdi2
Rab15
Tdg
Gfra2Emc1
Emc4
Cck0.5
0.0
0.5
1.0
1.5
0.2 0.4 0.6abs(99th percentile of permuted distribution (logFC))
True
obs
erva
tion
(logF
C)
Gpm6aUbc
Serinc1
Serinc3
Vcp rs
Pak1
Mfsd14aNck2
Gm5436
Slc25a46
Mir1242hg
Stx1b
Itfg1
Rtn1
Guk10.5
0.0
0.5
1.0
1.5
0.2 0.4 0.6 0.8
abs(99th percentile of permuted distribution (logFC))
True
obs
erva
tion
(logF
C)
Ubc
Serinc1
Hacd3
Prkar1b
Slc30a9
Lmbrd1Ankrd45 Pls3
Ctbp1 Atad1
Strip1
DnerTimm29
Vopp1
Trim35
Tmx1
Cdv3
Hmg20a
Inpp5f
Alg2
Rtn1
Psmb6
1110051M20Rik
Atp6v0bGm8797 Hint1
1
0
1
2
0.3 0.6 0.9abs(99th percentile of permuted distribution (logFC))
True
obs
erva
tion
(logF
C)
Ubc
Ptp4a1
Serinc1 Serinc3
Nsf
Slc25a46
Itfg1
Aplp1 Hnrnpk
Syt13
Dpysl4
Garnl3
Gm13340
Plekhb2
AI413582
Cycs
Elmod1
Rab24
Erp29
Sarnp
1
0
1
0.25 0.50 0.75abs(99th percentile of permuted distribution (logFC))
True
obs
erva
tion
(logF
C)
C0
- D
kkl1
(G
lut)
C1
- R
prm
(G
lut)
C2
- C
alb
2 (
GA
BA
)C
3 -
Te
sc (
Glu
t)C
4 -
Tn
faip
8l3
TRAPed FR vs NF Permutation test
40 0 40 80
Protein TransportIntracellular Protein Transport
Ephrin Receptor Signaling PathwayProtein Ubiquitination
Vesicle mediated TransportMembrane Organization
Nervous System DevelopmentUbiquitin dependent Protein Catabolic Process
Rhythmic ProcessSynapse Organization
ER to Golgi Vesicle mediated TransportLong term Synaptic Potentiation
Learning or MemoryNeuron Projection Development
MRNA ProcessingRegulation of Circadian Rhythm
Negative Regulation of Epidermal Growth Factor Receptor Signaling PathwayTricarboxylic Acid Cycle
Regulation of DNA binding Transcription Factor ActivityRegulation of Neuronal Synaptic Plasticity
Regulation of AxonogenesisCellular Process
Regulation of Synaptic PlasticityRNA Splicing
Neuron Projection MaintenanceGolgi to Plasma Membrane Protein Transport
Regulation of Synaptic Vesicle ExocytosisNegative Regulation of Neuron Apoptotic Process
ExocytosisViral Process
Antigen Processing and Presentation Via MHC Class IILearning
Positive Regulation of Synapse AssemblyRegulation of Alternative MRNA Splicing, Via Spliceosome
Protein DephosphorylationNegative Regulation of Protein Autophosphorylation
Regulation of TranslationRegulation of Catalytic Activity
Regulation of SNARE Complex AssemblyRegulation of Axon Extension
ATP Metabolic ProcessNeuron Apoptotic Process
Positive Regulation of GTPase ActivityProtein Stabilization
Phagosome AcidificationFc epsilon Receptor Signaling Pathway
Proteasome mediated Ubiquitin dependent Protein Catabolic ProcessRegulation of Macroautophagy
Protein DeubiquitinationMitochondrial Electron Transport, Cytochrome C to Oxygen
Post translational Protein ModificationATP Hydrolysis Coupled Cation Transmembrane Transport
Positive Regulation of Canonical Wnt Signaling PathwayStimulatory C type Lectin Receptor Signaling Pathway
Microtubule based ProcessProteolysis Involved in Cellular Protein Catabolic Process
Proteasomal Protein Catabolic ProcessTumor Necrosis Factor mediated Signaling Pathway
Regulation of MRNA StabilitySCF dependent Proteasomal Ubiquitin dependent Protein Catabolic Process
Mitochondrial Translational ElongationMitochondrial Translational Termination
Regulation of Mitotic Cell Cycle Phase TransitionElectron Transport Chain
Proteasomal Ubiquitin independent Protein Catabolic ProcessInterleukin 1 mediated Signaling Pathway
Proton Transmembrane TransportAntigen Processing and Presentation
Substantia Nigra DevelopmentCristae Formation
Mitochondrial ATP Synthesis Coupled Proton TransportNIK/NF kappaB Signaling
Regulation of Hematopoietic Stem Cell DifferentiationATP Synthesis Coupled Proton Transport
ATP Biosynthetic ProcessAnaphase promoting Complex dependent Catabolic Process
Negative Regulation of G2/M Transition of Mitotic Cell CycleWnt Signaling Pathway, Planar Cell Polarity Pathway
Regulation of Transcription From RNA Polymerase II Promoter in Response to HypoxiaRegulation of Cellular Amino Acid Metabolic Process
Translational InitiationViral Transcription
Oxidation reduction ProcessNuclear transcribed MRNA Catabolic Process, Nonsense mediated Decay
TranslationMitochondrial Respiratory Chain Complex I Assembly
SRP dependent Cotranslational Protein Targeting to MembraneMitochondrial Electron Transport, NADH to Ubiquinone
40 0 40 80
#DEGs
Score
downreg with FRupreg with FR
Remote memory-associated DEGs (99)
All DEGs (C0-C4) - TRAPed FR over NF
112
8
7
23
292
54
Yme1l1
PreplUap1Ndufs1Lgi1Ppp2r2aStx12Upf1Tmem263
Pdcd4NarsUba2Impact
CpeChchd2Hsp90aa1Cdc123Sra1Pcna
ps2Slc25a46Pak3
C0C1C3C2C4
log2 FC(DEG in 3/4 rep)
0
2
4
-4
-2
FR inactive vs NF inactivea
b
c
Extended Data Fig. 3 | Differential gene expression in distinct neuronal subtypes (FR over NF TRAPed populations). a, DEGs in each neuron subtype when inactive (tdT-) neurons are compared between FR and NF mice. b, Left, volcano plots of DEGs in FR vs NF mice for each neuron subtype (C0 to C4). DEGs found when all replicates are pooled analysed in a combined manner are shown in red. Recall-dependent DEGs (defined as being differentially expressed in at least 3 out of 4 replicates, when analysed replicates are analysed individually) are labelled in black. Right, permutations are performed for every recall-dependent DEG for each neuronal population. Upregulated DEGs lying above the y = x line (red) and downregulated DEGs lying below the y = x line
(blue) are considered to be above the 99th percentile of the permuted distribution. c, GO enrichment analysis of all up- and downregulated DEGs (941 DEGs up, 384 DEGs down, all neuron subtypes combined) when all replicates are pooled. Bars show the enrichment scores (GeneAnalytics) for the GO pathway and dot indicates the number of DEGs involved in that pathway. d, GO enrichment analysis of only the upregulated remote-memory-specific DEGs (from Fig. 3c) from all neuron subtypes combined. Bar indicates enrichment score (Gene Analytics) and number indicates number of recall-dependent DEGs involved in that pathway.
Article
1d 2d
No Salience (NS)
Salience (S) deprive
10 d
Food & 4-OHT
tdT production
0d
4-OHT injection
Active neuronal ensembleCollect single cells
Chow
WT
Homec
age
No Fea
r
Fear-R
ecall
No Sali
ence
Salien
ce0
1
2
3
experience
% o
f eve
nts
that
are
tdT+
in fl
ow
**p=0.002
p=0.013*
50
25
0
25
25 0 25tSNE_1
tSN
E_2
Fear paradigm (FR, NF, NR, HC)Food salience paradigm (S, NS)
50
25
0
25
25 0 25tSNE_1
tSN
E_2
C0-Dkkl1+C1-Rprm+
C2-Calb2+C3-Tesc+
C4-Tnfaip8l3C5-Tshz2C6-Lhx6
SalienceTRAPed
C0C1C2C3C4C5C6
neuronsubtype
FRTRAPed
Inactive
*
*
**
C0 C1 C2 C3 C4 C0 C1 C2 C3 C4Fear-Recall vs No Fear Food Salience vs No Salience
Pcna-ps2
avg log2(FC)
21
012
Gm13340
intersecting DEGs
a
b c
d e
deprive10 hr later
4-OHT
Extended Data Fig. 4 | Analysis of TRAPed ensembles in food salience (S) versus no salience (NS) mice. a, Schematic of experimental paradigm for generating TRAPed neuronal ensembles as a result of food salience (food deprivation followed by food return (salience) or no food return (no salience)). b, Percentage of events in flow cytometry that were tdT +, by experience (mean ± s.d.). c, tSNE of the merge of data from fear-recall experiments and
food-salience experiments coloured by neuron subtype (left) and experimental paradigm (right). d, Subtype composition differences between TRAPed fear-recalled ensembles and TRAPed food salience ensembles, as compared to background Inactive ensembles. e, Heat map of the average log2 fold change of DEGs in each neuron subtype when comparing fear-recall vs no-fear, and salience vs no salience. Only DEGs with FDR <0.01 are shown.
C0 C1
C2 C3
C4
Extended Data Fig. 5 | DEGs when comparing ensembles from food salience (S) to no salience (NS) mice. Volcano plots showing the log2 fold change and adjusted P values (in log10 scale) of genes when comparing food
salience over no salience groups within each neuron subtype (C0 through C4). Top DEGs per neuron subtype are labelled in red. Positive log2 fold change indicates upregulation in food salience (S) group.
Article
Stx1b
Emc1
Atp6v0b
Hnrnph2
Gdi2
Nck2
Rab24
Dpysl4
Pja2
Rab15
Vamp2
Cck Rab24
Pja2
Gsk3b
NsfStx1b
Gdi2
Rtn1
Gsk3b
Pdha1
Pfkm
Atp6v0c
Erp29
Aplp1
Nck2
Rtn3
Sdha
Cycs
Lmbrd1
Eif2ak1
Trim32
Sdha
Gdi2
Cycs
NsfNsf
Hid1
Rtn1
Nel l2
Hnrnpk
Mpc1
Pja2
Ctbp1
Vamp2
Cdc42se2
Rtn1
Pja2
Syt13
Hnrnph2Emc4
Hint1
I t fg1
Gpm6aStx1b
Ghitm
Dner
Rtn1Nsf
Rtn1
Cck
Sar1a
Cycs
Rtn1
Serinc1
Ankrd45
Vamp2
Emc4
Atp6v0c
Hnrnpk
Usp5Rab5a
Pak1
Psmb6Gsk3b
Rab5aRab5aApp
Trim32
Gsk3b
Vamp2
Rab5aRab5a
Trim32
Psmb6
Vamp2
Nsf
Rab24
Syt13
App
Sdha
Pdha1
Gdi2App
Hnrnpk
App
Atp5g3Sdha
Mmd
Abcf3
Tr im35
Clpp
Gdi2
Atp6v0c
Usp5
Dner
Rab24
Gsk3b
Atp6v0c
Ghitm
Pak1
App
Hint1
Aplp1
Serinc1Vamp2
Rab5a
Tmx1
Sdha
Vamp2
Syt13
Serinc1
Aplp1
App
Syt13
Rtn3
Pcsk2
AppApp
Pdha1
Serinc1
Rab5aSar1a
Serinc3
Up or down regulat ion(FR over NF)
Down
Up
Stx1b
Emc1
Atp6v0b
Hnrnph2
Gdi2
Nck2
Rab24
Dpysl4
Pja2
Rab15Cck Rab24
Pja2
Stx1b
Gdi2
Pdha1
Pfkm
Atp6v0c
Erp29
Aplp1
Nck2
Atp6v0b
Rtn3
Sdha
Cycs
Lmbrd1
Eif2ak1
Trim32
Sdha
Gdi2
Cycs
Hid1
Nell
Hnrnpk
Mpc1
Pja2
Ctbp1
Cdc42se2
Pja2
Syt13
Emc4
Hint1
Itfg1
Gpm6a Stx1b
Ghitm
Dner
Cck
Sar1a
Cycs
Rtn1
Ankrd45
Emc4
Atp6v0c
Hnrnpk
Usp5Rab5a
Pak1
Psmb6
Rab5aRab5aApp
Trim32
Rab5aRab5a
Trim32
Psmb6
Nsf
Rab24
Syt13
App
Sdha
Pdha1
Atp5g3
Gdi2App
Hnrnpk
App
Sdha
Mmd
Abcf3
Trim35
Clpp
Gdi2
Atp6v0c
Usp5
Dner
Rab24
Atp6v0c
Ghitm
Pak1
App
Hint1
Aplp1
Rab5a
Tmx1
Sdha
Syt13Aplp1
App
Syt13
Rtn3
Pcsk2
AppApp
Pdha1
Rab5aSar1a
Vamp2
Gsk3b
Serinc1
neuron subtype
C0
C1
C2
C3
C4
neurotransmitter
GABA
GLUT
Serinc3
a
0.1
0.2
0.3
0.4fra
ctio
n in
duct
ion
(C1
DEG
s)
C1 C0 C1 C2 C3 C4 C1
NF FRTRAP Inactive
0.2
0.4
0.6
fract
ion
indu
ctio
n (C
2 D
EGs)
TRAP InactiveC2 C0 C1 C2 C3 C4 C2
NF FR
0.20.40.6
TRAP InactiveC3 C0 C1 C2 C3 C4 C3fra
ctio
n in
duct
ion
(C3
DEG
s)
NF FR
Rtn
1H
sbp1
Vam
p2Sl
c25a
46Ab
cf3
Rab
24N
sfPr
kar1
bPl
ekhb
2Pi
p4k2
cD
pysl
4M
iga2
Kctd
10H
int1
Ghi
tmSl
c30a
9G
uk1
Cdv
3C
ckEm
c4Sd
haAt
p6v0
bC
lpp
Tmem
50a
Mfs
d14b
Lmbr
d1Ap
pAt
p6v0
cU
bcItf
g1C
rip2
Hac
d3Td
gPc
sk2
Gar
nl3
Hnr
nph2
Hm
g20a
Strip
1Sk
iv2l
2An
krd4
5Tm
x1H
nrnp
kPt
p4a1
Tmem
151a
Eif2
ak1
Mpc
1In
pp5f
Serin
c3C
dc42
se2
Sar1
aZf
p706
Hid
1Pd
ha1
Fam
131a
Tim
m29
Ctb
p1Em
c1Ap
lp1
Pigq
Acsf
3G
pm6a
1110
051M
20R
ikSe
rinc1
Trim
35D
mtn
Atp5
g3Vo
pp1
Cyc
sG
di2
Usp
5R
ab5a
Dne
rM
ir1242
hgR
tn3
Mfs
d14a
Elm
od1
Trim
32Pj
a2Su
lt4a1
Rab
15Er
p29
Pls3
ATCG
AG TCAGTC
G TAC
GATC
C TAG
TGCA
AC TGACGT
AC TGATCGTGACGACTGACTCGTAATGCACTGCGAT
GACT
ATCG
ATCG
AC TGCG TAAGCT
ATGC
TGCA
GACTCAGTCTGA
CATGCATGTGACAGCT
ATGCGATCTCAG
CTGACAGTCTGA
AGTC
CTAG
G ACT
ATCGGTAC
ACGT
C TGA
G ATC
ATCG
G ACTTCAG
GTACTAGC
ACGTACGT
CG TAC TGA
AG TCGCAT
AC TGACGTCGAT
AGCT
AGTC
CGAT
CGAT
GATC
AC TGGTAC
C TAG
ATCG
AGCT
AG TCATGC
AGCT
CG TAAC TGAC TGG TCA
AGTC
AGTC
TCGA
ACGT
AGCT
C TAG
ACGT
AG TCAG TCACGTACGTATCG
ATGC
GCTAGTCA
TGAC
AG TCAGTC
AC TGC TAG
G TCA
CG TACGTA
AC TGCG TAAG TCCG TAAGCT
ATGC
CTGA
CG TAACTG
CATG
CG TAAC TGACGT
GCAT
CG TACG TACGTA
C TAG
ACTG
AG TCTCGA
CATG
CG TAC TGA
CGTA
0 10 20 30 40
E2F1
ZNF652
JUN
Ddit3
Sox6
ZBTB12
POL008.1
MYBL1
HIF1a
HIF1b
POL013.1
TFAPC2
RHOXF1
GMEB2
Best match
Upregulated and Downregulated Exemplary DEGs (FR over NF)
% of Target or background
Motif score
0
5
10
15
Motif
0.2
0.4
0.6
TRAP InactiveC4 C0 C1 C2 C3 C4 C4
fract
ion
indu
ctio
n (C
4 D
EGs)
NF FR
c
b
Extended Data Fig. 6 | Neuron subtype-specific activation programs, hypothesized protein–protein interactions and upstream regulatory motifs. a, Fraction of cells in each neuron subtype that are induced with the transcriptional program (that is, DEGs) from a neuron subtype. Overall, the activation program of each TRAPed neuron subtype is found to be more specific to it than the inactive population, or other subtypes. b, Left, de novo regulator motif discovery: analysis was performed using HOMER on the subset of 99 remote-memory-associated DEGs by looking at the sequences -400 to +100 bp from the TSS. 12 de novo and 2 known motifs were found (only motifs with an enrichment P value <10−2) were kept). Heat map depicts the ‘motif score’ of each DEG for each motif, and genes and motifs were clustered via the ward.D
method. Right, bar graph depicting the percentage of the DEGs (target sequences) that possess a match for the motif within -400 to +100 bp from the TSS, vs the percentage of background sequences. For de novo motifs, the best match gene is listed on the right. HIF1b and HIF1a are matches to known motifs. c, Left, hypothesized protein–protein interactions of a subset of recall-dependent DEGs (TRAPed FR/NF) using the STRING database (https://string-db.org/). Only genes that are connected at a confidence level of 0.4 (medium) are shown. Connections indicate a possible existence of an interaction between two proteins. Genes are coloured by up of downregulation in FR/NF. Right, same network plot, with nodes coloured by the neuron subtype which differentially regulates the DEG.
0.0
0.1
0.2
0.3
0.4
0.5
Frac
tion
of T
RAP
ed c
ells
that
are
Dkk
l1+
RNA-scope scRNA-seq
0.0
0.1
0.2
0.3
0.4
Frac
tion
of T
RAP
ed c
ells
that
are
Tnf
+
RNA-scope scRNA-seq
0.0
0.1
0.2
0.3
0.4
Frac
tion
of T
RAP
ed c
ells
that
are
Tes
c+
RNA-scope scRNA-seq
0.00 0.02 0.04 0.06 0.08
NF
FR
Ratio of Nuclei that are TRAP+
**p=0.006
FRNF
a
bDAPIMerge tdTTnfaip8l3 Syt13
Merge DAPI tdT Vamp2Tesc
NF
FR
FR
NF
DAPIMerge tdTRprm Stx1bNF
FR
c
Extended Data Fig. 7 | In situ validation of tdT levels, neuronal subtype compositions and remote-memory-specific DEGs in the mPFC. a, Ratio of Nuclei that are tdT+ (mRNA level) per training condition. Each data point represents one region of interest. (mean ± s.d.) b, Ratio of TRAPed cells that are positive for a neuronal subtype marker obtained either via the RNA-scope method, or by scRNA-seq (mean ± s.e.m.) (see Fig. 2). TRAPed cells are defined as DAPI+/tdT+ in RNAscope quantification, and as tdT mRNA count >1 in
scRNA-seq (post-QC). No significant differences are found between FR and NF within either RNAscope or scRNA-methods, indicating no major changes in neuronal subtype composition of active populations in different training conditions. c, in situ hybridization of key remote-memory specific DEGs including Stx1b in Rprm+/tdT + cells, Syt13 in Tnfaip8l3+/tdT + cells, Vamp2 in Tesc+/tdT + cells. Scale bars, 100 μm.
Article
AgrnCol4a4Dusp18LplThbs2Lama2Col4a5AppNtng2NrtnCntn2A2mTncEfna5CalrHsp90b1Psen1PsapLrpap1ApoeSerpine2Fgf13MdkRph3aF8Efna2Nlgn3Nlgn1Nlgn2L1camNxph1Ntng1TnfC1qbF9Nxph3PlauGpc3VtnCtgfCol4a1Efna3Col4a3Lama1NgfPlatEfna1Efna4Col4a6Lamb2Serping1TfpiSerpine1Thbs1Pdgfb
Maged1
Atp1a3
Tnfrsf21
Lrrc4c
Lrp1
Ncstn
Epha7
Scn8a
Nrxn1
Gfra2
Cntn1
Cd47
Cd81
Rpsa
ECm
icrog
lia
OPC
olig
oas
trocy
te
C0 C1 C3 C2 C4
EXH INH 0 1
log2FC (FR/NF)
2 2
scaled avg eprs
FDR<0.05
Neuronal RECEPTORSenriched in FR over NF Avg expression of
non-neuronal LIGANDS
Adam15
Adcyap1
App
Calm1
Calm2
Calm3
Cck
Cx3cl1
Efna3
Efna5
Fgf14
Gad1
Gas6
Hsp90b1
Icam5
Ncam1
Nlgn1
Nlgn3
Pkm
Ptdss1
Ptma
Qdpr
Rgmb
Rps19
Sema4b
Sema4f
Sorbs1
Itga5
Itga9
Itgav
Itgb1
Itgb3
Adcyap1r1
Sctr
Vipr1
Vipr2
Cav1
Cd74
Gpc1
Lrp1
Ncstn
NgfrSlc45a3
Tnfrsf21
Abca1
Adcy8Adcyap1r1
Cacna1cCrhr1
Egfr
Fas
Grm3
Grm4
Grm5
Grm7
Hmmr
Htr2c
Insr
Kcnn4
Kcnq1
Kcnq3
Kcnq5
Mylk
Oprm1
Pde1a
Pde1b
Pde1c
Ptpra
Sctr
Trpc3
Trpc5
Vipr1
Abca1
Adcy8
Cacna1c
Egfr
Grm5
Grm7
Insr
Kcnq1
Kcnq3
Kcnq5
Mylk
Pde1a
Pde1b
Pde1c
Trpc5
Abca1
Adcy8
Egfr
Grm5
Grm7
Insr
Kcnq1
Kcnq3
Kcnq5
Mylk
Pde1a
Pde1b
Pde1c
Trpc5
Cckbr
Cx3cr1
Epha1
Epha2
Epha3
Epha4
Epha5
Epha6
Epha7
Ephb1
Ephb6
Epha1
Epha2
Epha3
Epha4
Epha5
Epha6
Epha7
Ephb1
Ephb2
Ephb6
Fgfr1
Fgfr2
Fgfr3
Grm4
Axl
Mertk
Tyro3
Asgr1
Erbb2
Lrp1
Tlr1
Tlr2
Tlr4
Tlr7
Tlr9
Itgb2
Cacna1c
Fgfr1
Fgfr2
Gfra1
Ptpra
Robo1
Nrxn1
Nrxn2
Nrxn3
Nrxn1
Nrxn2
Nrxn3
Cd44
Jmjd6
Scarb1
Vipr1
Dysf
Bmpr1b
Bmpr2
Neo1
C5ar1
Dcbld2
Nrp2
Insr
Itga1
Itgb5
C0 C1 C3 C2 C4
EXH INH
mic
rogl
iaE
CO
PC
olig
oas
trocy
te
Neuronal LIGANDSenriched in FR over NF
Avg expression of non-neuronal RECEPTORS
0 1
log2FC (FR/NF)
2 2
scaled avg eprs
FDR<0.05
d
e
Adam15
Adcyap1
App
Calm1
Cx3cl1
Fgf14
Gas6
Hsp90b1
Icam5
Ncam1
Nlgn1
Nlgn3
Rps19
Sorbs1
ItgavItgb1SctrNcstnTnfrsf21Abca1EgfrGrm3Grm5Kcnq3
Sctr
Cx3cr1Fgfr2Fgfr3MertkTlr2Tlr4Tlr7Itgb2
Fgfr2
Nrxn1C5ar1Itgb5
-log10(FDR)<1.32
4
6
21
012
log2FC (FR / NF)
C0 C1 C3 C2 C4
astro
cyte
micr
oglia
Perturbed neuronal ligands Perturbed glial receptorsc
0
200
400
astro
cyte EC
microg
liaoli
goOPC
# ce
lls
NFFR
Endothelial OPC Oligodendrocytea
b
Extended Data Fig. 8 | DEGs and potential cell–cell interactions in non-neuronal cells during memory consolidation. a, Volcano plots of non-neuronal cell types when comparing cells in FR over NF nice. DEGs (FDR >0.01, log2FC >1) are labelled in red, and exemplary DEGs (high log2FC and log10FDR) are labelled in black. b, Number of non-neuronal cells collected in this study, for each cell type and experimental condition. c, Heat map of a subset of neuronal ligands and glial receptors that are found to be differentially perturbed upon memory consolidation. Only receptors and ligands which were
found to be (differentially) expressed are shown. d, Left, heat map of the log2FC of DEGs (FR over NF) in neurons that are classified as ligands. Middle and right, Sankey plot of known ligand-receptor pairs and heat map of the average scaled expression level of the corresponding receptors in each cell type. e, Left, heat map of the log2FC of DEGs (FR over NF) in neurons that are classified as receptors. Middle and right, Sankey plot of known ligand-receptor pairs and heat map of the average scaled expression level of the corresponding ligands in each cell type.
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Extended Data Fig. 9 | Comparison of remote-memory DEGs with previously published datasets of experience-dependent transcriptional activity. a, Left, heat map of the log2FC of all 1,292 DEGs (FDR <0.01, FR over NF, all cells pooled) in this manuscript, and their log2FC values in previously published datasets of experience-dependent DEGs in activated neurons during: recent fear memory retrieval24, associative fear-learning22, post-visual stimulus23, or novel environment exposure21. A value of zero log2FC indicates the gene was not differentially expressed in a dataset. Right, same as left, but
now DEGs are filtered down to the ‘Recall-dependent DEG’ set derived from this manuscript. Only genes differentially expressed in three out of four replicates are remaining. b, Left, log2 fold change heat map of the recall-dependent DEGs between tdT + vs tdT − neurons in FR mice (genes are differentially expressed in >3/4 replicates) undergoing remote fear memory consolidation. Right, the log2FC values of these genes if they are found in previously published datasets of experience-dependent DEGs (see a). A value of zero log2FC indicates the gene was not differentially expressed in that dataset.
Article
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Rab15Pip4k2cPcsk2Syt13Nell2
Gfra2
Stx1b
Mir1242hgMfsd14b
Gsk3b
Pigq
Acsf3Mmd
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Kctd10Abcf3Miga2Sult4a1
HnrnpkDmtn
Fam134a
Tmem151aFam131aDpysl4Pak1Skiv2l2Mfsd14aNck2Vamp2Cdv3Ankrd45Strip1Hmg20aNsfTimm29Zfp706AppSdhaPfkmCtbp1Usp5Aplp1Prkar1bLmbrd1Hid1Emc1Pls3TdgVopp1Sar1aAtad1Plekhb2Trim35Itfg1Hacd3Rab5aSlc30a9DnerUbcRtn3GhitmGpm6aGdi2Pja2Tmx1Cdc42se2NcdnHnrnph2Serinc1Ptp4a1Pdha1Pcna ps2Slc25a46Trim32Alg2Serinc3Inpp5f
FR vs NF NR vs NF
Recall-specific DEGs
Fear-relatedDEGs
FR vs NF NR vs NF
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Comparison of Recall-specific and fear-related DEGs
All Recall-specific DEGs
Extended Data Fig. 10 | Comparison of remote-memory-specific DEGs and fear-experience-related DEGs. Left, log2 fold change heatmap of the union of DEGs from comparing FR vs NF (remote-memory specific) and NR vs NF (fear-related). Sustained transcriptional changes from the fear-experience
itself is shown in the yellow highlighted columns. Right, zoomed in view of the portion of the heat map within the green boxes where most fear experience-related DEGs are located.
1
nature research | reporting summ
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ctober 2018
Corresponding author(s): Chen MB, Jiang X, Quake SR, Sudhof TC.
Last updated by author(s): Jul 21, 2020
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Data collection - Sequences from the Nextseq or Novaseq were demultiplexed using bcl2fastq, and reads were aligned to the mm10 genome augmented with ERCC sequences, using STAR version 2.5.2b. Gene counts were made using HTSEQ version 0.6.1p1. All packages were called and run through a custom Snakemake pipeline - FreezeFrame (version 4)by Coulbourn Instruments
Data analysis - R (version 4.0.2 Taking Off Again) - RStudio Version 1.1.456 - Seurat v3 by the Satija Lab (https://satijalab.org/seurat/) - HOMER (version 4.8) (http://homer.ucsd.edu/homer/motif/) - GeneAnalytics (2020) (https://geneanalytics.genecards.org/)
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nature research | reporting summ
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ctober 2018- STRING database (https://string-db.org/) - Malacards: The human disease database (https://www.malacards.org/) - RNA-seq data from Rao-Ruiz, 2019 (NCBI GEO Accession GSE129024) - RNA-seq data from Hrvatin, 2017 (GSE102827) -RNA-seq data from Lacar, 2016 (NCBI GEO Accession Number: GSE77067) -RNA-seq data from Cho, 2016 (GSE85128) - Receptor-ligand data from Ramilowski, 2015 (http://fantom.gsc.riken.jp/5/suppl/Ramilowski_et_al_2015/)
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Sample size Sample sizes (both number of mouse replicates and number of cells sequences) were determined via a combination of factors including: the total expected size of the population of active neurons in the prefrontal cortex (~1.5%), (2) cost of sequencing and mice (3) expected margin of error and confidence levels
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Replication Replication was achieved by performing the behavioral training on 4 sets of mice, on 4 different independent days. Results were consistent in all replicates (4).
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Laboratory animals TRAP2 was generated in a 129Sv/SvJ background. For behavior experiments, they were backcrossed to C57Bl6/J for 3 generations. Mice were housed at room temperature and 40-60% humidity. We have described the usage of C57bl6J mice in the method part of the manuscript. Male mice at age between 6-7 weeks were used in this study.
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nature research | reporting summ
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ctober 2018Flow CytometryPlots
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Methodology
Sample preparation mPFC regions were micro-dissected from live vibratome sections (300 um thick) of the prefrontal cortex. Tissue pieces were enzymatically dissociated via a papain-based digestion system (Worthington, Cat # LK003150). Briefly, tissue chunks were incubated in 1mL of papain (containing L-cysteine and EDTA), DNAse, and kyneurenic acid for 1 hour at 37C and 5% CO2. After 10 min of incubation, tissues were triturated briefly with a P1000 wide bore pipette tip and returned. Cells were triturated another 4 times (~30 each) with a P200 pipette tip over the rest of the remaining incubation time. At room temperature, cell suspensions were centrifuged at 350g for 10 min, resuspended in 1mL of EBSS with 10% v/v ovomucoid inhibitor, 4.5% v/v DNAse and 0.1% v/v kyneurenic acid, and centrifuged again. Supernatant was removed and cells 1mL ACSF was added. ACSF was composed of: 1mM KCl, 7mM Mgcl2, 0.5 mM Cacl2, 1.3 mM NaH2PO4, 110 mM choline chloride, 24mM NaHCO3, 1.3 mM Na Ascorbate, 20mM glucose and 0.6mM sodium pyruvate. Cells were passed through a 70 um cell strainer to remove debris. Hoescht stain was added (1:2000, Life Technologies, Cat #H3570) and incubated in the dark at 4C for 10 min. Samples were centrifuged (350g for 8 min at 4C) and resuspended in 0.5mL of ACSF and kept on ice for flow cytometry.
Instrument Sony SH800
Software Sony SH800
Cell population abundance tdtomato+/ Hoescht+ cells were ~0.5-2% of all total events
Gating strategy There was gating on BSC or FSC as size distribution of neurons/non-neuronal cells of interest is unknown. Only gating for a population that was double positive for Hoescht (above 1000 arbitrary fluorescent units) and tdTomato (above 10e4 arbitrary fluorescent units) was used (see Extended Data 2 for examples).
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