1. Neuroscience
Download icon

Extensive and spatially variable within-cell-type heterogeneity across the basolateral amygdala

  1. Timothy P O'Leary
  2. Kaitlin E Sullivan
  3. Lihua Wang
  4. Jody Clements
  5. Andrew L Lemire
  6. Mark S Cembrowski  Is a corresponding author
  1. Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Canada
  2. Janelia Research Campus, Howard Hughes Medical Institute, United States
  3. Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Canada
  4. School of Biomedical Engineering, University of British Columbia, Canada
Tools and Resources
  • Cited 0
  • Views 1,655
  • Annotations
Cite this article as: eLife 2020;9:e59003 doi: 10.7554/eLife.59003

Abstract

The basolateral amygdala complex (BLA), extensively connected with both local amygdalar nuclei as well as long-range circuits, is involved in a diverse array of functional roles. Understanding the mechanisms of such functional diversity will be greatly informed by understanding the cell-type-specific landscape of the BLA. Here, beginning with single-cell RNA sequencing, we identified both discrete and graded continuous gene-expression differences within the mouse BLA. Via in situ hybridization, we next mapped this discrete transcriptomic heterogeneity onto a sharp spatial border between the basal and lateral amygdala nuclei, and identified continuous spatial gene-expression gradients within each of these regions. These discrete and continuous spatial transformations of transcriptomic cell-type identity were recapitulated by local morphology as well as long-range connectivity. Thus, BLA excitatory neurons are a highly heterogenous collection of neurons that spatially covary in molecular, cellular, and circuit properties. This heterogeneity likely drives pronounced spatial variation in BLA computation and function.

Introduction

The amygdala is a brain region that governs a variety of functions and behaviors (Janak and Tye, 2015). Classically, the amygdala has been studied for the role it plays in acquisition and expression of conditioned fear memory (Fanselow and LeDoux, 1999; Maren and Quirk, 2004), with more recent evidence also implicating this brain region to be involved in other aversive states like anxiety (Daviu et al., 2019). Complementing this work examining the role of the amygdala in negative valence settings, it is also becoming increasingly apparent that the amygdala participates in appetitive and reward-based behavior (Baxter and Murray, 2002; Wassum and Izquierdo, 2015). The mechanisms by which this single brain region mediates such a variety of functions is unclear.

One reductionist approach to understanding amygdala computation lies in identifying functional contributions of specific amygdalar regions (Beyeler and Dabrowska, 2020). The basolateral amygdala complex (BLA) is one region that has received particular attention, in large part due to its high degree of reciprocal long-range connectivity with other brain regions (Amir et al., 2018; Janak and Tye, 2015; Little and Carter, 2013; McGarry and Carter, 2017; Senn et al., 2014). This anatomical arrangement suggests a powerful role for the BLA in orchestrating a variety of long-range computations, and moreover, enables circuit-specific experimental access for mapping circuits onto function and behavior (Tovote et al., 2015). Harnessing this experimental tractability, a body of work has emerged demonstrating that BLA projections to different downstream regions control of a range of diverse, and sometimes bidirectional, functional and behavioral phenotypes (Beyeler et al., 2018; Beyeler et al., 2016; Burgos-Robles et al., 2017; Felix-Ortiz et al., 2013; Felix-Ortiz et al., 2016; Felix-Ortiz and Tye, 2014; Herry et al., 2008; Kim et al., 2013; Namburi et al., 2015; Tye et al., 2011).

One central consideration for interpreting circuit-based manipulations is understanding the extent to which BLA neurons can be considered to be an intrinsically homogeneous population (Namburi et al., 2015; Zirlinger et al., 2001). In principle, differential behavioral effects following circuit-specific BLA manipulations may simply reflect different downstream readouts of otherwise identical BLA inputs. Conversely, and not mutually exclusively, such differential behavior could also reflect variable intrinsic BLA neuron identity that conveys different long-range information independent of downstream readout. Indeed, as different BLA projections can emanate from different gross geographic locations (Beyeler et al., 2018; McGarry and Carter, 2017), there exists a potential organizational substrate wherein projection target and intrinsic identity of BLA neurons might spatially covary.

One perspective that can provide comprehensive insight into BLA neuronal identity is from transcriptomic ‘cell typing’, wherein cells with similar gene-expression properties can be identified and mapped onto higher order structural and functional roles (Cembrowski, 2019; Lein et al., 2017b; Zeng and Sanes, 2017). This approach has proven useful for deciphering cell-type-specific organization and operation in a wide variety of other brain regions, especially in cases where spatial variation in gene expression can be mapped to spatial variation in higher order properties (e.g. morphology, connectivity) (Cembrowski et al., 2018a; Cembrowski and Spruston, 2019; Economo et al., 2018; Lein et al., 2017a; Mandelbaum et al., 2019; Phillips et al., 2019; Wu et al., 2017). Thus, application of transcriptomic cell typing to the BLA has the potential to inform cell-type-specific identity rules and spatial organization, as well as to infer higher-order structural and functional correlates.

Here, we employed such a cell-typing approach to understand the extent and organization of heterogeneity within the excitatory neuronal population of the BLA. To initially assess variation within this population, we performed single-cell RNA sequencing, identifying both coarse discrete heterogeneity as well as fine-scale graded variation. To map this variation in space, we used single-gene and multiplexed in situ hybridization, mapping both discrete and graded transcriptomic variation onto spatial axes of the BLA. Finally, leveraging this spatial heterogeneity in gene expression, we identified similar spatial differences in BLA connectivity and morphology. Ultimately, our work demonstrates that excitatory neurons of the BLA are a highly heterogeneous collection of neurons that vary in space across molecular, cellular, and circuit properties. This prominent variation in intrinsic BLA identity likely contributes to pronounced circuit-specific and behavioural effects. To facilitate use of our data as a resource for future studies, we have created a web-based portal to allow easy access and analysis of the scRNA-seq data in our study (http://scrnaseq.janelia.org/amygdala; schematic: Figure 1).

Workflow for assessing heterogeneity within the basolateral amygdala complex.

(A) Atlas schematic of the basolateral amygdala complex (BLA), schematizing microdissection for scRNA-seq. (B) Overview of scRNA-seq data, as visualized through t-SNE dimensionality reduction. Data, along with analysis and visualization tools, available at http://scrnaseq.janelia.org/amygdala. (C) Analysis of coarse discrete heterogeneity within the BLA (see Figures 2 and 3). (D) Analysis of fine continuous heterogeneity within the BLA (see Figures 4 and 5). (E) Spatial registration of discrete and continuous heterogeneity (see Figure 6). (F) Higher order discrete and continuous heterogeneity within the BLA (see Figures 7 and 8).

Results

scRNA-seq reveals discrete gene-expression differences within the BLA

To perform scRNA-seq, we used our previously published manual approach, which facilitates capture of excitatory neurons due to their general abundance and post-dissociation viability (Cembrowski et al., 2018a; Cembrowski et al., 2018b). To capture BLA neurons, the BLA was microdissected, dissociated, and individual cells were manually obtained for scRNA-seq. After subsequent processing and sequencing (see Methods), high-depth scRNA-seq data was obtained for 1231 excitatory neurons (5.9 ± 1.2 thousand expressed genes/cell, mean ± SD). Broadly, these scRNA-seq profiles separated into two discretely separated groups via t-SNE visualization and principal component analysis, with this separation recapitulated by graph-based clustering (see Methods; Figure 2A,B). Such discrete separation was robust, as random classifiers trained on 100 randomly selected cells (~8.1% of total dataset) was sufficient to predict near-perfect classification accuracy of the remaining dataset (91.8% +/- 3.7% accuracy of remaining 1131 cells, N = 1000 trials, mean ± SD; Figure 2C). This discrete separation was also seen using UMAP dimensionality reduction (Figure 2—figure supplement 1A,B; Becht et al., 2019).

Figure 2 with 2 supplements see all
scRNA-seq analysis of coarse, discrete transcriptomic differences.

(A) Overview of scRNA-seq data, as visualized through t-SNE dimensionality reduction and colored according to cluster identity. The number of enriched genes for each cluster is provided. (B) As in (A), but with projections onto the first two principal components. (C) Random forest classification of subsampled data. (D) Expression of known control genes. Expression is colored from low (white) to high (red). Inset numerical values denote maximum CPM value across all cells. (E) As in (D), but with novel cluster-enriched marker genes. (F) Functionally relevant neuronal genes that are differentially expressed between clusters. Numerical values denote CPM values of right tick mark.

Broadly, sequenced cells expressed markers of neurons in general (e.g. Snap25), as well as more specific markers of excitatory neurons (e.g. Slc17a7, encoding Vglut1, and Camk2a; Figure 2D). As expected, expression of markers for inhibitory neurons (e.g., Gad1 and Slc32a1) was effectively absent. Thus, both coarse clusters of BLA cells represented excitatory neuronal populations. Such excitatory subtypes of BLA cells were not resolved in a recently published relatively low-depth droplet-based scRNA-seq dataset (Zeisel et al., 2018) (see Figure 2—figure supplement 2), highlighting the utility of our high-depth manual capture approach.

We next examined gene expression that differentiated the two clusters of excitatory neurons. Using an adjusted p-value threshold of 0.05, we identified a total of 415 differentially expressed genes between the two clusters. This differential expression incorporated genes that were relatively binary in expression (‘on’ vs. ‘off’: e.g., Cplx1 and Negr1; Figure 2E), as well as genes that exhibited expression in only a subset of neurons in the enriched cluster (e.g. Col5a2, Calb1; Figure 2E). Moreover, these differentially expressed genes encompassed a wide array of functionally relevant categories for neurons, including axon guidance and cell adhesion, ligands and receptors, calcium handling, synapses, and transcriptional regulation (Figure 2F). These results illustrate many differentially expressed genes across two BLA excitatory neuron subtypes, and suggest that this differential expression likely maps on to higher-order functional variability.

Discrete separation in gene expression maps onto the lateral vs. basal amygdala nuclei

We next mapped the spatial location of the two discrete clusters. To do this, we analyzed cluster-specific marker gene expression via chromogenic in situ hybridization (ISH) data from the Allen Mouse Brain Atlas (Lein et al., 2007Table 1). Marker gene expression was examined across the anterior-posterior axis of the BLA in coronal sections (Figure 3A–D), with the spatial extent of the BLA identifiable by Slc17a7 expression (Figure 3E–F).

Table 1
List of image series used from the Allen Mouse Brain Atlas.
GeneFigureImage series
Kcng12-S2C77340480
Negr12-S2F, 3D692
Cplx12-S2I, 3F, 6-S1D67752308
Strip22-S2L72283809
Zbtb202-S2O79568020
Slc17a73B70436317
Ddit4l3D71836878
Lynx13F655
Gad13-S1B79556706
Slc32a13-S1B72081554
Cpne83-S1E73520974
Gpr883-S1E79567811
Rspo23-S1H71016632
Cdh93-S1H72472764
Rorb5B79556597
Myl45B72129251
Cdh135B79490066
Otof5B73788043
Prss235E70634118
S100b5E79591593
Nnat5E77887874
Slc24A25E71924238
Adamts25E71924385
Etv15E72119595
Bdnf5E79587720
Nr4a25E732
Slc6a16-S1D79591685
Tshz16-S1D72129289
Meis26-S1D1231
Figure 3 with 2 supplements see all
Differentially expressed genes show spatially discrete variation within the BLA.

(A) Three-dimensional coronal volumetric rendering of the BLA in the mouse brain. Image modified from Allen Brain Explorer (Lau et al., 2008). (B,C) As in (A), but for sagittal (B) and horizontal (C) views of the mouse brain. Coronal sections defined in (D) are shown. (D) Coronal sections highlighting the anterior, intermediate, and posterior geometry of the BLA. Atlas definitions of LA vs. BA are shown (modified from Paxinos and Franklin, 2004). (E) Spatial expression of Slc17a7 via chromogenic ISH across the anterior, intermediate, and posterior BLA. Scale bar: 500 μm. Inset shows scRNA-seq expression. (F) Segmentation of Slc17a7-expressing cells from the BLA. (G,H) As in (E,F), but for Negr1 and Ddit4l, novel markers of cluster 1. For reference, locations of segmented cells are superimposed on the location of excitatory (Slc17a7-expressing) neurons within the BLA. (I,J) As in (G,H), but for Cplx1 and Lynx1, novel markers of cluster 2.

Marker genes for cluster 1 (e.g. Negr1, Ddit4l) were generally enriched in posterior sections, occupying the lateral-dorsal regions of the BLA (Figure 3G–H). Notably, such spatial enrichment corresponds to the lateral amygdala (LA; Figure 3D). Conversely, expression of marker genes for cluster 2 (e.g. Cplx1, Lynx1) was generally present in the basal amygdala (BA), abutting and largely nonoverlapping with expression for cluster one markers (Figure 3I–J). Thus, spatial registration of cluster 1 and cluster 2 indicated that these clusters corresponded to the LA and BA, respectively. Consistent with this, the relative representation of the two clusters in the scRNA-seq dataset was similar to the relative abundance of LA and BA neurons counted from ISH (scRNA-seq: 27% and 73% from LA and BA neurons respectively, cf. 39% and 61% from ISH using atlas delineation of LA vs. BA).

We note that some marker genes can indeed show expression in the opposite nuclei, albeit at a markedly reduced density (e.g. BA-enriched Cplx1 is sparsely expressed in the LA, Figure 3I). Such lower expression density in the depleted region was similar to the overall abundance of interneurons (Figure 3—figure supplement 1A,B), suggesting that such geographical expression ‘spill-over’ reflects interneuron labeling rather than displaced excitatory neurons. In support of this, many cluster markers showed minimal spill-over expression (e.g. Ddit4l, Lynx1: Figure 3E,G; additional marker genes: Figure 3—figure supplement 1C–F), with two-color ISH illustrating this spill-over predominantly reflected Gad1-expressing interneurons (see Figure 3—figure supplement 2).

Graded heterogeneity within the basal and lateral amygdala

Next, we examined the existence and organization of further fine-scale heterogeneity within each cluster. To begin, we allowed finer clustering of our scRNA-seq dataset, which split the LA into two subpopulations and the BA into four subpopulations (Figure 4A; see Materials and methods). Such subpopulations were generally abutting and/or interspersed when projections onto the first two principal components were examined (Figure 4B), as well as with UMAP dimensionality reduction (Figure 2—figure supplement 1C,D), suggesting that these subpopulations might reflect graded differences within the LA and BA rather than discrete subtypes. Reinforcing this, random forest classification was relatively error-prone, with neurons that occupied the interface of within-LA and within-BA subpopulations being poorly classified in particular (Figure 4C).

scRNA-seq analysis of fine, graded transcriptomic differences.

(A) Overview of scRNA-seq data, as visualized through t-SNE dimensionality reduction, at a relatively fine clustering resolution. (B) As in (A), but with projections onto the first two principal components. (C) Random forest classification of subsampled data. For comparison, results from the coarse 2-cluster scheme are also provided in light grey. Inset illustrates rates of misclassification on a per-cell basis, with misclassified cells typically found at the interface of fine clusters. Inset results depict averages across 1000 simulations for 800 training cells. (D) t-SNE illustration of fine-scale clustering within the LA, with number of enriched genes shown for each subpopulation. (E) Expression of example marker genes for the two subpopulations of LA neurons. Note high gene-to-gene variation in expression for marker genes associated with each subpopulation. Inset numerical values denote maximum CPM value across all cells. (F) Expression of functionally relevant genes that are enriched in each LA subpopulation. (G,H,I) As in (D,E), but for subpopulations of the BA.

We therefore considered the possibility that these subpopulations reflected graded, rather than discrete, differences (Cembrowski and Menon, 2018). We first pursued this by examining heterogeneity within the LA, leveraging the fact that gene expression between the two subpopulations varied substantially (e.g. 199 genes differentially expressed at padjusted <0.05; Figure 4D). Enriched genes within a given subpopulation exhibited a high degree of gene-to-gene variation, wherein the extent of expression of one marker gene was a poor predictor of the extent of expression in another marker gene (Figure 4E,F). Moreover, when comparing between the two subpopulations, differentially expressed genes did not exhibit a clear reciprocal boundary that classically embodies discrete separation (Cembrowski and Menon, 2018; Figure 4E). All of these observed signatures are suggestive of LA heterogeneity existing in a continuous spectrum, rather than adhering to discretely separated populations.

In a similar fashion, we considered the four BA subpopulations, examining the 147 genes that were enriched within individual subpopulations (Figure 4G). As with the LA, these enriched genes exhibited high gene-to-gene variability within a subpopulation, and as well lacked reciprocal boundaries between subpopulations (Figure 4H,I). These findings, coupled with the fact that these subpopulations were abutting and/or interleaved in dimensionally reduced spaces (Figure 4A,B) and exhibited poor separation at interfaces (Figure 4C), suggested that borders between subpopulations of the BA were graded rather than discrete.

Spatial variation within the lateral and basal amygdala

Motivated by scRNA-seq-graded variation within LA and BA clusters, we next sought to examine whether such gradients exhibited a spatial correlate. We began with the LA, examining ISH for markers of the two LA subpopulations (Figure 5A). Markers for the two subpopulations were enriched in grossly different regions of the LA: one set of subpopulation markers (e.g. Rorb, Myl4) was relatively enriched in the anterior and dorsal LA, whereas markers for the other subpopulation (e.g. Cdh13, Otof) were relatively enriched in posterior and ventral LA (Figure 5B). As expected from scRNA-seq, strict spatial boundaries between subpopulation marker genes were not apparent; similarly, prominent gene-to-gene variability in spatial expression within a given subpopulation was also present. In sum, such results suggest graded spatial variation of cell-type identity within the LA.

Graded spatial variation, within the LA and BA, of subpopulation-enriched marker genes.

(A) Representative ISH images for selected LA subpopulation-enriched genes. Scale bar: 500 μm. Insets show t-SNE visualization of scRNA-seq data for each gene. (B) Locations of segmented cells for pairs of genes for each LA subpopulation across anterior, intermediate, and posterior sections. For reference, locations of cells are superimposed on the location of excitatory (Slc17a7-expressing) neurons within the BLA. (C,D) As in (A,B), except for pairs of enriched genes for BA subpopulations.

Similar spatial organizational rules, consistent with graded cell-type identity, were present within the BA (Figure 5C,D). Marker genes for BA subpopulation one were generally enriched anterior and medial, whereas markers for BA subpopulation two were enriched in more posterior and lateral regions of the BA. Markers for BA subpopulation 3 and 4 showed spatial variation, wherein subpopulation three was enriched medially and subpopulation four was enriched laterally. As with the LA, no sharp spatial delineations were seen between subpopulations, and marker genes associated with the same BA subpopulation spatially varied on a gene-to-gene basis.

Importantly, two features of this variable cell identity suggest that gene expression differences can be largely attributed to graded baseline differences in cell type, rather than cell-state differences driven by activity. First, most enriched genes (Figure 4D–I) are not associated with activity dependence. Second, for the genes that have been associated with activity dependence (e.g. Bdnf, Nr4a2), they exhibit different patterns of expression across cells (Bdnf vs. Nr4a2: scRNA-seq: Figure 4H, ISH: Figure 5C,D; see also Figure 6—figure supplement 1 for within-sample mFISH comparison). Although we cannot completely discount activity states driving some gene-expression differences within the BLA population, the above results argue that activity-driven cell-state differences are small relative to baseline cell-type variability.

Multiplexed spatial mapping captures graded cell-type identities

From single-gene ISH, both BA and LA scRNA-seq heterogeneity can be mapped onto spatial gradients of cell-type identity. However, mapping single genes within individual sections means that gene-expression covariation within individual cells is lost. Additionally, chromogenic ISH is qualitative in nature, which precludes quantitative analyses of gene-expression. To circumvent these limitations, and simultaneously investigate both discrete and continuous heterogeneity with the BLA, we next employed multiplexed single-molecule fluorescent in situ hybridization (mFISH). With this multiplexed approach, we mapped 12 gene-expression targets in the same tissue, providing a comprehensive assessment of cell-type identity and organization at single-cell resolution in space. For gene selection, we used Slc17a7 to identity excitatory neurons, Negr1 and Cplx1 to study the putative discrete spatial divide between LA and BA, and the subpopulation marker genes Nr4a2, Otof, Cdh13, Rorb, Adamts2, Prss23, Bdnf, Slc5a5, and Nnat (Figures 25). Quantitative data on the expression of each gene was obtained on a per-cell basis, via calculating the number of signal-containing pixels normalized by the cell area (see Materials and methods).

Gene expression was mapped in anterior, intermediate, and posterior BLA sections (Figure 6A–C, Figure 6—figure supplement 1, Video 1). To initially study putative discrete differences between LA and BA, we derived a ‘phenotype index’ that corresponded to the relative expression of LA marker gene Negr1 and BA marker gene Cplx1 (defined on a per-cell basis according to (ECplx1-ENegr1)/(ECplx1+ENegr1), where E is the expression for the indicated gene). With this metric, cells exclusively expressing Negr1 have phenotypic indices of −1, cells exclusively expressing Cplx1 have indices of 1, and cells expressing non-zero amounts of both genes occupy intermediate values. Using this metric to study BLA phenotypes across space, it was apparent that phenotypes sharply transitioned across the LA-BA border (Figure 6D), with single-cell phenotypes exhibiting a bimodal distribution concentrated at extreme phenotypic index values (Figure 6E).

Figure 6 with 3 supplements see all
Multiplexed FISH simultaneously maps discrete and continuous heterogeneity.

(A) Overview of mFISH in the anterior amygdala. Scale bar: 400 µm. (B) As in (A), but for intermediate amygdala. Inset provides representative expansion, scale bar: 20 µm. (C) As in (B), but for posterior amygdala. (D) Overview of discrete heterogeneity in the BLA, obtained by assessing relative expression of Negr1 and Cplx1 across cells. (E) Histogram of phenotype identities for (D). (F) Illustration of gene-expression correlations for all cells, relative to the Negr1 phenotype seed cell highlighted in magenta. (G) Correlations computed and averaged across space, relative to the magenta seed cell shown in (F). Magenta and green points represent other cells within the posterior BLA with a binarized discrete cell-type identity. Blue and orange lines indicate binned average correlation across all cells at 100 µm spacing for the same and opposite phenotypes. (H,I) As in (F,G), but for a Cplx1 phenotype seed cell. (J) As in (G,I), but pooling and analyzing averages across all cells in the BLA. Blue and orange lines illustrate mean correlations to other cells of the same and opposite phenotypes, respectively, with spread about mean indicating one standard deviation.

Video 1
Overview of mFISH marker gene expression in the posterior BLA.

We next used mFISH to visualize graded spatial changes in gene-expression and cell-type identity within the LA and BA. To do this, a given seed cell was selected (e.g. magenta cell, Figure 6F), and correlations of mFISH-detected gene expression were computed to all remaining cells (Figure 6F; see Materials and methods). Distances between the seed cell and all remaining cells within the same section were calculated, with all cells assigned a LA or BA discrete phenotype (according to a winner-take-all binarization of the results of Figure 6D,E). This approach allowed the spatial profile of gene-expression correlation to be computed for a given seed cell, and to be examined relative to phenotypic identity (Figure 6G; see also Figure 6H,I). Averaging this process across all cells, we identified a spatial gradient of gene expression, providing direct evidence for graded cell-type identity within BA and LA (Figure 6J). Finally, clustering of mFISH data at different resolutions recapitulated both the discrete and graded cell-type identity changes, providing further evidence of these organizational patterns within the BLA (Figure 6—figure supplement 2).

scRNA-seq and ISH map interneuron subtypes and geography

In principle, the scRNA-seq and ISH approaches used previously may also provide insight into the inhibitory cell-type organization of the BLA. To examine this, we next analyzed the 51 interneuron single-cell transcriptomes obtained by scRNA-seq (Figure 6—figure supplement 3A–C). Despite the small number of cells in this dataset, this analysis identified two distinct subtypes of interneurons that differentially expressed dozens of genes. Via chromogenic ISH, we found these subtypes occupied distinct regions, with one subtype within the BLA and the other occupying the pericapsular region (Figure 6—figure supplement 3D,E). Such results illustrate that different subtypes of neurons occupy the pericapsular and interior regions of the BLA, and highlight that our high-read-depth approach can identify heterogeneity despite surveying relatively few cells.

Long-range projections recapitulate organization of spatial heterogeneity

As a focal point of BLA research involves long-range circuits of excitatory neurons (Janak and Tye, 2015), we next examined whether spatial gene-expression patterns we observed in the BLA mapped to specific circuits. To do so, we used the retrograde AAV tracer rAAV2-retro (Tervo et al., 2016), and in a given animal injected fluorescent tracers (either rAAV2-retro-GFP or rAAV2-retro-tdT) into downstream targets of the BLA. In total, six projections were examined: auditory cortex (ACX), nucleus accumbens (NAC), medial entorhinal cortex (MEC), ventral hippocampus (VHC), prefrontal cortex (PFC), and the retrosplenial cortex (RSC).

Projections to the ACX and NAC recapitulated the discrete LA-BA divide that was identified transcriptomically, such that ACX projections were enriched within the LA and NAC projections where enriched in the BA (Figure 7A–H; both obeyed p<0.001 for enrichment relative to random selected BLA neurons in N = 1000 Monte-Carlo simulations). Similarly, the remaining four projections – MEC, VHC, PFC and RSC – all were enriched with the BA (Figure 7I–X; p<0.001 via N = 1000 Monte-Carlo simulations). These projections furthermore exhibited spatially restricted regions reminiscent of BA subpopulation marker gene expression (Figure 7—figure supplement 1; p<0.001 via N = 1000 Monte-Carlo simulations when comparing to cell-to-cell distances expected by random selection of BA neurons). Thus, spatial heterogeneity was present for each BLA projection examined, consistent with organizational rules observed at a transcriptomic level.

Figure 7 with 1 supplement see all
BLA projection neurons show spatially discrete and continuous cellular variation.

(A) Atlas schematic showing retrograde viral injection site for the ACX (modified from Paxinos and Franklin, 2004). (B) Representative maximum intensity projections of labeled cells within the BLA. Scale bar: 500 μm. The boundary of the BLA is shown (solid line) along with the division between the lateral and basal sub-regions (dashed line) (C). Locations of all virally labeled cells along the A/P axis of the BLA (left to right). For reference, virally labeled cells (black) are superimposed over the position of excitatory neurons (Slc17a7-expressing cells), with cells colored to illustrate LA (magenta) and BA (green) per atlas definitions. (D) Percentage of total labeled cells located within the LA and BA, depicted as mean ± SEM. Grey lines indicate range of values within 95% confidence intervals for the expected number of cells in the LA and BA, via Monte Carlo simulations of random selection of excitatory neurons. E-X. As in (A–D), except for the NAC (E–H), PFC (I–L), VHC (M–P), MEC (Q–T) and RSC (U–X) injection sites.

Discrete morphological differences across the LA-BA axis

Finally, we leveraged our previous assays to see whether differences within cellular properties could be observed across the LA-BA axis. In particular, we considered whether there is a difference in cell-body size across the LA-BA border. Identifying such differences is important, as cell-body size can strongly shape passive electronic properties, the influence of active channels in the membrane, and biophysical processes that are shaped by surface area-to-volume ratios (Dayan and Abbott, 2001).

To begin, we examined cell-body area in our projection level dataset (N = 3–5 mice per injection site, 12–82 cells/mouse). In doing so, we identified a precipitous decline in the cell body size of ACX-projecting neurons, relative to projections from the VHC, MEC, PFC, NAC and RSC (p<0.005 via Mann-Whitney U test; Figure 8A,B). Of note, this drop covaried with the LA vs. BA projection site of these neurons. For pairwise comparisons within the BA (VHC, MEC, PFC, NAC, RSC), we saw no statistical differences in cell-body size (p>0.05 via Mann-Whitney U test for all pairwise within-BA comparisons).

LA and BA neurons vary by morphology.

(A) Representative images of labeled cells projecting to the ACX, NAC, PFC, VHC, MEC, and RSC. Scale bars: 25 μm. (B) The cell-body area of virally labeled cells for each projection site, depicted as mean ± SEM. (C) Top row: overview of mFISH signals for Slc17a7, Negr1, and Cplx1 in the intermediate BLA. Scale bars: 300 μm. Middle, bottom rows: expansions for the LA and BA, respectively. Scale bars: 40 μm. (D) Left: Cell-body area of Slc17a7-expressing cells segmented from (C). Middle, right: Cell-body area of Slc17a7-expressing cells that also express Negr1 or Cplx1, respectively. (E) Cell-body area for Slc17a7-expressing cells that also express Negr1 (magenta) or Cplx1 (green), depicted as mean ± SEM.

The previous results are shaped by the particular projections we examined, and thus, we next sought to take a complementary unbiased approach to survey cell-body area of LA vs. BA neurons. From our mFISH experiments (Figure 6), we extracted channels that corresponded to markers of excitatory neurons (Slc17a7), LA neurons (Negr1), and BA neurons (Cplx1) (Figure 8C). Leveraging the fact that dense Slc17a7 expression effectively acted as a cell-body fill, we examined cell-body areas of Slc17a7-expressing neurons that were either Negr1-expressing or Cplx1-expressing (see Materials and methods) (Figure 8D). In doing so, we again recapitulated a marked difference in cell body size between LA and BA excitatory neurons (p<2.2×10−16 via Mann-Whitney U Test, Figure 8E), further underscoring that a discrete cell-body size difference exists between LA and BA excitatory neurons.

Discussion

LA and BA as distinct entities

Frequently, the LA and BA are grouped together as elements of the basolateral amygdala complex (BLA). This aggregation is typically due to a lack of a clear cytoarchitectonic boundary between the two nuclei in most experimental settings (e.g. DAPI counterstain). As this LA-BA divide can be challenging to practically identify, this may also suggest that the LA and BA can be functionally aggregated. However, given that the amygdala is frequently referenced as a brain region that is relatively arbitrarily named in both structure and function (Swanson and Petrovich, 1998), such a BLA gross aggregation may conceal important underlying heterogeneity.

Indeed, a collection of disparate evidence illustrates specific features that divide the LA and BA. Classically, the LA and BA have been partitioned with respect to histochemical stains (e.g. acetylcholinesterase) and the orientation of fibers (LeDoux, 2007). There is a variety of work that shows LA and BA also exhibit differences in gene expression (Zirlinger et al., 2001), morphology (McDonald, 1984; Sah et al., 2003), and behavioral recruitment (Beyeler et al., 2018; Herry et al., 2008). Although these results argue for differences between the LA and BA, their disparate and multimodal nature make it challenging to assess the overall degree of separability between the LA and BA. It is also challenging to assess whether such heterogeneity emerges from continuous or discrete differences across the two nuclei.

An advantage of our transcriptomics-based analyses is that it allows complete quantification of a given feature; that is, quantified expression of every gene in the genome. Leveraging this complete approach, our work revealed hundreds of differentially expressed genes between the LA and BA, producing discrete separation between the two nuclei in both gene-expression space and geographical space (Figures 2, 3 and 6). In addition to this degree of differential expression being numerically large, many of these genes were also central to neuronal structure and function, thus suggesting that higher order structural and functional differences should be apparent within the BLA. This prediction was embodied by both differences in projection targets (Figure 7) and cell-body size (Figure 8). Taken in concert with previous findings, this work argues that excitatory neurons of the BA vs. LA should be considered as intrinsically distinct entities (summary: Figure 9).

Summary of heterogeneity within the basolateral amygdala.

Examples of spatially variable gene-expression, circuit, and cellular properties of excitatory neurons in the basolateral amygdala, as resolved in this study.

Graded spatial heterogeneity and functional implications

Our work here revealed pronounced graded transcriptomic variability in LA and BA excitatory neurons (Figure 4). In principle, discrete separation of BA subtypes may emerge with sampling additional cells; however, high-depth scRNA-seq data requires relatively few cells to resolve discrete subtypes (Figure 2C; Figure 2—figure supplement 2; Figure 6—figure supplement 3; Cembrowski et al., 2018b; Erwin et al., 2020). Thus, this graded transcriptomic identity seems to be a bona fide feature within the LA and BA.

Consistent with this, complementary techniques showed spatial gradients of marker-gene expression (Figure 6). Such a spatial arrangement is not necessarily expected a priori; indeed, there is evidence in many other brain regions wherein prominent graded transcriptomic heterogeneity does not have a spatial correlate (Harris et al., 2018; Stanley et al., 2020). The finding of a spatial covariate of this gene-expression heterogeneity is important, as it facilitates histological cross-validation of scRNA-seq data (Figures 5 and 6). Moreover, it provides a ‘Rosetta stone’ for registering and comparing our work to previous spatially resolved structural and function characterizations (Cembrowski and Spruston, 2019).

In atlas parcellations of the BLA, both the BA and LA are frequently partitioned into subnuclei, with the LA divided into the dorsolateral, ventrolateral, and ventromedial domains, and the BA divided into anterior and posterior domains. Although such discrete parcellations are provided to emphasize differences between these geographic regions, a body of evidence suggests that the heterogeneity underlying these subnuclei divisions may be spatially graded rather than discrete. For example, dendritic morphologies change gradually across subnuclei (McDonald, 1984; Sah et al., 2003), both the BA and LA have continua in firing patterns (Sah et al., 2003), and topographical gradients of connectivity are present across the BLA (Beyeler et al., 2018). Similar functional correlates, although typically sampled at a relatively coarser resolution in space, are also present within the BA along the anterior-posterior axis (Bergstrom et al., 2013; Goosens and Maren, 2001; Kim et al., 2016).

What are the potential functional advantages of such graded heterogeneity? One such answer may be found when considering this organization with respect to the computational demands required of the BLA. In particular, this brain region subserves neuronal and behavioural responses to fear, anxiety, and reward (Baxter and Murray, 2002; Janak and Tye, 2015; Maren and Quirk, 2004). In all these settings, graded neural representations are likely important for ensuring graded behavioral responses, and having a continuum in BA and LA cell-type identity could facilitate population-level coding of graded neural representations and responses. Indeed, such an organizational scheme is consistent with postulated graded representations within the BLA populations (Kyriazi et al., 2018), and thus such graded identity may allow cell-type variability to help match the space of necessary computations.

Cell-type classification: transcriptomics vs other methodologies

As our findings here leverage transcriptomics for cell-type classification, it is important to make the distinction between transcriptomically defined cell types relative to cell types defined by other criteria (Zeng and Sanes, 2017). Of particular note to the graded transcriptomic identity found here, cells that have a continuous gene-expression identity may nonetheless be functionally distinct in ways that are not predicted solely by transcriptomic organization (Kim et al., 2020). One such example of this is a gene-expression threshold needed for associated protein products and function, which can effectively impose a higher-order discrete phenotype (Cembrowski and Menon, 2018). Thus, although transcriptomic cell-type identities constrain and inform higher order classification, a perfect correspondence between transcriptomic and higher-order identities is not a necessity.

This point has particular relevance for consideration of within-BA differences, wherein published work argues for the discrete separation within the BA nucleus. Here, magnocellular and parvocellular populations have been proposed (Alheid, 2003). Putative markers for these populations, Ppp1r1b and Rspo2, have been identified from bulk microarray analysis and associated with different behavioral recruitment and function (Kim et al., 2016). Within our scRNA-seq data, Ppp1r1b and Rspo2 are enriched in opposite ends of the BA transcriptomic spectrum and do not conform to discrete subtypes. Thus, Ppp1r1b and Rspo2 populations may reflect graded subtypes at a transcriptomic level, with biological nonlinearities producing apparently step-wise differences in high-order function and behavior.

Implications for circuit manipulations

In previous work, projections from the LA and BA have been associated with different long-range targets (Beyeler et al., 2018; Hoover and Vertes, 2007; McGarry and Carter, 2017; Reppucci and Petrovich, 2016; Senn et al., 2014; Tsukano et al., 2019; Yang et al., 2016). At a finer spatial scale within the BA, gradients that vary upon projection site have also been reported (Beyeler et al., 2018; McGarry and Carter, 2017). Our study, revealing discrete and continuous differences in cell-type identity (Figures 26) that differentially map onto these long-range targets (Figure 7), shows that neurons comprising these projections encompass an intrinsically diverse collection of cells.

As long-range projections are frequently leveraged for BLA circuit-specific manipulations, our work here emphasizes that care should be taken to avoid confounds when comparing manipulations of different circuits. In particular, prominent heterogeneity within the BLA excitatory neuron population indicates that manipulations of different circuits target BLA neurons that can vary in intrinsic properties. In principle, such heterogeneity may make manipulation efficacy inherently variable between projections, especially for methodologies that rely on intrinsic properties of neurons to exert effects (e.g. DREADDs: Roth, 2016). A means of circumventing such limitations, and complementing existing circuit-specific manipulations, lies in examining intrinsic activity of the BLA across space agnostic to projection target. Such an approach leverages the spatial heterogeneity identified in our study, and will help to identify how spatial gene-expression differences map onto functional variability. In combination within circuit-specific manipulations, this work will provide an important understanding of how intrinsic- and circuit-level operations interact to evoke the wide array of BLA-associated phenotypes (Janak and Tye, 2015).

Informing and interpreting future experiments

Our work here has revealed the spatial transcriptomic organization of excitatory neurons in the BLA. For future studies, this spatial framework can effectively act as a ‘Rosetta Stone’, wherein different spatially resolved experimental modalities can be compared and integrated. In addition to this correlative work, our work here also reveals genes that can be leveraged to access and manipulate BLA cells (e.g. via transgenic mice), as well as molecular targets that can be manipulated to causally relate specific gene-expression products to computation and function (e.g. via CRISPR-Cas). Thus, our findings here will help to inform a broad range of observational and interventional experiments in future work.

To facilitate the continued use of our data, we have hosted our scRNA-seq data online in conjunction with analysis and visualization tools (http://scrnaseq.janelia.org/amygdala). This web portal will help guide further exploration and hypothesis generation from these complex data, with associated tools allowing this analysis to be conducted in a straightforward and intuitive fashion. The combination of this accessibility and utility will help further exploration of cellular and molecular heterogeneity of the BLA, and how this heterogeneity relates to computation, function, and behavior.

Materials and methods

Key resources table
Reagent type
(species) or
resource
DesignationSource or
reference
IdentifiersAdditional
information
Sequence-based reagentCplx1 ISH probeAdvanced Cell Diagnostics482531-C3Two-color FISH
Sequence-based reagentGad1 ISH probeAdvanced Cell Diagnostics400951-C2Two-color FISH
Sequence-based reagentNr4a2 ISH probeAdvanced Cell Diagnostics423351-T1Multiplexed FISH
Sequence-based reagentOtof ISH probeAdvanced Cell Diagnostics485671-T2Multiplexed FISH
Sequence-based reagentCdh13 ISH probeAdvanced Cell Diagnostics443251-T3Multiplexed FISH
Sequence-based reagentRorb ISH probeAdvanced Cell Diagnostics444271-T4Multiplexed FISH
Sequence-based reagentAdamts2 ISH probeAdvanced Cell Diagnostics806371-T5Multiplexed FISH
Sequence-based reagentPrss23 ISH probeAdvanced Cell
Diagnostics
447921-T6Multiplexed FISH
Sequence-based reagentBdnf ISH probeAdvanced Cell Diagnostics424821-T7Multiplexed FISH
Sequence-based reagentSlc5a5 ISH probeAdvanced Cell Diagnostics487721-T8Multiplexed FISH
Sequence-based reagentSlc17a7 ISH probeAdvanced Cell Diagnostics416631-T9Multiplexed FISH
Sequence-based reagentNnat ISH probeAdvanced Cell Diagnostics432631-T10Multiplexed FISH
Sequence-based reagentNegr1 ISH probeAdvanced Cell Diagnostics806361-T11Multiplexed FISH
Sequence-based reagentCplx1 ISH probeAdvanced Cell Diagnostics482531-T12Multiplexed FISH
Software,algorithmRhttps://www.r-project.orgSCR_001905-
Software,algorithmSeurathttps://satijalab.org/seurat/RRID:SCR_007322-
Software,algorithmFijihttps://imagej.net/FijiRRID:SCR_002285-
Software, algorithmPrismhttps://www.graphpad.com/scientific-software/prism/RRID:SCR_002798-
OtherAAV-SL1-CAG-tdTJanelia Viral Core--
OtherAAV-SL1-CAG-GFPJanelia Viral Core--
Strain, strain background Mus musculusPenk-creJanelia Research Campus--

Experimental procedures were approved by the Animal Care Committee at the University of British Columbia and the Institutional Animal Care and Use Committee at the Janelia Research Campus.

Single-cell RNA sequencing data acquisition and analysis

Request a detailed protocol

The single-cell RNA-seq dataset (5.9 ± 1.2 thousand expressed genes/cell from 123 ± 65 thousand reads/cell, mean ± SD) was generated according to a previously published protocol (Cembrowski et al., 2018a; Cembrowski et al., 2018b). To capture individual neurons from the basolateral amygdala (BLA), brain sections were obtained from five mature (≥8 weeks of age) male C57BL/6 mice. Three of these mice were WT, whereas the remaining two two mice were double-transgenic Penk2-cre x Ai14 mice (Madisen et al., 2010), used to leverage tdTomato expression to visually identify the BLA to facilitate complete microdissection (Oh et al., 2014). Cells extracted from both genetic backgrounds clustered together, indicating no effect of genetic background nor incomplete microdissection of BLA in WT mice. In all cases, the BLA was microdissected and dissociated, with manual purification (Hempel et al., 2007) used to capture cells and place into eight-well strips. For all datasets, library preparation, sequencing, and initial count-based quantification (Dobin et al., 2013; Trapnell et al., 2009) was performed according to previous methods (Cembrowski et al., 2018a). No blinding or randomization was used for the construction or analysis of this dataset. No a priori sample size was determined for the number of animals or cells to use; note that previous methods have indicated that several hundred cells from a single animal is sufficient to resolve heterogeneity within excitatory neuronal cell types (Cembrowski et al., 2018a; Cembrowski et al., 2018b). Raw and processed scRNA-seq datasets have been deposited in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus under GEO: GSE148866.

Computational analysis was performed in R (RRID:SCR_001905) (R Development Core Team, 2008) using a combination of Seurat (RRID:SCR_007322) (Satija et al., 2015) and custom scripts (Cembrowski et al., 2018a). Cells with <10,000 total counts were excluded from analysis (n = 69 of 1370 initial cells). For all remaining cells, counts were converted to Counts Per Million (CPM) for subsequent analysis. Putative non-neuronal cells (n = 19) were eliminated from the dataset by rejecting cells that exhibited CPM < 250 for Snap25, a pan-neuronal marker. For examining excitatory neurons (Figures 25), interneurons (n = 51) were eliminated from the dataset by rejecting cells that exhibited CPM > 100 for Gad1, an interneuron marker. Variable genes (n = 108) used for PCA were obtained with Seurat via FindVariableGenes(mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.125, x.high.cutoff = 3, y.cutoff = 1). Clusters were obtained with Seurat via FindClusters(reduction.type = ‘pca’, dims. use = 1:10), using resolution = 0.2 to obtain two coarse discrete clusters and resolution = 0.8 to examine finer-scale heterogeneity. For examining interneurons (Figure 6—figure supplement 3), 51 interneurons were retained (via CPM > 100 for Gad1), and variable genes and clusters were obtained via x.low.cutoff = 0.0125, x.high.cutoff = 3, y.cutoff = 3, resolution = 1, and dims. use = 1:2 parameters. In all cases, subpopulation-specific enriched genes obeying pADJ < 0.05 were obtained with Seurat via FindMarkers(), where is the pADJ is the adjusted p-value from Seurat based on Bonferroni correction. Functionally relevant differentially expressed genes were obtained using FindMarkers(), allowing for both cluster-specific enriched and depleted genes obeying pADJ < 0.05. t-SNE visualization van der Maaten and Hinton, 2008 used perplexity = 30 (interneuron analysis: 20), with 1000 iterations (sufficient for convergence) on the default seed. Qualitatively similar results were obtained for other seed values. UMAP visualization (Becht et al., 2019) was obtained via the R UMAP package, using default parameters, performed on the scaled Seurat dataset with highly variable genes. In total, this approach and associated parameters provided discrete clusters that were consistent with dimensionally reduced visualizations and robust to downsampling, and predicted organizations that were validated by complementary histological methodologies (Cembrowski and Spruston, 2017).

When plotting gene expression using t-SNE, color ranges from white (zero expression) to red (maximal expression), plotted logarithmically, with the maximum CPM value across all cells provided as an inset. For random forest classification (ClassifyCells() in Seurat), random subsets of graph-based clustered cells were taken (n = 50, 100, 200, 400, or 800 cells; n = 100 random subsets for each number of cells), and used to predict the cluster identities of the remaining cells in the dataset.

To compare our work to a recently published scRNA-seq dataset (Zeisel et al., 2018), we downloaded the ‘TEGLU22 (Excitatory neurons, amygdala)’ dataset from this publication (14,897 total cells). A minimum of 500 total counts was required to initially retain 13,826 total cells from this dataset (1.2 ± 0.7 thousand genes expressed/cell). Interneurons and non-neuronal cells were excluded via the same thresholds used in our scRNA-seq data, and excitatory neurons were further selected for by requiring CPM > 100 for the excitatory neuronal marker Slc17a7 for these remaining cells, resulting in 1975 total cells used for analysis. Subsequent analysis and visualization, including extraction of variable genes and clusters, was performed identically to our in-house dataset.

Chromogenic in situ hybridization

Request a detailed protocol

All chromogenic ISH images were obtained from the publicly available Allen Mouse Brain Atlas (AMBA) (Lein et al., 2007). At least one coronal section was selected for analysis within each of the anterior (−0.82 to −0.94 from bregma), intermediate (−1.34 to −1.70 from bregma) and posterior (−1.82 to −2.06 from bregma) regions of the BLA. To facilitate segmentation of expressing cells, images were processed with a gaussian blur filter, binarized with a manually determined intensity threshold, and adjacent cells separated with a watershed function. The locations of segmented cells between 100–400 µm2 were then measured within the BLA.

To spatially register cells to a common orientation, the BLA within each image was traced using six points, and then rotated and translated onto a BLA template using a procrustes transformation in R without scaling (Beyeler et al., 2018). The transformation was then applied to the location of segmented cells for the respective image, and then segmented cells were plotted relative to the dorsal-most point of the template. In a minority of cases, a small translation of segmented cells (typically <200 µm) was subsequently performed manually if the procrustes translation misaligned BLA borders. The number of cells within different sub-regions of the BLA were obtained after transformation of segmented cells. Sub-regional boundaries were created for each of the anterior, intermediate or posterior BLA templates, and analyses of all images within a respective BLA anterior-to-posterior region used the same sub-regional boundaries.

Fluorescent in situ hybridization

Request a detailed protocol

Two-color fluorescent ISH was performed according to previous protocols (Cembrowski et al., 2016), with extensions for multiplexed approaches (described below). All probes were purchased from Advanced Cell Diagnostics (Hayward, CA) and were as follows: Cplx1 (482531-C3) and Gad1 (400951-C2) for two-color FISH, and Nr4a2 (423351-T1), Otof (485671-T2), Cdh13 (443251-T3), Rorb (444271-T4), Adamts2 (806371-T5), Prss23 (447921-T6), Bdnf (424821-T7), Slc5a5 (487721-T8), Slc17a7 (416631-T9), Nnat (432631-T10), Negr1 (806361-T11), and Cplx1 (482531-T12) for 12-channel multiplexed FISH.

For multiplexed FISH, 12 probes with unique detection tails were hybridized to tissue and subsequently amplified. Cleavable fluorophores specifically targeting probes 1–4 were then added, the tissue stained for DAPI, and coverslipped with ProLong Gold antifade mounting medium. Within 1–2 days, tissue was imaged with a Leica SP8 confocal microscope with a 63x objective. After this first round of imaging, the coverslip was removed by soaking slides in 20x SSC, and the fluorophores cleaved via two consecutive incubations in a 10% TCEP cleaving solution. Cleavable fluorophores specific to probes 5–8 were then added, and the tissue was coverslipped and imaged. This same procedure was then repeated for probes 9–12. Each round of imaging visualized DAPI in the 405 channel, and the subsequent four probes in either Alexa 488, ATTO 550, ATTO 647N, or Alexa 750.

After the three rounds of imaging to visualize probes 1–4 (round 1), 5–8 (round 2), and 9–12 (round 3), respectively, the images were computationally analyzed via registration, segmentation and quantification from in-house code in Fiji (Schindelin et al., 2012) (RRID:SCR_002285). The registration code aligned the three rounds of imaging in X, Y, and Z coordinates by taking the maximum projections of the DAPI signal from each round, and via a fast Fourier transform (FFT), identified offsets and translated the DAPI and probe signals from the second round in X and Y. To align in the Z-axis, a slice through the original DAPI stacks was taken and coordinates for translation in Z were once again identified via FFT offset. These steps were repeated to align the third round to the first. Segmentation involved taking the maximum projection of the DAPI signal for the first round and manually thresholding to select nuclei from background. Fiji’s Watershed algorithm was run to separate any overlapping cells and then ROIs were selected using the Analyze Particles Fiji function. ROIs with an area less than 40 µm (typically reflecting partial cells) or greater than 200 µm (typically reflecting multiple abutting cells) were discarded, and the remaining ROIs dilated by a value of 5 µm to encompass the entire soma. Finally, quantification involved thresholding each of the 12 probe images to separate signal from background. Signals in the 750 channel (Rorb, Slc5a5, Cplx1) were quantified after applying a smoothing filter to reduce background. Using the ROIs selected from segmentation, the number of expressing pixels for each probe were summed and normalized by the total pixel area of the cell, producing values of counts per area (CPA).

The final dataset constituted three sections (anterior, intermediate, and posterior) from one WT mouse, with analysis of segmented, quantified cells proceeding as follows. First, non-excitatory neurons were removed by requiring Slc17a7 expression (threshold = 0.004 CPA). Cell-to-cell correlations were calculated as Pearson coefficients. For binary Cplx1 vs. Negr1 phenotyping, based upon the strongly bimodal structure of Cplx1 vs. Negr1 expression (Figure 6E), a winner-take-all strategy was used to assign each excitatory neuron to either Cplx1 or Negr1 phenotypes by identifying the gene with greater expression. Excitatory neurons that expressed neither gene were excluded from any phenotype-associated analysis (n = 364 cells). For computing cell area, maximum projection images of Slc17a7 from anterior, intermediate, and posterior sections were segmented in Fiji, similarly to DAPI. However, prior to thresholding a Gaussian blur filter was applied, but no watershed algorithm was used to prevent incorrect segmentation of cells due to the punctate in situ signal. ROIs with an area less than 75 μm2 or greater than 450 μm2 were discarded, and both Negr1 and Cplx1 signals then underwent quantification via the same approach mentioned previously. Clustering analysis pooled data from the anterior, intermediate, and posterior BLA sections. This aggregate data set was normalized such that CPA values for each cell added to equal 1. Hierarchical clustering of normalized data was performed in R, with a Euclidean distance metric and Ward’s D2 clustering method. t-SNE (perplexity = 35) was used to visualize the data in dimensionally reduced space, with resultant plots colored according to section position or cluster identity.

Viral tracing experiments and analysis

Request a detailed protocol

Mice underwent stereotaxic surgery, wherein either AAV-SL1-CAG-GFP or AAV-SL1-CAG-tdT was injected into a given region with known afferent input from the BLA; namely, the prefrontal cortex (A/P 1.7, M/L 0.60, D/V −2.3), nucleus accumbens (1.34, 1.0,–5.2), ventral hippocampus (−3.55, 3.25,–4.00), auditory cortex (−2.5, 4.5,–2.0), medial entorhinal cortex (−4.6, 3.3,–2.6) or retrosplenial cortex (−1.5, 0.5,–0.5). Dorsal/ventral coordinates were measured from the skull at site of injection (PFC, NAC, VHC), or from the pial surface (MEC, RSC). At each site, 100 nL of virus was injected over 2 min, and the needle remained at the injection site for 3 min post-injection. During surgery, mice were placed under isoflurane anesthesia, and received a local injection of bupivicane along the incision site. The day of surgery, and 2 days following surgery, mice received daily injections of Metacam for post-operative care. Mice were perfused (10 mL of PBS, followed by 50 mL of 4% PFA) and brains extracted 5–9 days following surgery to permit sufficient expression of GFP or tdT.

Brains were cryoprotected in 30% sucrose in PBS for at least 48 hr, and sectioned at either 20 µm or 100 µm using a cryostat. Sections were mounted onto glass slides and either stored at −80°C until use (20 µm sections), or were processed the following day (100 µm sections). Sections were counterstained with DAPI (1:1000) for 10 min, and coverslipped with PVA-Dabco mounting medium. The location and size of viral-labeled cell bodies was determined manually. As with chromogenic in situ hybridization, locations of labeled cells were translated onto a common BLA template. For the PFC, MEC, ACX and VHC, the location of 28–168 cells were measured per animal. For the NAC, the location of 260–469 cells were measured per animal, given a higher density of virally labelled cells compared to the other injection sites. For the RSC, 12–32 cells were measured per animal, given a lower number of virally labeled cells compared to other injection sites.

To establish a null model to compute statistically enriched projections from LA or BA, we used Slc17a7 (excitatory) neurons from LA and BA to establish chance estimates. For a given projection, N cells were selected at random, where N denotes the total number of cells from experimental data (summed across animals for a given projection). This random selection was repeated 1000 times, and the mean and 95% confidence intervals were calculated across iterations. Projections were denoted as statistically enriched if the empirically observed number fell outside of the 95% confidence interval.

Fluorescence imaging

Request a detailed protocol

All mFISH histological images were acquired with a 63x objective on a SP8 white light laser confocal microscope (Leica Microsystems, Concord, Ontario, Canada), with z-stacks with step size of 0.35 µm were acquired for each round of imaging. Due to the large number of channels (12), final images are shown by overlaying each channel in order from highest to lowest expression rather than by merging channels, with all images shown depicting maximum intensity projections of the original stacks. Single-channel aligned mFISH images are available at https://figshare.com/projects/BLA_heterogeneity/87476. All other histological images were acquired using a 20x objective on an LSM 880 confocal microscope (Carl Zeiss Microscopy, Jena, Germany). Single optical sections or maximum intensity projections are shown, with the relevant regions tiled in X and Y dimensions as needed. In some cases, channels were postprocessed in Fiji (Schindelin et al., 2012), with brightness adjustments applied to the entire image and/or pseudocoloring.

Statistical conventions

Request a detailed protocol

Central tendency and error bars denote mean ± SEM respectively, unless otherwise stated. Associated statistical parameters are reported within text or figure legends. Correlations were measured via Pearson correlation coefficients, and statistical significance was determined via unpaired Mann-Whitney U tests, unless otherwise stated. Statistical significance is denoted as follows: ns: p0.05; *: p<0.05, **: p<0.01, ***: p<0.001.

References

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
    Chapter 3 - Neuronal diversity of the amygdala and the bed nucleus of the stria terminalis
    1. A Beyeler
    2. J Dabrowska
    (2020)
    In: J. H Urban, J. A Rosenkranz, editors. Handbook of Amygdala Structure and Function. Elsevier. pp. 63–100.
  9. 9
  10. 10
  11. 11
  12. 12
  13. 13
  14. 14
  15. 15
  16. 16
  17. 17
  18. 18
    Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
    1. P Dayan
    2. LF Abbott
    (2001)
    Cambridge: Massachusetts Institute of Technology Press.
  19. 19
  20. 20
  21. 21
  22. 22
  23. 23
  24. 24
  25. 25
  26. 26
  27. 27
  28. 28
  29. 29
  30. 30
  31. 31
  32. 32
  33. 33
  34. 34
  35. 35
  36. 36
  37. 37
  38. 38
  39. 39
  40. 40
  41. 41
  42. 42
  43. 43
  44. 44
  45. 45
  46. 46
  47. 47
  48. 48
  49. 49
    The Mouse Brain in Stereotaxic Coordinates, Compact
    1. G Paxinos
    2. KBJ Franklin
    (2004)
    Amsterdam; Boston: Elsevier Academic Press.
  50. 50
  51. 51
    R: A language and environment for statistical computing, version 2.6.2
    1. R Development Core Team
    (2008)
    R Foundation for Statistical Computing, Vienna, Austria.
  52. 52
  53. 53
  54. 54
  55. 55
  56. 56
  57. 57
  58. 58
  59. 59
  60. 60
  61. 61
  62. 62
  63. 63
  64. 64
  65. 65
    Visualizing High-Dimensional data using t-SNE
    1. LJP van der Maaten
    2. GE Hinton
    (2008)
    Journal of Machine Learning Research 9:2579–2605.
  66. 66
  67. 67
    Detecting activated cell populations using Single-Cell RNA-Seq
    1. YE Wu
    2. L Pan
    3. Y Zuo
    4. X Li
    5. W Hong
    (2017)
    Neuron 96:316–329.
  68. 68
  69. 69
  70. 70
  71. 71

Decision letter

  1. Anne E West
    Reviewing Editor; Duke University School of Medicine, United States
  2. Laura L Colgin
    Senior Editor; University of Texas at Austin, United States
  3. Benjamin W Okaty
    Reviewer; Harvard Medical School, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Thank you for submitting your article "Extensive and spatially variable within-cell-type heterogeneity across the basolateral amygdala" for consideration by eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Laura Colgin as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Benjamin W Okaty (Reviewer #2).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation.

Summary:

The BLA is known to participate in a wide range of behaviors and neurons of the BLA are already known through tracing to connect to different regions. Here the authors ask whether there is heterogeneity in gene expression within different neurons of the BLA and conclude that these differences in gene expression may define neurons that participate in different BLA functions. Using scRNA-seq with in situ validation, the authors find transcriptomic separation of lateral from basal amygdala neurons as well as identifying less distinctly spatially segregated subpopulations of neurons in each of the two subdivisions. Overall this dataset will be helpful to researchers studying amygdala function.

Essential revisions:

The reviewers concurred on three points that they felt should be strengthened through text revision or data analysis to fully support the conclusions of the manuscript. I have included the full text of the reviewers' comments on these points including suggestions for further data or analyses because they offer insight into the nature of the concerns. These are just suggestions, however, and we invite the authors to decide how to respond.

1) The authors need to further justify the clustering parameters used.

One review said, "While I recognize it is not a trivial problem to identify clustering parameters to fully capture cell type diversity, I am concerned that the number of cells is in the dataset may be too low, which is limiting cluster resolution. Alternatively, the clustering parameters used for Figure 2 and beyond may not be optimized to capture more granular transcriptionally distinct cell types. Methods such as IKAP (https://www.biorxiv.org/content/10.1101/596817v1) have been developed to identify clustering parameters based on the particular dataset. Did the authors use this or some other method to arrive at their clustering parameters? Can they scientifically justify the parameters they chose?"

A second suggested, "Did the authors make any attempt to cluster cells based on mFISH CPA values of all 12 genes? If so, what did that look like; i.e. how well does this particular set of 12 genes serve to classify all six clusters identified by scRNAseq? Might it be possible to use this mFISH data to further characterize anterior/posterior or dorsal/ventral bias of finer-scale subclusters, rather than based solely on single marker genes? While the correlation analysis does a nice job of showing graded spatial heterogeneity, it would still be informative to see some sort of quantification of spatial bias of all the scRNA-seq subclusters, as inferred by mFISH."

2) A central and critical theme of the manuscript is the idea of continuous gradients of gene expression. However this concept raised two different sets of questions – one about the definition of cell types and one about the concept of continuous distributions in space.

a) While transcriptomic cell-typing is a powerful tool for neuron classification, cells that exist along a continuum in gene expression space may nonetheless constitute functionally distinct subtypes in ways that are not fully "reducible" to well-separated transcriptomic boundaries alone. Moreover, continuous expression gradients may become discretized at higher levels of regulation (e.g. a critical mRNA expression threshold may be required for protein expression, or gene/protein expression level may have some nonlinear relationship with higher-order cell phenotypes etc.). Could the authors perhaps give a more nuanced discussion of these possibilities? Specifically, I think it's important to explicitly make the distinction between transcriptomically defined neuron subtypes and subtypes defined by other criteria.

Certainly a desirable goal is to have a comprehensive measure of cell identity that subsumes multiple levels of description, and in ideal circumstances cellular transcriptome and other phenotypes may overlap in a clear-cut way. However, that may not always be the case. For example – when describing the results of Kim et al., 2016 with respect to there being functionally distinct subtypes of BLA neurons marked by expression of Ppp1r1b or Rspo2, the authors state: "In the context of our results here, we interpret these previous results as analyzing opposite extremes of a continuum; indeed, in our scRNA-seq data Ppp1r1b and Rspo2 are enriched in opposite ends of the BA transcriptomic spectrum and do not conform to distinct subtypes. Thus, results predicated on analyzing these two populations may reflect relatively arbitrary divisions of a continuum, rather than examination of well-separated subtypes."

Ppp1r1b and Rspo2 appear to be expressed at the highest levels in clusters BA1 and BA4, respectively. While the boundary between BA1 and BA4 in tSNE-space is more graded than discrete, these clusters are nonetheless separable by Louvain clustering, and random forest classification of subsampled data appears to fail mostly at the interface of these clusters, not at the "opposite extremes". Thus, if these two genes are enriched in "opposite ends of the BA transcriptomic spectrum", is it truly "arbitrary" to use these genes to parse BLA neurons into different subtypes? Moreover, Kim et al., 2016 found that neurons marked by one or the other gene exhibited different spatial biases, responded to different stimuli, and regulated different behaviors, all of which suggests that these indeed represent functionally distinct neuron subtypes.

b) My only concern relates to the way the authors discuss the concept of spatial gradients. I find the wording confusing both with respect to gene expression and with respect to the projections. One example is where the authors refer to a "continuous spectrum" of cell types defined by gene expression across the LA. This emerges again where it says "both the BA and LA exhibit variable cell-type identity that transformed continuously in space". However, especially since the authors are looking at one gene at a time, it is not clear it is quite continuous – which along with the word "graded" sounds to me like it goes from high to low so quantification would be required. With respect to the gene expression data that are analyzed by the multi-color in situ (which is a powerful experiment) it was unclear when the authors refer to "graded" versus "discrete" do they take into account levels of expression or is this just a +/- call above threshold? This is certainly a quantitative method but it is not clear in Figure 6 that the quantitative data were used. This would be nice to see and would likely make this concept more clear.

3) Two of the reviewers raised questions about BLA interneurons. The reviewers agreed that exploring interneuron transcriptomes was not essential in this paper but suggested that the authors clarify the following points:

a) The authors say they use a manual approach "that facilitates capture of excitatory neurons" but the methods offer no description of how that selectivity would be achieved other than via a careful dissection of a brain region primarily composed of those neurons. This should be clarified.

b) The paper largely ignores interneuron populations in and around the BLA. Including pericapsular GABAergic clusters. This is despite these cells being an integral component of BLA circuitry. To make the paper more comprehensive, the authors could include data from the 51 interneurons they collected, and provide some description of the cell types. If the authors cannot provide these data, it should be made explicitly clear in the text that these data are not included.

c) Subsection “Discrete separation in gene expression maps onto the lateral vs. basal amygdala nuclei” and Figure 3—figure supplement 1: If the authors want to make the strong statement that the "spill-over" is interneurons, they need to show that with double labeled in situ, not just argue that the pattern of expression is similar in number and general spatial distribution.

https://doi.org/10.7554/eLife.59003.sa1

Author response

Essential revisions:

The reviewers concurred on three points that they felt should be strengthened through text revision or data analysis to fully support the conclusions of the manuscript. I have included the full text of the reviewers' comments on these points including suggestions for further data or analyses because they offer insight into the nature of the concerns. These are just suggestions, however, and we invite the authors to decide how to respond.

1) The authors need to further justify the clustering parameters used.

One review said, "While I recognize it is not a trivial problem to identify clustering parameters to fully capture cell type diversity, I am concerned that the number of cells is in the dataset may be too low, which is limiting cluster resolution.

Generally, our plate-based manual capture approach is designed to produce high-read-depth transcriptomes, and thus is well-powered to detect gene-expression differences within profiled cells. Complementary approaches that are designed to survey a larger number of cells (e.g., droplet-based 10X Genomics Chromium) are more efficient at capturing rare cell types, but due to dropout effects and low depth, perform relatively poorly at detecting heterogeneity and subclusters within common cell types (see, e.g., “Direct Comparative Analysis of 10X Genomics Chromium and Smart-seq2”, Wang et al., bioRxiv, 2019). Thus, in principle, our approach is relatively well-positioned to reveal heterogeneity within classical types of the BLA.

In our revised manuscript, we now show this advantage is born out in practice. First, in examining transcriptomes of interneurons, we are able to identify and validate distinct interneuron subtypes despite only having tens of cells in each cluster (see new Figure 6—figure supplement 3; see also our response to comment 3). Requiring only tens of cells to resolve and validate separable clusters is also consistent with our previously published work (e.g., Cembrowski et al., 2018a; Cembrowski et al., 2018b). From these results, it seems unlikely that the >1,200 cells profiled from the BLA excitatory neuron population significantly limits cluster resolution.

To reinforce this, we also now perform a direct comparison to an existing, larger 10X

Genomics Chromium droplet-based dataset (see new Figure 2—figure supplement 2). Despite this droplet-based dataset having a great number of cells (n = 1,975 cells in this dataset; cf. 1,231 cells in our dataset), the relatively low-read-depth nature of this droplet-based dataset does not recover the clusters we discover in our work here.

Thus, multiple new lines of evidence suggest that our approach is well-suited to resolve transcriptomic heterogeneity of BLA excitatory neurons. Nonetheless, as with any transcriptomics study, there always exists the principle that acquiring more cells may illustrate further heterogeneity. We now mention this possibility, in conjunction with our above points, in our revised manuscript (see revised Discussion on “Graded spatial heterogeneity and functional implications”).

Alternatively, the clustering parameters used for Figure 2 and beyond may not be optimized to capture more granular transcriptionally distinct cell types. Methods such as IKAP (https://www.biorxiv.org/content/10.1101/596817v1) have been developed to identify clustering parameters based on the particular dataset. Did the authors use this or some other method to arrive at their clustering parameters? Can they scientifically justify the parameters they chose?"

Our approach for justifying our obtained scRNA-seq clusters is two-fold. First, we ensure agreement between clustering methods and other computational analyses. In our manuscript here, this took the form of ensuring clusters are in agreement with cluster-free visualizations of data in dimensionally-reduced space (e.g., Figure 2A, B; Figure 2—figure supplement 1), and that clusters are robust to down-sampling (e.g., Figure 2C). The parameters we chose in our manuscript here captured robust clusters across these complementary computational approaches.

Second, we next ensure that cell-type predictions from single-cell RNA-seq can be cross-validated by other experimental data. In our manuscript here, this took the form of cross-validation by the Allen Mouse Brain Atlas and mFISH (e.g., Figures 3, 5, 6). This approach, relying on different animals and methodologies (as well as laboratories, in the case of the Allen Mouse Brain Atlas), provides much stronger validation than can be provided by different analytical approaches applied to a fixed RNA-seq dataset.

In our revised manuscript, we now summarize this approach to cluster selection in our Methods section.

A second suggested, "Did the authors make any attempt to cluster cells based on mFISH CPA values of all 12 genes? If so, what did that look like; i.e. how well does this particular set of 12 genes serve to classify all six clusters identified by scRNAseq?”

We thank the reviewer for this suggestion, and in our revised manuscript, we now have performed clustering of mFISH data (new Figure 6—figure supplement 2). This analysis recapitulates the general organizational patterns we previously identified using scRNAseq and chromogenic ISH: two clusters recapitulate a BA vs. LA divide, and six clusters produces further graded heterogeneity within these discrete clusters. Thus, interestingly, this relatively small set of 12 genes is sufficient to reproduce the general organizational patterns of BLA heterogeneity.

Might it be possible to use this mFISH data to further characterize anterior/posterior or dorsal/ventral bias of finer-scale subclusters, rather than based solely on single marker genes? While the correlation analysis does a nice job of showing graded spatial heterogeneity, it would still be informative to see some sort of quantification of spatial bias of all the scRNA-seq subclusters, as inferred by mFISH.

In principle, we can use our mFISH data to infer fine-scale organization based on these 12 genes. However, we are wary to do this formally in the manuscript, as all of our analyses suggest within-BA and within-LA subclusters reflect elements of a continuum. Thus, emphasizing spatial domains of subclusters may lead the reader to incorrectly infer that subclusters reflect well-separated, distinct subtypes of cells. To avoid this potential confusion, we would prefer to avoid such a characterization.

2) A central and critical theme of the manuscript is the idea of continuous gradients of gene expression. However this concept raised two different sets of questions – one about the definition of cell types and one about the concept of continuous distributions in space.

a) While transcriptomic cell-typing is a powerful tool for neuron classification, cells that exist along a continuum in gene expression space may nonetheless constitute functionally distinct subtypes in ways that are not fully "reducible" to well-separated transcriptomic boundaries alone. Moreover, continuous expression gradients may become discretized at higher levels of regulation (e.g. a critical mRNA expression threshold may be required for protein expression, or gene/protein expression level may have some nonlinear relationship with higher-order cell phenotypes etc.). Could the authors perhaps give a more nuanced discussion of these possibilities? Specifically, I think it's important to explicitly make the distinction between transcriptomically defined neuron subtypes and subtypes defined by other criteria.

The reviewer makes valuable points concerning the definition of cell types within the brain. In our revised manuscript, we have now included a Discussion section that explicitly makes the distinction between transcriptomically defined neuron subtypes and subtypes defined by other criteria (see “Cell-type classification: transcriptomics vs. other methodologies”). This section includes the specific points raised by the reviewer here.

Certainly a desirable goal is to have a comprehensive measure of cell identity that subsumes multiple levels of description, and in ideal circumstances cellular transcriptome and other phenotypes may overlap in a clear-cut way. However, that may not always be the case. For example, when describing the results of Kim et al., 2016 with respect to there being functionally distinct subtypes of BLA neurons marked by expression of Ppp1r1b or Rspo2, the authors state: "In the context of our results here, we interpret these previous results as analyzing opposite extremes of a continuum; indeed, in our scRNA-seq data Ppp1r1b and Rspo2 are enriched in opposite ends of the BA transcriptomic spectrum and do not conform to distinct subtypes. Thus, results predicated on analyzing these two populations may reflect relatively arbitrary divisions of a continuum, rather than examination of well-separated subtypes."

Ppp1r1b and Rspo2 appear to be expressed at the highest levels in clusters BA1 and BA4, respectively. While the boundary between BA1 and BA4 in tSNE-space is more graded than discrete, these clusters are nonetheless separable by Louvain clustering, and random forest classification of subsampled data appears to fail mostly at the interface of these clusters, not at the "opposite extremes". Thus, if these two genes are enriched in "opposite ends of the BA transcriptomic spectrum", is it truly "arbitrary" to use these genes to parse BLA neurons into different subtypes? Moreover, Kim et al., 2016 found that neurons marked by one or the other gene exhibited different spatial biases, responded to different stimuli, and regulated different behaviors, all of which suggests that these indeed represent functionally distinct neuron subtypes.

The reviewer’s point is well-taken. Although ideally cell identity at the transcriptomic level would overlap with higher-order properties, there are many instances where this need not be the case. In our newly added section (“Cell-type classification: transcriptomics vs. other methodologies”; see also previous response point), we now discuss how discrete differences in higher-order phenotypes could emerge within the BA.

In particular, we suggest that this may occur for neuronal populations marked by the Ppp1r1b and Rspo2 genes, wherein a graded transcriptomic identity could form the underpinning for more step-like changes in higher-order function and behavior.

b) My only concern relates to the way the authors discuss the concept of spatial gradients. I find the wording confusing both with respect to gene expression and with respect to the projections. One example is where the authors refer to a "continuous spectrum" of cell types defined by gene expression across the LA. This emerges again where it says "both the BA and LA exhibit variable cell-type identity that transformed continuously in space". However, especially since the authors are looking at one gene at a time, it is not clear it is quite continuous – which along with the word "graded" sounds to me like it goes from high to low so quantification would be required.

We thank the reviewer for noting the confusion surrounding the use of spatial gradients in the manuscript. We have now revised our wording to remove phrases like “continuous spectrum” and “graded” when referring to chromogenic ISH, which is a non-quantitative readout of gene expression. We also now explicitly note these caveats, and use them to motivate our mFISH analyses, which provide single-molecule resolution and quantitative data.

For projection cells, spatial gradients are shown in Figure 7, and further quantified in

Figure 7—figure supplement 1. To avoid confusion, we now explicitly note that these are “cellular” gradients, as projection cell quantification occurs at a cellular level and no molecular readout is present in these datasets.

With respect to the gene expression data that are analyzed by the multi-color in situ (which is a powerful experiment) it was unclear when the authors refer to "graded" versus "discrete" do they take into account levels of expression or is this just a +/- call above threshold? This is certainly a quantitative method but it is not clear in Figure 6 that the quantitative data were used. This would be nice to see and would likely make this concept more clear.

We apologize that our wording was not clearer, and confirm that quantitative data were indeed used in all mFISH analyses. In our revised Results section, we now explicitly note the quantitative nature of the data as we introduce this method.

3) Two of the reviewers raised questions about BLA interneurons. The reviewers agreed that exploring interneuron transcriptomes was not essential in this paper but suggested that the authors clarify the following points:

As described below, we now provide written clarifications on BLA interneurons. Moreover, although we concur that transcriptomic exploration of BLA interneurons was not necessarily essential in this work, we have nonetheless performed additional experiments and analysis examining BLA interneurons. This has resulted in two new supplementary figures in our revised manuscript (see new Figure 2—figure supplement 2 and Figure 6—figure supplement 3), which we believe further strengthen our manuscript. These results are described in depth in points (b) and (c) below.

a) The authors say they use a manual approach "that facilitates capture of excitatory neurons" but the methods offer no description of how that selectivity would be achieved other than via a careful dissection of a brain region primarily composed of those neurons. This should be clarified.

We now clarify this statement in our revised Results section, emphasizing that we primarily collect excitatory neurons due to their general prevalence, as well as their viability post-dissociation.

b) The paper largely ignores interneuron populations in and around the BLA. Including pericapsular GABAergic clusters. This is despite these cells being an integral component of BLA circuitry. To make the paper more comprehensive, the authors could include data from the 51 interneurons they collected, and provide some description of the cell types. If the authors cannot provide these data, it should be made explicitly clear in the text that these data are not included.

This point is well-taken and in our revised manuscript we now include analysis of the 51 interneurons we collected (see new Figure 6—figure supplement 3A-C). We have additionally complemented this new work with further analysis using chromogenic ISH (see new Figure 6—figure supplement 3D, E). Of particular note, this new analysis identifies two spatially distinct subtypes of interneurons, one of which is the pericapsular GABAergic clusters noted by this reviewer.

c) Subsection “Discrete separation in gene expression maps onto the lateral vs. basal amygdala nuclei” and Figure 3—figure supplement 1: If they authors want to make the strong statement that the "spill-over" is interneurons they need to show that with double labeled in situ, not just argue that the pattern of expression is similar in number and general spatial distribution.

As requested, in our revised manuscript we have now performed new experiments using double-label in situ hybridization. Results from these experiments are consistent with interneuron spill-over, as postulated in our original manuscript (see new Figure 3—figure supplement 2).

https://doi.org/10.7554/eLife.59003.sa2

Article and author information

Author details

  1. Timothy P O'Leary

    Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, Canada
    Contribution
    Formal analysis, Investigation, Visualization, Methodology, Writing - review and editing
    Contributed equally with
    Kaitlin E Sullivan
    Competing interests
    No competing interests declared
  2. Kaitlin E Sullivan

    Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, Canada
    Contribution
    Formal analysis, Visualization, Methodology, Writing - review and editing
    Contributed equally with
    Timothy P O'Leary
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8043-7111
  3. Lihua Wang

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Contribution
    Resources, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  4. Jody Clements

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Contribution
    Resources, Data curation, Software, Writing - review and editing
    Competing interests
    No competing interests declared
  5. Andrew L Lemire

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Contribution
    Resources, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  6. Mark S Cembrowski

    1. Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, Canada
    2. Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    3. Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada
    4. School of Biomedical Engineering, University of British Columbia, Vancouver, Canada
    Contribution
    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing
    For correspondence
    mark.cembrowski@ubc.ca
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8275-7362

Funding

Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-04507)

  • Mark S Cembrowski

Canadian Institutes of Health Research (PJT-419798)

  • Mark S Cembrowski

Canada Foundation for Innovation (John R. Evans Leaders Fund 38369)

  • Mark S Cembrowski

University of British Columbia

  • Mark S Cembrowski

Howard Hughes Medical Institute

  • Mark S Cembrowski

Michael Smith Foundation for Health Research (SCH-2020-0383)

  • Mark S Cembrowski

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

The authors thank members of the Cembrowski laboratory for helpful discussion and comments on the manuscript. Funding was provided by the University of British Columbia (Department of Cellular and Physiological Sciences, Djavad Mowafaghian Centre for Brain Health, and the Faculty of Medicine Research Office to M.S.C.), the Natural Sciences and Engineering Resource Council of Canada (RGPIN-2019–04507 to M.S.C.), the Canadian Institutes of Health Research (PJT-419798 to M.S.C.), the Canadian Foundation for Innovation (John R Evans Leaders Fund 38369 to M.S.C.), the Michael Smith Foundation for Health Research (SCH-2020–0383 to M.S.C.), and the Howard Hughes Medical Institute.

Ethics

Animal experimentation: Experimental procedures were approved by the Animal Care Committee at the University of British Columbia (A18-0267; A18-0285) and the Institutional Animal Care and Use Committee at the Janelia Research Campus (11-78).

Senior Editor

  1. Laura L Colgin, University of Texas at Austin, United States

Reviewing Editor

  1. Anne E West, Duke University School of Medicine, United States

Reviewer

  1. Benjamin W Okaty, Harvard Medical School, United States

Publication history

  1. Received: May 16, 2020
  2. Accepted: August 26, 2020
  3. Accepted Manuscript published: September 1, 2020 (version 1)
  4. Version of Record published: September 11, 2020 (version 2)

Copyright

© 2020, O'Leary et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,655
    Page views
  • 254
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Neuroscience
    Tobias Lieblein et al.
    Research Article

    The formation of oligomers of the amyloid-β peptide plays a key role in the onset of Alzheimer's disease. We describe herein the investigation of disease-relevant small amyloid-β oligomers by mass spectrometry and ion mobility spectrometry, revealing functionally relevant structural attributes. In particular we can show that amyloid-β oligomers develop in two distinct arrangements leading to either neurotoxic oligomers and fibrils or non-toxic amorphous aggregates. Comprehending the key-attributes responsible for those pathways on a molecular level is a pre-requisite to specifically target the peptide's tertiary structure with the aim to promote the emergence of non-toxic aggregates. Here we show for two fibril inhibiting ligands, an ionic molecular tweezer and a hydrophobic peptide that despite their different interaction mechanisms, the suppression of the fibril pathway can be deduced from the disappearance of the corresponding structure of the first amyloid-β oligomers.

    1. Neuroscience
    Seung-Eon Roh et al.
    Research Article Updated

    Climbing fibers (CFs) generate complex spikes (CS) and Ca2+ transients in cerebellar Purkinje cells (PCs), serving as instructive signals. The so-called 'all-or-none' character of CSs has been questioned since the CF burst was described. Although recent studies have indicated a sensory-driven enhancement of PC Ca2+ signals, how CF responds to sensory events and contributes to PC dendritic Ca2+ and CS remains unexplored. Here, single or simultaneous Ca2+ imaging of CFs and PCs in awake mice revealed the presynaptic CF Ca2+ amplitude encoded the sensory input’s strength and directly influenced post-synaptic PC dendritic Ca2+ amplitude. The sensory-driven variability in CF Ca2+ amplitude depended on the number of spikes in the CF burst. Finally, the spike number of the CF burst determined the PC Ca2+ influx and CS properties. These results reveal the direct translation of sensory information-coding CF inputs into PC Ca2+, suggesting the sophisticated role of CFs as error signals.