DOI: 10.5281/zenodo.15006781
title: Opioid-driven disruption of the septum reveals a role for neurotensin-expressing neurons in withdrawal

description: This repository includes processed snRNAseq .rds files and processed HCR dataframes, pertinent to Simon et al., 2025. Relevant code can be found on Github: 

snRNAseq files

LS_sal - saline treated mice

LS_mor - morphine treated mice

LS_nal - morphine + naloxone treated mice

LS_one_mor - one morphine injection

LS_nal_no_mor - one naloxone injection, morphine naive

IEGs.xlsx - list of IEGs pulled from 

Correlation_mapping_results - Correlation mapping to the ABC atlas

HCR files

object_results_WT "" - base HCR experiment, raw data separated by animal

spot_counts.rds - base HCR experiment and correlation analysis

avg_intensity.rds - base HCR experiment

cell_intensity.rds - base HCR experiment

spots.rds - IEG HCR experiment

 
description: Because opioid withdrawal is an intensely aversive experience, persons with opioid use disorder (OUD) often relapse to avoid it. The lateral septum (LS) is a forebrain structure that is important in aversion processing, and previous studies have linked the LS to substance use disorders. It is unclear, however, which precise LS cell types might contribute to the maladaptive state of withdrawal. To address this, we used single-nucleus RNA-sequencing to interrogate cell type specific gene expression changes induced by chronic morphine and withdrawal. We discovered that morphine globally disrupts the transcriptional profile of LS cell types, but neurotensin-expressing neurons (Nts; LS-Nts neurons) are selectively activated by naloxone. Using two-photon calcium imaging and ex vivo electrophysiology, we next demonstrated that LS-Nts neurons receive enhanced glutamatergic drive in morphine-dependent mice and remain hyperactivated during opioid withdrawal. Finally, we showed that activating and silencing LS-Nts neurons during opioid withdrawal regulates pain coping behaviors and sociability. Together, these results suggest that LS-Nts neurons are a key neural substrate involved in opioid withdrawal and establish the LS as a crucial regulator of adaptive behaviors, specifically pertaining to OUD.
description: Single-nucleus RNA-sequencing and library prep. A total of 30 wildtype mice (~8 weeks) were used for single-nucleus RNA-sequencing. Animals were assigned to one of the following groups: saline (saline only), chronic morphine (escalating morphine), naloxone (escalating morphine plus naloxone), acute morphine (saline 13x, one dose of 10 mg/kg morphine) and naloxone only (saline 13x, one dose of 1 mg/kg naloxone). Each experimental group included 3 male and 3 female mice that were pooled into the same reaction. 

Each day for 3 days prior to tissue collection, all animals were habituated to the procedure room for ~2 hours to control for environmentally-induced immediate early gene activity. Our procedure for tissue collection has been detailed previously30,31. On the day of sacrifice, mice were sequentially euthanized with sodium pentobarbital (> 90 mg/kg Somnasol) and transcardially perfused with ice-cold NMDG (96 mM NMDG, 2.5 mM KCl, 1.35 mM NaH2PO4, 30 mM NaHCO3, 20 mM HEPES, 25 mM glucose, 2 mM thiourea, 5 mM Na+ ascorbate, 3 mM Na+pyruvate, 0.6 mM glutathione-ethyl-ester, 2 mM N-acetyl-cysteine, 0.5 mM CaCl2, 10 mM MgSO4; pH 7.35–7.40, 300-305 mOsm, continuously oxygenated with 95% O2 and 5% CO2). This NMDG solution was also used for tissue sectioning and for a collection bath. Both the perfusion solution and recovery bath included an inhibitor cocktail containing 500 nM TTX, 10 μM APV, 10 μM DNQX, 5 μM actinomycin, and 37.7 μM anysomycin, as described previously30. 250 μm thick coronal sections containing septum were rapidly harvested using a Leica Vibratome (VT1200). The septal complex was then rapidly microdissected, flash-frozen on powdered dry ice, and stored at -80 °C. We intended to reduce nucleus accumbens contamination as much as possible. To this end, our tissue sections primarily ranged from +1.10 mm through 0.0 mm with respect to Bregma. We also excluded the medial septum and diagonal band level to and ventral to the accumbens. Once tissue from all animals were collected and stored, we proceeded with nuclear isolation, a protocol we adapted from previously-published papers89,90. The following steps were all performed on ice. Also note that the PBS used here was Ca2+ and Mg2+ free. For each experimental group, we pooled all mice into the same tube containing a hypertonic lysis buffer (10mM Tris-HCl, 10 mM NaCl, 3 mM MgCl2, 0.1% Igepal, 0.2 U/μl Lucigen NxGen Rnase inhibitor). Nuclei were extracted using a glass dounce homogenizer (DWK, #885300-0002) via 5-10 reps of pestle A and 5 reps of pestle B. Nuclei were then incubated in lysis buffer for 5 min. Next, the nuclear extract was moved into a clean tube, diluted to ~5-6 ml using a wash buffer (1X PBS, 1% BSA, 0.2 U/μl Rnase inhibitor), and strained into a new 50 ml conical tube using a 40 μm cell strainer (pluriSelect, # 43-50040-51). Strained nuclei were centrifuged for 10 min, 500 x g at 4 °C. The resulting pellet was then retained. To purify our nuclei, we used density gradient centrifugation, where we prepared a two-layer column containing a 25% and 30% iodixanol (OptiPrep, Sigma-Aldrich, #D1556) gradient. Briefly, we suspended the nuclear pellet in a homogenate solution (0.25M Sucrose, 25 mM KCl, 5 mM MgCl2, 20 mM Tris-HCl, pH 7.8-8.0, and 0.2 U/μl Rnase inhibitor), and combined the resuspended pellet 1:1 with a 50% iodixanol solution (50% OptiPrep, 25 mM KCl, 5 mM MgCl2, 20 mM Tris-HCl, 1% BSA, 0.2 U/μl Rnase inhibitor, pH 7.8-8.0) in a clean 15-ml conical tube. This creates a 25% iodixanol layer. Next, we prepare a 30% iodixanol layer (30% OptiPrep, 25 mM KCl, 5 mM MgCl2, 20 mM Tris-HCl, 1% BSA, 0.2 U/μl Rnase inhibitor) and underlaid the 25% iodixanol layer with this 30% layer using a 18G needle. This density gradient column was then centrifuged for 15 min, 3000 x g at 4 °C. The resulting purified and invisible pellet was then resuspended in wash buffer. Nuclei were stained with propidium iodide and assessed for yield and quality using a disposable Neubauer hemocytometer (INCYTO, #DHC-N01) on a fluorescent microscope (Zeiss ApoTome.2). Final nuclear concentration was adjusted to 1,000 nuclei per μl in preparation for single nucleus capture.

Note that the discrepancy in kit version and sequencing platform may have obstructed our ability to compare the newer samples the older samples (saline, chronic morphine, withdrawal). Single nuclei were captured, and experimental libraries were prepared using 10X Genomics Chronium Single Cell 3′ V3 Kit (older samples) or 10X Genomics Chromium 3’ NextGEM kit (newer samples). We followed the manufacturer’s protocol and recommendations. Briefly, we captured ~10,000 barcoded nuclei for each experimental group and reverse-transcribed captured mRNAs to cDNA. cDNA libraries were PCR amplified, fragmented and ligated with sequencing adaptors. cDNA libraries from the older samples were sequenced on the Illumina 4400 HiSeq platform, whereas the newer samples were sequenced on the NovaSeq PE150 platform. Each sample was sequenced to a target output of ~350,000,000 reads. When introduced the two new samples (One Mor and Nal Only) into the original dataset (Sal, Mor, and Nal), cells were stably integrated into our existing clusters, which is why we retained these cells for our anatomical analyses. However, when we attempted to identify differentially expressed genes between our Sal group and One Mor or Nal only groups, we found that One Mor (acute morphine for 1 hour) induced 1,975 genes and Nal only (acute Nal only for 1 hour) induced 1,822. We found this to be unlikely to be true, because 1 week of morphine escalation (Mor) induced only 1,002 genes (Figure S2E-F). We believe these discrepancies may be because of differences in 10x Genomics kit versions (V3 vs NextGEM) and sequencing platform. Although there may be methods to extract some information from the newer samples, we decided that focusing on the Sal, Mor, and Nal groups for the remainder of the paper.

HCR procedure. 4 male and 4 female mice were anesthetized with isoflurane and rapidly decapitated. Brains were flash-frozen within powdered dry ice and stored at -80 °C. We collected 20 μm coronal sections directly onto glass slides using a Leica CM3050S, and each slide possessed LS-containing sections from ~+1.10 through ~+0.0 Bregma. 1 slide per animal underwent HCR. We used both standard and custom-designed probes provided by Molecular Instruments, and we followed their protocol with some modifications described in our previously-published work30. Probes used for this experiment included Nts, Sst, Crhr2, Drd2, Drd3, Foxp2, Esr1, Met, Tacr1, Pax6, Onecut2, Samd3, Slc32a1, and Slc17a6; we also used Fos in activation experiments. In brief, we fixed tissue sections with 4% PFA for 30 min, and performed successive dehydration steps using 50%, 75% and 100% x 2 EtOH. Protease IV (ACDBiosystem) was used to digest tissue for 5 min, and probes were hybridized to tissue at 37 °C. We quenched autofluorescence (Vector labs #SP-8400) and mounted using VectaShield Vibrance antifade mounting media. We acquired images on a Zeiss ApoTome.2 at 10x magnification using Zen (Blue edition). Following each round, tissue was treated with DNase I (Sigma-Aldrich, #4716728001) to deplete the previous round’s probes. To register all images for each section onto the same plane, we took brightfield images, which acted as the reference for each round. To validate the enrichment of Fos in LS-Nts and LS-Met cells following naloxone-induced withdrawal, we performed HCR on tissue from 4 mice treated with saline, 4 mice treated with chronic morphine, and 4 treated with chronic morphine plus naloxone. Each group contained 2 males and 2 females, and every slide contained 1 male and 1 female from the same group (2 slides per group, 4 mice per group). We were interested in the expression of both Met and Nts, which each ranged throughout the bulk of the septal complex, but we were limited in the number of sections we could test. Therefore, we collected serial sections from ~+0.85 through ~+0.0 Bregma, and 2 sections per mouse were retained for analysis. We imaged these sections at 20x magnification to resolve puncta.

Single-cell RNA-sequencing clustering and gene expression analysis. Once sequenced, reads were aligned to a pre-mRNA reference genome using the 10X Genomics’ Cell Ranger v3 pipeline, which provided digital expression matrices that were used for downstream analysis. Clustering, sample integration, cell type identification, and differential gene expression identification were all carried out using the Seurat V3 R package93. 

Filtering and quality control. We have previously published our general filtering parameters30,31, but in brief, genes expressed in fewer than 3 cells were removed from the dataset. We next retained cells that possessed between 500 and 25,000 UMIs and fewer than 1% mitochondrial read enrichment. Although we included the cutoffs normally used for scRNA-seq, snRNAseq data are mostly depleted of mitochondrial reads. Doublets were then computationally predicted and removed by the DoubletDecon R package23 using the default parameters.

Sample integration and clustering. Once putative doublets were omitted, we utilized Seurat’s integration algorithm based on canonical correlation analysis24 and mutual nearest neighbor analysis25. Gene transcript counts were scaled for total sequencing depth (factor of 10,000) and then natural-log transformed. Integration anchors were identified for each dataset and transformed by a correction vector to produce a “corrected” expression matrix, on which we performed principal component analysis (PCA). We used the first 30 PCs to identify cell clusters (using Louvain, resolution 0.80) and to plot their proximity in space using Uniform Manifold Approximation and Projection (UMAP). We evaluated the expression of canonical neuronal markers (e.g. Stmn2 and Thy1) to identify neuronal populations, and proceeded to subset these neurons out of the data and performed re-clustering to reveal intra-neuronal heterogeneity. In re-clustering neurons, we normalized and scaled expression data using scTransform94, which constructs a generalized linear model (GLM) to dissociate sequencing depth from gene expression without reducing cell type heterogeneity. We integrated datasets and clustered individual neurons based on these transformed data as described before, but used the original, linearly-scaled and normalized RNA values in downstream expression analyses.

To determine the optimal clustering resolution for our data (i.e. number of bioinformatically-determined neuron types), we utilized the Clustree R package95. This approach allowed us to assess the stability of clusters over a series of clustering granularities. We determined 14 clusters (2 glutamatergic and 12 GABAergic at a resolution of 0.20) to be optimal because it produced stable clusters with neurons yielding from each experimental group. Although it appears that perhaps a lower resolution would produce even more stable clusters (resolution = 0.10), we selected the higher value because we believe that Gaba12 neurons are bona-fide septal cells, distinct from Gaba7. They’re however rare, and so do not separate as readily as other cells might. Next, to identify potential molecular markers for each cell cluster, we computed the average expression of a given gene in one cluster, and then compared it to the expression of that gene in all other clusters combined. P-values were computed using Wilcoxon rank sum test and corrected for the total number of genes tested.