Brain-wide cell-type-specific noradrenergic modulation of the transcriptome

Brain-wide cell-type-specific noradrenergic modulation of the transcriptome | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Brain-wide cell-type-specific noradrenergic modulation of the transcriptome View ORCID Profile Cody Slater , View ORCID Profile Timothy Lantin , View ORCID Profile Steven M. Wellman , Litian Jia , Gabriel Gonzalez , View ORCID Profile José L. McFaline-Figueroa , View ORCID Profile Qi Wang doi: https://doi.org/10.1101/2025.05.09.653136 Cody Slater 1 Vagelos College of Physicians and Surgeons, Department of Biomedical Engineering, Columbia University , New York, NY Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Cody Slater Timothy Lantin 1 Vagelos College of Physicians and Surgeons, Department of Biomedical Engineering, Columbia University , New York, NY Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Timothy Lantin Steven M. Wellman 1 Vagelos College of Physicians and Surgeons, Department of Biomedical Engineering, Columbia University , New York, NY Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Steven M. Wellman Litian Jia 1 Vagelos College of Physicians and Surgeons, Department of Biomedical Engineering, Columbia University , New York, NY Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gabriel Gonzalez 1 Vagelos College of Physicians and Surgeons, Department of Biomedical Engineering, Columbia University , New York, NY Find this author on Google Scholar Find this author on PubMed Search for this author on this site José L. McFaline-Figueroa 1 Vagelos College of Physicians and Surgeons, Department of Biomedical Engineering, Columbia University , New York, NY Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for José L. McFaline-Figueroa Qi Wang 1 Vagelos College of Physicians and Surgeons, Department of Biomedical Engineering, Columbia University , New York, NY Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Qi Wang For correspondence: qi.wang{at}columbia.edu Abstract Full Text Info/History Metrics Preview PDF ABSTRACT Neuromodulatory systems such as the locus coeruleus–norepinephrine (LC-NE) system exert a widespread influence on brain function, yet the transcriptional consequences of such neuromodulatory perturbations remain largely unknown across the many unique cell types in the brain. In this study, we establish a generalizable framework to map brain-wide, cell-type-specific gene expression changes in mice following in vivo chemogenetic activation or inhibition of LC neurons. Single-nucleus RNA sequencing revealed that LC perturbation induces widespread but highly cell type- and region-specific transcriptional program changes, shaped by the distribution of adrenergic receptor subtypes. These findings support a model in which a shared global signal of neuromodulatory tone can produce discrete, context-dependent cellular outcomes through distinct molecular gating mechanisms of cell-type-specific adrenergic receptor subtype combinations. By establishing gene expression as a quantifiable metric of neuromodulatory control, this study lays the foundation for transcriptionally informed interventions capable of modulating brain functions with cellular precision. Introduction Neuromodulators play a critical role in mediating the flexibility of neural information processing underlying various perceptual and cognitive functions [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. Numerous molecules, including biogenic amines, neuropeptides, cytokines, and gases, act in concert to dynamically modify circuit output [ 3 , 12 , 8 , 13 , 14 , 15 ]. The result is a functional reconfiguration of otherwise hard-wired networks through the alteration of properties within individual neurons, on synapses, and across the local microenvironment [ 3 , 16 , 17 , 18 , 19 , 20, 21 ]. Most neuromodulatory systems use a volumetric mode of signaling instead of direct synaptic transmission, facilitating simultaneous influence over a broad population of cells [ 8 , 18 , 9 , 22 , 23 ]. This mechanism affects control over global patterns of brain activity and plays a critical role in switching between distinct brain states (e.g., sleep vs. arousal) and the transition between them [ 5 , 24 , 25 , 26 , 27 ]. A comprehensive understanding of the role neuromodulation plays in healthy brain function is far from complete and is impeded by the complexity of how these molecules shape neural activity. In mammals, the ascending arousal system comprises the most well-characterized set of neuromodulators and includes the noradrenergic, serotonergic, dopaminergic, and cholinergic systems [ 28 , 8 ]. The noradrenergic system is of particular interest due to its largely singular source, from one of the smallest but most extensively projecting nuclei in the brain. The primary source of norepinephrine (NE) is the locus coeruleus (LC), which is a small, bilateral pontine nucleus. Its axons project broadly throughout the forebrain, midbrain, and hindbrain, exerting widespread influence over arousal, learning, and sensory processing. [ 4 , 28 , 16 , 29 , 30 , 11 ]. Individual LC-NE neurons receive and integrate information from many brain regions and, likewise, broadcast widely across mostly non-specific projections [ 16 ]. As such, each LC-NE neuron integrates many inputs into a signal that is distributed widely throughout the brain. This input-output relationship is largely homogeneous, though there is evidence of some additional sub-circuit specificity, such as medulla-projecting LC-NE neurons receiving disproportionately less input from the central amygdala [ 16 ]. Others have described a limited modularity of the LC, including that individual LC neurons collateralize to innervate functionally related circuits such as somatosensory thalamus and somatosensory cortex [ 31 ]. The extent to which LC-NE sub-circuits can facilitate heterogeneous output remains insufficient to explain the full scope of context-dependent NE modulation. This is due, in part, to the diverse array of cell types with unique molecular and functional properties [ 32 ]. The noradrenergic system exerts its influence through a differentially expressed array of competing receptor subtypes that alter the intrinsic or extrinsic properties of a defined neural circuit in a region-specific manner [ 18 ]. These NE-mediated changes can be rapid and reversible or long-lasting and persistent, depending on the context and duration of signaling. In this setting, cell-type-specific adrenergic receptor (AR) subtypes play an important role in receiving and decoding global LC-NE signaling in a region- and circuit-specific manner. ARs are G protein-coupled receptors (GPCRs) divided into two main classes: alpha-adrenergic receptors ( α ARs) and beta-adrenergic receptors ( β ARs). Each class is further divided into subtypes, with α ARs including α 1 a , α 1 b , α 1 d , α 2 a , α 2 b , and α 2 c , and β ARs including β 1 , β 2 , and β 3 [ 33 , 34 , 35 ]. α 1 ARs activate the P LC - IP 3 - DAG signaling pathway through a G q/ 11 protein subunit, leading to increased intracellular calcium levels, activation of protein kinase C (PKC), and regulation of calcium-sensitive processes [ 36 , 37 ]. α 2 ARs inhibit adenylate cyclase through a G i subunit, decreasing cyclic adenosine monophosphate (cAMP) levels and protein kinase A (PKA), as well as activating K + and inhibiting L- and N-type Ca 2 + channels through the βγ and G O subunits, respectively, of the G i GPCR [ 38 , 37 ]. In direct contrast, all β ARs act through a G s GPCR subunit to activate adenylate cyclase, increase cAMP levels and promote neurotransmitter release [ 39 , 40 , 37 ]. The relative affinities of each subtype for NE is estimated to be α 2 > α 1 » β 1 > β 2 > β 3 , with α 1 ARs showing the strongest localization to specifically post-synaptic neurons [ 41 , 42 , 43 ]. The recent completion of several brain-wide transcriptomic atlases, such as the Allen Brain Cell (ABC) atlas [ 44 ], provides an unprecedented view of the molecular identity of individual cell types across functional subdivisions of the mouse brain [ 45 , 46 , 47 , 44 ]. The result is a high-resolution description of each transcriptionally distinct cell type, including cell-type-specific expression of certain receptors, such as ARs. These molecular atlases offer a new opportunity to observe a global view of neuromodulation. Volumetric release of NE exerts direct input on cells that express ARs within local circuits. The result of that input is determined by the functional properties of the receiving cell type and communication within the local network. There is now a substantial need to connect these descriptive atlases with methods to measure the impact on each cell type in a global manner. Recent advances in single-cell and single-nucleus RNA sequencing (snRNA-seq) and in vivo chemogenetic manipulation provide a powerful platform for mapping the transcriptional consequences of neuromodulatory signaling across the whole brain in a single experiment. To obtain a systematic understanding of how discrete modes of noradrenergic activation or inactivation affects the brain wide molecular states across diverse cell populations, we have developed a generalizable transcriptomic framework for profiling cell type- and region-specific gene expression changes following defined neuromodulatory perturbations. As a proof-of-principle, the murine LC-NE system was activated or inactivated in vivo using DREADDs (Designer Receptors Exclusively Activated by Designer Drugs), and neural tissue sampled from across the brain was profiled using snRNA seq. Our data offer molecular insights into how the LC exerts its broad physiological effect and demonstrates the utility of using transcriptomic readouts in intact, perturbed neural circuits to reveal the global influence of neurochemicals in the brain. By establishing gene expression as a measurable and controllable substrate for neural manipulation, this work lays a foundation for neuromodulatory therapies and brain-machine interfaces (BMIs) that can facilitate a more regulated and predictable control over functionally relevant volumetric neurotransmission. Results A customized pipeline enables in vivo noradrenergic modulation and brain-wide single-nucleus RNA profiling The LC-NE system is known to achieve a diverse set of functions in part through distinct modes of activity. Short-term periods of tonic LC hyperactivity are known to occur during events of high arousal or acute stress and can last for hours to days, resulting in physiological and behavioral changes. Similarly, LC-NE hypoactivity is observed in several pathologies, including narcolepsy, depression, and during a coma. To mimic such events and facilitate a comprehensive characterization of brain-wide transcriptional changes associated with LC-NE hyper- or hypoactivity, we used virus delivered Cre-dependent DREADDs to chemogenetically activate LC-NE neurons in Dbh cre mice ( Figure 1a, b ) . We profiled tissue uniformly sampled from a single hemisphere across all brain regions in eight female mouse brains. Download figure Open in new tab Figure 1: Experimental workflow and single-nucleus RNA sequencing library prep. a) Three groups of Dbh-Cre mice received bilateral LC injections of Gq-DREADD, Gi-DREADD, or saline. b) Representative histological images of the LC. c) Experimental timeline. d) EasySci combinatorial indexing library preparation. To characterize brain-wide transcriptional changes following LC activation, mice were bilaterally injected with adeno associated viruses (AAVs) encoding Gq- or Gi-coupled DREADD targeting LC noradrenergic neurons and administered JHU37160 (a DREADD agonist) twice per day for three days prior to sacrifice ( Figure 1c ). snRNA-seq was performed on representative sections of the whole brain, resulting in the recovery of ∼55k high-quality nuclei across 8 samples ( Figure 1d ) . A UMAP clustering of nuclei revealed clean separation between classes according to the conventions of the Allen Brain Cell Atlas ( Figure 2a ) . Cell class identities were defined using hierarchical clustering and marker gene expression, capturing a broad diversity of neuronal and non-neuronal populations. Nuclei were annotated by transcriptomic class ( Figure 2b ) , major brain region ( Figure 2c ) , and neurotransmitter identity ( Figure 2d ) . Download figure Open in new tab Figure 2: Clustering and composition of recovered nuclei. a) UMAP clustering labeled by cell type class. b) Composition of recovered nuclei by class, c) major brain region, and d) neurotransmitter type. Nuclei were assessed on standard measures of quality control including the distribution of unique molecular identifiers (UMIs) per nucleus and the distribution of features per nucleus. Distributions were visualized across the neuronal and non-neuronal divide ( Figure 3a ) , treatments ( Figure 3b ) , classes ( Figure 3c ) , and brain regions ( Figure 3d ) . Download figure Open in new tab Figure 3: UMI and unique gene distributions. a) UMI and unique gene distributions between nuclei of nonneuronal and neuronal cells, b) treatment groups, c) major brain regions, and d) classes. Adrenergic receptor subtype expression is highly cell-type-specific Expression levels of alpha- and beta-adrenergic receptors were quantified across classes ( Figure 4 ) . Receptor expression patterns were heterogeneous, with classes expressing varied levels of α 1 , α 2 , or β receptors. In some classes, certain receptor subtypes were expressed at far lower amounts than other adrenergic receptor subtypes. For example, there is a distinct lack of the α 2 b , α 2 c , and β 2 subtypes in the 30 Astro-Epen class, whereas 01 IT-ET Glut neurons express these adrenergic receptor subtypes. This finding suggests that different classes of cells exposed to the same levels of free norepinephrine may produce vastly different cellular outcomes. Download figure Open in new tab Figure 4: Adrenergic receptor subtype distribution across classes. Differential gene expression analysis reveals that LC activation affects the expression of more genes than LC inhibition Differential gene expression analysis was performed to assess the impact of sustained locus coeruleus (LC) hyperactivity across annotated cell classes ( Figure 5a,b ) . Among all populations, three classes exhibited a relatively high number of differentially expressed genes in response to tonic LC hyperactivation. Download figure Open in new tab Figure 5: Differential gene expression summary, tonic LC hyperactivity vs. controls. a) DEG summary across classes. Top: Number of differentially expressed genes across classes. Middle: Breakdown of nuclei proportion by brain region across classes. Bottom: Breakdown of nuclei proportion by treatment group across classes. b) DEG summary across dominant neurotransmitter identities. Top: Number of differentially expressed genes across dominant neurotransmitter identities. Middle: Breakdown of nuclei proportion by brain region across dominant neurotransmitter identities. Bottom: Breakdown of nuclei proportion by treatment group across dominant neurotransmitter identities. c-e) Volcano plots for the top three classes by number of differentially expressed genes. Significance thresholds set to absolute log 2 fold change > 2 and adj. p-value < 0.05. In 01 IT-ET Glut neurons, elevated expression of Hmbs, Ccrn2 , and Tex50 was observed, suggesting that long range intratelencephalic and extratelencephalic projection neurons undergo transcriptional alterations associated with metabolic and transcriptional regulation ( Figure 5c ) . In 05 OB-IMN GABA neurons, which includes neurons of the olfactory bulb, increased expression of Gm45893, Wrap73 , and Gnpda1 was detected, consistent with shifts in RNA processing and amino sugar metabolism ( Figure 5d ) . In 30 Astro-Epen glial population, encompassing astrocytes and ependymal cells, Ola1, Map7d2 , and Dok5 were among the genes upregulated, pointing to changes in cytoskeletal dynamics and MAPK signaling ( Figure 5e ) . Comparatively fewer DEGs were identified from tonic LC hypoactivity analysis ( Figure 6a,b ) . In 01 IT-ET Glut neurons, downregulation of Eif4ebp1 and upregulation of Gm24407 , and Acr was detected, indicating potential shifts in synaptic architecture ( Figure 6c ) . In 22 MB-HB Sero neurons, a midbrain-hindbrain serotonergic class, increased expression of Cpne7 and Slc8a1 was observed, suggesting alterations in calcium handling and membrane excitability in neuromodulatory brainstem circuits ( Figure 6d ) . Within the 30 Astro Epen population, decreased expression of Prlr and increased expression of Pde10a was identified, implicating effects on prolactin signaling and cyclic nucleotide metabolism in astroglial function ( Figure 6e ) . Download figure Open in new tab Figure 6: Differential gene expression summary, tonic LC hypoactivity vs. controls. a) DEG summary across classes. Top: Number of differentially expressed genes across classes. Middle: Breakdown of nuclei proportion by brain region across classes. Bottom: Breakdown of nuclei proportion by treatment group across classes. b) DEG summary across dominant neurotransmitter identities. Top: Number of differentially expressed genes across dominant neurotransmitter identities. Middle: Breakdown of nuclei proportion by brain region across dominant neurotransmitter identities. Bottom: Breakdown of nuclei proportion by treatment group across dominant neurotransmitter identities. c-e) Volcano plots for the top three classes by number of differentially expressed genes. Significance thresholds set to absolute log 2 fold change > 2 and adj. p-value < 0.05. These findings demonstrate that diminished tonic norepinephrine release leads to class-specific transcriptional adapta tions, with differential sensitivity across neuronal and glial populations. Together, the data suggest that the absence of noradrenergic tone reconfigures homeostatic signaling pathways in a manner distinct from the gene programs engaged by LC hyperactivity. Gene set enrichment analysis reveals perturbation-specific functional programs Gene set enrichment analysis revealed that tonic LC hyperactivity activated diverse molecular programs in a cell type-specific manner ( Figure 7 ) . In 01 IT-ET Glut neurons, enrichment was observed in pathways related to synaptic remodeling and neurotransmitter receptor organization. Notably, terms such as presynaptic membrane organization and NMDA selective glutamate receptor signaling pathway exhibited high enrichment scores, indicating that LC hyperactivity substantially modulates both synaptic architecture and receptor sensitivity in pyramidal neurons. In contrast, the 05 OB-IMN GABA neuronal class was enriched for terms including regulation of grooming behavior, positive regulation of the force of heart contraction , and regulation of heart rate by chemical signal , suggesting a broader physiological role for these neurons of the olfactory bulb. Additionally, significantly decreased enrichment scores were identified for histone H3K27 methyltransferase activity across multiple cell types, pointing to a shared epigenetic response across classes to tonic LC hyperactivity. Download figure Open in new tab Figure 7: Gene set enrichment analysis, tonic LC hyperactivity vs. controls. a) Heatmap of classes (rows) vs. gene ontology (GO) terms (columns). b-d) Bubble plots of highly relevant GO terms for the top three classes by number of DEGs. Methods Animals All procedures were approved by the Institutional Animal Care and Use Committee (IACUC) at Columbia University. B6.Cg-Dbh tm3.2(cre)Pjen/J (Dbh-Cre, JAX stock #033951) mice and C57BL/6J WT mice aged 8 weeks were used for all experiments. Mice were housed in a 12-hour light/dark cycle with ad libitum access to food and water. Stereotaxic surgery and viral injections Aseptic surgical procedures for the injection of AAV viral vectors are similar to those described previously [ 15 , 48 ]. Briefly, mice were anesthetized with isoflurane (5% induction, 1.5% maintenance) and subsequently secured in a stereotaxic frame (Kopf Instruments) with a heating pad (FHC, Bowdoinham, ME) to maintain body temperature. The skull was carefully leveled and fur above the scalp was clipped and then removed with depilatory cream. An alternating application of Betadine and 70% alcohol solution was used to disinfect the surgical site. Prior to incision, buprenorphine (0.05 mg/kg) and saline solution containing 5% dextrose (1 mL) were administered subcutaneously to provide analgesia and hydration for the duration of the surgery. A local anesthetic (lidocaine, 2%) was also applied to the incision site before a midline scalp incision was made. For all AAV injections, burr holes were made above the LC region, and saline was applied to each craniotomy to prevent desiccation of exposed brain surfaces. Pulled capillary glass micropipettes (Drummond Scientific, Broommall, PA) were back-filled with AAV solution and injected into the target brain regions at a rate of 23 nL/sec using a precision injection system (Nanoliter 2020, World Precision Instruments, Sarasota, FL). The micropipette was left in place for at least 3 minutes following each injection and then slowly withdrawn. To selectively target LC neurons for chemogenetic manipulations, either AAV-hSyn-DIO-hM4D(Gi)-mCherry (Addgene Cat. Number: 44362) or AAV-hSyn-DIO-hM3D(Gq)-mCherry (Addgene Cat. Number: 44361) was injected into the LC of both hemispheres (300 nL at each injection site, AP: -5.4 mm, ML: +/-1.0 mm, DV from dura: 2.5 mm, 3.0 mm, and 3.5 mm). Controls were injected with sterile phosphate buffered saline (PBS). At the conclusion of the surgery, Baytril (5 mg/kg) and carprofen (5 mg/kg) were administered. Four additional doses of Baytril and two additional doses of carprofen were administered every 24 hours after surgery. Animals’ weights were measured at least once per day for 5 days. Chemogenetic perturbation DREADD agonist JHU37160 (0.2 mg/kg; Hello Bio, Cat.#HB6261) dissolved in sterile saline was injected intraperi toneally into Dbh-Cre mice to inactivate hM4D(Gi)-expressing or activate hM3D(Gq)-expressing neurons in the target region. Saline of equivalent volume was administered as a control. Injections were given every 12 hours for 3 consecutive days, and each animal was euthanized through cervical dislocation 30 minutes following the final injection. Recovery of neural tissue Immediately following euthanasia, neural tissue was extracted, split into hemispheres down the midline, and flash frozen in chilled isopentane (2-methylbutane). Each right hemisphere was allocated for nuclei isolation, and the left hemisphere was allocated for histology. Recovery and purification of nuclei for single-nucleus RNA-sequencing For snRNAseq library preparation, brains were sagittaly sectioned at 10 µ m across the entirety of one hemisphere. Every 6th slice was collected in an Eppendorf tube for subsequent processing. Tissue was then dissociated and nuclei isolated using an optimized method [ 49 ], and combinatorial barcodes were added to each transcript following protocols established previously [ 50 , 51 ]. Library preparation Histology Frozen tissue sections were stored at –80°C until use. To begin staining, slides were equilibrated to room temperature and placed on a 60°C slide warmer for 5 minutes. A hydrophobic barrier was drawn around each section and allowed to dry for 10 minutes. Sections were rehydrated with two 5-minute washes in 1× PBS. Antigen retrieval was performed by incubating the slides in 0.01 M sodium citrate buffer (pH 6.0) at 60°C for 30 minutes. The buffer was composed of 135 mL dH 2 O, 2.7 mL 0.1 M citric acid, and 12.3 mL 0.1 M sodium citrate. Following antigen retrieval, endogenous peroxidase activity was blocked by incubating slides in a solution of 10% methanol and 3% hydrogen peroxide in dH 2 O for 20 minutes at room temperature on a shaker (150 rpm). Proper chemical waste disposal protocols were followed for methanol-containing solutions. Slides were then gently blotted to remove excess liquid and incubated in a pre-treatment solution containing 1× PBS, 10% donkey serum, and 1% Triton X-100 for 30 minutes at room temperature to permeabilize tissues and reduce nonspecific binding. For blocking of endogenous mouse IgG, sections were incubated for 2 hours in donkey anti-mouse IgG Fab fragments (1.3 mg/mL; diluted 1:10 in blocking buffer). This step was included for experiments involving fluorophores with emission in the 633–647 nm range. The Fab carrier solution consisted of 10% donkey serum, 1% Triton X-100 (from a 20% stock in PBS), and 1× PBS. Following blocking, slides were washed eight times for 4 minutes each: four times with PBS-T (PBS + 0.1% Tween-20) and four times with PBS alone (alternating between the two, with the final rinse in PBS only). Slides were then incubated with primary antibodies diluted in 1°Ab carrier solution (10% donkey serum, 1% Triton X-100, and 1× PBS) for 48 hours at 4°C in a humidified chamber. Antibody solutions were centrifuged briefly before dilution and vortexed thoroughly to ensure mixing. Control slides were incubated with carrier solution lacking primary antibody. On the second day, slides were washed three times for 5 minutes each in 1× PBS. Secondary antibody incubation was performed for 6 hours at room temperature in the dark, using antibodies diluted in PBS. After incubation, slides were again washed three times in 1× PBS in the dark. For mounting, sections were coverslipped using Fluoromount-G with or without DAPI and allowed to dry overnight in a dark, dry chamber at room temperature. Slides were sealed with clear nail polish (optional) and stored in the dark until imaging. Computational methods Loading data and examining nuclei recovery To begin the analysis, single-nucleus RNA sequencing data were loaded using load_adata() , a function that accounted for the operating system and local machine name. This ensured cross-platform compatibility and reproducibility of results. The resulting AnnData object served as the foundational data structure for all downstream analyses. To characterize the cellular composition of the dataset, nuclei were grouped according to transcriptionally defined cell classes derived from the Allen Brain Cell Atlas. The relative abundance of each class was computed on a per-sample basis, and the mean and standard deviation were visualized as horizontal bar plots with the class_composition() function. ( Figure 2B ) . This allowed for a comparative analysis of the recovery rates of various classes across samples and highlighted potential biases in nuclei recovery. To assess regional representation, nuclei were further stratified by anatomical location using the region_composition() function. Only nuclei uniquely registered to a single region were considered. For each brain region, the average percentage across samples and its standard deviation were plotted, with per sample variability overlaid as scatter points ( Figure 2C ) . To account for nuclei assigned to multiple regions, the region_composition() function includes a parameter, egistration_category=multi , that allows for using expanded annotations from multi-mapped registrations, wherein each region was given equal weight. Additionally, nuclei that lacked spatial assignments were included as a separate category labeled ‘None’, ensuring complete representation using region_composition_all_labels() . This approach enabled a holistic view of anatomical sampling density and spatial registration fidelity. Nuclei were then categorized by their dominant neurotransmitter phenotype with the neurotransmitter_composition() function. Sample-level percentages were computed for each neuro transmitter group, and their averages and standard deviations were visualized using horizontal bar plots ( Figure 2d ) . This analysis provided insight into the chemical diversity of the sampled population and facilitated downstream differential gene expression comparisons based on neurotransmitter identity. Quality control To evaluate data quality, the distributions of total UMI counts and detected gene features were visualized using umi_feature_class(), umi_feature_treatment() , and umi_feature_region() , which produced violin plots across different classes, experimental treatments, and brain regions, respectively ( Figure 3 ) . For each panel, data were filtered to exclude extreme low- and high-UMI counts, thereby removing outliers and damaged nuclei. This QC step ensured that comparisons across samples, treatments, and classes reflected true biological variability rather than technical noise. Cell type labeling All recovered cells were assigned a hierarchical cell type label using the MapMyCells tool provided by the Allen Institute [ 44 ]. Cells with a bootstrapping probability less than 0.7 for class assignment were excluded from dataset. Adrenergic receptor subtype expression Expression of adrenergic receptor subtypes was visualized across multiple classes using gene_expression_class() , which generated violin plots, color-coded to remain consistent with earlier functions. ( Figure 4 ) . It should be noted that this function can be used to probe transcriptional changes of any annotated gene in response to perturbation, at cell type resolution. All panels were restricted to cells expressing the gene of interest and used normalized expression values. To summarize the number of differentially expressed genes per class ( Figures 5a , 6a ) , a function, deg_class_summary() , created. Within this function, Scanpy’s built-in rank_genes_groups() function was used on each class subset, and DEGs were filtered based on fold change and adjusted p-value thresholds. Classes with insufficient experimental or control nuclei were excluded. The number of DEGs per class was visualized as a bar plot, reflecting the magnitude of response per cell type. In addition, deg_class_summary() also generates volcano plots for the top three classes by number of DEGs to illustrate the spread and significance of individual genes ( Figures 5c , 6c ) . The distribution of nuclei by major brain structure within each class was visualized using a stacked bar plot using region_proportion_class() . Counts were aggregated and normalized by class to obtain region proportions, and regions were color-coded based on a predefined palette. This revealed spatial biases or enrichment patterns in nuclei distribution for each class. To determine whether observed transcriptional differences could be attributed to treatment-related differences in class composition, the proportions of nuclei per class were compared between treatment and control conditions with treatment_proportion_class() . Nuclei counts were normalized across groups, and class-level proportions were visualized using a dual-stacked bar chart. This facilitated the interpretation of DEG results in the context of potential compositional shifts due to noradrenergic modulation. An analogous analysis was performed at the neurotransmitter level using deg_neurotransmitter_summary(), region_proportion_neurotransmitter() , and treatment_proportion_neurotransmiter() ( Figures 5b , 6b ) . The number of DEGs was computed for each neurotransmitter group using pseudo bulk profiles and Welch’s t-tests, mirroring the methodology used in class-based analysis. The regional composition of each neurotransmitter class was plotted to assess anatomical biases. The treatment composition per neurotransmitter group was visualized, enabling cross-comparison between chemical identity, spatial distribution, and treatment exposure. Gene set enrichment analysis To identify enriched biological processes, gene set enrichment analysis was performed using go_heatmap_clustered() , which precompiled gene sets from MSigDB and structured Gene Ontology (GO) terms from the Gene Ontology Consortium. For each class, expression differences between treatment and control conditions were computed and used to score gene sets based on mean expression shifts. Z-score normalization was applied, and the top most variable GO terms were selected. Gene sets were hierarchically clustered by Jaccard distance and visualized as a heatmap, revealing shared and divergent pathway-level responses across classes ( Figure 7a ) . To probe class-specific gene ontology trends at higher resolution, GO enrichment was summarized with go_class() at the individual class level using bubble plots ( Figure 7b ) . For a selected class, gene sets with the highest and lowest Z-scores were displayed, with bubble size encoding the number of contributing genes and color encoding expression bias. Term descriptions were fetched via API queries to ensure interpretability. This analysis provided focused insight into the specific biological processes modulated by treatment in individual cell classes. Discussion Using chemogenetic activation and inactivation of LC neurons and single-nucleus RNA sequencing their downstream molecular consequences, this study, for the first time, characterized brain-wide, cell type–specific transcriptional responses to repeated overactivation or inactivation of the LC. Our findings demonstrate that neuromodulatory perturba tion exerts widespread but highly cell type- and region-specific gene expression changes ( Figures 5 , 6b ) . These results highlight the capacity of a single neuromodulatory nucleus to enact broad, context-dependent molecular programs through volumetric signaling. The observed effects reinforce the understanding that neuromodulators such as norepinephrine influence neural circuits not solely through point-to-point synaptic transmission but also via diffuse, extrasynaptic mechanisms [ 52 ]. This volumetric mode of transmission enables broad modulation of neuronal excitability, plasticity, gene expression, and function over biological scales from the molecular level to the organismal level. The present data suggest that the specific transcriptional outcomes of LC-NE signaling are shaped by the distribution of adrenergic receptor subtypes across cell classes and support a model in which the adrenergic receptor combinations and ratios of each cell type functions as a molecular gate, filtering global NE signals into discrete, cell-type-specific responses ( Figure 4 ) . It is notable that diverse functional programs were activated in a cell type-specific manner ( Figure 7 ) . For example, in tonic LC hyperactivity, 01 IT-ET Glut exhibited enrichment in pathways related to synaptic remodeling and neurotransmitter receptor organization. Indeed, GO terms like presynaptic membrane organization, NMDA selective glutamate receptor signaling pathway had especially high enrichment scores, suggesting that LC hyperactivity heavily adjusts both the connections and sensitivity of intratelencephalic and extratelencephalic glutamatergic neurons. Under the same treatment condition, cells belonging to the 05 OB-IMN GABA class were enriched in genes relevant to the regulation of grooming behavior, positive regulation of the force of heart contraction, and regulation of heart rate by chemical signal, pointing to the potential influence of immature olfactory bulb neurons in broader behavior and physiology. Taken together, these results indicate that LC neuromodulation does not simply elicit a uniform molecular response but instead modulates functionally tailored transcriptional programs depending on the intrinsic identity and role of the target cells. Notably, however, this study also identified significantly lowered enrichment scores for histone H3K27 methyltransferase activity across at least three classes, illustrating the paradigm’s ability to also capture commonalities of molecular response to neural perturbation across cell types. The effects of LC perturbation observed in this study likely reflect a mixture of rapid, reversible signaling cascades and slower gene regulatory programs. While brief episodes of LC activation have been associated with short-term changes in synaptic dynamics and network activity during sensory and cognitive processing [ 11 , 53 ], the present paradigm lays the foundation for future inquiries into prolonged neuromodulatory tone shifts. Longitudinal investigations of LC modulation are relevant to pathophysiological states in which chronic LC hypoactivity (e.g., in narcolepsy, depression) or hyperactivity (e.g., in anxiety disorders or neurodegeneration) is observed. The LC-NE system is also essential for normal neural functions such as learning, cognition, circadian rhythm entrainment, and sleep in behavior [ 30 , 54 , 55 , 56 ]; the gene expression changes identified here may therefore provide a molecular framework for understanding how persistent alterations in neuromodulatory tone contribute to both the etiology of these pathophysiological states as well as the mechanisms of neural functions at baseline. The transcriptomic framework established in this study is broadly generalizable and can be applied to the investigation of other neuromodulatory systems. The combination of whole-brain snRNA-seq with cell-type-specific annotations allows for systematic mapping of the molecular consequences of neural perturbations with high spatial and cellular resolution. This approach provides a scalable method to evaluate how global neurotransmitter fluctuations are decoded across the brain and offers a foundation for the development of transcriptionally-informed interventions. Several future investigations are warranted based on the findings of this hypothesis-generating study. First, although we observed significant gene expression changes induced by LC perturbation, the perturbation in the present study was relatively short-term, consisting of twice-daily treatments over three days. However, the pathogenesis of many neuropsychiatric disorders often involves chronic hyperactivation or hypoactivation of the LC-NE system [ 57 ]. There fore, a brain-wide characterization of transcriptomic changes across diverse cell types in response to long-term LC-NE perturbation, which mimicks pathogenesis of neuropsychiatric disorders such as chronic stress, could yield critical insights into the molecular mechanisms underlying these disorders. Second, the integration of behavioral and physio logical measurements would allow for the connection of transcriptional changes to functional outcomes. For instance, concurrent assessment of behavior, neural dynamics (e.g., calcium imaging, electrophysiology), and gene expression during LC perturbation would enable causal inference regarding the impact of neuromodulatory tone cell-type-specific gene expression on perception, learning, or decision making. Such multimodal approaches would be essential for linking observations across biological scales, from molecular adaptations to systems-level functions and behavior. Finally, while the current study employed a binary model of neuromodulatory modulation (hyperactive vs. hypoactive), future studies could explore graded or temporally dynamic perturbations to better approximate endogenous LC activity patterns. Such studies would be instrumental in elucidating how the brain decodes continuous fluctuations in neuromodulator levels as well as the nature of nonlinear relationships between neuromodulatory tone, gene regulatory outcomes, and higher-order behavior. Together, this work demonstrates that defined neuromodulatory perturbations give rise to widespread, cell type-specific transcriptional changes across the brain. These findings advance our understanding of the molecular mechanisms underlying volumetric neuromodulation and establish a platform for the development of next-generation, transcription ally informed neuromodulatory therapies and brain-machine interfaces [ 58 , 59 , 60 ]. By framing gene expression as a programmable substrate of neuromodulatory control, this study lays the groundwork for engineering more predictable and targeted interventions that engage brain functions across biological and temporal scales relevant to both neural restoration and enhancement. Disclaimer Q.W. is the co-founder of Sharper Sense. T.L. owns stock options in Envisagenics from his previous employment. Footnotes cs3791{at}columbia.edu tim.lantin{at}columbia.edu smw2250{at}columbia.edu lj2612{at}columbia.edu gag2155{at}columbia.edu jm5200{at}columbia.edu qi.wang{at}columbia.edu References [1]. ↵ Thomas J. Brozoski , Roger M. Brown , H. E. Rosvold , and Patricia S. Goldman . Cognitive Deficit Caused by Regional Depletion of Dopamine in Prefrontal Cortex of Rhesus Monkey . Science , 205 ( 4409 ): 929 – 932 , August 1979 . Publisher: American Association for the Advancement of Science . OpenUrl Abstract / FREE Full Text [2]. ↵ S. G. Birnbaum , P. X. Yuan , M. Wang , S. 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