The work presented here was funded through NIH HLB U01 HL133360 awarded to JS and RV, NIDA R21 DA036372 awarded to JS and EVB, T32 AA-007463 awarded to Jan Hoek in support of SJO&#39;S, and National Institute of Alcoholism and Alcohol Abuse: R01 AA018873.

This study was carried out in accordance with the recommendations of Animal Care and Use Committee (IACUC) of Thomas Jefferson University. The protocol was approved by Thomas Jefferson University IACUC.
1. Animal model
<ol>
	<li>House male Sprague Dawley (&#62;120 g, Harlan, Indianapolis, IN, USA) rat triplets individually with free access to ethanol-chow (2 rats) or control-chow mixture (1 rat). 
	NOTE: This representative experiment employed the Lieber-DeCarli protoco...

<ol>
	<li>. Substance Use and Mental Health Indicators in the United States: Results from the 2019 National Survey on Drug Use and Health Available from: <a target="_blank" href="https://www.samhsa.gov/data/">https://www.samhsa.gov/data/</a> (2020)</li><li>Prevalence of Serious Mental Illness (SMI). NIH Available from: <a target="_blank" href="https://www.nimh.nih.gov/health/statistics/mental-illness.shtml">https://www.nimh.nih.gov/health/statistics/mental-illness.shtml</a> (2020)</li><li>Mattick, R. P., Kimber, J., Breen, C., Davoli, M., Mattick, R. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Buprenorphine+maintenance+versus+placebo+or+methadone+maintenance+for+opioid+dependence.">Buprenorphine maintenance versus placebo or methadone maintenance for opioid dependence.</a> Cochrane Database of Systematic Reviews. , (2008).</li><li>Mattick, R. P., Breen, C., Kimber, J., Davoli, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Methadone+maintenance+therapy+versus+no+opioid+replacement+therapy+for+opioid+dependence.">Methadone maintenance therapy versus no opioid replacement therapy for opioid dependence.</a> The Cochrane Database of Systematic Reviews. 2009 (3), (2009).</li><li>Miller, P. M., Book, S. W., Stewart, S. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Medical+treatment+of+alcohol+dependence:+A+systematic+review.">Medical treatment of alcohol dependence: A systematic review.</a> International Journal of Psychiatry in Medicine. 42 (3), 227-266 (2012).</li><li>Holmes, E. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Lancet+Psychiatry+Commission+on+psychological+treatments+research+in+tomorrow's+science.">The Lancet Psychiatry Commission on psychological treatments research in tomorrow's science.</a> The Lancet. Psychiatry. 5 (3), 237-286 (2018).</li><li>Ford, C. L., Young, L. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Translational+opportunities+for+circuit-based+social+neuroscience:+advancing+21st+century+psychiatry.">Translational opportunities for circuit-based social neuroscience: advancing 21st century psychiatry.</a> Current Opinion in Neurobiology. 68, 1-8 (2021).</li><li>Holmes, E. A., Craske, M. G., Graybiel, A. 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E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effects+of+ibudilast+on+the+subjective,+reinforcing,+and+analgesic+effects+of+oxycodone+in+recently+detoxified+adults+with+opioid+dependence.">Effects of ibudilast on the subjective, reinforcing, and analgesic effects of oxycodone in recently detoxified adults with opioid dependence.</a> Neuropsychopharmacology. 42 (9), 1825-1832 (2017).</li><li>Heinzerling, K. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=placebo-controlled+trial+of+targeting+neuroinflammation+with+ibudilast+to+treat+methamphetamine+use+disorder.">placebo-controlled trial of targeting neuroinflammation with ibudilast to treat methamphetamine use disorder.</a> Journal of Neuroimmune Pharmacology. 15 (2), 238-248 (2020).</li><li>Bogenschutz, M. P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Psilocybin-assisted+treatment+for+alcohol+dependence:+A+proof-of-concept+study.">Psilocybin-assisted treatment for alcohol dependence: A proof-of-concept study.</a> Journal of Psychopharmacology. 29 (3), 289-299 (2015).</li><li>O'Sullivan, S. J., Schwaber, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Similarities+in+alcohol+and+opioid+withdrawal+syndromes+suggest+common+negative+reinforcement+mechanisms+involving+the+interoceptive+antireward+pathway.">Similarities in alcohol and opioid withdrawal syndromes suggest common negative reinforcement mechanisms involving the interoceptive antireward pathway.</a> Neuroscience and Biobehavioral Reviews. 125, 355-364 (2021).</li><li>O'Sullivan, S. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+systems+neuroscience:+A+growing+frontier+in+mental+illness.">Single-cell systems neuroscience: A growing frontier in mental illness.</a> Biocell. 46 (1), 7-11 (2022).</li><li>O'Sullivan, S. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+glia+and+neuron+gene+expression+in+the+central+amygdala+in+opioid+withdrawal+suggests+inflammation+with+correlated+gut+dysbiosis.">Single-cell glia and neuron gene expression in the central amygdala in opioid withdrawal suggests inflammation with correlated gut dysbiosis.</a> Frontiers in Neuroscience. 13, 665 (2019).</li><li>O'Sullivan, S. J., McIntosh-Clarke, D., Park, J., Vadigepalli, R., Schwaber, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single+cell+scale+neuronal+and+glial+gene+expression+and+putative+cell+phenotypes+and+networks+in+the+nucleus+tractus+solitarius+in+an+alcohol+withdrawal+time+series.">Single cell scale neuronal and glial gene expression and putative cell phenotypes and networks in the nucleus tractus solitarius in an alcohol withdrawal time series.</a> Frontiers in Systems Neuroscience. 15, 739790 (2021).</li><li>O'Sullivan, S. J., Reyes, B. A. S., Vadigepalli, R., Van Bockstaele, E. J., Schwaber, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Combining+laser+capture+microdissection+and+microfluidic+qpcr+to+analyze+transcriptional+profiles+of+single+cells:+A+systems+biology+approach+to+opioid+dependence.">Combining laser capture microdissection and microfluidic qpcr to analyze transcriptional profiles of single cells: A systems biology approach to opioid dependence.</a> Journal of Visualized Experiments. (157), e60612 (2020).</li><li>Achanta, S., Vadigepalli, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single+cell+high-throughput+qRT-PCR+protocol.">Single cell high-throughput qRT-PCR protocol.</a> Protocols.io. , (2020).</li><li>O'Sullivan, S. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+interoceptive+antireward+pathway+and+gut+dysbiosis+in+addiction.">The interoceptive antireward pathway and gut dysbiosis in addiction.</a> Journal of Psychiatry, Depression &amp; Anxiety. 7 (40), 1-5 (2021).</li><li>Park, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+transcriptional+analysis+reveals+novel+neuronal+phenotypes+and+interaction+networks+involved+in+the+central+circadian+clock.">Single-cell transcriptional analysis reveals novel neuronal phenotypes and interaction networks involved in the central circadian clock.</a> Frontiers in Neuroscience. 10, 481 (2016).</li><li>Staehle, M. 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Alcohol use disorder remains a challenging disease to treat. Our group has approached this disorder by investigating antireward processes with a systems neuroscience perspective. We measured gene expression changes in single NTS neurons and microglia in an alcohol withdrawal time series16. The NTS was chosen for its prominent role in the autonomic dysregulation that occurs in alcohol withdrawal syndrome. We combine LCM with single-cell microfluidic RT-qPCR allowing for robust numbers of samples an...

Addiction remains a growing challenge in the developed world1,2. Despite major scientific and clinical advances, rates of addiction continue to increase while the efficacy of established treatments remains stable at best3,4,5. However, advances in biotechnology and scientific approaches have led to novel methods and hypotheses to further investigate the pathophysiology of substance dependence6,7,8. Indeed, recent developments suggest that novel concepts and treatment paradigms may lead to breakthroughs with social, economic, and political consequences9,10,11,12.
We investigated antireward in the withdrawal of alcohol and opioid dependence13,14,15,16. Methods are central to this paradigm17,18. Laser capture microdissection (LCM) can select single cells with high anatomic specificity. This functionality is integral to the neuroinflammation antireward hypothesis as both glia and neurons can be collected and analyzed from the same neuronal subnucleus in the same animal13,14,15,16,19. A relevant portion of the transcriptome of selected cells can then be measured with high-throughput microfluidic reverse transcription quantitative polymerase chain reactions (RT-qPCR) providing high-dimensional datasets for computational analysis yielding insights into functional networks20,21.
Measuring a subset of the transcriptome in neurons and glia in a specific brain nucleus generates a dataset that is robust in both sample number and genes measured and is sensitive and specific. These tools are optimal for a system&#39;s neuroscience approach to psychiatric disease because glia, mainly astrocytes and microglia, have demonstrated a central role in neurological and psychiatric disease over the past decade22,23. Our approach can measure the expressive response of glia and neurons concomitantly across numerous receptors and ligands involved in local paracrine signaling. Indeed, signaling can be inferred from these datasets using various quantitative methods such as fuzzy logic24. Further, the identification of cellular subphenotypes in neurons or glia and their function can provide insight into how brain cells in specific nuclei organize, respond to, and dysregulate at the single-cell level. The dynamics of this functional system can also be modeled with time series experiments16. Lastly, animal models can be perturbed anatomically or pharmacologically to lend a mechanistic condition to this system&#39;s approach.
Representative experiment: 
Below, we provide an example of the application of these methods. This study investigated rat neuronal and microglia gene expression in the solitary nucleus (NTS) in response to alcohol dependence and subsequent withdrawal16. Rat cohorts comprised 1) Control, 2) Ethanol-dependent (EtOH), 3) 8 h withdrawal (Wd), 4) 32 h Wd, and 5) 176 h Wd (Figure 1A). Following rapid decapitation, brainstems were separated from the forebrain and cryosectioned, and slices were stained for tyrosine hydroxylase-positive (TH +) neurons and microglia (Figure 1B). LCM was used to collect both TH+ and TH- neurons and microglia. All the cells were from the NTS and analyzed as samples of 10-cell pools. Four 96 x 96 microfluidic RT-qPCR dynamic arrays were run on the RT-qPCR platform measuring 65 genes (Figure 1B-C). Data were normalized using a -&#916;&#916;Ct method and analyzed using R, and single-cell selection was validated with molecular markers (Figure 1D-E). Technical validation was further verified by technical replicates analyzed within a single batch and across batches (Figure 2 and Figure 3). TH+ and TH- neurons organized into different sub phenotypes with similar inflammatory gene clusters but differing &#947;-aminobutyric acid (GABA) receptor (R) clusters (Figure 4 and Figure 5). Sub phenotypes that had elevated expression of inflammatory gene clusters were over-represented at 32 h Wd while GABA-receptor (GABAR) expression remained low in protracted alcohol withdrawal (176 h Wd). This work contributes to the antireward hypothesis of alcohol and opioid dependence which conjectures that interceptive feedback from the viscera in withdrawal contributes to the dysregulation of visceral-emotional neuronal nuclei (i.e., NTS and amygdala) resulting in more severe autonomic and emotional sequelae, which contribute to substance dependence (Figure 6).

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>20X DNA Binding Dye</td>
			<td>Fluidigm</td>
			<td>100-7609</td>
			<td>NA</td>
		</tr>
		<tr>
			<td>2x GE Assay Loading Reagent</td>
			<td>Fluidigm</td>
			<td>85000802-R</td>
			<td>NA</td>
		</tr>
		<tr>
			<td>96.96 Dynamic Array IFC for Gene Expression (referred to as qPCR chip in text)</td>
			<td>Fluidigm</td>
			<td>BMK-M-96.96</td>
			<td>NA</td>
		</tr>
		<tr>
			<td>Anti-Cd11&#946; Antibody</td>
			<td>Genway Biotech</td>
			<td>CCEC48</td>
			<td>Microglia Stain</td>
		</tr>
		<tr>
			<td>Anti-NeuN Antibody, clone A60</td>
			<td>EMD Millipore</td>
			<td>MAB377</td>
			<td>Neuronal Stain</td>
		</tr>
		<tr>
			<td>Anti-tyrosine hydroxylase antibody</td>
			<td>abcam</td>
			<td>ab112</td>
			<td>Stain for TH+ neurons</td>
		</tr>
		<tr>
			<td>ArcturusXT Laser Capture Microdissection System</td>
			<td>Arcturus</td>
			<td>NA</td>
			<td>NA</td>
		</tr>
		<tr>
			<td>Biomark HD</td>
			<td>Fluidigm</td>
			<td>NA</td>
			<td>RT-qPCR platform</td>
		</tr>
		<tr>
			<td>Bovine Serum Antigen</td>
			<td>Sigma-Aldrich</td>
			<td>B4287</td>
			<td></td>
		</tr>
		<tr>
			<td>CapSure Macro LCM Caps</td>
			<td>ThermoFisher Scientific</td>
			<td>&#160;LCM0211</td>
			<td>NA</td>
		</tr>
		<tr>
			<td>CellDirect One-Step qRT-PCR Kit</td>
			<td>ThermoFisher Scientific</td>
			<td>11753500</td>
			<td>Lysis buffer solution components</td>
		</tr>
		<tr>
			<td>CellsDirect Resuspension &#38; Lysis Buffer Kit</td>
			<td>ThermoFisher Scientific</td>
			<td>11739010</td>
			<td>Invitrogen</td>
		</tr>
		<tr>
			<td>DAPI</td>
			<td>ThermoFisher Scientific</td>
			<td>62248</td>
			<td>Nucleus Stain</td>
		</tr>
		<tr>
			<td>DNA Suspension Buffer</td>
			<td>TEKnova</td>
			<td>T0221</td>
			<td></td>
		</tr>
		<tr>
			<td>Donkey anti-Rabbit IgG (H+L) ReadyProbe Secondary Antibody, Donkey anti-Rabbit IgG (H+L) ReadyProbe Secondary Antibody, Alexa Fluor 488</td>
			<td>ThermoFisher Scientific</td>
			<td>R37118</td>
			<td>Seconadry Antibody</td>
		</tr>
		<tr>
			<td>Exonuclease I</td>
			<td>New Englnad BioLabs, Inc.</td>
			<td>M0293S</td>
			<td>NA</td>
		</tr>
		<tr>
			<td>ExtracSure Sample Extraction Device</td>
			<td>ThermoFisher Scientific</td>
			<td>LCM0208</td>
			<td>NA</td>
		</tr>
		<tr>
			<td>FisherbrandTM Superfrost Plus Microscope Slides</td>
			<td>ThermoFisher Scientific</td>
			<td>22-037-246</td>
			<td>Plain glass slides</td>
		</tr>
		<tr>
			<td>GeneAmp Thin-Walled Reaction Tube</td>
			<td>ThermoFisher Scientific</td>
			<td>N8010611</td>
			<td></td>
		</tr>
		<tr>
			<td>Goat anti-Mouse IgG (H+L), Superclona Recombinant Secondary Antibody, Alexa Fluor 555</td>
			<td>ThermoFisher Scientific</td>
			<td>A28180</td>
			<td>Seconadry Antibody</td>
		</tr>
		<tr>
			<td>IFC Controller</td>
			<td>Fluidigm</td>
			<td>NA</td>
			<td>NA</td>
		</tr>
		<tr>
			<td>RNaseOut</td>
			<td>ThermoFisher Scientific</td>
			<td>10777019</td>
			<td></td>
		</tr>
		<tr>
			<td>SsoFast EvaGreen Supermix with Low Rox</td>
			<td>Bio-Rad</td>
			<td>PN 172-5211</td>
			<td>NA</td>
		</tr>
		<tr>
			<td>SuperScript VILO cDNA Synthesis Kit</td>
			<td>ThermoFisher Scientific</td>
			<td>11754250</td>
			<td>Contains VILO and SuperScript</td>
		</tr>
		<tr>
			<td>T4 Gene 32 Protein</td>
			<td>New Englnad BioLabs, Inc.</td>
			<td>M0300S</td>
			<td>NA</td>
		</tr>
		<tr>
			<td>TaqMan PreAmp Master Mix</td>
			<td>ThermoFisher Scientific</td>
			<td>4391128</td>
			<td>NA</td>
		</tr>
		<tr>
			<td>TE Buffer</td>
			<td>TEKnova</td>
			<td>T0225</td>
			<td>NA</td>
		</tr>
		<tr>
			<td>TempPlate Semi-Skirted 96-Well PCR Plate, 0.2 mL</td>
			<td>USA Scientific</td>
			<td>1402-9700</td>
			<td>NA</td>
		</tr>
	</tbody>
</table>

investigating drivers of antireward in addiction behavior with anatomically specific single-cell gene expression methods

Increasing rates of addiction behavior have motivated mental health researchers and clinicians alike to understand antireward and recovery. This shift away from reward and commencement necessitates novel perspectives, paradigms, and hypotheses along with an expansion of the methods applied to investigate addiction. Here, we provide an example: A systems biology approach to investigate antireward that combines laser capture microdissection (LCM) and high-throughput microfluidic reverse transcription quantitative polymerase chain reactions (RT-qPCR). Gene expression network dynamics were measured and a key driver of neurovisceral dysregulation in alcohol and opioid withdrawal, neuroinflammation, was identified. This combination of technologies provides anatomic and phenotypic specificity at single-cell resolution with high-throughput sensitivity and specific gene expression measures yielding both hypothesis-generating datasets and mechanistic possibilities that generate opportunities for novel insights and treatments.

Validation of single-cell collection is performed visually during LCM procedures. Cell nuclei are assessed at the QC station. The cell type can be determined by emission of tagged fluorophore for that cell type and its general morphology. If non-desired cells have been selected on the cap, their genetic material can be destroyed with a UV laser at the QC station. Further validation by molecular analysis is also necessary. In this representative example16, two types of neurons were selected-tyrosin...

Watch this Scientific Journal Video about Investigating Drivers of Antireward in Addiction Behavior with Anatomically Specific Single-Cell Gene Expression Methods at JoVE.com

Investigating Drivers of Antireward in Addiction Behavior with Anatomically Specific Single-Cell Gene Expression Methods

The combination of laser capture microdissection and microfluidic RT-qPCR provides anatomic and biotechnical specificity in measuring the transcriptome in single neurons and glia. Applying creative methods with a system's biology approach to psychiatric disease may lead to breakthroughs in understanding and treatment such as the neuroinflammation antireward hypothesis in addiction.

Increasing rates of addiction behavior have motivated mental health researchers and clinicians alike to understand antireward and recovery. This shift ...

The protocol is highly sensitive and allows high throughput capture of gene expression profile at single cell resolution. The technique provides both anatomical spatial and molecular specificity at single cell resolution. After harvesting the brain, selecting the single cells, and preparing the mRNA, inject control line fluid into the qPCR chip for priming.Insert the qPCR chip into the microfluidic mixing device. Select the Prime script and run the program. After the completion of the program, remove the primed qPCR chip and pipette six microliters of the reaction from the PCR sample plate into the corresponding sample well in the primed qPCR chip.Now, pipette six microliters of the reaction from the PCR assay plate into the corresponding assay well in the primed qPCR chip. Then insert the chip into the microfluidic mixing device. Select the Load Mix script and run the program.Turn on the microfluidic RT-qPCR platform and warm up the bulb. Remove the qPCR chip from the microfluidic mixing device and peel the protective sticker from the bottom of the chip. Open the microfluidic RT-qPCR platform and load the qPCR chip into the platform.Launch the data collection software by clicking on Start a New Run. Verify the chip barcode and chip type. Then click on next.Select the chip run file and browse the file location for the data collection storage. Then click on Application Type and select Gene Expression. Select ROX for a passive reference, single probe, and EVAGreen for probe type.Click to select the thermal cycling program and select Biomark HD GE:Fast 96x96 PCR+Melt v2. pcl file. Download the data analysis software.Launch the software to analyze the microfluidic RT-qPCR experiment. Click Chip Run and open the ChipRun. bml file that was created by the experimenter.A window displaying the experimental details including the passive reference, probe, and PCR thermal program will pop up. Under the Chip Explorer tab, click Sample Plate Setup and create a new sample plate template. Copy and paste the sample labels into the software spreadsheet as per the experimental design.Enter the sample name and the RNA concentration used for the standard samples. Now, map the sample set up by clicking map from the task menu and selecting SBS96-Left.dsp. Next, select Detail Views to update these changes in the file and click Analyze.Select Detector Plate Setup and create a new assay plate. Select the appropriate container type and format. Paste the assay names as per the experimental design and click on Analyze.In the analysis settings pane, click the User and set the fit to auto. Now, manually review each reaction in the 96x96 chip. Visualize the amplification and melt curves to determine if each reaction followed the expected qPCR pattern.If the amplification or melt curve does not match with what is expected, fail that reaction. Following QC, export the data by selecting file, clicking export, and saving the dataset as a csv file. As the exported csv file has both a pass fail matrix and a matrix with raw CT values, use the pass fail matrix to replace any failed cells in the dataset with NA.Download the recent version of open Source R software and then download the R Studio application.For median centering, use the R software to calculate the median CT value calculated from all the CT values for an individual sample and then subtract all individual CT values from this median value to get a minus delta CT value. For housekeeping gene normalization, calculate the average expression of the housekeeping genes for each sample and use this value to subtract the individual CT values. To generate a minus delta delta CT value, run the code in the R software.Upload the normalized dataset into R and analyze the data using the scale function, generate the scaled data, and then use the R heat map function or separate software to generate a heat map. To organize the data set for other functionality, calculate the Pearson correlations between each gene followed by the melt function. Export and upload this data into a gene correlation network software.Neurons showed elevated expression of NeuN and microglial samples showed significant expression of the microglial markers Cd34 and Cx3xr1. The Th+neuronal samples demonstrated significantly elevated expression of Th compared to Th-neuronal and microglia samples. The Th-neurons displayed significant expression of Gcg, suggesting that Th-neuronal samples are enriched with neurons that use Glp1 as a neurotransmitter.Linear discriminate analysis showed that these three cell types had differing gene expression profiles across all 65 genes. Cellular sub phenotypes of Glp1 enriched neuron samples through an alcohol withdrawal time series were represented using heat maps. The sub phenotype A, which highly expresses the inflammatory gene cluster one increases in ratio at the eight hour withdrawal time point and reaches a maximum at 32 hour withdrawal.The inflammatory sub phenotype is normalized to control by 176 hour withdrawal. Sub phenotype B, which highly expresses GABA receptor gene cluster two demonstrates an overall suppression in the expression of this gene cluster by the 176 hour withdrawal condition. The gene expression of individual samples was combined into averages so that the expression of gene clusters and the protein location of that gene transcript could be visualized throughout the time series.This technique can be applied to any biological system or tissue to understand the cellular response to a disease or a perturbation. That is to decipher the molecular response at single cell resolution relating to the spatial and the anatomical architecture of the tissue.

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Research

JoVE Journal

Neuroscience

2.1K 조회수.  Thomas Jefferson University. 이 프로토콜은 매우 민감하며 단일 세포 분해능에서 유전자 발현 프로필의 높은 처리량 캡처를 허용합니다. 이 기술은 단일 세포 분해능에서 해부학적 공간 및 분자 특이성을 모두 제공합니다. 뇌를 수확하고, 단일 세포를 선택하고, mRNA를 준비한 후, 프라이밍을 위해 qPCR 칩에 컨트롤 라인 유체를 주입한다.qPCR 칩을 미세유체 혼합 장치에 삽입합니다. 프라임 스크립트를...