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 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.
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'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'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 -ΔΔCt 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 γ-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).
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
2. Sample harvesting
3. Cryosectioning
NOTE: A rat neuronal nuclei is approximately 10 µm. Thus, 10 µm is the optimal slice thickness for this animal model. Slice thickness is adjusted according to the animal model for the study.
4. Immunofluorescence staining of single cells
5. Standard ethanol and xylene tissue dehydration series
6. Select single cells using laser capture microdissection (LCM)
7. Run qPCR chip on a microfluidic RT-qPCR on platform
8. Data analysis
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-tyrosine hydroxylase (Th) positive (+) and Th negative (-), in addition to microglia. Figure 1D demonstrates that samples meant to be neurons demonstrated statistically significant elevated expression of the neuronal marker, NeuN. Concurrently, microglial samples had significant elevation of the microglial markers, CD34 and Cx3xr1, suggesting that sample selection was performed with high fidelity. Additionally, Th+ neuronal samples demonstrated significantly elevated expression of Th compared to Th- neuronal samples and microglia samples while Th- neurons demonstrated significantly elevated expression of GCG (a preproglucagon coding gene) suggesting that these Th- neuronal samples are enriched with neurons that use GLP-1 as a neurotransmitter (Figure 1D). A more global computational measure known as linear discriminate analysis showed that these three cell types had differing gene expression profiles across all 65 genes measured with neurons and microglia separating along the x-axis and Th+ and Th- neuronal samples dividing along the y-axis (Figure 1E).
The quality of the data was also validated with intra and inter-batch technical replicates (Figure 2 and Figure 3). Intra-batch replicates control for the quality of data from that specific batch. If intra-batch replicates do not align, another qPCR chip experiment can be performed from the same sample and assay plates that were made to run the first batch. Inter-batch replicates ensure that data from across batches can be compared. This is crucial for experiments in which large numbers of samples are assayed requiring multiple batches such as this representative example. There was one outlier in this experiment as sample 40 in batch 4 (Figure 3). However, sample 40 in batches 1, 2, and 3 correctly aligned, and the other replicates from batch 4 aligned with batches 1, 2, and 3 suggesting that this was an isolated bad reaction, which occurred in this one replicate. Comparing across batches also requires proper data normalization techniques. This experiment used median centering, though housekeeping genes (Actb, Gapdh, Ldha), were also assayed in this experiment and served as a control for the median-centering method. That is, both methods yielded highly analogous datasets further suggesting high data quality.
Figure 4 displays a heat map of GLP-1 enriched neuronal samples that is organized by cellular subphenotype. This figure not only demonstrates the importance of cellular subphenotypes in neurobiology, but also demonstrates how ratios of subphenotypes change through alcohol withdrawal. The heat map shows that subphenotype A, which highly expresses the inflammatory gene cluster 1, increases in ratio at the 8 h Wd time point and reaches a maximum at 32 h Wd. By 176 h Wd, this inflammatory subphenotype is normalizing its prevalence back to control. Subphenotype B, which highly expresses GABAR gene cluster 2, demonstrates an overall suppression in the expression of this gene cluster by the 176 h Wd condition. These findings demonstrate how this GLP-1 neuronal cell type becomes hyperexcitable by increasing its inflammatory subphenoype and concurrently decreasing the level of expression of GABARs in its GABA subphenotype during alcohol withdrawal. Figure 5 combines the gene expression of individual samples into averages so that the expression of clusters of genes and the location of the protein of that gene transcript can be visualized throughout the time series.
Figure 1: Experimental design and single-cell selection. (A) Rat triplets were randomly assigned to one of the five treatment conditions. (B) Single-cell transcriptome data generation. (C) Cartoon representation of genes assayed and their function. Genes in green were not assayed. Official gene symbol is used. (D) Gene expression of cell type markers. Error bars show standard error. Neurons compared to microglia, p-values = 0.0273, 3.94 x 10-10, 7.73 x 10-12, respectively. Th+ neurons showed elevated Th expression compared to Th- neurons (p = 4.56 x 10-11) and microglia (p = 2.95678 x 10-15). Th- neurons showed elevated expression of GCG compared to Th+ neurons (p = 0.0106) and microglia (p = 0.0435) indicating they are GLP-1+ neurons. *p < 0.05, ***p < 4 x 10-10. (E) Linear discriminate analysis of all samples displays the difference across all genes measured between the three cell types collected in a two-dimension space. Centroid distance between NE neurons and GLP-1 neurons = 3.30, NE neurons and microglia = 1.57, GLP-1 Neurons and microglia = 2.92. This figure has been modified from O'Sullivan et al. 202116. Please click here to view a larger version of this figure.
Figure 2: Technical replicate plots of raw Ct values within a batch. Each graph displays raw Ct values for intrachip technical replicates plotted against each other demonstrating technical experimental integrity. This figure has been modified from O'Sullivan et al. 202116. Please click here to view a larger version of this figure.
Figure 3: Technical replicate plots of raw Ct values between batches. Each graph displays raw Ct values for interchip technical replicates plotted against each other demonstrating that all batches are comparable to each other. This figure has been modified from O'Sullivan et al. 202116. Please click here to view a larger version of this figure.
Figure 4: Heat map of GLP-1 enriched neuronal samples. Heat map displays cellular subphenotypes of GLP-1 enriched neuron samples through an alcohol withdrawal time series. Rows represent 10-cell pooled samples with cellular subphenotype clusters labeled with uppercase letters. Columns represent the z-score of -ΔΔCt gene expression values on a -1 to +1 color scale for that gene in that sample. Gene clusters are labeled by number. This figure has been modified from O'Sullivan et al. 202116. Please click here to view a larger version of this figure.
Figure 5: Suphenotype gene expression in GLP-1 enriched neuronal samples. Cellular diagrams display boxes representing relative gene expression (average z-score of -ΔΔCt values) of subphenotypes shown in the heatmap in Figure 4. The diagrams were constructed using Cytoscape version 3.8.0 and z-scores were calculated using the scale function in R version 3.5.2. -ΔΔCt labels on the right show which boxes correspond to which gene and the color represents expression (blue is low expression and yellow is high expression). The location of the box represents the localization or function of the protein product from that gene transcript. Green numbers indicate subgroups within subphenotypes. This figure has been modified from O'Sullivan et al. 202116. Please click here to view a larger version of this figure.
Figure 6: Interoceptive vagal circuit, visceral-emotional neuraxis, and schematic of opponent-process model of addiction. (A) Interoceptive vagal afferents relay the state of the gut, which is highly influenced by gut microflora, and other peripheral organs to the nucleus tractus solitarius (NTS). This information is subsequently relayed to the amygdala and influences emotional states. (B) A simplified cartoon representation showing the integrative roles of the nucleus tractus solitarius (NTS) and the central nucleus of the amygdala (CeA) in emotion, stress, and autonomic regulation. Two neuronal subtypes, GLP-1 and NE neurons are highlighted. Many anatomical and functional connections are omitted for clarity. Abbreviations: NE = norepinephrine; GLP-1 = glucagon-like peptide 1; GABA = γ-aminobutyric acid; HPA axis = hypothalamic-pituitary-adrenal axis; CRF = corticotropin releasing hormone. (C) Alcohol and/or opioid exposure has two actions: stimulate reward, via the mesolimbic dopamine pathway, and inhibit antireward. These actions motivate substance use via positive reinforcement. (D) Alcohol and/or opioid withdrawal has two actions: inhibit reward, by inhibiting the mesolimbic dopamine pathway (not shown) and stimulate antireward. This study proposes that visceral-emotional neuroinflammation is an endpoint in antireward stimulation, though this hypothesis warrants further testing. These actions, whatever the mechanism, motivate substance dependence via negative reinforcement. This figure has been modified from O'Sullivan et al. 202116. Please click here to view a larger version of this figure.
Supplementary Table 1: Primers for microfluidic RT-qPCR. All primer pairs for mRNA amplification are listed along with the amplicon length formed by each primer pair. This table has been modified from O'Sullivan et al. 202116. Please click here to download this File.
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 and genes to be measured at low cost with anatomic and molecular sensitivity and specificity (Figure 1B–C). Single-cells were identified and selected by IHC staining, and these cellular subphenotype groupings were validated with molecular expression (Figure 1D–E). However, some GLP-1 enriched neuronal samples (TH- neurons) moderately expressed Th (Figure 4). The establishment of cell phenotype was made at cell selection based on fluorescent visualization, and this criterion was used for the study as molecular grouping based on the expression of a single gene, Th, failed to define expression patterns suggestive of a cellular phenotype16. Thus, the results characterize major NTS microglial and neuronal subphenotypes and their dynamics in alcohol withdrawal.
This manuscript describes a method for single-cell transcriptomics, and it is imperative that all the steps in the protocol are followed as described. Some modification may be required when working with a different organ tissues or cell types. One of the challenges of this protocol is maintaining RNA quality and integrity during LCM. We have established protocols for IHC staining, LCM, and microfluidic RT-qPCR to address the challenges as detailed above. Briefly, these processes include the addition of RNase inhibitor to all staining solutions to prevent RNA degradation, a fast-staining protocol to prevent possible RNA degradation, processing slides for LCM immediately after IHC staining and dehydration and maintaining proper cold conditions whenever possible during sample processing or transfer.
The transcriptomics assay is performed on lysed single cells without RNA isolation, followed by reverse transcription, pre-amplification, and qPCR. The pre-amplification step is to amplify cDNA molecules to detectable levels for qPCR selectively. There are several limitations to the pre-amplification step. These include gene transcripts that fail to be amplified, which will result in no detection in microfluidic RT-qPCR. There is also the possibility of primer dimer formation as the PCR reaction contains all primers for the experimental assay. That is, the formation of dimers with a forward and reverse sequence of a single primer pair and with all the forward and reverse sequences in the experiment is possible. Hence, primer testing using positive experimental control like RNA standards is advisable.
The RT-qPCR chip design is another crucial aspect of this protocol for quality control and batch effects. Positive controls consist of standard RNA from the animal and organ being assayed (rat brain in this representative example). An RNA dilution series can then be loaded into each chip and should demonstrate the appropriate quantitative signal for each gene assayed. Water can be used as a negative control. These controls are critical for proper normalization that controls for batch effects which may occur when combining data from different chips. This is discussed in detail in step 8.
In the representative experiment shown here, these methods were applied for hypothesis-generating and providing insight into possible mechanism. The study was performed in the context of another work and provides important information for the interceptive antireward hypothesis13. In brief, this model conjectures that antireward is stimulated in substance withdrawal by interoceptive signaling from the vagus via the NTS to the amygdala and that the initial substrate of this antireward is neuroinflammation (Figure 6). This work supports this hypothesis by establishing the inflammatory changes in gene expression in neurons and microglia upon alcohol withdrawal.
A major weakness of this study is its lack of mechanistic claims. However, these methods can be used to determine a mechanism after a baseline experiment such as this one is performed. For example, an animal model intervention or perturbations, such as a subdiaphragmatic vagotomy or gene knockout, can demonstrate anatomic or genetic mechanisms of inflammatory regulators. Future studies that take this approach can contribute to the development of the interoceptive antireward hypothesis, which aims to implement these insights into clinical practice for addiction treatment.
The authors have nothing to disclose.
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'S, and National Institute of Alcoholism and Alcohol Abuse: R01 AA018873.
20X DNA Binding Dye | Fluidigm | 100-7609 | NA |
2x GE Assay Loading Reagent | Fluidigm | 85000802-R | NA |
96.96 Dynamic Array IFC for Gene Expression (referred to as qPCR chip in text) | Fluidigm | BMK-M-96.96 | NA |
Anti-Cd11β Antibody | Genway Biotech | CCEC48 | Microglia Stain |
Anti-NeuN Antibody, clone A60 | EMD Millipore | MAB377 | Neuronal Stain |
Anti-tyrosine hydroxylase antibody | abcam | ab112 | Stain for TH+ neurons |
ArcturusXT Laser Capture Microdissection System | Arcturus | NA | NA |
Biomark HD | Fluidigm | NA | RT-qPCR platform |
Bovine Serum Antigen | Sigma-Aldrich | B4287 | |
CapSure Macro LCM Caps | ThermoFisher Scientific | LCM0211 | NA |
CellDirect One-Step qRT-PCR Kit | ThermoFisher Scientific | 11753500 | Lysis buffer solution components |
CellsDirect Resuspension & Lysis Buffer Kit | ThermoFisher Scientific | 11739010 | Invitrogen |
DAPI | ThermoFisher Scientific | 62248 | Nucleus Stain |
DNA Suspension Buffer | TEKnova | T0221 | |
Donkey anti-Rabbit IgG (H+L) ReadyProbe Secondary Antibody, Donkey anti-Rabbit IgG (H+L) ReadyProbe Secondary Antibody, Alexa Fluor 488 | ThermoFisher Scientific | R37118 | Seconadry Antibody |
Exonuclease I | New Englnad BioLabs, Inc. | M0293S | NA |
ExtracSure Sample Extraction Device | ThermoFisher Scientific | LCM0208 | NA |
FisherbrandTM Superfrost Plus Microscope Slides | ThermoFisher Scientific | 22-037-246 | Plain glass slides |
GeneAmp Thin-Walled Reaction Tube | ThermoFisher Scientific | N8010611 | |
Goat anti-Mouse IgG (H+L), Superclona Recombinant Secondary Antibody, Alexa Fluor 555 | ThermoFisher Scientific | A28180 | Seconadry Antibody |
IFC Controller | Fluidigm | NA | NA |
RNaseOut | ThermoFisher Scientific | 10777019 | |
SsoFast EvaGreen Supermix with Low Rox | Bio-Rad | PN 172-5211 | NA |
SuperScript VILO cDNA Synthesis Kit | ThermoFisher Scientific | 11754250 | Contains VILO and SuperScript |
T4 Gene 32 Protein | New Englnad BioLabs, Inc. | M0300S | NA |
TaqMan PreAmp Master Mix | ThermoFisher Scientific | 4391128 | NA |
TE Buffer | TEKnova | T0225 | NA |
TempPlate Semi-Skirted 96-Well PCR Plate, 0.2 mL | USA Scientific | 1402-9700 | NA |