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

This study was carried out in accordance with the recommendations of Animal Care and Use Committee (IACUC) of Thomas Jefferson University and Drexel University College of Medicine. The protocol was approved by the Thomas Jefferson University and Drexel University College of Medicine IACUC.
1. Animal model
<ol>
	<li>Insert two 75 mg slow-release morphine sulfate pellets or two placebo pellets subcutaneously in adult male Sprague-Dawley rats.
	<ol>
		<li>Use a gown and gloves appropriately for minor sterile surgery. Shave the rat dorsum with clippers if necessary.</li>
		<li>Apply vet ointment to the animal&#39;s eyes. Anesthetize the rat with approximately 20 s of isoflurane inhalation. Anesthesia is confirmed by loss of consciousness.</li>
		<li>Make a midline incision in the rat dorsum with bead-sterilized blunt scissors and separate the dermis from the body wall with a bead-sterilized probe. Insert the pellets under the dermis with bead-sterilized forceps. Suture the incision closed with a sterile needle. 
		NOTE: The entire procedure takes about 5 min per rat. Fresh sterile gloves are used for each rat.</li>
		<li>Place the rat into an isolation cage for postsurgical recovery. Check for a heartbeat and regular respiratory rhythm. Observe the rat until consciousness is regained. Assess for postsurgical pain.</li>
		<li>Assess the rats 8 h postsurgery and every 12 h after for recovery and infection. Place rats in a cage with the rest of the cohort when they are fully recovered from surgery, about 24 h postsurgery.</li>
	</ol>
	</li>
	<li>Give an intraperitoneal naltrexone injection (75 mg/kg) to the G and the Withdrawal cohorts following 6 days of morphine exposure. 
	NOTE: There were four rat cohorts in this representative experiment (see Figure 1).</li>
</ol>
2. Sample harvesting
<ol>
	<li>Harvest the brain 6 days following the pellet insertion or 24 h following the naltrexone injection.
	<ol>
		<li>Place the animal in an isoflurane chamber for approximately 30 s or until loss of consciousness occurs, indicated by a lack of motion and decreased respiratory rate.</li>
		<li>Use a sharp guillotine to rapidly decapitate the animal.</li>
		<li>Dissect the brain out from the animal&#39;s skull.</li>
		<li>Use a sharp handheld razor to make the following gross incisions to the removed brain: First, slice off the cerebellum and discard. Second, separate the brainstem from the forebrain with a transverse incision. Third, hemisect the forebrain and/or brainstem with a midline sagittal incision.</li>
		<li>Place the forebrain and brainstem into a plastic tissue-embedding mold with 3-4 cm of Optimal Cutting Temperature compound (OCT) in the bottom of the mold. Cover the rest of the sample with OCT.</li>
		<li>Immediately place the plastic tissue-embedding mold with the sample covered with OCT into a bath containing dry ice and methanol. Do not let the methanol spill into the tissue-embedding mold. Keep the embedding mold with the brain sample in the methanol-ice bath until tissue collection is finished (~10-15 min maximum).</li>
		<li>Place the brain sample into a -80 &#176;C freezer as soon as possible.</li>
	</ol>
	</li>
	<li>Harvest the gut samples concurrently.
	<ol>
		<li>Following rapid decapitation, make a midline incision in the animal&#39;s abdomen with a scalpel.</li>
		<li>Find the cecum and sever its connection to the ascending colon.</li>
		<li>Squeeze the cecal contents into a 15 mL conical tube.</li>
		<li>Immediately place the conical tube on dry ice and put into a -80 &#176;C freezer as soon as possible. 
		NOTE: The small intestine contents can also be collected using the same methods as a negative control.</li>
	</ol>
	</li>
</ol>
3. Slicing
<ol>
	<li>Slice the hemisected rat forebrain using a cryostat.
	<ol>
		<li>Remove the plastic embedding mold with the forebrain from the -80 &#176;C freezer and place into a -20 &#176;C cryostat.</li>
		<li>Remove the OCT-embedded hemisected forebrain sample from the embedding mold. Use a razor to slice the corners of the plastic embedding mold vertically if necessary. Mount the forebrain for rostral to caudal coronal slicing on a cryostat chuck using OCT. 
		NOTE: Anatomic landmarks to identify the CeA include the optic tract and stria terminalis (Figure 2B). The optic tract branches from the optic chiasm and tracks dorsal-lateral as the brain is sliced rostral to caudal. When the optic tract has a morphology similar to what is seen in a rat brain atlas bregma -2.12 mm10, test slices may be viewed under a microscope. The optic tract and stria terminalis morphology can be checked in a rat brain atlas10 to identify the bregma and whether the CeA surrounds the stria terminals.</li>
		<li>Slice 10 &#181;m thick coronal sections from the hemisected forebrain rostral to caudal until sections containing the CeA are reached. 
		NOTE: The width and height of the sections are approximately 200 mm.</li>
		<li>Collect 10 &#181;m sections containing the CeA, or the preferred brain region, by thaw-mounting 10 &#181;m sections onto plain glass slides. Immediately place the glass slides onto a metal pan resting on dry ice. Put the slides with brain sections into a -80 &#176;C freezer as soon as possible. 
		NOTE: Multiple slices may be placed on the same slide. If using a different cell type stain for slices on the same slide, leave about 100 mm between slices so a hydrophobic pen can be used to separate cell type-specific antibody solutions on the slide. Leave about 20 mm from the edge of the slide on each side of the slice.</li>
	</ol>
	</li>
</ol>
4. Immunofluorescence staining
<ol>
	<li>Stain the forebrain sections for the brain cell of choice (e.g., neuron, microglia, astrocyte, etc.) using immunofluorescence.
	<ol>
		<li>Remove one or more slides with 10 &#181;m sections of the CeA from the -80 &#176;C freezer.</li>
		<li>Fix the slides with 75% ethanol for 30 s. Remove the excess liquid.</li>
		<li>Block the slices for 30 s with 2% bovine serum antigen (BSA) in 1x phosphate buffer saline (PBS). Wash 1x with PBS.</li>
		<li>Add a primary antibody solution to the slide for 2 min. Wash 1x with 2% BSA solution. 
		NOTE: The primary antibody solution is composed of 2% primary antibody, 1% RNase Out, and 96% of same BSA PBS solution for the blocking step above (step 4.1.3). The representative experiment used an anti-NeuN antibody, an anti-Cd11&#946; antibody, and an anti-GFAP antibody in the following quantities: 3 &#181;L of primary, 1.88 &#181;L of RNA inhibitor, and 145.12 &#181;L of BSA solution.</li>
		<li>Add the secondary antibody solution to the slide for 3 min. Wash 1x with PBS. 
		NOTE: The secondary antibody solution is composed of 1 &#181;L of goat anti-mouse 488 nm fluorescent tag (1:500), 2.5 &#181;L of RNA inhibitor, 1.3 &#181;L of DAPI (1:10,000), and 196.5 &#181;L of 2% BSA.</li>
	</ol>
	</li>
</ol>
5. Ethanol and xylene dehydration series
<ol>
	<li>Dip the slides into 75% ethanol for 30 s. Immediately after, dip the slides in 95% ethanol for 30 s. Immediately after, dip the slides into 100% ethanol for 30 s. Immediately after, dip the slides into a second container containing 100% ethanol for 30 s.</li>
	<li>Following the ethanol dehydration series, dip the slides into freshly poured xylene for 1 min. Immediately after, dip the slides into a second container of xylene for 4 min.</li>
	<li>Remove the slides from the xylene bath and let air dry in the dark for 5 min.</li>
	<li>Place the slides in a desiccator for 5 min to dry further.</li>
</ol>
6. Laser capture microdissection
<ol>
	<li>If stained, place the slide into the microscope and find the region of interest (CeA) using anatomic landmarks (i.e., the optic chiasm and stria terminalis).</li>
	<li>Use fluorescence to identify the stained cell type and its nucleus in the region of interest. Choose one cell or multiple cells if doing single cell pooled samples. Mark the cells of interest using LCM software.</li>
	<li>Place the LCM cap on top of the slice on the region of interest.</li>
	<li>Use test shots of an infrared (IR) laser to adjust the IR laser strength, size, and duration so that the LCM cap adhesive melts only over the area of the selected single cell. This ensures that no other cells will be collected other than the cells selected. 
	NOTE: In this representative experiment, 10 cell pools of the same cell type were used as a single sample to limit the cell-to-cell variability in gene expression between samples with the same treatment. However, this method can be used for true single cell experiments1,3.</li>
	<li>Select the individual cells to be collected for analysis using the LCM software tools (Figure 2C). Cells selected must be in the anatomic area of the CeA (or the brain region of choice) based on the rat brain atlas and the bregma10. Cells should be at least 3 &#181;m from the adjacent stained nuclei.</li>
	<li>Fire the IR laser to collect the identified single cells.</li>
	<li>Place the cap in the quality control (QC) station and view it to ensure that only the desired cells were selected. If other cells were mistakenly selected, an ultraviolet laser can be used to destroy the unwanted cells while the cap remains in the QC station.</li>
	<li>Take a photo of the tissue section from where the cell was collected to document its anatomic specificity. Record the distance of the slice from the bregma if appropriate using a rat brain atlas as a reference10.</li>
	<li>Remove the LCM cap from the QC station, attach the sample extraction device, and pipette 5.5 &#181;L of lysis buffer onto the sample. 
	NOTE: The lysis buffer solution consists of 0.5 &#181;L of lysis enhancer and 5 &#181;L of resuspension buffer.</li>
	<li>Fit the ExtracSure device onto a 0.5 mL microcentrifuge tube and place on a hotplate at 75 &#176;C for 15 min.</li>
	<li>Spin down the sample and lysis buffer for 30 s at low speed (0.01-0.02 x g) and place the collected sample into a -80 &#176;C freezer.</li>
</ol>
7. Single-cell microfluidic RT-qPCR
<ol>
	<li>Preamplification of single cell mRNA for 96.96 Dynamic Array Chip
	<ol>
		<li>Combine the forward and reverse mRNA qPCR gene primers for all the genes being assayed in a primer pool for preamplification (500 nM concentration each primer). For example, 1 &#181;L of 100 &#181;M primers in 80 &#181;L plus 120 &#181;L of DNA Suspension Buffer. 
		NOTE: The primer sequences used for the representative experiment can be found in O&#39;Sullivan et al.4.</li>
		<li>In a new 96 x 96 PCR plate, add 1 &#181;L of 5x VILO to each well.</li>
		<li>Remove LCM single cell samples from the samples stored at -80 &#176;C, let thaw briefly, centrifuge briefly at a low speed (0.01-0.02 x g), and add 5.5 &#181;L of the lysed single-cell sample to the PCR plate. Each sample is added to its own well.</li>
		<li>Place the PCR plate with the samples and VILO into the thermocycler and heat at 65 &#176;C for 1.5 min. Spin the plate for 1 min at 1,300 x g at 4 &#176;C and place the plate on ice.</li>
		<li>Add 0.15 &#181;L of 10x cDNA synthesis master mix, 0.12 &#181;L T4 Gene 32 protein, and 0.73 &#181;L of DNA suspension buffer to each well.</li>
		<li>Place the PCR plate into the thermocycler and run the following protocol: 25 &#176;C for 5 min, 50 &#176;C for 30 min, 55 &#176;C for 25 min, 60 &#176;C for 5 min, 70 &#176;C for 10 min, 4 &#176;C to end.</li>
		<li>Add 7.5 &#181;L of Taq polymerase master mix to each well.</li>
		<li>Add 1.5 &#181;L of the primer pool (see above) to each well.</li>
		<li>Place the PCR plate in the thermocycler and run the following preamplification protocol: 95 &#176;C for 10 min, followed by 22 cycles of 96 &#176;C for 5 sec, 60 &#176;C for 4 min.</li>
		<li>Add 0.6 &#181;L of exonuclease I reaction buffer 10x, 1.2 &#181;L exonuclease I, and 4.2 &#181;L of DNA suspension buffer to each well.</li>
		<li>Place the PCR plate in the thermocycler and run the following protocol: 37 &#176;C for 30 min, 80 &#176;C for 15 min.</li>
		<li>Add 54 &#181;L of TE buffer to each well. Spin the PCR plate at 1,300 x g at 5 min. Store at 4 &#176;C if immediately continuing to next step. Store at -20 &#176;C if waiting more than 12 h for next step.</li>
	</ol>
	</li>
	<li>Prepare the sample plate for the 96.96 Dynamic Array Chip.
	<ol>
		<li>In a new 96 well PCR plate, add 0.45 &#181;L of 20x DNA binding dye and 4.55 &#181;L of low ROX mastermix to each well.</li>
		<li>Add 3 &#181;L of preamplified sample to each well, spin the PCR plate at 1,300 x g, then put the plate on ice.</li>
	</ol>
	</li>
	<li>Prepare the assay plate for the 96.96 Dynamic Array Chip.
	<ol>
		<li>In a new 96 well PCR plate, add 3.75 &#181;L of 2x GE assay loading reagent and 1.25 &#181;L of DNA suspension buffer to each well.</li>
		<li>Add 2.5 &#181;L of 10 &#181;M qPCR primer to each corresponding well. Spin the PCR plate at 1,300 x g for 5 min.</li>
	</ol>
	</li>
	<li>Load and run the 96.96 Dynamic Array Chip.
	<ol>
		<li>Prime the chip with control line fluid.</li>
		<li>Place the chip in an IFC Controller HX and run the Prime (136X) script.</li>
		<li>Add 6 &#181;L of the sample from the PCR sample plate into the corresponding sample wells in the 96.96 Dynamic Array Chip.</li>
		<li>Add 6 &#181;L of the sample from the PCR assay plate into the corresponding assay wells in the 96.96 Dynamic Array Chip.</li>
		<li>Use needles to pop any air bubbles in the wells of the 96.96 Dynamic Array Chip.</li>
		<li>Place the 96.96 Dynamic Array Chip into the IFC Controller HX and run the Load Mix (136x) script.</li>
		<li>Remove the chip from the IFC Controller HX, peel off the protective sticker, and place the 96.96 Dynamic Array Chip into a microfluidic RT-qPCR platform. Run the GE Fast 96 x 96 PCR protocol (30 cycles). 
		NOTE: The RNA quality and validity of the results are assessed by multiple methods, including assay validation via gel electrophoresis, melting temperature curves, sample and assay replicates, and standard dilution series plots. Additionally, transcriptional findings can be validated by independent methods on brain hemisection including Western blot and immunofluorescence assays.</li>
	</ol>
	</li>
</ol>
8. Measuring the bacterial abundance with microfluidic RT-qPCR
<ol>
	<li>Extract the bacterial DNA following the directions of the stool DNA extraction kit.</li>
	<li>Estimate the bacterial DNA concentration using qPCR.</li>
	<li>Add the extracted bacterial DNA to a new PCR plate. Add 1 &#181;L of extracted bacterial DNA and 9 &#181;L of DNA Suspension buffer.</li>
	<li>Prepare the assay plate for the 48.48 Dynamic Array Chip (see steps 7.2.1-7.2.2)</li>
	<li>Prepare the sample plate for the 48.48 Dynamic Array Chip (see steps 7.3.1-7.3.2)
	<ol>
		<li>In a new 96 well PCR plate, add 0.45 &#181;L of 20x DNA binding dye and 4.55 &#181;L of low ROX mastermix to 48 wells.</li>
		<li>Add 3 &#181;L of the sample from the PCR plate containing the bacterial DNA to the 48 wells and spin down the PCR plate at 1,300 x g for 5 min. Store at 4 &#176;C.</li>
	</ol>
	</li>
	<li>Load and run the 48.48 Dynamic Array Chip (see steps 7.4.1-7.4.7).</li>
</ol>

<ol>
	<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=Inputs+drive+cell+phenotype+variability.">Inputs drive cell phenotype variability.</a> Genome Research. 24, 930-941 (2014).</li><li>Park, J., Ogunnaike, B., Schwaber, J., 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=Identifying+functional+gene+regulatory+network+phenotypes+underlying+single+cell+transcriptional+variability.">Identifying functional gene regulatory network phenotypes underlying single cell transcriptional variability.</a> Progress in Biophysics and Molecular Biology. 117, 87-98 (2015).</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>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>Buettner, F., 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=Computational+analysis+of+cell-to-cell+heterogeneity+in+single+cell+RNA-sequencing+data+reveals+hidden+subpopulations+of+cells.">Computational analysis of cell-to-cell heterogeneity in single cell RNA-sequencing data reveals hidden subpopulations of cells.</a> Nature Biotechnology. 33, 155-160 (2015).</li><li>Papalexi, E., Satija, 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+RNA+sequencing+to+explore+immune+cell+heterogeneity.">Single-cell RNA sequencing to explore immune cell heterogeneity.</a> Nature Reviews Immunolology. 18, 35-45 (2018).</li><li>SEQC/MAQC-III Consortium. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comprehensive+assessment+of+RNA-seq+accuracy,+reproducibility+and+information+content+by+the+Sequencing+Quality+Control+Consortium.">A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium.</a> Nature Biotechnology. 32, 903-914 (2014).</li><li>Rowin, J., Xia, Y., Jung, B., Sun, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gut+inflammation+and+dysbiosis+in+human+motor+neuron+disease.">Gut inflammation and dysbiosis in human motor neuron disease.</a> Physiological Reports. 5, (2017).</li><li>Tamboli, C. P., Neut, C., Desreumaux, P., Colombel, J. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dysbiosis+in+inflammatory+bowel+disease.">Dysbiosis in inflammatory bowel disease.</a> Gut. 53, 1-4 (2004).</li><li>Paxinos, G., Watson, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> The Rat Brain in Stereotaxic Coordinates: Hard Cover Edition. , (2006).</li></ol>

The authors declare that they have no competing financial interests.

Single-cell biology has demonstrated the heterogeneity of cellular phenotypes and robustness of tissue function. These findings have provided insight into the organization of biological systems at both macro and micro scales. Here, we describe the combination of two methods, LCM and microfluidic qPCR, to obtain single-cell transcriptome measures that provide anatomic specificity and transcriptional accuracy at a relatively low cost (Figure 1). Our group takes a systems biology approach and o...

Measuring the gene expression profiles of single cells has demonstrated extensive phenotypic heterogeneity within a tissue. This complexity has complicated our understanding of the biological networks that govern tissue function. Our group and others have explored this phenomenon in many tissues and conditions1,2,3,4,5,6. These experiments not only suggest that regulation of gene expression networks underlie such heterogeneity, but also that single-cell resolution reveals a complexity in tissue function that tissue-level resolution fails to appreciate. Indeed, merely a small minority of cells may respond to a specific condition or challenge, but the impact of those cells on overall physiology may be substantial. Additionally, a system biology approach that applies multivariate methods to high dimensional datasets from multiple cell types and tissues can elucidate system-wide treatment effects.
We combine LCM and microfluidic RT-qPCR to obtain such datasets. We take this approach here in contrast to gathering single cells via fluorescence-activated cell sorting (FACS) and using RNA sequencing (RNA-seq) to measure their transcriptome. The advantage of LCM over FACS is that the exact anatomic specificity of single cells can be documented with LCM, relatively and absolutely. Further, while RNA-seq can measure more features that RT-qPCR, microfluidic RT-qPCR is less expensive and has a higher sensitivity and specificity7.
In this representative experiment, we investigated the effects of opioid dependence and naltrexone-precipitated opioid withdrawal on rat neuronal, microglia, and astrocyte gene expression in the central nucleus of the amygdala (CeA) and gut microflora abundance4. Four treatment groups were analyzed: 1) Placebo, 2) Morphine, 3) Naltrexone, and 4) Withdrawal (Figure 1). We found that opioid dependence did not substantially alter gene expression, but that opioid withdrawal induced the expression of inflammatory genes, Tnf in particular. Astrocytes were the most affected cell type. The gut microbiome was profoundly impacted by opioid withdrawal as indicated by a decrease in the Firmicutes to Bacteroides ratio, which is an established marker of gut dysbiosis8,9.

<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>48.48 Dynamic Array IFC for Gene Expression</td>
			<td>Fluidigm</td>
			<td>BMK-M-48.48</td>
			<td>NA</td>
		</tr>
		<tr>
			<td>96.96 Dynamic Array IFC for Gene Expression</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>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>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>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>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>Fisherbrand 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>GFAP Monoclonal Antibody</td>
			<td>ThermoFisher Scientific</td>
			<td>A-21294</td>
			<td>Astrocyte Stain</td>
		</tr>
		<tr>
			<td>Goat anti-Mouse IgG (H+L), Superclonal&#8482; Recombinant Secondary Antibody, Alexa Fluor 488</td>
			<td>ThermoFisher Scientific</td>
			<td>A28175</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>Rox master mix</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>
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combining laser capture microdissection and microfluidic qpcr to analyze transcriptional profiles of single cells: a systems biology approach to opioid dependence

Profound transcriptional heterogeneity in anatomically adjacent single cells suggests that robust tissue functionality may be achieved by cellular phenotype diversity. Single-cell experiments investigating the network dynamics of biological systems demonstrate cellular and tissue responses to various conditions at biologically meaningful resolution. Herein, we explain our methods for gathering single cells from anatomically specific locations and accurately measuring a subset of their gene expression profiles. We combine laser capture microdissection (LCM) with microfluidic reverse transcription quantitative polymerase chain reactions (RT-qPCR). We also use this microfluidic RT-qPCR platform to measure the microbial abundance of gut contents.

The selection of the single cells was validated both visually and molecularly. Visually, cellular morphology was viewed before cell collection. Cells collected were then viewed at the QC station and the cellular nuclei stain (DAPI) overlapped with the single cell selection marker fluorescence. Figure 2A shows representative images of a slide with hemisected rat forebrain containing the CeA. Subsequent images (Figure 2</st...

Watch this Scientific Journal Video about Combining Laser Capture Microdissection and Microfluidic qPCR to Analyze Transcriptional Profiles of Single Cells: A Systems Biology Approach to Opioid Depend

Combining Laser Capture Microdissection and Microfluidic qPCR to Analyze Transcriptional Profiles of Single Cells: A Systems Biology Approach to Opioid Dependence

This protocol explains how to collect single neurons, microglia, and astrocytes from the central nucleus of the amygdala with high accuracy and anatomic specificity using laser capture microdissection. Additionally, we explain our use of microfluidic RT-qPCR to measure a subset of the transcriptome of these cells.

Profound transcriptional heterogeneity in anatomically adjacent single cells suggests that robust tissue functionality may be achieved by cellular ...

combining-laser-capture-microdissection-microfluidic-qpcr-to-analyze

Research

JoVE Journal

Genetics

5.2K Views.  Thomas Jefferson University. This protocol explains how to collect single neurons, microglia, and astrocytes from the central nucleus of the amygdala with high accuracy and anatomic specificity using laser capture microdissection. Additionally, we explain our use of microfluidic RT-qPCR to measure a subset of the transcriptome of these cells.

Video: Combining Laser Capture Microdissection and Microfluidic qPCR to Analyze Transcriptional Profiles of Single Cells: A Systems Biology Approach to Opioid Dependence

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

Gene expression heterogeneity is an interesting feature to investigate in lymphoid populations. Gene expression in these cells varies during cell activation, stress, or stimulation. Single-cell multiplex gene expression enables the simultaneous assessment of tens of genes1,2,3. At the single-cell level, multiplex gene expression determines population heterogeneity4,5. It allows for the distinction of population heterogeneity by determining both the probable mix of diverse precursor stages among mature cells and also the diversity of cell responses to stimuli.
Innate lymphoid cells (ILC) have been recently described as a population of innate effectors of the immune response6,7. In this protocol, cell heterogeneity of the ILC hepatic compartment is investigated during homeostasis.
Currently, the most widely used technique to assess gene expression is RT-qPCR. This method measures gene expression only one gene at a time. Additionally, this method cannot estimate heterogeneity of gene expression, since multiple cells are needed for one test. This leads to the measurement of the average gene expression of the population. When assessing large numbers of genes, RT-qPCR becomes a time-, reagent-, and sample-consuming method. Hence, the trade-offs limit the number of genes or cell populations that can be evaluated, increasing the risk of missing the global picture.
This manuscript describes how single-cell multiplex RT-qPCR can be used to overcome these limitations. This technique has benefited from recent microfluidics technological advances1,2. Reactions occurring in multiplex RT-qPCR chips do not exceed the nanoliter-level. Hence, single-cell gene expression, as well as simultaneous multiple gene expression, can be performed in a reagent-, sample-, and cost-effective manner. It is possible to test cell gene signature heterogeneity at the clonal level between cell subsets within a population at different developmental stages or under different conditions4,5. Working on rare populations with large numbers of conditions at the single-cell level is no longer a restriction.

This protocol describes how to assess the expression of a large array of genes at the clonal level. Single-cell RT-qPCR produces highly reliable results with a strong sensitivity for hundreds of samples and genes.

Gene expression heterogeneity is an interesting feature to investigate in lymphoid populations. Gene expression in these cells varies during cell ...

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Gene expression measurements from bulk populations of cells can obscure the considerable transcriptomic variation of individual cells within those populations. Single-cell gene expression measurements can help assess the role of noise in gene expression, identify correlations in the expression of pairs of genes, and reveal subpopulations of cells that respond differently to a stimulus. Here, we describe a procedure to measure the expression of up to 96 genes in single mammalian cells isolated from a population growing in tissue culture. Cells are sorted into lysis buffer by fluorescence-activated cell sorting (FACS), and the mRNA species of interest are reverse-transcribed and amplified. Gene expression is then measured using a microfluidic real-time PCR machine, which performs up to 96 qPCR assays on up to 96 samples at a time. We also describe the generation and use of PCR amplicon standards to enable the estimation of the absolute number of each transcript. Compared with other methods of measuring gene expression in single cells, this approach allows for the quantification of more distinct transcripts than RNA FISH at a lower cost than RNA-Seq.

We describe a method to sort single mammalian cells and to quantify the expression of up to 96 target genes of interest in each cell. This method includes the use of internal qPCR standards to enable the estimation of absolute transcript counts.

Gene expression measurements from bulk populations of cells can obscure the considerable transcriptomic variation of individual cells within those ...

Single Molecule Analysis of Laser Localized Psoralen Adducts

The DNA Damage Response (DDR) has been extensively characterized in studies of double strand breaks (DSBs) induced by laser micro beam irradiation in live cells. The DDR to helix distorting covalent DNA modifications, including interstrand DNA crosslinks (ICLs), is not as well defined. We have studied the DDR stimulated by ICLs, localized by laser photoactivation of immunotagged psoralens, in the nuclei of live cells. In order to address fundamental questions about adduct distribution and replication fork encounters, we combined laser localization with two other technologies. DNA fibers are often used to display the progress of replication forks by immunofluorescence of nucleoside analogues incorporated during short pulses. Immunoquantum dots have been widely employed for single molecule imaging. In the new approach, DNA fibers from cells carrying laser localized ICLs are spread onto microscope slides. The tagged ICLs are displayed with immunoquantum dots and the inter-lesion distances determined. Replication fork collisions with ICLs can be visualized and different encounter patterns identified and quantitated.

Lasers are frequently used in studies of the cellular response to DNA damage. However, they generate lesions whose spacing, frequency, and collisions with replication forks are rarely characterized. Here, we describe an approach that enables the determination of these parameters with laser localized interstrand crosslinks.

The DNA Damage Response (DDR) has been extensively characterized in studies of double strand breaks (DSBs) induced by laser micro beam irradiation in live ...

Laser Microirradiation to Study In Vivo Cellular Responses to Simple and Complex DNA Damage

DNA damage induces specific signaling and repair responses in the cell, which is critical for protection of genome integrity. Laser microirradiation became a valuable experimental tool to investigate the DNA damage response (DDR) in vivo. It allows real-time high-resolution single-cell analysis of macromolecular dynamics in response to laser-induced damage confined to a submicrometer region in the cell nucleus. However, various laser conditions have been used without appreciation of differences in the types of damage induced. As a result, the nature of the damage is often not well characterized or controlled, causing apparent inconsistencies in the recruitment or modification profiles. We demonstrated that different irradiation conditions (i.e., different wavelengths as well as different input powers (irradiances) of a femtosecond (fs) near-infrared (NIR) laser) induced distinct DDR and repair protein assemblies. This reflects the type of DNA damage produced. This protocol describes how titration of laser input power allows induction of different amounts and complexities of DNA damage, which can easily be monitored by detection of base and crosslinking damages, differential poly (ADP-ribose) (PAR) signaling, and pathway-specific repair factor assemblies at damage sites. Once the damage conditions are determined, it is possible to investigate the effects of different damage complexity and differential damage signaling as well as depletion of upstream factor(s) on any factor of interest.

Laser Microirradiation to Study In Vivo Cellular Responses to Simple and Complex DNA Damage

The goal of this protocol is to describe how to use laser microirradiation to induce different types of DNA damage, including relatively simple strand breaks and complex damage, to study DNA damage signaling and repair factor assembly at damage sites in vivo.

DNA damage induces specific signaling and repair responses in the cell, which is critical for protection of genome integrity. Laser microirradiation ...

Adaptation of Hybridization Capture of Chromatin-associated Proteins for Proteomics to Mammalian Cells

The hybridization capture of chromatin-associated proteins for proteomics (HyCCAPP) technology was initially developed to uncover novel DNA-protein interactions in yeast. It allows analysis of a target region of interest without the need for prior knowledge about likely proteins bound to the target region. This, in theory, allows HyCCAPP to be used to analyze any genomic region of interest, and it provides sufficient flexibility to work in different cell systems. This method is not meant to study binding sites of known transcription factors, a task better suited for Chromatin Immunoprecipitation (ChIP) and ChIP-like methods. The strength of HyCCAPP lies in its ability to explore DNA regions for which there is limited or no knowledge about the proteins bound to it. It can also be a convenient method to avoid biases (present in ChIP-like methods) introduced by protein-based chromatin enrichment using antibodies. Potentially, HyCCAPP can be a powerful tool to uncover truly novel DNA-protein interactions. To date, the technology has been predominantly applied to yeast cells or to high copy repeat sequences in mammalian cells. In order to become the powerful tool we envision, HyCCAPP approaches need to be optimized to efficiently capture single-copy loci in mammalian cells. Here, we present our adaptation of the initial yeast HyCCAPP capture protocol to human cell lines, and show that single-copy chromatin regions can be efficiently isolated with this modified protocol.

This is a method to identify novel DNA-interacting proteins at specific target loci, relying on sequence-specific capture of crosslinked chromatin for subsequent proteomic analyses. No prior knowledge about potential binding proteins, nor cell modifications are required. Initially developed for yeast, the technology has now been adapted for mammalian cells.

The hybridization capture of chromatin-associated proteins for proteomics (HyCCAPP) technology was initially developed to uncover novel DNA-protein ...

Ultralow Input Genome Sequencing Library Preparation from a Single Tardigrade Specimen

Tardigrades are microscopic animals that enter an ametabolic state called anhydrobiosis when facing desiccation and can return to their original state when water is supplied. The genomic sequencing of microscopic animals such as tardigrades risks bacterial contamination that sometimes leads to erroneous interpretations, for example, regarding the extent of horizontal gene transfer in these animals. Here, we provide an ultralow input method to sequence the genome of the tardigrade, Hypsibius dujardini, from a single specimen. By employing rigorous washing and contaminant exclusion along with an efficient extraction of the 50 ~ 200 pg genomic DNA from a single individual, we constructed a library sequenced with a DNA sequencing instrument. These libraries were highly reproducible and unbiased, and an informatics analysis of the sequenced reads with other H. dujardini genomes showed a minimal amount of contamination. This method can be applied to unculturable tardigrades that could not be sequenced using previous methods.

Contamination during the genomic sequencing of microscopic organisms remains a large problem. Here, we show a method to sequence the genome of a tardigrade from a single specimen with as little as 50 pg of genomic DNA without whole genome amplification to minimize the risk of contamination.

Tardigrades are microscopic animals that enter an ametabolic state called anhydrobiosis when facing desiccation and can return to their original state ...

A Novel Saturation Mutagenesis Approach: Single Step Characterization of Regulatory Protein Binding Sites in RNA Using Phosphorothioates

Gene regulation plays an important role in development. Numerous DNA- and RNA-binding proteins bind their target sequences with high specificity to control gene expression. These regulatory proteins control gene expression either at the level of DNA (transcription) or at the level of RNA (pre-mRNA splicing, polyadenylation, mRNA transport, decay, and translation). Identification of regulatory sequences helps understand not only how a gene is switched on or off, but also which downstream genes are regulated by a particular regulatory protein. Here, we describe a one-step approach that allows saturation mutagenesis of a protein binding site in RNA. It involves doping DNA template with non-wild-type nucleotides within the binding site, synthesis of separate RNAs with each phosphorothioate nucleotide, and isolation of the bound fraction following incubation with protein. Interference from non-wild-type nucleotides results in their preferential exclusion from the protein-bound fraction. This is monitored by gel electrophoresis following selective chemical cleavage with iodine of phosphodiester bonds containing phosphorothioates (phosphorothioate mutagenesis or PTM). This single-step saturation mutagenesis approach is applicable to the characterization of any protein binding site in RNA.

Proteins that bind specific RNA sequences play critical roles in gene expression. Detailed characterization of these binding sites is crucial for our understanding of gene regulation. Here, a single-step approach for saturation mutagenesis of protein-binding sites in RNA is described. This approach is relevant for all protein-binding sites in RNA.

Gene regulation plays an important role in development. Numerous DNA- and RNA-binding proteins bind their target sequences with high specificity to ...

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

The Replica Set method is an approach to quantitatively measure lifespan or survival of Caenorhabditis elegans nematodes in a high-throughput manner, thus allowing a single investigator to screen more treatments or conditions over the same amount of time without loss of data quality. The method requires common equipment found in most laboratories working with C. elegans and is thus simple to adopt. The approach centers on assaying independent samples of a population at each observation point, rather than a single sample over time as with traditional longitudinal methods. Scoring entails adding liquid to the wells of a multi-well plate, which stimulates C. elegans to move and facilitates quantifying changes in healthspan. Other major benefits of the Replica Set method include reduced exposure of agar surfaces to airborne contaminants (e.g. mold or fungus), minimal handling of animals, and robustness to sporadic mis-scoring (such as calling an animal as dead when it is still alive). To appropriately analyze and visualize the data from a Replica Set style experiment, a custom software tool was also developed. Current capabilities of the software include plotting of survival curves for both Replica Set and traditional (Kaplan-Meier) experiments, as well as statistical analysis for Replica Set. The protocols provided here describe the traditional experimental approach and the Replica Set method, as well as an overview of the corresponding data analysis.

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

Here we describe the Replica Set method, an approach to quantitatively measure C. elegans lifespan/survival and healthspan in a high-throughput and robust manner, thus allowing screening of many conditions without sacrificing data quality. This protocol details the strategy and provides a software tool for analysis of Replica Set data.

The Replica Set method is an approach to quantitatively measure lifespan or survival of Caenorhabditis elegans nematodes in a high-throughput manner, thus ...

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is inherently a single-cell assay, however prior to molecular analysis, the target cells are often prospectively isolated in bulk, thereby losing single-cell resolution. Single-cell gene expression analysis provides a means to understand molecular differences between individual cells in heterogeneous cell populations. In bulk cell analysis an overrepresentation of a distinct cell type results in biases and occlusions of signals from rare cells with biological importance. By utilizing FACS index sorting coupled to single-cell gene expression analysis, populations can be investigated without the loss of single-cell resolution while cells with intermediate cell surface marker expression are also captured, enabling evaluation of the relevance of continuous surface marker expression. Here, we describe an approach that combines single-cell reverse transcription quantitative PCR (RT-qPCR) and FACS index sorting to simultaneously characterize the molecular and immunophenotypic heterogeneity within cell populations.
In contrast to single-cell RNA sequencing methods, the use of qPCR with specific target amplification allows for robust measurements of low-abundance transcripts with fewer dropouts, while it is not confounded by issues related to cell-to-cell variations in read depth. Moreover, by directly index-sorting single-cells into lysis buffer this method, allows for cDNA synthesis and specific target pre-amplification to be performed in one step as well as for correlation of subsequently derived molecular signatures with cell surface marker expression. The described approach has been developed to investigate hematopoietic single-cells, but have also been used successfully on other cell types.
In conclusion, the approach described herein allows for sensitive measurement of mRNA expression for a panel of pre-selected genes with the possibility to develop protocols for subsequent prospective isolation of molecularly distinct subpopulations.

Bulk gene expression measurements cloud individual cell differences in heterogeneous cell populations. Here, we describe a protocol for how single-cell gene expression analysis and index sorting by Florescence Activated Cell Sorting (FACS) can be combined to delineate heterogeneity and immunophenotypically characterize molecularly distinct cell populations.

Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is ...

Generation of Cancer Cell Clones to Visualize Telomeric Repeat-containing RNA TERRA Expressed from a Single Telomere in Living Cells

Telomeres are transcribed, giving rise to telomeric repeat-containing long noncoding RNAs (TERRA), which have been proposed to play important roles in telomere biology, including heterochromatin formation and telomere length homeostasis. Recent findings revealed that TERRA molecules also interact with internal chromosomal regions to regulate gene expression in mouse embryonic stem (ES) cells. In line with this evidence, RNA fluorescence in situ hybridization (RNA-FISH) analyses have shown that only a subset of TERRA transcripts localize at chromosome ends. A better understanding of the dynamics of TERRA molecules will help define their function and mechanisms of action. Here, we describe a method to label and visualize single-telomere TERRA transcripts in cancer cells using the MS2-GFP system. To this aim, we present a protocol to generate stable clones, using the AGS human stomach cancer cell line, containing MS2 sequences integrated at a single subtelomere. Transcription of TERRA from the MS2-tagged telomere results in the expression of MS2-tagged TERRA molecules that are visualized by live-cell fluorescence microscopy upon co-expression of a MS2 RNA-binding protein fused to GFP (MS2-GFP). This approach enables researchers to study the dynamics of single-telomere TERRA molecules in cancer cells, and it can be applied to other cell lines.

Here, we present a protocol to generate cancer cell clones containing a MS2 sequence tag at a single subtelomere. This approach, relying on the MS2-GFP system, enables visualization of the endogenous transcripts of telomeric repeat-containing RNA (TERRA) expressed from a single telomere in living cells.

Telomeres are transcribed, giving rise to telomeric repeat-containing long noncoding RNAs (TERRA), which have been proposed to play important roles in ...