This work was supported by grants from the NIH (5R01MH094705-04 and R01MH109665-01 to Z.J.H.), by the CSHL Robertson Neuroscience Fund (to Z.J.H.), and by a NARSAD Post-Doctoral Fellowship (to A.P.).

<ol>
	<li>Macosko, E. Z., 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=Highly+Parallel+Genome-wide+Expression+Profiling+of+Individual+Cells+Using+Nanoliter+Droplets.">Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.</a> Cell. 161 (5), 1202-1214 (2015).</li><li>Klein, A. M., 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=Droplet+barcoding+for+single-cell+transcriptomics+applied+to+embryonic+stem+cells.">Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.</a> Cell. 161 (5), 1187-1201 (2015).</li><li>Xin, Y., 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=Use+of+the+Fluidigm+C1+platform+for+RNA+sequencing+of+single+mouse+pancreatic+islet+cells.">Use of the Fluidigm C1 platform for RNA sequencing of single mouse pancreatic islet cells.</a> Proceedings of the National Academy of Sciences of the United States of America. 113 (12), 3293-3298 (2016).</li><li>Gao, R., 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=Nanogrid+single-nucleus+RNA+sequencing+reveals+phenotypic+diversity+in+breast+cancer.">Nanogrid single-nucleus RNA sequencing reveals phenotypic diversity in breast cancer.</a> Nature Communications. 8 (1), 228 (2017).</li><li>Yuan, J., Sims, P. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+Automated+Microwell+Platform+for+Large-Scale+Single+Cell+RNA-Seq.">An Automated Microwell Platform for Large-Scale Single Cell RNA-Seq.</a> Scientific Reports. 6, 33883 (2016).</li><li>Eberwine, 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=Analysis+of+gene+expression+in+single+live+neurons.">Analysis of gene expression in single live neurons.</a> Proceedings of the National Academy of Sciences of the United States of America. 89 (7), 3010-3014 (1992).</li><li>Hashimshony, T., Wagner, F., Sher, N., Yanai, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CEL-Seq:+Single-Cell+RNA-Seq+by+Multiplexed+Linear+Amplification.">CEL-Seq: Single-Cell RNA-Seq by Multiplexed Linear Amplification.</a> Cell Reports. 2 (3), 666-673 (2012).</li><li>Paul, 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=Transcriptional+Architecture+of+Synaptic+Communication+Delineates+GABAergic+Neuron+Identity.">Transcriptional Architecture of Synaptic Communication Delineates GABAergic Neuron Identity.</a> Cell. 171 (3), 522-539 (2017).</li><li>Zeisel, 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=Cell+types+in+the+mouse+cortex+and+hippocampus+revealed+by+single-cell+RNA-seq.">Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq.</a> Science. 1934, (2015).</li><li>Tasic, B., 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=Adult+mouse+cortical+cell+taxonomy+revealed+by+single+cell+transcriptomics.">Adult mouse cortical cell taxonomy revealed by single cell transcriptomics.</a> Nature Neuroscience. 19, 335-346 (2016).</li><li>Okaty, B. W., 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=Multi-Scale+Molecular+Deconstruction+of+the+Serotonin+Neuron+System.">Multi-Scale Molecular Deconstruction of the Serotonin Neuron System.</a> Neuron. 88 (4), 774-791 (2015).</li><li>Poulin, J. F., Tasic, B., Hjerling-Leffler, J., Trimarchi, J. M., Awatramani, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Disentangling+neural+cell+diversity+using+single-cell+transcriptomics.">Disentangling neural cell diversity using single-cell transcriptomics.</a> Nature Neuroscience. 19 (9), 1131-1141 (2016).</li><li>Crow, M., Paul, A., Ballouz, S., Huang, Z. J., Gillis, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exploiting+single-cell+expression+to+characterize+co-expression+replicability.">Exploiting single-cell expression to characterize co-expression replicability.</a> Genome Biology. 17, 101 (2016).</li><li>Crow, M., Paul, A., Ballouz, S., Huang, Z. J., Gillis, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Characterizing+the+replicability+of+cell+types+defined+by+single+cell+RNA-sequencing+data+using+MetaNeighbor.">Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor.</a> Nature Communications. 9 (1), (2018).</li><li>Hrvatin, S., 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+analysis+of+experience-dependent+transcriptomic+states+in+the+mouse+visual+cortex.">Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex.</a> Nature Neuroscience. 21 (1), 120-129 (2018).</li></ol>

Authors declare that there are no competing financial interests.

The manual sorting protocol is suitable for a supervised RNA sequencing of neuron populations that are either sparsely labeled in the mice brain or are representing a rare cell population that is otherwise not feasible to study using current high-throughput cell sorting and amplification methods. Cells subjected to FACS usually undergo sheath and sample line pressures in the range of ~9&#8211;14 psi, depending on nozzle size and desired event rates. In addition, upon being ejected from the nozzle, the cells can land hard on the surface of the collection tube or wells coated with sample buffer causing impact stress. During manual sorting, such high pressures are never applied, as the cells are sucked into the pipette by capillary action and expelled by gently blowing them out and simply breaking tips of the glass pipettes. The DIVA-Seq protocol is useful for RNA amplification from cells with small cellular volumes (&#60;8 &#181;L) and low starting material and consistently yields large numbers of detectable genes (8&#8211;10 K), which, when coupled with deep sequencing, allows for detailed reconstructions of a coherent molecular picture of cellular functions underlying cell identity8,13,14. Due to purity of cell collection steps, high sensitivity of gene detection, and the ability to perform absolute molecule counts, this method is useful for studying cellular states and disease pathophysiology with high depth and precision.
While the yield of amplified RNA and the degree of gene detection is relatively high in this protocol, certain procedural measures help maintain consistency. During second-strand synthesis, assembly must be done on ice, the thermal cycler must be pre-cooled before transfer to the unit, and the reaction must be done strictly at 16 &#176;C (or slightly below) to avoid formation of hairpins that may reduce aRNA yield. It is also advised not to exceed 2 h at 16 &#176;C during second-strand synthesis, and it is important to move to the cDNA purification step as soon as possible. During the IVT steps, incubation for less than 12 h might reduce aRNA yield, whereas exceeding 14 h of IVT time may result in some aRNA degradation.
We did not perform a comparative study with the same input sample from litter-mates subjected to FACS and manual sorting using DIVA-Seq; hence, we do not claim that any particular gene category is misregulated in FACS and not in manual sorting. Both FACS and manual sorting will likely introduce some degree of gene expression artifacts. For differential gene expression situations, any such effect should in theory cancel out one another, as it will be manifested in both the control and sample groups. Recently, a cocktail of transcription inhibitors have been used to prevent the activation of immediate early gene expression, and such steps can also be incorporated to this protocol15.
The manual sorting process is gentle and quicker (usually 90&#8211;160 min) compared to FACS (excluding the sample preparation times) that requires density gradient centrifugation, staining with viability, cytotracker dyes, and post-sorting visualization. Manual sorting does not subject the cells to high sheath pressure and impact stress upon sorting onto wells. It also allows near-constant access to oxygenated ACSF and overall provides a hospitable and less stressful sorting environment, which may be crucial for cells that are sensitive to stress such as fast spiking cells with high metabolic demands. In DIVA-Seq currently, up to 96 cells can be multiplexed to save reagent costs and provide absolute mRNA counting with high gene counts per cell.
However, there are drawbacks to this method; for example, manual sorting needs reliable fluorescently labeled cells as a starting population. It is inherently a low-throughput process, with each sorting session yielding 32&#8211;64 cells at its maximum, which is considerably lower than in FACS. Manual sorting also requires fine motor skills and some practice to manipulate glass pipettes under a dissection microscope and capture single cells in microcapillary pipettes. The DIVA-Seq amplification is 3&#39;-biased; hence, it cannot be used for whole transcriptome amplification and is also not suitable for splice isoform detection.

Single-cell RNA sequencing (RNA-seq) has transformed transcriptomic studies. While large-scale single-cell RNAseq can be performed using a variety of techniques, such as droplets1,2, microfluidics3, nanogrids4, and microwells5, most methods cannot sort defined cell types that express genetically encoded fluorophores. To isolate a select cell population, fluorescence-activated cell sorting (FACS) is often used to sort labeled cells in a single-cell mode. However, FACS has some restrictions and requires meticulous sample processing steps. First, a large number of input cells are typically needed (often several million cells per mL), with a significant fraction (&#62;15&#8211;20%) containing the labeled population. Second, cell preparations may require multiple rounds of density gradient centrifugation steps to remove glial fraction, debris, and cell clumps that might otherwise clog the nozzle or flow cell. Third, FACS usually employs staining and destaining steps for live/dead staining (e.g., 4&#8242;,6-diamidino-2-phenylindole (DAPI), propidium iodide (PI), and Cytotracker dyes), which take up additional time. Fourth, as a rule of thumb for two-color sorting (such as DAPI and green/red fluorescent protein (GFP/RFP)), usually two samples and one control are needed, requiring an unlabeled sample to be processed in addition to the desired mouse strain. Fifth, filtering is often performed multiple times before and during sample sorting to proactively prevent clogged sample lines in an FACS machine. Sixth, time must be allotted in most commonly used FACS setups to initialize and stabilize the fluid stream and perform droplet calibration. Seventh, control samples are typically run in sequence prior to the actual sample collection to set up compensation matrices, doublet rejection, setting gates, etc. Users either perform steps six and seven themselves ahead of time or require the assistance of a technician in parallel. Finally, post-FACS, there are often steps to ensure that only labeled single cells are present in each well; for example, by checking samples in a high-content screening setup such as a fast plate imager.
To circumvent the steps outlined above and facilitate a relatively quick, targeted sequencing of a small population of single fluorescently labeled neurons, we describe a manual sorting procedure followed by two rounds of a highly sensitive in vitro amplification protocol, called double in-vitro transcription with absolute counts sequencing (DIVA-Seq). The RNA amplification and cDNA library generation are adapted from Eberwine et al.6 and Hashimshony et al.7, with certain modifications to suit mouse interneurons that have smaller cellular volumes; furthermore, we have also found that it is equally useful for excitatory pyramidal neurons.

<table><tbody><tr><td>ERCC RNA Spike-In Control Mixes</td><td>Thermo Fisher</td><td>Cat# 4456740</td><td></td></tr><tr><td>SuperScript III</td><td>Thermo Fisher</td><td>Cat# 18080093</td><td></td></tr><tr><td>RNaseOUT Recombinant Ribonuclease Inhibitor</td><td>Thermo Fisher</td><td>Cat# 10777019</td><td></td></tr><tr><td>RNA fragmentation buffer</td><td>New England Biolabs</td><td>Cat# E6105S</td><td></td></tr><tr><td>RNA MinElute kit</td><td>Qiagen</td><td>Cat# 74204</td><td></td></tr><tr><td>Antarctic phosphatase</td><td>New England Biolabs</td><td>Cat# M0289</td><td></td></tr><tr><td>Poly nucleotide kinase</td><td>New England Biolabs</td><td>Cat# M0201</td><td></td></tr><tr><td>T4 RNA ligase2, truncated</td><td>New England Biolabs</td><td>Cat# M0242</td><td></td></tr><tr><td>Ampure Xp magnetic&amp;nbsp; beads</td><td>Beckman Coulter</td><td>Cat# A63880</td><td></td></tr><tr><td>SPRIselect size selection magnetic beads</td><td>Thermo Fisher</td><td>Cat# B23317</td><td></td></tr><tr><td>DL-AP5</td><td>Tocris</td><td>Cat# 0105</td><td></td></tr><tr><td>CNQX</td><td>Tocris</td><td>Cat# 1045</td><td></td></tr><tr><td>TTX</td><td>Tocris</td><td>Cat# 1078</td><td></td></tr><tr><td>Protease from Streptomyces griseus</td><td>Sigma-Aldrich</td><td>Cat# P5147</td><td></td></tr><tr><td>Message Amp II kit</td><td>Thermo Fisher</td><td>Cat# AM1751</td><td></td></tr><tr><td>Carbogen</td><td>Airgas</td><td>Cat# UN3156</td><td></td></tr><tr><td>Sylgard 184</td><td>Sigma-Aldrich</td><td>Cat# 761036</td><td></td></tr><tr><td>Illumina TrueSeq smallRNA kit</td><td>Illumina</td><td>Cat# RS-200-0012</td><td></td></tr><tr><td>Bioanalyzer RNA Pico chip</td><td>Agilent</td><td>Cat# 5067-1513</td><td></td></tr><tr><td>Bioanalyzer High Sensitvity DNA chip</td><td>Agilent</td><td>Cat# 5067-4626</td><td></td></tr><tr><td>Bioanalyzer 2100</td><td>Agilent</td><td></td><td></td></tr><tr><td>Dissection microscope with fluorescence and bright field illumination with DIC optics.&amp;nbsp; (Leica model MZ-16F).</td><td>Leica</td><td>Model MZ-16F</td><td></td></tr><tr><td>Glass microcapillary: Borosilicate capillary tubes 500/pk. OD= 1 mm, ID=0.58 mm, wall= 0.21 mm, Length= 150 mm.</td><td>Warner instruments</td><td>Model GC100-15, Order# 30-0017</td><td></td></tr><tr><td>Capillary pipette puller</td><td>Sutter Instruments Co</td><td>P-97</td><td></td></tr><tr><td>Vibratome</td><td>Thermo Microm</td><td>HM 650V</td><td></td></tr><tr><td>Vibratome tissue cooling unit</td><td>Thermo Microm</td><td>CU 65</td><td></td></tr></tbody></table>

single-cell rna sequencing of fluorescently labeled mouse neurons using manual sorting and double in vitro transcription with absolute counts sequencing (diva-seq)

Single-cell RNA sequencing (RNA-seq) is now a widely implemented tool for assaying gene expression. Commercially available single-cell RNA-sequencing platforms process all input cells indiscriminately. Sometimes, fluorescence-activated cell sorting (FACS) is used upstream to isolate a specifically labeled population of interest. A limitation of FACS is the need for high numbers of input cells with significantly labeled fractions, which is impractical for collecting and profiling rare or sparsely labeled neuron populations from the mouse brain. Here, we describe a method for manually collecting sparse fluorescently labeled single neurons from freshly dissociated mouse brain tissue. This process allows for capturing single-labeled neurons with high purity and subsequent integration with an in vitro transcription-based amplification protocol that preserves endogenous transcript ratios. We describe a double linear amplification method that uses unique molecule identifiers (UMIs) to generate individual mRNA counts. Two rounds of amplification results in a high degree of gene detection per single cell.

Using the protocol described above, GABAergic neurons were manually sorted (Figure 1) and RNA was amplified, then made into a cDNA library (Figure 2) and sequenced at high depth8. The amplified RNA (aRNA) products ranged between 200&#8211;4,000 bp in size, with a peak distribution slightly above 500 bp (Figure 3A). The bead-purified cDNA library was further size-restricted by a second round of purification using beads that eliminated smaller fragments less than 200 bp (Figure 3B and 3C) and with a peak at ~350 bp. Having shorter fragments will lead to empty reads (no mRNA inserts, only adapter and primer sequences), whereas longer fragments will occupy more space on the flow cells, reducing total read output. Upon sequencing, we routinely obtained a 4.8 x 105 median, or 6.9 x 105 average mapped reads per cell (Figure 4A). After duplicate RNA removal using UMIs, each single cell had a 1.0 x 105 median, or 1.4 x 105 average unique reads per cell (Figure 4B). In each single cell external RNA controls consortium (ERCC), spike-in RNA was used as an internal control for which the absolute number of molecules that are added to the sample can be calculated. There was a linear relationship of input to observed counts, with a slope of 0.92 and adjusted R2&#160;= 0.94 (Figure 4C). We detected on average ~10,000 genes per single neuron (ranging from ~7,500 to 12,000 genes/cell), with &#62; 95% of the single cells detecting &#62; 6,000 genes (Figure&#160;4D-4F). This number compares favorably against published data from similar mouse brain-derived single neurons (e.g., 1,865-4,760 genes in Zeisel et al.9, 7,250 genes in Tasic et al.10, and 8,000 genes in Okaty et al.11). Readers are directed to Poulin et al.12 for a detailed comparison.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58690/58690fig1.jpg" /> 
Figure 1:&#160;Workflow of manual sorting of neurons followed by DIVA-Seq. Fresh mouse brains were collected and sliced, and the region of interest was microdissected. Single neurons expressing fluorescent proteins were collected manually and amplified using two rounds of linear amplification by in vitro transcription. <a href="https://www.jove.com/files/ftp_upload/58690/58690fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58690/58690fig2.jpg" /> 
Figure 2: Schematic workflow of DIVA-Seq with two rounds of amplification while incorporating unique molecular identifiers (UMIs) or varietal-tags. Sample bar code (SBC) allows each single cell to be identified by its 9-nucleotide code (teal). UMI is a stretch of random nucleotides 10 bp in length that is different for each primer used. During the bioinformatics step, two mapped transcripts having the same UMI sequence will be counted only once, thus eliminating amplification duplicates and allowing for absolute transcript counting. RA5 and RA3 are sequencing primers, and T7-RA5 primer is needed to add the T7 sequences back to the first-round aRNA products so that the T7 RNA polymerase can rebind and perform a second round of linear amplification by in vitro transcription. <a href="https://www.jove.com/files/ftp_upload/58690/58690fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58690/58690fig3.jpg" /> 
Figure 3: Example bioanalyzer plots.&#160;(A)&#160;aRNA size distributions should be between 200-4,000 bp with a peak at around 500 bp. X-axis has arbitrary fluorescence unit [FU]. (B)&#160;Size distribution of cDNA library products after bead cleanup. (C)&#160;Size distribution after 0.7x SPRI size selection (step 6.6) with a peak around 350 bp.
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/58690/58690fig4.jpg" /> 
Figure 4: Example sequence read distributions and gene detection from manually-sorted neurons after DIVA-Seq8.&#160;(A)&#160;Total mapped read distribution. (B)&#160;Total unique reads distribution. (C)&#160;ERCC reads show linear relationship over 4 orders of magnitude. (D)&#160;Genes detected vs. read counts shows that &#62;95% single cells have &#62;6000 genes/cell. (E)&#160;Genes detected amongst 6 interneuron types are comparable. (F)&#160;Distribution of genes detected per cell, GEO accession #GSE92522. This figure has been adapted from Paul et al.8. <a href="https://www.jove.com/files/ftp_upload/58690/58690fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Buffer</td>
			<td>Item</td>
			<td>Concentration</td>
			<td>Amount (&#181;L)</td>
		</tr>
		<tr>
			<td>Sample Collection buffer</td>
			<td>Recombinant ribonuclease inhibitor</td>
			<td></td>
			<td>55</td>
		</tr>
		<tr>
			<td></td>
			<td>ERCC</td>
			<td>1:50K diluted</td>
			<td>110</td>
		</tr>
		<tr>
			<td></td>
			<td>Nuclease free water</td>
			<td></td>
			<td>605</td>
		</tr>
		<tr>
			<td></td>
			<td colspan="3">Aliquot 43.75 &#181;L of above into 16 tubes (2 strips of 8; 200 &#181;L PCR tubes); add following per tube</td>
		</tr>
		<tr>
			<td></td>
			<td>T7-UMI-primers (e.g. N10B1-N10B16)</td>
			<td>1 ng/&#181;L</td>
			<td>6.25&#160; &#181;L/tube</td>
		</tr>
		<tr>
			<td></td>
			<td colspan="3">Final volume in each tube 50 &#181;L (each tube can be split in 25 &#181;L aliquots and frozen at -80 &#176;C)</td>
		</tr>
		<tr>
			<td>Solutions:</td>
			<td>Item</td>
			<td>Concentration</td>
			<td>Amount</td>
		</tr>
		<tr>
			<td>ACSF: to make 5 L dissolve</td>
			<td>NaCl&#160;</td>
			<td>126 mM</td>
			<td>36.8 g</td>
		</tr>
		<tr>
			<td></td>
			<td>KCl&#160;</td>
			<td>3 mM</td>
			<td>1.15 g</td>
		</tr>
		<tr>
			<td></td>
			<td>NaH2PO4</td>
			<td>1.25 mM</td>
			<td>0.75 g</td>
		</tr>
		<tr>
			<td></td>
			<td>NaHCO3</td>
			<td>20 mM</td>
			<td>8.4 g</td>
		</tr>
		<tr>
			<td></td>
			<td colspan="3">To 500 mL of ACSF, bubble oxygen for 10-15 min then add following fresh each time:</td>
		</tr>
		<tr>
			<td></td>
			<td>D-glucose</td>
			<td>20 mM</td>
			<td>1.8 g</td>
		</tr>
		<tr>
			<td></td>
			<td>MgSO4</td>
			<td>2 mM</td>
			<td>0.5 mL from 4 M stock</td>
		</tr>
		<tr>
			<td></td>
			<td>CaCl2</td>
			<td>2 mM</td>
			<td>0.5 mL from 4 M stock</td>
		</tr>
		<tr>
			<td></td>
			<td>Keep ACSF oxygenated through out</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Solutions:</td>
			<td>Item</td>
			<td colspan="2">Concentration</td>
		</tr>
		<tr>
			<td colspan="2">Activity blocker cocktail: make a 100x stock</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>To 100 mL ACSF add</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>APV</td>
			<td>0.05 mM</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>CNQX</td>
			<td>0.02 mM</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>TTX</td>
			<td colspan="2">0.0001 mM (0.1 &#181;M)</td>
		</tr>
		<tr>
			<td>Solutions:</td>
			<td>Item</td>
			<td>Amount</td>
			<td></td>
		</tr>
		<tr>
			<td>Protease soltion (100 mL)</td>
			<td>Protease from Streptomyces griseus</td>
			<td>100 mg</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="3">Fetal bovine serum: commercial source, aliquot in 500 &#181;L for each use.</td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 1: List of solutions and buffers.
Table 2: List of primers and sequences. N10B1-N10B96 are first strand primers and T7-RA5 is the second-round primer.&#160;<a href="https://www.jove.com/files/ftp_upload/58690/Table-2.xlsx" target="_blank">Please click here to download this table.</a>

Watch this Scientific Journal Video about Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

This protocol describes the manual sorting procedure to isolate single fluorescently labeled neurons followed by in vitro transcription-based mRNA amplification for high-depth single-cell RNA sequencing.

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing (DIVA-Seq)

Single-cell RNA sequencing (RNA-seq) is now a widely implemented tool for assaying gene expression. Commercially available single-cell RNA-sequencing ...

single-cell-rna-sequencing-fluorescently-labeled-mouse-neurons-using

Nanyang Technological University

Research

JoVE Journal

Neuroscience

9.4K Views.  Cold Spring Harbor Laboratory. This protocol describes the manual sorting procedure to isolate single fluorescently labeled neurons followed by in vitro transcription-based mRNA amplification for high-depth single-cell RNA sequencing.

Video: Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

Production of RNA for Transcriptomic Analysis from Mouse Spinal Cord Motor Neuron Cell Bodies by Laser Capture Microdissection

Preparation of high-quality RNA from cells of interest is critical to precise and meaningful analysis of transcriptional differences among cell types or between the same cell type in health and disease or following pharmacologic treatments.&#160;In the spinal cord, such preparation from motor neurons, the target of interest in many neurologic and neurodegenerative diseases, is complicated by the fact that motor neurons represent &#60;10% of the total cell population.&#160;Laser capture microdissection (LMD) has been developed to address this problem.&#160;Here, we describe a protocol to quickly recover, freeze, and section mouse spinal cord to avoid RNA damage by endogenous and exogenous RNases, followed by staining with Azure B in 70% ethanol to identify the motor neurons while keeping endogenous RNase inhibited.&#160;LMD is then used to capture the stained neurons directly into guanidine thiocyanate lysis buffer, maintaining RNA integrity.&#160;Standard techniques are used to recover the total RNA and measure its integrity.&#160;This material can then be used for downstream analysis of the transcripts by RNA-seq and qRT-PCR.

High-quality total RNA has been prepared from cell bodies of mouse spinal cord motor neurons by laser capture microdissection after staining spinal cord sections with Azure B in 70% ethanol.&#160;Sufficient RNA (~40-60 ng) is recovered from 3,000-4,000 motor neurons to allow downstream RNA analysis by RNA-seq and qRT-PCR.

Preparation of high-quality RNA from cells of interest is critical to precise and meaningful analysis of transcriptional differences among cell types or ...

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Single cell mRNA sequencing has made significant progress in the last several years and has become an important tool in the field of developmental biology. It has been successfully used to identify rare cell populations, discover novel marker genes, and decode spatial and temporal developmental information. The single cell method has also evolved from the microfluidic based Fluidigm C1 technology to the droplet-based solutions in the last two to three years. Here we used the heart as an example to demonstrate how to profile the mouse embryonic tissue cells using the droplet based scRNA-Seq method. In addition, we have integrated two strategies into the workflow to profile multiple samples in a single experiment. Using one of the integrated methods, we have simultaneously profiled more than 9,000 cells from eight heart samples. These methods will be valuable to the developmental biology field by providing a cost-effective way to simultaneously profile single cells from different genetic backgrounds, developmental stages, or anatomical locations.

Here we presented a multiplexed single cell mRNA sequencing method to profile gene expression in mouse embryonic tissues. The droplet-based single cell mRNA sequencing (scRNA-Seq) method in combination with multiplexing strategies can profile single cells from multiple samples simultaneously, which significantly reduces reagent costs and minimizes experimental batch effects.

Single cell mRNA sequencing has made significant progress in the last several years and has become an important tool in the field of developmental ...

Developmental Biology

Reliable Identification of Living Dopaminergic Neurons in Midbrain Cultures Using RNA Sequencing and TH-promoter-driven eGFP Expression

In Parkinson&#39;s Disease (PD) there is widespread neuronal loss throughout the brain with pronounced degeneration of dopaminergic neurons in the SNc, leading to bradykinesia, rigidity, and tremor. The identification of living dopaminergic neurons in primary Ventral Mesencephalic (VM) cultures using a fluorescent marker provides an alternative way to study the selective vulnerability of these neurons without relying on the immunostaining of fixed cells. Here, we isolate, dissociate, and culture mouse VM neurons for 3 weeks. We then identify dopaminergic neurons in the cultures using eGFP fluorescence (driven by a Tyrosine Hydroxylase (TH) promoter). Individual neurons are harvested into microcentrifuge tubes using glass micropipettes. Next, we lyse the harvested cells, and conduct cDNA synthesis and transposon-mediated &#34;tagmentation&#34; to produce single cell RNA-Seq libraries1,2,3,4,5. After passing a quality-control check, single-cell libraries are sequenced and subsequent analysis is carried out to measure gene expression. We report transcriptome results for individual dopaminergic and GABAergic neurons isolated from midbrain cultures. We report that 100% of the live TH-eGFP cells that were harvested and sequenced were dopaminergic neurons. These techniques will have widespread applications in neuroscience and molecular biology.

In Parkinson&#39;s Disease (PD), Substantia Nigra (SNc) dopaminergic neurons degenerate, leading to motor dysfunction. Here we report a protocol for culturing ventral midbrain neurons from a mouse expressing eGFP driven by a Tyrosine Hydroxylase (TH) promoter sequence, harvesting individual fluorescent neurons from the cultures, and measuring their transcriptome using RNA-seq.

In Parkinson&#39;s Disease (PD) there is widespread neuronal loss throughout the brain with pronounced degeneration of dopaminergic neurons in the SNc, ...

Isolation and Genotyping of Gastrulating Mouse Embryo for Single-Cell Sequencing

Single-Cell RNA Sequencing of Mutant Whole Mouse Embryos: From the Epiblast to the End of Gastrulation

Over the last decade, single-cell approaches have become the gold standard for studying gene expression dynamics, cell heterogeneity, and cell states within samples. Before single-cell advances, the feasibility of capturing the dynamic cellular landscape and rapid cell transitions during early development was limited. In this paper, a robust pipeline was designed to perform single-cell and nuclei analysis on mouse embryos from embryonic day E6.5 to E8, corresponding to the onset and completion of gastrulation. Gastrulation is a fundamental process during development that establishes the three germinal layers: mesoderm, ectoderm, and endoderm, which are essential for organogenesis. Extensive literature is available on single-cell omics applied to wild-type perigastrulating embryos. However, single-cell analysis of mutant embryos is still scarce and often limited to FACS-sorted populations. This is partially due to the technical constraints associated with the need for genotyping, timed pregnancies, the count of embryos with desired genotypes per pregnancy, and the number of cells per embryo at these stages. Here, a methodology is presented designed to overcome these limitations. This method establishes breeding and timed pregnancy guidelines to achieve a higher chance of synchronized pregnancies with desired genotypes. Optimization steps in the embryo isolation process coupled with a same-day genotyping protocol (3 h) allow for microdroplet-based single-cell to be performed on the same day, ensuring the high viability of cells and robust results. This method further includes guidelines for optimal nuclei isolations from embryos. Thus, these approaches increase the feasibility of single-cell approaches of mutant embryos at the gastrulation stage. We anticipate that this method will facilitate the analysis of how mutations shape the cellular landscape of the gastrula.

Author Spotlight: A Pipeline to Analyze Lineage-Specific Mutant Embryos at Single-Cell Resolution

This paper establishes a pipeline for high-quality single-cell and nuclei suspensions of gastrulating mouse embryos for sequencing of single cells and nuclei.

Over the last decade, single-cell approaches have become the gold standard for studying gene expression dynamics, cell heterogeneity, and cell states ...

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

The discovery of cell type-specific markers can provide insight into cellular function and the origins of cellular heterogeneity. With a recent push for the improved understanding of neuronal diversity, it is important to identify genes whose expression defines various subpopulations of cells. The retina serves as an excellent model for the study of central nervous system diversity, as it is composed of multiple major cell types. The study of each major class of cells has yielded genetic markers that facilitate the identification of these populations. However, multiple subtypes of cells exist within each of these major retinal cell classes, and few of these subtypes have known genetic markers, although many have been characterized by morphology or function. A knowledge of genetic markers for individual retinal subtypes would allow for the study and mapping of brain targets related to specific visual functions and may also lend insight into the gene networks that maintain cellular diversity. Current avenues used to identify the genetic markers of subtypes possess drawbacks, such as the classification of cell types following sequencing. This presents a challenge for data analysis and requires rigorous validation methods to ensure that clusters contain cells of the same function. We propose a technique for identifying the morphology and functionality of a cell prior to isolation and sequencing, which will allow for the easier identification of subtype-specific markers. This technique may be extended to non-neuronal cell types, as well as to rare populations of cells with minor variations. This protocol yields excellent-quality data, as many of the libraries have provided read depths greater than 20 million reads for single cells. This methodology overcomes many of the hurdles presented by Single-cell RNA-Seq and may be suitable for researchers aiming to profile cell types in a straightforward and highly efficient manner.

Here, we present a combinatorial approach for classifying neuronal cell types prior to isolation and for the subsequent characterization of single-cell transcriptomes. This protocol optimizes the preparation of samples for successful RNA Sequencing (RNA-Seq) and describes a methodology designed specifically for the enhanced understanding of cellular diversity.

The discovery of cell type-specific markers can provide insight into cellular function and the origins of cellular heterogeneity. With a recent push for ...

Isolation of Region-specific Microglia from One Adult Mouse Brain Hemisphere for Deep Single-cell RNA Sequencing

As resident macrophages in the central nervous system, microglia actively control brain development and homeostasis, and their dysfunctions may drive human diseases. Considerable advances have been made to uncover the molecular signatures of homeostatic microglia as well as alterations of their gene expression in response to environmental stimuli. With the advent and maturation of single-cell genomic methodologies, it is increasingly recognized that heterogenous microglia may underlie the diverse roles they play in different developmental and pathological conditions. Further dissection of such heterogeneity can be achieved through efficient isolation of microglia from a given region of interest, followed by sensitive profiling of individual cells. Here, we provide a detailed protocol for the rapid isolation of microglia from different brain regions in a single adult mouse brain hemisphere. We also demonstrate how to use these sorted microglia for plate-based deep single-cell RNA sequencing. We discuss the adaptability of this method to other scenarios and provide guidelines for improving the system to accommodate large-scale studies.

We provide a protocol for isolation of microglia from different dissected regions of an adult mouse brain hemisphere, followed by semi-automated library preparation for deep single-cell RNA sequencing of full-length transcriptomes. This method will help to elucidate functional heterogeneity of microglia in health and disease.

As resident macrophages in the central nervous system, microglia actively control brain development and homeostasis, and their dysfunctions may drive ...

Live Animal Imaging and Cell Sorting Methods for Investigating Neurodegeneration in a C. elegans Excitotoxic Necrosis Model

Excitotoxic necrosis is a leading form of neurodegeneration. This process of regulated necrosis is triggered by the synaptic accumulation of the neurotransmitter glutamate, and the excessive stimulation of its postsynaptic receptors. However, information on the subsequent molecular events that culminate in the distinct neuronal swelling morphology of this type of neurodegeneration is lacking. Other aspects, such as changes in specific subcellular compartments, or the basis for the differential cellular vulnerability of distinct neuronal subtypes, remain under-explored. Furthermore, a range of factors that come into play in studies that use in vitro or ex vivo preparations might modify and distort the natural progression of this form of neurodegeneration. It is therefore important to study excitotoxic necrosis in live animals by monitoring the effects of interventions that regulate the extent of neuronal necrosis in the genetically amenable and transparent&#160;model system of the nematode Caenorhabditis elegans. This protocol describes methods of studying excitotoxic necrosis in C. elegans neurons, combining optical, genetic, and molecular analysis. To induce excitotoxic conditions in C. elegans, a&#160;knockout of a glutamate transporter gene (glt-3) is combined with a neuronal sensitizing genetic background (nuls5 [Pglr-1::G&#945;S(Q227L)]) to produce glutamate receptor hyperstimulation and neurodegeneration. Nomarski differential interference contrast (DIC), fluorescent, and confocal microscopy in live animals are methods used to quantify neurodegeneration, follow subcellular localization of fluorescently labeled proteins, and quantify mitochondrial morphology in the degenerating neurons. Neuronal Fluorescence Activated Cell Sorting (FACS) is used to distinctly sort at-risk neurons for cell-type specific transcriptomic analysis of neurodegeneration. A combination of live imaging and FACS methods as well as the benefits of the C. elegans model organism allow researchers to leverage this system to obtain reproducible data with a large sample size. Insights from these assays could translate to novel targets for therapeutic intervention in neurodegenerative diseases.

Live Animal Imaging and Cell Sorting Methods for Investigating Neurodegeneration in a C. elegans Excitotoxic Necrosis Model

In a C. elegans excitotoxicity model, this protocol employs in vivo imaging to analyze the regulation of necrotic neurodegeneration, the effect of genes encoding candidate mediators, and involvement of mitochondria. Cell dissociation and sorting is used to specifically obtain at-risk neurons for cell-specific transcriptomic analysis of neurodegeneration and neuroprotection mechanisms.

Excitotoxic necrosis is a leading form of neurodegeneration. This process of regulated necrosis is triggered by the synaptic accumulation of the ...

RNA Isolation from Cell Specific Subpopulations Using Laser-capture Microdissection Combined with Rapid Immunolabeling

Laser capture microdissection (LCM) allows the isolation of specific cells from thin tissue sections with high spatial resolution. Effective LCM requires precise identification of cells subpopulations from a heterogeneous tissue. Identification of cells of interest for LCM is usually based on morphological criteria or on fluorescent protein reporters. The combination of LCM and rapid immunolabeling offers an alternative and efficient means to visualize specific cell types and to isolate them from surrounding tissue. High-quality RNA can then be extracted from a pure cell population and further processed for downstream applications, including RNA-sequencing, microarray or qRT-PCR. This approach has been previously performed and briefly described in few publications. The goal of this article is to illustrate how to perform rapid immunolabeling of a cell population while keeping RNA integrity, and how to isolate these specific cells using LCM. Herein, we illustrated this multi-step procedure by immunolabeling and capturing dopaminergic cells in brain tissue from one-day-old mice. We highlight key critical steps that deserve special consideration. This protocol can be adapted to a variety of tissues and cells of interest. Researchers from different fields will likely benefit from the demonstration of this approach.

Gene expression analysis of a subset of cells in a specific tissue represents a major challenge. This article describes how to isolate high-quality total RNA from a specific cell population by combining a rapid immunolabeling method with laser capture microdissection.

Laser capture microdissection (LCM) allows the isolation of specific cells from thin tissue sections with high spatial resolution. Effective LCM requires ...

Biology

Fluorescence-Activated Nuclei Negative Sorting of Neurons Combined with Single Nuclei RNA Sequencing to Study the Hippocampal Neurogenic Niche

Adult Hippocampal Neurogenesis (AHN), which consists of a lifelong maintenance of proliferative and quiescent neural stem cells (NSCs) within the sub-granular zone (SGZ) of the dentate gyrus (DG) and their differentiation from newly born neurons into granule cells in the granule cell layer, is well validated across numerous studies. Using genetically modified animals, particularly rodents, is a valuable tool to investigate signaling pathways regulating AHN and to study the role of each cell type that compose the hippocampal neurogenic niche. To address the latter, methods combining single nuclei isolation with next generation sequencing have had a significant impact in the field of AHN to identify gene signatures for each cell population. Further refinement of these techniques is however needed to phenotypically profile rarer cell populations within the DG. Here, we present a method that utilizes Fluorescence Activated Nuclei Sorting (FANS) to exclude most neuronal populations from a single nuclei suspension isolated from freshly dissected DG, by selecting unstained nuclei for the NeuN antigen, in order to perform single nuclei RNA sequencing (snRNA-seq). This method is a potential steppingstone to further investigate intercellular regulation of the AHN and to uncover novel cellular markers and mechanisms across species.

Presented here is a method to sequence single nuclei isolated from the mouse dentate gyrus that excludes most neurons through fluorescence-activated nuclei (FAN)-sorting. This approach generates high-quality expression profiles and facilitates the study of most other cell types represented in the niche, including scarce populations such as neural stem cells.

Adult Hippocampal Neurogenesis (AHN), which consists of a lifelong maintenance of proliferative and quiescent neural stem cells (NSCs) within the ...

Isolating Fluorescently Labeled Neurons from a Murine Brain

Source: Paul, A., et al., <a href="https://app.jove.com/v/58690">Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing (DIVA-Seq).</a> J. Vis. Exp. (2018)
This video demonstrates a method to isolate fluorescently labeled neurons from a mouse brain.

Izolowanie fluorescencyjnie znakowanych neuronów z mysiego mózgu

All procedures involving sample collection have been performed in accordance with the institute&#39;s IRB guidelines.
1. Manual Sorting of Fluorescently ...

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing (DIVA-Seq)

Podsumowanie

Przeglądaj więcej filmów

Rozdziały w tym wideo

Production of RNA for Transcriptomic Analysis from Mouse Spinal Cord Motor Neuron Cell Bodies by Laser Capture Microdissection

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Reliable Identification of Living Dopaminergic Neurons in Midbrain Cultures Using RNA Sequencing and TH-promoter-driven eGFP Expression

Single-Cell RNA Sequencing of Mutant Whole Mouse Embryos: From the Epiblast to the End of Gastrulation

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Isolation of Region-specific Microglia from One Adult Mouse Brain Hemisphere for Deep Single-cell RNA Sequencing

Live Animal Imaging and Cell Sorting Methods for Investigating Neurodegeneration in a C. elegans Excitotoxic Necrosis Model

RNA Isolation from Cell Specific Subpopulations Using Laser-capture Microdissection Combined with Rapid Immunolabeling

Fluorescence-Activated Nuclei Negative Sorting of Neurons Combined with Single Nuclei RNA Sequencing to Study the Hippocampal Neurogenic Niche

Izolowanie fluorescencyjnie znakowanych neuronów z mysiego mózgu