We would like to acknowledge Jennifer Bair and Einat Snir, as well as the University of Iowa Institute for Human Genetics, for their assistance in preparing and handling samples.

<ol>
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The authors have nothing to disclose.

Our protocol demonstrates, through a quick and easy-to-use guide, a method to prepare single cells of identified morphological classes for high-quality sequencing, with little injury to the sample. In the present manuscript, intrinsically photosensitive retinal ganglion cells are morphologically characterized, isolated, and prepared for RNA-Seq. Cellular stresses may occur during retinal handling; for this reason, we replace each piece of tissue after no more than 4 h of use. We can assess the state of the cells by using the electrophysiology rig to record from these cells and to monitor their responses, which allows us to ensure that the cells are of good health. Our protocol allowed for the generation of successful cDNA libraries from 15 out of 23 samples processed, giving a success rate of 65%. Furthermore, the quality of our sequencing data was excellent, as the average number of genes expressed by our cells ranged from 2,316-10,353, with an average of 5,683 genes registering with a non-zero FPKM value. These numbers are similar to expressed gene counts found by other studies of neurons5, showing that our data is also of good quality.
While we have had success with this protocol for these specific neurons, it should be noted that minor amendments to this technique can be applied for a number of different applications. Using Fluorescence-activated Cell Sorter (FACS) within a separate mouse model, we have isolated small populations of cells (1,000 -25,000) labeled with a GFP marker from the central nervous system. The RNA from these populations was isolated, and 1 &#181;L (at or slightly below 12 ng/&#181;L) of this RNA was used to begin the reverse transcription at step 6.1. The only adjustment that should be made to the protocol after this step occurs at step 7.4, at which point one may choose to employ fewer PCR cycles. We have used 19 cycles in cases when the initial sample contained greater than 10,000 cells and have found that this produces good-quality cDNA libraries. For example, cell samples were prepared from a FACSorted population, and the number of genes expressed ranged from 12,340-14,052, with an average of 13,265 genes with a non-zero FPKM value. This technique can be applied to various mouse models for which a fluorescent reporter is present. Due to this amendment, researchers could potentially study any cell population for which a fluorescent reporter mouse is available, extending studies past the field of neuronal diversity.
A second adjustment can be made to profile cells based on functionality rather than just morphology. This project relies on a transgenic model, but this technique can be applied to neurons with no known molecular identifiers. For example, there are over 30 functional subtypes of retinal ganglion cells currently identified27, very few of which have unique markers. As this technique already relies on an electrophysiology rig for the cell filling, one could employ patch clamping to identify the functional response profile of each cell based on its spiking pattern. Using light-evoked spike recordings, the classification of retinal neurons could be determined prior to cell isolation. This technique works on retinal ganglion cells, but it may be extended to examine other retinal neurons. It should be noted, however, that if this protocol is used to type retinal bipolar or amacrine cells in the inner nuclear layer, for example, one must be careful to provide constant positive pressure at the tip of the electrode to avoid contacting cells as the electrode passes through the ganglion cell layer and inner plexiform layer. For retinal neurons within the inner nuclear layer, the preparation of retinal sections prior to cellular recordings would likely work best to consistently avoid contamination. Each cell would be isolated and prepared as described here following the classification step. Furthermore, we propose the use of this protocol for the study of neurons, but this technique may be applied to any cell type that can be morphologically characterized or isolated through the use of a transgenic model.
This technique can also be modified to work with previously prepared cDNA libraries, as we have generated in the past for microarray hybridization28. With a separately prepared library, one begins with cDNA quantities in the range of 50-150 ng and starts the protocol at step 8.4; higher and lower concentrations have not been attempted, although they may work as well. Purification of the cDNA using DNA beads would occur first, followed by tagmentation and PCR amplification. We have tested this adjustment with cDNA from library preparations, such as those for microarray hybridization29, and have successfully produced tagmented libraries for RNA-Seq.
Finally, this protocol can be used in the assessment of the success of a particular induction of cell populations from induced Pluripotent Stem Cells (iPSCs). The driving force behind differentiation of these re-programmed pluripotent cells relies on an understanding of the transcription factors that lead cell types to develop separately from one another. Driving iPSCs toward particular cell fates is a new method for studying neuronal diversity, as it can be used to selectively generate cell types. This protocol can be adapted to assess the quality of diversity among differentiated cells from this model. As previously mentioned, there are more than 30 functional subtypes of retinal ganglion cells, and many efforts have been made to generate functional RGCs from iPSCs. The identification of markers for subtypes of RGCs &#8211; in our case, ipRGCs &#8211; would allow the researcher to evaluate the success of their protocol in producing various subtypes of RGCs. The evaluation of cell types among these neurons would benefit from this protocol and may also serve as a tool for the continued investigation of subtype-specific markers as this model system becomes readily available.
In the present manuscript, we describe a simple technique for the isolation and preparation of single cells for transcriptomic analysis. Moreover, we suggest ways to edit the protocol so that this technique can be used for a multitude of experiments with a range of goals in mind. The protocol described here was adapted from a protocol described by Trombetta et al.24 as an adjustment of the recommended kit instructions for low-input RNA-Seq. The major differences lie within cell isolation and various volumetric changes that we have chosen to best suit the needs of this project. It is important to highlight that the most important step in this protocol is the isolation of cells from the retinal tissue. This is the point in the protocol during which most errors can occur, and it should be carried out with the most careful attention. Any amount of contamination can result in unusable samples, while the loss of cytoplasm may cause a severe depletion of mRNA molecules. This step should be carried out with care and by the same individual from experiment to experiment to ensure that the samples have been handled precisely.
In summary, this protocol describes a technique for the classification, isolation, and preparation of cells for high-quality RNA-Seq. This is an efficient, relatively low-cost way to optimize the quality of data from single cells. This technique is versatile and may be minimally modified for application to various studies.

Neuronal diversity is observed throughout the central nervous system, particularly in the vertebrate retina, a highly specialized tissue consisting of 1 glial and 6 neuronal cell types that arise from one population of retinal progenitor cells1,2,3. Many subtypes of cells can be classified functionally, morphologically, and genetically. The goal of this protocol is to tie the genetic variability of cell types to their identifiable functional and/or morphological characteristics. A number of genes have been identified for the classification of cells, but many subtypes continue to go uncharacterized, as they represent a small fraction of the overall population. The identification of genes within these specific subtypes will allow for a greater understanding of neuronal diversity within the retina and may also shed light on the diversification of neural cells elsewhere. Furthermore, single-cell studies allow for the uncovering of new cell types, which may have been overlooked due to their low representation among the overall population4,5,6,7.
One of the benefits of single-cell transcriptomics is that unique markers or combinations of markers that define a particular cellular subtype can be discovered. These can then be used to gain genetic access to that cell type for different manipulations. For example, we are using this protocol to characterize the cell type-specific genes of a subset of retinal ganglion cells that express the photopigment melanopsin. The use of a fluorescent marker in melanopsin-expressing retinal ganglion cells enables the study of these cells, as they are clustered together due to their expression of a known gene. Interestingly, there are five known subtypes of this cell population in the mouse retina8. Thus, in order to isolate RNA from cells of each type, we have used established morphological classifications within the transgenic model to identify each subtype prior to cell isolation. This technique allows for the characterization of cells as well as for their isolation directly from the retina, without the need for tissue dissociation, which may cause a stress response within cells and contamination due to severed dendrites9.
A multitude of new techniques have come to light in the past few years as the RNA-Seq method continues to develop. These tools allow for maximized cell acquisition and greater cost efficiency while approaching the question at hand4,7,10,11,12,13. However, while these techniques have been excellent stepping stones, there are a number of hurdles still encountered that this protocol is able to address. First, many of the current procedures isolate cells from dissociated tissue and attempt to use either principal component analysis or hierarchical clustering post-hoc to determine cell classification. Relying on these tools to classify subtypes may not produce reliable results and may force one to find new ways to validate this data for the correlation of a genetic marker to a functional cell type. The requirement for dissociation in other protocols can sometimes result in tissue damage and can cause neuronal processes to be severed, resulting in a potential loss of mRNA. Furthermore, in dissociated cell preparations, the stress responses may begin to affect the transcriptomes of these cells14. This protocol overcomes these challenges by determining the functional cell type prior to isolation, and it better maintains the health of the cells by keeping the retinal tissue intact.
One technique was introduced in 2014 and consisted of the in vivo analysis of the transcriptome of live cells15. While this technique allows for the examination of the transcriptome with minimal mechanical disruption to the tissue, it lacks the ability to classify specific cell types within the tissue before examining their transcriptomes without using a very specific reporter mouse. Our protocol does not require a specific reporter, as we utilize cell filling and electrophysiology to characterize cells before their isolation. Another limitation of this previous protocol is that it requires a specific wavelength to excite the photoactivatable element, whereas our protocol allows for the use of a fluorescent reporter and fluorescent dye, which are readily available or can be chosen by each lab individually. Still, other laboratories have married the two methods of electrophysiology and transcriptomics for the study of cellular diversity. The use of patch-clamp recordings to characterize the function of a cell prior to its isolation has been performed on dissociated neurons16 and, in some cases, it has preceded the use of microarray analysis17 for these studies. The same complications are encountered by those approaches, as they require tissue dissociation or the use of microarray technology, which relies on the hybridization of samples to available probes. One of the most recent advances has been the development of Patch-Seq, a technique that combines the use of patch-clamp recordings and RNA-Seq technology to understand cells from whole-brain slices18. While this technique has its similarities to the protocol presented here, it is again important to note that our approach allows the tissue to remain intact for the health of the cells. Here, we present a protocol for the optimization of this alliance, which generates high-quality, single-cell libraries for the use of RNA-Seq to obtain a high read depth and mapping coverage.

<table border="0" cellpadding="0" cellspacing="0" width="907">
	<tbody>
		<tr height="20">
			<td height="20" style="height:20px;width:257px;">Ames&#39; Medium</td>
			<td style="width:171px;">Sigma Aldrich</td>
			<td style="width:108px;">A1420-10X1L</td>
			<td style="width:372px;"></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Sodium Bicarbonate</td>
			<td>Sigma Aldrich</td>
			<td>S8875</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">K-gluconate</td>
			<td>Spectrum Chemical</td>
			<td>PO178</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">EGTA</td>
			<td>Sigma Aldrich</td>
			<td>E4378</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">HEPES</td>
			<td>Sigma Aldrich</td>
			<td>H3375</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Diethyl pyrocarbonate (DEPC)</td>
			<td>Sigma Aldrich</td>
			<td>D5758</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Alexa Fluor 594 Hydrazide</td>
			<td>Invitrogen</td>
			<td>A10442</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Collagenase</td>
			<td>Worthington Biochemical&#160;</td>
			<td>LS005273</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Hyaluronidase</td>
			<td>Worthington Biochemical&#160;</td>
			<td>LS002592</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Petri dish (35mm diameter)</td>
			<td>Thermo Fisher Scientific</td>
			<td>153066</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Ophthalmologic scissors</td>
			<td>Fine Science Tools</td>
			<td>15000-00</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">#5 Forceps</td>
			<td>Fine Science Tools</td>
			<td>11252-30</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Microplate Shaker</td>
			<td>Fisher Scientific</td>
			<td>13-687-708</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Glass Micropipette</td>
			<td>Sutter</td>
			<td>BF120-69-10</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Micropipette Puller</td>
			<td>Sutter</td>
			<td></td>
			<td>P-1000 horizontal pipette puller</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">1mL syringe</td>
			<td>Fisher Scientific</td>
			<td>14-823-2F</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Flexible tubing</td>
			<td>Fisher Scientific</td>
			<td>14-171</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">TCL lysis buffer</td>
			<td>Qiagen</td>
			<td>1031576</td>
			<td>Lysis Buffer 1</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">&#946;-mercaptoethanol</td>
			<td>Sigma Aldrich</td>
			<td>M3148</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">RNase-Free Water</td>
			<td>Qiagen</td>
			<td>129112</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">0.2 ml PCR tubes</td>
			<td>Eppendorf</td>
			<td>30124359</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Ethyl Alcohol, Pure</td>
			<td>Sigma Aldrich</td>
			<td>E7023</td>
			<td>Ethanol</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Analog Vortex Mixer</td>
			<td>Thermo Fisher Scientific</td>
			<td>02215365</td>
			<td>Vortex</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Mini Centrifuge</td>
			<td>Thermo Fisher Scientific</td>
			<td>05-090-100</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Agencourt RNAClean XP Beads</td>
			<td>Beckman Coulter</td>
			<td>A63987</td>
			<td>RNA magnetic beads</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">MagnaBlot II Magnetic Separator</td>
			<td>Promega</td>
			<td>V8351</td>
			<td>Magnetic stand</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">1.5 ml MCT Graduated Tubes</td>
			<td>Thermo Fisher Scientific</td>
			<td>05-408-129</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Smart-Seq v4 Ultra Low Input RNA Kit</td>
			<td>Clontech</td>
			<td>634888</td>
			<td>Reagents for Reverse Transcription and PCR Amplification</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">10X Lysis Buffer</td>
			<td></td>
			<td></td>
			<td>Lysis Buffer 2</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">5X Ultra Low First-Strand Buffer</td>
			<td></td>
			<td></td>
			<td>Buffer 1</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">3&#39; SMART-Seq CDS Primer II A</td>
			<td></td>
			<td></td>
			<td>Primer II</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">SMART-Seq v4 Oligonucleotide</td>
			<td></td>
			<td></td>
			<td>Oligonucleotide</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">SMARTScribe Rverse Transcriptase</td>
			<td></td>
			<td></td>
			<td>Reverse Transcriptase</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">2X SeqAmp PCR Buffer</td>
			<td></td>
			<td></td>
			<td>PCR Buffer</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">PCR Primer II A</td>
			<td></td>
			<td></td>
			<td>PCR Primer</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">SeqAmp DNA Polymerase</td>
			<td></td>
			<td></td>
			<td>DNA Polymerase</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Mastercycler pro S</td>
			<td>Eppendorf</td>
			<td>950030020</td>
			<td>Thermocycler</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Agencourt AMPure XP Beads</td>
			<td>Beckman Coulter</td>
			<td>A63881</td>
			<td>DNA magnetic beads</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">2100 Bioanalyzer</td>
			<td>Agilent Technologies</td>
			<td>G2939AA</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">HS Bioanalyzer Chips &#38; Reagents</td>
			<td>Agilent Technologies</td>
			<td>5067-4626</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Qubit HS Assay Kit</td>
			<td>Thermo Fisher Scientific</td>
			<td>Q32851</td>
			<td>For the calculation of sample concentrations</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Qubit Assay Tubes</td>
			<td>Thermo Fisher Scientific</td>
			<td>Q32856</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Qubit 2.0 Fluorometer</td>
			<td>Thermo Fisher Scientific</td>
			<td>Q32866</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Nextera XT DNA Sample Preparation Kit</td>
			<td>Illumina</td>
			<td>FC-131-1024</td>
			<td>Reagents for Tagmentation and Index Coupling</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">TD Buffer</td>
			<td></td>
			<td></td>
			<td>Buffer 2</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">ATM</td>
			<td></td>
			<td></td>
			<td>Tagmentation Mix</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">NT Buffer</td>
			<td></td>
			<td></td>
			<td>Tagmentation Neutralizing Buffer</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">NPM</td>
			<td></td>
			<td></td>
			<td>PCR Master Mix</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Nextera XT Index Kit</td>
			<td>Illumina</td>
			<td>FC-131-1001</td>
			<td>Indices for Tagmentation</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">N501</td>
			<td></td>
			<td></td>
			<td>White 1</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">N502</td>
			<td></td>
			<td></td>
			<td>White 2</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">N701</td>
			<td></td>
			<td></td>
			<td>Orange 1</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">N702</td>
			<td></td>
			<td></td>
			<td>Orange 2</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">HiSeq 2500</td>
			<td>Illumina</td>
			<td>SY-401-2501</td>
			<td>For completing sequencing of samples</td>
		</tr>
	</tbody>
</table>

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.

Cell types are easily classified following the dye injection
Figure 1 shows an example of a GFP+ RGC before and after fluorescent tracer filling. This cell was identified based on its expression of GFP in the transgenic line (Figure 1A). A tight seal was formed with a fine-tip, pulled-glass electrode onto the soma of this cell. In order to characterize the subtype, the fluorescent dye was injected into the soma and allowed to fill all associated processes (Figure 1B). The classification as an M4 ipRGC was enabled through the observation that this cell has a very large soma and that its processes terminate within the ON sublamina of the inner plexiform layer (Figure 1C). As an illustration of differences in stratification that can be discerned in an isolated retinal preparation, we filled M1 (OFF-stratifying), M3 (bistratified), and M4 (ON-stratifying) ipRGCs with a fluorescent tracer, fixed them, and performed confocal imaging of the ON and OFF sublaminae (Figure 1D). This visualization of the dendrites via confocal imaging is very similar to how the dendrites look in unfixed in vitro tissue when cells are filled with a fluorescent tracer (Schmidt and Kofuji 2009, 2010, &#38; 2011).
cDNA libraries from single cells
The use of an Oligo d(T) primer allows for the selective reverse transcription and amplification of mRNA. After generating and amplifying the cDNA libraries from each cell, bioanalyzer chips were used to assess the quality and size of the libraries. The results of this analysis show that the ideal cDNA smears are 0.5-2 Kb in a good sample (Figure 2A). Some samples show a few bands or smears below the 300 bp mark, suggesting that these libraries are of poor quality (see Table 1 for troubleshooting). An example of a good-quality bioanalyzer trace shows few or no DNA bands below 300bp and a robust smear between 500bp and 2Kb (Figure 2B). Empty control lanes should display lower and upper markers at 35 &#38; 10380 bp, respectively, as well as a steady baseline that does not fluctuate. Various libraries can produce quality data, as can be seen by both Figure 2B and 2C, which produced excellent read depths and high mapping rates. Only those samples with strong cDNA libraries of the expected size should be carried through to tagmentation (see Table 1 for troubleshooting).
Low-input tagmentation prepares high-quality samples for sequencing
This protocol is optimized for tagmentation with a minor amount of starting material. Just 250 pg works best for a successful tagmentation, as lower amounts will not amplify properly and higher amounts will not fragment completely. After completing the tagmentation and PCR amplification/sample clean-up, the samples were again analyzed on a bioanalyzer chip. The ideal cDNA smears at this point should be 0.2-1 Kb (Figure 3A). The trace should appear smooth and evenly distributed between those two sizes (Figure 3B); this trace corresponds to the sample in lane 1. Figure 3C shows an incomplete tagmentation, which can be resolved easily (see Table 1 for troubleshooting). We have performed data analysis on the samples and found that this protocol produced samples with excellent read depths, often surpassing 12 million reads per sample; averages of 69.1% mapping; and more than 5,000 expressed genes.
<img alt="Figure 1" src="/files/ftp_upload/55229/55229fig1.jpg" /> 
Figure 1: Identification of RGCs Types based on GFP Expression in Melanopsin-expressing ipRGCs and on Morphological Properties. (A) The ganglion-cell layer of the retina, visualized using IR-DIC in the whole-mount retina preparation. (B) The same preparation visualized in epifluorescence (~480 nm) to identify GFP-expressing ganglion cells. (C) A GFP-expressing cell targeted for patch-clamp recording and filled with a fluorescent tracer. The cell can be identified as an M4 ipRGC based on its very large soma size and ON stratifying dendrites25,26. (D) Confocal image of ipRGC dendrites imaged in the ON and/or OFF sublamina of the IPL (M1), in the ON sublamina of the IPL (M4), and in both the ON and OFF sublaminae of the IPL (M3). IR-DIC: infrared differential interference contrast. <a href="http://cloudflare.jove.com/files/ftp_upload/55229/55229fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/55229/55229fig2.jpg" /> 
Figure 2: Analysis of cDNA Library Quality with a Bioanalyzer Trace. (A) Bioanalyzer output example for multiple samples that have been reverse transcribed, amplified, and purified. Lanes 1-3 show the ideal DNA smears, with the majority of DNA larger than 300 bp. Libraries with smears in this range have consistently provided excellent sequencing data, showing an average of 5,683 genes expressed per cell. Lane 4 represents a sample that was unsuccessfully processed and thus produced no cDNA. The successful control lane has a constant baseline and two clean peaks at 35 and10,380 bp. (B) Example of a successful bioanalyzer trace with a high intensity of cDNA around 2 Kb. This smear corresponds to the sample in lane 1. (C) Example of a successful bioanalyzer trace with the cDNA centered around 500 bp. This trace corresponds to the sample in lane 3. <a href="http://cloudflare.jove.com/files/ftp_upload/55229/55229fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/55229/55229fig3.jpg" /> 
Figure 3: Tagmented Samples Show Robust Smears between 0.2 and 2 Kb. (A) A representative example bioanalyzer output following tagmentation, amplification, and PCR clean-up. (B) Trace of a successfully tagmented sample corresponding to lane 1 in (A). (C) Example of the trace for a sample with incomplete tagmentation, clear by the peak intensity around 1 Kb. <a href="http://cloudflare.jove.com/files/ftp_upload/55229/55229fig3large.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>Problem</td>
			<td>Possible Cause</td>
			<td colspan="2">Solution</td>
		</tr>
		<tr>
			<td>Step 3.3) Cannot form G&#937; seal</td>
			<td>Surface of cell is not clean enough</td>
			<td colspan="2">Clean further using positive pressure</td>
		</tr>
		<tr>
			<td>Step 3.3) Cell appears to deflate/die</td>
			<td></td>
			<td colspan="2">Prepare new pipette and target a new cell</td>
		</tr>
		<tr>
			<td>Step 3.4) Cannot determine terminal location of dendrites</td>
			<td>Alexafluor has not had enough time to diffuse throughout cell or concentration of Alexafluor is not high enough</td>
			<td colspan="2">Check to make sure gain and exposure time on camera are high enough. Wait an additional 5 min before visualizing dendrites. If dendrites still are not visible, prepare solution with higher Alexafluor concentration.</td>
		</tr>
		<tr>
			<td>Step 4.1) Unable to aspirate all of cytoplasm</td>
			<td></td>
			<td colspan="2"></td>
		</tr>
		<tr>
			<td>Step 5.5) Supernatant is not clear after 8 minutes</td>
			<td></td>
			<td colspan="2">Gently pipette entire solution twice, while still on magnetic separator, and let sit for another 5 min</td>
		</tr>
		<tr>
			<td>Step 5.5) Pellet disperses during pipetting</td>
			<td>Sample is too far from magnet</td>
			<td colspan="2">Keep tubes on magnetic separating device during all pipetting. Expel solution and allow beads to re-pellet for 5 min</td>
		</tr>
		<tr>
			<td>Step 5.7) After 5 min, samples still appear glossy</td>
			<td>Maximum amount of EtOH has not been removed</td>
			<td colspan="2">Continue to monitor samples during drying. Every 2 min, use a P10 pipette and remove all EtOH at bottom of tube</td>
		</tr>
		<tr>
			<td>Step 8.9) Pellet had cracks before rehydration</td>
			<td></td>
			<td colspan="2">Allow sample to rehydrate for a total of 4 min, rather than 2 (Step 8.10)</td>
		</tr>
		<tr>
			<td>Step 8.11) Small amount of bead was brought up with supernatant</td>
			<td></td>
			<td colspan="2">Eject entire sample back into tube and place on magnetic separator for 1 min; Pipet up gently and make sure to avoid pellet</td>
		</tr>
		<tr>
			<td>Step 8.12) DNA smears are inconsistent and fluorescence scale continuously changes</td>
			<td>Too high DNA concentration for a HS chip</td>
			<td colspan="2">Check concentration of sample and dilute between 1 - 10 ng/&#181;L. Rerun bioanalyzer</td>
		</tr>
		<tr>
			<td>Step 8.12) Low molecular weight smear</td>
			<td>RNA Degradation</td>
			<td colspan="2">Make sure to flash freeze cell immediately after collection. Discard this cDNA library</td>
		</tr>
		<tr>
			<td>Step 8.12) Fluctuating marker baseline in control lane</td>
			<td>Contamination or old reagent</td>
			<td colspan="2">Discard DNA marker and employ new tube for Bioanalyzer run</td>
		</tr>
		<tr>
			<td>Step 8.12) No DNA smear</td>
			<td>Failure to expel cell</td>
			<td colspan="2">Pull new needles with a slightly larger tip</td>
		</tr>
		<tr>
			<td></td>
			<td>RNA Bead Failure</td>
			<td colspan="2">Make sure beads have been fully resuspended before use and allow samples to incubate before placing on magnetic separator</td>
		</tr>
		<tr>
			<td>Step 8.12) Weak DNA smear</td>
			<td>Not enough amplification</td>
			<td colspan="2">Employ more PCR cycles</td>
		</tr>
		<tr>
			<td>Step 8.12) DNA smear outside of 500bp-2Kb range</td>
			<td></td>
			<td colspan="2">DNA smears below 0.5 and above 2 Kb are likely contamination. Discard sample</td>
		</tr>
		<tr>
			<td>Step 8.12) Regularly spaced spikes on DNA trace</td>
			<td>Contamination of sample</td>
			<td colspan="2">Always wear fresh gloves and make new ethanol for rinses; filter tips should be used at all times</td>
		</tr>
		<tr>
			<td>Step 9.3) Unable to detect concentration of sample on fluorometer</td>
			<td></td>
			<td colspan="2">Samples below detection level have too little DNA for tagmentation and cannot be used</td>
		</tr>
		<tr>
			<td>Step 10.10) Samples are not dry after 10 min</td>
			<td>Continue to remove excess EtOH</td>
			<td colspan="2">Allow samples to airdry longer, check on them every minutes</td>
		</tr>
		<tr>
			<td>Step 10.13) Smear skewed toward 2Kb</td>
			<td>Incomplete fragmentation</td>
			<td colspan="2">Re-dilute sample to a concentration of 0.15 ng/&#181;L, rather than 0.2 ng/&#181;L; rerun tagmentation</td>
		</tr>
		<tr>
			<td>Step 10.13) Smear skewed toward 200bp</td>
			<td>Too little DNA input</td>
			<td colspan="2">Dilute sample less, try a concentration of 0.4 ng/&#181;L; rerun tagmentation</td>
		</tr>
		<tr>
			<td></td>
			<td>Tagmentation reaction too long</td>
			<td colspan="2">Reduce reaction time of tagmentation to 8 min</td>
		</tr>
		<tr>
			<td>Step 10.13) Weak smears</td>
			<td>Improper Amplification of DNA</td>
			<td colspan="2">Dilute sample to concentration of 0.4 ng/&#181;L and rerun tagmentation</td>
		</tr>
		<tr>
			<td>Step 11.2) Maximum obtainable pool concentration is below 5 nM</td>
			<td></td>
			<td colspan="2">Identify which sample(s) have significantly low concentrations and rerun tagmentation with new dilution</td>
		</tr>
	</tbody>
</table>
Table 1: Solutions and Suggestions for Potential Hindrances in the Protocol. This table lists potential difficulties that may occur during this protocol and the steps at which one may encounter them. This table lists possible causes for many of these problems, as well as solutions that may help to solve any issues.

Watch this Scientific Journal Video about Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells at JoVE.com

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

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 ...

single-cell-rna-seq-of-defined-subsets-of-retinal-ganglion-cells

Nanyang Technological University

Research

JoVE Journal

Neuroscience

13.7K Views.  Iowa State University. 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.

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

An Isolated Retinal Preparation to Record Light Response from Genetically Labeled Retinal Ganglion Cells

The first steps in vertebrate vision take place when light stimulates the rod and cone photoreceptors of the retina 1. This information is then segregated into what are known as the ON and OFF pathways. The photoreceptors signal light information to the bipolar cells (BCs), which depolarize in response to increases (On BCs) or decreases (Off BCs) in light intensity. This segregation of light information is maintained at the level of the retinal ganglion cells (RGCs), which have dendrites stratifying in either the Off sublamina of the inner plexiform layer (IPL), where they receive direct excitatory input from Off BCs, or stratifying in the On sublamina of the IPL, where they receive direct excitatory input from On BCs. This segregation of information regarding increases or decreases in illumination (the On and Off pathways) is conserved and signaled to the brain in parallel.
The RGCs are the output cells of the retina, and are thus an important cell to study in order to understand how light information is signaled to visual nuclei in the brain. Advances in mouse genetics over recent decades have resulted in a variety of fluorescent reporter mouse lines where specific RGC populations are labeled with a fluorescent protein to allow for identification of RGC subtypes 2 3 4 and specific targeting for electrophysiological recording. Here, we present a method for recording light responses from fluorescently labeled ganglion cells in an intact, isolated retinal preparation. This isolated retinal preparation allows for recordings from RGCs where the dendritic arbor is intact and the inputs across the entire RGC dendritic arbor are preserved. This method is applicable across a variety of ganglion cell subtypes and is amenable to a wide variety of single-cell physiological techniques.

This article provides a description of how to dissect and record from the isolated retinal preparation in mouse. In particular, we describe how to record light responses from a fluorescently labeled ganglion cell population and subsequently identify and analyze its morphology.

The first steps in vertebrate vision take place when light stimulates the rod and cone photoreceptors of the retina 1.  This information is then ...

Transfection of Mouse Retinal Ganglion Cells by in vivo Electroporation

The targeting and refinement of RGC projections to the midbrain is a popular and powerful model system for studying how precise patterns of neural connectivity form during development. In mice, retinofugal projections are arranged in a topographic manner and form eye-specific layers in the Lateral Geniculate Nucleus (dLGN) of the thalamus and the Superior Colliculus (SC). The development of these precise patterns of retinofugal projections has typically been studied by labeling populations of RGCs with fluorescent dyes and tracers, such as horseradish peroxidase1-4. However, these methods are too coarse to provide insight into developmental changes in individual RGC axonal arbor morphology that are the basis of retinotopic map formation. They also do not allow for the genetic manipulation of RGCs.
Recently, electroporation has become an effective method for providing precise spatial and temporal control for delivery of charged molecules into the retina5-11. Current retinal electroporation protocols do not allow for genetic manipulation and tracing of retinofugal projections of a single or small cluster of RGCs in postnatal mice. It has been argued that postnatal in vivo electroporation is not a viable method for transfecting RGCs since the labeling efficiency is extremely low and hence requires targeting at embryonic ages when RGC progenitors are undergoing differentiation and proliferation6. 
In this video we describe an in vivo electroporation protocol for targeted delivery of genes, shRNA, and fluorescent dextrans to murine RGCs postnatally. This technique provides a cost effective, fast and relatively easy platform for efficient screening of candidate genes involved in several aspects of neural development including axon retraction, branching, lamination, regeneration and synapse formation at various stages of circuit development. In summary we describe here a valuable tool which will provide further insights into the molecular mechanisms underlying sensory map development.

Transfection of Mouse Retinal Ganglion Cells by in vivo Electroporation

We demonstrate an in vivo electroporation protocol for transfecting single or small clusters of retinal ganglion cells (RGCs) and other retinal cell types in postnatal mice over a wide range of ages. The ability to label and genetically manipulate postnatal RGCs in vivo is a powerful tool for developmental studies.

The targeting and refinement of RGC projections to the midbrain is a popular and powerful model system for studying how precise patterns of neural ...

An Optic Nerve Crush Injury Murine Model to Study Retinal Ganglion Cell Survival

Injury to the optic nerve can lead to axonal degeneration, followed by a gradual death of retinal ganglion cells (RGCs), which results in irreversible vision loss. Examples of such diseases in human include traumatic optic neuropathy and optic nerve degeneration in glaucoma. It is characterized by typical changes in the optic nerve head, progressive optic nerve degeneration, and loss of retinal ganglion cells, if uncontrolled, leading to vision loss and blindness.
The optic nerve crush (ONC) injury mouse model is an important experimental disease model for traumatic optic neuropathy, glaucoma, etc. In this model, the crush injury to the optic nerve leads to gradual retinal ganglion cells apoptosis. This disease model can be used to study the general processes and mechanisms of neuronal death and survival, which is essential for the development of therapeutic measures. In addition, pharmacological and molecular approaches can be used in this model to identify and test potential therapeutic reagents to treat different types of optic neuropathy.
Here, we provide a step by step demonstration of (I) Baseline retrograde labeling of retinal ganglion cells (RGCs) at day 1, (II) Optic nerve crush injury at day 4, (III) Harvest the retinae and analyze RGC survival at day 11, and (IV) Representative result.

This protocol shows how to retrogradely label retinal ganglion cells, and how to subsequently make an optic nerve crush injury in order to analyze retinal ganglion cell survival and apoptosis. It is an experimental disease model for different types of optic neuropathy, including glaucoma.

Injury to the optic nerve can lead to axonal degeneration, followed by a gradual death of retinal ganglion cells (RGCs), which results in irreversible ...

Live-cell Imaging of Sensory Organ Precursor Cells in Intact Drosophila Pupae

Since the discovery of Green Fluorescent Protein (GFP), there has been a revolutionary change in the use of live-cell imaging as a tool for understanding fundamental biological mechanisms. Striking progress has been particularly evident in Drosophila, whose extensive toolkit of mutants and transgenic lines provides a convenient model to study evolutionarily-conserved developmental and cell biological mechanisms. We are interested in understanding the mechanisms that control cell fate specification in the adult peripheral nervous system (PNS) in Drosophila. Bristles that cover the head, thorax, abdomen, legs and wings of the adult fly are individual mechanosensory organs, and have been studied as a model system for understanding mechanisms of Notch-dependent cell fate decisions. Sensory organ precursor (SOP) cells of the microchaetes (or small bristles), are distributed throughout the epithelium of the pupal thorax, and are specified during the first 12 hours after the onset of pupariation. After specification, the SOP cells begin to divide, segregating the cell fate determinant Numb to one daughter cell during mitosis. Numb functions as a cell-autonomous inhibitor of the Notch signaling pathway.
Here, we show a method to follow protein dynamics in SOP cell and its progeny within the intact pupal thorax using a combination of tissue-specific Gal4 drivers and GFP-tagged fusion proteins 1,2.This technique has the advantage over fixed tissue or cultured explants because it allows us to follow the entire development of an organ from specification of the neural precursor to growth and terminal differentiation of the organ. We can therefore directly correlate changes in cell behavior to changes in terminal differentiation. Moreover, we can combine the live imaging technique with mosaic analysis with a repressible cell marker (MARCM) system to assess the dynamics of tagged proteins in mitotic SOPs under mutant or wildtype conditions. Using this technique, we and others have revealed novel insights into regulation of asymmetric cell division and the control of Notch signaling activation in SOP cells (examples include references 1-6,7 ,8).

Live-cell Imaging of Sensory Organ Precursor Cells in Intact Drosophila Pupae

In this video, we describe a method for live cell imaging of asymmetrically dividing sensory organ progenitor cells and epidermal cells in intact Drosophila pupae

Since the discovery of Green Fluorescent Protein (GFP), there has been a revolutionary change in the use of live-cell imaging as a tool for understanding ...

Single-cell Profiling of Developing and Mature Retinal Neurons

Highly specialized, but exceedingly small populations of cells play important roles in many tissues. The identification of cell-type specific markers and gene expression programs for extremely rare cell subsets has been a challenge using standard whole-tissue approaches. Gene expression profiling of individual cells allows for unprecedented access to cell types that comprise only a small percentage of the total tissue1-7. In addition, this technique can be used to examine the gene expression programs that are transiently expressed in small numbers of cells during dynamic developmental transitions8.
This issue of cellular diversity arises repeatedly in the central nervous system (CNS) where neuronal connections can occur between quite diverse cells9. The exact number of distinct cell types is not precisely known, but it has been estimated that there may be as many as 1000 different types in the cortex itself10. The function(s) of complex neural circuits may rely on some of the rare neuronal types and the genes they express. By identifying new markers and helping to molecularly classify different neurons, the single-cell approach is particularly useful in the analysis of cell types in the nervous system. It may also help to elucidate mechanisms of neural development by identifying differentially expressed genes and gene pathways during early stages of neuronal progenitor development.
As a simple, easily accessed tissue with considerable neuronal diversity, the vertebrate retina is an excellent model system for studying the processes of cellular development, neuronal differentiation and neuronal diversification. However, as in other parts of the CNS, this cellular diversity can present a problem for determining the genetic pathways that drive retinal progenitors to adopt a specific cell fate, especially given that rod photoreceptors make up the majority of the total retinal cell population11. Here we report a method for the identification of the transcripts expressed in single retinal cells (Figure 1). The single-cell profiling technique allows for the assessment of the amount of heterogeneity present within different cellular populations of the retina2,4,5,12. In addition, this method has revealed a host of new candidate genes that may play role(s) in the cell fate decision-making processes that occur in subsets of retinal progenitor cells8. With some simple adjustments to the protocol, this technique can be utilized for many different tissues and cell types.

A method for the isolation of single retinal cells and subsequent amplification of their cDNAs is described. Single-cell transcriptomics reveals the degree of cellular heterogeneity present in a tissue and uncovers new marker genes for rare cell populations. The accompanying protocol can be adjusted to suit many different cell types.

Highly specialized, but exceedingly small populations of cells play important roles in many tissues. The identification of cell-type specific markers and ...

Viral Tracing of Genetically Defined Neural Circuitry

Classical methods for studying neuronal circuits are fairly low throughput. Transsynaptic viruses, particularly the pseudorabies (PRV) and rabies virus (RABV), and more recently vesicular stomatitis virus (VSV), for studying circuitry, is becoming increasingly popular. These higher throughput methods use viruses that transmit between neurons in either the anterograde or retrograde direction.
Recently, a modified RABV for monosynaptic retrograde tracing was developed. (Figure 1A). In this method, the glycoprotein (G) gene is deleted from the viral genome, and resupplied only in targeted neurons. Infection specificity is achieved by substituting a chimeric G, composed of the extracellular domain of the ASLV-A glycoprotein and the cytoplasmic domain of the RABV-G (A/RG), for the normal RABV-G1. This chimeric G specifically infects cells expressing the TVA receptor1. The gene encoding TVA can been delivered by various methods2-8. Following RABV-G infection of a TVA-expressing neuron, the RABV can transmit to other, synaptically connected neurons in a retrograde direction by nature of its own G which was co-delivered with the TVA receptor. This technique labels a relatively large number of inputs (5-10%)2 onto a defined cell type, providing a sampling of all of the inputs onto a defined starter cell type.
We recently modified this technique to use VSV as a transsynaptic tracer9. VSV has several advantages, including the rapidity of gene expression. Here we detail a new viral tracing system using VSV useful for probing microcircuitry with increased resolution. While the original published strategies by Wickersham et al.4 and Beier et al.9 permit labeling of any neurons that project onto initially-infected TVA-expressing-cells, here VSV was engineered to transmit only to TVA-expressing cells (Figure 1B). The virus is first pseudotyped with RABV-G to permit infection of neurons downstream of TVA-expressing neurons. After infecting this first population of cells, the virus released can only infect TVA-expressing cells. Because the transsynaptic viral spread is limited to TVA-expressing cells, presence of absence of connectivity from defined cell types can be explored with high resolution. An experimental flow chart of these experiments is shown in Figure 2. Here we show a model circuit, that of direction-selectivity in the mouse retina. We examine the connectivity of starburst amacrine cells (SACs) to retinal ganglion cells (RGCs).

A method of tracing synaptically connected neurons is described. We use TVA specificity of an upstream cell to probe whether a cell population of interest receives synaptic input from genetically defined cell types.

Classical methods for studying neuronal circuits are fairly low throughput. Transsynaptic viruses, particularly the pseudorabies (PRV) and rabies virus ...

Implementing Dynamic Clamp with Synaptic and Artificial Conductances in Mouse Retinal Ganglion Cells

Ganglion cells are the output neurons of the retina and their activity reflects the integration of multiple synaptic inputs arising from specific neural circuits. Patch clamp techniques, in voltage clamp and current clamp configurations, are commonly used to study the physiological properties of neurons and to characterize their synaptic inputs. Although the application of these techniques is highly informative, they pose various limitations. For example, it is difficult to quantify how the precise interactions of excitatory and inhibitory inputs determine response output. To address this issue, we used a modified current clamp technique, dynamic clamp, also called conductance clamp 1, 2, 3 and examined the impact of excitatory and inhibitory synaptic inputs on neuronal excitability. This technique requires the injection of current into the cell and is dependent on the real-time feedback of its membrane potential at that time. The injected current is calculated from predetermined excitatory and inhibitory synaptic conductances, their reversal potentials and the cell's instantaneous membrane potential. Details on the experimental procedures, patch clamping cells to achieve a whole-cell configuration and employment of the dynamic clamp technique are illustrated in this video article. Here, we show the responses of mouse retinal ganglion cells to various conductance waveforms obtained from physiological experiments in control conditions or in the presence of drugs. Furthermore, we show the use of artificial excitatory and inhibitory conductances generated using alpha functions to investigate the responses of the cells.

This video article illustrates the set-up, the procedures to patch cell bodies and how to implement dynamic clamp recordings from ganglion cells in whole-mount mouse retinae. This technique allows the investigation of the precise contribution of excitatory and inhibitory synaptic inputs, and their relative magnitude and timing to neuronal spiking.

Ganglion cells are the output neurons of the  retina and their activity reflects the integration of multiple synaptic inputs  arising from specific neural ...

Retrograde Labeling of Retinal Ganglion Cells in Adult Zebrafish with Fluorescent Dyes

As retrograde labeling retinal ganglion cells (RGCs) can isolate RGCs somata from dying sites, it has become the gold standard for counting RGCs in RGCs survival and regeneration experiments. Many studies have been performed in mammalian animals to research RGCs survival after optic nerve injury. However, retrograde labeling of RGCs in adult zebrafish has not yet been reported, though some alternative methods can count cell numbers in retinal ganglion cell layers (RGCL). Considering the small size of the adult zebrafish skull and the high risk of death after drilling on the skull, we open the skull with the help of acid-etching and seal the hole with a light curing bond, which could significantly improve the survival rate. After absorbing the dyes for 5 days, almost all the RGCs are labeled. As this method does not need to transect the optic nerve, it is irreplaceable in the research of RGCs survival after optic nerve crush in adult zebrafish. Here, we introduce this method step by step and provide representative results.

We introduce an efficient method to retrograde label retinal ganglion cells (RGCs) in adult zebrafish.

As retrograde labeling retinal ganglion cells (RGCs) can isolate RGCs somata from dying sites, it has become the gold standard for counting RGCs in RGCs ...

RNA-Seq Analysis of Differential Gene Expression in Electroporated Chick Embryonic Spinal Cord

In ovo electroporation of the chick neural tube is a fast and inexpensive method for identification of gene function during neural development. Genome wide analysis of differentially expressed transcripts after such an experimental manipulation has the potential to uncover an almost complete picture of the downstream effects caused by the transfected construct. This work describes a simple method for comparing transcriptomes from samples of transfected embryonic spinal cords comprising all steps between electroporation and identification of differentially expressed transcripts. The first stage consists of guidelines for electroporation and instructions for dissection of transfected spinal cord halves from HH23 embryos in ribonuclease-free environment and extraction of high-quality RNA samples suitable for transcriptome sequencing. The next stage is that of bioinformatic analysis with general guidelines for filtering and comparison of RNA-Seq datasets in the Galaxy public server, which eliminates the need of a local computational structure for small to medium scale experiments. The representative results show that the dissection methods generate high quality RNA samples and that the transcriptomes obtained from two control samples are essentially the same, an important requirement for detection of differential expression genes in experimental samples. Furthermore, one example is provided where experimental overexpression of a DNA construct can be visually verified after comparison with control samples. The application of this method may be a powerful tool to facilitate new discoveries on the function of neural factors involved in spinal cord early development.

This video shows the basic steps for performing whole transcriptome analysis on dissected chick embryonic spinal cord samples after transfection with in ovo electroporation.

In ovo electroporation of the chick neural tube is a fast and inexpensive method for identification of gene function during neural development. Genome ...

Optical Coherence Tomography: Imaging Mouse Retinal Ganglion Cells In Vivo

Structural changes in the retina are common manifestations of ophthalmic diseases. Optical coherence tomography (OCT) enables their identification in vivo&#8212;rapidly, repetitively, and at a high resolution. This protocol describes OCT imaging in the mouse retina as a powerful tool to study optic neuropathies (OPN). The OCT system is an interferometry-based, non-invasive alternative to common post mortem histological assays. It provides a fast and accurate assessment of retinal thickness, allowing the possibility to track changes, such as retinal thinning or thickening. We present the imaging process and analysis with the example of the Opa1delTTAG mouse line. Three types of scans are proposed, with two quantification methods: standard and homemade calipers. The latter is best for use on the peripapillary retina during radial scans; being more precise, is preferable for analyzing thinner structures. All approaches described here are designed for retinal ganglion cells (RGC) but are easily adaptable to other cell populations. In conclusion, OCT is efficient in mouse model phenotyping and has the potential to be used for the reliable evaluation of therapeutic interventions.

Optical Coherence Tomography: Imaging Mouse Retinal Ganglion Cells In Vivo

This manuscript describes a protocol for the in vivo imaging of the mouse retina with high-resolution spectral domain optical coherence tomography (SD-OCT). It focuses on retinal ganglion cells (RGC) in the peripapillary region, with several scanning and quantifying approaches described.

Structural changes in the retina are common manifestations of ophthalmic diseases. Optical coherence tomography (OCT) enables their identification in ...