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 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.
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.
All procedures were approved by the Institutional Animal Care and Use Committee (IACUC) at Northwestern University.
1. Preparation of Solutions for Electrophysiology (4 h)
2. Preparation of Retinal Tissue (2 h)
NOTE: All procedures in this section should be performed under dim red illumination
3. Visualization and Targeting of GFP+ Retinal Ganglion Cells (10 min)
NOTE: All procedures in this section should be performed under dim red illumination
4. Cell Isolation (2 min)
5. RNA Purification (30 min)
6. Reverse Transcription (10 min)
NOTE: Before beginning, thaw the necessary reagents for reverse transcription (RT; except for the enzyme) on ice. These include: primer II, buffer 1, oligonucleotide, and RNase inhibitor.
7. cDNA Amplification (2.5 h)
NOTE: Before beginning, thaw PCR buffer and PCR primer on ice and spin the tubes down in a tabletop mini centrifuge before making the PCR master mix.
8. Purification of Amplified cDNA (30 min)
9. Determine Concentrations and Tagment cDNA (20 min)
10. Index Coupling and Purification (1 h)
NOTE: Before beginning, bring the DNA beads and resuspension buffer to RT for at least 30 min. Decide which indices to use for each of the samples.
NOTE: These indices will be attached to the respective 5' and 3' ends of the fragmented DNA for the identification of samples following sequencing. Ensure that no two pairings are the same for samples that may be sequenced together. For example, if sample 1 will use indices white 1 and orange 1, sample two should use white 1 and orange 2 or white 2 and orange 2, but never the same combination of indices. This kit contains 4 distinct white and 6 distinct orange indices. All of the different possible combinations allow up to 24 samples to be pooled in one sequencing lane. Although we typically only pool 10 samples in a lane, one could also use the kit containing 24 indices, which would allow for the pooling of 96 samples in a single lane of sequencing, if desired.
11. Pooling of Samples (10 min)
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, & 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 & 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.
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. Please click here to view a larger version of this figure.
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. Please click here to view a larger version of this figure.
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. Please click here to view a larger version of this figure.
Problem | Possible Cause | Solution | |
Step 3.3) Cannot form GΩ seal | Surface of cell is not clean enough | Clean further using positive pressure | |
Step 3.3) Cell appears to deflate/die | Prepare new pipette and target a new cell | ||
Step 3.4) Cannot determine terminal location of dendrites | Alexafluor has not had enough time to diffuse throughout cell or concentration of Alexafluor is not high enough | 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. | |
Step 4.1) Unable to aspirate all of cytoplasm | |||
Step 5.5) Supernatant is not clear after 8 minutes | Gently pipette entire solution twice, while still on magnetic separator, and let sit for another 5 min | ||
Step 5.5) Pellet disperses during pipetting | Sample is too far from magnet | Keep tubes on magnetic separating device during all pipetting. Expel solution and allow beads to re-pellet for 5 min | |
Step 5.7) After 5 min, samples still appear glossy | Maximum amount of EtOH has not been removed | Continue to monitor samples during drying. Every 2 min, use a P10 pipette and remove all EtOH at bottom of tube | |
Step 8.9) Pellet had cracks before rehydration | Allow sample to rehydrate for a total of 4 min, rather than 2 (Step 8.10) | ||
Step 8.11) Small amount of bead was brought up with supernatant | Eject entire sample back into tube and place on magnetic separator for 1 min; Pipet up gently and make sure to avoid pellet | ||
Step 8.12) DNA smears are inconsistent and fluorescence scale continuously changes | Too high DNA concentration for a HS chip | Check concentration of sample and dilute between 1 – 10 ng/µL. Rerun bioanalyzer | |
Step 8.12) Low molecular weight smear | RNA Degradation | Make sure to flash freeze cell immediately after collection. Discard this cDNA library | |
Step 8.12) Fluctuating marker baseline in control lane | Contamination or old reagent | Discard DNA marker and employ new tube for Bioanalyzer run | |
Step 8.12) No DNA smear | Failure to expel cell | Pull new needles with a slightly larger tip | |
RNA Bead Failure | Make sure beads have been fully resuspended before use and allow samples to incubate before placing on magnetic separator | ||
Step 8.12) Weak DNA smear | Not enough amplification | Employ more PCR cycles | |
Step 8.12) DNA smear outside of 500bp-2Kb range | DNA smears below 0.5 and above 2 Kb are likely contamination. Discard sample | ||
Step 8.12) Regularly spaced spikes on DNA trace | Contamination of sample | Always wear fresh gloves and make new ethanol for rinses; filter tips should be used at all times | |
Step 9.3) Unable to detect concentration of sample on fluorometer | Samples below detection level have too little DNA for tagmentation and cannot be used | ||
Step 10.10) Samples are not dry after 10 min | Continue to remove excess EtOH | Allow samples to airdry longer, check on them every minutes | |
Step 10.13) Smear skewed toward 2Kb | Incomplete fragmentation | Re-dilute sample to a concentration of 0.15 ng/µL, rather than 0.2 ng/µL; rerun tagmentation | |
Step 10.13) Smear skewed toward 200bp | Too little DNA input | Dilute sample less, try a concentration of 0.4 ng/µL; rerun tagmentation | |
Tagmentation reaction too long | Reduce reaction time of tagmentation to 8 min | ||
Step 10.13) Weak smears | Improper Amplification of DNA | Dilute sample to concentration of 0.4 ng/µL and rerun tagmentation | |
Step 11.2) Maximum obtainable pool concentration is below 5 nM | Identify which sample(s) have significantly low concentrations and rerun tagmentation with new dilution |
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.
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 µL (at or slightly below 12 ng/µL) 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 – in our case, ipRGCs – 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.
The authors have nothing to disclose.
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.
Ames' Medium | Sigma Aldrich | A1420-10X1L | |
Sodium Bicarbonate | Sigma Aldrich | S8875 | |
K-gluconate | Spectrum Chemical | PO178 | |
EGTA | Sigma Aldrich | E4378 | |
HEPES | Sigma Aldrich | H3375 | |
Diethyl pyrocarbonate (DEPC) | Sigma Aldrich | D5758 | |
Alexa Fluor 594 Hydrazide | Invitrogen | A10442 | |
Collagenase | Worthington Biochemical | LS005273 | |
Hyaluronidase | Worthington Biochemical | LS002592 | |
Petri dish (35mm diameter) | Thermo Fisher Scientific | 153066 | |
Ophthalmologic scissors | Fine Science Tools | 15000-00 | |
#5 Forceps | Fine Science Tools | 11252-30 | |
Microplate Shaker | Fisher Scientific | 13-687-708 | |
Glass Micropipette | Sutter | BF120-69-10 | |
Micropipette Puller | Sutter | P-1000 horizontal pipette puller | |
1mL syringe | Fisher Scientific | 14-823-2F | |
Flexible tubing | Fisher Scientific | 14-171 | |
TCL lysis buffer | Qiagen | 1031576 | Lysis Buffer 1 |
β-mercaptoethanol | Sigma Aldrich | M3148 | |
RNase-Free Water | Qiagen | 129112 | |
0.2 ml PCR tubes | Eppendorf | 30124359 | |
Ethyl Alcohol, Pure | Sigma Aldrich | E7023 | Ethanol |
Analog Vortex Mixer | Thermo Fisher Scientific | 02215365 | Vortex |
Mini Centrifuge | Thermo Fisher Scientific | 05-090-100 | |
Agencourt RNAClean XP Beads | Beckman Coulter | A63987 | RNA magnetic beads |
MagnaBlot II Magnetic Separator | Promega | V8351 | Magnetic stand |
1.5 ml MCT Graduated Tubes | Thermo Fisher Scientific | 05-408-129 | |
Smart-Seq v4 Ultra Low Input RNA Kit | Clontech | 634888 | Reagents for Reverse Transcription and PCR Amplification |
10X Lysis Buffer | Lysis Buffer 2 | ||
5X Ultra Low First-Strand Buffer | Buffer 1 | ||
3' SMART-Seq CDS Primer II A | Primer II | ||
SMART-Seq v4 Oligonucleotide | Oligonucleotide | ||
SMARTScribe Rverse Transcriptase | Reverse Transcriptase | ||
2X SeqAmp PCR Buffer | PCR Buffer | ||
PCR Primer II A | PCR Primer | ||
SeqAmp DNA Polymerase | DNA Polymerase | ||
Mastercycler pro S | Eppendorf | 950030020 | Thermocycler |
Agencourt AMPure XP Beads | Beckman Coulter | A63881 | DNA magnetic beads |
2100 Bioanalyzer | Agilent Technologies | G2939AA | |
HS Bioanalyzer Chips & Reagents | Agilent Technologies | 5067-4626 | |
Qubit HS Assay Kit | Thermo Fisher Scientific | Q32851 | For the calculation of sample concentrations |
Qubit Assay Tubes | Thermo Fisher Scientific | Q32856 | |
Qubit 2.0 Fluorometer | Thermo Fisher Scientific | Q32866 | |
Nextera XT DNA Sample Preparation Kit | Illumina | FC-131-1024 | Reagents for Tagmentation and Index Coupling |
TD Buffer | Buffer 2 | ||
ATM | Tagmentation Mix | ||
NT Buffer | Tagmentation Neutralizing Buffer | ||
NPM | PCR Master Mix | ||
Nextera XT Index Kit | Illumina | FC-131-1001 | Indices for Tagmentation |
N501 | White 1 | ||
N502 | White 2 | ||
N701 | Orange 1 | ||
N702 | Orange 2 | ||
HiSeq 2500 | Illumina | SY-401-2501 | For completing sequencing of samples |