The development of this project method was funded by the Department of Pathology and Laboratory Medicine of Weill Cornell Medical College. We acknowledge the Tri-Institutional Training Program in Computational Biology and Medicine for partial funding. We would like to thank the scientists who shared their time and knowledge with us, especially Maryke Appel; Dan Burgess; Iwanka Kozarewa; Chad Locklear; and everyone from the Weill Cornell Medical College Genomics Core Facility, including Jenny Zhang, Xiaobo (Shawn) Liang, Dong Xu, Wei Zhang, Huimin Shang, Tatiana Batson, and Tuo Zhang.

<ol>
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The authors have nothing to disclose.

Future applications or directions after mastering this technique
This work allows for exome sequencing from samples containing at least 10 ng of DNA. In the clinical context, this limit excludes most fine-needle aspiration samples due to insufficient material, but it includes adequate core biopsies and excisional biopsy samples. This will enable the acquisition of data from a larger set of possible samples.
Critical steps within the protocol
Proper freezing and dissociation techniques are critical to the success of the experiment. The entire sample should be dissociated, including fibrotic sections, as much as possible. Utilizing a 130 &#181;m nozzle for cell sorts appears to be critical to maximize the yield of the large HRS cells and DNA. While rigorous controlled experiments with multiple nozzle sizes were not performed, significant increases in cell yields were observed using the larger nozzle compared to the 100-&#181;m, and virtually no cells were detected on the slide post 85-&#181;m nozzle sort (unpublished observation).
Modifications and troubleshooting
When working with less than 100 ng masses of input DNA for library construction, care should be taken to reduce all unnecessary processing and cleanup steps, especially prior to PCR amplification, since every lost unique molecule results in a reduction of the complexity of the final library. The protocol deliberately recommends eluting extracts in 50 &#181;L because this value is compatible with both sonication and end-repair using the equipment recommended. In this way, it is not necessary to reduce the elution volume using a centrifugal evaporator or a column for the sonication step or to reduce the sonication volume for the end-repair step. Additional modifications to this protocol are possible; for example, new library construction kits may improve adapter ligation efficiency. One could troubleshoot/optimize by comparing adapter-mediated library amplification curves using real-time amplification on libraries made with different kits from the same quantity and quality of input DNA.
Limitations of the technique
At least 10 ng of purified HRS DNA (approximately 1,000 sorted cells after losses in the sorting and extraction steps) appears to be the minimal amount for high-quality results. In pilot experiments, DNA from a single sample of purified T cells was first used to make libraries spanning a range of input masses. 100 ng (the standard recommended amount) of input DNA was compared to libraries generated using this low-input protocol for 10 ng and 1 ng of input DNA. The library generated from 10 ng of input DNA had no significant differences from the 100-ng input library in magnitude and distribution of coverage across the target, or in PCR duplication fraction. However, the library generated using 1 ng of input DNA resulted in a significantly higher PCR duplicate fraction and a corresponding decrease (approximately 3-fold) in mean coverage across the target. Moreover, the 1 ng library also exhibited deviation from uniformity in coverage, which created false-positive copy number alterations with respect to the 100 ng library, unlike the 10 ng input library, which was copy-neutral in the same assessment. For this reason, exert caution using less than 10 ng of input DNA using this method.
Significance of the technique with respect to existing/alternative methods
Following the protocol will allow full exome sequencing of HRS cells from primary Hodgkin lymphoma samples. CHL lymph node samples that contain at least 2 x 107 cells are suitable for this procedure. This was not previously available to researchers, who relied on techniques such as laser capture microdissection and whole genome amplification. It is predicted that genomic approaches to primary CHL cases will continue to be valuable for further understanding CHL pathogenesis. Data also points to significant genome-level heterogeneity within this disease entity, with potentially at least two defined subsets that loosely follow morphological stratification between nodular sclerosis and mixed cellularity CHL. Finally, it is believed that genome-level data will lead to the development of targeted therapy for difficult-to-treat relapsed/refractory CHL cases.

Advancements in cancer genomics as a result of next-generation sequencing have led to significant breakthroughs in the identification of therapeutic targets and in prognostication for many hematologic and non-hematologic neoplasms. New individualized treatment strategies based on specific genomic alterations are rapidly being introduced in many tumor types (reviewed in references1,2). Despite significant progress in lymphoma genomics, the genome of the neoplastic HRS cells in classical Hodgkin lymphoma (CHL) had been underexplored. The investigations have been hampered by the scarcity of neoplastic HRS cells within a reactive microenvironment, making it difficult to isolate purified HRS cell populations3.
The method for isolating viable HRS cells from primary tumors was developed by Fromm et al.4,5,6. The method uses an eight-antibody cocktail, consisting of CD30, CD15, CD40, CD95, CD45 CD20, CD5, and CD64, to unequivocally identify HRS cells from a CHL tumor suspension. Using this methodology, we are able to isolate at least 1,000 viable HRS cells from fresh or frozen cell suspensions from tumor biopsies consisting of at least 107 cells (approximately 10 mg of tissue). The purity is greater than 90% by flow cytometric analysis and is estimated to be least 80% by exome genomic analysis of ten consecutive cases.
We have refined a flow cytometric cell isolation technique that has greatly eased the process, allowing for the rapid isolation of thousands of viable HRS cells from primary CHL tumors7. We have utilized the technique to produce what is believed to be the first whole-exome sequence of the tumor cells in primary cases of Hodgkin lymphoma. Our studies demonstrate the feasibility of high-throughput, genome-wide studies of individual CHL cases and have already led to the identification of novel genomic alterations with the potential to explain aspects of CHL pathogenesis.
We further developed a pipeline to utilize the extracted DNA for high-throughput genomic studies. In order to achieve reliable results from as few as 1,000 sorted HRS cells (thhe minimum obtained from sequential cases), we further developed a modified next-generation DNA library construction procedure8 that allowed us to increase adaptor ligation efficiency and to generate DNA fragment libraries without excessive amplification. This method allows for the analysis of routine clinical samples and the detection of recurrent mutations and chromosomal alterations7.

<table border="0" cellpadding="0" cellspacing="0" width="1576">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:571px;">Petri or Cell Culture Dish (sterile)</td>
			<td style="width:352px;"></td>
			<td style="width:119px;"></td>
			<td style="width:535px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:571px;">RPMI-1640 Media</td>
			<td>Roswell Park Memorial Institute</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Fetal Calf Serum (FCS), (heat inactivated)</td>
			<td style="width:352px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td colspan="2" height="21" style="height:21px;">Freezing Media (RPMI, 20% FCS, 10% dimethylsulfoxide (DMSO))-make fresh and keep sterile</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">RPMI with 2% FCS (make fresh or store for up to 1 month)</td>
			<td style="width:352px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">scalpel with fresh blade</td>
			<td style="width:352px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">10 ml syringe (no needle)</td>
			<td style="width:352px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Cryogenic vials</td>
			<td style="width:352px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">50 ml conical centrifuge tubes, force</td>
			<td style="width:352px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Centrifuge</td>
			<td style="width:352px;"></td>
			<td style="width:119px;"></td>
			<td>capable of handling 50 ml conical centrifuge tubes and providing 400g</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Hepes buffer(1M, cell culture grade)</td>
			<td style="width:352px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">phosphate buffered saline (PBS)</td>
			<td style="width:352px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Pluoronic-F68</td>
			<td style="width:352px;">Thermo-Fisher</td>
			<td style="width:119px;">24040-032</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">DNAase-I</td>
			<td style="width:352px;">Sigma-Aldrich, St. Louis, MO</td>
			<td style="width:119px;">D4527-10KU</td>
			<td>store as 5mg/ml in RPMI in -200C</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Bovine Serum Albumin (BSA)</td>
			<td style="width:352px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Sort Media (PBS+2%BSA+25mM HEPES+ Pluoronic &#8211;F68 (1X))</td>
			<td style="width:352px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">CD64-FITC (22)</td>
			<td style="width:352px;">Beckman Coulter, Miami, FL</td>
			<td style="width:119px;"></td>
			<td>20 uL suggested starting volume; Titering is suggested</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:571px;">CD30-PE (BerH83)</td>
			<td style="width:352px;">BD Biosciences, San Jose, CA</td>
			<td style="width:119px;"></td>
			<td>20 uL suggested starting volume; Titering is suggested</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:571px;">CD5-ECD (BL1a)</td>
			<td style="width:352px;">Beckman Coulter, Miami, FL</td>
			<td style="width:119px;"></td>
			<td>10 uL suggested starting volume; Titering is suggested</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:571px;">CD40-PerCP-eFluor 710 (1C10)</td>
			<td style="width:352px;">Ebiosciences, San Diego, CA</td>
			<td style="width:119px;"></td>
			<td>5 uL suggested starting volume; Titering is suggested</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:571px;">CD20-PC7 (B9E9)</td>
			<td style="width:352px;">Beckman Coulter, Miami, FL</td>
			<td style="width:119px;"></td>
			<td>10 uL suggested starting volume; Titering is suggested</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:571px;">CD15-APC (HI98)</td>
			<td style="width:352px;">BD Biosciences, San Jose, CA</td>
			<td style="width:119px;"></td>
			<td>20 uL suggested starting volume; Titering is suggested</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:571px;">CD45 APC-H7 (2D1)</td>
			<td style="width:352px;">BD Biosciences, San Jose, CA</td>
			<td style="width:119px;"></td>
			<td>Can be substituted with 10 uL suggested volume of CD45-Krome Orange (J.33, Beckman Coulter); Titering is suggested</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:571px;">CD95-Pacific Blue (DX2)</td>
			<td style="width:352px;">Life Technologies, Grand Island, NY</td>
			<td style="width:119px;"></td>
			<td>5 uL suggested starting volume; Titering is suggested</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:571px;">CD2 (5 &#956;g; clone RPA-2.10)</td>
			<td style="width:352px;">Biolegend, San Diego, CA</td>
			<td style="width:119px;"></td>
			<td>For optional protocol; Titering is suggested</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">CD54 (10 &#956;g; clone 84H10)</td>
			<td style="width:352px;">Serotec, Oxford, United Kingdom</td>
			<td style="width:119px;"></td>
			<td>For optional protocol; Titering is suggested</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">CD58 (10 &#956;g; clone TS2/9)</td>
			<td style="width:352px;">eBioscience, San Diego, CA</td>
			<td style="width:119px;"></td>
			<td>For optional protocol; Titering is suggested</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">LFA-1 (12 &#956;g; clone MHM23)</td>
			<td style="width:352px;">Novus Biologicals, Littleton, CO</td>
			<td style="width:119px;"></td>
			<td>For optional protocol; Titering is suggested</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">BD CS&#38;T Beads</td>
			<td style="width:352px;">BD Biosciences, San Jose, CA</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">BD Accudrop Beads</td>
			<td style="width:352px;">BD Biosciences, San Jose, CA</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">BC Versa Comp antibody capture beads</td>
			<td style="width:352px;">Beckman Coulter, Miami, FL</td>
			<td style="width:119px;"></td>
			<td>Compensation Beads</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">BD-FACS ARIA special research order instrument using 5 lasers</td>
			<td style="width:352px;">BD Biosciences, San Jose, CA</td>
			<td style="width:119px;"></td>
			<td>any BD-FACS aria with capabilities to detect the fluorochromes in the antibody panel should be sufficient</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:571px;">Wizard</td>
			<td style="width:352px;">Promega</td>
			<td>A2360</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">10 mM Tris-Cl buffer</td>
			<td style="width:352px;">NA</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Qubit dsDNA HS Assay kit</td>
			<td>Life Technologies, Carlsbad, CA</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:571px;">S2 Sonicator</td>
			<td style="width:352px;">Covaris, Woburn, MA</td>
			<td style="width:119px;"></td>
			<td>Alternatives may be substituted</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:571px;">microTUBE</td>
			<td style="width:352px;">Covaris, Woburn, MA</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Low-Throughput Library Preparation Kit</td>
			<td style="width:352px;">Kapa Biosystems, Wilmington, MA</td>
			<td>KK8221</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:571px;">Sybr Green</td>
			<td style="width:352px;">Sigma-Aldrich, St. Louis, MO</td>
			<td style="width:119px;">S9430</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:571px;">Agencourt AMPure XP Beads</td>
			<td style="width:352px;">Beckman Coulter, Miami, FL</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:571px;">Bioanalyzer</td>
			<td style="width:352px;">Agilent Technologies, Santa Clara, CA</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">SeqCap EZ Exome v.3.0</td>
			<td style="width:352px;">Roche Nimblegen</td>
			<td style="width:119px;">6465684001</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:571px;">HiSeq</td>
			<td style="width:352px;">Illumina</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:571px;">TruSeq-style Universal adapter</td>
			<td style="width:352px;">Integrated DNA Technologies (IDT), Coralville, Iowa</td>
			<td style="width:119px;"></td>
			<td>HPLC purification; AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGAT*C*T</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:571px;">TruSeq-style index adapter</td>
			<td style="width:352px;">Integrated DNA Technologies (IDT), Coralville, Iowa</td>
			<td style="width:119px;"></td>
			<td>HPLC purification; /5Phos/GATCGGAAGAGCACACGTCTGAACTCCAGTCACNNNNNNATCTCGTATGCCGTCTTCTGCTTG</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:571px;">TruSeq-style PCR primer 1</td>
			<td style="width:352px;">Integrated DNA Technologies (IDT), Coralville, Iowa</td>
			<td style="width:119px;"></td>
			<td>AATGATACGGCGACCACCGAGA</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:571px;">TruSeq-style PCR primer 2</td>
			<td style="width:352px;">Integrated DNA Technologies (IDT), Coralville, Iowa</td>
			<td style="width:119px;"></td>
			<td>CAAGCAGAAGACGGCATACGAG</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:571px;">Nuclease Free Duplex Buffer</td>
			<td style="width:352px;">Integrated DNA Technologies (IDT), Coralville, Iowa</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:571px;">BD FACSDIVA software</td>
			<td style="width:352px;">BD Biosciences, San Jose, CA</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:571px;">BD Falcon Tubes</td>
			<td style="width:352px;">BD Biosciences, San Jose, CA</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:571px;">BD Flow Tubes</td>
			<td style="width:352px;">BD Biosciences, San Jose, CA</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
	</tbody>
</table>

flow-sorting and exome sequencing of the reed-sternberg cells of classical hodgkin lymphoma

The Hodgkin Reed-Sternberg cells of classical Hodgkin lymphoma are sparsely distributed within a background of inflammatory lymphocytes and typically comprise less than 1% of the tumor mass. Material derived from bulk tumor contains tumor content at a concentration insufficient for characterization. Therefore, fluorescence activated cell sorting using eight antibodies, as well as side- and forward-scatter, is described here as a method of rapidly separating and concentrating with high purity thousands of HRS cells from the tumor for subsequent study. At the same time, because standard protocols for exome sequencing typically require 100-1,000 ng of input DNA, which is often too high, even with flow sorting, we also provide an optimized, low-input library construction protocol capable of producing high-quality data from as little as 10 ng of input DNA. This combination is capable of producing next-generation libraries suitable for hybridization capture of whole-exome baits or more focused targeted panels, as desired. Exome sequencing of the HRS cells, when compared against healthy intratumor T or B cells, can identify somatic alterations, including mutations, insertions and deletions, and copy number alterations. These findings elucidate the molecular biology of HRS cells and may reveal avenues for targeted drug treatments.

A bioanalyzer plot should be taken after library amplification and 0.8x bead cleanup. One should see a &#34;normal-like&#34; distribution of fragment sizes in the desired range (Figure 2a). Deviations from this shape, such as a visible &#34;shoulder&#34; in the curve, indicate the presence of a high or low molecular weight artifact. For example, Figure 2b-2d shows examples of libraries containing visible artifacts that should ideally not be sequenced. If a library is significantly compromised, it may be worthwhile to repeat the library construction if DNA is available and/or the cell sorting to start fresh.
An adapter dimer may sometimes carry over through the first 0.8x bead cleanup. If this occurs, it will be visible as a sharp peak centered around 125 - 130 bp (see Figure 2c). In this case, it is advisable to repeat an additional 0.8x bead cleanup and to repeat the bioanalyzer to ensure the successful removal of the dimer.
Using the specifications described in this protocol and multiplexing the sequencing at the level of four samples per lane in the sequencer listed in the Materials spreadsheet, a median depth of coverage of 50 - 100x across the target (after the bioinformatics removal of PCR duplicate reads) is achievable (see Figure 3a). In addition, libraries that were not overamplified and that resulted in libraries with adequate coverage should produce clean copy number plots. During early work, this library construction protocol was tested over three orders of magnitude of input mass, and variable results were discovered (see the Discussion section). Copy number plots generated using a library generated from 1 ng, a sub-optimal amount of input DNA, resulted in spurious copy number gains and losses (Figure 3b, bottom).
Approximately 70% of cases of CHL carry B2M and A20 mutations and/or deletions7. B2M mutations predominantly involve a translational start site, a first exon stop site, and a first exon stop site. A20 mutations are a mix of copy number losses and indels. The mutations appear to be clonal and can be used to estimate relative tumor content. If T cells are used as somatic controls, HRS sequencing will show significant copy number gains in the positions corresponding to T-cell receptor alpha/delta (14q11 - 12) and beta positions (7q32 - 35), as well as significant losses in the B-cell receptor heavy chain (14q32) and kappa light chain positions (2p11) (see Figure 4). Overall sequencing shows a range from 100 to 400 somatic mutations per case. Copy number variation is highly variable from case to case, with some cases showing numerous segmental gains and losses and other cases showing only a few alterations.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/54399/54399fig1.jpg" /> 
Figure 1: Multiparameter Gating for Flow-sorting of Viable HRS, B, and T cells. Population names are shown on plots and indicate the parent population of any gate. (A)shows all cells: On the FSC-H versus FSC-A histogram, draw a SINGLETS gate that includes populations with proportional FSC-H and FSC-A increases. Exclude doublets that have high FSC-A and lower FSC-H. Hodgkin cells are very large; expand the gate to include high FSC-H and FSC-A events. (B) Exclude debris, dead cells, and unlysed RBCs that normally have lower FSC-A and slight increases in SSC-H without cutting out viable populations (VIABLE gate). (C) Gate MONONUCLEAR cells on FSC-A vs. SSC-H to exclude granulocytes with high SSC-H; note that the gate is only used for the identification of T and B cells. (D) Gate T CELLS positively identified by CD5 (green) without CD20 and B CELLS (blue), which have CD20 expression without CD5. (E) Draw a LARGER gate on the CD45/SSH plot, including 10 - 20% of the lymphoid cells and all granulocytic cells. (F) Gate CD30-positive events with dim to negative CD64/FITC autofluorscence. (G) Among CD30 positive events, gate CD40- and CD95-positive events (CD30/40/95 HRS gate, red). (H-L) Observe the confirmatory immunophenotypic features on HRS cells; HRS cells generally do not express CD20 (plots H and L). The expression of CD5 is correlated with CD45 due to T-cell rosetting (I); most cases will show expression of CD15 and will be bright for CD71 (J). CD40 and CD95 expression is generally above the level of B and T cells, respectively (K). This research was originally published in Blood7. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54399/54399fig1large.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/54399/54399fig2.jpg" /> 
Figure 2: Size Distribution Plots of Samples after Adapter Ligation and Amplification. Ideally, libraries with visible artifacts should be repeated and not sequenced. (a) Well-formed library; no visible artifacts. (b) Asymmetry in the curve reveals a problem. (c) Significant adapter dimer (peak at 128) and a high molecular weight artifact (&#62;400 bp). The dimer may be cleaned with an additional 0.8x bead cleanup. (d) Significant high molecular weight artifact. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54399/54399fig2large.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/54399/54399fig3.jpg" /> 
Figure 3: Bioinformatic Quality Control Measurements for Higher-quality Libraries made from 10 ng of Input DNA Compared to Lower-quality Libraries made from 1 ng of Input DNA. The magnitude and distribution of the unique read depth of coverage expectations. (a) A median depth of coverage of at least 50 is expected when using 10-100 ng of input DNA. (b) Each of 2 panels depicts copy number variation analysis results comparing data between 2 sequenced libraries. Exonic probe segments (x-axis) versus copy number change on a log2 scale (y-axis) are plotted for a single representative chromosome (chr 6). Comparing data from a 10 ng low-input library from intratumoral T-cell DNA to a 100 ng normal-input library from intratumoral T-cell DNA from the same case showed no significant false-positive results; that is, low-input and normal-input libraries are copy-neutral (top). Numerous false-positive segmental copy number alterations were called when data from a 1 ng low-input library from intratumoral T-cell DNA was compared to a 100 ng normal-input library from intratumoral T-cell DNA from the same case (bottom). This research was originally published in Blood7. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54399/54399fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/54399/54399fig4.jpg" /> 
Figure 4: Recurrent Copy Number Gains and Losses, with Tumor Cells (Primary HRS and Cell Lines) Compared Against Healthy Intratumor T Cells from Primary Cases. If T cells are used as the somatic control against B cell-derived HRS and cell line populations, recurrent gains can be seen in T-cell receptors alpha/delta and beta. Likewise, recurrent losses are detectable in immunoglobulin heavy and kappa chains. This research was originally published in Blood7. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54399/54399fig4large.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>DNA input mass (n)</td>
			<td>1-10 ng</td>
			<td>25 ng</td>
			<td>100 ng</td>
		</tr>
		<tr>
			<td>Adapter : insert molar ratio (r)</td>
			<td>65:1</td>
			<td>25:1</td>
			<td>15:1</td>
		</tr>
	</tbody>
</table>
Table 1: DNA Input Mass and the Corresponding Adapter:Insert Molar Ratio. The values in this table can be used as a guide to calculate the amount of adapter oligo to add during the adapter ligation step of library construction.

Watch this Scientific Journal Video about Flow-sorting and Exome Sequencing of the Reed-Sternberg Cells of Classical Hodgkin Lymphoma at JoVE.com

Flow-sorting and Exome Sequencing of the Reed-Sternberg Cells of Classical Hodgkin Lymphoma

Here, we describe a combined flow cytometric cell sorting and low-input, next-generation library construction protocol designed to produce high-quality, whole-exome data from the Hodgkin Reed-Sternberg (HRS) cells of classical Hodgkin lymphoma (CHL).

The Hodgkin Reed-Sternberg cells of classical Hodgkin lymphoma are sparsely distributed within a background of inflammatory lymphocytes and typically ...

flow-sorting-exome-sequencing-reed-sternberg-cells-classical-hodgkin

Nanyang Technological University

Research

JoVE Journal

Genetics

9.9K Views.  Memorial Sloan Kettering Cancer Center. Here, we describe a combined flow cytometric cell sorting and low-input, next-generation library construction protocol designed to produce high-quality, whole-exome data from the Hodgkin Reed-Sternberg (HRS) cells of classical Hodgkin lymphoma (CHL).

Video: Flow-sorting and Exome Sequencing of the Reed-Sternberg Cells of Classical Hodgkin Lymphoma

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and microbial populations. Traditional methods for assessing genetic heterogeneity have been limited by low resolution, low sensitivity, and/or low specificity. Single cell sequencing has emerged as a powerful tool for detecting genetic heterogeneity with high resolution, high sensitivity and, when appropriately analyzed, high specificity. Here we provide a protocol for the isolation, whole genome amplification, sequencing, and analysis of single cells. Our approach allows for the reliable identification of megabase-scale copy number variants in single cells. However, aspects of this protocol can also be applied to investigate other types of genetic alterations in single cells.

Single cell sequencing is an increasingly popular and accessible tool for addressing genomic changes at high resolution. We provide a protocol that uses single cell sequencing to identify copy number alterations in single cells.

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and ...

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Next generation sequencing (NGS) technologies have revolutionized the nature of biological investigation. Of these, RNA Sequencing (RNA-Seq) has emerged as a powerful tool for gene-expression analysis and transcriptome mapping. However, handling RNA-Seq datasets requires sophisticated computational expertise and poses inherent challenges for biology researchers. This bottleneck has been mitigated by the open access Galaxy project that allows users without bioinformatics skills to analyze RNA-Seq data, and the Database for Annotation, Visualization, and Integrated Discovery (DAVID), a Gene Ontology (GO) term analysis suite that helps derive biological meaning from large data sets. However, for first-time users and bioinformatics' amateurs, self-learning and familiarization with these platforms can be time-consuming and daunting. We describe a straightforward workflow that will help C. elegans researchers to isolate worm RNA, conduct an RNA-Seq experiment and analyze the data using Galaxy and DAVID platforms. This protocol provides stepwise instructions for using the various Galaxy modules for accessing raw NGS data, quality-control checks, alignment, and differential gene expression analysis, guiding the user with parameters at every step to generate a gene list that can be screened for enrichment of gene classes or biological processes using DAVID. Overall, we anticipate that this article will provide information to C. elegans researchers undertaking RNA-Seq experiments for the first time as well as frequent users running a small number of samples.

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Galaxy and DAVID have emerged as popular tools that allow investigators without bioinformatics training to analyze and interpret RNA-Seq data. We describe a protocol for C. elegans researchers to perform RNA-Seq experiments, access and process the dataset using Galaxy and obtain meaningful biological information from the gene lists using DAVID.

Next generation sequencing (NGS) technologies have revolutionized the nature of biological investigation. Of these, RNA Sequencing (RNA-Seq) has emerged ...

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Numerous techniques have been developed to follow the progress of DNA replication through the S phase of the cell cycle. Most of these techniques have been directed toward elucidation of the location and timing of initiation of genome duplication rather than its completion. However, it is critical that we understand regions of the genome that are last to complete replication, because these regions suffer elevated levels of chromosomal breakage and mutation, and they have been associated with both disease and aging. Here we describe how we have extended a technique that has been used to monitor replication initiation to instead identify those regions of the genome last to complete replication. This approach is based on a combination of flow cytometry and high throughput sequencing. Although this report focuses on the application of this technique to yeast, the approach can be used with any cells that can be sorted in a flow cytometer according to DNA content.

We describe a technique for combining flow cytometry and high throughput sequencing to identify late replicating regions of the genome.

Numerous techniques have been developed to follow the progress of DNA replication through the S phase of the cell cycle. Most of these techniques have ...

Robust DNA Isolation and High-throughput Sequencing Library Construction for Herbarium Specimens

Herbaria are an invaluable source of plant material that can be used in a variety of biological studies. The use of herbarium specimens is associated with a number of challenges including sample preservation quality, degraded DNA, and destructive sampling of rare specimens. In order to more effectively use herbarium material in large sequencing projects, a dependable and scalable method of DNA isolation and library preparation is needed. This paper demonstrates a robust, beginning-to-end protocol for DNA isolation and high-throughput library construction from herbarium specimens that does not require modification for individual samples. This protocol is tailored for low quality dried plant material and takes advantage of existing methods by optimizing tissue grinding, modifying library size selection, and introducing an optional reamplification step for low yield libraries. Reamplification of low yield DNA libraries can rescue samples derived from irreplaceable and potentially valuable herbarium specimens, negating the need for additional destructive sampling and without introducing discernible sequencing bias for common phylogenetic applications. The protocol has been tested on hundreds of grass species, but is expected to be adaptable for use in other plant lineages after verification. This protocol can be limited by extremely degraded DNA, where fragments do not exist in the desired size range, and by secondary metabolites present in some plant material that inhibit clean DNA isolation. Overall, this protocol introduces a fast and comprehensive method that allows for DNA isolation and library preparation of 24 samples in less than 13 h, with only 8 h of active hands-on time with minimal modifications.

This article demonstrates a detailed protocol for DNA isolation and high-throughput sequencing library construction from herbarium material including rescue of exceptionally poor-quality DNA.

Herbaria are an invaluable source of plant material that can be used in a variety of biological studies. The use of herbarium specimens is associated with ...

Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

The Full-Length Individual Proviral Sequencing (FLIPS) assay is an efficient and high-throughput method designed to amplify and sequence single, near full-length (intact and defective), HIV-1 proviruses. FLIPS allows determination of the genetic composition of integrated HIV-1 within a cell population. Through identifying defects within HIV-1 proviral sequences that arise during reverse transcription, such as large internal deletions, deleterious stop codons/hypermutation, frameshift mutations, and mutations/deletions in cis acting elements required for virion maturation, FLIPS can identify integrated proviruses incapable of replication. The FLIPS assay can be utilized to identify HIV-1 proviruses that lack these defects and are&#160;therefore potentially replication-competent. The FLIPS protocol involves: lysis of HIV-1 infected cells, nested PCR of near full-length HIV-1 proviruses (using primers targeted to the HIV-1 5&#39; and 3&#39; LTR), DNA purification and quantification, library preparation for Next-generation Sequencing (NGS), NGS, de novo assembly of proviral contigs, and a simple process of elimination for identifying replication-competent proviruses. FLIPS provides advantages over traditional methods designed to sequence integrated HIV-1 proviruses, such as single-proviral sequencing. FLIPS amplifies and sequences near full-length proviruses enabling replication competency to be determined, and also uses fewer amplification primers, preventing the consequences of primer mismatches. FLIPS is a useful tool for understanding the genetic landscape of integrated HIV-1 proviruses, especially within the latent reservoir, however, its utilization can extend to any application in which the genetic composition of integrated HIV-1 is required.

Full-length individual proviral sequencing (FLIPS) provides an efficient and high-throughput method for the amplification and sequencing of single, near full-length (intact and defective) HIV-1 proviruses and allows for determination of their potential replication-competency. FLIPS overcomes limitations of previous assays designed to sequence the latent HIV-1 reservoir.

The Full-Length Individual Proviral Sequencing (FLIPS) assay is an efficient and high-throughput method designed to amplify and sequence single, near ...

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and effect sizes of regulatory elements to a target gene. Some progress has been made with the bioinformatic prediction of the existence and function of proximal epigenetic modifications associated with activated gene expression using conserved transcription factor binding sites. Chromatin conformation capture studies have revolutionized our ability to discover physical chromatin contacts between sequences and even within an entire genome. Circular chromatin conformation capture coupled with next-generation sequencing (4C-seq), in particular, is designed to discover all possible physical chromatin interactions for a given sequence of interest (viewpoint), such as a target gene or a regulatory enhancer. Current 4C-seq strategies directly sequence from within the viewpoint but require numerous and diverse viewpoints to be simultaneously sequenced to avoid the technical challenges of uniform base calling (imaging) with next generation sequencing platforms. This volume of experiments may not be practical for many laboratories. Here, we report a modified approach to the 4C-seq protocol that incorporates both an additional restriction enzyme digest and qPCR-based amplification steps that are designed to facilitate a greater capture of diverse sequence reads and mitigate the potential for PCR bias, respectively. Our modified 4C method is amenable to the standard molecular biology lab for assessing chromatin architecture.

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

The identification of physical interactions between genes and regulatory elements is challenging but has been facilitated by chromosome conformation capture methods. This modification to the 4C-seq protocol mitigates PCR bias by minimizing over-amplification of PCR templates and maximizes the mappability of reads by incorporating an addition restriction enzyme digest step.

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and ...

Isolating, Sequencing and Analyzing Extracellular MicroRNAs from Human Mesenchymal Stem Cells

Extracellular and circulating RNAs (exRNA) are produced by many cell types of the body and exist in numerous bodily fluids such as saliva, plasma, serum, milk and urine. One subset of these RNAs are the posttranscriptional regulators &#8211; microRNAs (miRNAs). To delineate the miRNAs produced by specific cell types, in vitro culture systems can be used to harvest and profile exRNAs derived from one subset of cells. The secreted factors of mesenchymal stem cells are implicated in alleviating numerous diseases and is used as the in vitro model system here. This paper describes the process of collection, purification of small RNA and library generation to sequence extracellular miRNAs. ExRNAs from culture media differ from cellular RNA by being low RNA input samples, which calls for optimized procedures. This protocol provides a comprehensive guide to small exRNA sequencing from culture media, showing quality control checkpoints at each step during exRNA purification and sequencing.

This protocol demonstrates how to purify extracellular microRNAs from cell culture media for small RNA library construction and next generation sequencing. Various quality control checkpoints are described to allow readers to understand what to expect when working with low input samples like exRNAs.

Extracellular and circulating RNAs (exRNA) are produced by many cell types of the body and exist in numerous bodily fluids such as saliva, plasma, serum, ...

Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy

Chromosomal aneuploidy, one of the main causes leading to embryonic development arrest, implantation failure, or pregnancy loss, has been well documented in human embryos. Preimplantation genetic testing for aneuploidy (PGT-A) is a genetic test that significantly improves reproductive outcomes by detecting chromosomal abnormalities of embryos. Next-generation sequencing (NGS) provides a high-throughput and cost-effective approach for genetic analysis and has shown clinical applicability in PGT-A. Here, we present a rapid and low-cost semiconductor sequencing-based NGS method for screening of aneuploidy in embryos. The first step of the workflow is whole genome amplification (WGA) of the biopsied embryo specimen, followed by construction of sequencing library, and subsequent sequencing on the semiconductor sequencing system. Generally, for a PGT-A application, 24 samples can be loaded and sequenced on each chip generating 60&#8722;80 million reads at an average read length of 150 base pairs. The method provides a refined protocol for performing template amplification and enrichment of sequencing library, making the PGT-A detection reproducible, high-throughput, cost-efficient, and timesaving. The running time of this semiconductor sequencer is only 2&#8722;4 hours, shortening the turnaround time from receiving samples to issuing reports into 5 days. All these advantages make this assay an ideal method to detect chromosomal aneuploidies from embryos and thus, facilitate its wide application in PGT-A.

Here, we introduce a semiconductor sequencing method for preimplantation genetic testing for aneuploidy (PGT-A) with the advantages of short turnaround time, low cost, and high throughput.

Chromosomal aneuploidy, one of the main causes leading to embryonic development arrest, implantation failure, or pregnancy loss, has been well documented ...

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Sequencing in remote locations and resource-poor settings presents unique challenges. Nanopore sequencing can be successfully used under such conditions, and was deployed to West Africa during the recent Ebola virus epidemic, highlighting this possibility. In addition to its practical advantages (low cost, ease of equipment transport and use), this technology also provides fundamental advantages over second-generation sequencing approaches, particularly the very long read length, ability to directly sequence RNA, and real-time availability of data. Raw read accuracy is lower than with other sequencing platforms, which represents the main limitation of this technology; however, this can be partially mitigated by the high read depth generated. Here, we present a field-compatible protocol for sequencing of the mRNAs encoding for Niemann-Pick C1, which is the cellular receptor for ebolaviruses. This protocol encompasses extraction of RNA from animal blood samples, followed by RT-PCR for target enrichment, barcoding, library preparation, and the sequencing run itself, and can be easily adapted for use with other DNA or RNA targets.

Nanopore sequencing is a novel technology that allows cost-effective sequencing in remote locations and resource-poor settings. Here, we present a protocol for sequencing of mRNAs from whole blood that is compatible with such conditions.

Sequencing in remote locations and resource-poor settings presents unique challenges. Nanopore sequencing can be successfully used under such conditions, ...

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Pancreatic islets comprise of endocrine cells with distinctive hormone expression patterns. The endocrine cells show functional differences in response to normal and pathological conditions. The goal of this protocol is to generate high-quality, large-scale transcriptome data of each endocrine cell type with the use of a droplet-based microfluidic single-cell RNA sequencing technology. Such data can be utilized to build the gene expression profile of each endocrine cell type in normal or specific conditions. The process requires careful handling, accurate measurement, and rigorous quality control. In this protocol, we describe detailed steps for human pancreatic islets dissociation, sequencing, and data analysis. The representative results of about 20,000 human single islet cells demonstrate the successful application of the protocol.

Here, we present a protocol to generate high-quality, large-scale transcriptome data of single cells from isolated human pancreatic islets using a droplet-based microfluidic single-cell RNA sequencing technology.

Pancreatic islets comprise of endocrine cells with distinctive hormone expression patterns. The endocrine cells show functional differences in response to ...