이 프로젝트 방법의 개발은 Weill Cornell Medical College의 병리학 및 실험 의학과에서 지원되었습니다. 우리는 전산 생물학 및 의학에서의 삼중 제도 연수 프로그램을 부분 기금으로 인정합니다. 우리는 시간과 지식을 우리와 공유 한 과학자, 특히 Maryke Appel에게 감사드립니다. Dan Burgess; 일간카 코자 레와; 차드로 클리어; Jenny Zhang, Xiaobo (Shawn) Liang, Dong Xu, Wei Zhang, Huimin Shang, Tatiana Batson 및 Tuo Zhang을 포함하여 Weill Cornell Medical College Genomics 핵심 시설의 모든 사람들이 있습니다.

<ol>
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E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+pathways+of+adhesion+in+spontaneous+rosetting+of+T-lymphocytes+to+the+Hodgkin's+cell+line+L428.">Molecular pathways of adhesion in spontaneous rosetting of T-lymphocytes to the Hodgkin's cell line L428.</a> Cancer Res. 48 (1), 37-40 (1988).</li><li>Biosciences. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> FACSAria II User's Guide. Part No. 644832, Revision A. , (2009).</li><li>. Qubit 3.0 Fluorometer, Catalog Number Q33216 Available from: <a target="_blank" href="https://www.thermofisher.com/order/catalog/product/Q33216">https://www.thermofisher.com/order/catalog/product/Q33216</a> (2017)</li><li>Roche Nimblegen. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> SeqCap EZ Library SR User's Guide ver. 4.1. , (2013).</li><li>Li, H., Durbin, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+and+accurate+short+read+alignment+with+Burrows-Wheeler+transform.">Fast and accurate short read alignment with Burrows-Wheeler transform.</a> Bioinformatics. 25 (14), 1754-1760 (2009).</li><li>Li, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Sequence+Alignment/Map+format+and+SAMtools.">The Sequence Alignment/Map format and SAMtools.</a> Bioinformatics. 25 (16), 2078-2079 (2009).</li><li>Saunders, C. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Strelka:+accurate+somatic+small-variant+calling+from+sequenced+tumor-normal+sample+pairs.">Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs.</a> Bioinformatics. 28 (14), 1811-1817 (2012).</li><li>Cingolani, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+program+for+annotating+and+predicting+the+effects+of+single+nucleotide+polymorphisms,+SnpEff:+SNPs+in+the+genome+of+Drosophila+melanogaster+strain+w1118;+iso-2;+iso-3.">A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3.</a> Fly (Austin). 6 (2), 80-92 (2012).</li><li>Dobin, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=STAR:+ultrafast+universal+RNA-seq+aligner.">STAR: ultrafast universal RNA-seq aligner.</a> Bioinformatics. 29 (1), 15-21 (2013).</li><li>Thorvaldsdottir, H., Robinson, J. T., Mesirov, J. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrative+Genomics+Viewer+(IGV):+high-performance+genomics+data+visualization+and+exploration.">Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration.</a> Briefings in bioinformatics. 14 (2), 178-192 (2013).</li><li>. DNA copy number data analysis. v.R package version 1.34.0 Available from: <a target="_blank" href="https://omictools.com/dnacopy-tool">https://omictools.com/dnacopy-tool</a> (2013)</li></ol>

저자는 공개 할 것이 없습니다.

이 기술을 습득 한 이후의 응용 프로그램 또는 지시 사항 이 작품은 적어도 10 NG의 DNA를 포함하는 샘플에서 exome 시퀀싱을 허용합니다. 임상 적 맥락에서이 한계는 불충분 한 물질로 인해 대부분의 미세 바늘 흡인 표본을 제외하지만 적절한 핵심 생검 및 절제 생검 표본을 포함합니다. 이렇게하면 더 많은 샘플 세트에서 데이터를 수집 할 수 있습니다. <p class="j...

<html> 차세대 시퀀싱의 결과로서의 암 유전체학의 진보는 치료 표적의 확인 및 많은 혈액학 및 비 혈액 종양의 예후에 중요한 돌파구를 가져왔다. 특정 게놈 변형에 기초한 새로운 개별 치료 전략이 많은 종양 유형에 급속히 도입되고있다 (참고 문헌 1 , 2 에서 검토 됨). 림프종 유전체학에서 상당한 발전에도 불구하고, 고전적 Hodgkin lymphoma (CHL)에서 종양 HRS 세포의 게놈은 과소 평가되었다. 이 연구는 반응성 미세 환경 내에서 신생 HRS 세포의 부족으로 인해 어려움을 겪었으므로 정제 된 HRS 세포 집단을 분리하기가 어렵습니다 3 . 원발성 종양에서 생존 가능한 HRS 세포를 분리하는 방법은 Fromm et al. 도 4에 도시 된 바와 같이,이 방법은 CD30, CD15, CD40, CD95, CD45 CD20, CD5 및 CD64로 구성된 8 가지 항체 칵테일을 사용하여 CHL 종양 현탁액에서 HRS 세포를 확실하게 식별합니다. 는 적어도 10 7 개의 세포 (약 10 mg 조직)로 구성된 종양 생검에서 얻은 신선 또는 고정 세포 현탁액에서 최소 1,000 개의 생존 가능한 HRS 세포를 분리 할 수 ​​있습니다. 순도는 유세포 분석을 통해 90 % 이상이며 10 개의 연속적인 사례의 exome 게놈 분석에 의해 최소 80 %. 당사는 초기의 CHL 종양에서 수천 개의 생존 가능한 HRS 세포를 신속하게 분리 할 수 ​​있도록 프로세스를 대폭 개선 한 유세포 분석기 분리 기술을 개선했습니다. 우리는 호 지킨 림프종의 일차적 인 경우에서 종양 세포의 첫 번째 전체 exome 서열로 여겨지는 것을 생산하는 기술을 이용했습니다. 우리의 연구는 일개별적인 CHL 사례에 대한 높은 처리량, 게놈 차원의 연구의 타당성 및 CHL 발병 기전의 측면을 설명 할 수있는 잠재력을 지닌 새로운 게놈 변형의 확인을 이끌어 냈다. 우리는 고 처리량 게놈 연구를 위해 추출 된 DNA를 활용하는 파이프 라인을 개발했습니다. 순차적으로 발견 된 최소 1,000 개의 HRS 세포로부터 안정적인 결과를 얻기 위해 Adaptor Ligation 효율을 높이고 DNA 단편 라이브러리를 생성 할 수있는 변형 된 차세대 DNA 라이브러리 제작 절차 8 을 개발했습니다 과도한 증폭없이. 이 방법은 일상적인 임상 시료의 분석과 재발 성 돌연변이 및 염색체 변형의 검출을 가능하게합니다 7 .

<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

고전적인 Hodgkin 림프종의 Hodgkin Reed-Sternberg 세포는 염증성 림프구의 배경 내에 희박하게 분포하며 전형적으로 종양 질량의 1 % 미만을 구성합니다. 벌크 종양에서 유래 한 물질은 특성화하기에 충분하지 않은 농도의 종양 함량을 포함합니다. 따라서 8 개의 항체를 이용한 형광 활성 세포 선별 및 측방 및 전방 산란은 후속 연구를 위해 종양에서 수천 개의 HRS 세포를 신속하게 분리 및 농축하는 방법으로 여기에 설명됩니다. 동시에 exome 시퀀싱을위한 표준 프로토콜은 일반적으로 흐름 정렬을 사용하는 경우에도 종종 100-1000 ng의 입력 DNA를 필요로하기 때문에 우리는 고품질의 생산이 가능한 최적화 된 저 입력 라이브러리 구성 프로토콜을 제공합니다 최소 10 ng의 입력 DNA로부터의 데이터. 이 조합은 전체 전자의 하이브리드 화 캡처에 적합한 차세대 라이브러리를 생성 할 수 있습니다.xome baits 또는 더 집중된 대상 패널을 원하는대로 선택할 수 있습니다. 건강한 종양의 T 또는 B 세포와 비교할 때 HRS 세포의 exome sequencing은 돌연변이, 삽입 및 결실, 사본 번호 변경을 포함한 신체적 변화를 확인할 수 있습니다. 이러한 발견은 HRS 세포의 분자 생물학을 밝혀주고 표적 약물 치료를위한 방법을 제시 할 수 있습니다.

생물학적 분석기 플롯은 라이브러리 증폭 및 0.8x 비드 세정 후에 수행해야합니다. 원하는 범위에서 단편 크기의 &quot;정상적인&quot;분포를보아야합니다 ( 그림 2a ). 커브의 눈에 보이는 &quot;어깨&quot;와 같은이 모양의 편차는 고 분자량 또는 저 분자량 아티팩트의 존재를 나타냅니다. 예를 들어, 그림 2b -2d 는 이상적...

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

여기에서는 고전적인 Hodgkin lymphoma (CHL)의 Hodgkin Reed-Sternberg (HRS) 세포로부터 고품질, 전체 exome 데이터를 생산하도록 고안된 결합 된 유동 세포 계측법 세포 분류 및 저 입력, 차세대 라이브러리 구성 프로토콜을 설명합니다.

고전적인 Hodgkin 림프종의 Reed-Sternberg 세포의 흐름 정렬 및 Exome 시퀀싱

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

Research

JoVE Journal

Genetics

9.9K 조회수.  Memorial Sloan Kettering Cancer Center.  여기에서는 고전적인 Hodgkin lymphoma (CHL)의 Hodgkin Reed-Sternberg (HRS) 세포로부터 고품질, 전체 exome 데이터를 생산하도록 고안된 결합 된 유동 세포 계측법 세포 분류 및 저 입력, 차세대 라이브러리 구성 프로토콜을 설명합니다. 

비디오: 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.

단일 셀 시퀀싱을 사용하여 복사 번호 수선의 검출

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

연구

유전학

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.

의 Transcriptomic 분석<em&gt; C</em&gt;.<em&gt; 간스</em갤럭시 프로젝트에 턱시도 제품군을 통해&gt; RNA 시퀀싱 데이터

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.

G2-seq: 높은 처리량 시퀀싱 기반 기술을 늦게 게놈의 복제 확인

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.

강력한 DNA 고립과 표 본관 표본에 대 한 높은 처리량 시퀀싱 도서관 건축

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.

다음-세대 시퀀싱에 대 한 전체 길이 HIV-1 Proviruses 근처의 증폭

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.

차세대 시퀀싱의 원형 염색체 구조를 사용 하 여 유전자 규제 시퀀스의 높은 처리량 Id (4 C-seq)를 캡처

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.

격리, 시퀀싱 및 인간 중간 엽 줄기 세포에서 세포 외 MicroRNAs 분석

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.

이류성 에 대한 착상 전 유전자 검사를위한 반도체 시퀀싱

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.

나노 포어 시퀀싱을 사용 하 여 전 혈에서 mRNA의 시퀀싱

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.

단세포 RNA 시퀀싱 및 인간 췌장 섬 분석

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

고전적인 Hodgkin 림프종의 Reed-Sternberg 세포의 흐름 정렬 및 Exome 시퀀싱

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