우리는 샘플을 준비하고 취급하는 데 도움을 준 Jennifer Bair와 Einat Snir, Iowa Institute for Human Genetics를 인정하고 싶습니다.

<ol>
	<li>Austin, C., Cepko, C. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Specification+of+Cell+Fate+in+the+Vertebrate+Retina.">Specification of Cell Fate in the Vertebrate Retina.</a> Neural Cell Specif. 3, 139-143 (1995).</li><li>Masland, R. 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+Neuronal+Organization+of+the+Retina.">The Neuronal Organization of the Retina.</a> Neuron. 76 (2), 266-280 (2012).</li><li>Rodieck, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> The First Steps in Seeing. , (1998).</li><li>Chiu, I. 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Neurosci. 19 (2), 335-346 (2016).</li><li>Trapnell, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Defining+cell+types+and+states+with+single-cell+genomics.">Defining cell types and states with single-cell genomics.</a> Genome Res. 25 (10), 1491-1498 (2015).</li><li>Zeisel, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cell+types+in+the+mouse+cortex+and+hippocampus+revealed+by+single-cell+RNA-seq.">Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq.</a> Science. 347 (6226), 1138-1142 (2015).</li><li>Schmidt, T. M., Do, M. T. H., Dacey, D., Lucas, R., Hattar, S., Matynia, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Melanopsin-Positive+Intrinsically+Photosensitive+Retinal+Ganglion+Cells:+From+Form+to+Function.">Melanopsin-Positive Intrinsically Photosensitive Retinal Ganglion Cells: From Form to Function.</a> J. Neurosci. 31 (45), 16094-16101 (2011).</li><li>Eberwine, J., Miyashiro, K., Kacharmina, J. E., Job, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Local+translation+of+classes+of+mRNAs+that+are+targeted+to+neuronal+dendrites.">Local translation of classes of mRNAs that are targeted to neuronal dendrites.</a> Proc. Natl. Acad. 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Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Highly+Parallel+Genome-wide+Expression+Profiling+of+Individual+Cells+Using+Nanoliter+Droplets.">Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.</a> Cell. 161 (5), 1202-1214 (2015).</li><li>Usoskin, D., 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=Unbiased+classification+of+sensory+neuron+types+by+large-scale+single-cell+RNA+sequencing.">Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing.</a> Nat. Neurosci. 18 (1), 145-153 (2015).</li><li>Poulin, J. F., Zou, J., Drouin-Ouellet, J., Kim, K. Y. A., Cicchetti, F., Awatramani, R. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Defining+midbrain+dopaminergic+neuron+diversity+by+single-cell+gene+expression+profiling.">Defining midbrain dopaminergic neuron diversity by single-cell gene expression profiling.</a> Cell. Rep. 9 (3), 930-943 (2014).</li><li>Gross, A., Schoendube, J., Zimmermann, S., Steeb, M., Zengerle, R., Koltay, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Technologies+for+Single-Cell+Isolation.">Technologies for Single-Cell Isolation.</a> Int. J. Mol. Sci. 16, 16897-16919 (2015).</li><li>Lovatt, D., 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=Transcriptome+in+vivo+analysis+(TIVA)+of+spatially+defined+single+cells+in+live+tissue.">Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue.</a> Nat Methods. 11 (2), 190-196 (2014).</li><li>Qiu, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-neuron+RNA-Seq:+Technical+feasibility+and+reproducibility.">Single-neuron RNA-Seq: Technical feasibility and reproducibility.</a> Front. Genet. 3 (124), 1-8 (2012).</li><li>Subkhankulova, T., Yano, K., Robinson, H. P. C., Livesey, F. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Grouping+and+classifying+electrophysiologically-defined+classes+of+neocortical+neurons+by+single+cell,+whole-genome+expression+profiling.">Grouping and classifying electrophysiologically-defined classes of neocortical neurons by single cell, whole-genome expression profiling.</a> Front. Mol. Neurosci. 3 (10), 1-11 (2010).</li><li>Fuzik, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integration+of+electrophysiological+recordings+with+single-cell+RNA-seq+data+identifies+neuronal+subtypes.">Integration of electrophysiological recordings with single-cell RNA-seq data identifies neuronal subtypes.</a> Nat. Biotechnol. 34 (2), 175-183 (2016).</li><li>Schmidt, T. M., Kofuji, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+isolated+retinal+preparation+to+record+light+response+from+genetically+labeled+retinal+ganglion+cells.">An isolated retinal preparation to record light response from genetically labeled retinal ganglion cells.</a> J. Vis. Exp. (47), e2367 (2011).</li><li>Schmidt, T. M., Kofuji, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+and+morphological+differences+among+intrinsically+photosensitive+retinal+ganglion+cells.">Functional and morphological differences among intrinsically photosensitive retinal ganglion cells.</a> J Neurosci. 29 (2), 476-482 (2009).</li><li>Schmidt, T. M., Kofuji, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structure+and+Function+of+Bistratified+Intrinsically+Photosensitive+Retinal+Ganglion+Cells+in+the+Mouse.">Structure and Function of Bistratified Intrinsically Photosensitive Retinal Ganglion Cells in the Mouse.</a> J Comp Neurol. 519 (8), 1492-1504 (2011).</li><li>Schmidt, T. M., Kofuji, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Differential+cone+pathway+influence+on+intrinsically+photosensitive+retinal+ganglion+cell+subtypes.">Differential cone pathway influence on intrinsically photosensitive retinal ganglion cell subtypes.</a> J Neurosci. 30 (48), 16262-16271 (2010).</li><li>Clontech Laboratories I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> SMARTerTM Ultra Low RNA Kit for Illumina&#174; Sequencing. , (2013).</li><li>Trombetta, J. J., Gennert, D., Lu, D., Satija, R., Shalek, A. K., Regev, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Preparation+of+single-cell+RNA-Seq+libraries+for+next+generation+sequencing.">Preparation of single-cell RNA-Seq libraries for next generation sequencing.</a> Curr. Protoc. Mol. Biol. 4 (22), 1-17 (2014).</li><li>Ecker, J. L., 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=Melanopsin-expressing+retinal+ganglion-cell+photoreceptors:+Cellular+diversity+and+role+in+pattern+vision.">Melanopsin-expressing retinal ganglion-cell photoreceptors: Cellular diversity and role in pattern vision.</a> Neuron. 67 (1), 49-60 (2010).</li><li>Schmidt, T. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Role+for+Melanopsin+in+Alpha+Retinal+Ganglion+Cells+and+Contrast+Detection.">A Role for Melanopsin in Alpha Retinal Ganglion Cells and Contrast Detection.</a> Neuron. 82 (4), 781-788 (2014).</li><li>Sanes, J. R., Masland, R. 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+Types+of+Retinal+Ganglion+Cells:+Current+Status+and+Implications+for+Neuronal+Classification.">The Types of Retinal Ganglion Cells: Current Status and Implications for Neuronal Classification.</a> Annu. Rev. Neurosci. 38, 221-246 (2014).</li><li>Goetz, J. J., Trimarchi, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+Profiling+of+Developing+and+Mature+Retinal+Neurons.">Single-cell Profiling of Developing and Mature Retinal Neurons.</a> J. Vis. Exp. , e3824 (2012).</li><li>Trimarchi, J. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+Heterogeneity+of+Developing+Retinal+Ganglion+and+Amacrine+Cells+Revealed+through+Single+Cell+Gene+Expression+Profiling.">Molecular Heterogeneity of Developing Retinal Ganglion and Amacrine Cells Revealed through Single Cell Gene Expression Profiling.</a> J. Comp. Neurol. 502, 1047-1065 (2007).</li></ol>

저자는 공개 할 것이 없습니다.

우리의 프로토콜은 신속하고 사용하기 쉬운 가이드를 통해 표본에 거의 손상을주지 않으면 서 높은 수준의 서열 분석을 위해 확인 된 형태 학적 분류의 단일 세포를 준비하는 방법을 보여줍니다. 본 원고에서 본질적으로 감광성 망막 신경절 세포는 형태 학적으로 특성화되고 분리되고 RNA-Seq에 대해 준비된다. 망막 처리 도중 세포 응력이 발생할 수 있습니다. 이러한 이유로 우리는 4 시간 이상 사...

뉴런의 다양성은 중추 신경계, 특히 척수 망막에서 관찰되며 망막 전구 세포 1 , 2 , 3 의 한 집단에서 유래 한 1 개의 신경교 세포와 6 개의 신경 세포 유형으로 구성된 고도로 전문화 된 조직이다. 세포의 많은 아형들은 기능적으로, 형태 학적으로, 그리고 유 전적으로 분류 될 수 있습니다. 이 프로토콜의 목표는 세포 유형의 유전 적 다양성을 식별 가능한 기능적 및 / 또는 형태 학적 특성에 연결하는 것입니다. 세포의 분류를 위해 많은 유전자가 확인되었지만 많은 하위 유형은 전체 인구의 작은 부분을 대표하므로 특성화되지 않고 계속됩니다. 이러한 특정 아형 내에서의 유전자의 동정은 망막 내에서의 연결의 다양성에 대한 더 큰 이해를 가능하게 할 것이고, 다른 곳에서 신경 세포의 다양 화를 밝힐 수도있다. 부더구나, 단세포 연구는 새로운 세포 유형의 발견을 허용하며, 이는 전체 인구 중 낮은 수치로 인해 간과되었을지도 모르는 4 , 5 , 6 , 7 . 단일 세포 transcriptomics의 이점 중 하나는 특정 세포 아형을 정의하는 고유 마커 또는 마커의 조합을 발견 할 수 있다는 것입니다. 이것들은 다른 조작을 위해 그 세포 유형에 대한 유전자 접근을 얻기 위해 사용될 수 있습니다. 예를 들어, 우리는 photopigment melanopsin을 표현하는 망막 신경절 세포의 하위 집합의 세포 유형 특정 유전자를 특성화하기 위해이 프로토콜을 사용하고 있습니다. 망막 신경절 세포를 나타내는 멜라닌 신 (melanopsin)을 발현하는 세포에서 형광 마커를 사용하면 알려진 유전자의 발현으로 인해 이들 세포가 함께 모이기 때문에 이들 세포를 연구 할 수 있습니다. 흥미롭게도,이 세포 popu의 다섯 가지 알려진 아형이 있습니다마우스 망막 8에서 . 따라서, 각 유형의 세포에서 RNA를 분리하기 위해, 우리는 세포 분리 전에 각 아형을 확인하기 위해 형질 전환 모델 내에서 확립 된 형태 학적 분류를 사용했다. 이 기술은 조직 해리가 필요없는 세포의 특성화와 망막에서의 직접적인 분리를 가능하게합니다. 조직 해리없이 세포 내에서 스트레스 반응을 일으키고 절단 된 수상 돌기로 인한 오염을 유발할 수 있습니다. RNA-Seq 방법이 계속 발전함에 따라 지난 몇 년 동안 수많은 새로운 기술이 밝혀졌습니다. 이 도구를 사용하면 4 , 7 , 10 , 11 , 12 , 13 번 문제에 접근하면서 최대의 셀 획득 및 비용 효율성을 극대화 할 수 있습니다. 그러나,이 기술은 우수한 디딤돌이었으며, 여전히이 프로토콜이 해결할 수있는 많은 장애물이 있습니다. 첫째, 현재의 많은 절차는 해리 된 조직으로부터 세포를 분리하고, 세포 분류를 결정하기 위해 주성분 분석 또는 사후 (post-hoc) 계층 적 클러스터링을 사용하려고 시도한다. 이러한 유형의 도구를 사용하여 하위 유형을 분류하면 신뢰할만한 결과를 얻을 수 없으며 기능성 세포 유형에 대한 유전 적 표지의 상관 관계에 대해이 데이터의 유효성을 검사하는 새로운 방법을 강요 할 수 있습니다. 다른 프로토콜에서 해리에 대한 요구 사항은 때때로 조직 손상을 초래할 수 있으며, 신경 프로세스가 절단되어 mRNA의 잠재적 손실을 초래할 수 있습니다. 또한, 해리 세포 준비에서 스트레스 반응은 이러한 세포의 transcriptomes 14 영향을 시작할 수 있습니다. 이 프로토콜은 격리 이전에 기능 세포 유형을 결정함으로써 이러한 문제를 극복하고, h망막 조직을 손상시키지 않으면 서 세포의 증식. 한 기술은 2014 년에 도입 되었고 생체 내 전 사체의 생체 내 분석으로 구성되었습니다 15 . 이 기술은 조직에 대한 기계적 파괴를 최소화하면서 전 사체의 검사를 허용하지만 매우 특이적인 기자 마우스를 사용하지 않고 전 사체를 검사하기 전에 조직 내 특정 세포 유형을 분류하는 능력이 부족합니다. 우리의 프로토콜은 특정 리포터를 필요로하지 않습니다. 왜냐하면 우리는 세포 충전 및 전기 생리학을 이용하여 격리 전에 세포를 특성화하기 때문입니다. 이 이전 프로토콜의 또 다른 한계는 특정 파장이 광활성 요소를 여기시키는 데 필요하다는 것인데, 우리 프로토콜은 형광 리포터와 형광 염료의 사용을 허용하고 있습니다.이 리포터와 형광 염료는 쉽게 사용할 수 있거나 각 실험실별로 개별적으로 선택할 수 있습니다. 아직도, 다른 실험실은 electr의 2 가지의 방법을 결혼했다세포 다양성 연구를위한 생리학 및 전이 학. 분리 이전에 세포의 기능을 특성화하기위한 패치 - 클램프 기록의 사용은 해리 된 뉴런 16에서 수행 되었고, 어떤 경우에는 이러한 연구를 위해 마이크로 어레이 분석 17 을 사용하기도했다. 이러한 접근법에서 동일한 합병증이 발생합니다. 조직 분해 또는 이용 가능한 프로브에 대한 시료의 하이브리드 화에 의존하는 마이크로 어레이 기술의 사용이 필요하기 때문입니다. 가장 최근의 진보 중 하나는 전체 뇌 조각에서 세포를 이해하기 위해 패치 클램프 녹음과 RNA-Seq 기술을 결합한 기술인 Patch-Seq의 개발이었습니다. 이 기술은 여기에 제시된 프로토콜과 유사하지만, 우리의 접근 방식은 조직이 세포의 건강을 유지하기 위해 그대로 유지된다는 것을 다시 한번 주목하는 것이 중요합니다. 여기서는 최적화를위한 프로토콜을 제시합니다.이는 RNA-Seq을 사용하여 높은 판독 깊이와 매핑 범위를 얻기 위해 고품질의 단일 세포 라이브러리를 생성합니다.

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

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

세포 유형별 마커의 발견은 세포 기능과 세포 이질의 기원에 대한 통찰력을 제공 할 수 있습니다. 최근 뉴런의 다양성에 대한 이해를 높이기 위해 발현이 세포의 여러 하위 집단을 정의하는 유전자를 확인하는 것이 중요합니다. 망막은 중추 신경계의 다양성 연구를위한 훌륭한 모델로, 여러 주요 세포 유형으로 구성되어 있습니다. 각 주요 부류의 세포에 대한 연구는 이러한 개체군의 동정을 용이하게하는 유전자 마커를 산출했다. 그러나, 세포의 여러 하위 유형이 각각의 주요 망막 세포 클래스 내에 존재하며, 이들 아형의 유형 중 일부는 형태 학적 또는 기능적 특징으로 특징 지어졌지만 유전 적 마커를 가지고있다. 개별 망막 아형에 대한 유전 적 마커에 대한 지식은 특정 시각 기능과 관련된 뇌 표적의 연구와지도 작성을 가능하게하고 유전자 네트워크에 대한 통찰력을 제공한다.세포 다양성을 유지하라. 아형의 유전 적 표지자를 확인하는 데 사용되는 현재의 방법은 서열 분석에 따른 세포 유형의 분류와 같은 단점을 가지고있다. 이는 데이터 분석에 대한 도전 과제이며 클러스터에 동일한 기능의 셀이 포함되도록하기 위해 엄격한 검증 방법이 필요합니다. 우리는 분리 및 시퀀싱 이전에 세포의 형태 및 기능을 확인하여 하위 유형별 마커를 쉽게 식별 할 수있는 기술을 제안합니다. 이 기술은 비 신경 세포 유형뿐만 아니라 사소한 변형이있는 희귀 한 세포 집단까지 확장 될 수 있습니다. 이 프로토콜은 우수한 품질의 데이터를 제공하며, 많은 라이브러리가 단일 셀에 대해 2,000 만 건 이상의 읽기 깊이를 제공합니다. 이 방법론은 Single-cell RNA-Seq이 제시하는 장애물 중 많은 부분을 극복하고 직접적이고 매우 효율적인 방법으로 세포 유형을 규명하고자하는 연구자에게 적합 할 수 있습니다.

세포 유형은 염료 주입에 따라 쉽게 분류됩니다. 그림 1 은 형광 추적 충전 전후의 GFP + RGC의 예를 보여줍니다. 이 세포는 유전자 변형 라인 ( 그림 1A )에서 GFP의 표현에 따라 확인되었습니다. 견고한 씰이이 셀의 소마 (soma) 위에 미세 팁의 인발 유리 전극으로 형성되었습니다. 아형을 특성화하기 위해, ?...

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

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

여기에서, 우리는 단클론 성 전 사체의 단리 및 후속 특성 규명을 위해 연결 세포 유형을 분류하기위한 조합 접근법을 제시한다. 이 프로토콜은 성공적인 RNA 시퀀싱 (RNA-Seq)을위한 샘플 준비를 최적화하고 세포 다양성의 강화 된 이해를 위해 특별히 고안된 방법을 설명합니다.

망막 신경절 세포의 정의 된 서브 세트의 단일 세포 RNA-Seq

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

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

Research

JoVE Journal

Neuroscience

13.7K 조회수.  Iowa State University.  여기에서, 우리는 단클론 성 전 사체의 단리 및 후속 특성 규명을 위해 연결 세포 유형을 분류하기위한 조합 접근법을 제시한다. 이 프로토콜은 성공적인 RNA 시퀀싱 (RNA-Seq)을위한 샘플 준비를 최적화하고 세포 다양성의 강화 된 이해를 위해 특별히 고안된 방법을 설명합니다. 

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

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

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

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

유전적으로 망막 신경절 라벨 셀에서 라이트 응답을 기록하기 위해 절연 망막 준비

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

연구

신경과학

Transfection of Mouse Retinal Ganglion Cells by in vivo Electroporation

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

Transfection of Mouse Retinal Ganglion Cells by in vivo Electroporation

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

마우스 망막 신경절 세포 Transfection로 생체내에 Electroporation

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

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

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

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

망막 신경절 세포의 생존을 공부 시신경의 호감 상해 Murine 모델

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

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

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

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

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

손상의 감각 오르간 전구체 세포 라이브 셀 이미징 Drosophila Pupae

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

Single-cell Profiling of Developing and Mature Retinal Neurons

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

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

개발하고 성숙한 망막 신경 세포의 단일 세포 프로 파일링

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

Viral Tracing of Genetically Defined Neural Circuitry

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

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

유 전적으로 정의 된 신경 회로의 추적 바이러스

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

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

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

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

마우스 망막 신경절 세포의 시냅스와 인공 컨덕턴스와 동적 클램프 구현

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

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

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

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

형광 염료와 성인 Zebrafish의 망막 신경절 세포의 퇴행성 라벨링

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

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

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

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

electroporation하여 병아리 배아 척수에서 차동 유전자 발현의 RNA-SEQ 분석

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

Optical Coherence Tomography: Imaging Mouse Retinal Ganglion Cells In Vivo

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

Optical Coherence Tomography: Imaging Mouse Retinal Ganglion Cells In Vivo

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

광학 일관성 단층 촬영: 마우스 망막 신경 절 세포에서 Vivo에서 화상 진 찰

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