私たちは、ファントム実験を行うためのマレクワイラーに感謝します。この作品は、欧州研究会議（ERC開始グラント309495：NeoNaNo）によってサポートされていました、ノルトラインヴェストファーレン、ドイツ連邦州（NRW; High-Tech.NRW/EU-Ziel 2 - のprogramm（EFRE）; ForSaTum）、ドイツ語教育研究省（BMBF）（資金調達プログラム仮想肝臓（0315743）、LungSys（0315415C）、LungSys2（0316042F）、Photonik Forschungドイツ（13N13355））、アーヘン工科大学（I 3 TMのシード基金）、フィリップスリサーチ（アーヘン、ドイツ）。

<ol>
	<li>Hoffman, R. M., Yang, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Subcellular+imaging+in+the+live+mouse.">Subcellular imaging in the live mouse.</a> Nature Protocols. 1 (2), 775-782 (2006).</li><li>Ale, A., Ermolayev, V., Herzog, E., Cohrs, C., Angelis, M. H., Ntziachristos, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=FMT-XCT:+in+vivo+animal+studies+with+hybrid+fluorescence+molecular+tomography-X-ray+computed+tomography.">FMT-XCT: in vivo animal studies with hybrid fluorescence molecular tomography-X-ray computed tomography.</a> Nature Methods. 9 (9), 615-620 (2012).</li><li>Eaton, V. L., Vasquez, K. O., Goings, G. E., Hunter, Z. N., Peterson, J. D., Miller, S. 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O., Casavant, C., Peterson, J. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+Whole+Body+Biodistribution+of+Fluorescent-Labeled+Agents+by+Non-Invasive+Tomographic+Imaging.">Quantitative Whole Body Biodistribution of Fluorescent-Labeled Agents by Non-Invasive Tomographic Imaging.</a> PLoS ONE. 6 (6), e20594 (2011).</li><li>Theek, B., Gremse, F., 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=Characterizing+EPR-mediated+passive+drug+targeting+using+contrast-enhanced+functional+ultrasound+imaging.">Characterizing EPR-mediated passive drug targeting using contrast-enhanced functional ultrasound imaging.</a> Journal of Controlled Release. 182 (1), 83-89 (2014).</li><li>Hyde, D., Schulz, R., Brooks, D., Miller, E., Ntziachristos, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Performance+dependence+of+hybrid+x-ray+computed+tomography/fluorescence+molecular+tomography+on+the+optical+forward+problem.">Performance dependence of hybrid x-ray computed tomography/fluorescence molecular tomography on the optical forward problem.</a> Journal of the Optical Society of America. A, Optics, Image Science, and Vision. 26 (4), 919-923 (2009).</li><li>Jacques, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optical+properties+of+biological+tissues:+a+review.">Optical properties of biological tissues: a review.</a> Physics in Medicine and Biology. 58 (11), R37 (2013).</li><li>Loening, A. M., Gambhir, S. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=AMIDE:+a+free+software+tool+for+multimodality+medical+image+analysis.">AMIDE: a free software tool for multimodality medical image analysis.</a> Molecular Imaging. 2 (3), 131-137 (2003).</li><li>Gremse, F., Schulz, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Qualitative+and+Quantitative+Data+Analysis.">Qualitative and Quantitative Data Analysis.</a> Small Animal Imaging. , 363-378 (2011).</li><li>Baiker, M., Milles, 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=Atlas-based+whole-body+segmentation+of+mice+from+low-contrast+Micro-CT+data.">Atlas-based whole-body segmentation of mice from low-contrast Micro-CT data.</a> Medical Image Analysis. 14 (6), 723-737 (2010).</li><li>Sevick-Muraca, E. M., Rasmussen, J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+imaging+with+optics:+primer+and+case+for+near-infrared+fluorescence+techniques+in+personalized+medicine.">Molecular imaging with optics: primer and case for near-infrared fluorescence techniques in personalized medicine.</a> Journal of Biomedical Optics. 13 (4), 041303 (2008).</li></ol>

フェリックスGremseはGremse-IT、フィリップスとアーヘン工科大学の実験的分子イメージングのための部門との協力における医用画像解析のためのソフトウェアとサービスを提供しています新興企業の創設者であり、所有者です。

我々は、説明し、マルチモーダルμCT-FMTイメージングのためのプロトコルを適用します。 17,21 -私たちは、市販されており、広く使用されているFMTとμCTデバイス3,11,15を使用します。プロトコルは、特定のFMTを必要とするがμCTは、同様の機能と同等の走査パラメータを持つ別のμCTに置き換えることができ、例えば、視野がマーカーを含むマウスのベッドをカバーするのに十分な大きさでなければなりません。 FMTは、μCTまたはMRI 21と組み合わせることなく、生体分布分析のために使用されてきたが、解剖学的データは、セグメンテーションをμCTデータ10に表示された臓器の境界に基づいて行うことができるため、再現性を高めるために有益です。統合μCT-FMT装置は2,7を開発されてきたが、これらはまだ市販されていません。さらに、2つの別々のデバイスを使用する配管は、可能にする、すなわち 、次のマウスCAを最初のマウスは、スループットを向上させるために、FMTである間のnμCTで画像化すること。 手動の作業負荷を低減するために、我々は、自動化されたマーカーの検出および融合を行います。さらに、マウスの形状を自動的にセグメント化され、この情報はかなりの蛍光再構成11,13,22を改善します。定量的蛍光再構成のために、吸収及び散乱マップは13,23を必要としています。我々は、自動化されたμCTデータのセグメント化といくつかの組織タイプ（肺、骨、皮膚、脂肪、および残りの軟組織）の既知の散乱係数を割り当てる24により散乱マップを導出します。その後、我々は、心臓や肝臓13,20だけでなく、灌流臓器のために特に重要である光生データからの吸収マップを再構築します。 複数の時点で、いくつかのマウスをスキャンするとすぐに分析されるデータセットの数が多いことになります。 biodis用tributionの研究は、いくつかの器官は、各μCT-FMTスキャンのためにセグメント化される必要があります。マウスが新たに繰り返しマウス床に位置しているので残念ながら、セグメンテーションは、再利用することはできません。私たちは私たちの研究所で開発された対話型のセグメント化のためのツールを使用するが、他のツールも25適切であるかもしれません。これらは、楕円やキューブ26のような単純な形状よりも複雑な器官へのより良い一致しているため私たちは、ボクセルワイズセグメンテーションを生成します。自動化された全動物のセグメンテーションは、さらに手動の作業負荷27を減少させるのに有用であろうが、インタラクティブセグメンテーションツールは依然としてセグメント化誤差を補正するために必要とされるであろう。また、自動化されたセグメント化ツールは、正確に病状などの特別な場合をほとんど予想することはできません。私たちはネイティブμCTスキャンを使用するため、脾臓などのいくつかの器官があっても、手動でセグメントに非常に困難です。造影剤は役立つだろうが、忍容性に問題があり、それはmaintaiすることが困難です縦イメージングを通じてNA安定した造影剤の分布。 我々ファントム研究は、蛍光再構成のために形状情報を使用した場合の信号定位が改善されることを示している。 インビボでは 、プローブの大量に既にある場合にも同様の改善が、早い時点（注射後15分）のために明らかです膀胱。ヒドロキシアパタイト結合プローブは、骨や関節に蓄積します。それはつまり 、この問題が発生したどのくらいの速顕著で、信号は注射後脊柱15分ですでにはっきりと見えます。これはおそらく、標的領域へ​​の高速血管外漏出と拡散を可能にプローブの低分子量によって引き起こされます。プローブがその標的ヒドロキシアパタイトに共有結合し、結合していないプローブが排泄されます。後の時点では、6時間および注射後24時間の間、脊椎における信号強度はおそらく、比較的安定したままで、ほとんどすべての光再ため、蛍光を漂白する深いマウスに痛みます。私たちの研究のために、我々は、注入前に取得したスキャンの明らかなように、低バックグラウンド蛍光を生じさせる750 nmのチャネルを使用します。低い波長では、より多くのバックグラウンド信号28を期待することができます。 要約すると、我々は、商業的に入手可能なFMTとμCTデバイスのためのマルチモーダルイメージングプロトコルを記述します。我々は、組み合わせは、蛍光再構成のための利点を提供することを示しています。我々は、生体内分布曲線をインタラクティブ臓器セグメンテーション及びバッチ処理によって大量の画像データから抽出される方法を示します。我々は、この標準化されたワークフローは、薬剤開発および蛍光標識したプローブを用いて、他のイメージング研究のために役立つことができると信じています。

蛍光媒介トモグラフィーは、とも呼ばれる蛍光分子トモグラフィー（FMT）は、定量的にそのような麻酔したマウス、さらには人間の身体組織、 例えば 、乳房や指の関節などのびまん性組織における蛍光分布を評価するための有望な技術です。細胞下解像度1の表面的な標的のイメージングを可能にする非侵襲的な顕微鏡技術とは対照的に、FMTは、低解像度2であるが、数センチメートルの深さで蛍光源の三次元再構成を可能にします。 5 -多くのターゲットに蛍光プローブは、画像、血管新生、アポトーシス、炎症、その他2にご利用いただけます。いくつかのプローブは、 例えば 、活性化可能である。、蛍光色素のunquenchingにつながる特定の酵素切断によって。また、蛍光タンパク質を発現するレポーター遺伝子は、腫瘍細胞遊走6を追跡するため、 例えば 、画像化することができます。 FMTは強く解剖学的モダリティ、 例えば 、μCT2,7またはMRI 8と組み合わせから利益を得ます。スタンドアロンFMT装置9市販されているが、蛍光画像は、解剖学的な参照情報なしに解釈するのは困難です。最近では、融合された解剖学的画像データは、より堅牢な分析10を可能にすること、を示すことができました。解剖学的データはまた、正確な光造形および蛍光再構成11のために重要であるマウスの外形として予備知識を提供するために使用することができます。また、光散乱および吸収マップは、組織タイプのセグメント化を用いて推定し、クラス固有の係数12,13を割り当てることにより得ることができます。近赤外光は、ヘモグロビンはメラニンや毛皮14以外にも、マウスでは、吸収性本体です。相対血液量が桁違いに地域変化するので、吸収マップは、泉のために特に重要ですtitative蛍光再建13。 非侵襲性の撮像素子を使用することの一つの利点は、マウスを複数の時点で、すなわち 、縦方向に画像化することができるということです。これは、プローブの動的挙動、 即ち、それらの標的の蓄積、生体分布および排泄10,15を評価するため、または疾患の進行16を評価することが重要です。複数の時点で、いくつかのマウスを画像化する場合、画像データが大量に発生します。比較可能性を有効にするには、これらは明確に定義され、文書化プロトコルを使用して、 すなわち 、体系的な方法で取得する必要があります。スキャン多数の画像データから定量的な測定を抽出するために必要とされる画像解析のための課題を提起します。 私たちの研究の目的は、我々はいくつかの研究10,13,15,17,18全体で使用され、最適化μCT-FMT撮像プロトコルの詳細な説明を提供することにあります。我々は説明しデータセットが生成されるか、処理して可視化し、分析しました。これは、ヒドロキシアパタイト19に結合確立分子プローブ、OsteoSenseを用いて実証され、画像の骨疾患に使用され、2を改造することができます。動物に関わるすべての手順は、動物のケアに関する政府の審査委員会によって承認されました。

<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>FMT (Fluorescence molecular tomography) FMT2500 LX</td>
			<td>PerkinElmer</td>
			<td>FMT2000</td>
			<td>Device for fluorescence molecular tomography</td>
		</tr>
		<tr>
			<td>&#181;CT (micro computed tomography) Tomoscope Duo</td>
			<td>CT Imaging GmbH</td>
			<td>Tomoscope Duo</td>
			<td>Device for micro computed tomography</td>
		</tr>
		<tr>
			<td>Multimodal Mouse Bed</td>
			<td>CT Imaging GmbH</td>
			<td>Experimental builder</td>
			<td>Partially transparent animal holder</td>
		</tr>
		<tr>
			<td>IsoFlo (isoflurane, USP)</td>
			<td>Abbott</td>
			<td>05260-05</td>
			<td>Isoflurane Inhalation anesthesia</td>
		</tr>
		<tr>
			<td>Small animal anesthesia system</td>
			<td>Harvard apparatus</td>
			<td>726419</td>
			<td>Complete Isoflurane Table-Top System</td>
		</tr>
		<tr>
			<td>Chlorophyll-free mouse food</td>
			<td>Ssniff</td>
			<td>E15051</td>
			<td>low chlorophyll / low fluorescence food</td>
		</tr>
		<tr>
			<td>OsteoSense 750EX</td>
			<td>PerkinElmer</td>
			<td>NEV10053EX</td>
			<td>Animal FMT contrast agent</td>
		</tr>
		<tr>
			<td>Portex Fine Bore Polythene Tubing</td>
			<td>Smith medical</td>
			<td>800/100/120</td>
			<td>Tube for injection catheter</td>
		</tr>
		<tr>
			<td>Sterican 30g</td>
			<td>BBraun</td>
			<td>4656300</td>
			<td>Hypodermic needle for catheter</td>
		</tr>
		<tr>
			<td>Imeron</td>
			<td>Altana pharma</td>
			<td>INLA F.1/0203/3.5337.69</td>
			<td>CT contrast agent for the phantom inclusions</td>
		</tr>
		<tr>
			<td>Agarose</td>
			<td>Sigma</td>
			<td>90-12-36-6</td>
			<td>Agarose for phantom production</td>
		</tr>
		<tr>
			<td>TiO2</td>
			<td>Applichem</td>
			<td>A1900,1000</td>
			<td>Titanium oxyde as phantom scattering agent</td>
		</tr>
		<tr>
			<td>Trypan blue</td>
			<td>Fluka</td>
			<td>93595</td>
			<td>Trypan blue to adjust phantom light propagation</td>
		</tr>
		<tr>
			<td>Cy7</td>
			<td>Lumiprobe</td>
			<td>15020</td>
			<td>Fluorochrome for the phantom inclusions</td>
		</tr>
		<tr>
			<td>Lipovenoes 20%</td>
			<td>Fresenius Kabi</td>
			<td>3094740</td>
			<td>Lipid emulsion, scattering agent for FMT contrast agents</td>
		</tr>
		<tr>
			<td>Definiens Developer XD Server</td>
			<td>Definiens AG</td>
			<td>Server XD</td>
			<td>Software platform for automated segmentation</td>
		</tr>
		<tr>
			<td>Imalytics Preclinical</td>
			<td>ExMI/Gremse-IT</td>
			<td>Version 2.0.1</td>
			<td>Software for image fusion, reconstruction and analysis</td>
		</tr>
		<tr>
			<td>NVIDIA Geforce Titan</td>
			<td>Asus</td>
			<td>GTXTITAN6GD5</td>
			<td>High end computer graphics card, 6GB Memory</td>
		</tr>
	</tbody>
</table>

hybrid &#181;ct-fmt imaging and image analysis

蛍光媒介トモグラフィー（FMT）は、インビボでの蛍光分布の縦及び定量決意を可能にし、新たなプローブの体内分布を評価するために、確立された分子プローブまたはレポーター遺伝子を用いて、疾患の進行を評価することができます。解剖学的モダリティ、 例えば 、マイクロコンピュータ断層撮影（μCT）との組み合わせは、画像解析のための蛍光再構成のために有益です。我々は、定量的な測定値を抽出するために必要な画像処理ステップを含むマルチモーダルμCT-FMTイメージングのためのプロトコルを説明します。マウスを調製し、画像化を行った後に、マルチモーダルデータセットが登録されています。続いて、改善された蛍光の再構成を考慮にマウスの形状をとる、行われます。定量分析のために、臓器セグメンテーションをインタラクティブセグメンテーションツールを使用して、解剖学的データに基づいて生成されます。最後に、生体内分布のCurvesは、バッチ処理機能を使用して生成されます。我々は、骨や関節に結合するよく知られているプローブの生体内分布を評価することによって方法の適用可能性を示しています。

私たちは、ヒドロキシアパタイトに結合する標的化プローブ、OsteoSenseの生体内分布を評価するために記載されたプロトコールを適用しました。 3匹のマウス（C57BL / 6アポ - / - AHSG - / - 二重ノックアウトマウス、10週齢）は撮像された前、15分、2時間、4時間、6時間、2ナノモルのOsteoSenseの静脈内注射の24時間後。当社のソフトウェアは自動的にFMT（ 図2C、D）で行わ蛍光復興解剖学的μCTデータの融合を可能にしたマルチモーダルマウスのベッドに組み込まれたマーカー（ 図1、図2A、B）を 、検出されました。 OsteoSenseは、低分子量を有するプローブであることから、膀胱における高速腎排泄ので、高信号が期待されます。 FMTの蛍光再建の融合は、膀胱（ 図2C、D）の外に、このような見当違いの信号などの問題を明らかにしました。 FMTはマウスの真の形状を知っていて、ブロック状を呈していないため、これらの問題が発生します。オウRの再構築は、μCTデータから正確な形状を決定し、散乱と吸収が膀胱（ 図2E、F）のために特に顕著である良好な信号の局在化、とのより正確な蛍光再構成を可能にするために、13にマッピングが生成されます。 適切な領域に再構成された蛍光を割り当てるには、我々は対話的に当社のソフトウェア（ 図3）を使用して、いくつかの器官をセグメント化。 18のスキャンのそれぞれについて、7領域はμCTデータ、 すなわち 、心臓、肺、肝臓、腎臓、脊椎、腸および膀胱に基づいてセグメント化されました。次いで、ソフトウェア126の各領域の平均蛍光濃度を計算するために使用されました。幸いなことに、ソフトウェアは、すべての値を計算し、単一のスプレッドシートに保存しますバッチモードを提供します。 蛍光分布を可視化するために、3Dレンダリングは、各時点で生成されました、同等のウィンドウ設定（ 図4A-F）を使用して。定量化された臓器の値を使用して、生体内分布は、3匹のマウス（ 図4G）で臓器値を平均することにより算出しました。注射の前に取得事前スキャンは、無視できるバックグラウンドシグナルを示しました。 15分注射後、最も強い信号があるため、高速腎排泄のため、膀胱に登場しました。その後の時点で、残りのプローブは、骨および関節に蓄積していました。 <img alt="図1" src="/files/ftp_upload/52770/52770fig1.jpg" /> 図1.マルチモーダルマウスベッド。（A）マルチモーダルマウスベッドはしっかりとマウスを保持している2つのアクリルガラスプレートが含まれています。締め付けは、2本のネジを使用して調整されます。マウスのベッドは、画像融合のためのマーカー（空孔）が含まれています。麻酔ガスをtで固定されるフレキシブルチューブを使用して供給されます。猿。 （B）マウスの床は、金属ホルダに取り付けられた回転μCTガントリの中心に保持されます。そうしないと、マーカーが誤って間違ったの融合につながる割り当てることができるため、（C）は 、マウスのベッドと金属ホルダとの間のギャップを避けてください。麻酔ガス管はチューブコネクタに接続する必要があります。 （D）は、マウスのベッドは正しい自動融合を可能にするために、フロント最初にFMTに挿入する必要があります。 （E）マーカーは、自動化されたマーカーの検出および融合のために使用されるFMTカメラ、に表示されます。 <a href="https://www.jove.com/files/ftp_upload/52770/52770fig1large.jpg" target="_blank">この図の拡大版を表示するには、こちらをクリックしてください。</a> <img alt="図2" src="/files/ftp_upload/52770/52770fig2.jpg" /> 図2.画像の融合と再構成。（A、B）マーカーとMOUの外形SEは、自動セグメンテーションアルゴリズムによって決定されます。 （C、D）OsteoSenseの注射後15分、プローブのかなりの量は、すでに膀胱中に排泄されています。 μCTデータとベンダー提供の再建を融合した後、問題が見えてきます。信号の大部分は、膀胱の周りではなく、膀胱の内側に表示され、いくつかの信号があっても、空気に表示されます。 FMTは、ブロック状のマウスを想定しているために発生します。 （E、F）私たちの改善された蛍光の再構築、μCTデータから派生したマウスの形状を使用して、膀胱内の蛍光のより良いローカライズをもたらす。 <a href="https://www.jove.com/files/ftp_upload/52770/52770fig2large.jpg" target="_blank">この図の拡大版を表示するには、こちらをクリックしてください。</a> <img alt="図3" src="/files/ftp_upload/52770/52770fig3.jpg" /> 図3.インタラクティブオルガンSegmentat蛍光分布を定量化するために、イオン（A）、いくつかの器官がセグメント化されています。心（赤）、肺（ピンク）、肝臓（茶色）、胃（ベージュ）、脊椎（紫）、腎臓（黄色）、腸（緑） 、および膀胱（金）。 （B）に強く、周囲の組織と比較して対比される肺は、しきい値および領域の充填を使用してセグメント化されます。膀胱、腎臓、および心臓などの（C）ラウンド器官は、「落書き」を使用してセグメント化されます。 （D）より複雑な形状の器官は、 例えば 、肝臓と胃を走り書きを使用してインクリメンタルにセグメント化されます。セグメントに背骨、高しきい値は、すべての骨のセグメントに適用されます。そしていくつかの骨、 例えば脊椎が残るまで、リブは、切り取られている。 <a href="https://www.jove.com/files/ftp_upload/52770/52770fig3large.jpg" target="_blank">この図の拡大版を表示するには、こちらをクリックしてください。</a> 図4：生体内分布。生体内分布を評価するために、マウスを、いくつかの時点（AF）に走査されます。 （A）プリスキャンは、注射の前に、750 nmのチャンネルに少し背景信号を示しています。 （B）15分注入後、プローブのかなりの量は、膀胱内に既にあります。 （C）2時間の時点で、マウスは、マウスの外で蛍光をもたらす、排尿していました。後の時点（DF）において、信号は、脊椎や膝に、骨や関節、 すなわち 。で優先的に表示されます。 （G）蛍光定量化濃度は、選択された臓器のために示されています。

Watch this Scientific Journal Video about Hybrid ÂµCT-FMT imaging and image analysis at JoVE.com

Hybrid  &amp;#181;CT-FMT imaging and image analysis

We describe a protocol for hybrid imaging, combining fluorescence-mediated tomography (FMT) with micro computed tomography (&#181;CT). After fusion and reconstruction, we perform interactive organ segmentation to extract quantitative measurements of the fluorescence distribution.

ハイブリッドμCT-FMT撮像と画像解析

Fluorescence-mediated tomography (FMT) enables longitudinal and quantitative determination of the fluorescence distribution in vivo and can be used to ...

Research

JoVE Journal

Bioengineering

Hybrid &#181;CT-FMT imaging and image analysis

13.0K ビュー.  RWTH Aachen University. We describe a protocol for hybrid imaging, combining fluorescence-mediated tomography (FMT) with micro computed tomography (µCT). After fusion and reconstruction, we perform interactive organ segmentation to extract quantitative measurements of the fluorescence distribution.

ビデオ: Hybrid  &#181;CT-FMT imaging and image analysis

Specimen Preparation, Imaging, and Analysis Protocols for Knife-edge Scanning Microscopy

Major advances in high-throughput, high-resolution, 3D microscopy techniques have enabled the acquisition of large volumes of neuroanatomical data at submicrometer resolution. One of the first such instruments producing whole-brain-scale data is the Knife-Edge Scanning Microscope (KESM)7, 5, 9, developed and hosted in the authors' lab. KESM has been used to section and image whole mouse brains at submicrometer resolution, revealing the intricate details of the neuronal networks (Golgi)1, 4, 8, vascular networks (India ink)1, 4, and cell body distribution (Nissl)3. The use of KESM is not restricted to the mouse nor the brain. We have successfully imaged the octopus brain6, mouse lung, and rat brain. We are currently working on whole zebra fish embryos. Data like these can greatly contribute to connectomics research10; to microcirculation and hemodynamic research; and to stereology research by providing an exact ground-truth. 
In this article, we will describe the pipeline, including specimen preparation (fixing, staining, and embedding), KESM configuration and setup, sectioning and imaging with the KESM, image processing, data preparation, and data visualization and analysis. The emphasis will be on specimen preparation and visualization/analysis of obtained KESM data. We expect the detailed protocol presented in this article to help broaden the access to KESM and increase its utilization.

The full process from brain specimen preparation to serial sectioning imaging using the Knife-Edge Scanning Microscope, to data visualization and analysis is described. This technique is currently used to acquire mouse brain data, but it is applicable to other organs, other species.

ナイフエッジスキャン顕微鏡用試料作製、イメージング、および解析プロトコル

Major advances in high-throughput, high-resolution, 3D microscopy techniques have enabled the acquisition of large volumes of neuroanatomical data at ...

生物工学

Optical Frequency Domain Imaging of Ex vivo Pulmonary Resection Specimens: Obtaining One to One Image to Histopathology Correlation

Lung cancer is the leading cause of cancer-related deaths1. Squamous cell and small cell cancers typically arise in association with the conducting airways, whereas adenocarcinomas are typically more peripheral in location. Lung malignancy detection early in the disease process may be difficult due to several limitations: radiological resolution, bronchoscopic limitations in evaluating tissue underlying the airway mucosa and identifying early pathologic changes, and small sample size and/or incomplete sampling in histology biopsies. High resolution imaging modalities, such as optical frequency domain imaging (OFDI), provide non-destructive, large area 3-dimensional views of tissue microstructure to depths approaching 2 mm in real time (Figure 1)2-6. OFDI has been utilized in a variety of applications, including evaluation of coronary artery atherosclerosis6,7 and esophageal intestinal metaplasia and dysplasia6,8-10.
Bronchoscopic OCT/OFDI has been demonstrated as a safe in vivo imaging tool for evaluating the pulmonary airways11-23 (Animation). OCT has been assessed in pulmonary airways16,23 and parenchyma17,22 of animal models and in vivo human airway14,15. OCT imaging of normal airway has demonstrated visualization of airway layering and alveolar attachments, and evaluation of dysplastic lesions has been found useful in distinguishing grades of dysplasia in the bronchial mucosa11,12,20,21. OFDI imaging of bronchial mucosa has been demonstrated in a short bronchial segment (0.8 cm)18. Additionally, volumetric OFDI spanning multiple airway generations in swine and human pulmonary airways in vivo has been described19. Endobronchial OCT/OFDI is typically performed using thin, flexible catheters, which are compatible with standard bronchoscopic access ports. Additionally, OCT and OFDI needle-based probes have recently been developed, which may be used to image regions of the lung beyond the airway wall or pleural surface17.
While OCT/OFDI has been utilized and demonstrated as feasible for in vivo pulmonary imaging, no studies with precisely matched one-to-one OFDI:histology have been performed. Therefore, specific imaging criteria for various pulmonary pathologies have yet to be developed. Histopathological counterparts obtained in vivo consist of only small biopsy fragments, which are difficult to correlate with large OFDI datasets. Additionally, they do not provide the comprehensive histology needed for registration with large volume OFDI. As a result, specific imaging features of pulmonary pathology cannot be developed in the in vivo setting. Precisely matched, one-to-one OFDI and histology correlation is vital to accurately evaluate features seen in OFDI against histology as a gold standard in order to derive specific image interpretation criteria for pulmonary neoplasms and other pulmonary pathologies. Once specific imaging criteria have been developed and validated ex vivo with matched one-to-one histology, the criteria may then be applied to in vivo imaging studies. Here, we present a method for precise, one to one correlation between high resolution optical imaging and histology in ex vivo lung resection specimens. Throughout this manuscript, we describe the techniques used to match OFDI images to histology. However, this method is not specific to OFDI and can be used to obtain histology-registered images for any optical imaging technique. We performed airway centered OFDI with a specialized custom built bronchoscopic 2.4 French (0.8 mm diameter) catheter. Tissue samples were marked with tissue dye, visible in both OFDI and histology. Careful orientation procedures were used to precisely correlate imaging and histological sampling locations. The techniques outlined in this manuscript were used to conduct the first demonstration of volumetric OFDI with precise correlation to tissue-based diagnosis for evaluating pulmonary pathology24. This straightforward, effective technique may be extended to other tissue types to provide precise imaging to histology correlation needed to determine fine imaging features of both normal and diseased tissues.

Optical Frequency Domain Imaging of Ex vivo Pulmonary Resection Specimens: Obtaining One to One Image to Histopathology Correlation

A method to image ex vivo pulmonary resection specimens with optical frequency domain imaging (OFDI) and obtain precise correlation to histology is described, which is essential to developing specific OFDI interpretation criteria for pulmonary pathology. This method is applicable to other tissue types and imaging techniques to obtain precise imaging to histology correlation for accurate image interpretation and assessment. Imaging criteria established with this technique would then be applicable to image assessment in future in vivo studies.

の光周波数領域イメージング<em&gt;エクスビボ</em&gt;肺切除標本：組織病理相関へのOne to Oneイメージの入手

Lung cancer is the leading cause of cancer-related deaths1. Squamous cell and small cell cancers typically arise in association with the conducting ...

High-resolution Spatiotemporal Analysis of Receptor Dynamics by Single-molecule Fluorescence Microscopy

Single-molecule microscopy is emerging as a powerful approach to analyze the behavior of signaling molecules, in particular concerning those aspect (e.g., kinetics, coexistence of different states and populations, transient interactions), which are typically hidden in ensemble measurements, such as those obtained with standard biochemical or microscopy methods. Thus, dynamic events, such as receptor-receptor interactions, can be followed in real time in a living cell with high spatiotemporal resolution. This protocol describes a method based on labeling with small and bright organic fluorophores and total internal reflection fluorescence (TIRF) microscopy to directly visualize single receptors on the surface of living cells. This approach allows one to precisely localize receptors, measure the size of receptor complexes, and capture dynamic events such as transient receptor-receptor interactions. The protocol provides a detailed description of how to perform a single-molecule experiment, including sample preparation, image acquisition and image analysis. As an example, the application of this method to analyze two G-protein-coupled receptors, i.e., &#946;2-adrenergic and &#947;-aminobutyric acid type B (GABAB) receptor, is reported. The protocol can be adapted to other membrane proteins and different cell models, transfection methods and labeling strategies.

This protocol describes how to use total internal reflection fluorescence microscopy to visualize and track single receptors on the surface of living cells and thereby analyze receptor lateral mobility, size of receptor complexes as well as to visualize transient receptor-receptor interactions. This protocol can be extended to other membrane proteins.

単一分子蛍光顕微鏡による受容体ダイナミクスの高分解能時空間解析

Single-molecule microscopy is emerging as a powerful approach to analyze the behavior of signaling molecules, in particular concerning those aspect (e.g., ...

Magnetic Levitation Coupled with Portable Imaging and Analysis for Disease Diagnostics

Currently, many clinical diagnostic procedures are complex, costly, inefficient, and inaccessible to a large population in the world. The requirements for specialized equipment and trained personnel require that many diagnostic tests be performed at remote, centralized clinical laboratories. Magnetic levitation is a simple yet powerful technique and can be applied to levitate cells, which are suspended in a paramagnetic solution and placed in a magnetic field, at a position determined by equilibrium between a magnetic force and a buoyancy force. Here, we present a versatile platform technology designed for point-of-care diagnostics which uses magnetic levitation coupled to microscopic imaging and automated analysis to determine the density distribution of a patient's cells as a useful diagnostic indicator. We present two platforms operating on this principle: (i) a smartphone-compatible version of the technology, where the built-in smartphone camera is used to image cells in the magnetic field and a smartphone application processes the images and to measures the density distribution of the cells and (ii) a self-contained version where a camera board is used to capture images and an embedded processing unit with attached thin-film-transistor (TFT) screen measures and displays the results. Demonstrated applications include: (i) measuring the altered distribution of a cell population with a disease phenotype compared to a healthy phenotype, which is applied to sickle cell disease diagnosis, and (ii) separation of different cell types based on their characteristic densities, which is applied to separate white blood cells from red blood cells for white blood cell cytometry. These applications, as well as future extensions of the essential density-based measurements enabled by this portable, user-friendly platform technology, will significantly enhance disease diagnostic capabilities at the point of care.

We present a magnetic levitation technique coupled with automated imaging and analysis in both a smartphone-compatible device and a device with embedded imaging and processing. This is applied to measure the density distribution of cells with two demonstrated biomedical applications: sickle cell disease diagnosis and separating white and red blood cells.

病の診断のためのポータブルイメージングおよび分析と相まって磁気浮上

Currently, many clinical diagnostic procedures are complex, costly, inefficient, and inaccessible to a large population in the world. The requirements for ...

Quantifying Intermembrane Distances with Serial Image Dilations

A recently-described extracellular nanodomain, termed the perinexus, has been implicated in ephaptic coupling, which is an alternative mechanism for electrical conduction between cardiomyocytes. The current method for quantifying this space by manual segmentation is slow and has low spatial resolution.We developed an algorithm that uses serial image dilations of a binary outline to count the number of pixels between two opposing 2 dimensional edges.This algorithm requires fewer man hours and has a higher spatial resolution than the manual method while preserving the reproducibility of the manual process.In fact, experienced and novice investigators were able to recapitulate the results of a previous study with this new algorithm.The algorithm is limited by the human input needed to manually outline the perinexus and computational power mainly encumbered by a pre-existing pathfinding algorithm.However, the algorithm&#39;s high-throughput capabilities, high spatial resolution and reproducibility make it a versatile and robust measurement tool for use across a variety of applications requiring the measurement of the distance between any 2-dimensional (2D) edges.

The purpose of this algorithm is to continuously measure the distance between two 2-dimensional edges using serial image dilations and pathfinding. This algorithm can be applied to a variety of fields such as cardiac structural biology, vascular biology, and civil engineering.

シリアル イメージの肥大と膜間距離を定量化

A recently-described extracellular nanodomain, termed the perinexus, has been implicated in ephaptic coupling, which is an alternative mechanism for ...

Substructure Analyzer: A User-Friendly Workflow for Rapid Exploration and Accurate Analysis of Cellular Bodies in Fluorescence Microscopy Images

The last decade has been characterized by breakthroughs in fluorescence microscopy techniques illustrated by spatial resolution improvement but also in live-cell imaging and high-throughput microscopy techniques. This led to a constant increase in the amount and complexity of the microscopy data for a single experiment. Because manual analysis of microscopy data is very time consuming, subjective, and prohibits quantitative analyses, automation of bioimage analysis is becoming almost unavoidable. We built an informatics workflow called Substructure Analyzer to fully automate signal analysis in bioimages from fluorescent microscopy. This workflow is developed on the user-friendly open-source platform Icy and is completed by functionalities from ImageJ. It includes the pre-processing of images to improve the signal to noise ratio, the individual segmentation of cells (detection of cell boundaries) and the detection/quantification of cell bodies enriched in specific cell compartments. The main advantage of this workflow is to propose complex bio-imaging functionalities to users without image analysis expertise through a user-friendly interface. Moreover, it is highly modular and adapted to several issues from the characterization of nuclear/cytoplasmic translocation to the comparative analysis of different cell bodies in different cellular sub-structures. The functionality of this workflow is illustrated through the study of the Cajal (coiled) Bodies under oxidative stress (OS) conditions. Data from fluorescence microscopy show that their integrity in human cells is impacted a few hours after the induction of OS. This effect is characterized by a decrease of coilin nucleation into characteristic Cajal Bodies, associated with a nucleoplasmic redistribution of coilin into an increased number of smaller foci. The central role of coilin in the exchange between CB components and the surrounding nucleoplasm suggests that OS induced redistribution of coilin could affect the composition and the functionality of Cajal Bodies.

We present a freely available workflow built for rapid exploration and accurate analysis of cellular bodies in specific cell compartments in fluorescence microscopy images. This user-friendly workflow is designed on the open-source software Icy and also uses ImageJ functionalities. The pipeline is affordable without knowledge in image analysis.

サブ構造アナライザー:蛍光顕微鏡画像における細胞体の迅速な探査と正確な分析のためのユーザフレンドリーなワークフロー

The last decade has been characterized by breakthroughs in fluorescence microscopy techniques illustrated by spatial resolution improvement but also in ...

Imaging and Quantification of the Area of Fast-Moving Microbubbles Using a High-Speed Camera and Image Analysis

An experimental and image analysis technique is presented for imaging cavitation bubbles and calculating their area. The high-speed imaging experimental technique and image analysis protocol presented here can also be applied for imaging microscopic bubbles in other fields of research; therefore, it has a wide range of applications. We apply this to image cavitation around dental ultrasonic scalers. It is important to image cavitation to characterize it and to understand how it can be exploited for various applications. Cavitation occurring around dental ultrasonic scalers can be used as a novel method of dental plaque removal, which would be more effective and cause less damage than current periodontal therapy techniques. We present a method for imaging the cavitation bubble clouds occurring around dental ultrasonic scaler tips using a high-speed camera and a zoom lens. We also calculate the area of cavitation using machine learning image analysis. Open source software is used for image analysis. The image analysis presented is easy to replicate, does not require programming experience, and can be modified easily to suit the application of the user.

Cavitation microbubbles are imaged using a high-speed camera attached to a zoom lens. The experimental setup is explained, and image analysis is used to calculate the area of the cavitation. Image analysis is done using ImageJ.

高速カメラによる高速マイクロバブルのイメージングと定量化

An experimental and image analysis technique is presented for imaging cavitation bubbles and calculating their area. The high-speed imaging experimental ...

High-Dimensionality Flow Cytometry for Immune Function Analysis of Dissected Implant Tissues

The success of implanting laboratory-grown tissue or a medical device in an individual is subject to the immune response of the recipient host. Considering an implant as a foreign body, a hostile and dysregulated immune response may result in the rejection of the implant, while a regulated response and regaining of homeostasis can lead to its acceptance. Analyzing the microenvironments of implants dissected out under in vivo or ex vivo&#160;settings can help in understanding the pattern of immune response, which can ultimately help in developing new generations of biomaterials. Flow cytometry is a well-known technique for characterizing immune cells and their subsets based on their cell surface markers. This review describes a protocol based on manual dicing, enzymatic digestion, and filtration through a cell strainer for the isolation of uniform cell suspensions from dissected implant tissue. Further, a multicolor flow cytometry staining protocol has been explained, along with steps for initial cytometer settings to characterize and quantify these isolated cells by flow cytometry.

Isolation of cells from dissected implants and their characterization by flow cytometry can significantly contribute to understanding the pattern of immune response against implants. This paper describes a precise method for the isolation of cells from dissected implants and their staining for flow cytometric analysis.

解剖されたインプラント組織の免疫機能解析のための高次元フローサイトメトリー

The success of implanting laboratory-grown tissue or a medical device in an individual is subject to the immune response of the recipient host. ...

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Quantification of cells is necessary for a wide range of biological and biochemical studies. Conventional image analysis of cells typically employs either fluorescence detection approaches, such as immunofluorescent staining or transfection with fluorescent proteins or edge detection techniques, which are often error-prone due to noise and other non-idealities in the image background. 
We designed a new algorithm that could accurately count and distinguish macrophages and fibroblasts, cells of different phenotypes that often colocalize during tissue regeneration. MATLAB was used to implement the algorithm, which differentiated distinct cell types based on differences in height from the background. A primary algorithm was developed using an area-based method to account for variations in cell size/structure and high-density seeding conditions. 
Non-idealities in cell structures were accounted for with a secondary, iterative algorithm utilizing internal parameters such as cell coverage computed using experimental data for a given cell type. Finally, an analysis of coculture environments was carried out using an isolation algorithm in which various cell types were selectively excluded based on the evaluation of relative height differences within the image. This approach was found to accurately count cells within a 5% error margin for monocultured cells and within a 10% error margin for cocultured cells.

We present a method, which utilizes a generalizable area-based image analysis approach to identify cell counts. Analysis of different cell populations exploited the significant cell height and structure differences between distinct cell types within an adaptive algorithm.

マクロファージ-線維芽細胞共培養の定量化のための領域ベースの画像解析アルゴリズム

Quantification of cells is necessary for a wide range of biological and biochemical studies. Conventional image analysis of cells typically employs either ...

Hybrid Cell Analysis System to Assess Structural and Contractile Changes of Human iPSC-Derived Cardiomyocytes for Preclinical Cardiac Risk Evaluation

Cardiac contractility assessment is of immense importance for the development of new therapeutics and their safe transition into clinical stages. While human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) hold promise to serve as a human-relevant model in preclinical phases of drug discovery and safety pharmacology, their maturity is still controversial in the scientific community and under constant development. We present a hybrid contractility and impedance/extracellular field potential (EFP) technology, adding significant pro-maturation features to an industry-standard 96-well platform.
The impedance/EFP system monitors cellular functionality in real-time. Besides the beat rate of contractile cells, the electrical impedance spectroscopy readouts detect compound-induced morphological changes like cell density and integrity of the cellular monolayer. In the other component of the hybrid cell analysis system, the cells are cultured on bio-compliant membranes that mimic the mechanical environment of real heart tissue. This physiological environment supports the maturation of hiPSC-CMs in vitro, leading to more adult-like contractile responses including positive inotropic effects after treatment with isoproterenol, S-Bay K8644, or omecamtiv mecarbil. Parameters such as the amplitude of contraction force (mN/mm2) and beat duration also reveal downstream effects of compounds with influence on electrophysiological properties and calcium handling.
The hybrid system provides the ideal tool for holistic cell analysis, allowing preclinical cardiac risk assessment beyond the current perspectives of human-relevant cell-based assays.

The analysis of changes in contractile function and cellular integrity of human iPSC-derived cardiomyocytes is of immense importance for nonclinical drug development. A hybrid 96-well cell analysis system addresses both parameters in a real-time and physiological manner for reliable, human-relevant results, necessary for a safe transition into clinical stages.

前臨床心臓リスク評価のためのヒトiPS細胞由来心筋細胞の構造・収縮変化評価のためのハイブリッド細胞解析システム

Cardiac contractility assessment is of immense importance for the development of new therapeutics and their safe transition into clinical stages. While ...