Whole-brain tissue mapping toolkit using large-scale highly multiplexed immunofluorescence imaging and deep neural networks.
Whole-brain tissue mapping toolkit using large-scale highly multiplexed immunofluorescence imaging and deep neural networks.
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DOI：
10.1038/s41467-021-21735-x
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发表时间：
2021-03-10
影响因子：
16.6
通讯作者：
Roysam B
中科院分区：
文献类型：
--
作者：
Maric D;Jahanipour J;Li XR;Singh A;Mobiny A;Van Nguyen H;Sedlock A;Grama K;Roysam B
绘制脑组织中的生物过程需要将多个组织样本的大量组织学观察结果拼凑在一起。我们提出了一种直接方法，可以从连续全脑切片中生成全面的生物标记物组的读数，表征所有主要脑细胞类型，其尺度范围从亚细胞区室、单个细胞、局部多细胞生态位到每个切片的全脑区域。我们使用优化的 10 重免疫染色和 10 色落射荧光成像的迭代循环，从单个全脑切片中积累高度丰富的图像数据集，从中重建无缝信号校正马赛克。通过计算分离感兴趣的特定荧光信号，抑制自发荧光、成像噪声、跨通道渗漏和交叉标记。使用深度神经网络实现可靠的大规模细胞检测和分割。细胞表型分析是通过分析适当的亚细胞区室中独特的生物标志物组合来进行的。这种方法可以通过在其天然解剖背景下同时分析多个生物过程来加速临床前药物评估和系统级脑组织学研究。绘制脑组织中的复杂过程具有挑战性。作者在这里报告了一个工具包，可实现大规模多重 IHC 和自动细胞分类，他们使用传统的落射荧光显微镜和深度神经网络来对大脑的所有主要细胞类别进行表型分析。
Mapping biological processes in brain tissues requires piecing together numerous histological observations of multiple tissue samples. We present a direct method that generates readouts for a comprehensive panel of biomarkers from serial whole-brain slices, characterizing all major brain cell types, at scales ranging from subcellular compartments, individual cells, local multi-cellular niches, to whole-brain regions from each slice. We use iterative cycles of optimized 10-plex immunostaining with 10-color epifluorescence imaging to accumulate highly enriched image datasets from individual whole-brain slices, from which seamless signal-corrected mosaics are reconstructed. Specific fluorescent signals of interest are isolated computationally, rejecting autofluorescence, imaging noise, cross-channel bleed-through, and cross-labeling. Reliable large-scale cell detection and segmentation are achieved using deep neural networks. Cell phenotyping is performed by analyzing unique biomarker combinations over appropriate subcellular compartments. This approach can accelerate pre-clinical drug evaluation and system-level brain histology studies by simultaneously profiling multiple biological processes in their native anatomical context. It is challenging to map complex processes in brain tissue. Here the authors report a toolkit enabling large-scale multiplexed IHC and automated cell classification whereby they use a conventional epifluorescence microscope and deep neural networks to phenotype all major cell classes of the brain.
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影响因子：
4.6
作者：
He L;Vanlandewijck M;Raschperger E;Andaloussi Mäe M;Jung B;Lebouvier T;Ando K;Hofmann J;Keller A;Betsholtz C
通讯作者：
Betsholtz C
DOI：
10.1073/pnas.1507125112
发表时间：
2015-06-09
影响因子：
11.1
作者：
Darmanis S;Sloan SA;Zhang Y;Enge M;Caneda C;Shuer LM;Hayden Gephart MG;Barres BA;Quake SR
通讯作者：
Quake SR
影响因子：
3
作者：
Bjornsson, Christopher S.;Lin, Gang;Roysam, Badrinath
通讯作者：
Roysam, Badrinath
影响因子：
16.6
作者：
Lai HM;Liu AKL;Ng HHM;Goldfinger MH;Chau TW;DeFelice J;Tilley BS;Wong WM;Wu W;Gentleman SM
通讯作者：
Gentleman SM
影响因子：
5.1
作者：
Dixon AR;Bathany C;Tsuei M;White J;Barald KF;Takayama S
通讯作者：
Takayama S