Practical fluorescence reconstruction microscopy for large samples and low-magnification imaging.
Practical fluorescence reconstruction microscopy for large samples and low-magnification imaging.
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DOI：
10.1371/journal.pcbi.1008443
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发表时间：
2020-12
影响因子：
4.3
通讯作者：
Cohen DJ
中科院分区：
文献类型：
--
作者：
LaChance J;Cohen DJ
荧光重建显微镜 (FRM) 描述了一类技术，其中透射光图像被传递到卷积神经网络，然后输出预测的落射荧光图像。这种方法具有许多优点，包括降低光毒性、释放荧光通道、简化样品制备以及重新处理遗留数据以获得新见解的能力。然而，FRM 的实施起来可能很复杂，并且当前的 FRM 基准是抽象的，很难与重建的价值或可信度联系起来。在这里，我们将传统的基准和演示与实际和熟悉的细胞生物学分析联系起来，以证明 FRM 应该在上下文中进行判断。我们进一步证明，即使使用较低放大倍率的显微镜数据（如筛选和高内涵成像中经常收集的数据），它也表现得非常好。具体来说，我们在细胞核、细胞-细胞连接和精细特征重建方面提出了有希望的结果；提供数据驱动的实验设计指南；并提供研究人员友好的代码、完整的样本数据和研究人员手册，以促进 FRM 的更广泛采用。生物学研究通常需要使用荧光成像来检测细胞内荧光标记的蛋白质，但这种成像本质上是有毒的，并且使实验设计和成像变得复杂。机器学习和人工智能的进步可以帮助解决这些问题，研究人员可以训练神经网络来检测透射光图像中的一些蛋白质，而无需荧光数据。我们将这类技术称为荧光重建显微镜 (FRM)，并致力于在三个关键方面使最终用户更容易使用它。首先，我们将 FRM 扩展到具有挑战性的低放大倍率、低分辨率显微镜，这是日益流行的高内涵筛查所需要的。其次，我们独特地将 FRM 性能与最终用户的日常价值指标联系起来，例如细胞计数、大小和特征检测，而不是从计算机视觉中抽象性能指标。第三，我们提供易于使用的软件工具和 FRM 特征，旨在帮助研究人员测试 FRM 并将其纳入自己的研究中。
Fluorescence reconstruction microscopy (FRM) describes a class of techniques where transmitted light images are passed into a convolutional neural network that then outputs predicted epifluorescence images. This approach enables many benefits including reduced phototoxicity, freeing up of fluorescence channels, simplified sample preparation, and the ability to re-process legacy data for new insights. However, FRM can be complex to implement, and current FRM benchmarks are abstractions that are difficult to relate to how valuable or trustworthy a reconstruction is. Here, we relate the conventional benchmarks and demonstrations to practical and familiar cell biology analyses to demonstrate that FRM should be judged in context. We further demonstrate that it performs remarkably well even with lower-magnification microscopy data, as are often collected in screening and high content imaging. Specifically, we present promising results for nuclei, cell-cell junctions, and fine feature reconstruction; provide data-driven experimental design guidelines; and provide researcher-friendly code, complete sample data, and a researcher manual to enable more widespread adoption of FRM. Biological research often requires using fluorescence imaging to detect fluorescently labeled proteins within a cell, but this kind of imaging is inherently toxic and complicates the experimental design and imaging. Advances in machine learning and artificial intelligence can help with these issues by allowing researchers to train neural networks to detect some of these proteins in a transmitted light image without needing fluorescence data. We call this class of technique Fluorescence Reconstruction Microscopy (FRM) and work here to make it more accessible to the end-users in three key regards. First, we extend FRM to challenging low-magnification, low-resolution microscopy as is needed in increasingly popular high content screening. Second, we uniquely relate FRM performance to every-day metrics of value to the end-user, such as cell counts, size, and feature detection rather than to abstract performance metrics from computer vision. Third, we provide accessible software tools and characterizations of FRM intended to aid researchers in testing and incorporating FRM into their own research.
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DOI：
10.1007/978-1-59745-060-7_14
发表时间：
2009
期刊：
Methods in molecular biology (Clifton, N.J.)
影响因子：
--
作者：
通讯作者：
--
DOI：
10.1073/pnas.1612208113
发表时间：
2016-12-20
影响因子：
11.1
作者：
Cohen, Daniel J.;Gloerich, Martijn;Nelson, W. James
通讯作者：
Nelson, W. James
影响因子：
48
作者：
Caicedo JC;Cooper S;Heigwer F;Warchal S;Qiu P;Molnar C;Vasilevich AS;Barry JD;Bansal HS;Kraus O;Wawer M;Paavolainen L;Herrmann MD;Rohban M;Hung J;Hennig H;Concannon J;Smith I;Clemons PA;Singh S;Rees P;Horvath P;Linington RG;Carpenter AE
通讯作者：
Carpenter AE
DOI：
10.1002/cyto.990090102
发表时间：
1988-01-01
期刊：
CYTOMETRY
影响因子：
--
作者：
ERBA, E;UBEZIO, P;DINCALCI, M
通讯作者：
DINCALCI, M
计算机标签：预测未标记图像中的荧光标签。
影响因子：
64.5
作者：
Christiansen EM;Yang SJ;Ando DM;Javaherian A;Skibinski G;Lipnick S;Mount E;O'Neil A;Shah K;Lee AK;Goyal P;Fedus W;Poplin R;Esteva A;Berndl M;Rubin LL;Nelson P;Finkbeiner S
通讯作者：
Finkbeiner S