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Polychromatic digital holographic microscopy: a quasicoherent-noise-free imaging technique to explore the connectivity of living neuronal networks
Neurophotonics ( IF 5.3 ) Pub Date : 2020-10-01 , DOI: 10.1117/1.nph.7.4.040501
Céline Larivière-Loiselle 1 , Erik Bélanger 1 , Pierre Marquet 1
Affiliation  

Significance: Over the past decade, laser-based digital holographic microscopy (DHM), an important approach in the field of quantitative-phase imaging techniques, has become a significant label-free modality for live-cell imaging and used particularly in cellular neuroscience. However, coherent noise remains a major drawback for DHM, significantly limiting the possibility to visualize neuronal processes and precluding important studies on neuronal connectivity. Aim: The goal is to develop a DHM technique able to sharply visualize thin neuronal processes. Approach: By combining a wavelength-tunable light source with the advantages of hologram numerical reconstruction of DHM, an approach called polychromatic DHM (P-DHM), providing OPD images with drastically decreased coherent noise, was developed. Results: When applied to cultured neuronal networks with an air microscope objective (20 × , 0.8 NA), P-DHM shows a coherent noise level typically corresponding to 1 nm at the single-pixel scale, in agreement with the 1 / N-law, allowing to readily visualize the 1-μm-wide thin neuronal processes with a signal-to-noise ratio of ∼5. Conclusions: Therefore, P-DHM represents a very promising label-free technique to study neuronal connectivity and its development, including neurite outgrowth, elongation, and branching.

中文翻译:

多色数字全息显微术:一种无准相干噪声成像技术,用于探索活体神经元网络的连通性

启示:在过去的十年中,基于激光的数字全息显微镜(DHM)是定量相成像技术领域中的一种重要方法,已成为活细胞成像的一种重要的无标记方式,尤其是在细胞神经科学中使用。然而,相干噪声仍然是DHM的主要缺点,极大地限制了可视化神经元过程的可能性,并排除了有关神经元连通性的重要研究。目的:目标是开发一种DHM技术,能够清晰地观察薄的神经元过程。方法:通过将波长可调光源与DHM全息图数值重建的优势相结合,开发了一种称为多色DHM(P-DHM)的方法,该方法可为OPD图像提供大幅降低的相干噪声。结果:当将P-DHM用于带有空气显微镜物镜(20×,0.8 NA)的培养的神经元网络时,与1 / N律一致,在单像素范围内,其相干噪声水平通常对应于1 nm。可以直观地看到1μm宽的薄神经元过程,信噪比约为5。结论:因此,P-DHM代表了一种非常有前途的无标记技术,用于研究神经元的连接性及其发展,包括神经突的生长,伸长和分支。
更新日期:2020-10-17
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