Temporal unmixing Optoacoustics – Machine learning to enable routine whole animal Optoacoustic imaging of genetically encoded photo-modulatable labels.

时间分离光声学 â 机器学习可实现基因编码光可调制标签的常规整体动物光声成像。

• 批准号: 447748737
• 负责人: Dr. Andre Stiel, Ph.D.
• 金额: --
• 依托单位: Institut für Biologische und Medizinische Bildgebung (IBMI)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/447748737?language=en
• 关键词: Temporal; unmixing; Optoacoustics; Machine; learning

Optoacoustic (OA) or Photoacoustic imaging is an emerging imaging modality that provides unsurpassed penetration depth at high resolution. It delivers more comprehensive time-resolved 3D in vivo images at depths far beyond the reach of optical methods. Moreover, with OA only requiring an optical excitation source and ultrasound detector, in terms of infrastructure, it is more comparable to optical microscopy than to cost intensive radiological methods. The application of OA in life sciences is new and the few existing genetically encodable labels (a prerequisite for targetable in vivo imaging) lack sufficiently high OA signal for whole animal studies. This prevents the separation from the predominant signal of blood hemoglobin in tissue. This challenge can be overcome by employing labels based on genetically encoded photochromatic proteins (hereafter called reversibly switchable OA proteins, rsOAP). These proteins’ signal can be modulated by light which allows a clean separation of modulating label signal from non-modulating background.The potential of rsOAPs for studying the dynamics of cells at the level of the whole organism has recently been demonstrated. Labeled cells were visualized in vivo at a depth of up to one centimeter and at low numbers (~500). So far, parallel visualization (multiplexing) of up to three labels is possible based on different modulation characteristics. However, those studies used dedicated high-end OA setups and showed that the interpretation of the modulation kinetics is demanding and prone to artifacts. This hampers the routine application, especially with regard to very small cell numbers deep in the tissue or entangled populations of different labels, which are typical situations for many biological studies. Recently, we showed that the interpretation of a multitude of features (e.g. signal strength, depth, kinetics, background-noise) that were analyzed via machine learning (ML) strongly improves the analysis of rsOAP-based measurements conducted with of-the-shelf instrumentation. Yet, further improvement in accuracy and sensitivity are necessary before this approach can become a routine application enabling OA whole animal imaging in the life sciences.In this project, we will address the challenges of the ML approach and optimize it with a focus on multiplexed detection of small numbers of differently labeled cells. The proposed work will help to fully enable the routine use of rsOAP imaging in OA. OA as a standard life-science imaging modality will allow its wider use and provide researchers with an indispensable tool to visualize the interactions of small cell populations in vivo in whole organisms. Moreover, multiplexing will allow interpretation of dynamic interactions of cells on the larger scale. Observations of this kind are crucial for immunology, developmental and tumor biology, allowing insights into the dynamic interplay that underlies disease mechanisms such as in cancer.

光声（OA）或光声成像是一种新兴的成像方式，提供无与伦比的穿透深度在高分辨率。它提供了更全面的时间分辨的3D体内图像，其深度远远超出了光学方法的范围。此外，由于OA仅需要光学激发源和超声检测器，就基础设施而言，它与光学显微镜相比更具有成本密集型放射学方法的可比性。OA在生命科学中的应用是新的，并且少数现有的遗传编码标记（可靶向体内成像的先决条件）缺乏足够高的OA信号用于整个动物研究。这防止了从组织中的血液血红蛋白的主要信号分离。这一挑战可以通过采用基于遗传编码的光致变色蛋白（以下称为可逆转换OA蛋白，rsOAP）的标记来克服。这些蛋白质的信号可以被光调制，这允许调制标记信号与非调制背景的干净分离。最近已经证明了rsOAPs在整个生物体水平上研究细胞动力学的潜力。标记的细胞在体内以高达1厘米的深度和低数量（~500）可视化。到目前为止，基于不同的调制特性，多达三个标签的并行可视化（复用）是可能的。然而，这些研究使用了专用的高端OA设置，并表明调制动力学的解释要求很高，容易出现伪影。这阻碍了常规应用，特别是对于组织深处非常小的细胞数量或不同标记物的缠结群体，这是许多生物学研究的典型情况。最近，我们表明，通过机器学习（ML）分析的众多特征（例如信号强度，深度，动力学，背景噪声）的解释大大改善了使用现成仪器进行的基于rsOAP的测量的分析。然而，在这种方法成为生命科学中OA整体动物成像的常规应用之前，还需要进一步提高准确性和灵敏度。在本项目中，我们将解决ML方法的挑战并对其进行优化，重点关注少量不同标记细胞的多重检测。拟议的工作将有助于充分实现rsOAP成像在OA中的常规使用。OA作为一种标准的生命科学成像模式将允许其更广泛的使用，并为研究人员提供一个不可或缺的工具，以可视化整个生物体内小细胞群的相互作用。此外，多路复用将允许在更大规模上解释细胞的动态相互作用。这种观察对于免疫学、发育和肿瘤生物学至关重要，可以深入了解疾病机制（如癌症）背后的动态相互作用。