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

• Applicant: Andre Stiel, Ph.D.
• Subject Area: Biophysics ; Bioinformatics and Theoretical Biology
• Term: since 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 447748737

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.

• DFG Programme: Research Grants