Towards a Wireless Fluorescence Microscope on A Chip
Rozhan Rabbani
EECS Department, University of California, Berkeley
Technical Report No. UCB/
May 1, 2024
http://www2.eecs.berkeley.edu/Pubs/TechRpts/Hold/143f87512c3afa10bd5b62c69d7366ea.pdf
Real-time access to multicellular information from dynamic biological processes in the body is crucial for understanding disease progression and treatment response. An impactful application is cancer immunotherapy, an effective therapeutic that unleashes the immune system to better identify and attack cancer. While immunotherapy has shown high survival rates in responders, low response rates among patients necessitate a deeper understanding of complex resistance mechanisms early in the treatment to improve outcomes. However, current clinical imaging techniques such as MRI, CT and PET lack the molecular contrast, resolution, and chronic usability to enable early recognition of non-responders and adaption to more personalized therapeutic regimens. Moreover, invasive tissue collection methods such as biopsies are impractical on a repeated basis limiting detection to snap-shots of the tumor microenvironment. Fluorescence microscopy (via injection of fluorescently tagged cell-specific probes) circumvents low sensitivity and long delays of the existing modalities but is yet to be deployed on a platform compatible with long-term implantation. This thesis presents a miniaturized lensless fluorescence microscope-on-a-chip capable of 1) chip-scale imaging of multiple cell types with an image sensor and an optical frontend for multicolor imaging, 2) in-situ illumination through device-level integration of light sources, and 3) wireless power transfer and communication via ultrasound (US) for chronic implantation at depth. The first-generation sensor serves as a proof-of-concept for single-color wireless fluorescence imaging incorporating a CMOS chip, a micro laser diode, a mm-sized piezoceramic and off-chip storage capacitors. The chip consists of a 36x40 array of capacitive trans-impedance amplifier-based pixels, wireless power management and communication via US and a laser driver all controlled by a Finite State Machine. The piezoceramic harvests energy from the acoustic waves at a depth of 2 cm to power up the chip and transfer 11.52 kbits/frame via backscattering. During Charge-Up, the off-chip capacitor operates with 905 mW/cm2 of US power density and stores charge to later supply the instantaneous power of the laser during Imaging. Proof of concept of the imaging front end is shown by imaging distributions of CD8+ T-cells, an indicator of the immune response to cancer, ex vivo, in the lymph nodes of a functional immune system (BL6 mice) against colorectal cancer consistent with the results of a fluorescence microscope. The overall system performance is verified by detecting 140 µ m features on a resolution target wirelessly transmitted via US backscattering. Next, we expand the work to a fully wireless image sensor specifically designed for multicolor fluorescence imaging deep in tissue. The new sensor operates deeper at 5 cm depth in oil, harvesting energy with 221 mW/cm2 (4x lower than the first sensor) incident US power density and backscattering data at 13 kbps with a bit error rate < 10^-6. In-situ fluorescence excitation is controlled with a wirelessly programmable on-chip driver. An optical frontend combining a multi-bandpass interference filter and a fiber optic plate provides > 60 dB attenuation of the excitation background and enables three-color fluorescence imaging for multi-cell-type detection. The resolution is 125 µ m. The system’s performance is validated through wireless, dual-color fluorescence imaging of effector and suppressor immune cells in ex vivo mouse tumor samples with and without immunotherapy. These results show promise for rapid identification of the underlying control mechanisms in therapeutic response, guiding more effective therapies. Finally, we apply deep learning models to images obtained with our customized contact image sensors to enable 3D reconstruction and depth estimation from 2D images beyond conventional linear optimization techniques.
Advisors: Vladimir Stojanovic
BibTeX citation:
@phdthesis{Rabbani:31356,
Author= {Rabbani, Rozhan},
Editor= {Anwar, Mekhail and Stojanovic, Vladimir and Muller, Rikky and Niknejad, Ali},
Title= {Towards a Wireless Fluorescence Microscope on A Chip},
School= {EECS Department, University of California, Berkeley},
Year= {2024},
Number= {UCB/},
Abstract= {Real-time access to multicellular information from dynamic biological processes in the body is crucial for understanding disease progression and treatment response. An impactful application is cancer immunotherapy, an effective therapeutic that unleashes the immune system to better identify and attack cancer. While immunotherapy has shown high survival rates in responders, low response rates among patients necessitate a deeper understanding of complex resistance mechanisms early in the treatment to improve outcomes. However, current clinical imaging techniques such as MRI, CT and PET lack the molecular contrast, resolution, and chronic usability to enable early recognition of non-responders and
adaption to more personalized therapeutic regimens. Moreover, invasive tissue collection methods such as biopsies are impractical on a repeated basis limiting detection to snap-shots of the tumor microenvironment. Fluorescence microscopy (via injection of fluorescently tagged cell-specific probes) circumvents low sensitivity and long delays of the existing modalities but is yet to be deployed on a platform compatible with long-term implantation. This thesis presents a miniaturized lensless fluorescence microscope-on-a-chip capable of 1) chip-scale imaging of multiple cell types with an image sensor and an optical frontend for multicolor imaging, 2) in-situ illumination through device-level integration of light sources, and 3) wireless power transfer and communication via ultrasound (US) for chronic implantation at depth. The first-generation sensor serves as a proof-of-concept for single-color wireless fluorescence imaging incorporating a CMOS chip, a micro laser diode, a mm-sized piezoceramic and off-chip storage capacitors. The chip consists of a 36x40 array of capacitive trans-impedance amplifier-based pixels, wireless power management and communication via US and a laser driver all controlled by a Finite State Machine. The piezoceramic harvests energy from the acoustic waves at a depth of 2 cm to power up the chip and transfer 11.52 kbits/frame via backscattering. During Charge-Up, the off-chip capacitor operates with 905 mW/cm2 of US power density and stores charge to later supply the instantaneous power of the laser during Imaging. Proof of concept of the imaging front end is shown by imaging distributions of CD8+ T-cells, an indicator of the immune response to cancer, ex vivo, in the lymph nodes of a functional immune system (BL6 mice) against colorectal cancer consistent with the results of a fluorescence microscope. The overall system performance is verified by detecting 140 µ m features on a resolution target wirelessly transmitted via US backscattering. Next, we expand the work to a fully wireless image sensor specifically designed for multicolor fluorescence imaging deep in tissue. The new sensor operates deeper at 5 cm depth in oil, harvesting energy with 221 mW/cm2 (4x lower than the first sensor) incident US power density and backscattering data at 13 kbps with a bit error rate < 10^-6. In-situ fluorescence excitation is controlled with a wirelessly programmable on-chip driver. An optical frontend combining a multi-bandpass interference filter and a fiber optic plate provides > 60 dB attenuation of the excitation background and enables three-color fluorescence imaging for multi-cell-type detection. The resolution is 125 µ m. The system’s performance is validated through wireless, dual-color fluorescence imaging of effector and suppressor immune cells in ex vivo mouse tumor samples with and without immunotherapy. These results show promise for rapid identification of the underlying control mechanisms in therapeutic response, guiding more effective therapies. Finally, we apply deep learning models to images obtained with our customized contact image sensors to enable 3D reconstruction and depth estimation from 2D images beyond conventional linear optimization techniques.},
}
EndNote citation:
%0 Thesis %A Rabbani, Rozhan %E Anwar, Mekhail %E Stojanovic, Vladimir %E Muller, Rikky %E Niknejad, Ali %T Towards a Wireless Fluorescence Microscope on A Chip %I EECS Department, University of California, Berkeley %D 2024 %8 May 1 %@ UCB/ %F Rabbani:31356