CP1: Nanoscale imaging of neural function in epilepsy and Alzheimer’s disease
Investigator: Chung, Hee Jung, UIUC,
Grant Number 5R01NS083402-05, 5R01NS097610-04, Funding Period: 5/15/2015-3/31/2020, 05/15/2016-04/30/2021
Description: The goal of the ongoing proposal (5R01NS083402, Role: PI) is to identify the molecular mechanisms of Kv7 axonal targeting and determine the roles that this targeting plays in neuronal excitability and plasticity. The goal of another proposal (5R01NS097610, Role: MPI) is to improve and apply high-resolution single-molecule fluorescence imaging technologies to dissect the mechanisms underlying abnormal synaptic plasticity in Alzheimer's disease.
Push: We will apply SLIM (TRD 1) to label-free investigations of mitochondria dynamics in axons of hippocampal neuronal culture during seizure-like activity. GLIM will be used to advance the mitochondria dynamics studies in neuronal axons in 3D spheroids during the development of temporal lobe epilepsy (TRDs 1, 3).
Pull: The Chung group is in need to study mitochondria dynamics in acute brain slices, which represents a new challenge for the Center. We will advance GLIM and AI for visualization of live brain slices with high penetration depth and axial resolution, with computational specificity to mitochondria.
CP2: Optical imaging of pancreas cancer organoids for drug development and personalized treatment
Melissa Skala and Kevin Eliceiri, University of Wisconsin, Grant Number NCI 1R01 CA211082-01A1, Funding Period: 8/1/2019-7/31/2022
Description: The goal of the ongoing proposal is to develop novel cellular-level imaging technologies to predict treatment response in individual PDAC patients, using macro-suspensions of the patients’ tumors maintained in a 3D culture (“organoids”). These non-invasive technologies will enable improved drug development and treatment planning in pancreatic cancer, thus improving morbidity and mortality.
Push: The technology developed in TRDs 1-3 will be deployed to enhance our understanding of drug response by eliminating the constraints due to fluorescence (photobleaching, phototoxicity, throughput) and providing quantitative, intrinsic markers for treatment efficacy. We will apply high-throughput GLIM (TRD 1) and OCT (TRD 2) to image organoids under treatment and develop quantitative markers for viability, based on cell morphology, dynamics and growth. We will employ the AI algorithms developed in TRD 3 to computationally predict NADH and FAD location from phase images.
Pull: The Wisconsin group will challenge the center to extract 3D information, unavailable from SHG, from the tumor microenvironment using interferometric imaging. The Center will develop new imaging and software tools in TRDs 1, 3 for quantifying, for the first time, 3D stromal fiber alignment in microtumor microenvironment and validate it as a treatment effectiveness predictor.
CP3: Dynamic Imaging of 3D Organoid Physiology
Investigator: Roger Kamm, Massachusetts Institute of Technology,
Grant Number 5U01CA214381-02A1, Funding Period: 9/1/17-8/31/22
Description: The goal of this research is to extend the capabilities and extensively evaluate a novel in vitro tumor model assay to study tumor immune responses directly in patient samples using both melanoma and thyroid cancer specimens. Samples are introduced into a microfluidic system containing a 3D matrix and tested for their response to immune checkpoint blockage treatments.
Push: The technology developed in TRDs 1, 3 will enable visualization of tumor fragments in label-free conditions. We will deploy GLIM to enable both assessing tumor response and, in combination with the numerical methods of TRD 3, allow the identification of different cell types within the cluster, and over time.
Pull: In order to study viability, we aim to monitor individual nuclei over long periods of time. The fluorescence imaging data from MIT will be shared such that the AI tools in TRD 3 will be optimized for computational specificity, in order to identify the different cell populations present, especially various types of immune cells.
CP4: Deep-learning-enabled goggles for image-guided surgery
Investigator: Sam Achilefu, Washington University;
Grant Number 1R01EB030987-01A1; Funding Period: 4/1/2021-12/31/2024.
Description: The ability to probe molecular processes noninvasively or using tissue-selective imaging agents and nanoparticles has made it possible to localize, identify the stage, and determine the functional status of pathological lesions.(1) The challenges in detecting cancer particularly have driven the development of diverse imaging technologies. While earlier cancer imaging methods enabled preoperative evaluation, the need to track and visualize cancer location in the operating room itself has ushered in new systems capable of providing concurrent images of cancer during surgery. Intraoperative use of conventional clinical imaging modalities is often limited by bulky hardware design, prohibitive cost, lack of real-time image display, and compatibility with conventional hardware interfaces.(2) For these reasons, focus on fluorescence-guided surgery (FGS) devices has increased to take advantage of real-time, high-resolution, functional imaging with hardware that has become increasingly amenable to miniaturization.(3) In particular, the adaptation of wearable devices for FGS presents hands-free capability for optimal navigation during cancer surgery.
This project will 1) develop and validate an automated fluorescence thresholding algorithm for tumor delineation; 2) develop and validate automated registration of augmented reality in the system; and 3) develop and evaluate clinical software to improve user experience (Fig. 1). The deep-learning tools developed at CLIMB will enable real-time detection and classification of suspicious lesions, with an order of magnitude boost in computational time.
CP5: Cell-level 3D label-free nanoscopy
Investigator: Krishna Agarwal;
Grant Number: ERC-STG 804233, Funding Period: 7/2019 – 6/2024
Description: The aim of this ERC project is to develop a label-free imaging technology that supports <100 nm resolution in 3D over a sample height of 10 um and square area of approximately 30 um. Toward this goal, we will develop a nonlinear inverse electromagnetic scattering solver to reconstruct the refractive index profile from intensity-only measurements. In order to show the proof-of-principle, we will collaborate with an autophagy or cardiovascular biology expert for imaging mitophagy processes in epithelial cancer cells (HeLa) and cardiomyocytes (H9C2) cells.
Push: We will apply GLIM at UiT to extend label-free imaging to thick specimens and combine the data with those from cell-level 3D label-free nanoscopy obtained by ERC starting grant (EU Horizon 2020) and FRIPRO Young Talent grant (Research Council of Norway). AI tools (PICS) for SLIM/GLIM technology (TRDs 1, 3) will be implemented into the projects at UiT and its network of biological collaborators to achieve molecular specificity without the restrictions of fluorescence.
Pull: The UiT group will challenge the CLIMB team to obtain label-free superresolution performance from SLIM and GLIM imaging, real-time, to study pathophysiology in cancer cell models. Correlative fluorescence nanoscopy modality for GLIM technology will enhance the specificity by providing a large database for PICS training, testing and optimization.
CP6: Nanoscale nuclear architecture measured by phase imaging with computational specificity (PICS) for cancer risk prediction
Investigator: Yang Liu, University of Pittsburgh;
Grant Number: 1R01CA232593-01A1, Funding Period: 4/2019 – 3/2024
Description: We are developing a reliable risk taxonomy of nanoscale nuclear architecture mapping (nanoNAM) markers that are associated with CRC progression, and rigorously validate their ability to consistently predict CRC progression risk in the context of colorectal adenoma.
Push: We will apply PICS, developed in TRDs 1, 3 to identify the nuclei with high specificity from our label-free imaging of unstained tissue slides. Once the map of nuclei is identified, we will perform nanoscale tissue architecture measurements (e.g., disorder parameters, spatial correlations) to improve the performance of the CRC progression risks prediction.
Pull: The Pittsburgh group will push the AI + SLIM as an end-to-end solution for inferring automatically risk progression information. Toward this end, the Liu group we will share data and analysis back to the center, which will allow the development of AI models for outputting directly a risk metric from a label-free image input.
CP 7: Using quantitative morphology information to extract robust IR spectra for histopathology
Investigators: Rohit Bhargava, BioE, Andre Balla, UIC; Grant Number EB029766, Funding Period: 2019-09-20 to 2022-05-30
Description: The goal of the existing project is to combine novel chemical imaging measurements with morphological measurements to provide label-free pathology.
Push: We will use the technology developed in TRD projects 1-3 to take advantage of the high-resolution morphological data and complementary information available. The technology developed in TRD 1 will be used provide a detailed morphological basis to understand the origin of molecular signatures. We will apply the techniques developed for translation in TRDs 1-3 as we seek to improve the throughput of our imaging and accuracy of patient outcome prediction.
Pull: One major challenge is to test the hypothesis that SLIM+AI can return chemical data with similar specificity as IR, but with a 1,000x throughput boost. The Bhargava group will provide new ground truth chemical imaging data for characterizing the chemistry behind the morphology. They will also bring well-characterized samples and AI infrastructure developed to classify images.
CP8: Larina – In vivo analysis of mammalian fertilization
Investigator: Irina Larina, Baylor College;
Grant Number 5R01EB027099-02; Funding Period: 12/1/2019-11/30/2022.
Description: This study is taking advantage of new technological developments in OCT imaging and will allow for quantitative assessment of reproductive processes in vivo in mouse models. We are studying hormonal regulation of oviduct cilia beating and muscle contractions, and functional analysis of fertility failures in mouse models of human defects. This study will likely provide new insight on the process of mammalian fertilization in its native state and lead to a better understanding of pathologies resulting in infertility.
Push: The imaging methods developed in TRD 1, 2 and computational algorithms developed in TRD 3 will be integrated for optimized analysis of cilia beat frequency and individual sperm tracking in the fallopian tube in vivo.
Advancement to SP: The methods developed at the Center will enhance the outcomes of Aims 3-4 of the existing project to investigate normal cilia beat frequency and determine functional consequences of hormonal regulations and genetic disruptions of fertility in vivo.