SP1: Dissecting the pathogenesis and outcomes of PSC using multi-omics by studying the exposome and genome
Investigators: Ravi Iyer and Kostas Lazaridis - subcontract Grant Number NIH 1RC2DZK118619-01 Funding Period: 9/1/18-7/31/20
Description: In the current project we generate the first multi-omics translational study and comprehensive data resources for Primary Sclerosing Cholangitis (PSC), a chronic, progressive liver disease without effective medical therapy. PSC reduces survival, is highly associated with inflammatory bowel disease and strongly predisposes to cholangiocarcinoma (i.e. bile duct cancer) and colon cancer. Our proposal is predicated on the hypothesis that multi-omics analyses of data capturing environmental exposures and the associated biological responses (i.e. the exposome), including interaction with the genome, will reveal networks or pathways influencing PSC pathogenesis and outcomes.
Push: We will use label-free imaging (interferometry, spectroscopy, nonlinear) of liver sections to add a new channel of data to improve our predictive models. The new data streams will be combined with the observed correlations among different variables into a unifying framework to boost the performance of our forecast accuracy of PSC outcomes.
SP2: Diffuse fast optical imaging for cognitive neuroscience
Investigators: Gabriele Gratton and Monica Fabiani; Grant Numbers: NIA R01 AG059878, NIA RF AG062666; Funding periods: 2018/2019-2023/2024
Description: The goal of the two current projects is to study the relationships between changes in cerebral arterial stiffness, as measured with diffuse optical tomography (DOT), and cognitive aging, a possible precursor of Alzheimer Disease. The first of these grants investigates the role of a series of risk factors for cerebral arteriosclerosis in brain structural and cognitive decline. The second investigates the effects of cerebral arteriosclerosis on age-related declines in cerebral function, with focus on cognitive control, a function related to goal directed behavior, central to cognitive performance, and linked to the ability to quickly switch on and off or maintain brain representations related to complex task demands.
Push: The technologies developed in TRDs 1-3 will be used to achieve fast optical imaging of neural function based on intrinsic scattering signals that are associated with brain activity, including cognitive processes. The instruments developed within TRDs 1-2 will boost our understanding of how these micrometer level conformational changes lead to changes at the millimeter level scattering properties of cortical tissue investigated using diffuse optical. Computational imaging and intelligent specificity (TRD 3) will boost the spatial specificity and signal-to-noise ratio of diffuse fast optical imaging methods, both issues that currently limit their widespread use.
SP 3: Studying DNA replication and damage response using PICS
Investigators: Supriya Prasanth, Cell and Developmental Biology, UIUC; Grant Number 1R01 GM125196-01; Funding Period: 8/1/2018-5/31/2022.
Description: Errors in DNA replication and repair mechanisms are deleterious and cause genetic aberrations leading to malignant cellular transformation and tumorigenesis. Origin recognition complex (ORC) proteins are critical for the initiation of DNA replication. Mutations within several Orc genes, including Orc1, Orc4 and Orc6, have also been linked to Meier Gorlin Syndrome, a rare genetic disorder in children characterized by primordial dwarfism. The goal of this project is to understand how Origin Recognition Complex (ORC) executes and coordinates various aspects of cell growth, including cell proliferation and survival.
Push: We will the SLIM and GLIM technology augmented by artificial intelligence (PICS) to image the dynamics of DNA replication and DNA damage response in unlabeled cells. We will also test the capability of PICS to identify DNA damage foci during the cell cycle without photodamage or toxicity.
SP 4: Ultrasensitive chemical microscopy by interferometric probing of photothermal effects
Investigator: Ji-Xin Cheng, Boston University;
Grant Number 5R01GM126049-02; Funding Period: 9/6/2018-7/31/2022.
Description: Our scientific premise is that after the mid-infrared photons induce the molecule to vibrate, the subsequent vibrational relaxation into heat causes a local change of the refractive index. Such change creates a phase delay and a thermal lens, both of which can be detected at sub-micron spatial resolution by a visible probe beam. In a collaboration with the Popescu Lab, we have proven the principle of “Bond-selective transient phase imaging”, which reached an imaging speed of 50 fps, a lateral resolution of 0.5 micrometers, and micro-molar detection sensitivity for the endogenous C=O bond (Light: Science & Applications 8(1): 1-12, 2019).
Push: The collaborators will apply the ultrasensitive phase imaging developed at the CLIMB Center (TRDs 1, 3) to further improve the detection sensitivity of the photothermal imaging. We will combine a GLIM with an IR pump to achieve high-throughput label-free chemical imaging in thick specimens.
SP5: 3D imaging of the interface between tissue and bio-integrated electronics
Investigator: John Rogers, Northwestern University;
Grant Number 1R01EB026572-01A1; Funding Period: 8/9/2019-4/30/2023.
Description: Bladder enterocystoplasty causes many severe complications due to anatomical and physiological differences between bladder tissue and the bowel tissue used to augment the bladder’s capacity. Several strategies have been reported to replace enterocystoplasty and regenerate bladder tissue but these have mostly failed clinically. This proposal will develop unprecedented regenerative engineering tools and technologies via the integration of stem cell science, advanced biomaterials, and bio-integrated electronics to enable the regeneration of functional bladder tissue and the non-invasive, real-time assessment thereof to better predict outcome.
Push: We propose to use the techniques developed at CLIMB, in particular epi-GLIM, confocal phase imaging (TRD 1) and OCT (TRD 2), to study the tissue- material interface with subcellular resolution, over a period of many days. The interface between tissue and the bio-integrated, deformable electronics informs about the regeneration process and, thus, is crucial to the success of the ongoing project.
SP6: Smartphone-linked system for diagnosis and epidemiological reporting of pathogens at the point of care
Investigators: B.T. Cunningham, R. Bashir, M. Do, UIUC;
Grant Number: R01AI139401, Funding Period: 9/2019 – 9/2023
Description: The project will develop a smartphone-based handheld instrument, microfluidic cartridge, and cloud-based service system for detection and reporting the presence and concentration of a panel of viral pathogens (Zika, Dengue, and, now, COVID-19) from whole blood. Using chemical lysis, the system will yield results in less than 10 minutes, using automated image processing of acquired fluorescence image sequences of the LAMP reactions in the cartridge. The platform is a sensitive, inexpensive, and rapid point-of-care tool for detection and reporting of infectious disease to facilitate physician communication with the patient and epidemiological management by health authorities.
Push: The phase imaging with computational specificity (PICS) developed in TRDs 1, 3 will enable intact viral pathogens to be detected through their optical scattering characteristics, so they can be rapidly counted with digital precision by a simple label-free assay protocol. We envision a single-step assay approach and low-cost optical detection instrument that can be deployed in point-of-care diagnostic settings.
SP7: Wang – Label-free intraoperative photoacoustic microscopy for rapid diagnosis of tissue biopsies
Investigator: Lihong Wang, Caltech;
Grant Number 5R01EB028277; Funding Period: 07/1/2019-03/31/2023.
Description: The current project will develop a multi-channel subcellular resolution PAM system for rapid analysis and diagnosis of tissue biopsies. Optimize Image acquisition in order to maximize discrimination of nuclear, cytoplasmic, and stromal properties and develop algorithms for synthetic H&E pseudocolors.
Push: The computational algorithms developed in TRD 3 will allow the segmentation of the subcellular components and stromal regions in the PAM images. The deep learning models from TRD 3 will operate in real-time.
Advancement to SP: Using this technology developed TRD 3, the tissue classification will be performed in parallel with the PAM acquisition, allowing for real time synthetic H&E output.
SP 8: Super-resolution microscopy of dynamic neuronal synapses
Investigators: Paul Selvin, Physics, and Andrew Smith, BioE, UIUC;
Grant Number 5R01NS100019-03, Funding Period: 07/01/2-17-06/30/2021
Description: AMPA- and NMDA-type glutamate receptors are neurotransmitter receptors that regulate learning, memory, and numerous neurodevelopmental and neurodegenerative diseases. We will develop new microscopy tools, probes, and methods to visualize their trafficking into and out of synapses to help understand these processes, exemplified by some “proof-of-principle” experiments. We will apply these to proof-of-principle experiments to understand in what way the receptors move into and around the synapse during homeostatic and synaptic plasticity, and whether endocytosed receptors communicate with each other between synapses on the same neuron.
Push: The team will take advantage of the high-resolution morphological data resulting from the methods developed in TRDs 1-3. Specifically, phase imaging with computational specificity (PICS) will be used to obtain label-free super-resolution imaging of neural synapses and quantify transport in and out of the synapse in Alzheimer’s and abnormal myelination models.
Pull: The Center will be pushed to develop a high-speed GLIM system to super-resolve the synapse and provide electrophysiology information in contactless mode. The collaborators will generate fluorescence data based on PALM instrumentation, which be used to train, test and validate the neural networks to predict superresolved images dynamically in disease models.