Service Projects
SP1: Studying DNA replication and damage response using PICS
Investigators: Supriya Prasanth, Cell and Developmental Biology, University of Illinois - Urbana-Champaign; 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.
SP2: 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 Bhargava 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.
SP3: 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.
SP4: 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.
SP5: 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. 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.
SP6: Machine-learning-based Optimization of Fluorescence Imaging in The Analysis of Adipose Tissue
Investigator: Andrew Smith, University of Illinois - Urbana-Champaign; Grant Number R01DK139924, R01CA288207; Funding Period: 6/25/24 – 5/31/29, 4/1/24 – 3/31/29.
Description: State-of-the-art deep neural networks and statistical learning techniques will be employed to correlate diagnostic information from biomarkers of inter-droplet structures with morphological information from close-packed lipid droplets. The overall goals are to employ learned virtual contrast to reduce the number of fluorescence labels needed to characterize adipose tissue and to begin data collection for the future study of the role of purely label-free imaging in adipose tissue characterization.
Push: Neural-network-based image-to-image translation will be employed at CLIMB to translate images of the interstitial labels to images of the lipid droplets; this is a sort of virtual lipid imaging. The utility of virtual lipid imaging in the classification of various stages of adipose tissue growth and contraction will be evaluated using modern statistical image analysis by CLIMB personnel. CLIMB personnel also will supervise the development of a digital image processing and analysis toolbox (TRD3) to automatically detect the potential crown-like structures, and analyze the relation of detected structures to the observed lipid morphology. The immediate goal is to enable Dr. Smith’s group to more robustly and more rapidly interpret their current image data; the ultimate goal is to enable them to make better use of their channel-limited imaging by obviating one of the colorimetric markers. The analysis techniques employed for this project also will be of use to the greater obesity research community studying the changes in adipose tissue with diet.
The service project has at least one additional synergy with CLIMB. The Smith Lab shall provide both adipose tissue samples and their own fluorescence imaging of those samples. Team members from TRD1 will image parts of these samples via quantitative phase imaging (QPI). QPI provides label-free visualization of adipose tissue, which complements confocal fluorescence imaging. The paired QPI and fluorescence images will be used for future development of virtual staining of label-free images. CLIMB will package, archive, and share these valuable data for future research in computational label-free imaging.