Dr. Irene Georgakoudi, Dartmouth College
Label-free, non-linear optical techniques provide unique insights into biological systems by exploiting multiphoton processes that offer intrinsic chemical and structural contrast. In particular, two-photon excited fluorescence (TPEF) intensity and lifetime imaging enable direct measurement of cellular metabolic function through endogenous fluorophores such as NAD(P)H and FAD. Combined with second harmonic generation (SHG), which probes the ordered structure of non-centrosymmetric molecules like collagen, these methods reveal complementary information about extracellular matrix organization and biomechanics. Integrating quantitative image analysis with detailed spectroscopic approaches, we map metabolic states and structural changes across multiple scales and biological contexts. In this talk, I will present how TPEF and SHG—applied to engineered brain tissue models of traumatic brain injury, animal models of osteoarthritis, and even living humans—can uncover critical functional parameters without the artifacts introduced by exogenous labels. Such capabilities can ultimately offer new paradigms for detecting, treating, and monitoring a wide range of processes, from normal aging to cancer.
Irene Georgakoudi joined the Thayer School of Engineering at Dartmouth College in June 2025, as Professor and co-director of the Translational Engineering in Cancer program at the Dartmouth Cancer Center. She studied Physics at Dartmouth College, and Biophysics at the University of Rochester. She was introduced to label-free, optical diagnostics as a postdoctoral fellow at MIT and continued to pursue this line of research as an independent investigator at Tufts University, after being an Instructor at the Massachusetts General Hospital/Harvard Medical School. Her work focuses on the development and application of imaging modalities that exploit endogenous light scattering and fluorescence contrast to characterize quantitatively tissue function and morphology to improve understanding, diagnosis, and monitoring of human diseases. She has over 140 peer reviewed publications and holds several patents on optical technologies that assess cell and tissue properties. She is a fellow of the American Institute for Biomedical Engineering, Optica, and SPIE.
Wednesday, June 4, 2025, 1:45 - 2:30 PM in Gray
Session Chairs: CCBIR Trainee Subcommittee
Junior Investigators join together for networking Bingo. Meet fellow Junior Investigators and compete to win a prize!
Inter-center collaboration to address unmet needs
Thursday, June 5, 2025, 3:00 - 4:00 PM
Session Chairs: Vadim Backman and Hao F. Zhang (Northwestern), and Kevin Eliceiri (University of Wisconsin-Madison)
More info TBD.
Thursday, June 5, 2025, 3:00 - 4:00 PM in Gray
Session Chairs: CCBIR Trainee Subcommittee
Junior Investigators will be split into groups to develop a “Shark Tank” pitch. The best pitch, as determined by your peers, will win a prize.
Northwestern University Center for Chromatin NanoImaging in Cancer
Vadim Backman, Northwestern University
Center for 3D Imaging in Cancer Cell Biology
Denis Wirtz, Johns Hopkins University
Center for Multiparametric Imaging of Tumor Immune Microenvironments
Paolo Provenzano, University of Minnesota
Imaging Mechanisms of Metastatic Tumor Formation In Situ
Kevin Dean, UT Southwestern
Mapping fibrotic and cellular immunosuppression within pancreatic adenocarcinomas in space and time
Paolo Provenzano, University of Minnesota
Pancreatic ductal adenocarcinoma (PDA) is characterized by profound desmoplasia that impedes effective T cell sampling of the tumor volume. Here, we present an integrated experimental and computational framework aimed at defining the migration dynamics of engineered T cells within the PDA tumor microenvironment (TME). Utilizing genetically engineered mouse models and live tumor slices, we employ multiplexed multiphoton microscopy to capture real-time interactions between T cells and their environment. Our findings indicate a dual role for stromal collagen, where it serves as a directional contact guidance cue that facilitates rapid, ballistic T cell migration along collagen fiber tracks while simultaneously imposing migration anisotropy that limits off-axis movement and creates localized immune exclusion zones. Colocalized with collagen fibers, immunosuppressive myeloid cells are identified as key modulators of T cell behavior by engaging in MHC complex and immune checkpoint, ultimately leading to T cell sequestration. To quantitatively dissect immune infiltration barriers, we developed TME-CARTographer (TME-CART), a computational pipeline that integrates high-dimensional imaging data with graph theory, behavior analysis, and interpretable deep learning, offering granular insights into distinct T cell-TME interplay. Our integrated data analysis platform offers a robust, quantitative tool aimed at optimizing engineering criteria for next-gen cell-based immunotherapies in solid tumors.
Other authors: Guhan Qian, Hongrong Zhang, Kevin Eliceiri
Cyclically Multiplexed Expansion Microscopy for High-Resolution, Multi-Target Imaging of Subcellular Architectures Kevin Dean, UT Southwestern Medical Center
Despite significant advances in fluorescence microscopy, challenges such as spectral overlap and spatial resolution continue to hinder visualization of diverse cellular architectures. To address these issues, we introduce Cyclically Multiplexed Expansion Microscopy (Cy-ExM)—a novel approach that integrates optimized cryo-fixation, expansion microscopy, and cyclic immunofluorescence labeling to enable multiplexed, nanoscale imaging of cellular processes. We demonstrate Cy-ExM on cultured cells by imaging diverse organelles and cytoskeletal structures with spinning disk, oblique plane, and axially swept light-sheet microscopy modalities, enabling high-resolution, volumetric imaging across different optical platforms. Cy-ExM thus combines the spatial precision of expansion microscopy with the molecular depth of cyclic labeling strategies. This method holds promise for cancer cell biology, where we aim to enable imaging of protein localization in a multiplexed format throughout the metastatic cascade, with a resolution approaching 20 nm.
Other authors: Seweryn Gałecki, Qionghua Shen
Accelerated Discovery of Cell Migration Regulators Using Label-Free Deep Learning-Based Automated Tracking Pei-Hsun Wu, Johns Hopkins University
Cell migration plays a key role in normal developmental programs and in disease, including immune responses, tissue repair, and metastasis. Unlike other cell functions, such as proliferation which can be studied using high-throughput assays, cell migration requires more sophisticated instruments and analysis, which decreases throughput and has led to more limited mechanistic advances in our understanding of cell migration. Current assays either preclude single-cell level analysis, require tedious manual tracking, or use fluorescently labeled cells, which greatly limit the number of extracellular conditions and molecular manipulations that can be studied in a reasonable amount of time. Using the migration of cancer cells as a testbed, we established a workflow that images large numbers of cells in real time, using a 96-well plate format. We developed and validated a machine-vision and deep-learning analysis method, DeepBIT, to automatically detect and track the migration of individual cells from time-lapsed videos without cell labeling and user bias. We demonstrate that our assay can examine cancer cell motility behavior in many conditions, using different small-molecule inhibitors of known and potential regulators of migration, different extracellular conditions such as different contents in extracellular matrix and growth factors, and different CRISPR-mediated knockouts. About 1500 cells per well were tracked in 840 different conditions, for a total of ~1.3M tracked cells, in 30h (2 min per condition). Manual tracking of these cells by a trained user would take ~5.5 years. This demonstration reveals previously unidentified molecular regulators of cancer cell migration and suggests that collagen content can change the sign of how cytoskeletal molecules can regulate cell migration.
Other authors: Tiffany Chu, Denis Wirtz
Chromatin and DNA Imaging via Multi-Label and Label-Free Spectroscopic Nanoscopy Ruyi Gong, Northwestern University
Super-resolution microscopy has transformed our ability to visualize structures below the diffraction limit. To unravel chromatin complexities, we developed a multi-laser spectroscopic super-resolution microscope coupled enabling simultaneous, multi-color imaging of chromatin components at the nanoscale. This system allowed us to capture the unified, domain-centered architecture of chromatin, where heterochromatin domains serve as structural anchors around which transcriptionally active regions and euchromatin elements are spatially arranged. Our investigation further quantified dye size effects in super-resolution images via physics-based polymer simulations and experiments. Moreover, to fundamentally circumvent the need for external labeling, we explored the endogenous fluorescence of DNA and demonstrated that both single-stranded and double-stranded DNA emit stochastic fluorescence with distinct spectral fingerprints dependent on their lengths and sequences, paving the way for a label-free super-resolution imaging modality for chromatin. Collectively, our work not only refines super-resolution fluorescence microscopy for detailed chromatin architecture analysis but also establishes a database for intrinsic fluorescence of DNA, providing new trajectories in future chromatin studies.
Other authors: (Ruyi Gong), Nicolas Acosta, Yuanzhe (Patrick) Su, Rivaan Kakkaramadam, Wing Shun Li, Juntong Jing, I Chae (Rachel) Ye, Boshuo Yang, Jane Frederick, Karla Isabel Medina, Cody Dunton, Luay Almassalha, Geng Wang, Vadim Backman
CODAvision: best practices and a user-friendly interface for rapid, customizable segmentation of medical images Valentina Matos Romero, Johns Hopkins University
Image-based machine learning tools have emerged as powerful resources for analyzing medical images, with deep learning-based semantic segmentation commonly utilized to enable spatial quantification of structures in images. However, customization and training of segmentation algorithms require advanced programming skills and intricate workflows, limiting their accessibility to many investigators.
To address this challenge, we present a protocol and software for automatic deep learning-based segmentation of medical images guided by a graphical user interface (GUI) using the CODAvision algorithm. This GUI guided workflow simplifies the process of semantic segmentation by enabling users to train highly customizable deep learning models without extensive coding expertise. This protocol outlines best practices for creating robust training datasets, configuring model parameters, and optimizing performance for diverse biomedical image types. CODAvision enhances the usability of the CODA algorithm (Nature Methods, 2022) by streamlining parameter configuration, model training, and performance evaluation, automatically generating quantitative results and comprehensive reports.
We demonstrate robust performance across numerous medical image modalities and diverse biological questions. We provide sample results in data types including histology, magnetic resonance imaging (MRI), and computed tomography (CT), and in applications including quantification of metastatic burden in in vivo models, and deconvolution of spot-based spatial transcriptomics datasets.
Overall, this protocol is designed for researchers with an interest in the rapid design of highly customizable semantic segmentation algorithms and a basic understanding of programming and anatomy.
Other authors: Valentina Matos Romero
fishAtlas: A Zebrafish Xenograft Platform to Map Organotropism in Ewing Sarcoma Hanieh Mazloom Farsibaf, UT Southwestern Medical Center
Metastasis is the leading cause of cancer associated death. However, not all cancer cells spread equally throughout the body. Understanding the tendency of specific cancer cells to migrate to and colonize specific organs, known as organotropism, is a first step toward more personalized and effective therapeutic strategies. Ewing Sarcoma (EwS) is an aggressive cancer of bone and soft tissue that primarily affects children and young adults. At diagnosis, approximately 25% of patients present with metastatic disease, with survival rates dropping below 30%. Here, we present the fishAtlas framework to investigate the organotropic behavior of EwS cells using a zebrafish xenograft model. Zebrafish are uniquely suited for this research due to their optical transparency, capacity to produce 200-300 eggs per week, and physiological complexity – including a complete circulatory system and complex tissue environments. This makes them ideal for high-throughput imaging and metastasis modeling and provides the statistical power to study the heterogeneity of cancer cells spreading. In our experiments, we prepared xenografts of human EwS cells derived from both primary and metastatic tumors of the same patient into zebrafish larvae and imaged approximately 100 fish per condition. Anatomical alignment and mapping of metastatic sites revealed that cells from the metastatic tumor disseminated more extensively than those from the primary tumor. This framework allows us to identify distinct organotropic profiles for different cancer types and offers a scalable platform to investigate how genetic and epigenetic features of tumor cells or drug perturbations may change these patterns.
Other authors: Ingrid Lekk, James Amatruda, Gaudenz Danuser
Chromatin beyond the connections Luay Almassalha, Northwestern University
The advent of chromatin conformation capture technologies allowed unprecedented access to the finest-scale features of the genome: topologically associating domains (TADs) and loop domains. These connections frequently span over hundreds of kilobasepairs (KBp) and are thought to guide signal responses in human tissues. Although TADs and loops are associated with transcriptional signals, their manipulation has muted effects on gene expression patterns. This naturally leads to questions about how connections integrate with the space-filling volumes that regulate chemical reactions to synthesize RNA. We show that TADs and loop domains are "impossible objects” when considered in the context of space filling features of individual cells. Instead, we describe how these features are partial projections of space-filling volumes in individual cells but that this information is insufficient to recreate the 3-D geometry governing transcription reactions. We then show how packing domains observed on chromatin electron microscopy (ChromEM) are the reaction volumes that guide the transcriptional reactions which define cellular function. Packing domains are self-assembling structures coordinated by transcription and matured by nucleosome remodeling enzymes based on physical principles. Transcriptional signals guide the initiation of domain formation and the maturation results in a physically encoded memory of the signaling state. We conclude by showing how this physical system integrates information on cellular state (nuclear volume, redox potential, and ion concentrations) to act as a geometric processor of signals in the context of cell state.
Other authors: Marcelo Carignano, Ruyi Gong, Wing Shun Li, Lucas Carter, Kyle L MacQuarrie, Igal Szleifer, and Vadim Backman
InterpolAI: Deep learning based optical flow interpolation and restoration of biomedical images for improved 3D tissue mapping André Forjaz, Johns Hopkins University
Recent advances in imaging and computation have enabled analysis of large three-dimensional (3D) biological datasets, revealing spatial composition, morphology, cellular interactions, and rare events. However, the accuracy of these analyses is limited by image quality, which can be compromised by missing data, tissue damage, or low resolution due to mechanical, temporal, or financial constraints. Here, we introduce InterpolAI, a method for interpolation of synthetic images between a pairs of authentic images in a stack of images, by leveraging frame interpolation for large image motion (FILM), an optical flow-based AI model. InterpolAI outperforms both linear interpolation and state-of-the-art optical flow-based method XVFI, preserving microanatomical features and cell counts, and image contrast, variance, and luminance. InterpolAI repairs tissue damages and reduces stitching artifacts. We validated InterpolAI across multiple imaging modalities, species, staining techniques, and pixel resolutions. This work demonstrates the potential of AI in improving the resolution, throughput, and quality of biological image datasets to enable improved 3D imaging.
Other authors: André Forjaz, Kyu Sang Han, Yu Shen, Vasco Queiroga, Florin A. Selaru, Marie Gérard, Daniel Xenes, Jordan Matelsky, Brock Wester4, Arrate Munoz Barrutia, Ashley L. Kiemen, Pei-Hsun Wu, and Denis Wirtz
Microscopy image analysis as graph theory: Describing cellular and subcellular structures Meghan Driscoll, University of Minnesota
Cells within tumors change shape as they migrate, phagocytose, and perform other critical processes. These shape changes directly enable various cell functions and are indicative of cell state. A cell’s morphology is typically characterized by first segmenting the cell, or finding its outline, and then mathematically describing this outline. However, segmentation is notoriously difficult, and even a perfect segmentation would still lose much of the highly convoluted plasma membrane structure we wish to capture. Here, we introduce a graph-theoretic analysis of cellular and sub-cellular structure. Graphs are constructed by sampling pixel intensities, analogous to sampling from the collection of photons that comprise an image. We show that we can calculate morphological curvature, and abstractions of curvature, directly from the graph. We also show that, often without segmentation, we can calculate morphological spectra as the “sound of the cells”, and use graph theory based machine learning to characterize shape. We have begun to apply these methods to immune and cancer cell structures.
Other authors: Aya Aqeel, Kyler Sood, Jeff Calder, Meghan Driscoll
Volumetric imaging of metastatic niches in Zebrafish xenografts with high throughput Conor McFadden, UT Southwestern Medical Center
Metastases are a leading cause of poor cancer survival, driven by the functional heterogeneity of cancer cells that enables them to migrate and proliferate in distant tissues. Cancer cells exhibit site-dependent morphology and gene expression changes which allow them to adapt and survive in diverse anatomical niches. While sequencing can reveal genetic diversity among metastatic cells at the population level, imaging their spatial organization and signaling at the single-cell level within the tissue context remains challenging. Zebrafish tumor xenograft models, due to their transparency, are well-suited for in vivo fluorescence imaging. Our research integrates high-throughput zebrafish imaging (VAST, Union Biometrica) with advanced fluorescence oblique plane microscopy (OPM) and adaptive optics to capture high-resolution 3D images of metastatic niches. This system aims to generate detailed, statistically robust insights into how cancer cells survive and proliferate in diverse tissue environments.
Other authors: Bingying Chen, Stephan Daetwyler, Xiaoding Wang, Divya Rajendran, Kevin M. Dean, and Reto Fiolka
Long-term imaging of cancer growth and metastasis in zebrafish xenografts with self-driving, multi-scale microscopy Stephan Daetwyler, UT Southwestern Medical Center
Zebrafish xenografts have become a powerful model to study cancer progression and metastasis in a near-physiological environment in vivo. They provide a short generation time, high-throughput and thus high statistical power. In addition, their optical translucency makes them ideally suited for optical imaging. However, imaging entire living zebrafish organisms over long-time periods with simultaneous imaging of selected sub-cellular cancer processes has remained challenging. To image across these spatial scales, we introduce a novel, versatile multi-resolution light-sheet microscope platform [1] that enables observation and quantification of subcellular behaviors alongside entire zebrafish organisms. Importantly, the microscope is self-driving, i.e. it automatically focuses image acquisition onto the cells and regions of biological interest. This enables long term observations over many hours to days while cancer cells migrate or while the zebrafish embryo develops or changes. We demonstrate the power of our imaging platform with studies of melanoma, Ewing Sarcoma, osteosarcoma, breast cancer and glioblastoma xenografts. In detail, we imaged tumor-immune cell interactions and show how cancer cell–immune cell interactions induce shape changes in macrophages and trigger behavioral changes of macrophages throughout a whole organism. Moreover, we show how more aggressive melanoma cells appear to have mechanisms to evade immune responses despite close and repeated contacts with macrophages, in contrast to less metastatic cancer cell lines. Beyond these interactions, the applicability of these new microscopy tools to identify and study processes in development and disease in zebrafish is imminent.
[1] https://www.nature.com/articles/s41592-025-02598-2
Other authors: Hanieh Mazloom-Farsibaf, Felix Y. Zhou, Dagan Segal, Subhrajit Banerjee, Maria Del Carmen Lafita-Navarro, Christopher Kuo, James Amatruda, Maralice Conacci-Sorrell, William A. Prinz, Gaudenz Danuser, Reto Fiolka
Immune Engineering Strategies to Enhance T Lymphocyte Dynamics in Pancreatic Ductal Adenocarcinoma Priyanila Magesh, University of Minnesota
Pancreatic Ductal Adenocarcinoma (PDA) stands as one of the leading contributors to cancer-associated mortality in the United States, in part, due to treatment resistance. Nevertheless, there is strong potential with Chimeric Antigen Receptor (CAR) T cell therapy, which harnesses T cells from the patient’s body modified with synthetic cell-surface receptors to target cancer cells efficiently. However, these CAR T cells face challenges in PDA: they display limited persistence, and poor tumor infiltration due to the dense desmoplastic stroma, and the immunosuppressive nature of the tumor microenvironment (TME). Hence, my research aims to identify and genetically engineer key focal adhesion-related genes in CAR T cells, enhancing their ability to sample within the tumor by overcoming the fibrotic stromal barriers. Preliminary experiments reveal that T cell migration on ECM substrates follows a biphasic pattern, suggesting that tumor microenvironment rich in adhesion ligands may hinder T cell motility. We initially screened for potential targets using bulk RNA sequencing of meso-CAR T cells, selectively identifying upregulated adhesion-associated genes such as integrins alpha-4, alpha-L, beta-1, and beta-2. To evaluate their effect on T cell migration, we performed two-photon time-series imaging of T cells moving in 3D ECM-like gels while blocking the target receptors with function-blocking antibodies. We observed that integrins alpha-4 and alpha-L blocking increases 3D speed, motility coefficient, and persistence. Targeting these integrins also results in unique morphology changes quantified by circularity and surface area in CAR T cells. Similarly, integrin-blockade migration experiments on mouse and human tumor slices, mimicking the TME, confirmed that integrin alpha-L blockade positively correlates with enhanced motility characteristics and sampling. Therefore, employing the CRISPR Cas9 knockout strategy to genetically modify the integrin alpha-L adhesion receptor could enhance the therapeutic potential of CAR T cells to navigate the desmoplastic stroma and infiltrate tumors in vivo, eventually offering an effective therapeutic for PDA patients.
Other authors: Dr. Paolo Provenzano
Pushing the precision limit in spectroscopic single-molecule localization microscopy Hao F. Zhang, Northwestern
The commonly achievable spatial precision in single-molecule localization microscopy (SMLM) is approximately 20 nm, primarily limited by the detected photon counts from single-molecule emissions and the detector’s noise floor. Our recent efforts to further improve the localization precision in spectroscopic single molecule location microscopy (sSMLM) focus on a new optical design to physically reduce photon loss and a new computational method to increase photon count numerically. In our optical solution, we developed the symmetrical dispersion dual-wedge prism assembly to use all detected photons for both spatial localization and spectral analysis. In our computational method, we developed a random labeling protocol with spectral tagging to combine photons emitted by the same single molecules but detected in different frames. We achieved a spatial precision of 1.07 nm and resolved the well-defined double octagon morphology of nuclear pores subjected to labeling efficiency distribution.
Other authors: Wei-Hong Yeo, Benjamin Brenner, Menglin Shi, George Rabadi, Cheng Sun, and Hao F. Zhang
Imaging deep into metastatic sites to study rare melanoma cells Torikul Islam, UT Southwestern
Treating metastatic cancer is a significant challenge because of the microenvironmental differences among metastatic sites, where cancer cells adapt to survive and proliferate. We have developed xenograft models in which human patient-derived melanomas engraft into immunocompromised NOD/SCID IL2Rγ-/- (NSG) mice. We found that some melanomas preferentially metastasize to the lung, whereas others metastasize broadly to many sites. We became interested in whether the differences in the metastasis patterns of melanomas from different patients reflect differences in their ability to migrate, survive, and/or proliferate in metastatic sites. To study this question, we developed new tools enabling us to detect and study metastatic cancer cells at single-cell resolution within the metastatic sites such as the lungs, liver, and kidneys. We transplanted luciferase-expressing broadly metastasizing and lung-only metastasizing, patient-derived melanomas into NSG mice and collected tissues at different timepoints. Next, we stained and chemically cleared the whole organs to image deep into the tissues using a Leica Stellaris CTR6500 confocal microscope. We identified cancer cells in the metastatic sites at a single cell resolution that bioluminescence imaging is not sensitive enough to detect. We are acquiring images from lungs, liver, and kidneys across many different timepoints. Thus, with our newly developed tools, we are scanning a large number and volume of organs to characterize organotropism. We are also optimizing methods to assess cell survival and proliferation using deep imaging of cleared tissues. These data will create a new approach to study organotropism and elucidate mechanisms that regulate the formation of metastasis.
Other authors: Derek Santiago, Sean J. Morrison
Precision Mapping of antibody-HER2 Engagement in Tumors Using Meditope-Engineered Antibodies and Mesoscopic FLIM-FRET Imaging Amit Verma, Albany Medical College
Effective target engagement of antibody drugs is critical for successful cancer treatment. Trastuzumab (TZM), a humanized monoclonal antibody, is widely used against HER2+ tumors. However, variability in HER2 expression and limited tumor penetration can reduce TZM-HER2 binding, leading to poor therapeutic response. To address this, we developed a near-infrared fluorescence lifetime FRET (NIR-FLI-FRET) imaging approach to precisely map TZM-HER2 binding within the tumor microenvironment in tumor xenografts.
Stochastic N-hydroxysuccinimide (NHS)-ester labeling creates heterogeneous dye-antibody conjugates, leading to variable target engagement. To overcome this, we developed site-specific NIR labeling with two dyes per IgG via cyclic meditope peptides on each Fab arm (TZM-MDT), to enhance tumor binding, penetration, and NIR-FLI-FRET imaging accuracy.
We used in vitro NIR-FLI-FRET microscopy and ex vivo time-domain mesoscopic fluorescence molecular tomography (TD-MFMT) to compare the binding of TZM-MDT versus TZM-NHS to HER2+ AU565 breast cancer cells and tumors. In cells, TZM-MDT showed more consistent lifetimes and higher FRET donor fractions, indicating enhanced HER2 binding. TD-MFMT imaging of AU565 xenografts confirmed increased TZM-MDT binding and deeper tumor penetration. Pixel-wise FRET donor profiles demonstrated uniform tumor-wide TZM-MDT-HER2 engagement, in contrast to peripheral binding seen with TZM-NHS. FRET data were validated via spatial correlation with color-deconvoluted immunohistochemistry (IHC) images. Manual (ImageJ) and automated (GNU Image Manipulation Program) ROI alignment and quantitation, followed by Pearson’s correlation, showed strong concordance between FRET donor fraction and IHC anti-TZM and anti-HER2 optical density. This integration of MDT-antibody technology with TD-MFMT FRET offers a robust platform for precision imaging and enhanced anti-HER2 therapy.
Other authors: Catherine Sherry, Taylor Humphrey, Shan Gao, Saif Ragab, Vikas Pandey, Tynan Young, John C Williams, Xavier Intes, Margarida Barroso
Quantitative Mitochondrial Analysis of Brequinar’s Effect on Platinum Resistant Ovarian Cancer Cells Xingyue Hao, Northwestern University
High grade serious ovarian cancer (HGSOC) is lethal due to the development of cisplatin-resistant cells, which enable resistance to platinum-based chemotherapy. Recent findings suggest that cisplatin-resistant cells are dependent on de novo pyrimidine synthesis and as such are vulnerable to inhibitors of this pathway, like Brequinar, which has been found to disrupt mitochondrial metabolic activity in cisplatin-resistant ovarian cancer cells and inhibit tumor growth. The effect of Brequinar on mitochondria structural are not well understood as few methods exist to quantify mitochondrial morphological alterations at nanoscale resolutions. Three-dimensional single-molecule localization microscopy (SMLM) can visualize such nanoscale alterations, allowing for quantitative measurements of mitochondrial structure. Using SMLM, we quantified mitochondrial morphologies in both OVCAR5 cells to examine how Brequinar affects mitochondrial structure.
OVCAR5 cisplatin-resistant and cisplatin-sensitive cells were both treated with DMSO and 1uM of Brequinar, respectively. Cells were fixed, stained with TOM-20, a mitochondrial membrane protein, and imaged using our SMLM system. We measured individual mitochondrial length, width, and volume. To normalize these values, we calculated the aspect ratio, which is defined as the ratio between mitochondrial length and width.
We found that though there was no significant change in aspect ratio between cell types, Brequinar treatment caused a significant increase in aspect ratio (p
Other authors: Yinu Wang, Horacio Cardenas, Daniela Matei, Hao F. Zhang
Multiscale fluorescence microscopy to shed light on cancer metastasis Reto Fiolka, UT Southwestern Medical Center
Fluorescence microscopy combined with optically transparent model organisms such as Zebrafish has the potential to unravel the functional heterogeneity and adaptation of cancer cells that underlies their metastatic potential. However, this requires bridging disparate scales, as metastatic sites might form across an entire organism. This stretches the capabilities of current microscope technology and manual operation is typically limited to anecdotal observations. To overcome this lack in technology, we have developed a self-driving, multi-resolution light-sheet microscopy platform capable of long-term in vivo imaging of sub-cellular processes alongside entire organisms. We demonstrate its application to imaging of developmental processes and models of cancer invasion, metastasis, and immune-cancer cell interactions in vitro and in vivo. To increase the throughput, we have combined an automated fluidic zebrafish screening platform with a custom-built single objective light-sheet microscope. This platform allows us to collect zebrafish embryos from a multiwell plate, screen them at low resolution for metastatic niches, and perform on demand high resolution 3D imaging of cancer cells. Combined, these new microscopes allow us to image the metastatic cascade at different spatial and temporal scales and also increase the statistical power of our observations.
Other authors: Stephan Daetwyler, Conor McFadden
γδ CAR-T cells show enhanced migration into pancreatic cancer Chris Zahm, University of Minnesota
Current immunotherapies have revolutionized the treatment of blood cancers, but solid tumors remain a challenge. Pancreatic ductal adenocarcinoma (PDAC) is particularly difficult due to multiple levels of immunosuppression and the failure of immune cells to effectively infiltrate their dense fibrotic stroma. As such, chimeric antigen receptor (CAR) T cell therapies targeting PDAC must be engineered with enhanced migratory abilities to penetrate the complicated TME. In this study we assessed the migratory capacity and metabolism of γδ CAR-T cells and explore their use as a cellular therapy for PDAC. Using multiphoton microscopy we generated fluorescent, second harmonic generation (SHG), and fluoresce lifetime (FLIM) images of γδ and αβ CAR-T cells in 3D matrices and compared their migration kinetics and metabolic profiles. We found that γδ CAR-Ts moved faster and further through collagen and showed increased motility coefficients in both collagen and spheroids of Panc-1 or Mia-Paca cells when compared to αβ-CAR-Ts. Furthermore, NAD(P)H FLIM revealed the metabolic profile of γδ CAR-T’s as more oxidative, resembling a stem memory phenotype while the αβ CAR were more glycolytic, resembling later stage cytotoxic T cells. Taken together these data indicate that γδ CAR-T’s may have an enhanced ability to migrate into and persist for longer periods in the PDAC TME when compared to their αβ counterparts. While in vivo studies are needed this data signifies the use of γδ CAR-T cells to treat PDAC and suggests they may play an essential role in the future of cellular therapies.
Other authors: Christopher D. Zahm, Shambojit Roy, Joseph G. Skeate, Ethan Niemeyer, Beau R. Webber, Branden S. Moriarity, Paolo P. Provenzano