Swati Padmanabhan
University of Minnesota · Industrial and Systems Engineering
Active 2022–2025
About
Swati Padmanabhan is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Minnesota. She holds a Ph.D. from the University of Washington, completed in 2023, and has conducted postdoctoral research at MIT. Her research focuses on algorithms for continuous optimization, and she is currently accepting new undergraduate and graduate research students. Her academic and research activities are centered around optimization techniques, contributing to the field through her scholarly work and teaching.
Research topics
- Cell biology
- Medicine
- Biology
- Pathology
- Machine Learning
- Artificial Intelligence
- Computer Science
- Data Mining
- Internal medicine
- Optoelectronics
- Physics
- Chemistry
- Algorithm
- Nanotechnology
- Materials science
- Cancer research
Selected publications
iScience · 2025-08-26 · 1 citations
articleOpen accessCancer patients undergoing transplantation-based treatments can develop graft-versus-host disease (GVHD), an inflammatory condition that increases mortality risk. In this study, we analyzed three cancer patients with severe inflammatory disorders following SARS-CoV-2 infection using high-resolution microscopy and spatial transcriptomics on pre- and post-infection gastrointestinal (GI) biopsies. We found that up to 49 days after infection, the duodenal epithelium retained COVID viral elements, showed increased expression of viral receptors, inflammatory genes, and interferon activity, and exhibited tissue scarring. Notably, SERPINA1 was a persistent marker of infection and inflammation, also present in ∼600 GI tumor samples, suggesting its role as a broader inflammation marker. These findings indicate that the GI epithelium can serve as a long-term COVID reservoir, potentially driving inflammatory syndromes similar to GVHD. This persistent viral presence may pose additional risks for cancer patients undergoing transplantation, highlighting the need for further investigation into post-COVID inflammatory complications in cancer patients.
Protocol for applying Tumor Treating Fields in mouse models of cancer using the inovivo system
STAR Protocols · 2025-01-08 · 2 citations
articleOpen accessTumor Treating Fields (TTFields) are electric fields clinically approved for cancer treatment, delivered via arrays attached to the patient’s skin. Here, we present a protocol for applying TTFields to torso orthotopic and subcutaneous mouse tumor models using the inovivo system. We guide users on proper system component connections, study protocol design, mouse fur depilation, array application, and treatment condition adjustment and monitoring. The inovivo system allows for the concurrent application of TTFields with standard cancer therapies. For complete details on the use and execution of this protocol, please refer to Barsheshet et al. 1 • Instructions for setup of the inovivo system • Steps for assembly of TTFields and sham arrays to mice torsos • Guidance on monitoring an inovivo experiment Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Tumor Treating Fields (TTFields) are electric fields clinically approved for cancer treatment, delivered via arrays attached to the patient’s skin. Here, we present a protocol for applying TTFields to torso orthotopic and subcutaneous mouse tumor models using the inovivo system. We guide users on proper system component connections, study protocol design, mouse fur depilation, array application, and treatment condition adjustment and monitoring. The inovivo system allows for the concurrent application of TTFields with standard cancer therapies.
Tunneling Nanotubes: Implications for Chemoresistance
Results and problems in cell differentiation · 2024 · 5 citations
1st authorCorresponding- Cell biology
- Nanotechnology
- Biology
2023-07-06
peer-reviewOpen accessCancer-directed treatment using Tumor-Treating Fields spatially modulates the tumor microenvironment at the cellular and molecular levels, decreasing nanotube-based cellular networks of communication while creating a microenvironment more susceptible to immunotherapeutic strategies.
eLife · 2023 · 13 citations
- Cancer research
- Medicine
- Cell biology
Disruption of intercellular communication within tumors is emerging as a novel potential strategy for cancer-directed therapy. Tumor-Treating Fields (TTFields) therapy is a treatment modality that has itself emerged over the past decade in active clinical use for patients with glioblastoma and malignant mesothelioma, based on the principle of using low-intensity alternating electric fields to disrupt microtubules in cancer cells undergoing mitosis. There is a need to identify other cellular and molecular effects of this treatment approach that could explain reported increased overall survival when TTFields are added to standard systemic agents. Tunneling nanotube (TNTs) are cell-contact-dependent filamentous-actin-based cellular protrusions that can connect two or more cells at long-range. They are upregulated in cancer, facilitating cell growth, differentiation, and in the case of invasive cancer phenotypes, a more chemoresistant phenotype. To determine whether TNTs present a potential therapeutic target for TTFields, we applied TTFields to malignant pleural mesothelioma (MPM) cells forming TNTs in vitro. TTFields at 1.0 V/cm significantly suppressed TNT formation in biphasic subtype MPM, but not sarcomatoid MPM, independent of effects on cell number. TTFields did not significantly affect function of TNTs assessed by measuring intercellular transport of mitochondrial cargo via intact TNTs. We further leveraged a spatial transcriptomic approach to characterize TTFields-induced changes to molecular profiles in vivo using an animal model of MPM. We discovered TTFields induced upregulation of immuno-oncologic biomarkers with simultaneous downregulation of pathways associated with cell hyperproliferation, invasion, and other critical regulators of oncogenic growth. Several molecular classes and pathways coincide with markers that we and others have found to be differentially expressed in cancer cell TNTs, including MPM specifically. We visualized short TNTs in the dense stromatous tumor material selected as regions of interest for spatial genomic assessment. Superimposing these regions of interest from spatial genomics over the plane of TNT clusters imaged in intact tissue is a new method that we designate Spatial Profiling of Tunneling nanoTubes (SPOTT). In sum, these results position TNTs as potential therapeutic targets for TTFields-directed cancer treatment strategies. We also identified the ability of TTFields to remodel the tumor microenvironment landscape at the molecular level, thereby presenting a potential novel strategy for converting tumors at the cellular level from 'cold' to 'hot' for potential response to immunotherapeutic drugs.
Neuro-Oncology · 2022-11-01
articleOpen accessAbstract BACKGROUND Tunneling nanotubes (TNTs) are cellular structures connecting cell membranes and mediating intercellular communication. TNTs are manually identified and counted by a trained investigator; however, this process is time-intensive. We therefore sought to develop an automated approach for quantitative analysis of TNTs. METHODS We used the convolutional neural network (U-Net) deep learning model to segment phase contrast microscopy images of both cancer and non-cancer cells. Our method was composed of preprocessing and model development. We developed a new preprocessing method to label TNTs on a pixel-wise basis. Two sequential models were employed to detect TNTs. First, we identified the regions of images with TNTs by implementing a classification algorithm. Second, we fed parts of the image classified as TNT-containing into a modified U-Net model to estimate TNTs on a pixel-wise basis. RESULTS The U-Net model detected 73.3% of human expert-identified TNTs, counted TNTs and cells, and calculated the TNT-to-cell ratio (TCR). We obtained a precision of 0.88, recall of 0.67, and f-1 score of 0.76 on a test data set. The predicted and true TCRs were not significantly different between the training and test data sets. CONCLUSIONS In summary, we report application of an automated model generated by deep learning and trained to accurately label and detect TNTs and cells imaged in culture. Continued application and refinement of this process will provide a new approach to the analysis of TNTs, which form to connect cancer and other cells. This approach has the potential to enhance the drug screens intended to assess therapeutic efficacy of experimental agents, and to reproducibly assess TNTs as a potential biomarker of response to therapy in cancer.
Cancers · 2022 · 7 citations
- Artificial Intelligence
- Computer Science
- Artificial Intelligence
BACKGROUND: Tunneling nanotubes (TNTs) are cellular structures connecting cell membranes and mediating intercellular communication. TNTs are manually identified and counted by a trained investigator; however, this process is time-intensive. We therefore sought to develop an automated approach for quantitative analysis of TNTs. METHODS: We used a convolutional neural network (U-Net) deep learning model to segment phase contrast microscopy images of both cancer and non-cancer cells. Our method was composed of preprocessing and model development. We developed a new preprocessing method to label TNTs on a pixel-wise basis. Two sequential models were employed to detect TNTs. First, we identified the regions of images with TNTs by implementing a classification algorithm. Second, we fed parts of the image classified as TNT-containing into a modified U-Net model to estimate TNTs on a pixel-wise basis. RESULTS: = 0.78). CONCLUSIONS: Our automated approach labeled and detected TNTs and cells imaged in culture, resulting in comparable TCRs to those determined by human experts. Future studies will aim to improve on the accuracy, precision, and recall of the algorithm.
bioRxiv (Cold Spring Harbor Laboratory) · 2022-12-30
preprintOpen accessAbstract Intercellular communication is critical for the development of invasive cancers. Multiple forms of intercellular communication have been well characterized, involving diffusible soluble factors or contact-dependent channels for immediately adjacent cells. Over the past 1-2 decades, the emergence of a unique form of F-actin-based cellular protrusion known as tunneling nanotubes (TNTs) has filled the niche of long-range cell-contact dependent intercellular communication that facilitates cell growth, differentiation, and in the case of invasive cancer phenotypes, a more chemoresistant phenotype. The cellular machinery of TNT-mediated transport is an area of active investigation, and microtubules have been implicated in this process as they are in other membranous protrusions. Tumor-Treating Fields (TTFields) therapy is a novel therapeutic strategy in clinical use for patients with advanced cancers, based on the principle of using low-intensity alternating electric fields to disrupt microtubules in cancer cells undergoing mitosis. Other mechanisms of action have also been demonstrated. In this study, we investigated the effects of TTFields on TNTs in malignant pleural mesothelioma (MPM) in vitro and also on the spatial transcriptomic landscape in vivo . We found that applying TTFields at 1.0 V/cm significantly suppressed TNT formation in a biphasic MPM cell line (MSTO-211H), but not in sarcomatoid MPM (VAMT). At these parameters, TTFields significantly reduced cell count in MSTO-211H, but did not significantly alter intercellular transport of mitochondria via intact TNTs. To understand how TTFields may impact expression of genes with known involvement to TNT formation and overall tumor growth, we performed spatial genomic assessment of TTFields-treated tumors from an in vivo animal model of MPM, and detected upregulation of immuno-oncologic biomarkers with simultaneous downregulation of pathways associated with cell hyperproliferation, invasion, and other critical regulators of oncogenic growth. Several molecular classes and pathways coincide with markers that we and others have found to be differentially expressed in cancer cell TNTs, including MPM specifically. In this study, we report novel cellular and molecular effects of TTFields in relation to tumor communication networks enabled by TNTs and related molecular pathways. These results position TNTs as potential therapeutic targets for TTFields-directed cancer treatment strategies; and also identify the ability of TTFields to potentially remodel the tumor microenvironment, thus enhancing response to immunotherapeutic drugs.
Frequent coauthors
- 10 shared
Emil Lou
- 8 shared
Karina Deniz
Twin Cities Orthopedics
- 6 shared
Akshat Sarkari
Twin Cities Orthopedics
- 4 shared
Katherine J. Ladner
The Ohio State University
- 4 shared
Sophie Korenfeld
University of Minnesota
- 3 shared
Phillip Wong
Memorial Sloan Kettering Cancer Center
- 3 shared
Chirag B. Patel
The University of Texas MD Anderson Cancer Center
- 2 shared
Hamza Ergüder
Yıldız Technical University
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