New Imaging Methods for Identifying Structural Differences in Cancer Cells

ASPIRE Award (2019-2021)

Robert F. Murphy, PhD (Principal) and Min Xu, PhD, Carnegie Mellon University; Yi-Wei Chang, PhD, University of Pennsylvania

Robert Murphy, PhD

Min Xu, PhD

Yi-Wei Chang, PhD

The diversity of cancer types is reflected in the physical heterogeneity of tumor cells. This includes the shape, number, and spatial distribution of the cellular components that carry out various cellular functions. Current imaging technologies do not have high enough resolution to capture the differences in these cellular substructures, which limits doctors’ ability to diagnose cancers based on physical differences in tumor cells. Electron cryotomography (ECT) imaging is a relatively new technology that can produce 3D images of cells with resolutions under five nanometers. It allows for detailed imaging of the cellular interior, which makes it ideally suited for imaging and classification of tumor cells. However, to classify different cell types, ECT must be adapted for human cells and accompanied by advanced computer-based analysis methods that have yet to be developed. Researchers in the Murphy, Xu, and Chang labs are designing and testing classification and statistical analysis software that can be used to evaluate subcellular structural differences in different cancer cell lines. They will use four cultured breast tissue cell lines that range from non-cancerous to invasive carcinoma to train the software to recognize these differences. If successful, they should be able to analyze ECT images to differentiate both the diverse structures inside cells and differentiate cell lines based on the physical variability of these structures. In the future, these imaging methods may be translatable to tumor cells excised from patients, allowing doctors to use them to diagnose patients and predict their prognosis.

published research

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Yu L, Li R, Zeng X, Wang H, Jin J, Ge Y, Jiang R, Xu M. Few shot domain adaptation for in situ macromolecule structural classification in cryoelectron tomograms. Bioinformatics. 2021.

Zeng Y, Howe G, Yi K, Zeng X, Zhang J, Chang YW, Xu M. Unsupervised Domain Alignment Based Open Set Structural Recognition of Macromolecules Captured by Cryo-Electron Tomography. Proc Int Conf Image Proc. 2021.

Zhou B, Yu H, Zeng X, Yang X, Zhang J, Xu M. One-Shot Learning With Attention-Guided Segmentation in Cryo-Electron Tomography. Front Mol Biosci. 2021.

Bandyopadhyay H, Deng Z, Ding L, Liu S, Uddin MR, Zeng X, Behpour S, Xu M. Cryo-shift: reducing domain shift in cryo-electron subtomograms with unsupervised domain adaptation and randomization. Bioinformatics. 2022.

Chandra S, Gourisaria MK, Gm H, Konar D, Gao X, Wang T, Xu M. Prolificacy Assessment of Spermatozoan via State-of-the-Art Deep Learning Frameworks. IEEE Access. 2022.

Chharia A, Upadhyay R, Kumar V, Cheng C, Zhang J, Wang T, Xu M. Deep-Precognitive Diagnosis: Preventing Future Pandemics by Novel Disease Detection with Biologically-Inspired Conv-Fuzzy Network. IEEE Access. 2022.

Zhu X, Chen J, Zeng X, Liang J, Li C, Liu S, Behpour S, Xu M. Weakly Supervised 3D Semantic Segmentation Using Cross-Image Consensus and Inter-Voxel Affinity Relations. Proc IEEE Int Conf Comput Vis. 2022.

Zeng X, Howe G, Xu M. End-to-end robust joint unsupervised image alignment and clustering. Proc IEEE Int Conf Comput Vis. 2022.

Wu X, Li C, Zeng X, Wei H, Deng HW, Zhang J, Xu M. CryoETGAN: Cryo-Electron Tomography Image Synthesis via Unpaired Image Translation. Frontiers in Physiology. 2022.

Zeng X, Lin Z, Uddin MR, Zhou B, Cheng C, Zhang J, Freyberg Z, Xu M. Structure Detection in Three-Dimensional Cellular Cryoelectron Tomograms by Reconstructing Two-Dimensional Annotated Tilt Series. J Comput Biol. 2022.

Gupta T, He X, Uddin MR, Zeng X, Zhou A, Zhang J, Freyberg Z, Xu M. Self-supervised learning for macromolecular structure classification based on cryo-electron tomograms. Front Physiol. 2022.

Uddin MR, Howe G, Zeng X, Xu M. Harmony: A Generic Unsupervised Approach for Disentangling Semantic Content from Parameterized Transformations. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2022.

Wang T, Li B, Zhang J, Zeng X, Uddin MR, Wu W, Xu M. Deep Active Learning for Cryo-Electron Tomography Classification. Proc Int Conf Image Proc. 2022.

Hyun-Sook Kim H, Rafid Uddin M, Xu M, Chang YW. Computational methods toward unbiased pattern mining and structure determination in cryo-electron tomography data. J Mol Biol. 2023.

Zeng X, Kahng A, Xue L, Mahamid J, Chang YW, Xu M. High-throughput cryo-ET structural pattern mining by unsupervised deep iterative subtomogram clustering. Proc Natl Acad Sci U S A. 2023.