publications

2022

1. IEEE

   Automated Cell Phenotyping for Imaging Mass Cytometry

   Sindhura Thirumal, Amoon Jamzad, Tiziana Cotechini, and 2 more authors

   In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2022

   Abs Bib LINK

   Imaging mass cytometry (IMC) is a new advancement in tissue imaging that is quickly gaining wider usage since its recent launch. It improves upon current tissue imaging methods by allowing for a significantly higher number of proteins to be imaged at once on a single tissue slide. For most analyses of IMC data, determining the phenotype of each cell is a crucial step. Current methods of phenotyping require sufficient biological knowledge regarding the protein expression profile of the various cell types. Here, we develop a deep convolutional autoencoder-classifier to automate the cell phenotyping process into four basic cell types. Biopsy tissue from bladder cancer patients is used to evaluate the efficacy of the classification. The model is evaluated and validated through feature importance, confirming that the significant features are biologically relevant. Our results demonstrate the potential of deep learning to automate the task of cell phenotyping for high-dimensional IMC data.

   @inproceedings{9871071,
     author = {Thirumal, Sindhura and Jamzad, Amoon and Cotechini, Tiziana and Siemens, D. Robert and Mousavi, Parvin},
     booktitle = {2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)},
     title = {Automated Cell Phenotyping for Imaging Mass Cytometry},
     year = {2022},
     volume = {},
     number = {},
     pages = {426-429},
     doi = {10.1109/EMBC48229.2022.9871071},
   }

2. Cytometry A

   TITAN: An end-to-end data analysis environment for the Hyperion™ imaging system

   Sindhura Thirumal, Amoon Jamzad, Tiziana Cotechini, and 4 more authors

   Cytometry Part A 2022

   Abs Bib LINK

   Imaging Mass Cytometry (IMC) is a powerful high-throughput technique enabling resolution of up to 37 markers in a single fixed tissue section while also preserving in situ spatial relationships. Currently, IMC processing and analysis necessitates the use of multiple different software, labour-intensive pipeline development, different operating systems and knowledge of bioinformatics, all of which are a barrier to many potential users. Here we present TITAN – an open-source, single environment, end-to-end pipeline that can be utilized for image visualization, segmentation, analysis and export of IMC data. TITAN is implemented as an extension within the publicly available 3D Slicer software. We demonstrate the utility, application, reliability and comparability of TITAN using publicly available IMC data from recently-published breast cancer and COVID-19 lung injury studies. Compared with current IMC analysis methods, TITAN provides a user-friendly, efficient single environment to accurately visualize, segment, and analyze IMC data for all users.

   @article{thirumal2022titan,
     title = {TITAN: An end-to-end data analysis environment for the Hyperion™ imaging system},
     author = {Thirumal, Sindhura and Jamzad, Amoon and Cotechini, Tiziana and Hindmarch, Charles T and Graham, Charles H and Siemens, D Robert and Mousavi, Parvin},
     journal = {Cytometry Part A},
     volume = {101},
     number = {5},
     pages = {423--433},
     year = {2022},
     publisher = {Wiley Online Library},
   }

2021

1. IEEE

   Utility of high-throughput imaging mass cytometry for cancer research: A feasibility study

   Sindhura Thirumal, Amoon Jamzad, Tiziana Cotechini, and 8 more authors

   In 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) 2021

   Abs Bib LINK

   The Hyperion Imaging System is a novel technology that uses imaging mass cytometry (IMC) to improve upon current methods of tissue imaging, enabling sub-cellular spatial resolution and acquisition of up to 37 proteins on a single tissue slide. The technology is fairly new, and thus we want to explore the types of analysis possible with these data. Here, we introduce an analysis pipeline to utilize machine learning-based analysis for IMC data using data from a muscle invasive bladder cancer patient cohort. We also propose a novel augmentation method to handle the challenge of low number of tissue samples from IMC studies. Our augmentation method was validated and shown to perform better than when only using the original data. Both our pipeline and augmentation method show promise for applications in future research studies and clinical evaluation of this technology. Our results indicate the feasibility of using the proposed framework with a more robust data set to identify prognostic features, which is an important foundation for further clinical research.

   @inproceedings{thirumal2021utility,
     title = {Utility of high-throughput imaging mass cytometry for cancer research: A feasibility study},
     author = {Thirumal, Sindhura and Jamzad, Amoon and Cotechini, Tiziana and Hindmarch, Charles T and Hardy, C{\'e}line and Kim, Nathalia and Simpson, Amber and Graham, Charles H and Berman, David M and Siemens, D Robert and others},
     booktitle = {2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)},
     pages = {1--4},
     year = {2021},
     organization = {IEEE},
   }

2. J Urol

   PD01-03 CHARACTERIZING THE IMMUNE AND INFLAMMATORY MICROENVIRONMENTS OF HUNNER LESIONS IN INTERSTITIAL CYSTITIS/BLADDER PAIN SYNDROME WITH IMAGING MASS CYTOMETRY: A NEW LOOK AT AN OLD DISEASE

   Tiziana Cotechini, Sindhura Thirumal, Charlie Hindmarch, and 6 more authors

   The Journal of Urology 2021

   Bib LINK

   @article{cotechini2021pd01,
     title = {PD01-03 CHARACTERIZING THE IMMUNE AND INFLAMMATORY MICROENVIRONMENTS OF HUNNER LESIONS IN INTERSTITIAL CYSTITIS/BLADDER PAIN SYNDROME WITH IMAGING MASS CYTOMETRY: A NEW LOOK AT AN OLD DISEASE},
     author = {Cotechini, Tiziana and Thirumal, Sindhura and Hindmarch, Charlie and Mousavi, Parvin and Berman, David and Siemens, D Robert and Nickel, J Curtis and Graham, Charles and Doiron, R Christopher},
     journal = {The Journal of Urology},
     volume = {206},
     number = {Supplement 3},
     pages = {e31--e31},
     year = {2021},
     publisher = {Wolters Kluwer Philadelphia, PA},
   }