Tumor stiffness has been associated with malignancy and increased risk for metastasis. Extensive research has been done investigating breast cancer cell lines’ responsiveness to surfaces of varying rigidities as well as examining the biophysical properties of breast cancer tumor samples. However, there is a critical gap regarding the relationship between cells’ mechanosensitivity in conjunction to biophysical properties of their extracellular matrix environment. To explore this relationship, we will analyze single-cell mechanosensitivity in comparison to tumor rigidity via shearwave ultrasound elastogrophy (SWE). Given the putative affiliation, we hypothesize that cells expressing invasive mechanosensitivity profiles will correlate with stiffer tumor regions. Using collagen gels containing different cell types, we derived biopsy-sized samples allowing us to optimize single-cell mechanosensitivity analysis. Cells were stained using different dyes corresponding to invasiveness. Subsequently, we analyzed their morphology. Morphological identification within organoid environments would allow for single-cell analysis without the aggression of tissue digestion, though preliminary results suggest high heterogeneity may not allow for confident cell identification solely on morphology. Thus, inquisition into cell viability and integrity was explored by analyzing the effects of tissue digestion with HyQtase on single-cells. Cell count and live-dead stain via flow cytometry allowed for analysis of single-cell viability. Lastly, cell integritymore »
Phenotyping Immune Cells in Tumor and Healthy Tissue Using Flow Cytometry Data
We present an automated pipeline capable of distinguishing the phenotypes of myeloid-derived suppressor cells (MDSC) in healthy and tumor-bearing tissues in mice using flow cytometry data. In contrast to earlier work where samples are analyzed individually, we analyze all samples from each tissue collectively using a representative template for it. We demonstrate with 43 flow cytometry samples collected from three tissues, naive bone-marrow, spleens of tumor-bearing mice, and intra-peritoneal tumor, that a set of templates serves as a better classifier than popular machine learning approaches including support vector machines and neural networks. Our "interpretable machine learning" approach goes beyond classification and identifies distinctive phenotypes associated with each tissue, information that is clinically useful. Hence the pipeline presented here leads to better understanding of the maturation and differentiation of MDSCs using high-throughput data.
- Award ID(s):
- 1637534
- Publication Date:
- NSF-PAR ID:
- 10109989
- Journal Name:
- BCB '18: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
- Sponsoring Org:
- National Science Foundation
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