Public Data Commons

This project is centered on training cytometry systems to enable label-free inline recognition of pancreatic and ovarian cancer cells derived from xenografts and model lines, based on their distinct electrophysiology characteristics. The goal is to quantify alterations in these cells due to their sensitivity to common chemotherapies and their phenotypic characteristics versus stromal cells in complex biopsy-like samples.

For this purpose, data from single-cell impedance cytometry on pancreatic ductal adenocarcinoma (PDAC) cells, conducted at several superimposed frequencies, will be analyzed by comparison to label-based fluorescence cytometry. This analysis aims to develop metrics for label-free phenotypic recognition during cytometry. Current tumor cell recognition approaches require prior knowledge of the cell death mechanism to develop fluorescent staining protocols, which often exhibit overlapping signals from apoptosis, necrosis, or autophagy.

To achieve label-free cell phenotype recognition at high throughput (~300-500 cells/s), impedance-based metrics will be developed and compared to flow cytometry within the following experiments:

1. Distinction of the electrophysiology phenotypes of apoptotic, necrotic, and viable cells in gemcitabine-treated tumor cells with varying degrees of drug resistance.
2. Analysis of apoptotic bodies to assess the onset of drug-induced apoptosis.
3. Distinction of pancreatic cancer cells versus associated fibroblast cells.
4. Distinction of ovarian cancer-induced metabolism alterations.

These recognition capabilities will eventually enable label-free quantification of tumor cell phenotypes in response to in vivo drug treatments. This will allow the screening of patient-derived tumors for drug sensitivity with single-cell sensitivity, using samples derived from less invasive fine needle biopsies.

1. “Apoptotic bodies in the pancreatic tumor cell culture media enable label-free drug sensitivity assessment by impedance cytometry”, Carlos Honrado+, Sara Adair, John H. Moore, Armita Salahi, Todd Bauer, Nathan S. Swami, Advanced Biology (2021), 2100438. (PMID: 34015194 PMCID: PMC8349864 DOI: 10.1002/adbi.202100438)
2. “Self-aligned sequential lateral field non-uniformities over channel depth for high throughput dielectrophoretic cell deflection”, XuHai Huang, Karina Torres-Castro, Walter Varhue+, Armita Salahi, Ahmed Rasin+, Carlos Honrado+, Audrey Brown, Jennifer Guler, and Nathan S. Swami*, Lab on a Chip (2021), 21 (5), 835-843. PMID: 33532812. PMCID: PMC8019514
3. “Label-Free Quantification of Cell Cycle Synchronicity of Human Neural Progenitor Cells Based on Electrophysiology Phenotypes”, C. Honrado+, N. Michel, J. H. Moore, A. Salahi, V. Porterfield, M. J. McConnell, N. S. Swami*; ACS Sensors (2021) 6, 1, 156–165.PMID: 33325234.
4. “Progression-mediated changes in mitochondrial morphology promotes adaptation to hypoxic peritoneal conditions in serous ovarian cancer”, J. P. Grieco, M. E. Allen, J. B. Perry, Y. Wang, Y. Song, A. Rohani, S. L. E. Compton, J. W Smyth, N. S. Swami, D. A. Brown, E. M. Schmelz, Frontiers in Oncology (2020) 10, 3027.PMID: 33520711. PMCID: PMC7838066
5. “Self-aligned microfluidic contactless dielectrophoresis device fabricated by single-layer imprinting on cyclic olefin copolymer”, A Salahi, W.B. Varhue+, V. Farmehini, A.R. Hyler, E.M. Schmelz, R.V. Davalos, N.S. Swami*; Analytical and bioanalytical chemistry (2020) 412 (16), 3881-3889. PMID: 32372273. PMCID: PMC7325389
6. Salahi A, Honrado C, Moore J, Adair S, Bauer TW, Swami NS. Supervised learning on impedance cytometry data for label-free biophysical distinction of pancreatic cancer cells versus their associated fibroblasts under gemcitabine treatment. Biosens Bioelectron. 2023 Jul 1;231:115262. doi: 10.1016/j.bios.2023.115262. Epub 2023 Mar 30. PMID: 37058962; PMCID: PMC10134450.
7. “Dielectrophoretic enrichment of live chemo-resistant circulating-like pancreatic cancer cells from the media of drug-treated adherent solid tumor cultures”, Aditya Rane, Javad Jarmoshti, Abdullah-Bin Siddique, Sara Adair, Karina Torres-Castro, Carlos Honrado, Todd W. Bauer, Nathan S. Swami (under review).