Research

Short pulses (ms to ms) of infrared light (1400-2100 nm) can excite action potentials in neurons; however, the mechanism driving this phenomenon is unknown. We are currently testing the hypothesis that infrared light alters the metabolic or redox balance of a cell. In support of our hypothesis, we have developed a model of infrared light and thermal dynamics on cell metabolism and enzyme binding properties. We are also using fluorescence lifetime imaging to reveal insights into infrared light effects enzyme binding dynamics and fast, wide-field fluorescence imaging to evaluate infrared light effects on cellular metabolism.

Recent Publications:

• Fluorescence intensity and lifetime redox ratios detect metabolic perturbations in T cells. Hu L, Wang N, Cardona E, and Walsh AJ, Biomedical Optics Express, 2020 (11)10: 5674-5688.
• Classification of T-cell activation via autofluorescence lifetime imaging. Walsh AJ*, Mueller KP, Tweed K, Jones I, Walsh CM, Piscopo NJ, Niemi NM, Pagliarini DJ, Saha K, Skala MC*, Nature Biomedical Engineering, 2020.

Currently, the activation of immune cells is assessed by label-based methods including flow cytometry and immunohistochemistry.  Activated immune cells require high rates of glycolysis to maintain immune activities.  Therefore, we are using fluorescence lifetime imaging of NAD(P)H and FAD which reports cellular metabolic information, to evaluate immune cell function.  We have shown that the combination of machine learning classification of fluorescence imaging features achieves high accuracy (~98%) for classification of activation of T cells.  Label-free imaging assays of immune cell function will allow unprecedented study of immune cell behaviors, functional assessment of immune cells in small volume samples (e.g. neonatal blood draws), and quality control/quality assurance of biomanufactured immune cells.

Cell-based therapies harness functional cells or tissues to mediate healing and treat disease. These cells or cell products must be cultured and manufactured in a manner that maintains sterility and cell viability and function. Currently, methods that can assess the viability and function of cells are destructive and/or reply on contrast agents that are not compatible for the use of the cells as a therapy. Optical imaging of endogenous collagen, by second-harmonic generation, and the metabolic coenzymes NADH and FAD, by autofluorescence microscopy, provides tissue structure and cellular information. We are working on optimizing label-free imaging systems and image analysis for continuous, in-line or on-line monitoring of biomanufactured cells.

Tumor drug resistance due to heterogeneous cell populations remains a significant challenge for cancer treatment.  Optical imaging of the fluorescence lifetimes of NADH and FAD, coenzymes of metabolism, can be used to track anti-cancer drug response in a label-free, non-contact manner. This imaging technique, “optical metabolic imaging,” can be performed on cancer cells and tumors to evaluate novel anti-cancer drugs or investigate mechanisms of resistance. Furthermore, multiphoton microscopy provides depth imaging of 3D samples such as patient derived primary tumor organoids. Optical metabolic imaging of organoids provides a predictive screen for individualized patient cancer treatment.

Optical metabolic microscopy can be combined with image segmentation for quantification of intra-population heterogeneity. Our work measuring and modeling cellular population dynamics reveals inherent and drug induced heterogeneity within cancer cells, providing a unique identification method of drug resistant cells. 

Recent Papers:

• POSEA: A novel algorithm to evaluate the performance of multi-object instance image segmentation, Nianchao Wang, Linghao Hu, Alex J Walsh, PlosOne 18(3): e0283692.=, 2023.
• Evolution of cisplatin resistance through coordinated metabolic reprogramming of the cellular reductive state, Yu, W., Y. Chen, N. Putluri, A. Osman, C. Coarfa, V. Putluri, A. H. M. Kamal, J. K. Asmussen, P. Katsonis, J. N. Myers, S. Y. Lai, W. Lu, C. C. Stephan, R. T. Powell, F. M. Johnson, H. D. Skinner, J. Kazi, K. M. Ahmed, L. Hu, A. Threet, M. D. Meyer, J. A. Bankson, T. Wang, J. Davis, K. R. Parker, M. A. Harris, M. L. Baek, G. V. Echeverria, X. Qi, J. Wang, A. I. Frederick, A. J. Walsh, O. Lichtarge, M. J. Frederick and V. C. Sandulache, British Journal of Cancer, 2023.
• Identification of rare cell populations in autofluorescence lifetime image data. Elizabeth Cardona, Alex J. Walsh, Cytometry Part A, 2022.
• Autofluorescence Imaging to Evaluate Cellular Metabolism. Anna Theodossiou, Linghao Hu, Nianchao Wang, Uyen Nguyen, Alex J. Walsh, J. Vis. Exp. (177), e63282.