Fig. 1: NADH, NADPH, and FAD are metabolic co-enzymes involved in hundreds of reactions in cells. The fluorescence of NADH and NADPH overlap and are collectively denoted as NAD(P)H. The fluorescence intensity and lifetimes of NAD(P)H and FAD provide insight into the redox ratio and enzyme binding activity across hundreds of reactions per cell, which reflect functional changes in cancer, immune cell behavior, and stem cell trajectory.

We develop label-free optical imaging technologies and quantitative analysis tools to study metabolic heterogeneity in cancer, stem cell function, and immune cell behavior. Optical metabolic imaging (OMI) uses two-photon fluorescence lifetime microscopy of metabolic co-enzymes NADH and FAD (Fig. 1) to quantify cell redox state and enzyme-binding activity. This approach is advantageous because fluorophores that are already present in the cells can be used to monitor metabolism with single-cell resolution. We have developed OMI alongside image analysis tools and population density models to quantify cellular heterogeneity within intact 3D samples. Ongoing projects require active collaborations and mentorship of trainees from diverse backgrounds in medicine, engineering, biochemistry, and biology.

  • Optical methods to monitor cell function, metabolism, and cellular diversity
    We have developed label-free optical imaging techniques to monitor cellular metabolism for cancer treatment, stem cell differentiation, and immune cell function. We use single cell segmentation and population density modeling to quantify metabolic diversity within intact samples, which we validated in breast cancer cell lines (Fig. 2A-B). Recently, we discovered that NAD(P)H lifetimes can robustly classify primary human T cells by activation state and function, which enables label-free monitoring of T cell function within intact samples. We are investigating the same approach to monitor immune cell function across numerous cell types in blood samples (Fig. 2C). We have similarly found that OMI can predict stem cell differentiation efficiencies early in production. Additional studies used OMI to characterize macrophage metabolism during migration within the tumor microenvironment using non-invasive 3D imaging and novel microdevices with primary human tumor samples. Prior studies characterized NAD(P)H fluorescence lifetimes across a series of metabolic inhibitors to show that NAD(P)H lifetime imaging can distinguish metabolic shunts that do not alter NAD(P)H fluorescence intensities.

    Figure 2

    Representative Publications

    • Walsh AJ*, Mueller KP, Tweed K, Jones I, Walsh CM, Piscopo NJ, Niemi NM, Pagliarini DJ, Saha K, Skala MC*. Classification of T-cell activation via autofluorescence lifetime imaging. Nat Biomed Eng. 2021;5(1):77-88. PMCID: PMC7854821.
    • Qian T*, Heaster TM, Houghtaling AR, Sun K, Samimi K, Skala MC*. Label-free imaging for quality control of cardiomyocyte differentiation. Nat Commun. 2021 Jul 28;12(1):4580. PMCID: PMC8319125
    • Heaster TM, Humayun M, Yu J, Beebe DJ, Skala MC*. Autofluorescence imaging of 3D tumor-macrophage microscale cultures resolves spatial and temporal dynamics of macrophage metabolism. Cancer Res. 2020;80(23):5408-23. PMCID: PMC7718391.
    • Sharick JT, Favreau PF, Gillette AA, Sdao SM, Merrins MJ, Skala MC*. Protein-bound NAD(P)H Lifetime is Sensitive to Multiple Fates of Glucose Carbon. Sci Rep. 2018;8(1):5456. PMCID: PMC5883019.
  • Optical metabolic imaging of organoids for drug development and clinical treatment planning
    OMI is a novel method to monitor cell metabolism within intact organoids so that changes in cell-level metabolic heterogeneity can be monitored over time. Primary tumor organoids are advantageous compared to traditional 2D culture because organoids retain multiple cell types from the original tumor in a 3D environment that maintains the cell-cell communication, genetic expression, and drug response of the original tumor. Primary tumor organoids also provide improved throughput compared to in vivo mouse models. We have shown that OMI accurately predicts drug response in organoids with respect to standard in vivo tumor volume. We further defined metrics of metabolic heterogeneity in tumor organoids that predict drug response across breast, pancreatic, oral, neuroendocrine, and colon cancers (Fig. 3). Current efforts are focused on patient-matched drug screens and developing new drugs that target metabolic heterogeneity in cancer. These methods are also used to monitor, characterize, and provide quality control of organoids derived from stem cells (Fig. 3).

    Figure 3

    Representative Publications

    • Datta R, Sivanand S, Lau AN*, Florek LV, Barbeau AM, Wyckoff J, Skala MC*, Vander Heiden MG*. Interactions with stromal cells promote a more oxidized cancer cell redox state in pancreatic tumors. Sci Adv. 2022 Jan 21;8(3):eabg6383. PMCID: PMC8782446.
    • Gil DA, Deming D, Skala MC*. Patient-derived cancer organoid tracking with wide-field one-photon redox imaging to assess treatment response. J Biomed Opt. 2021 Mar;26(3):036005. PMCID: PMC7983069.
    • Sharick JT, Walsh CM, Sprackling CM, Pasch CA, Pham DL, Esbona K, Choudhary A, Garcia-Valera R, Burkard ME, McGregor SM, Matkowskyj KA, Parikh AA, Meszoely IM, Kelley MC, Tsai S, Deming DA, Skala MC*. Metabolic Heterogeneity in Patient Tumor-Derived Organoids by Primary Site and Drug Treatment. Front Oncol. 2020;10:553. PMCID: PMC7242740.
    • Pasch CA, Favreau PF, Yueh AE, Babiarz CP, Gillette AA, Sharick JT, Karim MR, Nickel KP, DeZeeuw AK, Sprackling CM, Emmerich PB, DeStefanis RA, Pitera RT, Payne SN, Korkos DP, Clipson L, Walsh CM, Miller D, Carchman EH, Burkard ME, Lemmon KK, Matkowskyj KA, Newton MA, Ong IM, Bassetti MF, Kimple RJ, Skala MC, Deming DA*. “Patient-Derived Cancer Organoid Cultures to Predict Sensitivity to Chemotherapy and Radiation.” Clin Cancer Res. 2019;25(17):5376-87. PMCID: PMC6726566.
  • In vivo optical metabolic imaging of drug response
    Figure 4

    Fig. 4: OMI monitors cell metabolism in vivo. NAD(P)H mean fluorescence lifetime image of a mouse mammary tumor.

    Animal models have provided critical in vivo context to validate OMI of cell metabolism. Our studies found that metabolic diversity of tumor cells is predictive of tumor drug response, and that metabolic differences between tumor cells drive most of this metabolic diversity. We also found that metabolic heterogeneity was similar in organoids in vitro and mouse tumors in vivo under control and treatment conditions. Recent studies used novel mouse and zebrafish models to investigate macrophage metabolism in vivo with single cell resolution, and identify OMI features of pro- and anti-inflammatory macrophages.

    Representative Publications

    • Miskolci V, Tweed KE, Lasarev MR, Britt EC, Walsh AJ, Zimmerman LJ, McDougal CE, Cronan MR, Fan J, Sauer JD, Skala MC*, Huttenlocher A*. In vivo fluorescence lifetime imaging of macrophage intracellular metabolism during wound responses in zebrafish. Elife. 2022 Feb 24;11:e66080. PMCID: PMC8871371
    • Heaster TM, Heaton AR, Sondel PM, Skala MC*. Intravital Metabolic Autofluorescence Imaging Captures Macrophage Heterogeneity Across Normal and Cancerous Tissue. Front Bioeng Biotechnol. 2021 Apr 20;9:644648. PMCID: PMC8093439
    • Sharick JT, Jeffery JJ, Karim MR, Walsh CM, Esbona K, Cook RS, Skala MC*. Cellular metabolic heterogeneity in vivo is recapitulated in tumor organoids. Neoplasia. 2019;21(6):615-26. PMCID: PMC6514366.
    • Heaster TM, Landman BA, Skala MC*. Quantitative Spatial Analysis of Metabolic Heterogeneity Across in vivo and in vitro Tumor Models. Front Oncol. 2019 Nov 1;9:1144. PMCID: PMC6839277
  • Photothermal optical coherence tomography (PT-OCT) for molecular imaging.
    Optical coherence tomography is a powerful 3D imaging tool that is routinely used in clinical ophthalmology. However, it achieves poor molecular specificity. We developed a new technique, photothermal optical coherence tomography (PT-OCT), which enables molecular contrast through microscopic thermoelastic expansions in vivo. Photothermal optical coherence tomography provides molecular contrast in a spatial regime between microscopy (poor penetration depth) and ultrasound (poor resolution). Our work focuses on technology development and in vivo applications in ophthalmology and cancer, using endogenous contrast such as melanin (Fig. 5) and exogenous contrast agents such as indocyanine green.

    Figure 5

    Representative Publications

    • Lapierre-Landry M, Huckenpahler AL, Link BA, Collery RF, Carroll J, Skala MC. Imaging Melanin Distribution in the Zebrafish Retina Using Photothermal Optical Coherence Tomography. Transl Vis Sci Technol. 2018;7(5):4. PMCID: PMC6126953.
    • Lapierre-Landry M, Connor TB, Carroll J, Tao YK, Skala MC. Photothermal optical coherence tomography of indocyanine green in ex vivo eyes. Opt Lett. 2018;43(11):2470-3. PMCID: PMC8148624.
    • Lapierre–Landry M, Gordon AY, Penn JS, Skala MC. In vivo photothermal optical coherence tomography of endogenous and exogenous contrast agents in the eye. Sci Rep. 2017;7(1):9228. PMCID: PMC5569082.
    • Tucker-Schwartz JM, Beavers KR, Sit WW, Shah AT, Duvall CL, Skala MC. In vivo imaging of nanoparticle delivery and tumor microvasculature with multimodal optical coherence tomography. Biomed Opt Express. 2014;5(6):1731-43. PMCID: PMC4052907.
  • Collaborators