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

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. In parallel, we have developed photothermal optical coherence tomography (PT-OCT) to monitor changes in absorber concentrations in the eye and in tumors. Ongoing projects require active collaborations and mentorship of trainees from diverse backgrounds in medicine, engineering, biochemistry, and biology. Please see publications for a comprehensive view of projects and applications of these technologies.

  • Optical methods to monitor cell function, metabolism, and cellular diversity
    We have developed label-free optical imaging techniques to monitor changes in cellular metabolism with cancer treatment, stem cell differentiation, and immune cell function. Single cell segmentation and population density modeling are used to quantify metabolic diversity within intact samples, and this approach was validated in breast cancer cell lines (Fig. 2A-B). The same approach is under investigation to monitor immune cell function in blood samples (Fig. 2C). Flow cytometry based on NAD(P)H and FAD intensities was optimized for these low-yield autofluorescent molecules, and we successfully performed autofluorescence flow sorting of metabolically distinct breast cancer cell lines. Additional 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. We have also shown that the fluorescence intensities and lifetimes of NAD(P)H and FAD can discriminate metabolic changes due to distinct phases of the cell cycle on a single cell level, further characterizing these optical signals. Future technology development is focused on label-free quality control techniques for cell manufacturing including CAR T-cell therapies and stem cell therapies.

    Figure 2
    Fig. 2: OMI quantifies heterogeneous cell populations. Two breast cancer cell lines (SKBr3 and MDA-MB-231) are plated at varying known proportions and imaged with OMI. (A) Histograms of NAD(P)H mean lifetime, quantified per cell, are normalized to have a total area of 1 (total population >200 cells). Data (bars) are fit to the best mixed-model Gaussian distribution (blue lines). The red dashed lines represent the two component contributions. Errors in the mean (x̄) and proportion (p) for each population are shown as percentages. (B) Images with SKBr3 cells color-coded red and MDA-MB-231 cells color-coded blue, based on the distribution density model. Adapted from Walsh, Skala. Biomed Opt Express. (2015) (C) Representative NAD(P)H fluorescence lifetime images of immune cells isolated from human blood donors reveal metabolic differences between cells with different functions.

    Representative Publications

    • Walsh AJ, Skala MC. “Optical metabolic imaging quantifies heterogeneous cell populations.” Biomed Opt Express. (2015) Jan 15;6(2):559-73. PMID: 25780745
    • Shah AT, Cannon TM, Higginbotham JN, Coffey RJ, Skala MC. “Autofluorescence flow sorting of breast cancer cell metabolism.” J Biophotonics. (2017) Aug;10(8):1026-1033. PMID: 27730745
    • 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) Apr 3;8(1):5456. PMID: 29615678
    • Heaster TM, Walsh AJ, Zhao Y, Hiebert SW, Skala MC. “Autofluorescence imaging identifies tumor cell-cycle status on a single-cell level.” J Biophotonics. (2018) Jan;11(1). PMID: 28485124
  • Optical metabolic imaging of organoids for drug development and clinical treatment planning
    Optical metabolic imaging 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 optical metabolic imaging 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
    Fig. 3: OMI monitors cell metabolism in patient-derived tumor organoids. Representative images of mean NAD(P)H fluorescence lifetimes in organoids derived from oral, breast, pancreatic, neuroendocrine, and colon cancer, with (far right) a retinal photoreceptor organoid derived from human induced pluripotent stem cells (radius = 500 µm).

    Representative Publications

    • AJ Walsh, RS Cook, ME Sanders, L Aurisicchio, G Ciliberto, CL Arteaga, MC Skala. “Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer.” Cancer Research (2014) Sep 15; 74 (18): 5184–5194. PMID: 25100563
    • AJ Walsh, JA Castellanos, NS Nagathihalli, NB Merchant, MC Skala. “Optical imaging of drug-induced metabolism changes in murine and human pancreatic cancer organoids reveals heterogeneous drug response.” Pancreas (2016) Jul; 45 (6): 863–9. PMID: 26495796
    • Shah AT, Heaster TM, Skala MC. “Metabolic Imaging of Head and Neck Cancer Organoids.” PLoS One (2017) Jan 18;12(1):e0170415. PMID: 28099487
    • 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) Sep 1;25(17):5376-5387. PMID: 31175091
    • Capowski EE, Samimi K, Mayerl SJ, Phillips MJ, Pinilla I, Howden SE, Saha J, Jansen AD, Edwards KL, Jager LD, Barlow K, Valiauga R, Erlichman Z, Hagstrom A, Sinha D, Sluch VM, Chamling X, Zack DJ, Skala MC, Gamm DM. Reproducibility and staging of 3D human retinal organoids across multiple pluripotent stem cell lines. Development. 2019 Jan 9;146(1). PMID: 30567931
  • 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.

    Mouse models have provided critical in vivo context to validate optical metabolic imaging of cell metabolism. Optical metabolic imaging in vivo provides early (1-3 days post-treatment) measures of drug response compared to standard tumor volume in animal models of cancer. Metabolic diversity within tumors in vivo was defined by a heterogeneity index, derived from similar measures in ecology and information theory. This heterogeneity index also predicts tumor treatment response in vivo early in the course of treatment. Later studies correlated in vivo tumor cell diversity measured with optical metabolic imaging compared to standard immunohistochemical stains. We also experimentally confirmed that metabolic diversity in tumor organoids and matched in vivo tumors is similar under both control and treatment conditions. Later studies defined new statistical frameworks to quantify spatial metabolic diversity in vivo and in organoids. We have now generated mouse models that express fluorescent reporters in macrophages or T cells so that in vivo immune cell metabolism can be monitored in response to cancer immunotherapy. These studies could guide the development of more effective cancer immunotherapies.

    Representative Publications

    • AJ Walsh, RS Cook, HC Manning, DJ Hicks, A Lafontant, CL Arteaga, MC Skala. “Optical metabolic imaging identifies breast cancer glycolytic levels, sub-types, and early treatment response.” Cancer Research (2013) Oct 15; 73 (20): 6164–6174. PMID: 24130112
    • Shah AT, Diggins KE, Walsh AJ, Irish JM, Skala MC. “In Vivo Autofluorescence Imaging of Tumor Heterogeneity in Response to Treatment.” Neoplasia (2015) Dec; 17(12):862-870. PMID: 26696368
    • 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) June; 21:6, , pg. 615-626. PMID: 31078067
    • Heaster TM, Landman BA, Skala MC. “Quantitative spatial analysis of metabolic heterogeneity across in vivo and in vitro tumor models.” Frontiers in Oncology (2019) 9, 1144. PMID: 31737571
  • Development of 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
    Fig. 5: PT-OCT of melanin in mosaic zebrafish. (A) En face OCT image of the retinal pigment epithelium showing areas of pigment (white) and non-pigment (dark). (B) OCT (gray) and PT-OCT (green, overlaid) B-scans of the retina. The location of the B-scans is indicated by the lines in (A). Orange lines denote pigmented areas and black lines denote non-pigmented areas. Red circles indicates presence of blood vessels. Adapted from Lapierre-Landry et al., Transl Vis Sci Technol (2018)

    Representative Publications

    • 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) May 1;5(6):1731-43. PMID: 24940536
    • M Lapierre–Landry, AY Gordon, JS Penn, MC Skala. “In vivo photothermal optical coherence tomography of endogenous and exogenous contrast agents in the eye.” Scientific Reports (2017), Aug 23; 7 (1): 9228. PMID: 28835698
    • M Lapierre-Landry, TB Connor, J Carroll, YK Tao, MC Skala. “Photothermal optical coherence tomography of indocyanine green in ex vivo eyes.” Opt Lett. (2018) Jun 1;43(11):2470-2473. PMID: 29856406
    • M Lapierre-Landry, AL Huckenpahler, BA Link, RF Collery, J Carroll*, MC Skala. “Imaging Melanin Distribution in the Zebrafish Retina Using Photothermal Optical Coherence Tomography.” Transl Vis Sci Technol. (2018) Sep 4;7(5):4. PMID: 30197836
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