About

Melissa Skala is a Professor in the Department of Biomedical Engineering at the University of Wisconsin-Madison and holds the Carol Skornicka Chair in Biomedical Imaging. Her lab develops biomedical optical imaging technologies for cancer research, cell therapy, and immunology. Her current projects focus on tumor immunology and immunotherapy, cell-level metabolic heterogeneity, and cell-cell interactions. She leverages photonics-based tools for clinical problems such as quality control in T cell and stem cell therapies, designing personalized cancer treatments, monitoring diseases in the eye, and discovering new therapies for various diseases. Her research spans translational studies, hypothesis-driven questions, and algorithm or instrumentation development.

Research topics

• Computer Science
• Biology
• Computational biology
• Physics
• Optics
• Materials science
• Nanotechnology
• Ecology
• Genetics
• Cell biology
• Molecular biology
• Data science
• Immunology
• Chemistry
• Cancer research

Selected publications

• Atovaquone-induced therapeutic rewiring of melanoma metabolism

  Journal of Investigative Dermatology · 2026-03-01

  articleOpen access
  PublisherOA PDFDOI

• An automated image analysis pipeline for wide-field optical redox imaging of patient-derived cancer organoids

  Scientific Reports · 2026-02-18 · 1 citations

  articleOpen accessSenior author

  Wide-field optical redox imaging provides a fast and accessible method to monitor metabolic changes in cells and has recently been developed for drug screening in patient-derived cancer organoids (PDCOs). However, manual analysis of wide-field optical redox images is inefficient and laborious for large-scale drug screens. Here, we developed an automated pipeline for PDCO segmentation, single-PDCO tracking, and background correction in autofluorescence images. This pipeline was tested on two imaging systems over a 3-day time-course with two drug doses to demonstrate generalizability across imaging systems. Segmentation was performed using a fine-tuned Cellpose 3 model, which when compared to manual masks, outperformed Cellpose-SAM (4) and achieved high dice and recall scores across systems, indicating high reproducibility. Automated single-PDCO tracking was compared to manual tracking and the accuracy of the tracking algorithm exceeded 94% by two metrics, recall and Jaccard index. For background correction, the automated pipeline uses the full field-of-view to reduce sampling bias. Compared to the manual analysis pipeline, the automated pipeline resolves single-PDCO responses with comparable sensitivity to drug treatment but with over 127× faster processing time. This novel automated image analysis pipeline improves throughput and robustness in PDCO image analysis, which increases the accessibility and scalability of wide-field optical redox imaging for PDCO drug screening.

  PublisherOA PDFDOI

• Autofluorescence lifetime imaging resolves cell heterogeneity within peripheral blood mononuclear cells

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-08

  articleOpen accessSenior authorCorresponding

  Significance: Standard methods to characterize peripheral blood mononuclear cells (PBMCs) are often destructive, lack metabolic information, or do not provide single-cell resolution. Label-free tools that non-destructively measure single-cell metabolism within PBMCs can provide new layers of information to characterize disease state and cell therapy potential. Aim: Determine whether non-destructive fluorescence lifetime imaging microscopy (FLIM) of endogenous metabolic co-factors NAD(P)H and FAD, or optical metabolic imaging (OMI), can identify immune cell subsets and activation state within heterogeneous PBMC cultures. Approach: OMI measured single-cell metabolism of PBMCs from 3 different human donors in the quiescent or activated (phorbol 12-myristate 13-acetate and ionomycin) state. Fluorescent antibodies were used as ground truth labels for single-cell classifiers of immune cell subtypes. Results: OMI identified quiescent vs. activated PBMCs with 93% accuracy at only 2 hours post-stimulation, identified monocytes within quiescent and activated PBMCs with 96% and 88% accuracy, respectively, and identified NK cells within quiescent and activated PBMCs with 74% accuracy. Conclusion: OMI identifies activation state and immune cell subpopulations within PBMCs, enabling single-cell and label-free measurements of metabolic heterogeneity within complex PBMC samples. Therefore, OMI could enhance PBMC immunophenotyping for diagnostic and therapeutic applications. Statement of Discovery: We demonstrate that autofluorescence lifetime imaging can resolve functional and phenotypic metabolic subpopulations within a mixed culture of immune cells from human blood. This provides a new technique to characterize metabolic activity within immune cells from the peripheral blood of patients, which could improve disease diagnostics and the production of cell therapies.

  PublisherOA PDFDOI

• FLIM playground: an interactive, end-to-end graphical user interface for analyzing single cell fluorescence lifetime imaging microscopy

  2026-03-04

  articleSenior author

  FLIM Playground is an open source, cross platform, Python based interactive graphical platform that unifies all stages of fluorescence lifetime imaging microscopy (FLIM) workflows. The Data Extraction module organizes image metadata, calibrates instrument response function (IRF) shift, extracts single-cell features including fitted, fit-free and others, merges datasets, and assigns categorical labels to each cell. Next, the Visualization and Classification modules offer diverse analysis methods, including dimension reduction, heterogeneity analysis, and machine learning classifiers. The platform offers interactive widgets to enable code free, real time navigation and exploration of large datasets. By adopting best practices and offering flexibilities through interactivity, FLIM Playground accelerates hypothesis driven discovery and promotes reproducibility.

  PublisherDOI

• Autofluorescence imaging reveals the impact of cryopreservation on T cell metabolism and activation response

  2026-03-04

  article1st authorCorresponding
  PublisherDOI

• Assessing Intracellular Metabolism of Immune Cells In Situ in Live Zebrafish Larvae by Autofluorescence Lifetime Imaging Microscopy of NAD(P)H and FAD

  Methods in molecular biology · 2026-01-01

  book-chapter
  PublisherDOI

• Example and validation datasets for FLIM Playground

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-25

  datasetOpen access
  PublisherDOI

• Label-free optical metabolic imaging of Jurkat T cell response to in vitro tumor microenvironmental conditions

  2026-01-15

  articleSenior author
  PublisherDOI

• Abstract 2617: Rapid assessment of patient derived cancer organoids using label-free imaging and an automated analysis pipeline.

  Cancer Research · 2026-04-03

  articleSenior author

  Abstract Background: Tumor heterogeneity presents a major challenge in effective cancer treatment, particularly in colorectal cancer (CRC), by limiting the efficacy of therapies and driving resistance. Patient-derived cancer organoids (PDCOs) have emerged as powerful preclinical models that faithfully recapitulate the genomic, morphological, and metabolic profiles of primary tumors. However, current methods for rapidly and reproducibly assessing PDCOs are limited. Label-free imaging methods are a promising tool to measure organoid level heterogeneity and rapidly screen drug response in PDCOs. However, manual analysis of wide-field optical redox images is inefficient and laborious for large-scale drug screens. Here, we developed an automated pipeline for PDCO segmentation, single-PDCO tracking, and background correction in autofluorescence images. Methods: Wide field optical redox imaging (WF ORI) provided organoid-level measurements of treatment response without labels or additional reagents by measuring the autofluorescence intensity of the metabolic co-enzymes NAD(P)H and FAD, and the optical redox ratio, defined as the fluorescence intensity of [NAD(P)H/NAD(P)H+FAD], was used to measure the oxidation-reduction state of multiple CRC PDCO lines. Development of leading-edge analysis tools, isolating the ORI measurement to a 32μm region at the outer edge of the PDCOs, helped to maximize the sensitivity and reproducibility of treatment response measurements using WF ORI in CRC PDCOs. The automated pipeline includes segmentation using a fine-tuned Cellpose model, automated single-PDCO tracking over time via custom python code, and background correction. Glass’s delta (GΔ) is used to measure the PDCO treatment effect size. Results: Leading-edge analysis improves sensitivity to redox changes in treated PDCOs (GΔ = 1.462 vs GΔ = 1.233). Automated segmentation, when compared to manual masks, achieved mean Dice scores ≥0.8, indicating high reproducibility. Additionally, automated PDCO tracking accuracy exceeded 94% by two metrics, recall and Jaccard index, when compared to manual tracking. Importantly, the automated pipeline resolves single-PDCO responses over time with comparable sensitivity to drug treatment with over 127× faster processing time compared to the manual process. Conclusion: Overall, we demonstrate that combining PDCOs with accessible imaging and analysis techniques enables high-throughput detailed evaluation of tumor heterogeneity and therapeutic response. Citation Format: Amani A. Gillette, Angela Hsu, Shirsa Udgata, Alexa Schmitz, Dustin A. Deming, Melissa Skala. Rapid assessment of patient derived cancer organoids using label-free imaging and an automated analysis pipeline [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2617.

  PublisherDOI

• High-throughput autofluorescence lifetime flow cytometry of immune cells

  2026-03-05

  articleSenior author
  PublisherDOI

Recent grants

• NIH Grant R01CA211082

  NIH · $3.5M · 2023

• CAREER: High-throughput Imaging of Drug Response in Tumor Organoids

  NSF · $509k · 2016–2021

• Functional optical imaging for rapid, label-free predictions of treatment response and clonal evolution in patient-derived cancer organoids

  NIH · $3.1M · 2023–2028

• NIH Grant F32CA130309

  NIH · $110k · 2010

• NIH Grant R01CA185747

  NIH · $1.6M · 2021

Frequent coauthors

• Alex J. Walsh

  164 shared

• David J. Beebe

  University of Wisconsin Carbone Cancer Center

  137 shared

• Dustin A. Deming

  University of Wisconsin Carbone Cancer Center

  129 shared

• José M. Ayuso

  University of Wisconsin–Madison

  120 shared

• Amani A. Gillette

  Morgridge Institute for Research

  119 shared

• Tiffany M. Heaster

  99 shared

• Krishanu Saha

  University of Wisconsin–Madison

  85 shared

• Kristina A. Matkowskyj

  Mayo Clinic in Arizona

  76 shared

Education

• Postdoctoral , Biomedical Engineering

  Duke University

  2010

• PhD, Biomedical Engineering

  Duke University

  2007

• MS, Biomedical Engineering

  University of Wisconsin Madison

  2005

• BS, Physics

  Washington State University

  2002

Awards & honors

• Morgridge Institute for Research, Carol Skornicka Chair in B…
• Retina Research Foundation, Daniel M. Albert Chair (2021)
• Fellow of AIMBE (2019)
• Fellow of Optica (formerly OSA, Optical Society of America)…
• Fellow of SPIE (2019)