Biology subjects

Walsh, A. J.

Publications and source records attributed to Walsh, A. J..

7 recordsLinked to original sources

bioRxiv2025

Fluorescence lifetime imaging microscopy for metabolic analysis of LDHB inhibition in triple negative breast cancer

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with no targeted treatments currently available. TNBC cells participate in metabolic symbiosis, a process that optimizes tumor growth by balancing metabolic processes between glycolysis and oxidative phosphorylation through increased activity by the enzyme lactate dehydrogenase B (LDHB). Metabolic symbiosis allows oxidative cancer cells to function at a similar rate as glycolytic cancer cells, increasing overall metabolic activity and proliferation. Here, fluorescence lifetime imaging microscopy (FLIM) is used to analyze the metabolism of TNBC cells with inhibition of LDHB using a multiphoton microscope to measure the fluorescent lifetimes of two metabolic coenzymes, NAD(P)H and FAD. LDHB is inhibited via an indole derivative known as AXKO-0046 in varying concentrations. Understanding how TNBC cell metabolism changes due to LDHB inhibition will provide further insight into metabolic symbiosis and potential new TNBC treatment options.

bioengineering↗

bioRxiv2025

Timelapse and volumetric imaging of mitochondrial networking using NAD(P)H autofluorescence via 2-photon microscopy

SignificanceMitochondria are dynamic organelles that play a key role in energy production and maintaining cellular homeostasis. The regulation of mitochondrial dynamics, involving both fission and fusion, is vital for maintaining a healthy population of mitochondria within the cell. Alterations in mitochondrial dynamics have been associated with various disease states, such as metabolic and neurodegenerative diseases and cancer. AimWe describe a protocol for imaging and analyzing NAD(P)H intensity to visualize the movement of mitochondria over time and in 3D to visualize the distribution of the mitochondrial network within the cell. ApproachA multiphoton (MP) laser scanning microscope was used to image NAD(P)H autofluorescence signal of MDA-MB-231 cells at 750 nm excitation. A mitochondrial fluorescent dye, MitoSpy Orange, was used to validate the signal. Laser power, image size, dwell time, interval time, and imaging duration were optimized for timelapse and 3D imaging to minimize photodamage and maximize autofluorescence signal. ResultsThe NAD(P)H signal in 2D was imaged with a frame rate of 0.4 frames per second (FPS) allowing for visualization of mitochondria movement. 3D imaging was performed with a frame rate of 0.5 FPS for a single cell with a thickness of approximately 15 microns that allowed for visualization of the mitochondria network within the cell. ConclusionsThis protocol motivates using label-free imaging techniques to study mitochondrial dynamics in a non- destructive manner, suitable for drug screening and understanding the effects of mitochondrial dynamic alterations in disease.

bioengineering↗

bioRxiv2023

Comparison of polystyrene and hydrogel microcarriers for optical imaging of adherent cells

The ability to observe and monitor cell density and morphology has been imperative for assessing the health of a cell culture and for producing high quality, high yield cell cultures for decades. Microcarrier-based cultures, used for large-scale cellular expansion processes, are not compatible with traditional visualization-based methods such as widefield microscopy due to their thickness and material composition. Light-sheet tomography using label-free elastic scattering contrast from planar side illumination can achieve optical sectioning of thick samples, permitting non-invasive and non-destructive, in toto, three-dimensional, high-resolution visualization of cells cultured on microcarriers. Here, we assess the optical imaging capabilities of commercial polystyrene microcarriers versus custom-fabricated gelatin methacrylate (gelMA) microcarriers for non-destructive and non-invasive visualization of the entire microcarrier surface, direct cell enumeration, and sub-cellular visualization of mesenchymal stem/stromal cells (MSCs). The polystyrene microcarrier prevents visualization of cells on the distal half of the microcarrier using either fluorescence or elastic scattering contrast, whereas the gelMA microcarrier allows high fidelity visualization of cell morphology and quantification of cell density using light sheet fluorescence microscopy and tomography. The combination of optical-quality gelMA microcarriers and label-free light-sheet tomography will facilitate enhanced control of bioreactor-microcarrier cell culture processes.

biophysics↗

bioRxiv2023

Autofluorescence lifetime imaging classifies human lymphocyte activation and subtype

New non-destructive tools are needed to reliably assess lymphocyte function for immune profiling and adoptive cell therapy. Optical metabolic imaging (OMI) is a label-free method that measures the autofluorescence intensity and lifetime of metabolic cofactors NAD(P)H and FAD to quantify metabolism at a single-cell level. Here, we investigate whether OMI can resolve metabolic changes between human quiescent versus IL4/CD40 activated B cells and IL12/IL15/IL18 activated memory-like NK cells. We found that quiescent B and NK cells were more oxidized compared to activated cells. Additionally, the NAD(P)H mean fluorescence lifetime decreased and the fraction of unbound NAD(P)H increased in the activated B and NK cells compared to quiescent cells. Machine learning classified B cells and NK cells according to activation state (CD69+) based on OMI parameters with up to 93.4% and 92.6% accuracy, respectively. Leveraging our previously published OMI data from activated and quiescent T cells, we found that the NAD(P)H mean fluorescence lifetime increased in NK cells compared to T cells, and further increased in B cells compared to NK cells. Random forest models based on OMI classified lymphocytes according to subtype (B, NK, T cell) with 97.8% accuracy, and according to activation state (quiescent or activated) and subtype (B, NK, T cell) with 90.0% accuracy. Our results show that autofluorescence lifetime imaging can accurately assess lymphocyte activation and subtype in a label-free, non-destructive manner. TeaserLabel-free optical imaging can assess the metabolic state of lymphocytes on a single-cell level in a touch-free system.

bioengineering↗

bioRxiv2022

Autofluorescence is a biomarker of neural stem cell activation state

Neural stem cells (NSCs) in the adult brain are primarily quiescent but can activate and enter the cell cycle to produce newborn neurons. NSC quiescence can be regulated by disease, injury, and age, however our understanding of NSC quiescence is limited by technical limitations imposed by the bias of markers used to isolate each population of NSCs and the lack of live-cell labeling strategies. Fluorescence lifetime imaging (FLIM) of autofluorescent metabolic cofactors has previously been used in other cell types to study shifts in cell states driven by metabolic remodeling that change the optical properties of these endogenous fluorophores. Here we asked whether autofluorescence could be used to discriminate NSC activation state. We found that quiescent NSCs (qNSCs) and activated NSCs (aNSCs) each have unique autofluorescence intensity and fluorescence lifetime profiles. Additionally, qNSCs specifically display an enrichment of a specific autofluorescent signal localizing to lysosomes that is highly predictive of cell state. These signals can be used as a graded marker of NSC quiescence to predict cell behavior and track the dynamics of quiescence exit at single cell resolution in vitro and in vivo. Through coupling autofluorescence imaging with single-cell RNA sequencing in vitro and in vivo, we provide a high-resolution resource revealing transcriptional features linked to rapid NSC activation and deep quiescence. Taken together, we describe a single-cell resolution, non-destructive, live-cell, label-free strategy for measuring NSC activation state in vitro and in vivo and use this tool to expand our understanding of adult neurogenesis.

neuroscience↗

bioRxiv2020

In vivo fluorescence lifetime imaging captures metabolic changes in macrophages during wound responses in zebrafish

The effector functions of macrophages across the spectrum of activation states in vitro are linked to profound metabolic rewiring. However, the metabolism of macrophages remains poorly characterized in vivo. To assess changes in the intracellular metabolism of macrophages in their native inflammatory microenvironment, we employed two-photon fluorescence lifetime imaging microscopy (FLIM) of metabolic coenzymes NAD(P)H and FAD. We found that pro-inflammatory activation of macrophages in vivo was associated with a decrease in the optical redox ratio [NAD(P)H/(NAD(P)H+FAD)] relative to a pro-resolving population during both infected and sterile inflammation. FLIM also resolved temporal changes in the optical redox ratio and lifetime variables of NAD(P)H in macrophages over the course of sterile inflammation. Collectively, we show that non-invasive and label-free imaging of autofluorescent metabolic coenzymes is sensitive to dynamic changes in macrophage activation in interstitial tissues. This imaging-based approach has broad applications in immunometabolism by probing in real time the temporal and spatial metabolic regulation of immune cell function in a live organism. SignificanceMetabolic regulation of macrophage effector functions has recently emerged as a key concept in immune cell biology. Studies rely on in vitro and ex vivo approaches to study macrophage metabolism, however the high plasticity of these cells suggest that removal from their native microenvironment may induce changes in their intracellular metabolism. Here, we show that fluorescence lifetime imaging microscopy of metabolic coenzymes captures dynamic changes in the metabolic activity of macrophages while maintaining them in their endogenous microenvironment. This approach also resolves variations on a single-cell level, in contrast to bulk measurements provided by traditional biochemical assays, making it a potentially valuable tool in the field of immunometabolism.

immunology↗

bioRxiv2019

Classifying T cell activity in autofluorescence intensity images with convolutional neural networks

The importance of T cells in immunotherapy has motivated developing technologies to better characterize T cells and improve therapeutic efficacy. One specific objective is assessing antigen-induced T cell activation because only functionally active T cells are capable of killing the desired targets. Autofluorescence imaging can distinguish T cell activity states of individual cells in a non-destructive manner by detecting endogenous changes in metabolic co-enzymes such as NAD(P)H. However, recognizing robust patterns of T cell activity is computationally challenging in the absence of exogenous labels or information-rich autofluorescence lifetime measurements. We demonstrate that advanced machine learning can accurately classify T cell activity from NAD(P)H intensity images and that those image-based signatures transfer across human donors. Using a dataset of 8,260 cropped single-cell images from six donors, we meticulously evaluate multiple machine learning models. These range from traditional models that represent images using summary statistics or extract image features with CellProfiler to deep convolutional neural networks (CNNs) pre-trained on general non-biological images. Adapting pre-trained CNNs for the T cell activity classification task provides substantially better performance than traditional models or a simple CNN trained with the autofluorescence images alone. Visualizing the images with dimension reduction provides intuition into why the CNNs achieve higher accuracy than other approaches. However, we observe that fine-tuning all layers of the pre-trained CNN does not provide a classification performance boost commensurate with the additional computational cost. Our software detailing our image processing and model training pipeline is available as Jupyter notebooks at https://github.com/gitter-lab/t-cell-classification.

bioinformatics↗