KEYWORDS: Near infrared, Fluorescence, Tumors, Fluorescence lifetime imaging, In vivo imaging, Multiplexing, 3D modeling, Fluorescence tomography, Fluorescence imaging, Fluorescent proteins
The development of fluorescent proteins emitting in the near infrared (NIR) range (i.e., 650 nm-950 nm) has improved our capabilities for lifetime multiplexing and fluorescence imaging in vivo. Wavelengths in the NIR window experience reduced scattering and increased penetration depth through living tissue. Additionally, autofluorescence of cells and tissues is less prevalent in the NIR range, further improving signal to noise ratio. We performed fluorescence lifetime imaging (FLI) on breast cancer (AU565) and ovarian cancer (SKOV3) cell lines expressing the NIR fluorescent proteins (FPs), miRFP680 and emiRFP670. Confocal microscopy with time-correlated single-photon counting (TCSPC) reveals unique fluorescence decays for these NIR FPs, allowing for lifetime-based multiplexing on a single channel. Despite similar emission spectra, we were able to unmix fluorescence signals from a co-culture of SKOV3 expressing emiRFP670 and AU565 expressing miRFP680 based upon their unique fluorescence decays. We then generated 3D liquid overlay tumor spheroids using SKOV3 expressing emiRFP670 or miRFP680 for lifetime imaging via mesoscopic fluorescence molecular tomography (MFMT). 2D lifetime values and images acquired from MFMT corroborated our findings. Future investigation includes 3D light sheet mesoscopic imaging of tumor spheroids, as well as imaging of in vivo tumor xenografts expressing NIR-FPs. The long wavelengths and unique fluorescence lifetimes of emiRFP670 and miRFP680 make them ideal for multiplexed imaging, as well as for defining tumor volumes in vivo, while also leveraging the benefits of NIR imaging.
Breast cancer cell analysis has traditionally focused on cell and intracellular organelle morphology. Recent research has demonstrated that organelle topology-based cancer cell classification is considerably more accurate when using handcrafted feature extraction and machine learning-based classifiers on fluorescent confocal microscopy images. However, feature extraction and classification through this methodology requires manual segmentation and computational organelle rendering. Herein, we employ convolutional neural networks (CNN) and Gradient-weighted Class Activation Mapping (GradCAM) for fast end-to-end classification and visual interpretation of confocal fluorescent microscopy images based on spatial organelle features. First, raw 3D images are filtered and preprocessed into 2D image patches for the CNN. To replicate feature analysis of the surface-surface contact area, marginal intermediate fusion CNN is implemented to classify each patch. GradCAM is then used post hoc to generate a representative heatmap of important areas used for classification. All relevant heatmap patches are then reconstructed based on the extraction of their respective patches to obtain an overall heatmap of the entire microscopy image. Furthermore, finer-grained heatmaps were obtained through the use of patch overlap and weighting during initial patch preprocessing. On a dataset consisting of 6 different breast cancer cell lines, this methodology resulted in a classification accuracy of 95.7% while also providing visualization of areas indicative of certain cancer cell lines. These findings demonstrate the efficacy of using deep learning and GradCAM for fast and interpretable organelle-based cancer cell classification.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.