A Co-Culture-Based Multiparametric Imaging Technique to Dissect Local H2O2 Signals with Targeted HyPer7

Multispectral live-cell imaging is an informative approach that permits detecting biological processes simultaneously in the spatial and temporal domain by exploiting spectrally distinct biosensors. However, the combination of fluorescent biosensors with distinct spectral properties such as different sensitivities, and dynamic ranges can undermine accurate co-imaging of the same analyte in different subcellular locales. We advanced a single-color multiparametric imaging method, which allows simultaneous detection of hydrogen peroxide (H2O2) in multiple cell locales (nucleus, cytosol, mitochondria) using the H2O2 biosensor HyPer7. Co-culturing of endothelial cells stably expressing differentially targeted HyPer7 biosensors paved the way for co-imaging compartmentalized H2O2 signals simultaneously in neighboring cells in a single experimental setup. We termed this approach COMPARE IT, which is an acronym for co-culture-based multiparametric imaging technique. Employing this approach, we detected lower H2O2 levels in mitochondria of endothelial cells compared to the cell nucleus and cytosol under basal conditions. Upon administering exogenous H2O2, the cytosolic and nuclear-targeted probes displayed similarly slow and moderate HyPer7 responses, whereas the mitochondria-targeted HyPer7 signal plateaued faster and reached higher amplitudes. Our results indicate striking differences in mitochondrial H2O2 accumulation of endothelial cells. Here, we present the method’s potential as a practicable and informative multiparametric live-cell imaging technique.

Indexable signal amplification for multiparametric imaging

Multiparametric imaging allows researchers to measure the expression of many biomarkers simultaneously, allowing detailed characterization of cell microenvironments. One such technique, CODEX, allows fluorescence imaging of >30 proteins in a single tissue section. In the commercial CODEX system, primary antibodies are conjugated to DNA barcodes. This modification can result in antibody dysfunction, and development of a custom antibody panel can be very costly and time consuming as trial and error of modified antibodies proceeds. To address these challenges, we developed novel tyramide-conjugated DNA barcodes that can be used with primary antibodies via peroxidase-conjugated secondary antibodies. This approach results in signal amplification and imaging without the need to conjugate primary antibodies. When combined with commercially available barcode-conjugated primary antibodies, we can very quickly develop working antibody panels. We also present methods to perform antibody staining using a commercially available automated tissue stainer and in situ hybridization imaging on a CODEX platform.

Differentiating IDH status in human gliomas using machine learning and multiparametric MR/PET

Background The purpose of this study was to develop a voxel-wise clustering method of multiparametric magnetic resonance imaging (MRI) and 3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine (FDOPA) positron emission tomography (PET) images using an unsupervised, two-level clustering approach followed by support vector machine in order to classify the isocitrate dehydrogenase (IDH) status of gliomas. Methods Sixty-two treatment-naïve glioma patients who underwent FDOPA PET and MRI were retrospectively included. Contrast enhanced T1-weighted images, T2-weighted images, fluid-attenuated inversion recovery images, apparent diffusion coefficient maps, and relative cerebral blood volume maps, and FDOPA PET images were used for voxel-wise feature extraction. An unsupervised two-level clustering approach, including a self-organizing map followed by the K-means algorithm was used, and each class label was applied to the original images. The logarithmic ratio of labels in each class within tumor regions was applied to a support vector machine to differentiate IDH mutation status. The area under the curve (AUC) of receiver operating characteristic curves, accuracy, and F1-socore were calculated and used as metrics for performance. Results The associations of multiparametric imaging values in each cluster were successfully visualized. Multiparametric images with 16-class clustering revealed the highest classification performance to differentiate IDH status with the AUC, accuracy, and F1-score of 0.81, 0.76, and 0.76, respectively. Conclusions Machine learning using an unsupervised two-level clustering approach followed by a support vector machine classified the IDH mutation status of gliomas, and visualized voxel-wise features from multiparametric MRI and FDOPA PET images. Unsupervised clustered features may improve the understanding of prioritizing multiparametric imaging for classifying IDH status.

Creating virtual H&E images using samples imaged on a commercial CODEX platform

ABSTRACTMultiparametric fluorescence imaging via CODEX allows the simultaneous imaging of many biomarkers in a single tissue section. While the digital fluorescence data thus obtained can provide highly specific characterizations of individual cells and microenvironments, the images obtained are different from those usually interpreted by pathologists (i.e., H&E slides and DAB-stained immunohistochemistry slides). Having the fluorescence data plus co-registered H&E or similar data could facilitate adoption of multiparametric imaging into regular workflows, as well as facilitate the transfer of algorithms and machine learning previous developed around H&E slides. Since commercial CODEX instruments do not produce H&E-like images by themselves, we developed a staining protocol and associated image processing to make “virtual H&E” images that can be incorporated into the CODEX workflow. While there are many ways to achieve virtual H&E images, including use of a fluorescent nuclear stain and tissue autofluorescence to simulate eosin staining, we opted to combine fluorescent nuclear staining (via DAPI) with actual eosin staining. We also output images derived from fluorescent nuclear staining and autofluorescence images for additional evaluation.