MOTIVATION: Ultra-multiplexed fluorescence imaging has revolutionized our understanding of biological systems, enabling the simultaneous visualization and quantification of multiple targets within biological specimens. A recent breakthrough in this field is PICASSO, a mutual-information-based technique capable of demixing up to 15 fluorophores without their spectra, thereby significantly simplifying the application of ultra-multiplexed fluorescence imaging. However, this study has identified a limitation of mutual information (MI)-based techniques. They do not differentiate between spatial colocalization and spectral mixing. Consequently, MI-based demixing may incorrectly interpret spatially co-localized targets as non-colocalized, leading to overcorrection. RESULTS: We found that selecting regions within a multiplex image with low-spatial similarity for measuring spectroscopic mixing results in more accurate demixing. This method effectively minimizes overcorrections and promises to accelerate the broader adoption of ultra-multiplex imaging. AVAILABILITY AND IMPLEMENTATION: The codes are available at https://github.com/xing-lab-pitt/mosaic-picasso.

BACKGROUND: Automated segmentation of nuclei in microscopic images has been conducted to enhance throughput in pathological diagnostics and biological research. Segmentation accuracy and speed has been significantly enhanced with the advent of convolutional neural networks. A barrier in the broad application of neural networks to nuclei segmentation is the necessity to train the network using a set of application specific images and image labels. Previous works have attempted to create broadly trained networks for universal nuclei segmentation; however, such networks do not work on all imaging modalities, and best results are still commonly found when the network is retrained on user specific data. Stochastic optical reconstruction microscopy (STORM) based super-resolution fluorescence microscopy has opened a new avenue to image nuclear architecture at nanoscale resolutions. Due to the large size and discontinuous features typical of super-resolution images, automatic nuclei segmentation can be difficult. In this study, we apply commonly used networks (Mask R-CNN and UNet architectures) towards the task of segmenting super-resolution images of nuclei. First, we assess whether networks broadly trained on conventional fluorescence microscopy datasets can accurately segment super-resolution images. Then, we compare the resultant segmentations with results obtained using networks trained directly on our super-resolution data. We next attempt to optimize and compare segmentation accuracy using three different neural network architectures. RESULTS: Results indicate that super-resolution images are not broadly compatible with neural networks trained on conventional bright-field or fluorescence microscopy images. When the networks were trained on super-resolution data, however, we attained nuclei segmentation accuracies (F1-Score) in excess of 0.8, comparable to past results found when conducting nuclei segmentation on conventional fluorescence microscopy images. Overall, we achieved the best results utilizing the Mask R-CNN architecture. CONCLUSIONS: We found that convolutional neural networks are powerful tools capable of accurately and quickly segmenting localization-based super-resolution microscopy images of nuclei. While broadly trained and widely applicable segmentation algorithms are desirable for quick use with minimal input, optimal results are still found when the network is both trained and tested on visually similar images. We provide a set of Colab notebooks to disseminate the software into the broad scientific community ( https://github.com/YangLiuLab/Super-Resolution-Nuclei-Segmentation ).

This paper presents a case study of a Grid-enabled implementation of light scattering spectroscopy (LSS). The LSS technique allows noninvasive detection of precancerous changes in human epithelium, differentiating from traditional biopsies by allowing in vivo diagnosis of tissue samples and quantitative analyses of parameters related to cancerous changes via numerical techniques. This paper describes the architecture of GridLSS and its integration with a Web-based Grid computing portal. GridLSS solves an optimization problem of determining the light scattering spectrum that best fits experimental spectral data among a large set of spectra computed analytically using rigorous Mie theory. The novel approach taken in this paper is based on the precomputation and storage of Mie theory spectra in lookup databases that are queried during the minimization process. The paper makes three important contributions: 1) it presents a novel parallel application for LSS analysis that delivers high performance in wide-area distributed computing environment; 2) it evaluates and analyzes the performance of this application in cluster-based high-performance computing environments that are typical of Grid deployments; and 3) it shows that the performance of GridLSS benefits significantly from the use of on-demand Grid data transfers based on virtualized distributed file systems and from user-level caches for remote file system data.