Fluorescence Signal Correction and Deep Cell Population Profiling Algorithms for Analyzing Multiplex Images of Whole Rat Brain Slices



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The altered brain needs our urgent attention. While international brain mapping initiatives remain focused on the structure and working of the neuronal networks, conditions like concussion, stroke, Alzheimer's and experimental drug treatments inflict complex and multi-scale brain cellular alterations that deserve to be mapped in a comprehensive manner. At the cellular level, these conditions initiates a complex web of pathological alterations in all the types of brain cells, ranging from individual cells to multi-cellular functional units. These alterations represent a mixture of changes associated with the primary injury and secondary injuries. Many of these alterations can be subtle and/or latent, only discernible by sensing changes in cell morphology and/or the expression and/or intra-cellular distribution of specific molecular markers. Current immunohistochemistry (IHC) methods based on Hematoxylin and Eosin staining (H&E) or 3–5 channels of fluorescence immunostaining reveal only a fraction of these alterations at a time, miss the many other alterations and side effects that are occurring concurrently, and do not provide quantitative readouts.

Having the goal of advancing brain histology for pre-clinical studies, we propose a novel and comprehensive approach to seamlessly integrate highly multiplexed imaging (10-50 channels) with image processing and machine learning techniques. We describe a combination of signal reconstruction, deep cell detection, profiling and high-dimensional data analysis approaches to generate quantitative readouts of cellular alterations at multiple scales ranging from individual cells to multi-cellular units for comparative analysis. The acquired multiplex images are analyzed computationally to extract the specific fluorescent signals of interest while rejecting non-specific intra-channel and inter-channel signals. The reconstructed image data are analyzed using deep neural networks that detect each cell in the montage, and generate the information of cell location, cell type and cell functional status based on uniquely meaningful combinations of the molecular markers. The cellular measurements are exported for visualization and statistical profiling using other software tools. The proposed pipeline has applications in clinical studies and brain system biology. These data can be used for testing hypotheses, screening individual drugs and combination therapies, and initiating system-level studies.



Image processing, Machine learning, Deep learning, Cell Detection, Cell Classification, Fluorescence Imaging, Fluorescence, Signal correction, Cell Profiling