Computational delineation and cellular profiling of murine cortical cell layers using multiplex immunofluorescence...

The adult mammalian cerebral cortex has a vertical laminar organization consisting of six neuronal layers, with each layer subserving a specific function. Accurate delineation and assignment of cortical neurons to their appropriate layer is important to understanding normal and diseased cerebral cortex function.

We present a data-driven method for delineating cortical cell layers in coronal brain sections imaged using multiplexed immunofluorescence microscopy. Our method is based on spatial cluster analysis of neuronal features using an active machine learning-enhanced Dirichlet Process Mixture Model (Actively Informed Dirichlet Process Mixture Model; AIDPMM). It enables cytometric measurements to be parcellated based on cortical cell layer, facilitating unbiased, comprehensive, and quantitative profiling of the cell layers with respect to their thickness, cellular composition, cell-phenotypic status, and spatial arrangement. Cell profiles can be compared using conventional and spatial statistical methods across layers within the same brain, or across different experimental groups, allowing researchers to analyze the effects of manipulations with cell layer-specificity.

The accuracy of the AIDPMM cortical layer delineation was validated by comparing layer-specific marker staining to AIDPMM delineated layers (intersection over union; IoU = 92.5%), and by measuring the concordance between computational and human-delineated cortical cell layer midlines (R2 = 93.5%). Applying our method to a mild traumatic brain injury model, we detected layer-specific microglia and astrocyte activation 14 days post injury that was modified by lithium+valproate treatment.

Delineating cortical layers has primarily been accomplished using either of two methods. The first method, manual delineation, is labor intensive and is generally performed for a limited number of regions as required by the study. Due to its subjective nature, this approach is prone to human error and bias. The second method involves the use of markers expressed in neurons belonging to specific cortical layer. Limitations of this approach include overlapping expression of the marker between layers, and a dearth of unique molecular markers. Neither of these methods provides any kind of validation or profiling for the layer delineation, and are subject to the experimenter's subjectivity.

Our results indicate that AIDPMM is efficient, versatile, and readily amenable to visual inspection and proofreading, and provides an efficient, unbiased method for delineating cortical neuronal layers and profiling cytometric data by cell layer.