Voxel-Based Statistical Analysis of 3D Immunostained Tissue Imaging

The mouse brain after foot shock in four dimensions: Temporal dynamics at a single-cell resolution

Acute stress involves the majority of brain areas, which can be sequentially organized in functional brain networks as shown by our study with foot shock in mice. We used whole-brain microscopy to investigate different spatial resolutions over time. From mesoscale region–based analyses, we identified the order of activation of brain areas. With single-cell analyses, we analyzed shifts in activation over time within small nuclei—a result impossible to achieve with functional MRI’s resolution. These findings required the development of a four-dimensional (4D) analytical pipeline, which is made available as an R package. This “atlas” of foot shock can be visualized in 4D in our interactive web portal.

Acute stress leads to sequential activation of functional brain networks. A biologically relevant question is exactly which (single) cells belonging to brain networks are changed in activity over time after acute stress across the entire brain. We developed a preprocessing and analytical pipeline to chart whole-brain immediate early genes’ expression—as proxy for cellular activity—after a single stressful foot shock in four dimensions: that is, from functional networks up to three-dimensional (3D) single-cell resolution and over time. The pipeline is available as an R package. Most brain areas (96%) showed increased numbers of c-fos+ cells after foot shock, yet hypothalamic areas stood out as being most active and prompt in their activation, followed by amygdalar, prefrontal, hippocampal, and finally, thalamic areas. At the cellular level, c-fos+ density clearly shifted over time across subareas, as illustrated for the basolateral amygdala. Moreover, some brain areas showed increased numbers of c-fos+ cells, while others—like the dentate gyrus—dramatically increased c-fos intensity in just a subset of cells, reminiscent of engrams; importantly, this “strategy” changed after foot shock in half of the brain areas. One of the strengths of our approach is that single-cell data were simultaneously examined across all of the 90 brain areas and can be visualized in 3D in our interactive web portal.

Reduced and stable feature sets selection with random forest for neurons segmentation in histological images of macaque brain

In preclinical research, histology images are produced using powerful optical microscopes to digitize entire sections at cell scale. Quantification of stained tissue relies on machine learning driven segmentation. However, such methods require multiple additional information, or features, which are increasing the quantity of data to process. As a result, the quantity of features to deal with represents a drawback to process large series or massive histological images rapidly in a robust manner. Existing feature selection methods can reduce the amount of required information but the selected subsets lack reproducibility. We propose a novel methodology operating on high performance computing (HPC) infrastructures and aiming at finding small and stable sets of features for fast and robust segmentation of high-resolution histological images. This selection has two steps: (1) selection at features families scale (an intermediate pool of features, between spaces and individual features) and (2) feature selection performed on pre-selected features families. We show that the selected sets of features are stables for two different neuron staining. In order to test different configurations, one of these dataset is a mono-subject dataset and the other is a multi-subjects dataset to test different configurations. Furthermore, the feature selection results in a significant reduction of computation time and memory cost. This methodology will allow exhaustive histological studies at a high-resolution scale on HPC infrastructures for both preclinical and clinical research.

Tissue Clearing and Expansion Methods for Imaging Brain Pathology in Neurodegeneration: From Circuits to Synapses and Beyond

Studying the structural alterations occurring during diseases of the nervous system requires imaging heterogeneous cell populations at the circuit, cellular and subcellular levels. Recent advancements in brain tissue clearing and expansion methods allow unprecedented detailed imaging of the nervous system through its entire scale, from circuits to synapses, including neurovascular and brain lymphatics elements. Here, we review the state-of-the-art of brain tissue clearing and expansion methods, mentioning their main advantages and limitations, and suggest their parallel implementation for circuits-to-synapses brain imaging using conventional (diffraction-limited) light microscopy -such as confocal, two-photon and light-sheet microscopy- to interrogate the cellular and molecular basis of neurodegenerative diseases. We discuss recent studies in which clearing and expansion methods have been successfully applied to study neuropathological processes in mouse models and postmortem human brain tissue. Volumetric imaging of cleared intact mouse brains and large human brain samples has allowed unbiased assessment of neuropathological hallmarks. In contrast, nanoscale imaging of expanded cells and brain tissue has been used to study the effect of protein aggregates on specific subcellular structures. Therefore, these approaches can be readily applied to study a wide range of brain processes and pathological mechanisms with cellular and subcellular resolution in a time- and cost-efficient manner. We consider that a broader implementation of these technologies is necessary to reveal the full landscape of cellular and molecular mechanisms underlying neurodegenerative diseases.

Automated Individualization of Size-Varying and Touching Neurons in Macaque Cerebral Microscopic Images

In biomedical research, cell analysis is important to assess physiological and pathophysiological information. Virtual microscopy offers the unique possibility to study the compositions of tissues at a cellular scale. However, images acquired at such high spatial resolution are massive, contain complex information, and are therefore difficult to analyze automatically. In this article, we address the problem of individualization of size-varying and touching neurons in optical microscopy two-dimensional (2-D) images. Our approach is based on a series of processing steps that incorporate increasingly more information. (1) After a step of segmentation of neuron class using a Random Forest classifier, a novel min-max filter is used to enhance neurons' centroids and boundaries, enabling the use of region growing process based on a contour-based model to drive it to neuron boundary and achieve individualization of touching neurons. (2) Taking into account size-varying neurons, an adaptive multiscale procedure aiming at individualizing touching neurons is proposed. This protocol was evaluated in 17 major anatomical regions from three NeuN-stained macaque brain sections presenting diverse and comprehensive neuron densities. Qualitative and quantitative analyses demonstrate that the proposed method provides satisfactory results in most regions (e.g., caudate, cortex, subiculum, and putamen) and outperforms a baseline Watershed algorithm. Neuron counts obtained with our method show high correlation with an adapted stereology technique performed by two experts (respectively, 0.983 and 0.975 for the two experts). Neuron diameters obtained with our method ranged between 2 and 28.6 μm, matching values reported in the literature. Further works will aim to evaluate the impact of staining and interindividual variability on our protocol.