A Guide to Perform 3D Histology of Biological Tissues with Fluorescence Microscopy

Brain-wide immunolabeling and tissue clearing applications for engram research

In recent years, there has been significant progress in memory research, driven by genetic and imaging technological advances that have given unprecedented access to individual memory traces or engrams. Although Karl Lashley argued since the 1930s that an engram is not confined to a particular area but rather distributed across the entire brain, most current studies have focused exclusively on a single or few brain regions. However, this compartmentalized approach overlooks the interactions between multiple brain regions, limiting our understanding of engram mechanisms. More recently, several studies have begun to investigate engrams across the brain, but research is still limited by a lack of standardized techniques capable of reconstructing multiple ensembles at single-cell resolution across the entire brain. In this review, we guide researchers through the latest technological advancements and discoveries in immediate early gene (IEG) techniques, tissue clearing methods, microscope modalities, and automated large-scale analysis. These innovations could propel the field forward in building brain-wide engram maps of normal and disease states, thus, providing unprecedented new insights. Ultimately, this review aims to bridge the gap between research focused on single brain regions and the need for a comprehensive understanding of whole-brain engrams, revealing new approaches for exploring the neuronal mechanisms underlying engrams.

Novel Spatial Approaches to Dissect the Lung Cancer Immune Microenvironment

Lung cancer is a deadly disease with the highest rates of mortality. Over recent decades, a better understanding of the biological mechanisms implicated in its pathogenesis has led to the development of targeted therapies and immunotherapy, resulting in improvements in patient outcomes. To better understand lung cancer tumor biology and advance towards precision oncology, a comprehensive tumor profile is necessary. In recent years, novel in situ spatial multiomics approaches have emerged offering a more detailed view of the spatial location of tumor and tumor microenvironment cells, identifying their unique composition and functional status. In this sense, novel multiomics platforms have been developed to evaluate tumor heterogeneity, gene expression, metabolic reprogramming, signaling pathway activation, cell-cell interactions, and immune cell programs. In lung cancer research, several studies have used these spatial technologies to locate cells and associated them with histological features that are relevant to the pathogenesis of lung adenocarcinoma. These advancements may unveil further molecular and immune mechanisms in tumor biology that will lead to the discovery of biomarkers for treatment prediction and prognosis. In this review, we provide an overview of more widely used and emerging pathology-based approaches for spatial immune profiling in lung cancer and how they enhance our understanding of tumor biology and immune response.

Delivery and kinetics of immersion optical clearing agents in tissues: Optical imaging from ex vivo to in vivo

Advanced optical imaging provides a powerful tool for the structural and functional analysis of tissues with high resolution and contrast, but the imaging performance decreases as light propagates deeper into the tissue. Tissue optical clearing technique demonstrates an innovative way to realize deep-tissue imaging and have emerged substantially in the last two decades. Here, we briefly reviewed the basic principles of tissue optical clearing techniques in the view of delivery strategies via either free diffusion or external forces-driven advection, and the commonly-used optical techniques for monitoring kinetics of clearing agents in tissue, as well as their ex vivo to in vivo applications in multiple biomedical research fields. With future efforts on the even distribution of both clearing agents and probes, excavation of more effective clearing agents, and automation of tissue clearing processes, tissue optical clearing should provide more insights into the fundamental questions in biological events clinical diagnostics.

The potential of x-ray virtual histology in the diagnosis of skin tumors

BACKGROUND

Histopathological analysis represents the gold standard in clinical practice for diagnosing skin neoplasms. While the current diagnostic workflow has specialized in producing robust and accurate results, interpreting tissue architecture and malignant cellular morphology correctly remains one of the greatest challenges for pathologists. This paper aims to explore the prospect of applying x-ray virtual histology to human skin tumor excisions and correlating it with the histological validation.

MATERIALS AND METHODS

Seven skin biopsies containing intriguing melanoma types and pigmented skin lesions were scanned using x-ray Computed micro-Tomography (μCT) and then sectioned for conventional histology assessment.

RESULTS

The tissue microarchitecture reconstructed by μCT offers detailed insights into diagnosing the malignancy or benignity of the skin lesions. Three-dimensional reconstruction via x-ray virtual histology reveals infiltrative patterns in basal cell carcinoma and evaluated invasiveness in melanoma. The technology enables the identification of pagetoid distributions of neoplastic cells and the assessment of melanoma depth in three dimensions.

CONCLUSION

Although the proposed approach is not intended to replace conventional histology, the non-destructive nature of the sample and the clarity provided by virtual inspection demonstrate the promising impact of μCT as a valid support method prior to conventional histological sectioning. Indeed, μCT images can suggest the optimal sectioning position before using a microtome, as is commonly performed in histological practice. Moreover, the three-dimensional nature of the proposed approach paves the way for a more accurate assessment of significant prognostic factors in melanoma, such as Breslow thickness, by considering the whole micro-volume rather than a two-dimensional observation.

Expansion and Light-Sheet Microscopy for Nanoscale 3D Imaging

Expansion Microscopy (ExM) and Light-Sheet Fluorescence Microscopy (LSFM) are forefront imaging techniques that enable high-resolution visualization of biological specimens. ExM enhances nanoscale investigation using conventional fluorescence microscopes, while LSFM offers rapid, minimally invasive imaging over large volumes. This review explores the joint advancements of ExM and LSFM, focusing on the excellent performance of the integrated modality obtained from the combination of the two, which is refer to as ExLSFM. In doing so, the chemical processes required for ExM, the tailored optical setup of LSFM for examining expanded samples, and the adjustments in sample preparation for accurate data collection are emphasized. It is delve into various specimen types studied using this integrated method and assess its potential for future applications. The goal of this literature review is to enrich the comprehension of ExM and LSFM, encouraging their wider use and ongoing development, looking forward to the upcoming challenges, and anticipating innovations in these imaging techniques.

Quantitative cytoarchitectural phenotyping of deparaffinized human brain tissues

Advanced 3D imaging techniques and image segmentation and classification methods can profoundly transform biomedical research by offering deep insights into the cytoarchitecture of the human brain in relation to pathological conditions. Here, we propose a comprehensive pipeline for performing 3D imaging and automated quantitative cellular phenotyping on Formalin-Fixed Paraffin-Embedded (FFPE) human brain specimens, a valuable yet underutilized resource. We exploited the versatility of our method by applying it to different human specimens from both adult and pediatric, normal and abnormal brain regions. Quantitative data on neuronal volume, ellipticity, local density, and spatial clustering level were obtained from a machine learning-based analysis of the 3D cytoarchitectural organization of cells identified by different molecular markers in two subjects with malformations of cortical development (MCD). This approach will grant access to a wide range of physiological and pathological paraffin-embedded clinical specimens, allowing for volumetric imaging and quantitative analysis of human brain samples at cellular resolution. Possible genotype-phenotype correlations can be unveiled, providing new insights into the pathogenesis of various brain diseases and enlarging treatment opportunities.

An end-to-end workflow for nondestructive 3D pathology

Recent advances in 3D pathology offer the ability to image orders of magnitude more tissue than conventional pathology methods while also providing a volumetric context that is not achievable with 2D tissue sections, and all without requiring destructive tissue sectioning. Generating high-quality 3D pathology datasets on a consistent basis, however, is not trivial and requires careful attention to a series of details during tissue preparation, imaging and initial data processing, as well as iterative optimization of the entire process. Here, we provide an end-to-end procedure covering all aspects of a 3D pathology workflow (using light-sheet microscopy as an illustrative imaging platform) with sufficient detail to perform well-controlled preclinical and clinical studies. Although 3D pathology is compatible with diverse staining protocols and computationally generated color palettes for visual analysis, this protocol focuses on the use of a fluorescent analog of hematoxylin and eosin, which remains the most common stain used for gold-standard pathological reports. We present our guidelines for a broad range of end users (e.g., biologists, clinical researchers and engineers) in a simple format. The end-to-end workflow requires 3-6 d to complete, bearing in mind that data analysis may take longer.

Engineering the future of 3D pathology

In recent years, technological advances in tissue preparation, high-throughput volumetric microscopy, and computational infrastructure have enabled rapid developments in nondestructive 3D pathology, in which high-resolution histologic datasets are obtained from thick tissue specimens, such as whole biopsies, without the need for physical sectioning onto glass slides. While 3D pathology generates massive datasets that are attractive for automated computational analysis, there is also a desire to use 3D pathology to improve the visual assessment of tissue histology. In this perspective, we discuss and provide examples of potential advantages of 3D pathology for the visual assessment of clinical specimens and the challenges of dealing with large 3D datasets (of individual or multiple specimens) that pathologists have not been trained to interpret. We discuss the need for artificial intelligence triaging algorithms and explainable analysis methods to assist pathologists or other domain experts in the interpretation of these novel, often complex, large datasets.

An end-to-end workflow for non-destructive 3D pathology

Recent advances in 3D pathology offer the ability to image orders-of-magnitude more tissue than conventional pathology while providing a volumetric context that is lacking with 2D tissue sections, all without requiring destructive tissue sectioning. Generating high-quality 3D pathology datasets on a consistent basis is non-trivial, requiring careful attention to many details regarding tissue preparation, imaging, and data/image processing in an iterative process. Here we provide an end-to-end protocol covering all aspects of a 3D pathology workflow (using light-sheet microscopy as an illustrative imaging platform) with sufficient detail to perform well-controlled preclinical and clinical studies. While 3D pathology is compatible with diverse staining protocols and computationally generated color palettes for visual analysis, this protocol will focus on a fluorescent analog of hematoxylin and eosin (H&E), which remains the most common stain for gold-standard diagnostic determinations. We present our guidelines for a broad range of end-users (e.g., biologists, clinical researchers, and engineers) in a simple tutorial format.