Label-free imaging of large samples: 3D rendering and morphological analysis within histological workflows using serial block face imaging

Abstract

AbstractSerial block face imaging (SFBI) is a method used to generate 3-dimensional (3D) reconstruction of a sample via serial image acquisition. Several SBFI approaches have been proposed for large samples, differing in the ability to generate contrast as well as in the nature of the detected signal. We propose a new system that detects the endogenous autofluorescence signal of paraffin-embedded samples. The sample preparation is simplified compared to other approaches, and adapted to be integrated into a routine histological preparation. More specifically, it was designed to limit reagent toxicity and to be compatible with downstream histological processing. We show the usefulness of the technique with a wide range of tissues based on the intrinsic autofluorescence signal. Optimization of quality section recovery offers the possibility to develop correlative approaches and multimodal analysis between the 3D dataset with the 2-dimensional (2D) sections. In addition, contrast and resolution of block-face images allow us to successfully perform post processing analysis and morphology quantifications. Overall, our methodology offers a simple, cost effective and rapid approach to obtain quantitative data on a large sample with no specific staining.