Autofluorescence microscopy as a label-free tool for renal histology and glomerular segmentation

Abstract

Functional tissue units (FTUs) composed of multiple cells like the glomerulus in the kidney nephron play important roles in health and disease. Histological staining is often used for annotation or segmentation of FTUs, but chemical stains can introduce artefacts through experimental factors that influence analysis. Secondly, many molecular -omics techniques are incompatible with common histological stains. To enable FTU segmentation and annotation in human kidney without the need for histological staining, we detail here the use of widefield autofluorescence (AF) microscopy as a simple, label-free modality that provides detailed renal morphology comparable to periodic acid-Schiff (PAS) stained tissue in both formalin-fixed paraffin-embedded (FFPE) and fresh frozen samples and with no tissue processing beyond sectioning. We demonstrate automated deep learning-based glomerular unit recognition and segmentation on PAS and AF images of the same tissue section from 9 fresh frozen samples and 9 FFPE samples. All training comparisons were carried out using registered AF microscopy and PAS stained whole slide images originating from the same section, and the recognition models were built with the exact same training and test examples. Measures of recognition performance, such as the Dice-Sorensen coefficient, the true positive rate, and the positive predictive value differed less than 2% between standard PAS and AF microscopy for both preservation methods. These results demonstrate that AF is a potentially powerful tool to study human kidney tissue, that it can serve as a label-free source for automated and manual annotation of tissue structures.

Highly multiplexed, label-free proteoform imaging of tissues by individual ion mass spectrometry

“…The identification of kidney internal structures was supported by autofluorescence microscopy ( Fig. 4B ) ( 44 ) and periodic acid–Schiff (PAS) staining histology ( Fig. 4C ).…”

A Lipid Atlas of the Human Kidney

“…For instance, MALDI-IMS provides untargeted, spatial information for hundreds of metabolites and lipids nearing cellular resolution 27 . Autofluorescence (AF) microscopy is a label-free microscopy method, which is used as the basis for computational segmentation of the tissue section into known FTUs 28 . The molecular markers corresponding to specific FTUs can subsequently be extracted from the multimodal combination of IMS-supplied mass spectra and AF-supplied FTU masks generated using an interpretable machine learning workflow (Figure 1D.1).…”

Microscopy-Directed Imaging Mass Spectrometry for Rapid High Spatial Resolution Molecular Imaging of Glomeruli

“…Segmentation of glomeruli was performed using previously described methods. 30 Briefly, several glomeruli in the pre-IMS autofluorescence images were manually annotated using polygon regions in QuPath 35 learning model that recognizes glomeruli based on an autofluorescence image input. 35 Specifically, a Mask-RCNN (101 ResNet backbone) convolutional neural network, as implemented in the detectron2 package, 29,30 was trained to deliver glomeruli segmentations based on an autofluorescence image.…”