Single cell resolution 3D imaging and segmentation within intact live tissues
Epithelial cells form diverse structures from squamous spherical organoids to densely packed pseudostratified tissues. Quantification of cellular properties in these contexts requires high-resolution deep imaging and computational techniques to achieve truthful three-dimensional (3D) structural features. Here, we describe a detailed step-by-step protocol for sample preparation, imaging and deep-learning-assisted cell segmentation to achieve accurate quantification of fluorescently labelled individual cells in 3D within live tissues. We share the lessons learned through troubleshooting 3D imaging of Drosophila wing discs, including considerations on the choice of microscopy modality and settings (objective, sample mounting) and available segmentation methods. In addition, we include a computational pipeline alongside custom code to assist replication of the protocol. While we focus on the segmentation of cell outlines from membrane labelling, this protocol applies to a wide variety of samples, and we believe it be valuable for studying other tissues that demand complex analysis in 3D.
Code (1)
Tasks
Cell SegmentationSegmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Interpretable Embeddings for Segmentation-Free Single-Cell Analysis in Multiplex Imaging
Multiplex Imaging (MI) enables the simultaneous visualization of multiple biological markers in separate imaging channels at subcellular resolution, providing valuable insights into cell-type heterogeneity and spatial or…
Cell Segmentationfeature selectionSegmentationDISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging
Calcium imaging is one of the most important tools in neurophysiology as it enables the observation of neuronal activity for hundreds of cells in parallel and at single-cell resolution. In order to use the data gained wi…
Cell SegmentationClusteringInstance SegmentationSegmentation+1End-to-end learning of pharmacological assays from high-resolution microscopy images
Predicting the outcome of pharmacological assays based on high-resolution microscopy images of treated cells is a crucial task in drug discovery which tremendously increases discovery rates. However, end-to-end learning …
Cell SegmentationDrug DiscoverySegmentationacia-workflows: Automated Single-cell Imaging Analysis for Scalable and Deep Learning-based Live-cell Imaging Analysis Workflows
Live-cell imaging (LCI) technology enables the detailed spatio-temporal characterization of living cells at the single-cell level, which is critical for advancing research in the life sciences, from biomedical applicatio…
Cell SegmentationPushing the limits of cell segmentation models for imaging mass cytometry
Imaging mass cytometry (IMC) is a relatively new technique for imaging biological tissue at subcellular resolution. In recent years, learning-based segmentation methods have enabled precise quantification of cell type an…
Cell SegmentationSegmentation