Automated detection of fluorescent cells in in‐resin fluorescence sections for integrated light and electron microscopy

Delpiano, José; Pizarro, Luis; Peddie, Christopher J.; Jones, Martin L.; Griffin, Lewis D.; Collinson, Lucy M.

doi:10.1111/jmi.12700

Cited by 15 publications

(16 citation statements)

References 26 publications

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“…With respect to the cell segmentation, the results show a rather low inter-observer agreement with an F-score of 0.39 for cell segmentation in DAPI-stained images and 0.45 for cell segmentation in PHH3-stained images. This low agreement has also been reported for other fluorescence microscopy datasets, and is likely related to the smaller surface area to volume ratio of cell structures compared to tissues [30] as well as blurry cell edges even where the focus is optimal. In order to assess the extent to which the quality of the scans affects the human and automatic segmentation, the image focus assessment method proposed by Yang et al [11] was used.…”

Section: Cell Segmentationsupporting

confidence: 47%

Segmentation of Tissues and Proliferating Cells in Light-Sheet Microscopy Images using Convolutional Neural Networks

Vercio¹,

Rm²,

Robertson³

et al. 2021

Preprint

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Background and Objective: A variety of genetic mutations are known to affect cell proliferation and apoptosis during organism development, leading to structural birth defects such as facial clefting. Yet, the mechanisms how these alterations influence the development of the face remain unclear. Cell proliferation and its relation to shape variation can be studied in high detail using Light-Sheet Microscopy (LSM) imaging across a range of developmental time points. However, the large number of LSM images captured at cellular resolution precludes manual analysis. Thus, the aim of this work was to develop and evaluate automatic methods to segment tissues and proliferating cells in these images in an accurate and efficient way. Methods: We developed, trained, and evaluated convolutional neural networks (CNNs) for segmenting tissues, cells, and specifically proliferating cells in LSM datasets. We compared the automatically extracted tissue and cell annotations to corresponding manual segmentations for three specific applications: (i) tissue segmentation (neural ectoderm and mesenchyme) in nuclear-stained LSM images, (ii) cell segmentation in nuclear-stained LSM images, and (iii) segmentation of proliferating cells in Phospho-Histone H3 (PHH3)-stained LSM images. Results: The automatic CNN-based tissue segmentation method achieved a macro-average F-score of 0.84 compared to a macro-average F-score of 0.89 comparing corresponding manual segmentations from two observers. The automatic cell segmentation method in nuclear-stained LSM images achieved an F-score of 0.57, while comparing the manual segmentations resulted in an F-score of 0.39. Finally, the automatic segmentation method of proliferating cells in the PHH3-stained LSM datasets achieved an F-score of 0.56 for the automated method, while comparing the manual segmentations resulted in an F-score of 0.45. Conclusions: The proposed automatic CNN-based framework for tissue and cell segmentation leads to results comparable to the inter-observer agreement, accelerating the LSM image analysis. The trained CNN models can also be applied for shape or morphological analysis of embryos, and more generally in other areas of cell biology.

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Section: Cell Segmentationsupporting

confidence: 47%

Segmentation of Tissues and Proliferating Cells in Light-Sheet Microscopy Images using Convolutional Neural Networks

Vercio¹,

Rm²,

Robertson³

et al. 2021

Preprint

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“…Integrated CLEM instruments (with the LM built in the EM) greatly facilitate retracing of the ROI from FM to EM (Koning et al, 2019; Liv et al, 2013), since the coordinate systems of the FM-EM are shared. Recently, multiple integrated (confocal) FM and volume-EM systems were reported (Ando et al, 2018; Brama et al, 2016; Delpiano et al, 2018; Gorelick et al, 2019; Lane et al, 2019). We add to this a home-built system integrating a Confocal Laser Scanning Microscope (CLSM) into a Focused Ion Beam / Scanning Electron Microscope (FIB.SEM), similar in geometry to a previously reported (Timmermans et al, 2016) integrated system and as outlined in Supplementary Figure 1.…”

Section: Resultsmentioning

confidence: 99%

“…We and others have shown that linking functional or dynamic information obtained with live-cell imaging to the underlying fine structure of the cell opens up powerful possibilities to study mechanistic processes with respect to their ultrastructure (Collinson et al, 2017; Fermie et al, 2018; Russell et al, 2016). Correlation with live-cell FM also aids the identification and capture of rare cellular structures or events, which is very challenging, time demanding, and at times basically impossible without smart tracking for EM imaging (Burel et al, 2018; Delpiano et al, 2018). An integrated CLEM platform provides exactly these requirements by enabling a direct translation between live-cell FM and volume-EM and the targeting identified live-cell events for follow-up ultrastructural imaging.…”

Section: Introductionmentioning

confidence: 99%

Correlative Organelle Microscopy: fluorescence guided volume electron microscopy of intracellular processes

Loginov

Fermie

Fokkema

et al. 2021

Preprint

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Intracellular processes depend on a strict spatial and temporal organization of proteins and organelles. Directly linking molecular to nanoscale ultrastructural information is therefore crucial to understand cellular physiology. Volume or 3-dimensional (3D) correlative light and electron microscopy (volume-CLEM) holds unique potential to explore cellular physiology at high-resolution ultrastructural detail across cell volumes. Application of volume-CLEM is however hampered by limitations in throughput and 3D correlation efficiency. Addressing these limitations, we here describe a novel pipeline for volume-CLEM that provides high-precision (<100nm) registration between 3D fluorescence microscopy (FM) and 3D electron microscopy (EM) data sets with significantly increased throughput. Using multi-modal fiducial nanoparticles that remain fluorescent in epoxy resins and a 3D confocal fluorescence microscope integrated in a Focused Ion Beam Scanning Electron Microscope (FIB.SEM), our approach uses FM to target extremely small volumes of even single organelles for imaging in volume-EM, and obviates the need for post correlation of big 3D datasets. We extend our targeted volume-CLEM approach to include live-cell imaging, adding information on the motility of intracellular membranes selected for volume-CLEM. We demonstrate the power of our approach by targeted imaging of rare and transient contact sites between endoplasmic reticulum (ER) and lysosomes within hours rather than days. Our data suggest that extensive ER-lysosome and mitochondria-lysosome interactions restrict lysosome motility, highlighting the unique capabilities of our integrated CLEM pipeline for linking molecular dynamic data to high-resolution ultrastructural detail in 3D.

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“…Regions of interest (ROI) were selected between imaging rounds for successive, targeted acquisitions down to 4 nm/px resolution, thereby reducing the time it would otherwise take to fully image the full brain at high resolution. Similarly, Delpiano et al (2018) used detection of in-resin preserved fluorescence in an integrated light and electron microscope for automated guiding to ROIs for subsequent acquisition. Other approaches involve parallelizing the imaging load across multiple instruments.…”

Section: Introductionmentioning

confidence: 99%

Optimization of negative stage bias potential for faster imaging in large-scale electron microscopy

Lane

Vos

Wolters

et al. 2020

Preprint

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Large-scale electron microscopy (EM) allows analysis of both tissues and macromolecules in a semi-automated manner, but acquisition rate forms a bottleneck. We reasoned that a negative bias potential may be used to enhance signal collection, allowing shorter dwell times and thus increasing imaging speed. Negative bias potential has previously been used to tune penetration depth in block-face imaging. However, optimization of negative bias potential for application in thin section imaging will be needed prior to routine use and application in large-scale EM. Here, we present negative bias potential optimized through a combination of simulations and empirical measurements. We find that the use of a negative bias potential generally results in improvement of image quality and signal-to-noise ratio (SNR). The extent of these improvements depends on the presence and strength of a magnetic immersion field. Maintaining other imaging conditions and aiming for the same image quality and SNR, the use of a negative stage bias can allow for a 20-fold decrease in dwell time, thus reducing the time for a week long acquisition to less than 8 hours. We further show that negative bias potential can be applied in an integrated correlative light electron microscopy (CLEM) application, allowing fast acquisition of a high precision overlaid LM-EM dataset. Application of negative stage bias potential will thus help to solve the current bottleneck of image acquisition of large fields of view at high resolution in large-scale microscopy.

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Automated detection of fluorescent cells in in‐resin fluorescence sections for integrated light and electron microscopy

Cited by 15 publications

References 26 publications

Segmentation of Tissues and Proliferating Cells in Light-Sheet Microscopy Images using Convolutional Neural Networks

Segmentation of Tissues and Proliferating Cells in Light-Sheet Microscopy Images using Convolutional Neural Networks

Correlative Organelle Microscopy: fluorescence guided volume electron microscopy of intracellular processes

Optimization of negative stage bias potential for faster imaging in large-scale electron microscopy

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