Three-dimensional imaging of human stem cells using soft X-ray tomography

Niclis, Jonathan C.; Murphy, Sean V.; Parkinson, Dilworth Y.; Zedan, Amr; Sathananthan, A. Henry; Cram, David S.; Heraud, Philip

doi:10.1098/rsif.2015.0252

Cited by 18 publications

(17 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For the sake of completeness, soft X‐ray tomography may be mentioned. It has been used for characterization of organelles in frozen samples (http://www.bioopticsworld.com/articles/print/volume-6/issue-2/features/whole-cell-tomography-molecular-biology-structural-biology-affo.html; ) and, at the same time, provides information on the cell volume .…”

Section: Quantitative Phase Imaging and Similar Techniquesmentioning

confidence: 99%

Methods for cell volume measurement

Model

2017

Cytometry Pt A

View full text Add to dashboard Cite

Volume is an essential characteristic of a cell, and this review describes the main methods of its measurement that have been used in the past several decades. The discussed methods include various implementations of light scattering, estimates based on one or two cell dimensions, surface scanning, fluorescence confocal and transmission slice-by-slice imaging, intracellular volume markers, displacement of extracellular solution, quantitative phase imaging, radioactive methods, and some others. Suitability of these methods to some typical samples and applications is discussed. © 2017 International Society for Advancement of Cytometry.

show abstract

Section: Quantitative Phase Imaging and Similar Techniquesmentioning

confidence: 99%

Methods for cell volume measurement

Model

2017

Cytometry Pt A

View full text Add to dashboard Cite

show abstract

“…Although Raman-based methods (confocal Raman imaging) have high resolution to interrogate a single cell and organelles inside the cell, they cannot provide structural information like X-ray [40][41][42]. This is a reason why researchers combine the Raman-based methods with AFM that has an ultrahigh spatial resolution of fractions of nm.…”

Section: Introductionmentioning

confidence: 99%

A look into the use of Raman spectroscopy for brain and breast cancer diagnostics: linear and non-linear optics in cancer research as a gateway to tumor cell identity

Abramczyk

Brożek-Płuska

Jarota

et al. 2020

Expert Review of Molecular Diagnostics

View full text Add to dashboard Cite

Introduction: Currently, intensely developing of linear and non-linear optical methods for cancer detection provides a valuable tool to improve sensitivity and specificity. One of the main reasons for insufficient progress in cancer diagnostics is related to the fact that most cancer types are not only heterogeneous in their genetic composition but also reside in varying microenvironments and interact with different cell types. Until now, no technology has been fully proven for effective detecting of invasive cancer, which infiltrating the extracellular matrix. Areas covered: This review investigates the current status of Raman spectroscopy and Raman imaging for brain and breast cancer diagnostics. Moreover, the review provides a comprehensive overview of the applicability of atomic force microscopy (AFM), linear and non-linear optics in cancer research as a gateway to tumor cell identity. Expert commentary: A combination of linear and non-linear optics, particularly Raman-driven methods, has many additional advantages to identify alterations in cancer cells that are crucial for their proliferation and that distinguish them from normal cells.

show abstract

“…Additionally, even if an adequate 3D ground truth can be prepared, access to convenient tools for building/deploying these deep learning models is currently prohibitive for many biologists. Existing tools, such as NiftyNet [Gibson et al, 2018] or DLTK [Pawlowski et al, 2017], are difficult to use without sufficient experience in deep learning and computer vision. Other tools, e.g., Aivia Cloud [DRVISION, 2018], are easier to use but not openly accessible.…”

Section: Introductionmentioning

confidence: 99%

The Allen Cell and Structure Segmenter: a new open source toolkit for segmenting 3D intracellular structures in fluorescence microscopy images

Chen

Ding

Viana

et al. 2018

Preprint

View full text Add to dashboard Cite

A continuing challenge in quantitative cell biology is the accurate and robust 3D segmentation of structures of interest from fluorescence microscopy images in an automated, reproducible, and widely accessible manner for subsequent interpretable data analysis. We describe the Allen Cell Structure Segmenter, a new Python-based open source toolkit developed for 3D segmentation of intracellular structures in fluorescence microscope images. This toolkit brings together classic image segmentation and iterative deep learning workflows first to generate initial high-quality 3D intracellular structure segmentations and then to easily curate these results to generate the ground truths for building robust and accurate deep learning models. The toolkit takes advantage of the high-replicate 3D live cell image data collected at the Allen Institute for Cell Science of over 30 endogenous fluorescently tagged human induced pluripotent stem cell (hiPSC) lines. Each cell line represents a different intracellular structure with one or more distinct localization patterns within undifferentiated hiPS cells and hiPSC-derived cardiomyocytes. The Allen Cell Structure Segmenter consists of two complementary elements, a classic image segmentation workflow with a restricted set of algorithms and parameters and an iterative deep learning segmentation workflow. We created a collection of 20 classic image segmentation workflows based on 20 distinct and representative intracellular structure localization patterns as a "lookup table" reference and starting point for users. The iterative deep learning workflow can take over when the classic segmentation workflow is insufficient. Two straightforward "human-in-the-loop" curation strategies convert a set of classic image segmentation workflow results into a set of 3D ground truth images for iterative model training without the need for manual painting in 3D. The deep learning model architectures used in this toolkit were designed and tested specifically for 3D fluorescence microscope images and implemented as readable scripts. This toolkit was applied to the robust segmentation of fluorescent lamin B1, which exhibits significant variability in its localization pattern during the cell cycle. The Allen Cell Structure Segmenter thus leverages state of the art computer vision algorithms in an accessible way to facilitate their application by the experimental biology researcher.

show abstract

Three-dimensional imaging of human stem cells using soft X-ray tomography

Cited by 18 publications

References 60 publications

Methods for cell volume measurement

Methods for cell volume measurement

A look into the use of Raman spectroscopy for brain and breast cancer diagnostics: linear and non-linear optics in cancer research as a gateway to tumor cell identity

The Allen Cell and Structure Segmenter: a new open source toolkit for segmenting 3D intracellular structures in fluorescence microscopy images

Contact Info

Product

Resources

About