Quantitative Geometric Modeling of Blood Cells from X-ray Histotomograms of Whole Zebrafish Larvae
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Curated by eLife
eLife assessment
Tissue phenotyping is central to nearly all areas of biology. In this study, the authors use an advanced form of micro-CT (X-ray histotomography) in zebrafish to phenotype blood cells in the intact animal. These approaches build upon prior work from this group and others showing this is a scalable imaging method that could readily be applied to other cell types, and provide an excellent complement to histological analysis of tissues. This is important work, as it demonstrates that the method can provide an approach that is orthogonal to conventional histology. The strength of the presented data is compelling, with description of both the hardware and software needed to implement the protocol, which will make it accessible to other researchers in the field.
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Abstract
Tissue phenotyping is foundational to understanding and assessing the cellular aspects of disease in organismal context and an important adjunct to molecular studies in the dissection of gene function, chemical effects, and disease. As a first step toward computational tissue phenotyping, we explore the potential of cellular phenotyping from 3-Dimensional (3D), 0.74 µm isotropic voxel resolution, whole zebrafish larval images derived from X-ray histotomography, a form of micro-CT customized for histopathology. As proof of principle towards computational tissue phenotyping of cells, we created a semi-automated mechanism for the segmentation of blood cells in the vascular spaces of zebrafish larvae, followed by modeling and extraction of quantitative geometric parameters. Manually segmented cells were used to train a random forest classifier for blood cells, enabling the use of a generalized cellular segmentation algorithm for the accurate segmentation of blood cells. These models were used to create an automated data segmentation and analysis pipeline to guide the steps in a 3D workflow including blood cell region prediction, cell boundary extraction, and statistical characterization of 3D geometric and cytological features. We were able to distinguish blood cells at two stages in development (4- and 5-days-post-fertilization) and wild-type vs. polA2 huli hutu ( hht ) mutants. The application of geometric modeling across cell types to and across organisms and sample types may comprise a valuable foundation for computational phenotyping that is more open, informative, rapid, objective, and reproducible.
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eLife assessment
Tissue phenotyping is central to nearly all areas of biology. In this study, the authors use an advanced form of micro-CT (X-ray histotomography) in zebrafish to phenotype blood cells in the intact animal. These approaches build upon prior work from this group and others showing this is a scalable imaging method that could readily be applied to other cell types, and provide an excellent complement to histological analysis of tissues. This is important work, as it demonstrates that the method can provide an approach that is orthogonal to conventional histology. The strength of the presented data is compelling, with description of both the hardware and software needed to implement the protocol, which will make it accessible to other researchers in the field.
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Joint Public Review:
The authors explored previously developed pan-resolution x-ray tomographic imaging pipelines for quantitative analysis of thousands of blood cells within 4 and 5 dpf zebrafish. By performing automatic segmentation of individual cells within the zebrafish embryo, the authors tried to demonstrate the applicability of x-ray tomography to quantitative analysis of cell phenotypes at the tissue level. The combination of random forest classification and automatic segmentation based on cell pose is promising, especially considering the open access and the general applicability of these tools. However, the key features claimed by the authors, that is, visualisation of all blood cells in the embryo and quantitative analysis of blood cell phenotypes, were not sufficiently supported by the presented data. Additionally, I see …
Joint Public Review:
The authors explored previously developed pan-resolution x-ray tomographic imaging pipelines for quantitative analysis of thousands of blood cells within 4 and 5 dpf zebrafish. By performing automatic segmentation of individual cells within the zebrafish embryo, the authors tried to demonstrate the applicability of x-ray tomography to quantitative analysis of cell phenotypes at the tissue level. The combination of random forest classification and automatic segmentation based on cell pose is promising, especially considering the open access and the general applicability of these tools. However, the key features claimed by the authors, that is, visualisation of all blood cells in the embryo and quantitative analysis of blood cell phenotypes, were not sufficiently supported by the presented data. Additionally, I see limitations in applicability to other cell types, as mentioned by authors as well, and similar analysis on other organisms due to differences in cell size, packing, and tissue background.
When supported by additional data, the manuscript has the potential to be a useful pipeline for cell phenotype analysis and an impactful method for the zebrafish community and beyond.
Major points:
1. The authors report that pan-resolution x-ray tomography enables visualisation of blood cells in the whole zebrafish embryo. These observations are based on a comparative analysis of EM data and histology with x-ray tomography. Not EM, nor histology shows the distribution of all blood cells (or comparable volume) as in x-ray tomography. At this point, it would be important to supplement the work with the 3D distribution of blood cells visualized by complementary methods, for example, light-sheet microscopy. Such data can be compared to the cells visualized by x-ray tomography like in Figure 6 in terms of cell numbers and distribution throughout the organs. Without such comparative analysis, it is unclear whether X-ray tomography visualizes all blood cells in the organism.2. Some critical information is missing for the optimisation of automatic segmentation. For example, how was the manual segmentation performed? For example, how cells of 3 pixels in diameter were segmented (Figure 8)? On how many cells? Taking that the F1 score is often biologically not meaningful, see Lena Maier-Hein, Bjoern Menze, et al. it would be important to make careful evaluation of segmentation results. For example, in Figure 2 it would be important to add the histogram of volume distribution in these datasets not just one mean value. The same type of histogram would be important to add to Figure 5 and compare these results to Figure 2.
3. For the comparison of blood cell shape between different samples, there is a lack of statistics and validation. How many embryos per condition were used? Considering that blood cells should be possible to obtain from zebrafish embryos. It would be important to see something like FACs data on blood cells from the same type of specimens. Would the size distribution obtained by FACs be comparable to X-ray tomography data? Without validation by other methods and statistically meaningful analysis, the results from x-ray tomography are simply not substantiated.
Minor points:
1. Please put some details on the parameters and usage of Cellpose.2. The claim in the Discussion on 'was able to show differences between data sets sufficient to classify new, unknown blood cells into these groups' is not supported by the data.
3. The key resource table should include all reagents, including sample preparation. This resource table should also include data sets as a resource, which are currently in the 'Data availability statement'.
4. Provide tables with the results on manual segmentation, automatic segmentation, and analysis of cellular phenotypes used for LDA.
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