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CeLaVi: An Interactive Cell Lineage Visualisation Tool

Irepan Salvador-Martínez, Marco Grillo, Michalis Averof, Maximilian J. Telford

Preprint posted on 15 December 2020 https://www.biorxiv.org/content/10.1101/2020.12.14.422765v1

Article now published in Nucleic Acids Research at http://dx.doi.org/10.1093/nar/gkab325

CeLaVi: a cool name and a novel software for cell lineage visualisation

Selected by Mariana De Niz

Categories: cell biology

Background

The development of multicellular organisms starts from single cells that undergo a coordinated process of cell division, morphogenesis and cell differentiation. Cell divisions constitute a genealogical tree, which can provide a framework for understanding how and when cell fate decisions are made. Cell lineage visualization has relied on following successive cell divisions of developing organisms under the microscope. Recent developments in imaging technologies have simplified this process, making it possible to image larger numbers of cells at a time. In parallel, genetic lineage recorders have been developed, which make it possible to reconstruct the entire lineage history of a complex organism. A complex goal, however, is to integrate the description of cell relationships with information about the spatial distribution and identities of the cells those divisions produce. It is not uncommon in biology, however, that as technologies are developed allowing further investigation of biological phenomena, the software to handle vast amounts of data lags behind. To bridge this gap, in their work, Salvador-Martinez et al (1) present CeLaVi (Cell Lineage Visualization tool), which is a web-based tool that allows users to navigate and explore cell lineage while also making it possible to visualize the spatial distribution, identities and properties of cells. Moreover, it allows 3D viewing and annotation of various cell qualities, including gene expression, cell type and tissue layer. They present this software as open-source, with example datasets and video tutorials http://www.celavi.pro.

Figure 1. Example of CeLaVi user interface (from Ref.1).

Key findings and developments

Key developments.

Developments. CeLaVi is built using HTML5, Ajax, jQuery, CSS and the visualization libraries D3.js and Plotly.js. It was tested in multiple operating systems (Linux, MacOS, Windows) and browsers (Chrome, Firefox, Safari). The user interface consists of two interactive spaces: the lineage viewer, and the 3D viewer, which are tightly integrated. The users can explore various features including visualizing individual branches of the cell lineage tree, exploring the spatial distribution of cells in 3D, visualizing the spatial distribution of cell clones, selecting cells to display their lineage history, and visualizing the clonal relationship of any cell to others in 3D. To visualize a cell lineage tree and relate it to a 3D representation of the positions of each cell, two files are required: one containing the lineage tree, and one containing the 3D coordinates of the same cells.

The authors then describe in detail the two viewers, with the following key points:

Lineage tree viewer. CeLaVi’s lineage tree viewer can accommodate very large trees while retaining tree legibility. The viewer allows the user to focus on specific branches of the lineage tree by expanding, collapsing or pruning selected branches. Interacting with each cell in the lineage tree can provide specific cell information, for instance, cell ID, and total number of descendants.

3D viewer. CeLaVi’s 3D viewer renders cells as spheres, which users can interact with. It allows visualizing the degree of lineage relationship of all cells relative to one selected cell, showing the nested set of clones to which the cell belongs.

Information visualization. CeLaVi allows visualizing cell clones, and multiple clones originating at a given tree depth can be simultaneously visualized with unique colour coding. The software allows users to save selected cells or cell clones with customized annotations. The software allows two important pieces of further information to be uploaded and associated with the two viewer windows, namely, cell type information (a file containing cell annotations), and gene expression data, to see relative expression levels of a gene of interest.

Key findings.

The authors then include 4 example datasets in which they demonstrate CeLaVi’s features. These include a) C. elegans L1 larva including information from previous studies, on 357 cells viewed using confocal microscopy; b) Ciona intestinalis gastrula combining previously available 3D imaging and gene expression data; c) Parhyale hawaiensis limb, including previously available data from fluorescently-labeled embryos imaged with multi-view light-sheet microscopy at high spatiotemporal resolution over several days of embryogenesis; d) in silico organoid, from a simulation of morphogenesis.

Altogether, the authors present CeLaVi as the first interactive web-based visualization tool for cell lineages, with simple representations of that enables fast rendering in 3D, which is key for data exploration.

What I like about this paper

I like this preprint for three key things: first, that usually software lags behind technological advances that make certain types of experiments possible, which usually results in analysis being challenging for some time, until the relevant software tools catch up and allow it. I like that the authors bridge this gap here. The second reason is that I like the principles of open science and open access, and here the authors make their tool available to the scientific community. Not only that, but it is web-based and simple to use, translating as greater accessibility. Finally, the title is great!

References

  1. Salvador-Martinez et al, CeLaVi: an interactive cell lineage visualization tool, bioRxiv, 2020.

 

Posted on: 23 December 2020

doi: https://doi.org/10.1242/prelights.26642

Read preprint (No Ratings Yet)

Author's response

Irepan Salvador-Martinez shared

Open questions

1. Great tool! Is it possible to upload and compile files from various sources- for instance, different gene expression analyses from different studies, simultaneously?

First of all, thank you very much for highlighting our preprint!

Yes, the files that you can upload to CeLaVi can come from different sources, with the condition they are in a correct format. We have tried to keep things simple so the accepted file formats are generic and commonly used (eg csv format).

Right now it is not possible to upload 2 different gene expression datasets at the same time (it is an interesting suggestion!). An alternative that comes to my mind would be, if you want to visually compare the gene expression data from different studies, to load the gene expression datasets separately and export the visualisation of your gene of interest as images (using a built-in function to export images as png or svg) to compare these later.

2. Given the great potential of the tool, as well as being standalone, could it be integrated into existing large databases – like geneDB or so, whereby the information on gene expression is already there, but from there now users can have access to all the information that CeLaVi allows?

I agree it is very important to integrate our tool to existing databases/software. As our tool is focused on the visualisation of cell lineages, we thought it would good to be able to visualise files coming from MaMuT (Wolff et al 2018), a cell tracking software that is widely used by the developmental biology community to annotate and reconstruct cell lineages from large multi-dimensional microscopy data. Luckily, Ko Sugawara (IGFL, Lyon, France) wrote a python script that can be used to convert the MaMuT XML output into CeLaVi input files. Ko kindly made available the script from his GitHub repository, at https://github.com/ksugar/celavi-tools.

3. You tested in your work, the performance of CeLaVi on multiple unrelated organisms. Is it possible to link information on cell morphology or lineage between organisms? For instance, if you select all that is known about a specific cell, and are able to find related cells in different organisms?

We haven’t implemented a function that can directly compare the information between organisms or samples. The main intended use of CeLaVi is as a tool for exploring the data that are being produced through the new analyses of cell lineages. The many ways in which both the lineage trees and 3D cells can be interacted with mean there are many potential ways in which this tool may be used – not all of which we can predict.

4. What do you envisage to further implement as next steps into CeLaVi?

We think it would be nice to further improve the integration of CeLaVi with cell tracking softwares and eventually it would be great to include a temporal dimension to the 3D visualisation, so you can replay how development progresses through time.

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