materials_methods

Welcome to the material & methods section of this wiki. Here you can find summary of quantification and analysis pipeline logic to present to publication as well as citation instruction for the use of this package.

Citation

Sequential Fish package

As of date this package is not published through peer review but is licenced under BSD 2-Clause License allowing free usage and reproduction with appropriate credits to the licence. Feel free to participate to this package developpement currently hosted on github by oppening a pull request or an issue on the appropriate pages.

Any peer review publication needs to join the code used for quantification through permenant archive, to that end the code is also published on zenodo through regular release (version). One can find the version of this package in the __init__.py file of the installation as well as in the Acquisition data table column 'pipeline_version'.

To cite, preferably use the DOI including all past and future release : https://doi.org/10.5281/zenodo.15683711 . If you need to discriminate between pipeline versions find appropriate release on zenodo project page and look for the details section, it contains an unique DOI for each release.

If later on a peer reviewed publication is released we kindly as you to also cite the paper in your work.

Other published work

Segmentation (cellpose)
Cellpose-SAM: superhuman generalization for cellular segmentation
Marius Pachitariu, Michael Rariden, Carsen Stringer
bioRxiv 2025.04.28.651001; doi: https://doi.org/10.1101/2025.04.28.651001

Single molecule quantification (big-fish)
Arthur Imbert, Wei Ouyang, Adham Safieddine, Emeline Coleno, Christophe Zimmer, Edouard Bertrand, Thomas Walter, Florian Mueller. FISH-quant v2: a scalable and modular analysis tool for smFISH image analysis. bioRxiv (2021) https://doi.org/10.1101/2021.07.20.453024

Methods

Quantification pipeline

Image quantifications were based Sequential Fish python package an open-source software implenting an image analysis pipeline including cell segmentation, threshold-based spot detection, dense region deconvolution, and a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Firstly, nuclei and cytoplasm segmentation were inferred in 2D/3D on image stack mean projection using Cellpose neural network [cit]. Secondly, single molecules were detected as 3D spots using the Big-FISH package [cit], which relies on an intensity Laplacian of Gaussian (LoG) filter followed by a local maxima filter and a user-adjusted threshold. During analysis, we ensured that the same thresholds were applied to images made for the same RNA, for all cell treatments. As local maxima detection can lead to a misestimation of single molecule number in bright regions where individual molecules cannot be distinguished, a dense region deconvolution step was added. It consists of fitting the median detected spot with a 3D-gaussian to construct a reference spot and then reconstructing the bright region signal using multiple reference spots, thus providing the number of single RNAs in bright regions. Thirdly, a DBSCAN was used to assign spots to clusters (foci). At this point, detected spots were divided in clustered spots (spots assigned to a cluster) or free spots. Finally, a centroid calculation was performed to assign coordinates to each cluster. This single-molecule quantification process was repeated on all smFISH or SunTag channels, enabling quantification of RNA colocalization. During analysis, filtering was used to discard aberrations: (i) cells without nucleus or with more than one nuclei; (ii) any cell whose mask laid on the border of the field of view and that was thus considered incomplete; (iii) any spot detected outside of segmented cells. Distances between spots were computed using the exact Euclidian distance. Additonaly, to correct for any error due to microscope failing to return to exact same position after imaging rounds a translational correction is applied using Fourier space cross correlation matching on the dapi signal.

Co-localization pipeline

Following quantification pipeline, a co-localization pipeline was applied to sequential fish data. Co-localization rates are calculated individually in each cell and we display their mean value in the heatmap. To assess measured co-localization rates meaningfulness against random co-localization we compute Z-scores against a probabilistic model for random co-localization (#TODO add reference) using formula $z= \frac{p_{measured} - p_{model}}{\sigma_{model}}$. Again, Z-scores are computed individually for all cells and is retained the median value of the population. Finally a Wilcoxon signed rank test is applied to derive a pvalue assessing if observed co-localization differs significally from random co-localization.

Statistical description template

Here you can find a template for statistical description based on nature portfolio requirements.