nf-core/lsmquant

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Introduction

nf-core/lsmquant is a bioinformatics pipeline that performs preprocessing and analysis of light-sheet microscopy images of tissue cleard samples. The pipeline takes 2D single-channel 16-bit .tif images as input. The preprocessing consists of intesity adjustment, channel alignemnt, and tile stitching to reconstruct the 3D image. For mousebrain samples it offers a regsitration to the Allen Mouse Brain Reference Atlas for precise region annotation. Cell nuclei quantification is perfomed on the nuclear channel by a 3D-Unet.

Basic workflow

Preprocessing

1. Intensity Adjustment
2. Channel Alignment
3. Iterative Stitching

Analysis

4. ARA Registration subworkflow (optional)
5. Cell Nuclei Quantification

Pipeline Summary

The pipeline consists of two major stages, the preprocessingstage and the analysisstage.

Preprocessing

Preprocessing is performed on raw 2D single-channel 16-bit .tif images produced by a light sheet microscope. Three individual steps are perfomed :

• Measuring and adjustemnts for intensities
• Image channel alignemnt for at least two different channels
• Image tile stitching to recustruct the full image for each channel and z-slice

Analysis

Analysis is performed using a 3D-Unet to qunatify the amount of cell-nuclei in the given sample. The quantification is performed on the nuclear channel only, assuming that the corresponding image file names contain the pattern C1.

Optionally registration to the Allen Refernce Atlas (ARA) for functional brain region annotation can be perfomed before segmentation. This includes the following two steps:

• Downsampling of the high resolution stitched images
• Registration to the ARA

Usage

To run the pipeline you need to provide a samplesheet with your data in the following structure:

samplesheet.csv

sample_id,img_directory,parameter_file
TEST1,path/to/image-files,path/to/parameter/file.csv

The parameter csv file includes sample specific parameters that are used for processing the given data.It needs to follow a specific structure.

Please get the basic tempalte file here ( include maybe link to template csv which can be found in the repo ?) parametersheet.csv

Now, you can run the pipeline using:

nextflow run nf-core/lsmquant \
   -profile <docker/singularity/.../institute> \
   --input <samplesheet.csv> \
   --outdir <OUTDIR> \
   --stage <stage>

For more details and further functionality, please refer to the usage documentation and the parameter documentation.

Pipeline output

To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation.

Credits

nf-core/lsmquant was originally written by Carolin Schwitalla.

The pipeline is mainly based on the NuMorph (Nuclear-Based Morphometry) toolbox developed by Krupa et al., 2021.

NuMorph: Tools for cortical cellular phenotyping in tissue-cleared whole-brain images

Krupa O, Fragola G, Hadden-Ford E, Mory JT, Liu T, Humphrey Z, Rees BW, Krishnamurthy A, Snider WD, Zylka MJ, Wu G, Xing L, Stein JL.

Cell Rep. 2021 Oct 12, doi: 10.1016/j.celrep.2021.109802

We thank the following people for their extensive assistance in the development of this pipeline:

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don’t hesitate to get in touch on the Slack #lsmquant channel (you can join with this invite).

Citations

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.