Introduction

nf-core/lsmquant is a bioinformatics pipeline that performs preprocessing and analysis of light-sheet microscopy images of tissue cleared samples. The pipeline takes raw images from a directory or a zip archive as input. The images need to be in a 2D single-channel 16-bit tifformat.

Pipeline Summary

The pipeline consists of 3 major components: Preprocessing, Cell-Nuclei quantification, and Allen Reference Atlas registration. A detailed explanation on each method can be found in the Methods description section.

Preprocessing

This stage reconstructs the 3D image from raw light-sheet data. Here two different workflows can be chosen:

1. align_stitch for multi-channel brain images:
   Performs intensity adjustment, channel alignment, and iterative tile stitching

2. stitch for single-channel images:
   Performs intensity adjustment and interactive tile stitching.

Allen Brain Atlas Registration (Optional)

This workflow registers full brain images to the Allen Brain Reference Atlas. This is an optional workflow and can be chosen by setting the parameter: ara_registartion

Cell Nuclei Quantification

Quantification of cell-nuclei is performed using a 3D-Unet and it is performed on the nuclear channel only. This is an optional workflow and can be chosen by setting the parameter:nuclei_quantification

Usage

Note

If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with -profile test before running the workflow on actual data.

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 template file here. 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>

Warning

Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters; see docs.

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 at the Quantitative Biology Center Tuebingen (QBiC).

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:

Matthias Hörtenhuber
Famke Bäuerle
Mark Polster
Susi Jo
Luis Kuhn Cuellar
Daniel Straub
Tatiana Woller
Niklas Grote
Jason Stein
Felix Kyere
Ian Curtin

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.