Introduction

nf-core/tumorscope is a bioinformatics pipeline for analyzing multi-channel microscopy videos in TIFF format. The pipeline processes time-lapse microscopy data with multiple fluorescent channels (Caspase 3, Actin/Tubulin, Calcium, Brightfield) and performs cell segmentation using Cellpose on the Actin/Tubulin channel.

The pipeline performs the following steps:

1. Multi-channel TIFF preprocessing - Extracts individual channels from multi-channel TIFF files and resizes images for optimal processing
2. Cell segmentation - Uses Cellpose with the cyto3 model to segment cells from the Actin/Tubulin channel
3. Quality control reporting - Generates comprehensive reports with MultiQC

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.

First, prepare a samplesheet with your input data that looks as follows:

samplesheet.csv:

csv sample,tiff sample1,/path/to/your/microscopy_video1.tif sample2,/path/to/your/microscopy_video2.tif

Each row represents a multi-channel TIFF file containing microscopy time-lapse data. The TIFF files should have 4 channels in the following order: - Channel 0: Caspase 3 - Channel 1: Actin/Tubulin (used for segmentation) - Channel 2: Calcium - Channel 3: Brightfield

Now, you can run the pipeline using:

bash nextflow run nf-core/tumorscope \ -profile <docker/singularity/.../institute> \ --input samplesheet.csv \ --outdir results \ --diameter 45 \ --model_type cyto3

You can customize the Cellpose segmentation parameters: - --diameter 45: Expected cell diameter in pixels (number, default: 45) - --model_type cyto3: Cellpose model to use (string, options: cyto, cyto2, cyto3, nuclei) - --flow_threshold 0.8: Flow error threshold (number, default: 0.8) - --cellprob_threshold -1.0: Cell probability threshold (number, default: -1.0) - --gpu false: Enable GPU acceleration (boolean, default: false)

[!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/tumorscope was originally written by Seqera AI.

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 #tumorscope 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.