nf-core pipeline · 3dunet, image-analysis, image-processing
nf-core/lsmquant
Preprocesses and analyses light-sheet microscopy images of tissue-cleared samples.
What nf-core/lsmquant is
nf-core/lsmquant is a bioinformatics pipeline designed for preprocessing and analysing light-sheet microscopy data from tissue-cleared specimens. It was created by Carolin Schwitalla at the Quantitative Biology Center (QBiC) and the University of Tübingen in collaboration with the Stein Lab at the University of North Carolina.
The pipeline automates the complex computational steps required to handle volumetric light-sheet data, spanning 3D image reconstruction, nuclear quantification, and brain atlas mapping. It is primarily based on the NuMorph (Nuclear-Based Morphometry) toolbox for cellular phenotyping.
What the pipeline does
The pipeline ingests raw 2D single-channel 16-bit TIFF files from a folder or ZIP archive, guided by a samplesheet and a sample-specific parameter CSV. For 3D image reconstruction, users can select one of three preprocessing workflows: iterative tile stitching alone (stitch_only), intensity adjustment with iterative stitching (int_stitch), or full intensity adjustment, channel alignment, and iterative tile stitching (int_align_stitch).
Following preprocessing, two optional downstream workflows can be executed. Nuclear channel data can be segmented and quantified using a 3D U-Net neural network architecture, and full-brain image reconstructions can be mapped onto the Allen Brain Reference Atlas.
Key facts
- Pipeline engine
- Nextflow
- Core methodology
- NuMorph toolbox (Krupa et al., 2021)
- Segmentation method
- 3D U-Net
- Input format
- 2D single-channel 16-bit TIFF (in directories or ZIP archives)
- Reference atlas
- Allen Brain Reference Atlas
- Latest release
- 1.0.3 (2026-08-10)
What you can use it for
- 3D Tile Reconstruction and Stitching
- Reconstructing full 3D volumes from raw 2D tiled light-sheet microscopy images with optional intensity adjustment and channel alignment.
- Nuclear Quantification
- Segmenting and quantifying cell nuclei within tissue-cleared specimens by applying a 3D U-Net directly to the nuclear channel.
- Whole-Brain Coordinate Registration
- Registering reconstructed whole-brain cleared tissue images directly to the standardized Allen Brain Reference Atlas.
Run it here
The research agent runs nf-core/lsmquant with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
Strengths and limits
Good at
- Provides three configurable preprocessing routes to tailor tile stitching, intensity adjustment, and channel alignment.
- Incorporates a 3D U-Net model designed for cell-nuclei segmentation in volumetric microscopy data.
- Automates spatial registration of cleared full-brain reconstructions to the Allen Brain Reference Atlas.
- Accepts raw image inputs organized as either directories or compressed ZIP archives.
Watch out for
- Accepts image inputs strictly formatted as 2D single-channel 16-bit TIFF files.
- Cell nuclei quantification is limited exclusively to the nuclear channel.
- Requires a separate, specifically formatted sample parameter CSV file in addition to the primary samplesheet.
Questions
Can I run nf-core/lsmquant without my own cluster?
The research agent runs nf-core/lsmquant with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
What licence is nf-core/lsmquant under?
MIT, like every nf-core pipeline.
What input image formats are accepted?
Raw images must be provided in 2D single-channel 16-bit TIFF format, stored either within an uncompressed directory or a ZIP archive.
What preprocessing options are available for image stitching?
The pipeline provides three preprocessing choices: stitch_only for iterative tile stitching, int_stitch for intensity adjustment and iterative stitching, and int_align_stitch for intensity adjustment, channel alignment, and iterative tile stitching.
How is cell nuclei quantification conducted?
Nuclei quantification is an optional workflow that applies a 3D U-Net to the nuclear channel.
Is reference atlas registration required for all samples?
No, Allen Brain Reference Atlas registration is an optional workflow intended for full-brain datasets.
How to cite it
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. NuMorph: Tools for cortical cellular phenotyping in tissue-cleared whole-brain images. Cell Rep. 2021 Oct 12, doi: 10.1016/j.celrep.2021.109802
Written from nf-core/lsmquant's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.