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Introduction

Fluorescence microscopy experiments enable researchers to visualize specific biological macromolecules in tissues or cell cultures, using probes, antibody staining, or fluorescent protein conjugation, to visualize proteins of interest. These samples are imaged in multiple channels to capture each fluorophore separately. Using image analysis techniques, the molecules of interest can be quantified to study disease mechanisms and infer biological pathways. The first step in per-cell quantification is to perform cellular segmentation to determine which pixels in a microscopy image belong to a single cell. cellpose is a set of segmentation network models for anatomical segmentation of cells in microscopy images. We have written the Mantle cellpose-segmentation pipeline to read image directories from your Mantle Database, perform segmentation using cellpose, and write the resulting masks to your Mantle Database.

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Managing image data

In your Mantle Database, data is stored as entities, which have properties that can be files or metadata. Sets of microscopy images can be stored in Mantle as entities of the image_directory data type. Expand to view the properties of image_directory entities:
For demonstration purposes, your Mantle Database comes with images from the BBBC014v1 dataset from the Broad Bioimage Benchmark Collection:
Ljosa, V., Sokolnicki, K. L., & Carpenter, A. E. (2012). Annotated high-throughput microscopy image sets for validation. Nature Methods, 9(7), 637-637.
These images are taken from human MCF7 (human breast adenocarcinoma) and A549 (human alveolar basal epithelial) cell lines that are stained for 1) DNA and 2) NFκB. The cells are plated on a 96-well plate and each well was treated with one of 12 concentration points of TNFα. Higher concentrations of TNFα should induce cytoplasm to nucleus translocation of NFκB. 2-channel images were acquired from this experiment (channel 1: DNA, channel 2: NFκB).

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Producing cytoplasm and nuclei masks from images

The Mantle cellpose-segmentation pipeline adapts the Cellpose CLI to allow it to take image_directory entities as inputs and to structure the output directory of masks into a segmentation_mask_directory entity. Expand to view the parameters for the cellpose-segmentation pipeline: