Fluorescence Microscopy Tutorial

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 amount of the molecules of interest can be characterized to study disease mechanisms and infer biological pathways.

Mantle can be used for data management, preprocessing, and downstream analysis of fluoresence imaging data.

All data, pipelines, and analysis environments in this tutorial are available in your Mantle account.

Raw data

In Mantle, you can store images and associated metadata together as datasets. Here, a folder of images is stored as an image_directory data type that contains the name of the dataset and the path to the directory.

In this tutorial, images from the BBBC014v1 dataset, available from the Broad Bioimage Benchmark Collection, are processed and analyzed.

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) and uploaded to Mantle as an image_directory dataset.

Data processing

Many image analysis workflows include a step for image segmentation to identify biological structures within an image, like nuclei and cytoplasm. Cellpose is a popular software tool for segmentation of biological images and is included as the cellpose-segmentation pipeline in Mantle.

To run the pipeline, an input dataset needs to be specified along with parameters for running Cellpose. Expand to view the parameters for the cellpose-segmentation pipeline:

Input parameters

cellpose_model

string

required

Name of the pre-trained Cellpose model (e.g. “nuclei”, “cyto3”).

See the Cellpose documentation for more information.

nuclei_channel

string

required

Value for the channel with a nuclei marker.

0 = None, 1 = red, 2 = green, 3 = blue

cytoplasm_channel

string

required

Value for the channel with a cytoplasm marker.

For nuclei segmentation, this can be the same as “nuclei_channel” to load a single-channel grayscale image of just the nuclei channel.

0 = grayscale, 1 = red, 2 = green, 3 = blue

diameter

string

required

Expected pixel diameter of the objects being segmented.

None can be used for automatic estimation of the object diameter by Cellpose.

Running the Cellpose pipeline generates segmentation masks for each image from the input dataset. The masks are stored as a directory in a segmentation_mask_directory dataset. Masks for the nuclei and the cytoplasm could be used with the original images to characterize biological features in the samples (demonstration coming soon!).

Wrapping up

Mantle provides an easy way to store large imaging datasets with their corresponding metadata. Image processing pipelines, like Cellpose, can be used to extract features from these images and analysis notebooks can turn these features into insights. Try running Cellpose on your own fluorescence microscopy data!