Extract SCV structures from the fluorescence images with Lamp1 and DAPI

Brief introduction

It is a workflow to extract Salmonella-containing vacuoles (SCVs) by the assistance of the machine learning tool ilastik for further quantification requests.

#Examples

• Tissues or cells stained with DAPI and Lamp1

#Description

By the coexistence of the lysosomal membrane proteins such as LAMP1 and the bacterial DNA, SCV can be recognized easily by human eyes. However, these staining patterns are mixed with cellular lysosomes without bacteria and the nucleus of the parasitic cell respectively . By simply binarize the DNA staining or LAMP1 staining channel, SCVs are either under- or over-estimated .Here I established a workflow to extract SCVs by the assistance of the machine learning tool ilastik for further quantification requests.

Instruction

1. Three channels images were contained in the original dataset from an expansion microscopy processed cell. Channel 1 Mitochondrial citrate carrier (CIC) Channel 2 Lamp1 Channel 3 DAPI The workflow is composed by 3 parts.
2. Extract bacterial DNA and add back as an additional channel to the raw dataset from Leica lif file by a Fiji script (SCV extractor from lif_A_ilastik.ijm). A trained ilastik project for 3D images is also included in this GitHub (ExM_Salmonella DNA.ilp). Please be aware that the pretrained project may not fit diverse infection conditions. It is required to train your own model by your own dataset if it is needed.
3. Semi-automatically remove non-infected cells by another Fiji script (SCV extractor from lif_B_remove uninfected cells.ijm).
4. Generate Lamp1, SCV and CIC 3D surfaces with Imaris in the batch format. The parameters for batch processing is included (SCV extractor.icsx).

How to cite

If this helps your assay in your research, please cite the doi below.

1. 10.5281/zenodo.13756849

2. Intracellular Salmonella hijacks the mitochondrial citrate carrier to mitigate host oxidative defense. Chieh-Hua Fu, Nai-Shu Chen, Shao-Chun Hsu, Hsueh-Wen Hu, Ting-Yin Wu, Yi-Jou Huang, Ying-Chu Chen, An-Chi Luo, Yu-Tsung, Huang, Shu-Jung Chang. (Submitting)

Tutorial