StoriesMitochondrial analysis of Allen Institute Cells
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Using the MitoHacker pipeline to analyze mitochondrial morphology of a publicly available data set.
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Copyright (c) 2025 David Kashatus
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Chapter 1 - Cell Segmentation
We set out to generate single-cell mitochondrial morphology data from 6 Images downloaded from the Allen Institute for Cell Science. www.allencell.org
The images we analyzed (Available in the Data tab) are of Human IPS cells expressing EGFP-tagged Tom20.
First, we set out to segment the multi-cell images into 88 individual cell images using the Cell Catcher tool. For a description of this tool, click on the Chapter 1 slides. This tool was first described in Rohani, etal. (under the Publications tab).
The input and output images can be found in the Projects tab, along with the parameters that were used to perform the segmentation. The resulting 88 single-cell images were then available for mitochondrial segmentation (Chapter 2).
Chapter 1: Datasets
AICS-Boundaries
Chapter 1: Linked Projects
AICS (Cell Catcher)
Chapter 1: Citations
Mito Hacker: a set of tools to enable high-throughput analysis of mitochondrial network morphology
10.1038/s41598-020-75899-5
Ali Rohani; Jennifer A. Kashatus; Dane T. Sessions; Salma Sharmin; David F. Kashatus
Scientific Reports
(2020)
Chapter 2 - Mitochondrial Segmentation
Following the cell segmentation, we next performed Mito Catcher Analysis on the 88 individual cells identified and segmented using Cell Catcher to created a binary mask of the mitochondrial staining within each cell. For a description of this tool, click on the Chapter 2 slides. This tool was first described in Rohani, etal. (under the Publications tab).
The input and output images can be found in the Projects tab, along with the parameters that were used to perform the mitochondrial segmentation.
The resulting set of 88 single-cell images were then available for mitochondrial analysis (Chapter 3).
Chapter 2: Linked Projects
AICS-SingleCells (Mito Catcher)
Chapter 2: Citations
Mito Hacker: a set of tools to enable high-throughput analysis of mitochondrial network morphology
10.1038/s41598-020-75899-5
Ali Rohani; Jennifer A. Kashatus; Dane T. Sessions; Salma Sharmin; David F. Kashatus
Scientific Reports
(2020)
Chapter 3 - Mitochondrial Analysis
Using the 88 images generated by Mito Catcher, we next set out to generate a database of structural data on the mitochondrial network using the MiA tool. For a description of this tool, click on the Chapter 3 slides. This tool was first described in Rohani, etal. (under the Publications tab).
Using MiA, we were able to measure >125 features of the mitochondrial network of each cell. The input images and resulting database are available in the Projects tab.
We next used the Inspector tool to generate a series of plots and visualize the data. These plots can be found under the Data tab.
Chapter 3: Datasets
AICS-MiA Charts
Chapter 3: Linked Projects
AICS-MiA (MiA)
Chapter 3: Citations
Mito Hacker: a set of tools to enable high-throughput analysis of mitochondrial network morphology
10.1038/s41598-020-75899-5
Ali Rohani; Jennifer A. Kashatus; Dane T. Sessions; Salma Sharmin; David F. Kashatus
Scientific Reports
(2020)
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We are fascinated by organelle dynamics and their intersection with disease pathology
David Kashatus is a Associate Professor at the Kashatus Lab
Dave received his bachelor’s degree in Ecology and Evolutionary Biology from Princeton University in 1997 and his Ph.D. in Genetics and Molecular Biology from the University of North Carolina at Chapel Hill in 2006, studying under Al Baldwin. After a postdoctoral fellowship with Chris Counter at Duke University, he moved to UVA in 2012. Dave’s lab is interested in the role of mitochondrial dynamics in tumorigenesis.
I believe scientists’ pursuit of truth should not be limited by their access to tools but by the strength of their ideas.
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Story References
Mito Hacker: a set of tools to enable high-throughput analysis of mitochondrial network morphology
10.1038/s41598-020-75899-5
Ali Rohani; Jennifer A. Kashatus; Dane T. Sessions; Salma Sharmin; David F. Kashatus
Scientific Reports
(2020)
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