Intelligent image‐activated sorting of Chlamydomonas reinhardtii by mitochondrial localization

• Published in: Cytometry. Part A Vol. 101; no. 12; pp. 1027 - 1034
• Main Authors: Harmon, Jeffrey, Findinier, Justin, Ishii, Natsumi Tiffany, Herbig, Maik, Isozaki, Akihiro, Grossman, Arthur, Goda, Keisuke
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.12.2022; Wiley Subscription Services, Inc
• Subjects: Artificial neural networks; Carbon; Carbon - metabolism; cell sorting; Chlamydomonas reinhardtii; Chlamydomonas reinhardtii - genetics; Chlamydomonas reinhardtii - metabolism; Cytology; Cytoplasm; deep learning; Genotypes; imaging flow cytometry; Inorganic carbon; Localization; Metabolism; microalgae; Mitochondria; Mitochondria - metabolism; Morphology; Mutants; Neural networks; Neural Networks, Computer; Phenotypes; Protein Transport; Relocation; microalgae; deep learning; imaging flow cytometry; cell sorting
• ISSN: 1552-4922; 1552-4930; 1552-4930
• DOI: 10.1002/cyto.a.24661

Organelle positioning in cells is associated with various metabolic functions and signaling in unicellular organisms. Specifically, the microalga Chlamydomonas reinhardtii repositions its mitochondria, depending on the levels of inorganic carbon. Mitochondria are typically randomly distributed in the Chlamydomonas cytoplasm, but relocate toward the cell periphery at low inorganic carbon levels. This mitochondrial relocation is linked with the carbon‐concentrating mechanism, but its significance is not yet thoroughly understood. A genotypic understanding of this relocation would require a high‐throughput method to isolate rare mutant cells not exhibiting this relocation. However, this task is technically challenging due to the complex intracellular morphological difference between mutant and wild‐type cells, rendering conventional non‐image‐based high‐event‐rate methods unsuitable. Here, we report our demonstration of intelligent image‐activated cell sorting by mitochondrial localization. Specifically, we applied an intelligent image‐activated cell sorting system to sort for C. reinhardtii cells displaying no mitochondrial relocation. We trained a convolutional neural network (CNN) to distinguish the cell types based on the complex morphology of their mitochondria. The CNN was employed to perform image‐activated sorting for the mutant cell type at 180 events per second, which is 1–2 orders of magnitude faster than automated microscopy with robotic pipetting, resulting in an enhancement of the concentration from 5% to 56.5% corresponding to an enrichment factor of 11.3. These results show the potential of image‐activated cell sorting for connecting genotype–phenotype relations for rare‐cell populations, which require a high throughput and could lead to a better understanding of metabolic functions in cells. Intelligent image‐activated cell sorting (iIACS) based on the complex morphology of the mitochondria in C. reinhardtii. Using a CNN with a 5 ms processing time, we achieved enrichment of C. reinhardtii populations from 50% to 80.5% and from 5% to 56.5%, resulting in an enrichment factor of 1.6, and 11.3 respectively. These results show the capability iIACS for high event‐rate sorting based on complex intracellular morphologies.