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Scientists at Calico have developed an automated image analysis tool called Nellie to help researchers study organelles, the tiny structures inside cells that perform vital functions. Tracking organelles is difficult due to their small size and constant movement, but Nellie simplifies this by offering segmentation, tracking, and feature extraction from 2D and 3D live-cell microscopy images.
A Senior Data Scientist created Nellie by combining elements from various specialized tools, allowing it to analyze a wide range of organelles without requiring manual adjustments. The tool was tested on different applications, including studying drug-induced changes in mitochondria and endoplasmic reticulum dynamics, proving to be more accurate than existing solutions.
Nellie was released online last year and has now been published in Nature Methods. The tool has been downloaded over 900 times from GitHub, showing its growing impact among researchers. It eliminates the need for extensive parameter tuning, making advanced image analysis more accessible.
One of Nellie’s key strengths is its ability to analyze different organelle types from a single fluorescence channel without requiring adjustments, streamlining complex cellular studies. Scientists aim to further improve Nellie by integrating deep-learning capabilities and enhancing its ability to study organelle interactions.
Future developments will focus on enabling simultaneous analysis of multiple organelles, which could provide a more detailed understanding of how cellular structures interact over time. The ultimate goal is to use Nellie to drive new discoveries in cell biology and aging research.
