Our lab specializes in developing advanced image analysis solutions to address complex questions in cellular biology. We design powerful computational tools capable of accurately quantifying cellular dynamics across a variety of experimental conditions, enabling deeper biological insights with greater efficiency and precision.
Through integrating state-of-the-art algorithms with customized analytical workflows, our software significantly streamlines data processing, reduces manual intervention, and ensures reproducibility of results. Whether investigating cell growth, morphological changes, or cellular responses to environmental stresses, our tools empower researchers to explore biological phenomena with exceptional clarity and detail.
Driven by continuous innovation in algorithm development, workflow automation, and user-friendly software design, we equip researchers with the resources needed to confidently pursue ambitious biological research, advancing our collective understanding of cellular systems under diverse conditions.
Fluorescent microscopy time-lapse of bacterial cells dividing in a mother machine. White: phase contrast, Red: cytoplasm, Yellow: foci. Yellow outlines indicate automated segmentation tracking cell boundaries over time.
Project introduction: MMAP (Mother Machine Analysis Platform)
The Mother Machine Analysis Platform (MMAP) is a versatile and user-friendly tool designed to streamline the analysis of microfluidic experiments utilizing the mother machine setup. MMAP enables researchers to efficiently process, analyze, and visualize time-lapse microscopy data with minimal manual intervention while maintaining flexibility for customized adaptations.
MMAP supports a wide range of microscopy data formats, including nd2, tif, and other common file types. It automatically extracts and reads metadata from image files, ensuring accurate data handling while allowing manual modifications when needed. The integrated data viewer facilitates image inspection, cropping, and alignment to optimize downstream analysis.
The protocol incorporates state-of-the-art segmentation algorithms such as ilastik, Omnipose, and DeLTA, enabling robust and precise cell identification across various experimental conditions. Additionally, MMAP offers built-in tools for kymograph generation, allowing users to track cellular dynamics over time effectively.
Designed for high-throughput analysis, MMAP supports batch processing, enabling users to apply pre-set analysis parameters across large datasets efficiently. The software also provides comprehensive output options, generating materials suitable for presentation, further analysis, and evaluation.
By integrating automated workflows with customizable settings, MMAP offers a powerful, adaptable solution for researchers working with microfluidic-based cell imaging, enhancing both data processing efficiency and analytical precision.
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