Label-free TIMING: an Efficient, Reliable and Scalable AI workflow for Automated Profiling of Cell-cell interaction behaviors in Nanowell Arrays

Anuj S Todkar¹Shyam Reddy Kotha²Lin Bai¹Saikiran Mandula²Hannah Wilson²Daniel D Meyer²Rebecca Berdeaux²Badrinath Roysam^1*Navin Varadarajan^1*

• ¹University of Houston, Houston, United States
• ²CellChorus Inc, Houston, United States

Time-lapse imaging microscopy in nanowell grids (TIMING) is an integrated method for dynamic profiling of live immune–target cell interactions at single-cell resolution with broad applications and impact in immunology, immunotherapy and infectious diseases. Notwithstanding these applications, the current TIMING workflows necessitate fluorescent labeling of cells for automated image analysis operations including cell classification, segmentation, and tracking. Leveraging advances in computer vision methods for label-free phase contrast time-lapse microscopy and constraints specific to TIMING, especially spatial confinement of interacting cell cohorts in an array of nanoliter-capacity wells (nanowells); and temporal consistency, we show that TIMING analysis can now be performed in a fully label-free manner, with an accuracy comparable to the fluorescence-based TIMING. The proposed label-free TIMING (LF-TIMING) method offers reduced cellular phototoxicity and fluorescence photobleaching, reduced dye-induced artifacts that can interfere with physiological accuracy and enhanced live-cell imaging duration by eliminating reliance on fluorescent labels. Importantly, it expands the versatility of TIMING by enabling direct profiling of precious patient derived cells without the need for labeling while also freeing up fluorescence channels for investigating experimental structural or functional reporters, thus extending the molecular/subcellular features that can be profiled.