Organoids are powerful models for studying tissue dynamics across multiple cellular generations and offer key insights into organ homeostasis, cellular differentiation and disease. Live imaging of organoids is an essential tool for understanding dynamic processes on the cell level, while still in the tissue context. It allows for simultaneous quantification of gene expression and reconstruction of lineage trees, while tracking cells over space and time. Here, we present protocols for long-term imaging and quantitative analysis of intestinal organoids. Our workflow consists of sample preparation, experimental manipulations (such as drug treatments, laser ablations and fluorescence recovery after photobleaching), long-term live imaging (120 h), in situ fixation and permeabilization, multiplexed antibody staining and single-cell tracking with lineage reconstruction. The workflow is broadly applicable to studying dynamic cellular processes in both 2D and 3D organoids, at timescales from minutes to days. We outline strategies for optimizing organoid health, imaging conditions and experimental interventions to ensure efficient downstream analysis. In addition, we describe how this workflow enables real-time quantification of gene expression using fluorescent reporters and inferring cell type changes by combining multiplexed antibody staining with cell-tracking data. The skills required for following these protocols are organoid culturing and standard live-cell confocal microscopy, and the full experimental protocol takes ~2 weeks. Together, these protocols provide a comprehensive approach for studying cellular behavior, lineage trees, expression dynamics and cell–cell interactions in organoids.

Organoids are a major new tool to study tissue renewal. However, characterizing the underlying differentiation dynamics remains challenging. Here, we developed TypeTracker, which identifies cell fates by AI-enabled cell tracking and propagating end point fates back along the branched lineage trees. Cells that ultimately migrate to the villus commit to their new type early, when still deep inside the crypt, with important consequences: (i) Secretory cells commit before terminal division, with secretory fates emerging symmetrically in sister cells. (ii) Different secretory types descend from distinct stem cell lineages rather than an omnipotent secretory progenitor. (iii) The ratio between secretory and absorptive cells is strongly affected by proliferation after commitment. (iv) Spatial patterning occurs after commitment through type-dependent cell rearrangements. This "commit-then-sort" model contrasts with the conventional conveyor belt picture, where cells differentiate by moving up the crypt-villus axis and hence raises new questions about the underlying commitment and sorting mechanisms.

During renewal of the intestine, cells are continuously generated by proliferation. Proliferation and differentiation must be tightly balanced, as any bias toward proliferation results in uncontrolled exponential growth. Yet, the inherently stochastic nature of cells raises the ques-tion how such fluctuations are limited. We used time-lapse microscopy to track all cells in crypts of growing mouse intestinal organoids for multiple generations, allowing full reconstruction of the underlying lineage dynamics in space and time. Proliferative behavior was highly symmetric between sister cells, with both sisters either jointly ceasing or continuing proliferation. Simulations revealed that such symmetric proliferative behavior minimizes cell number fluctuations, explaining our obser-vation that proliferating cell number remained constant even as crypts increased in size considerably. Proliferative symmetry did not reflect positional symmetry but rather lineage control through the mother cell. Our results indicate a concrete mechanism to balance proliferation and differentiation with minimal fluctuations that may be broadly relevant for other tissues.

Time-lapse microscopy is routinely used to follow cells within organoids, allowing direct study of division and differentiation patterns. There is an increasing interest in cell tracking in organoids, which makes it possible to study their growth and homeostasis at the singlecell level. As tracking these cells by hand is prohibitively time consuming, automation using a computer program is required. Unfortunately, organoids have a high cell density and fast cell movement, which makes automated cell tracking difficult. In this work, a semi-automated cell tracker has been developed. To detect the nuclei, we use a machine learning approach based on a convolutional neural network. To form cell trajectories, we link detections at different time points together using a min-cost flow solver. The tracker raises warnings for situations with likely errors. Rapid changes in nucleus volume and position are reported for manual review, as well as cases where nuclei divide, appear and disappear. When the warning system is adjusted such that virtually error-free lineage trees can be obtained, still less than 2% of all detected nuclei positions are marked for manual analysis. This provides an enormous speed boost over manual cell tracking, while still providing tracking data of the same quality as manual tracking.