Tumors arise from uncontrolled cell proliferation driven by mutations in genes that regulate stem cell renewal and differentiation. Intestinal tumors, however, retain some hierarchical organization, maintaining both cancer stem cells (CSCs) and cancer differentiated cells (CDCs). This heterogeneity, coupled with cellular plasticity enabling CDCs to revert to CSCs, contributes to therapy resistance and relapse. Using genetically encoded fluorescent reporters in human tumor organoids, combined with our machine-learning-based cell tracker, CellPhenTracker, we simultaneously traced cell-type specification, metabolic changes, and reconstructed cell lineage trajectories during tumor organoid development. Our findings reveal distinctive metabolic phenotypes in CSCs and CDCs. We find that lactate regulates tumor dynamics, suppressing CSC differentiation and inducing dedifferentiation into a proliferative CSC state. Mechanistically, lactate increases histone acetylation, epigenetically activating MYC. Given that lactate's regulation of MYC depends on the bromodomain-containing protein 4 (BRD4), targeting cancer metabolism and BRD4 inhibitors emerge as a promising strategy to prevent tumor relapse.

Cell tracking is an indispensable tool for studying development by time-lapse imaging. However, existing cell trackers cannot assign confidence to predicted tracks, which prohibits fully automated analysis without manual curation. We present a fundamental advance: an algorithm that combines neural networks with statistical physics to determine cell tracks with error probabilities for each step in the track. From these, we can obtain error probabilities for any tracking feature, from cell cycles to lineage trees, that function like P values in data interpretation. Our method, OrganoidTracker 2.0, greatly speeds up tracking analysis by limiting manual curation to rare low-confidence tracking steps. Importantly, it also enables fully automated analysis by retaining only high-confidence track segments, which we demonstrate by analyzing cell cycles and differentiation events at scale for thousands of cells in multiple intestinal organoids. Our approach brings cell dynamics-based organoid screening within reach and enables transparent reporting of cell-tracking results and associated scientific claims.

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.

Linking single-cell genomic or transcriptomic profiles to functional cellular characteristics, in particular time-varying phenotypic changes, could help unravel molecular mechanisms driving the growth of tumour-cell subpopulations. Here we show that a custom-built optical microscope with an ultrawide field of view, fast automated image analysis and a dye activatable by visible light enables the screening and selective photolabelling of cells of interest in large heterogeneous cell populations on the basis of specific functional cellular dynamics, such as fast migration, morphological variation, small-molecule uptake or cell division. Combining such functional single-cell selection with single-cell RNA sequencing allowed us to (1) functionally annotate the transcriptomic profiles of fast-migrating and spindle-shaped MCF10A cells, of fast-migrating MDA-MB-231 cells and of patient-derived head-and-neck squamous carcinoma cells, and (2) identify critical genes and pathways driving aggressive migration and mesenchymal-like morphology in these cells. Functional single-cell selection upstream of single-cell sequencing does not depend on molecular biomarkers, allows for the enrichment of sparse subpopulations of cells, and can facilitate the identification and understanding of the molecular mechanisms underlying functional phenotypes.

Organoids have emerged as powerful model systems to study organ development and regeneration at the cellular level. Recently developed microscopy techniques that track individual cells through space and time hold great promise to elucidate the organizational principles of organs and organoids. Applied extensively in the past decade to embryo development and 2D cell cultures, cell tracking can reveal the cellular lineage trees, proliferation rates, and their spatial distributions, while fluorescent markers indicate differentiation events and other cellular processes. Here, we review a number of recent studies that exemplify the power of this approach, and illustrate its potential to organoid research. We will discuss promising future routes, and the key technical challenges that need to be overcome to apply cell tracking techniques to organoid biology.

Dormancy is colloquially considered as extending lifespan by being still. Starved yeasts form dormant spores that wake-up (germinate) when nutrients reappear but cannot germinate (die) after some time. What sets their lifespans and how they age are open questions because what processes occur-and by how much-within each dormant spore remains unclear. With single-cell-level measurements, we discovered how dormant yeast spores age and die: spores have a quantifiable gene-expressing ability during dormancy that decreases over days to months until it vanishes, causing death. Specifically, each spore has a different probability of germinating that decreases because its ability to-without nutrients-express genes decreases, as revealed by a synthetic circuit that forces GFP expression during dormancy. Decreasing amounts of molecules required for gene expression-including RNA polymerases-decreases gene-expressing ability which then decreases chances of germinating. Spores gradually lose these molecules because they are produced too slowly compared with their degradations, causing gene-expressing ability to eventually vanish and, thus, death. Our work provides a systems-level view of dormancy-to-death transition.