. We show that this responsiveness arises because the SAC primes kinetochore phosphatases to induce negative feedback and silence its own signal. Active SAC signalling recruits PP2A-B56 to kinetochores where it antagonises Aurora B to promote PP1 recruitment. PP1 in turn silences the SAC and delocalises PP2A-B56. Preventing or bypassing key regulatory steps demonstrates that this spatiotemporal control of phosphatase feedback underlies rapid signal switching at the kinetochore by; 1) allowing the SAC to quickly transition to the ON state in the absence of antagonising phosphatase activity, and 2) ensuring phosphatases are then primed to rapidly switch the SAC signal OFF when kinetochore kinase activities are diminished by force-producing microtubule attachments.The spindle assembly checkpoint (SAC) is globally activated at mitotic entry and only extinguished when all kinetochores have established force-producing microtubule attachments 2,3 . At each individual kinetochore however, the SAC responses are much more dynamic. Here, localised SAC signalling switches rapidly between the ON and OFF states depending on microtubule occupancy [2][3][4][5][6] .Exactly how kinetochores manage to achieve this rapid signal switching remains unknown. To address this we initially focussed on characterising the kinetochore phosphatases responsible for SAC silencing in mammalian cells. We performed a targeted screen with siRNAs to 222 individual phosphatase subunits to identify those that regulate mitotic exit in mammalian cells. 48 hours after siRNA transfection, cells were synchronised in mitosis using the microtubule poison nocodazole, after which mitotic exit was forced by the small molecule MPS1 inhibitor reversine 7 for 1 hour. (Fig.1b). Live monitoring of endogenous Cyclin B1 levels 16 showed that B56 depletion prevented efficient APC/C activation following MPS1 inhibition (Fig.1c). This indicated that PP2A-B56 depletion did not simply delay mitotic exit, but in fact prevented SAC silencing. PP2A-B56 has recently been shown to localise to the outer kinetochore via interaction with a short phosphorylated motif in BUBR1 (termed KARD) [17][18][19] . We found that all B56 isoforms that we tested (B56, ,  1 ,  3 , ) localised to the centromere/kinetochores regions of mitotic chromosomes, with some more clearly enriched on kinetochores than others (B56 1 ,  3 , ; Supplementary Fig.1a). We next deleted the B56 binding motif from BUBR1 (BUBR1 ∆KARD ; Supplementary Fig.1b-d), which specifically abolished B56 kinetochore localisation (Fig.1d, e and supplementary fig.1e), and delayed mitotic exit following MPS1 inhibition with either reversine (Fig.1f) or the distinct inhibitor ( Supplementary Fig.1f). These delays were accentuated by concomitant B56 depletion, which even allowed cells to mount a prolonged arrest with a high dose of reversine or . This was unrelated to effects on centromeric PP2A-B56 because SGO1 depletion caused mitotic arrest due to reduced centromeric PP2A and loss of sister chromatid cohesion, as expe...

Single-cell methods are beginning to reveal the intrinsic heterogeneity in cell populations, arising from the interplay of deterministic and stochastic processes. However, it remains challenging to quantify single-cell behaviour from time-lapse microscopy data, owing to the difficulty of extracting reliable cell trajectories and lineage information over long time-scales and across several generations. Therefore, we developed a hybrid deep learning and Bayesian cell tracking approach to reconstruct lineage trees from live-cell microscopy data. We implemented a residual U-Net model coupled with a classification CNN to allow accurate instance segmentation of the cell nuclei. To track the cells over time and through cell divisions, we developed a Bayesian cell tracking methodology that uses input features from the images to enable the retrieval of multi-generational lineage information from a corpus of thousands of hours of live-cell imaging data. Using our approach, we extracted 20,000 + fully annotated single-cell trajectories from over 3,500 h of video footage, organised into multi-generational lineage trees spanning up to eight generations and fourth cousin distances. Benchmarking tests, including lineage tree reconstruction assessments, demonstrate that our approach yields high-fidelity results with our data, with minimal requirement for manual curation. To demonstrate the robustness of our minimally supervised cell tracking methodology, we retrieve cell cycle durations and their extended inter- and intra-generational family relationships in 5,000 + fully annotated cell lineages. We observe vanishing cycle duration correlations across ancestral relatives, yet reveal correlated cyclings between cells sharing the same generation in extended lineages. These findings expand the depth and breadth of investigated cell lineage relationships in approximately two orders of magnitude more data than in previous studies of cell cycle heritability, which were reliant on semi-manual lineage data analysis.