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Abstract Cellular senescence is a phenotypic state that contributes to the progression of age-related disease through secretion of pro-inflammatory factors known as the senescence-associated secretory phenotype (SASP). Understanding the process by which healthy cells become senescent and develop SASP factors is critical for improving the identification of senescent cells and, ultimately, understanding tissue dysfunction. Here, we reveal how the duration of cellular stress modulates the SASP in distinct subpopulations of senescent cells. We used multiplex, single-cell imaging to build a proteomic map of senescence induction in human epithelial cells induced to senescence over the course of 31 days. We map how the expression of SASP proteins increases alongside other known senescence markers such as p53, p21, and p16^INK4a. The aggregated population of cells responded to etoposide with an accumulation of stress response factors over the first 11 days, followed by a plateau in most proteins. At the single-cell level, however, we identified two distinct senescence cell populations, one defined primarily by larger nuclear area and the second by higher protein concentrations. Trajectory inference suggested that cells took one of two discrete molecular paths from unperturbed healthy cells, through a common transitional subpopulation, and ending at the discrete terminal senescence phenotypes. Our results underscore the importance of using single-cell proteomics to identify the mechanistic pathways governing the transition from senescence induction to a mature state of senescence characterized by the SASP. 

Abstract BackgroundCurrent methods for analyzing single-cell datasets have relied primarily on static gene expression measurements to characterize the molecular state of individual cells. However, capturing temporal changes in cell state is crucial for the interpretation of dynamic phenotypes such as the cell cycle, development, or disease progression. RNA velocity infers the direction and speed of transcriptional changes in individual cells, yet it is unclear how these temporal gene expression modalities may be leveraged for predictive modeling of cellular dynamics. ResultsHere, we present the first task-oriented benchmarking study that investigates integration of temporal sequencing modalities for dynamic cell state prediction. We benchmark ten integration approaches on ten datasets spanning different biological contexts, sequencing technologies, and species. We find that integrated data more accurately infers biological trajectories and achieves increased performance on classifying cells according to perturbation and disease states. Furthermore, we show that simple concatenation of spliced and unspliced molecules performs consistently well on classification tasks and can be used over more memory intensive and computationally expensive methods. ConclusionsThis work illustrates how integrated temporal gene expression modalities may be leveraged for predicting cellular trajectories and sample-associated perturbation and disease phenotypes. Additionally, this study provides users with practical recommendations for task-specific integration of single-cell gene expression modalities. 

Abstract Eukaryotic chromosomes contain regions of varying accessibility, yet DNA replication factors must access all regions. The first replication step is loading MCM complexes to license replication origins during the G1 cell cycle phase. It is not yet known how mammalian MCM complexes are adequately distributed to both accessible euchromatin regions and less accessible heterochromatin regions. To address this question, we combined time-lapse live-cell imaging with immunofluorescence imaging of single human cells to quantify the relative rates of MCM loading in euchromatin and heterochromatin throughout G1. We report here that MCM loading in euchromatin is faster than that in heterochromatin in early G1, but surprisingly, heterochromatin loading accelerates relative to euchromatin loading in middle and late G1. This differential acceleration allows both chromatin types to begin S phase with similar concentrations of loaded MCM. The different loading dynamics require ORCA-dependent differences in origin recognition complex distribution. A consequence of heterochromatin licensing dynamics is that cells experiencing a truncated G1 phase from premature cyclin E expression enter S phase with underlicensed heterochromatin, and DNA damage accumulates preferentially in heterochromatin in the subsequent S/G2 phase. Thus, G1 length is critical for sufficient MCM loading, particularly in heterochromatin, to ensure complete genome duplication and to maintain genome stability.