Biology subjects

Shariati, S. A.

Publications and source records attributed to Shariati, S. A..

5 recordsLinked to original sources

bioRxiv2026

Quantitative CDK2 Dynamics Are Linked to Cell Fate Decisions in Differentiating Trophoblast Stem Cells

During early development of the placenta, a subset of murine trophectoderm stem cells (TSCs) undergo endoreplication, an unusual form of cell division cycle that decouples DNA synthesis from cytokinesis, resulting in physiological polyploidy. Oscillations in CDK2 activity are essential for the orderly progression of the cell cycle to ensure replicated DNA is accurately partitioned into two daughter cells. However, it remains underexplored how the dynamics of CDK2 activity regulate endoreplication in the context of TSCs differentiation. To address this question, we leveraged the variability in cell fate decisions in an established in vitro system of TSCs differentiation that relies on removal of a growth factor, FGF4, to induce endoreplication. Using quantitative single-cell live confocal microscopy of a precise CDK2 biosensor, DHB-Venus, we identified at least three different outcomes upon FG4 removal: self-renewal, endoreplication, and migration. Our quantitative analyses showed high levels of Cdk2 activity in self-renewing cells whereas intermediate DHB-Venus turnover is linked to increased nuclear and cell size, indicating a shift to endoreplication. Importantly, we also characterize a third class of differentiating TSCs with migratory characteristics that correlate with low levels Cdk2 activity without a change in nuclear size. In sum, our results demonstrated a correlation between different fate outcomes and specific thresholds of CDK2 activity. Our findings show that TSCs can distinguish between different outcomes through modulating the central kinase of the cell cycle, CDK2, positioning it as a key regulator of early trophoblast differentiation. Summary StatementThis study investigates the oscillatory behavior of CDK2 activity during murine trophectoderm differentiation and its potential role in guiding cell fate decisions.

developmental biology↗

bioRxiv2026

Direct Mapping of CDK2 Substrates in Embryonic Stem Cells Uncovers an AP-Site Repair Mechanism via HMCES Phosphorylation

Embryonic stem cells (ESCs) proliferate rapidly while robustly maintaining genomic integrity and exhibiting high cell-cycle kinase activity. How this activity contributes to genome integrity remains unclear. Here, using mouse ESCs engineered to express an analog-sensitive CDK2, we combine thiophosphate labeling with mass spectrometry to define a high-confidence CDK2 substrate landscape. We uncovered 65 CDK2 substrates in total, including both known and previously unrecognized substrates. Among these, HMCES, a sensor of apurinic/apyrimidinic (AP) sites, was identified as a specific cyclin E-CDK2 substrate. We mapped three CDK2-dependent phosphorylation sites in HMCES and showed that phosphorylation of these sites decreased HMCES binding to ssDNA. Mutational analysis further revealed that HMCES docks to cyclin E-CDK2 complexes via the hydrophobic patch on cyclin E. Finally, we demonstrated that HMCES phosphorylation contributes to AP-site repair and promotes ESC proliferation. Together, our findings uncover a CDK2-HMCES signaling axis that links rapid cell-cycle progression to the preservation of genome stability in mouse ESCs.

developmental biology↗

bioRxiv2024

Enhanced Cell Tracking Using A GAN-based Super-Resolution Video-to-Video Time-Lapse Microscopy Generative Model

Cells are among the most dynamic entities, constantly undergoing various processes such as growth, division, movement, and interaction with other cells as well as the environment. Time-lapse microscopy is central to capturing these dynamic behaviors, providing detailed temporal and spatial information that allows biologists to observe and analyze cellular activities in real-time. The analysis of time-lapse microscopy data relies on two fundamental tasks: cell segmentation and cell tracking. Integrating deep learning into bioimage analysis has revolutionized cell segmentation, producing models with high precision across a wide range of biological images. However, developing generalizable deep-learning models for tracking cells over time remains challenging due to the scarcity of large, diverse annotated datasets of time-lapse movies of cells. To address this bottleneck, we propose a GAN-based time-lapse microscopy generator, termed tGAN, designed to significantly enhance the quality and diversity of synthetic annotated time-lapse microscopy data. Our model features a dual-resolution architecture that adeptly synthesizes both low and high-resolution images, uniquely capturing the intricate dynamics of cellular processes essential for accurate tracking. We demonstrate the performance of tGAN in generating high-quality, realistic, annotated time-lapse videos. Our findings indicate that tGAN decreases dependency on extensive manual annotation to enhance the precision of cell tracking models for time-lapse microscopy.

systems biology↗

bioRxiv2024

Self-organization of embryonic stem cells into a reproducible embryo model through epigenome editing

Embryonic stem cells (ESCs) can self-organize in vitro into developmental patterns with spatial organization and molecular similarity to that of early embryonic stages. This self-organization of ESCs requires transmission of signaling cues, via addition of small molecule chemicals or recombinant proteins, to induce distinct embryonic cellular fates and subsequent assembly into structures that can mimic aspects of early embryonic development. During natural embryonic development, different embryonic cell types co-develop together, where each cell type expresses specific fate-inducing transcription factors through activation of non-coding regulatory elements and interactions with neighboring cells. However, previous studies have not fully explored the possibility of engineering endogenous regulatory elements to shape self-organization of ESCs into spatially-ordered embryo models. Here, we hypothesized that cell-intrinsic activation of a minimum number of such endogenous regulatory elements is sufficient to self-organize ESCs into early embryonic models. Our results show that CRISPR-based activation (CRISPRa) of only two endogenous regulatory elements in the genome of pluripotent stem cells is sufficient to generate embryonic patterns that show spatial and molecular resemblance to that of pre-gastrulation mouse embryonic development. Quantitative single-cell live fluorescent imaging showed that the emergence of spatially-ordered embryonic patterns happens through the intrinsic induction of cell fate that leads to an orchestrated collective cellular motion. Based on these results, we propose a straightforward approach to efficiently form 3D embryo models through intrinsic CRISPRa-based epigenome editing and independent of external signaling cues. CRISPRa-Programmed Embryo Models (CPEMs) show highly consistent composition of major embryonic cell types that are spatially-organized, with nearly 80% of the structures forming an embryonic cavity. Single cell transcriptomics confirmed the presence of main embryonic cell types in CPEMs with transcriptional similarity to pre-gastrulation mouse embryos and revealed novel signaling communication links between different embryonic cell types. Our findings offer a programmable embryo model and demonstrate that minimum intrinsic epigenome editing is sufficient to self-organize ESCs into highly consistent pre-gastrulation embryo models

developmental biology↗

bioRxiv2021

DeepSea: An efficient deep learning model for automated cell segmentation and tracking

Dynamics and non-genetic heterogeneity are two fundamental characteristics of basic processes of life such as cell division or differentiation. Time-lapse microscopy is the only method that can directly capture the dynamics and heterogeneity of fundamental cellular processes at the singlecell level with high temporal resolution. Successful application of single-cell time-lapse microscopy requires automated segmentation and tracking of hundreds of individual cells over several time points. Recently, deep learning models have ushered in a new era in the quantitative analysis of microscopy images. However, integrated segmentation and tracking of single cells remain challenges for the analysis of time-lapse microscopy images. This work presents a versatile and trainable deep-learning software, termed DeepSea, that allows for both segmentation and tracking of single cells in sequences of phase-contrast live microscopy images. Our segmentation model can easily be trained to segment phase-contrast images of different cell types with higher precision than existing models. Our tracking model allows for quantification of dynamics of several cell biological features of individual cells, such as cell division cycle, mitosis, cell morphology, and cell size, with high precision using phase-contrast images. We showcase the application of DeepSea by analyzing cell size regulation in embryonic stem cells. Our findings show that embryonic stem cells exhibit cell size control in the G1 phase of the cell cycle despite their unusual fast division cycle. Our training dataset, user-friendly software, and code are available here https://deepseas.org.

systems biology↗