Biology subjects

Gligorovski, V.

Publications and source records attributed to Gligorovski, V..

9 recordsLinked to original sources

bioRxiv2026

Love-thy-neighbor: Neural networks for tracking and lineage tracing in budding yeast

Tracking and lineage tracing are widely needed tasks in biological image analysis. For cells that grow and divide, tracking is challenging because cells change in number, shape, and size throughout a recording. As the time interval between images increases, it becomes more difficult to establish correspondences between cells across timepoints. Consequently, tracking has to be performed between consecutive or temporally close images, which leads to exponentially decreasing tracking accuracy and thus high sensitivity to error rates. For budding yeast, this challenge is further heightened by the similarity of cells in colonies, their dense packing, the asymmetric nature of cell divisions, and movement due to growth of the colony. A related task, lineage tracing, is similarly challenging without fluorescent markers due to multiple potential mother cells surrounding a new daughter cell. Here, we present neural networks for budding yeast tracking and lineage tracing, named LYN-track and LYN-trace, respectively. These methods leverage fine geometric features of cells and their neighborhoods. To train and test the algorithms, we recorded and annotated new budding and fission yeast microscopy movies (78,852 frame-to-frame tracklets, 2,512 images), which we make freely available. On these and existing datasets, our neural network-based methods demonstrate robust, above state-of-the-art performance. Both tools have been integrated into graphical user interfaces (GUIs), available on Github, and can be straightforwardly retrained with custom data if desired.

bioinformatics↗

bioRxiv2025

LDLR variant classification through activity-normalized prime editing screening

BackgroundInherited variants in the LDL receptor (LDLR) gene are the most common cause of familial hypercholesterolemia (FH), significantly increasing coronary artery disease risk. Early identification of pathogenic LDLR variants enables prompt intervention with lipid-lowering therapies; however, the majority of LDLR variants observed in the population have uncertain or absent clinical classifications, limiting the potential to improve clinical management. MethodsWe developed an innovative, activity-normalized prime editing screening pipeline to measure the impact of 5,184 LDLR coding variants on LDL-cholesterol (LDL-C) uptake. Through pairing a genotypic outcome reporter with every prime editing guide RNA (pegRNA), we adjust phenotypic measurements to account for variable editing efficiency, extending activity normalization to prime editing for the first time at this scale. Further, we use a statistical estimation approach that leverages measurements for all missense variants at a given position to denoise the resulting scores. ResultsWe show that prime editing-mediated reporter editing correlates with endogenous variant installation frequency, allowing activity normalization to improve imputation of LDLR variant effect. Our optimized prime editing assay identifies a broad, continuous spectrum of variant functional effects. We achieve robust separation of pathogenic vs. benign ClinVar variants and concordance between experimentally derived functional scores and LDL-C levels measured in UK Biobank participants. Further, when calibrating the strength of evidence provided by this functional screening data to align with the ACMG/AMP variant interpretation guidelines, and integrating additional sources of evidence, a majority of currently unclassified rare LDLR variants meet evidence thresholds for reclassification. We use the broad coverage of this screen to gain insight into how apolipoproteins bind to LDLR. In particular, we identify and characterize rare LDLR variants that enhance LDL-C uptake through increased interaction with apolipoprotein B. Finally, we compare prime editing-based functional scores with those derived from recent base editing and cDNA-based LDLR variant screens, showing that these approaches all show robust correlation with clinically observed LDL-C levels and computational scores, while prime editing identifies candidate splice-altering coding variants that are not modeled by cDNA screening. ConclusionsAltogether, our approach demonstrates the power of prime editing to significantly improve understanding of how variants in LDLR impact function and contribute to FH.

genetics↗

bioRxiv2025

Optogenetic tripwires resolve models of how cells count DNA breaks

DNA end resection is a critical step in DNA damage repair and activation of the DNA damage checkpoint (DDC). To date, resection has been studied primarily in bulk cell populations. However, single-cell analyses are essential for uncovering cell-to-cell variability and can powerfully support or contradict system-level models where traditional genetic perturbations face limitations. We present a single-cell method to quantify resection by integrating an optogenetic expression system at defined distances from an inducible double-strand DNA break (DSB) site in the budding yeast genome. Using this system, we test competing, unresolved models of how the DDC counts DSBs and determines when to override the checkpoint. Current models propose that the extent of DNA damage is signaled by resection, either through liberated single-stranded DNA (ssDNA) or proteins bound along the non-resected strand. Although mechanistically plausible and widely known, these models rely on inconclusive evidence from gene knockout studies. An alternative hypothesis is that the DDC counts DSBs digitally, using factors located at 3 break ends or at ss/dsDNA junctions. Here, we leverage natural cell-to-cell variability in resection rates as an intrinsic perturbation, avoiding the limitations of prior genetic approaches. Our single-cell data challenge models in which DNA damage is inferred from the extent or rate of resection or from proteins bound along resected DNA. To explore alternative mechanisms, we investigated DDC proteins localized at 3 DSB ends or ss/dsDNA boundaries. By dynamically depleting candidate proteins after checkpoint arrest, we identified ss/dsDNA boundary proteins Ddc1, Dpb11, and Rad9 as essential for DDC maintenance and promising candidates for a cascade that acts as a digital DSB counter. Our findings demonstrate that quantitative, system-level single-cell approaches, coupled with dynamic perturbations, can resolve fundamental questions in DNA repair and checkpoint signaling.

molecular biology↗

bioRxiv2025

A distinct phase of cyclin B (Cdc13) nuclear export at mitotic entry in S. pombe

In eukaryotes, cell division requires coordination between the nucleus and cytoplasm. Entry into cell division is driven by cyclin-dependent kinases (CDKs), which need a cyclin binding partner for their activity. In Schizosaccharomyces pombe (fission yeast), the B-type cyclin Cdc13 is essential and sufficient for cell cycle progression and is strongly enriched in the nucleus. Here, we show that a fraction of Cdc13 is exported from the nucleus to the cytoplasm just prior to mitosis. This export could be critical to propagate CDK activity throughout the cell. Mutating three Cdc13 nuclear localization signals (NLSs) led to precocious enrichment of Cdc13 in the cytoplasm but did not accelerate mitotic entry, indicating that the export is not sufficient to trigger entry into mitosis. The export coincides with spindle pole body integration into the nuclear envelope and may be required to coordinate nuclear and cytoplasmic signaling required for this integration. The onset and stop of Cdc13 nuclear export are remarkably abrupt, underscoring that S. pombe mitotic entry consists of several switch-like transitions over the course of minutes. Our findings add another instance to the various cyclin nuclear transport events known to occur at critical cell cycle transitions throughout eukaryotes.

cell biology↗

bioRxiv2024

Automated plasmid design for marker-free genome editing in budding yeast

The ease of genome editing has contributed to the popularity of budding yeast as a model organism. However, the palette of selectable markers is in principle limited as most can only be used once. Some markers such as URA3 and TRP1 can be recycled through counterselection. This permits seamless genome modification with pop-in/pop-out (PIPO), in which a DNA construct first integrates in the genome and, subsequently, homologous regions recombine and excise undesired sequences. Popular approaches for creating such constructs use oligonucleotides and polymerase chain reaction (PCR). The drawbacks are that long oligonucleotides are unstable, can form secondary structures that interfere with PCR, cannot be regenerated in a typical biological laboratory, and are only widely available for lengths less than about 120, which limits the homology and efficiency that can be attained. With the rapid reduction in price, synthesizing custom DNA sequences in specific plasmid backbones has become an appealing alternative. For designing plasmids for seamless PIPO gene tagging or deletion, there are a number of factors to consider. To create only the shortest DNA sequences necessary, avoid errors in manual design, specify the amount of homology desired, and customize restriction sites, we created the computational tool PIPOline. Using it, we tested the ratios of homology that improve pop-out efficiency when targeting the genes HTB2 or WHI5. We supply optimal PIPO plasmid sequences for tagging or deleting almost all S288C budding yeast open reading frames (ORFs). Finally, we demonstrate how the histone variant Htb2 marked with a red fluorescent protein can be used as a cell-cycle stage marker, alternative to superfolder GFP (sfGPF), reducing light toxicity. We expect PIPOline to streamline genome editing in budding yeast.

bioinformatics↗

bioRxiv2024

Light-directed evolution of dynamic, multi-state, and computational protein functionalities

Directed evolution is a powerful method in biological engineering. Current approaches draw on time-invariant selection mechanisms, ideal for evolving steady-state properties such as enzymatic activity or fluorescence intensity. A fundamental problem remains how to continuously evolve dynamic, multi-state, or computational functionalities, e.g., on-off kinetics, state-specific activity, stimulus-responsiveness, or switching and logic capabilities. These require selection pressure on all of the states of a protein of interest (POI) and the transitions between them. We realized that optogenetics and cell cycle oscillations could be leveraged for a novel directed evolution paradigm ( optovolution) that is germane for this need: We designed a signaling cascade in budding yeast where optogenetic input switches the POI between off (0) and on (1) states. In turn, the POI controls a Cdk1 cyclin, which in the re-engineered cell cycle system is essential for one cell cycle stage but poisonous for another. Thus, the cyclin must oscillate (1-0-1-0...) for cell proliferation. In this system, evolution can act efficiently on the POIs different states, input-output relations, and dynamics on the timescale of minutes in every cell cycle. Further, controlling the pacemaker, light, directs and tunes selection pressures. Optovolution is in vivo, continuous, self-selecting, and efficient. We first evolved two optogenetic systems, which relay 0/1 input to 0/1 output: We obtained 19 new variants of the LOV transcription factor El222 that were stronger, less leaky, or green light responsive in vivo. We demonstrate the utility of the latter mutations for orthogonal color-multiplexing with only LOV domains for the first time. Evolving the PhyB-Pif3 optogenetic system, we discovered that loss of YOR1 makes supplementing the chromophore phycocyanobilin (PCB) unnecessary. Finally, we demonstrate the generality of the method by evolving a destabilized rtTA transcription factor, which performs an AND operation between transcriptional and doxycycline input. Optovolution makes coveted, difficult-to-change protein functionalities continuously evolvable.

synthetic biology↗

bioRxiv2020

Multidimensional single-cell benchmarking of inducible promoters for precise dynamic control in budding yeast

For quantitative systems biology, simultaneous readout of multiple cellular processes as well as precise, independent control over different genes activities are essential. In contrast to readout systems such as fluorescent proteins, control systems such as inducible transcription-factor-promoter systems have only been characterized in an ad hoc fashion, impeding precise system-level manipulations of biological systems and reliable modeling. We designed and performed systematic benchmarks involving easy-to-communicate units to characterize and compare inducible transcriptional systems. We built a comprehensive single-copy library of inducible systems controlling standardized fluorescent protein expression in budding yeast, including GAL1pr, GALL, MET3pr, CUP1pr, PHO5pr, tetOpr, terminator-tetOpr, Z3EV system, the blue-light optogenetic systems El222-LIP, El222-GLIP and the red-light inducible PhyB-PIF3 system. To analyze these systems dynamic properties, we performed high-throughput time-lapse microscopy. The analysis of >100 000 cell images was made possible by the recently developed convolutional neural network YeaZ. We report key kinetic parameters, scaling of noise levels, impacts on growth, and, crucially, the fundamental leakiness of each system. Our multidimensional benchmarking additionally uncovers unexpected disadvantages of widely used tools, e.g., nonmonotonic activity of the MET3 and GALL promoters, slow off kinetics of the doxycycline and estradiol-inducible systems tetOpr and Z3EV, and high variability of PHO5pr and red-light activated PhyB-PIF3 system. We introduce two new tools for controlling gene expression: strongLOV, a more light-sensitive El222 mutant, and ARG3pr that functions as an OR gate induced by the lack of arginine or presence of methionine. To demonstrate the ability to finely control genetic circuits, we experimentally tuned the time between cell cycle Start and mitotic entry in budding yeast, artificially simulating near-wild-type timing. The characterizations presented here define the compromises that need to be made for quantitative experiments in systems and synthetic biology. To calibrate perturbations across laboratories and to allow new inducible systems to be benchmarked, we deposited single-copy reporter yeast strains, plasmids, and computer analysis code in public repositories. Furthermore, this resource can be accessed and expanded through the website https://promoter-benchmark.epfl.ch/.

synthetic biology↗

bioRxiv2020

Optimizing checkpoint strategies based on first principles predicts experimental DNA damage checkpoint override times

Why biological quality-control systems fail is often mysterious. Specifically, checkpoints such as the DNA damage checkpoint or the spindle assembly checkpoint are overriden after prolonged arrests allowing cells to continue dividing despite the continued presence of errors.1-4 Although critical for biological systems, checkpoint override is poorly understood quantitatively by experiment or theory. Override may represent a trade-off between risk and speed, a fundamental principle explaining biological phenomena.5,6 Here, we derive the first, general theory of optimal checkpoint strategies, balancing risk and opportunities for growth. We demonstrate that the mathematical problem of finding the optimal strategy maps onto the question of calculating the optimal absorbing boundary for a random walk, which we show can be solved efficiently recursively. The theory predicts the optimal override strategy without any free parameters based on two inputs, the statistics i) of error correction and ii) of survival. We apply the theory to the prominent example of the DNA damage checkpoint in budding yeast (Saccharomyces cerevisiae) experimentally. Using a novel fluorescent construct which allowed cells with DNA breaks to be isolated by flow cytometry, we quantified i) the probability distribution function of repair for a double-strand DNA break (DSB), including for the critically important, rare events deep in the tail of the distribution, as well as ii) the survival probability if the checkpoint was overridden. Based on these two measurements, the optimal checkpoint theory predicted remarkably accurately the DNA damage checkpoint override times as a function of DSB numbers, which we measured precisely at the single-cell level. Our multi-DSB results refine well-known bulk culture measurements7 and show that override is a more general phenomenon than previously thought. Further, we show for the first time that override is an advantageous strategy in cells with wild-type DNA repair genes. The universal nature of the balance between risk and self-replication opportunity is in principle relevant to many other systems, including other checkpoints, developmental decisions8, or reprogramming of cancer cells9, suggesting potential further applications of the theory.

systems biology↗

bioRxiv2020

YeaZ: A convolutional neural network for highly accurate, label-free segmentation of yeast microscopy images

The processing of microscopy images constitutes a bottleneck for large-scale experiments. A critical step is the establishment of cell borders ( segmentation), which is required for a range of applications such as growth or fluorescent reporter measurements. For the model organism budding yeast (Saccharomyces cerevisiae), a number of methods for segmentation exist. However, in experiments involving multiple cell cycles, stress, or various mutants, cells crowd or exhibit irregular visible features, which necessitate frequent manual corrections. Furthermore, budding events are visually subtle but important to detect. Convolutional neural networks (CNNs) have been successfully employed for a range of image processing applications. They require large, diverse training sets. Here, we present i) the first set of publicly available, high-quality segmented yeast images (>10000 cells) including mutants, stressed cells, and time courses, ii) a corresponding U-Net-based CNN, iii) a Python-based graphical user interface (GUI) to efficiently use the system, and iv) a web application to test it (www.quantsysbio.com). A key feature is a cell-cell boundary test which avoids the need for additional input from fluorescent channels. A bipartite graph matching algorithm tracks cells in time with high reliability. Our network is highly accurate and outperforms existing methods on benchmark images recorded by others, suggesting it transfers well to other conditions. Furthermore, new buds are detected early with high reliability. We apply the system to detect differences in geometry between wild-type and cyclin mutant cells. Our results indicate that morphogenesis control occurs unexpectedly early in the cell cycle and is gradual, demonstrating how the efficient processing of large numbers of cells uncovers new biology. Our system can serve as a resource to the community, expanded continuously with new images. Furthermore, the techniques we develop here are likely to be useful for other organisms as well. The identification of cell borders ( segmentation) in microscopy images constitutes a bottleneck for large-scale experiments. For the model organism Saccharomyces cerevisiae, current segmentation methods face challenges when cells bud, crowd, or exhibit irregular features. Here, we present i) the first set of publicly available, high-quality segmented yeast images (>10000 cells) including mutants, stressed cells, and time courses, ii) a corresponding convolutional neural network (CNN), iii) a graphical user interface and a web application (www.quantsysbio.com) to efficiently employ, test, and expand the system. A key feature is a cell-cell boundary test which avoids the need for fluorescent markers. Our CNN is highly accurate, including for buds, and outperforms existing methods on benchmark images, indicating it transfers well to other conditions. To demonstrate how efficient, large-scale image processing uncovers new biology, we analyzed the geometries of {approx}2200 wild-type and cyclin mutant cells and found that morphogenesis control occurs unexpectedly early and gradually.

bioinformatics↗