Daniel Needleman

Abstract Cells adjust their internal circuits in response to changes in their environment. Hence, exposing cells to changing conditions provides a way to probe the intrinsic dynamics of cellular internal circuits. Metabolic networks are examples of such circuits since metabolic fluxes dynamically adjust when environmental conditions are transiently altered. Most existing theoretical frameworks focus on cellular metabolic steady states and do not consider the dynamics of changes in metabolic fluxes. In this work, we applied transfer function analysis from control theory to analyze the changes of NADH oxidative fluxes in the mitochondria and cytoplasm in mouse oocytes in response to dynamical perturbations of oxygen depletion and recovery. We observed an overshoot of NADH oxidative flux in the cytoplasm upon oxygen recovery which is absent in the mitochondrial NADH oxidative flux. Metabolic perturbation experiments and transfer function analysis indicate that this cytoplasmic NADH overshoot results from the coupling of the mitochondrial and cytoplasmic NADH cycles. The degree of overshoot is determined by competing timescales associated with the exchange rates of lactate and pyruvate with the media and their interconversion rates catalyzed by lactate dehydrogenase. Applying control theory to the data enables the inference of the exchange and conversion rates of pyruvate and lactate, allowing predictions of the contribution of lactate to mitochondrial respiration. Our work indicates that the oocytes maintain a homeostatic respiration rate across nutrient conditions by modulating the contribution of lactate to mitochondrial respiration.

While methods exist to indirectly quantify the metabolism of biological systems, directly measuring metabolic rates in living samples remains challenging. Here, we describe a calorimetric sensor with a sensitivity of ~100 pW at 23.1 mHz, suitable for measurements on living organisms, surpassing previously reported sensitivities. The sensor measures minute temperature differences between a capillary that contains the sample and two reference capillaries, directly relating this temperature difference to the heat produced by the sample. The sensor provides high responsivity (23 to 100 nV/nW), a fast thermal response time (~7.9 s), and supports real-time, long-term monitoring of biological processes, such as the proliferation and growth of small numbers of bacteria. These capabilities offer opportunities to advance our understanding of complex biological phenomena. We demonstrate the utility of the sensor by measuring the growth rate of Escherichia coli . The technique enables estimation of the oxygen consumption rate per cell, the heat production per cell, and the associated contributions from respiration and fermentation. We further show how the growth rate changes in response to different concentrations of chloramphenicol, rifampicin, and ampicillin, three antibiotics with distinct mechanisms of action. The sensor shows significant potential for determining the minimum inhibitory concentrations of antibiotics, performing antibiotic susceptibility testing of pathogens, and enabling fundamental studies of microorganism metabolism.

Abstract Microtubule asters are central to positioning organelles and organizing intracellular architectures. We show that the multi-stage choreography of microtubule asters which positions pronuclei during the first cell division in Caenorhabditis elegans , is governed by a cellular analog of geometric optics. Large-scale electron tomography reveals that astral microtubules reach cortical and pronuclear surfaces largely along line-of-sight trajectories, leaving complementary regions shadowed and inaccessible. Laser ablation identifies surface-anchored motors pulling on microtubules as the dominant drivers of motion. We develop a biophysical model incorporating dynamic terminator curves that separate microtubule-accessible and -inaccessible surfaces, and quantitatively recapitulate aster separation, pronuclear migration, centering, and rotation in control and genetically perturbed embryos. More broadly, robust positioning emerges from a feedback loop wherein intracellular geometry gates microtubule access, thus sculpting the distribution and magnitude of pulling forces, which in turn reshape that geometry.

Abstract Chromosome segregation is a tightly-regulated process that normally occurs with high fidelity. Errors in chromosome segregation are associated with aging, cancer, and infertility. Initially erroneously attached chromosomes are corrected over the course of mitosis, with the spindle assembly checkpoint preventing entry into anaphase until this error correction is complete. Despite extensive work on the molecular basis of error correction and the spindle assembly checkpoint, it is still unclear how disruption of these processes contribute to chromosome segregation errors. Here, we develop and experimentally test a coarse-grained model of error correction in the presence of a faulty spindle assembly checkpoint. We use the resulting model to disentangle the impact of various small molecule and genetic perturbations on both error correction and the spindle assembly checkpoint, and to compare chromosomally stable hTERT-RPE-1 cells and chromosomally unstable U2-OS cells. We find that the probability of error-free chromosome segregation is determined by the ratio of the checkpoint failure rate to the error correction rate, and validate a simple heuristic for understanding the source of chromosome segregation errors: perturbations which cause errors by disrupting the spindle assembly checkpoint decrease anaphase times, while those that disrupt error correction increase anaphase times. Taken together, this work provides a quantitative framework for understanding how error correction and the spindle assembly checkpoint contribute to mitotic timing and fidelity.

How thousands of microtubules (MTs) and molecular motors self-organize into spindles remains poorly understood. By combining static, nanometer-resolution, large-scale electron tomography reconstructions and dynamic, optical-resolution, polarized light microscopy, we test an active liquid crystal continuum theory of mitotic spindles in human tissue culture cells. At micron length scales, probed by optical microscopy, the continuum theory accurately captures spindle morphology and fluctuation spectra, indicating that local interactions-polymerization, alignment, diffusion, and polar transport-govern the collective behaviors of MTs in human mitotic spindles. Electron tomography data enables tests of the continuum theory at submicron scales, revealing that chromosome-attached kinetochore microtubules (KMTs) show distinctive lateral organization not explained by the coarse-grained theory, while the non-KMTs that make up the bulk of the spindle follow the theory down to ∼300 nm length scales. At length scales below ∼300 nm, fluctuations arising from the intrinsic discreteness of the microtubule ensemble dominate over the collective correlations predicted from the continuum theory. Taken together, these findings show that an active liquid-crystal theory can quantitatively capture the self-organization of human mitotic spindles on long length scales and provides a means to measure the spindle's material properties, while also pointing to the existence of additional processes contributing to the behaviors of KMTs.

In-vitro fertilization (IVF) has become standard practice to address infertility, which affects more than one in ten couples in the US. However, current protocols yield relatively low success rates of about 20% per treatment cycle. A critical but complex and time-consuming step is the grading and selection of embryos for implantation. Although incubators with time-lapse microscopy have enabled computational analysis of embryo development, existing automated approaches either require extensive manual annotations or use opaque deep learning models that are hard for clinicians to validate and trust. We present EmbryoProfiler, a visual analytics system collaboratively developed with embryologists, biologists, and machine learning researchers to support clinicians in visually assessing embryo viability from time-lapse microscopy imagery. Our system incorporates a deep learning pipeline that automatically annotates microscopy images and extracts clinically interpretable features relevant for embryo grading. Our contributions include: (1) a semi-automatic, visualization-based workflow that guides clinicians through fertilization assessment, developmental timing evaluation, morphological inspection, and comparative analysis of embryos; (2) innovative interactive visualizations, such as cell-shape plots, designed to facilitate efficient analysis of morphological and developmental characteristics; and (3) an integrated, explainable machine learning classifier offering transparent, clinically-informed embryo viability scoring to predict live birth outcomes. Quantitative evaluation of our classifier and qualitative case studies conducted with practitioners demonstrate that EmbryoProfiler enables clinicians to make better-informed embryo selection decisions, potentially leading to improved clinical outcomes in IVF treatments.

How embryos adapt their internal cellular machinery to reductions in cell size during development remains a fundamental question in cell biology1–11. Here, we use high-resolution lattice light-sheet fluorescence microscopy and automated image analysis to quantify lineage-resolved mitotic spindle and chromosome segregation dynamics from the 2- to 64-cell stages in Caenorhabditis elegans embryos. While spindle length scales with cell size across both wild-type and size-perturbed embryos, chromosome segregation dynamics remain largely invariant, suggesting that distinct mechanisms govern these mitotic processes. Combining femtosecond laser ablation12,13 with large-scale electron tomography14, we find that central spindle microtubules mediate chromosome segregation dynamics and remain uncoupled from cell size across all stages of early development. In contrast, spindle elongation is driven by cortically anchored motor proteins and astral microtubules, rendering it sensitive to cell size12,13,15–17. Incorporating these experimental results into an extended stoichiometric model for both the spindle and chromosomes, we find that allowing only cell size and microtubule catastrophe rates to vary reproduces elongation dynamics across development. The same model also accounts for centrosome separation and pronuclear positioning in the one-cell C. elegans embryo18, spindle-length scaling across nematode species spanning ~100 million years of divergence17, and spindle rotation in human cells19. Thus, a unified stoichiometric framework provides a predictive, mechanistic account of spindle and nuclear dynamics across scales and species.

Abstract While cryo-electron microscopy (cryo-EM) has come to prominence in the last decade due to its ability to resolve biomolecular complexes at atomic resolution, advancements in experimental and computational methods have made cryo-EM promising for investigating intracellular organization and heterogeneous molecular states. A primary challenge for these alternative applications is the development of techniques for cryo-EM data analysis, which are very computationally demanding. To this end, it is advantageous to leverage advanced scientific computing frameworks for statistical analysis. One such framework is JAX, an emerging array-oriented Python numerical computing package for automatic differentiation and vectorization with a growing ecosystem for statistical inference and machine learning. We have developed cryoJAX, a cryo-EM image simulation library for building computational data analysis applications in JAX. CryoJAX is a flexible modeling language for cryo-EM image formation and therefore can support a wide range of data analysis downstream. By integrating with the JAX ecosystem, cryoJAX enables the development and deployment of algorithms for the growing breadth of scientific applications for cryo-EM. Synopsis The authors have developed cryoJAX, a cryo-EM image simulation library for developing data analysis techniques across cryo-EM modalities. CryoJAX is built on JAX, an emerging scientific computing framework in Python well suited for cryo-EM data analysis.

Abstract How embryos adapt their internal cellular machinery to reductions in cell size during development remains a fundamental question in cell biology 1–11 . Here, we use high-resolution lattice light-sheet fluorescence microscopy and automated image analysis to quantify lineage-resolved mitotic spindle and chromosome segregation dynamics from the 2-to 64-cell stages in Caenorhabditis elegans embryos. While spindle length scales with cell size across both wild-type and size-perturbed embryos, chromosome segregation dynamics remain largely invariant, suggesting that distinct mechanisms govern these mitotic processes. Combining femtosecond laser ablation 12,13 with large-scale electron tomography 14 , we find that central spindle microtubules mediate chromosome segregation dynamics and remain uncoupled from cell size across all stages of early development. In contrast, spindle elongation is driven by cortically anchored motor proteins and astral microtubules, rendering it sensitive to cell size 12,13,15–17 . Incorporating these experimental results into an extended stoichiometric model for both the spindle and chromosomes, we find that allowing only cell size and microtubule catastrophe rates to vary reproduces elongation dynamics across development. The same model also accounts for centrosome separation and pronuclear positioning in the one-cell C. elegans embryo 18 , spindle-length scaling across nematode species spanning ~100 million years of divergence 17 , and spindle rotation in human cells 19 . Thus, a unified stoichiometric framework provides a predictive, mechanistic account of spindle and nuclear dynamics across scales and species.

A great deal is known about biochemical aspects of transcription, but we still lack an understanding of how transcription is causally regulated in space and time. A major unanswered question is the extent to which transcription at different locations in the nucleus are independent from each other or, instead, are spatially coordinated. We propose two classes of models of coordination: 1) the shared environment model, in which neighboring loci exhibit coordinated transcriptional dynamics due to sharing the same local biochemical environment; 2) the mechanical crosstalk model, in which forces propagate from one actively transcribing locus to affect transcription of another. Determining the prevalence of the spatial coordination of transcription, and the underlying mechanisms when it occurs, is an exciting challenge in nuclear biophysics.

In clinical In-Vitro Fertilization (IVF), identifying the most viable embryo for transfer is important to increasing the likelihood of a successful pregnancy. Traditionally, this process involves embryologists manually assessing embryos' static morphological features at specific intervals using light microscopy. This manual evaluation is not only time-intensive and costly, due to the need for expert analysis, but also inherently subjective, leading to variability in the selection process. To address these challenges, we develop a multimodal model that leverages both time-lapse video data and Electronic Health Records (EHRs) to predict embryo viability. One of the primary challenges of our research is to effectively combine time-lapse video and EHR data, owing to their inherent differences in modality. We comprehensively analyze our multimodal model with various modality inputs and integration approaches. Our approach will enable fast and automated embryo viability predictions in scale for clinical IVF.

During eukaryotic cell division, a microtubule-based structure called the spindle exerts forces on chromosomes. The best-studied spindle forces, including those responsible for the separation of sister chromatids, are directed parallel to the spindle's long axis. By contrast, little is known about forces perpendicular to the spindle axis, which determine the metaphase plate configuration and thus the location of chromosomes in the subsequent nucleus. Using live-cell microscopy, we find that metaphase chromosomes are spatially anti-correlated in mouse oocyte spindles, evidence of previously unknown long-range forces acting perpendicular to the spindle axis. We explain this observation by showing that the spindle's microtubule network behaves as a nematic liquid crystal and that deformation of the nematic field around embedded chromosomes causes long-range repulsion between them.

A long-standing observation is that in fast-growing cells, respiration rate declines with increasing growth rate and is compensated by an increase in fermentation, despite respiration being more efficient than fermentation. This apparent preference for fermentation even in the presence of oxygen is known as aerobic glycolysis, and occurs in bacteria, yeast, and cancer cells. Considerable work has focused on understanding the potential benefits that might justify this seemingly wasteful metabolic strategy, but its mechanistic basis remains unclear. Here we show that aerobic glycolysis results from the saturation of mitochondrial respiration and the decoupling of mitochondrial biogenesis from the production of other cellular components. Respiration rate is insensitive to acute perturbations of cellular energetic demands or nutrient supplies, and is explained simply by the amount of mitochondria per cell. Mitochondria accumulate at a nearly constant rate across different growth conditions, resulting in mitochondrial amount being largely determined by cell division time. In contrast, glucose uptake rate is not saturated, and is accurately predicted by the abundances and affinities of glucose transporters. Combining these models of glucose uptake and respiration provides a quantitative, mechanistic explanation for aerobic glycolysis. The robustness of specific respiration rate and mitochondrial biogenesis, paired with the flexibility of other bioenergetic and biosynthetic fluxes, may play a broad role in shaping eukaryotic cell metabolism.

International audience

Error correction is central to many biological systems and is critical for protein function and cell health. During mitosis, error correction is required for the faithful inheritance of genetic material. When functioning properly, the mitotic spindle segregates an equal number of chromosomes to daughter cells with high fidelity. Over the course of spindle assembly, many initially erroneous attachments between kinetochores and microtubules are fixed through the process of error correction. Despite the importance of chromosome segregation errors in cancer and other diseases, there is a lack of methods to characterize the dynamics of error correction and how it can go wrong. Here, we present an experimental method and analysis framework to quantify chromosome segregation error correction in human tissue culture cells with live cell confocal imaging, timed premature anaphase, and automated counting of kinetochores after cell division. We find that errors decrease exponentially over time during spindle assembly. A coarse-grained model, in which errors are corrected in a chromosome-autonomous manner at a constant rate, can quantitatively explain both the measured error correction dynamics and the distribution of anaphase onset times. We further validated our model using perturbations that destabilized microtubules and changed the initial configuration of chromosomal attachments. Taken together, this work provides a quantitative framework for understanding the dynamics of mitotic error correction.

The forces that orient the spindle in human cells remain poorly understood due to a lack of direct mechanical measurements in mammalian systems. We use magnetic tweezers to measure the force on human mitotic spindles. Combining the spindle's measured resistance to rotation, the speed at which it rotates after laser ablating astral microtubules, and estimates of the number of ablated microtubules reveals that each microtubule contacting the cell cortex is subject to ∼5 pN of pulling force, suggesting that each is pulled on by an individual dynein motor. We find that the concentration of dynein at the cell cortex and extent of dynein clustering are key determinants of the spindle's resistance to rotation, with little contribution from cytoplasmic viscosity, which we explain using a biophysically based mathematical model. This work reveals how pulling forces on astral microtubules determine the mechanics of spindle orientation and demonstrates the central role of cortical dynein clustering.

STUDY QUESTION: Can fluorescence lifetime imaging microscopy (FLIM) detect associations between the metabolic state of cumulus cell (CC) samples and the clinical outcome of the corresponding embryos? SUMMARY ANSWER: FLIM can detect significant variations in the metabolism of CC associated with the corresponding embryos that resulted in a clinical pregnancy versus those that did not. WHAT IS KNOWN ALREADY: CC and oocyte metabolic cooperativity are known to be necessary for the acquisition of developmental competence. However, reliable CC biomarkers that reflect oocyte viability and embryo developmental competency have yet to be established. Quantitative measures of CC metabolism could be used to aid in the evaluation of oocyte and embryo quality in ART. STUDY DESIGN, SIZE, DURATION: A prospective observational study was carried out. In total, 223 patients undergoing IVF with either conventional insemination or ICSI at a tertiary care center from February 2018 to May 2020 were included, with no exclusion criteria applied. PARTICIPANTS/MATERIALS, SETTING, METHODS: This cohort had a mean maternal age of 36.5 ± 4.4 years and an average oocyte yield of 16.9 (range 1-50). One to four CC clusters from each patient were collected after oocyte retrieval and vitrified. CC metabolic state was assessed using FLIM to measure the autofluorescence of the molecules NAD(P)H and FAD+, which are essential for multiple metabolic pathways. CC clusters were tracked with their corresponding oocytes and associated embryos. Patient age, Day 3 and Day 5/6 embryo morphological grades, and clinical outcomes of embryos with traceable fate were recorded. Nine FLIM quantitative parameters were obtained for each CC cluster. We investigated associations between the FLIM parameters and patient maternal age, embryo morphological rank, ploidy, and clinical outcome, where false discovery rate P-values of <0.05 were considered statistically significant. MAIN RESULTS AND THE ROLE OF CHANCE: A total of 851 CC clusters from 851 cumulus-oocyte complexes from 223 patients were collected. Of these CC clusters, 623 were imaged using FLIM. None of the measured CC FLIM parameters were correlated with Day 3 morphological rank or ploidy of the corresponding embryos, but FAD+ FLIM parameters were significantly associated with morphological rank of blastocysts. There were significant differences for FAD+ FLIM parameters (FAD+ fraction engaged and short lifetime) from CC clusters linked with embryos resulting in a clinical pregnancy compared with those that did not, as well as for CC clusters associated with embryos that resulted in a live birth compared those that did not. LIMITATIONS, REASONS FOR CAUTION: Our data are based on a relatively low number of traceable embryos from an older patient population. Additionally, we only assessed CCs from 1 to 4 oocytes from each patient. Future work in a younger patient population with a larger number of traceable embryos, as well as measuring the metabolic state of CCs from all oocytes from each patient, would provide a better understanding of the potential utility of this technology for oocyte/embryo selection. WIDER IMPLICATIONS OF THE FINDINGS: Metabolic imaging via FLIM is able to detect CC metabolic associations with maternal age and detects variations in the metabolism of CCs associated with oocytes leading to embryos that result in a clinical pregnancy and a live birth versus those that do not. Our findings suggest that FLIM of CCs may be used as a new approach to aid in the assessment of oocyte and embryo developmental competence in clinical ART. STUDY FUNDING/COMPETING INTEREST(S): National Institutes of Health grant NIH R01HD092550-03 (to C.R., and D.J.N.). Becker and Hickl GmbH and Boston Electronics sponsored research with the loaning of equipment for FLIM. D.J.N. and C.R. are inventors on patent US20170039415A1. TRIAL REGISTRATION NUMBER: N/A.

STUDY QUESTION: Can the BlastAssist deep learning pipeline perform comparably to or outperform human experts and embryologists at measuring interpretable, clinically relevant features of human embryos in IVF? SUMMARY ANSWER: The BlastAssist pipeline can measure a comprehensive set of interpretable features of human embryos and either outperform or perform comparably to embryologists and human experts in measuring these features. WHAT IS KNOWN ALREADY: Some studies have applied deep learning and developed 'black-box' algorithms to predict embryo viability directly from microscope images and videos but these lack interpretability and generalizability. Other studies have developed deep learning networks to measure individual features of embryos but fail to conduct careful comparisons to embryologists' performance, which are fundamental to demonstrate the network's effectiveness. STUDY DESIGN, SIZE, DURATION: We applied the BlastAssist pipeline to 67 043 973 images (32 939 embryos) recorded in the IVF lab from 2012 to 2017 in Tel Aviv Sourasky Medical Center. We first compared the pipeline measurements of individual images/embryos to manual measurements by human experts for sets of features, including: (i) fertilization status (n = 207 embryos), (ii) cell symmetry (n = 109 embryos), (iii) degree of fragmentation (n = 6664 images), and (iv) developmental timing (n = 21 036 images). We then conducted detailed comparisons between pipeline outputs and annotations made by embryologists during routine treatments for features, including: (i) fertilization status (n = 18 922 embryos), (ii) pronuclei (PN) fade time (n = 13 781 embryos), (iii) degree of fragmentation on Day 2 (n = 11 582 embryos), and (iv) time of blastulation (n = 3266 embryos). In addition, we compared the pipeline outputs to the implantation results of 723 single embryo transfer (SET) cycles, and to the live birth results of 3421 embryos transferred in 1801 cycles. PARTICIPANTS/MATERIALS, SETTING, METHODS: In addition to EmbryoScope™ image data, manual embryo grading and annotations, and electronic health record (EHR) data on treatment outcomes were also included. We integrated the deep learning networks we developed for individual features to construct the BlastAssist pipeline. Pearson's χ2 test was used to evaluate the statistical independence of individual features and implantation success. Bayesian statistics was used to evaluate the association of the probability of an embryo resulting in live birth to BlastAssist inputs. MAIN RESULTS AND THE ROLE OF CHANCE: The BlastAssist pipeline integrates five deep learning networks and measures comprehensive, interpretable, and quantitative features in clinical IVF. The pipeline performs similarly or better than manual measurements. For fertilization status, the network performs with very good parameters of specificity and sensitivity (area under the receiver operating characteristics (AUROC) 0.84-0.94). For symmetry score, the pipeline performs comparably to the human expert at both 2-cell (r = 0.71 ± 0.06) and 4-cell stages (r = 0.77 ± 0.07). For degree of fragmentation, the pipeline (acc = 69.4%) slightly under-performs compared to human experts (acc = 73.8%). For developmental timing, the pipeline (acc = 90.0%) performs similarly to human experts (acc = 91.4%). There is also strong agreement between pipeline outputs and annotations made by embryologists during routine treatments. For fertilization status, the pipeline and embryologists strongly agree (acc = 79.6%), and there is strong correlation between the two measurements (r = 0.683). For degree of fragmentation, the pipeline and embryologists mostly agree (acc = 55.4%), and there is also strong correlation between the two measurements (r = 0.648). For both PN fade time (r = 0.787) and time of blastulation (r = 0.887), there's strong correlation between the pipeline and embryologists. For SET cycles, 2-cell time (P < 0.01) and 2-cell symmetry (P < 0.03) are significantly correlated with implantation success rate, while other features showed correlations with implantation success without statistical significance. In addition, 2-cell time (P < 5 × 10-11), PN fade time (P < 5 × 10-10), degree of fragmentation on Day 3 (P < 5 × 10-4), and 2-cell symmetry (P < 5 × 10-3) showed statistically significant correlation with the probability of the transferred embryo resulting in live birth. LIMITATIONS, REASONS FOR CAUTION: We have not tested the BlastAssist pipeline on data from other clinics or other time-lapse microscopy (TLM) systems. The association study we conducted with live birth results do not take into account confounding variables, which will be necessary to construct an embryo selection algorithm. Randomized controlled trials (RCT) will be necessary to determine whether the pipeline can improve success rates in clinical IVF. WIDER IMPLICATIONS OF THE FINDINGS: BlastAssist provides a comprehensive and holistic means of evaluating human embryos. Instead of using a black-box algorithm, BlastAssist outputs meaningful measurements of embryos that can be interpreted and corroborated by embryologists, which is crucial in clinical decision making. Furthermore, the unprecedentedly large dataset generated by BlastAssist measurements can be used as a powerful resource for further research in human embryology and IVF. STUDY FUNDING/COMPETING INTEREST(S): This work was supported by Harvard Quantitative Biology Initiative, the NSF-Simons Center for Mathematical and Statistical Analysis of Biology at Harvard (award number 1764269), the National Institute of Heath (award number R01HD104969), the Perelson Fund, and the Sagol fund for embryos and stem cells as part of the Sagol Network. The authors declare no competing interests. TRIAL REGISTRATION NUMBER: Not applicable.

Summary Error correction is central to many biological systems and is critical for protein function and cell health. During mitosis, error correction is required for the faithful inheritance of genetic material. When functioning properly, the mitotic spindle segregates an equal number of chromosomes to daughter cells with high fidelity. Over the course of spindle assembly, many initially erroneous attachments between kinetochores and microtubules are fixed through the process of error correction. Despite the importance of chromosome segregation errors in cancer and other diseases, there is a lack of methods to characterize the dynamics of error correction and how it can go wrong. Here, we present an experimental method and analysis framework to quantify chromosome segregation error correction in human tissue culture cells with live cell confocal imaging, timed premature anaphase, and automated counting of kinetochores after cell division. We find that errors decrease exponentially over time during spindle assembly. A coarse-grained model, in which errors are corrected in a chromosome autonomous manner at a constant rate, can quantitatively explain both the measured error correction dynamics and the distribution of anaphase onset times. We further validated our model using perturbations that destabilized microtubules and changed the initial configuration of chromosomal attachments. Taken together, this work provides a quantitative framework for understanding the dynamics of mitotic error correction.

Connecting the large-scale emergent behaviors of active cytoskeletal materials to the microscopic properties of their constituents is a challenge due to a lack of data on the multiscale dynamics and structure of such systems. We approach this problem by studying the impact of depletion attraction on bundles of microtubules and kinesin-14 molecular motors. For all depletant concentrations, kinesin-14 bundles generate comparable extensile dynamics. However, this invariable mesoscopic behavior masks the transition in the microscopic motion of microtubules. Specifically, with increasing attraction, we observe a transition from bi-directional sliding with extension to pure extension with no sliding. Small-angle X-ray scattering shows that the transition in microtubule dynamics is concurrent with a structural rearrangement of microtubules from an open hexagonal to a compressed rectangular lattice. These results demonstrate that bundles of microtubules and molecular motors can display the same mesoscopic extensile behaviors despite having different internal structures and microscopic dynamics. They provide essential information for developing multiscale models of active matter.

In clinical In-Vitro Fertilization (IVF), identifying the most viable embryo for transfer is important to increasing the likelihood of a successful pregnancy. Traditionally, this process involves embryologists manually assessing embryos' static morphological features at specific intervals using light microscopy. This manual evaluation is not only time-intensive and costly, due to the need for expert analysis, but also inherently subjective, leading to variability in the selection process. To address these challenges, we develop a multimodal model that leverages both time-lapse video data and Electronic Health Records (EHRs) to predict embryo viability. One of the primary challenges of our research is to effectively combine time-lapse video and EHR data, owing to their inherent differences in modality. We comprehensively analyze our multimodal model with various modality inputs and integration approaches. Our approach will enable fast and automated embryo viability predictions in scale for clinical IVF.

Few techniques are available for studying the nature of forces that drive subcellular dynamics. Here we develop two complementary ones. The first is femtosecond stereotactic laser ablation, which rapidly creates complex cuts of subcellular structures and enables precise dissection of when, where and in what direction forces are generated. The second is an assessment of subcellular fluid flows by comparison of direct flow measurements using microinjected fluorescent nanodiamonds with large-scale fluid-structure simulations of different force transduction models. We apply these techniques to study spindle and centrosome positioning in early Caenorhabditis elegans embryos and to probe the contributions of microtubule pushing, cytoplasmic pulling and cortical pulling upon centrosomal microtubules. Based on our results, we construct a biophysical model to explain the dynamics of centrosomes. We demonstrate that cortical pulling forces provide a general explanation for many behaviours mediated by centrosomes, including pronuclear migration and centration, rotation, metaphase spindle positioning, asymmetric spindle elongation and spindle oscillations. This work establishes methodologies for disentangling the forces responsible for cell biological phenomena. Cell division is governed by the positioning of a cytoskeletal structure called the spindle. Two methods, one based on laser ablation and the other on fluid flow assessments, are now shown to be useful tools for studying spindle positioning.

Abstract Recent studies have highlighted the significance of the spindle midzone, the region between the segregating chromosomes, in ensuring proper chromosome segregation. By combining 3D electron tomography, cutting-edge light microscopy and a novel single cell in vitro essay allowing single molecule tracking, we have discovered a previously unknown role of the regulation of microtubule dynamics within the spindle midzone of C. elegans by the chromokinesin KLP-19, and its relevance for proper spindle function. Using Fluorescence recovery after photobleaching and a combination of second harmonic generation and two-photon fluorescence microscopy, we found that the length of the antiparallel microtubule overlap zone in the spindle midzone is constant throughout anaphase, and independent of cortical pulling forces as well as the presence of the microtubule bundling protein SPD-1. Further investigations of SPD-1 and KLP-19 in C. elegans , the homologs of PRC1 and KIF4a, suggest that KLP-19 regulates the overlap length and functions independently of SPD-1. Our data shows that KLP-19 plays an active role in regulating the length of microtubules within the midzone as well as the size of the antiparallel overlap region throughout mitosis. Depletion of KLP-19 in mitosis leads to an increase in microtubule length and thus microtubule-based interactions in the spindle midzone, which affects spindle dynamics and force transmission. Our data shows that by localizing KLP-19 to the spindle midzone in anaphase microtubule dynamics can be locally controlled allowing the formation of a functional midzone. Summary KLP-19 controls microtubule length in the spindle midzone of C. elegans , affecting spindle dynamics and force transmission during mitosis.

Summary The forces which orient the spindle in human cells remain poorly understood due to a lack of direct mechanical measurements in mammalian systems. We use magnetic tweezers to measure the force on human mitotic spindles. Combining the spindle’s measured resistance to rotation, the speed it rotates after laser ablating astral microtubules, and estimates of the number of ablated microtubules reveals that each microtubule contacting the cell cortex is subject to ∼1 pN of pulling force, suggesting that each is pulled on by an individual dynein motor. We find that the concentration of dynein at the cell cortex and extent of dynein clustering are key determinants of the spindle’s resistance to rotation, with little contribution from cytoplasmic viscosity, which we explain using a biophysically based mathematical model. This work reveals how pulling forces on astral microtubules determine the mechanics of spindle orientation and demonstrates the central role of cortical dynein clustering. Highlights Cytoplasmic viscosity does not determine the spindle’s resistance to rotation Each astral microtubule that contacts the cell cortex is pulled on by a single dynein motor Pulling forces on astral microtubules determine the mechanics of spindle orientation The mechanics of spindle orientation is regulated by clustering of dynein motors at the cell cortex Graphical Abstract

Abstract Study question Is there any relationship between the relative telomere length (RTL) within cumulus cells (CCs) and the outcome of assisted reproductive treatment using the corresponding oocyte? Summary answer Lower RTLs in CCs were significantly associated with embryos chosen for transfer or cryopreservation. In contrast, embryos considered non-viable (discarded) tended to have higher RTLs. What is known already Cumulus cells fulfil vital roles in support of oocyte development, including the transduction of external signals and the provision of resources via transzonal projections. Given their essential role in the acquisition of oocyte developmental competence, the biology of CCs is of clinical relevance. Telomeres are specialised structures protecting the ends of chromosomes, composed of repetitive DNA sequences and associated proteins. Telomeres shorten with each mitotic division, as well as due to oxidative damage, eventually reaching a critical threshold at which point cellular senescence occurs. Currently, published data on CCs telomere length and relationship with oocyte potential are conflicting. Study design, size, duration The study involved 182 human CC samples collected from 52 IVF patients. Quantitative PCR (qPCR) was used to measure the relative telomere length in each of the CC samples. Telomere lengths were assessed for associations with various patient characteristics (e.g. age, body mass index, infertility diagnosis). Additionally, potential relationships with clinically relevant oocyte/embryo features were investigated (fertilisation; development/morphology), as well as the eventual fate of the associated embryo (transferred; cryopreserved for potential future use; discarded). Participants/materials, setting, methods Real-time quantitative PCR was carried out using PCR primers specific for the telomere repeat. A single-copy gene was also amplified from each CC sample. Quantification of this gene was used for normalisation of the telomere data, allowing control for variation in the number of cumulus cells in each sample. Associations between RTL in CCs and patient, embryonic, and clinical factors were assessed using various statistical methods, with P-values &amp;lt;0.05 considered significant. Main results and the role of chance No associations were identified between RTL and any patient characteristics, except for BMI. The amount of CC telomeric DNA tended to be greater for patients with higher BMI (P = 0.002). When considering links between RTL and oocyte or embryonic factors, a significant relationship was detected between the quantity of telomeric DNA in CCs and whether the corresponding embryo was considered non-viable (discarded) or whether it was transferred or cryopreserved (P = 0.019). This finding raises the possibility that measurement of RTL in CCs could provide a pre-conception, non-invasive assessment of oocyte quality. In the context of fertility preservation, RTL measurement could assist in evaluating a cohort of oocytes, indicating whether the cryopreserved eggs are likely to be sufficient or whether additional cycles to generate more would be advisable. If future studies confirm that CC RTL has a strong predictive value, the possibility of limiting fertilisation to oocytes considered to have high likelihood of viability could also be considered for routine IVF cycles. It is unclear why shorter CC telomeres might be associated with oocytes of superior potential, but one possibility is that the cells may have undergone a greater proliferation (more mitoses), resulting in a more extensive cumulus mass supporting the enclosed oocyte. Limitations, reasons for caution Before drawing definitive conclusions, confirmation of the findings within a larger, independent data set is necessary. Even if confirmed, determination of the true clinical value of telomere assessment will require further, appropriately designed studies. The current study was not powered to evaluate relationships between CC telomere lengths and IVF outcomes. Wider implications of the findings Currently, simplistic morphological evaluation is the only method for assessing oocyte competence prior to fertilisation. If CCs telomere measurement is confirmed to have predictive value, a preconception test of oocyte potential could be offered. This would be extremely valuable for patients cryopreserving oocytes for fertility preservation and for donor banks. Trial registration number NA

Blastomere instance segmentation is important for analyzing embryos’ abnormality. To measure the accurate shapes and sizes of blastomeres, their amodal segmentation is necessary. Amodal instance segmentation aims to recover an object’s complete silhouette even when the object is not fully visible. For each detected object, previous methods directly regress the target mask from input features. However, images of an object under different amounts of occlusion should have the same amodal mask output, making it harder to train the regression model. To alleviate the problem, we propose to classify input features into intermediate shape codes and recover complete object shapes. First, we pre-train the Vector Quantized Variational Autoencoder (VQ-VAE) model to learn these discrete shape codes from ground truth amodal masks. Then, we incorporate the VQ-VAE model into the amodal instance segmentation pipeline with an additional refinement module. We also detect an occlusion map to integrate occlusion information with a backbone feature. As such, our network faithfully detects bounding boxes of amodal objects. On an internal embryo cell image benchmark, the proposed method outperforms previous state-of-the-art methods. To show generalizability, we show segmentation results on the public KINS natural image benchmark. Our method would enable accurate measurement of blastomeres in In Vitro Fertilization (IVF) clinics, potentially increasing the IVF success rate.

Incomplete labels are common in multi-task learning for biomedical applications due to several practical difficulties, e.g., expensive annotation efforts by experts, limit of data collection, different sources of data. A naive approach to enable joint learning for partially labeled data is adding self-supervised learning for tasks without ground truths by augmenting an input image and forcing the multi-task model to return the same outputs for both the input and augmented images. However, the partially labeled setting can result in imbalanced learning of tasks since not all tasks are trainable with ground truth supervisions for each data sample. In this work, we propose a multi-task curriculum learning method tailored for partially labeled data. For balanced learning of tasks, our multitask curriculum prioritizes less performing tasks during training by setting different supervised learning frequencies for each task. We demonstrate that our method outperforms standard approaches on one biomedical and two natural image datasets. Furthermore, our learning method with partially labeled data performs better than the standard multi-task learning methods with fully labeled data for the same number of annotations.

Living systems are intrinsically nonequilibrium: They use metabolically derived chemical energy to power their emergent dynamics and self-organization. A crucial driver of these dynamics is the cellular cytoskeleton, a defining example of an active material where the energy injected by molecular motors cascades across length scales, allowing the material to break the constraints of thermodynamic equilibrium and display emergent nonequilibrium dynamics only possible due to the constant influx of energy. Notwithstanding recent experimental advances in the use of local probes to quantify entropy production and the breaking of detailed balance, little is known about the energetics of active materials or how energy propagates from the molecular to emergent length scales. Here, we use a recently developed picowatt calorimeter to experimentally measure the energetics of an active microtubule gel that displays emergent large-scale flows. We find that only approximately one-billionth of the system's total energy consumption contributes to these emergent flows. We develop a chemical kinetics model that quantitatively captures how the system's total thermal dissipation varies with ATP and microtubule concentrations but that breaks down at high motor concentration, signaling an interference between motors. Finally, we estimate how energy losses accumulate across scales. Taken together, these results highlight energetic efficiency as a key consideration for the engineering of active materials and are a powerful step toward developing a nonequilibrium thermodynamics of living systems.