The Needleman Lab

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.

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 micro- tubule 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 clus- tering 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.