About

Daniel Needleman is the Gordon McKay Professor of Applied Physics and Professor of Molecular and Cellular Biology at Harvard University. His research combines physics and cell biology to study biological self-organizing systems. He seeks to obtain a quantitative understanding of biological systems, focusing on how they result from mechanics and energetics, and how their constituents self-organize. His work explores how these systems are perturbed in disease and change over evolution, considering biological self-organization as an example of active matter driven out of equilibrium by energy transduction at the molecular level. His long-term goals include uncovering general principles governing non-equilibrium systems and developing predictive theories of biological organization and behaviors. His approach involves studying complex biological systems through a close interplay between quantitative experiments and theory, developing new methods to produce the necessary data.

Research topics

• Biology
• Physics
• Materials science
• Nanotechnology
• Cell biology
• Genetics
• Biochemistry
• Engineering
• Thermodynamics
• Chemical physics
• Biophysics
• Biological system
• Biochemical engineering

Selected publications

• Contributions of error correction and the spindle assembly checkpoint to mitotic timing and fidelity

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-13

  articleOpen accessSenior authorCorresponding

  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.

  PublisherDOI

• Geometric optics organizes organelle interactions and positioning

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-29

  article

  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.

  PublisherDOI

• Human mitotic spindles as active liquid crystals: From collective behaviors to discrete filaments

  Proceedings of the National Academy of Sciences · 2026-02-10

  articleOpen accessSenior author

  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.

  PublisherDOI

• Author Reply to Peer Reviews of Chromokinesin Klp-19 regulates microtubule overlap and dynamics during anaphase in C. elegans

  2025-03-25

  peer-review
  PublisherDOI

• Cell size reduction drives spindle scaling but not chromosome segregation in C. elegans

  Research Square · 2025-12-01

  preprintOpen access
  PublisherOA PDFDOI

• EmbryoProfiler: A Visual Clinical Decision Support System for IVF

  IEEE Transactions on Visualization and Computer Graphics · 2025-12-05 · 1 citations

  article

  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.

  PublisherDOI

• cryoJAX: A Cryo-electron Microscopy Image Simulation Library In JAX

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-10-24 · 6 citations

  preprintOpen access

  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.

  PublisherOA PDFDOI

• Cell size reduction scales spindle elongation but not chromosome segregation in <i>C. elegans</i>

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-10-14

  preprintOpen access

  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.

  PublisherOA PDFDOI

• Nuclear biophysics: Spatial coordination of transcriptional dynamics?

  Current Opinion in Cell Biology · 2025-06-13 · 2 citations

  reviewSenior authorCorresponding
  PublisherDOI

• BlastAssist: a deep learning pipeline to measure interpretable features of human embryos

  Human Reproduction · 2024-02-23 · 17 citations

  articleOpen access

  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.

  PublisherOA PDFDOI

Recent grants

• CAREER: Physical Aspects of Spindle Self-Organization

  NSF · $800k · 2009–2014

• Nonequilibrium Physics of Spindle Assembly: Understanding the Response of the Spindle to Perturbations

  NSF · $480k · 2013–2016

• MRI-R2: Development of a System for Dynamic, Multipoint, Dual-Color Fluorescence Fluctuation Spectroscopy

  NSF · $418k · 2010–2013

• PFI-TT: Development of Metabolic Imaging to Improve Treatment of Infertility

  NSF · $286k · 2018–2021

• Spindle Self-Organization and Bioenergetics in Vivo

  NSF · $930k · 2020–2025

Frequent coauthors

• Michael Shelley

  102 shared

• Reza Farhadifar

  60 shared

• Peter Foster

  Brandeis University

  55 shared

• Catherine Racowsky

  Brigham and Women's Hospital

  47 shared

• Bezia Lemma

  Harvard University

  42 shared

• Uri Raviv

  Hebrew University of Jerusalem

  37 shared

• Hai‐Yin Wu

  Harvard University

  36 shared

• Jan Brugués

  Max Planck Institute of Molecular Cell Biology and Genetics

  35 shared

Labs

• Daniel Needleman LabPI