About

Noah Mitchell, PhD, is an Assistant Professor of Molecular Genetics and Cell Biology at The University of Chicago. His research focuses on the dynamic self-organization of visceral organ tissues, exploring how tissues transform into specific shapes vital for their function through the collective behaviors of cells and mechanical interactions between mesodermal and epithelial tissue layers. His lab combines techniques such as whole-organ live imaging, genetic perturbations, computational microscopy, and approaches from soft matter physics to investigate the links between genes and tissue geometry. Dr. Mitchell holds a PhD and an MS in Physics from The University of Chicago and a degree from the University of California Santa Barbara. His scholarly contributions include developing a morphodynamic atlas for Drosophila development, studying topological interactions in embryonic fate decisions, and analyzing tissue deformation and cell motility. His work aims to elucidate the physical principles underlying tissue morphogenesis and shape formation in developmental biology.

Research topics

• Biology
• Physics
• Nanotechnology
• Anatomy
• Cell biology
• Thermodynamics
• Chemical physics
• Classical mechanics
• Particle physics
• Biophysics
• Mechanics
• Materials science

Selected publications

• DynamicAtlas: a morphodynamic atlas for Drosophila development

  Nature Methods · 2025-12-24 · 1 citations

  articleOpen access

  Living organisms develop their shape through the interplay of gene expression and mechanics. While atlases of static samples characterize cell fates and gene regulation, understanding dynamic shape changes requires live imaging. Here we present DynamicAtlas: a 'morphodynamic atlas' of live and static datasets from 500 Drosophila melanogaster embryos (wild type and 18 mutants), aligned to a common morphological timeline. Surprisingly, characterizing wild-type surface tissue flows reveals distinct 'morphodynamic modules'-time periods in which the global pattern of motion is stationary-corresponding to key developmental stages. Mutant analysis shows stationary flow patterns depend on genes that break spatial symmetry along the dorsal-ventral axis. Temperature perturbations indicate that morphodynamic modules change in response to accumulated tissue deformation, rather than elapsed time. Extending our approach to the embryonic Drosophila midgut, we find modules in covariant measures of the dynamic three-dimensional surface. DynamicAtlas provides a high-resolution framework for studying shape formation across living systems.

  PublisherDOI

• Topological interactions drive the first fate decision in the Drosophila embryo

  Nature Physics · 2025-02-25 · 3 citations

  articleOpen access
  PublisherOA PDFDOI

• Learning a conserved mechanism for early neuroectoderm morphogenesis

  PubMed · 2024-05-28 · 1 citations

  articleOpen access

  Morphogenesis is the process whereby the body of an organism develops its target shape. The morphogen BMP is known to play a conserved role across bilaterian organisms in determining the dorsoventral (DV) axis. Yet, how BMP governs the spatio-temporal dynamics of cytoskeletal proteins driving morphogenetic flow remains an open question. Here, we use machine learning to mine a morphodynamic atlas of Drosophila development, and construct a mathematical model capable of predicting the coupled dynamics of myosin, E-cadherin, and morphogenetic flow. Mutant analysis shows that BMP sets the initial condition of this dynamical system according to the following signaling cascade: BMP establishes DV pair-rule-gene patterns that set-up an E-cadherin gradient which in turn creates a myosin gradient in the opposite direction through mechanochemical feedbacks. Using neural tube organoids, we argue that BMP, and the signaling cascade it triggers, prime the conserved dynamics of neuroectoderm morphogenesis from fly to humans.

  PublisherOA PDFDOI

• Protocol for 3D surface texture modeling and quantitative spectral decomposition analysis in Drosophila border cell clusters

  STAR Protocols · 2024-07-27

  articleOpen access

  Drosophila border cell clusters model collective cell migration. Airyscan super-resolution microscopy enables fine-scale description of cluster shape and texture. Here we describe how to convert Airyscan images of border cell clusters into 3D models of the surface and detect regions of convex and concave curvature. We use spectral decomposition analysis to compare surface textures across genotypes to determine how genes of interest impact cluster surface geometry. This protocol applies to border cells and could generalize to additional cell types. For complete details on the use and execution of this protocol, please refer to Gabbert et al. 1 • Drosophila cell-specific transgene expression and ovary dissection and fixation • Airyscan image acquisition of border cell clusters with high-resolution z stacks • Extraction of cluster surface to generate 3D models of the surface shape and texture • Spectral decomposition analysis to quantify differences in surface geometry Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Drosophila border cell clusters model collective cell migration. Airyscan super-resolution microscopy enables fine-scale description of cluster shape and texture. Here we describe how to convert Airyscan images of border cell clusters into 3D models of the surface and detect regions of convex and concave curvature. We use spectral decomposition analysis to compare surface textures across genotypes to determine how genes of interest impact cluster surface geometry. This protocol applies to border cells and could generalize to additional cell types.

  PublisherDOI

• TubULAR: tracking in toto deformations of dynamic tissues via constrained maps

  Nature Methods · 2023-12-01 · 14 citations

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Geometric control of myosin II orientation during axis elongation

  eLife · 2023-01-30 · 36 citations

  articleOpen access

  The actomyosin cytoskeleton is a crucial driver of morphogenesis. Yet how the behavior of large-scale cytoskeletal patterns in deforming tissues emerges from the interplay of geometry, genetics, and mechanics remains incompletely understood. Convergent extension in Drosophila melanogaster embryos provides the opportunity to establish a quantitative understanding of the dynamics of anisotropic non-muscle myosin II. Cell-scale analysis of protein localization in fixed embryos suggests that gene expression patterns govern myosin anisotropy via complex rules. However, technical limitations have impeded quantitative and dynamic studies of this process at the whole embryo level, leaving the role of geometry open. Here, we combine in toto live imaging with quantitative analysis of molecular dynamics to characterize the distribution of myosin anisotropy and the corresponding genetic patterning. We found pair rule gene expression continuously deformed, flowing with the tissue frame. In contrast, myosin anisotropy orientation remained approximately static and was only weakly deflected from the stationary dorsal-ventral axis of the embryo. We propose that myosin is recruited by a geometrically defined static source, potentially related to the embryo-scale epithelial tension, and account for transient deflections by cytoskeletal turnover and junction reorientation by flow. With only one parameter, this model quantitatively accounts for the time course of myosin anisotropy orientation in wild-type, twist , and even-skipped embryos, as well as embryos with perturbed egg geometry. Geometric patterning of the cytoskeleton suggests a simple physical strategy to ensure a robust flow and formation of shape.

  PublisherDOI

• Creation of an isolated turbulent blob fed by vortex rings

  Nature Physics · 2023 · 34 citations

  • Physics
  • Mechanics
  • Classical mechanics
  PublisherOA PDFDOI

• Synthetic dataset 2

  Figshare · 2023-01-01

  datasetOpen access1st authorCorresponding

  Synthetic dataset of nuclei on a tube-like tissue that changes shape, for analysis demonstration with TubULAR. <br> TubULAR is a set of tools for working with 3D data of surfaces – potentially complex and dynamic – that can be described as tubes. Developing guts, pumping hearts, and other visceral organs can be treated as tubes with potentially complex and dynamic shapes. With TubULAR, we can describe the tissue motion on the tube-like surface and quantify how it changes over time. <br> This synthetic dataset is a tube of cells with nuclei and membrane that coils into a loop, then uncoils into a straight tube. To generate the dataset, the surface geometry was encoded numerically. We placed 120 nuclei-like blobs of intensity centered at locations across the surface. Locations were chosen as a solution to an iterative farthest-point search, so that nuclei are well-spaced from each other. We then performed a Voronoi tessellation to create a channel mimicking `cell-cell junctions'. The nuclei sizes were determined based on the distance of each nucleus to the nearest membrane location. <br> For more on the codebase, visit: https://npmitchell.github.io/tubular/ https://github.com/npmitchell/tubular

  PublisherDOI

• Example timeseries -- zebrafish heart, analyzed data

  Figshare · 2023-01-01

  datasetOpen access1st authorCorresponding

  Analysis of a beating zebrafish heart dataset using TubULAR. The data was acquired by Sebastian Streichan and Michael Leibling at UC Santa Barbara. <br> Financial support provided via Streichan Lab under NSF Grant No. PHY-2047140 <br> This data was acquired using the techniques described in: K. G. Chan, S. J. Streichan, L. A. Trinh and M. Liebling, "Simultaneous Temporal Superresolution and Denoising for Cardiac Fluorescence Microscopy," in IEEE Transactions on Computational Imaging, vol. 2, no. 3, pp. 348-358, Sept. 2016, doi: 10.1109/TCI.2016.2579606.

  PublisherDOI

• Turbulence through sustained vortex ring collisions

  Physical Review Fluids · 2023-11-16 · 1 citations

  articleOpen access

  This paper is associated with a video winner of a 2022 American Physical Society's Division of Fluid Dynamics (DFD) Gallery of Fluid Motion Award for work presented at the DFD Gallery of Fluid Motion. The original video is available online at the Gallery of Fluid Motion, https://doi.org/10.1103/APS.DFD.2022.GFM.V0008

  PublisherOA PDFDOI

Frequent coauthors

• William T. M. Irvine

  Fermi National Accelerator Laboratory

  21 shared

• Kristen B. W. McQuinn

  13 shared

• Evan D. Skillman

  11 shared

• Sebastian J. Streichan

  University of California, Santa Barbara

  11 shared

• Dillon Cislo

  Rockefeller University

  11 shared

• Lisa M. Nash

  7 shared

• Ari M. Turner

  7 shared

• Zvonimir Dogic

  University of California, Santa Barbara

  7 shared

Labs

• Mitchell LabPI

Education

• Ph.D., Physics

  University of Chicago

  2018

• M.S., Physics

  University of Chicago

  2013

• Other, Quantitative developmental biology

  University of California

  2024