High-volume, label-free imaging for quantifying single-cell dynamics in induced pluripotent stem cell colonies.

To facilitate the characterization of unlabeled induced pluripotent stem cells (iPSCs) during culture and expansion, we developed an AI pipeline for nuclear segmentation and mitosis detection from phase contrast images of individual cells within iPSC colonies. The analysis uses a 2D convolutional neural network (U-Net) plus a 3D U-Net applied on time lapse images to detect and segment nuclei, mitotic events, and daughter nuclei to enable tracking of large numbers of individual cells over long times in culture. The analysis uses fluorescence data to train models for segmenting nuclei in phase contrast images. The use of classical image processing routines to segment fluorescent nuclei precludes the need for manual annotation. We optimize and evaluate the accuracy of automated annotation to assure the reliability of the training. The model is generalizable in that it performs well on different datasets with an average F1 score of 0.94, on cells at different densities, and on cells from different pluripotent cell lines. The method allows us to assess, in a non-invasive manner, rates of mitosis and cell division which serve as indicators of cell state and cell health. We assess these parameters in up to hundreds of thousands of cells in culture for more than 36 hours, at different locations in the colonies, and as a function of excitation light exposure.

Experimentally Measuring Rolling and Sliding in Three-Dimensional Dense Granular Packings.

We experimentally measure a three-dimensional (3D) granular system's reversibility under cyclic compression. We image the grains using a refractive-index-matched fluid, then analyze the images using the artificial intelligence of variational autoencoders. These techniques allow us to track all the grains' translations and 3D rotations with accuracy sufficient to infer sliding and rolling displacements. Our observations reveal unique roles played by 3D rotational motions in granular flows. We find that rotations and contact-point motion dominate the dynamics in the bulk, far from the perturbation's source. Furthermore, we determine that 3D rotations are irreversible under cyclic compression. Consequently, contact-point sliding, which is dissipative, accumulates throughout the cycle. Using numerical simulations whose accuracy our experiment supports, we discover that much of the dissipation occurs in the bulk, where grains rotate more than they translate. Our observations suggest that the analysis of 3D rotations is needed for understanding granular materials' unique and powerful ability to absorb and dissipate energy.

Label-free cell tracking enables collective motion phenotyping in epithelial monolayers.

Collective cell migration is an umbrella term for a rich variety of cell behaviors, whose distinct character is important for biological function, notably for cancer metastasis. One essential feature of collective behavior is the motion of cells relative to their immediate neighbors. We introduce an AI-based pipeline to segment and track cell nuclei from phase-contrast images. Nuclei segmentation is based on a U-Net convolutional neural network trained on images with nucleus staining. Tracking, based on the Crocker-Grier algorithm, quantifies nuclei movement and allows for robust downstream analysis of collective motion. Because the AI algorithm required no new training data, our approach promises to be applicable to and yield new insights for vast libraries of existing collective motion images. In a systematic analysis of a cell line panel with oncogenic mutations, we find that the collective rearrangement metric, D2 min, which reflects non-affine motion, shows promise as an indicator of metastatic potential.

Memory in three-dimensional cyclically driven granular material.

We perform experimental and numerical studies of a granular system under cyclic compression to investigate reversibility and memory effects. We focus on the quasistatic forcing of dense systems, which is most relevant to a wide range of geophysical, industrial, and astrophysical problems. We find that soft-sphere simulations with proper stiffness and friction quantitatively reproduce both the translational and rotational displacements of the grains. We then utilize these simulations to demonstrate that such systems are capable of storing the history of previous compressions. While both mean translational and rotational displacements encode such memory, the response is fundamentally different for translations compared to rotations. For translational displacements, this memory of prior forcing depends on the coefficient of static interparticle friction, but rotational memory is not altered by the level of friction.

Collinear antiferromagnetism in trigonal SrMn2As2 revealed by single-crystal neutron diffraction.

Iron pnictides and related materials have been a topic of intense research for understanding the complex interplay between magnetism and superconductivity. Here we report on the magnetic structure of SrMn2As2 that crystallizes in a trigonal structure ([Formula: see text]) and undergoes an antiferromagnetic (AFM) transition at [Formula: see text] K. The magnetic susceptibility remains nearly constant at temperatures [Formula: see text] with [Formula: see text] whereas it decreases significantly with [Formula: see text]. This shows that the ordered Mn moments lie in the [Formula: see text] plane instead of aligning along the [Formula: see text]-axis as in tetragonal BaMn2As2. Single-crystal neutron diffraction measurements on SrMn2As2 demonstrate that the Mn moments are ordered in a collinear Néel AFM phase with [Formula: see text] AFM alignment between a moment and all nearest neighbor moments in the basal plane and also perpendicular to it. Moreover, quasi-two-dimensional AFM order is manifested in SrMn2As2 as evident from the temperature dependence of the order parameter.