We present a method, open-source software, and experiments which embed arbitrary deformation vector fields produced by any method (e.g., ANTs or VoxelMorph) in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework. This decouples formal diffeomorphic shape analysis from image registration, which has many practical benefits. Shape analysis can be added to study designs without modification to already chosen image registration methods and existing databases of deformation fields can be reanalyzed within the LDDMM framework without repeating image registrations. Pairwise time series studies can be extended to full time series regression with minimal added computing. The diffeomorphic rigor of image registration methods can be compared by embedding deformation fields and comparing projection distances. Finally, the added value of formal diffeomorphic shape analysis can be more fairly evaluated when it is derived from and compared to a baseline set of deformation fields. In brief, the method is a straightforward use of geodesic shooting in diffeomorphisms with a deformation field as the target, rather than an image. This is simpler than the image registration case which leads to a faster implementation that requires fewer user derived parameters.

Messenger RNA (mRNA) transfection enables rapid, transient protein expression without nuclear entry, providing a powerful alternative to DNA or viral delivery in post-mitotic and otherwise difficult-to-transfect cells. Although in vitro transcribed (IVT) mRNAs have revolutionized therapeutic applications, their adoption in experimental biology remains limited by challenges in synthesis, variability across cell types, and concerns about cytotoxicity. Here, we define design principles that maximize IVT mRNA performance across diverse cellular and organismal systems. Through systematic comparison of capping strategies and base modifications, including N1-methyl-pseudouridine, 5-methylcytidine, and 5-methoxyuridine, we identify modifications that enhance translation while minimizing activation of cellular stress responses. Optimized transcripts drive robust protein expression within four hours, persist for up to one week, and support multiplexed expression of structurally and functionally distinct proteins in mammalian cells, including cancer cell lines, iPSC-derived systems, primary cells, and organoids, as well as in vivo in zebrafish embryos and in less genetically tractable models such as Danionella cerebrum and sea urchin embryos. To further expand accessibility for community use, we developed mRNAbow, a platform for generating low-toxicity mRNAs encoding organelle-targeted fluorescent proteins and biosensors for multiplex imaging, with corresponding plasmids made publicly available. Together, these advances establish a generalizable framework for IVT mRNA design and expand experimental access to synthetic mRNA technologies for dissecting cellular architecture and dynamics.

Understanding how neural signals control muscle activity during behavior is a key challenge in motor neuroscience. To this end, recent advances in intramuscular multielectrode arrays have enabled high-quality multichannel recordings of many motor unit action potentials (MUAPs) in freely moving subjects. However, identifying individual MUAP events within multichannel recordings is a significant challenge for existing spike sorting methods, which are typically optimized for identifying action potentials from neurons in the brain. To overcome this challenge, we developed the Enhanced Motor Unit sorter (EMUsort), an extension of Kilosort4 (KS4) that achieves high-performance MUAP spike sorting. We applied EMUsort to high-resolution intramuscular recordings from rat forelimb during locomotion and monkey forelimb during a reaching task. EMUsort improves upon prior methods by addressing key challenges encountered with MUAP datasets, including: 1) long time delays across electrodes due to propagation along muscle fibers, 2) more complex waveform shapes compared to neuronal action potentials, and 3) a high degree of MUAP overlap due to cumulative motor unit recruitment. We compared EMUsort to existing spike sorting methods quantitatively using simulated datasets that closely emulated the rat and monkey datasets we recorded. EMUsort provided median error rate reductions of 67.5% and 49.9% during periods of high motor unit activation for the rat and monkey datasets, respectively. In sum, EMUsort provides a substantial improvement to MUAP spike sorter accuracy, especially during regions of high MUAP overlap, in an easy-to-use software package.

Single-walled carbon nanotubes (SWCNTs) functionalized with single-stranded DNAs can function as near-infrared nanosensors for molecular analytes. However, predicting which analytes elicit strong optical responses for specific nanosensors remains challenging. We developed machine learning (ML) models to predict analyte-induced fluorescence changes in a DNA–SWCNT dopamine nanosensor. Using a data set of 63 small molecules sampling chemical space around dopamine, we encoded analytes with RDKit fingerprints, with or without HOMO and LUMO energies, and applied principal component analysis to identify structural motifs associated with optical response strength. We trained support vector regression and classification models using two strategies: ensembles of 200 models and cross-validation. Regression models achieved mean R2 values of 0.2–0.4, with cross-validation outperforming ensembles, while classifiers reached mean F1 scores of ∼0.8. Cross-validation performed best for predictions on a blind set of 21 molecules. These findings show that ML can capture structure–response patterns in modest data sets and guide in silico DNA–SWCNT nanosensor design.

Dendrites integrate synaptic inputs to trigger action potentials, and dendrites carry back-propagating action potentials (bAPs) to synapses where these signals contribute to plasticity. Despite strong evidence for a rich repertoire of nonlinear dendritic excitations, the in vivo roles of these excitations in dendritic integration and back-propagation remain uncertain. Here, we used high-speed voltage imaging through a chronically implanted microprism to map membrane potential dynamics from basal to apical dendrites of CA1 neurons in mice navigating in a virtual reality environment. Despite complex dendritic branch morphology, the dynamics were largely captured by 2 or 3 electrical compartments: basal, soma, and apical. Fast dendritic spikes almost always started from bAPs, indicating that dendritic spikes are primarily a consequence rather than a cause of somatic spiking. These fast spikes sometimes triggered slower apical dendritic plateau depolarizations, which drove complex spikes at the soma. We found that the biophysics of dendritic excitability determined the distribution of simple and complex spikes across a place field. Our results show how CA1 pyramidal neurons convert synaptic inputs to spiking outputs and suggest a primary role of dendritic nonlinearities in mediating activity-dependent plasticity.

Online monitoring and quantification of neural signals has tremendous value both for neurofeedback experiments and for brain-computer interfaces. Unfortunately, established methods of online monitoring primarily involve the use of thresholded neural activity rather than sorted single-neuron spikes. The recent introduction of large-scale, high-density electrophysiology has enabled the recording of activity from hundreds of neurons simultaneously in both model organisms and human participants. This development highlights the need for a robust and easily implementable system for sorting spikes during data collection for ‘live’ analyses of neuronal signals. Here, we describe a system for live sorting of neuronal activity (LSS) based on the widely used Kilosort platform. The LSS workflow utilizes an initial period of recorded neural data to identify waveform templates using Kilosort 4. LSS then interfaces with the SpikeGLX API to retrieve small batches (e.g. 50 ms) of data and for processing online. We measured the similarity of single-neuron activity sorted live by LSS to that sorted offline in neurophysiological recordings from macaque visual cortex using Neuropixels probes. We show that LSS closely replicates the post-stimulus time histograms and visual response tuning curves of single-neurons obtained using offline sorting. Furthermore, we show that decoding neural signals online with LSS consistently outperforms online decoding of thresholded activity, and that LSS can achieve the same performance as that obtained with offline sorting.

Two invasive adelgids are associated with widespread damage to several North American conifer species. Adelges tsugae, hemlock woolly adelgid, was introduced from Japan and reproduces parthenogenetically in North America, where it has rapidly decimated Tsuga canadensis and Tsuga caroliniana (eastern and Carolina hemlocks, respectively). Adelges abietis, eastern spruce gall adelgid, introduced from Europe, forms distinctive pineapple-shaped galls on several native spruce species. While not considered a major forest pest, it weakens trees and increases susceptibility to additional stressors. Broad-spectrum insecticides that are often used to control adelgid populations can have off-target impacts on beneficial insects. Whole genome sequencing was performed on both species to aid in development of targeted solutions that may minimize ecological impact. Adelges abietis was sequenced using barcoded linked-reads from 30 pooled individuals, with Hi-C scaffolding performed using data from a single individual collected from the same host plant. Adelges tsugae used long-read sequencing from pooled nymphs. The assembled A. tsugae and A. abietis genomes, pooled from several parthenogenetic females, are 220.75 Mbp and 253.16 Mbp, respectively. Each consists of eight autosomal chromosomes, as well as two sex chromosomes (X1/X2), supporting the XX-XO sex determination system. The genomes are over 96% complete based on BUSCO assessment. Genome annotation identified 11,424 and 12,060 protein-coding genes in A. tsugae and A. abietis, respectively. Comparative analysis of proteins across 29 hemipteran species and 14 arthropod outgroups identified 31,666 putative gene families. Gene family evolution analysis with CAFE revealed lineage-specific expansions in immune-related aminopeptidases (ERAP1) and juvenile hormone binding proteins (JHBP), contractions in juvenile hormone acid methyltransferases (JHAMT), and conservation of nicotinic acetylcholine receptors (nAChR). These genes were explored as candidate families towards a long-term objective of developing adelgid-selective insecticides. Structural comparisons of proteins across seven focal species (Adelges tsugae, Adelges abietis, Adelges cooleyi, Rhopalosiphum maidis, Apis mellifera, Danaus plexippus, and Drosophila melanogaster) revealed high conservation of nAChR and ERAP1, while JHAMT exhibited species-specific structural divergence. The potential of JHAMT as a lineage-specific target for pest control was explored through virtual screening of drugs and pesticides.

bioRxiv preprint: https://doi.org/10.1101/2024.11.21.624573

Neural representations of information are shaped by long-range input and local network interactions. Previous studies linking neural coding and cortical connectivity have focused on input-driven activity in the sensory cortex. Here we studied neural activity in the motor cortex while mice gathered rewards with multidirectional tongue reaching. This behaviour does not require training, allowing us to probe neural coding and connectivity before activity is shaped by extended learning. Motor cortex neurons were tuned to target location and reward outcome, and typically responded during and after movements. We studied the underlying network interactions in vivo by estimating causal neural connections using an all-optical method. Mapping connectivity between more than 20,000,000 excitatory neuron pairs showed a multi-scale columnar architecture in layer 2/3 of the motor cortex. Neurons displayed local (less than 100 µm) like-to-like excitatory connectivity according to target-location tuning, and inhibition over longer spatial scales. Connectivity patterns comprised a continuum, with abundant sparsely connected neurons and rare densely connected neurons that function as network hubs. Hub neurons were weakly tuned to target location and reward outcome but influenced more neighbouring neurons. This network of neurons, encoding location and outcome of movements to different motor goals, may be a general substrate for rapid learning of complex, goal-directed behaviours.

Cells exhibit a mysterious form of selective heritable short-term memory, influencing outcomes as diverse as cell fate decisions in embryos and environmental responses in cancer cells and bacteria. Here, we present a simple theoretical framework explaining how this selective memory can arise from the reactions regulating molecular levels in cells. Our key insight is that related cells retain more similar molecular concentrations relative to random cells when a greater variance of possible concentration states is created during a single cell generation than is created by cell division across a population. This persistence of molecular similarity down a lineage constitutes a form of heritable short-term memory. We identify the biochemical networks that produce, modify, and degrade molecules as an underexplored source of these additional molecular concentration states. Using experimentally informed simulations, we find that the strength and duration of molecular similarity down a lineage depend on tunable network properties, explaining why some cellular traits persist only briefly while others last generations. These contributions to molecular concentration variance from biochemical reaction networks act in concert with gene expression and other regulatory processes to shape the protein composition of cells. Our framework yields clear, testable predictions for determining how biochemical network architectures drive non-genetic cellular inheritance.

The endoplasmic reticulum (ER) is a highly interconnected membrane network that serves as a central site for protein synthesis and maturation. A crucial subset of ER-associated transcripts, termed secretome mRNAs, encode secretory, lumenal and integral membrane proteins, representing nearly one-third of human protein-coding genes. Unlike cytosolic mRNAs, secretome mRNAs undergo co-translational translocation, and thus require precise coordination between translation and protein insertion. Disruption of this process, such as through altered elongation rates, activates stress response pathways that impede cellular growth, raising the question of whether secretome translation is spatially organized to ensure fidelity. Here, using live-cell single-molecule imaging, we demonstrate that secretome mRNA translation is preferentially localized to ER junctions that are enriched with the structural protein lunapark and in close proximity to lysosomes. Lunapark depletion reduced ribosome density and translation efficiency of secretome mRNAs near lysosomes, an effect that was dependent on eIF2-mediated initiation and was reversed by the integrated stress response inhibitor ISRIB. Lysosome-associated translation was further modulated by nutrient status: amino acid deprivation enhanced lysosome-proximal translation, whereas lysosomal pH neutralization suppressed it. These findings identify a mechanism by which ER junctional proteins and lysosomal activity cooperatively pattern secretome mRNA translation, linking ER architecture and nutrient sensing to the production of secretory and membrane proteins.