Biology subjects

Sheridan, A.

Publications and source records attributed to Sheridan, A..

13 recordsLinked to original sources

bioRxiv2026

Split intein vectors permit oversize FLIM-FRET biosensor neuronal expression

Adeno-associated virus (AAV) packaging limits constrain the design, performance, and in vivo application of genetically encoded biosensors. We developed a split intein-mediated reconstitution strategy enabling modular delivery and reassembly of oversized fluorescence lifetime-based biosensors. Using this platform, we engineered an oversized cAMP sensor compatible with one-photon fluorescence lifetime measurements, enabling monitoring of intracellular signaling dynamics in distinct neuronal subtypes in freely behaving mice.

bioengineering↗

bioRxiv2026

Spatial imprints of emergent cardiomyocyte states in the pressure-overloaded heart

Resilience to cardiac stress is essential for health, yet the relationship between cardiomyocyte (CM) stress response and local microenvironment remains unclear. Here, we combined MERFISH spatial transcriptome profiling with Cellouette, an improved cell segmentation method, to determine CM-microenvironment relationships in a mouse model of ventricular pressure overload. We report the shape, transcription profile, spatial organization, and physical connectivity for >400,000 cells across stressed and healthy tissues. Under stress, CMs adopted a spectrum of emergent transcriptional states, with advanced states marked by a metabolic and pro-fibrotic shift. To discover CM-environment relationships, we performed a network analysis of physical cell connectivity combined with cell-type-specific profiling. We found that pro-fibrotic CM progression was tightly linked to distinct local microenvironments, and CM metabolic shifts could be inferred from transcriptional patterns in neighboring non-CM cells, revealing microenvironmental imprints of disease. We thus provide a resource for understanding the heterogeneity of outcome during cardiac pressure overload. HighlightsO_LICellouette provides accurate segmentation for single-cell spatial transcriptomics in cardiac tissue. C_LIO_LIPressure overload creates spatial gradients of cardiomyocyte pro-fibrotic states. C_LIO_LICardiomyocyte pro-fibrotic progression is linked to changes in local cell composition and gene expression. C_LIO_LITranscriptional states of non-muscle cells predict metabolic state of adjacent cardiomyocytes. C_LI

genomics↗

bioRxiv2025

Combinatorial protein barcodes enable self-correcting neuron tracing with nanoscale molecular context

Mapping nanoscale neuronal morphology with molecular annotations is critical for understanding healthy and dysfunctional brain circuits. Current methods are constrained by image segmentation errors and by sample defects (e.g., signal gaps, section loss). Genetic strategies promise to overcome these challenges by using easily distinguishable cell identity labels. However, multicolor approaches are spectrally limited in diversity, whereas nucleic acid barcoding lacks a cellfilling morphology signal for segmentation. Here, we introduce PRISM (Protein-barcode Reconstruction via Iterative Staining with Molecular annotations), a platform that integrates combinatorial delivery of antigenically distinct, cell-filling proteins with tissue expansion, multi-cycle imaging, barcode-augmented reconstruction, and molecular annotation. Protein barcodes increase label diversity by >750-fold over multicolor labeling and enable morphology reconstruction with intrinsic error correction. We acquired a [~]10 million {micro}m3 volume of mouse hippocampal area CA2/3, multiplexed across 23 barcode antigen and synaptic marker channels. By combining barcodes with shape information, we achieve an 8x increase in automatic tracing accuracy of genetically labelled neurons. We demonstrate PRISM supports automatic proofreading across micron-scale spatial gaps and reconnects neurites across discontinuities spanning hundreds of microns. Using PRISMs molecular annotation capability, we map the distribution of synapses onto traced neural morphology, characterizing challenging synaptic structures such as thorny excrescences (TEs), and discovering a size correlation among spatially proximal TEs on the same dendrite. PRISM thus supports selfcorrecting neuron reconstruction with molecular context.

neuroscience↗

bioRxiv2024

A deep learning-based strategy for producing dense 3D segmentations from sparsely annotated 2D images

Analyzing volumetric microscopy data requires dense segmentation, yet training accurate machine learning models demands prohibitively expensive 3D ground-truth. To overcome this bottleneck, we present a lightweight framework that bootstraps dense 3D instance segmentations directly from sparse 2D annotations. A 2D network trained on sparse labels predicts complete boundaries on every section, and a 3D network pre-trained on synthetic data infers inter-section connectivity to build coherent 3D volumes. We validated this 2D[->]3D method across diverse datasets spanning electron, expansion, and live-cell microscopy. Strikingly, 3D models trained on these rapidly generated pseudo ground-truths achieve accuracy comparable to those trained on dense expert annotations, yielding up to a 1,000-fold reduction in human annotation time. Even accounting for downstream proofreading, total reconstruction costs drop by an order of magnitude. This approach democratizes the generation of dense 3D training data, seamlessly extending 2D foundation models into the third dimension.

neuroscience↗

bioRxiv2021

Structured connectivity in the cerebellum enables noise-resilient pattern separation

The cerebellum is thought to detect and correct errors between intended and executed commands1-3 and is critical for social behaviors, cognition and emotion4-6. Computations for motor control must be performed quickly to correct errors in real time and should be sensitive to small differences between patterns for fine error correction while being resilient to noise7. Influential theories of cerebellar information processing have largely assumed random network connectivity, which increases the encoding capacity of the networks first layer8-13. However, maximizing encoding capacity reduces resiliency to noise7. To understand how neuronal circuits address this fundamental tradeoff, we mapped the feedforward connectivity in the mouse cerebellar cortex using automated large-scale transmission electron microscopy (EM) and convolutional neural network-based image segmentation. We found that both the input and output layers of the circuit exhibit redundant and selective connectivity motifs, which contrast with prevailing models. Numerical simulations suggest these redundant, non-random connectivity motifs increase discriminability of similar input patterns at a minimal cost to the networks overall encoding capacity. This work reveals how neuronal network structure can balance encoding capacity and redundancy, unveiling principles of biological network architecture with implications for artificial neural network design.

neuroscience↗

bioRxiv2021

Content-aware frame interpolation (CAFI): Deep Learning-based temporal super-resolution for fast bioimaging

The development of high-resolution microscopes has made it possible to investigate cellular processes in 4D (3D over time). However, observing fast cellular dynamics remains challenging as a consequence of photobleaching and phototoxicity. These issues become increasingly problematic with the depth of the volume acquired and the speed of the biological events of interest. Here, we report the implementation of two content-aware frame interpolation (CAFI) deep learning networks, Zooming SlowMo (ZS) and Depth-Aware Video Frame Interpolation (DAIN), based on combinations of recurrent neural networks, that are highly suited for accurately predicting images in between image pairs, therefore improving the temporal resolution of image series as a post-acquisition analysis step. We show that CAFI predictions are capable of understanding the motion context of biological structures to perform better than standard interpolation methods. We benchmark CAFIs performance on six different datasets, obtained from three different microscopy modalities (point-scanning confocal, spinning-disk confocal and confocal brightfield microscopy). We demonstrate its capabilities for single-particle tracking methods applied to the study of lysosome trafficking. CAFI therefore allows for reduced light exposure and phototoxicity on the sample and extends the possibility of long-term live-cell imaging. Both DAIN and ZS as well as the training and testing data are made available for use by the wider community via the ZeroCostDL4Mic platform.

cell biology↗

bioRxiv2021

Ultrastructural readout of in vivo synaptic activity for functional connectomics.

Large-volume ultrastructural mapping approaches yield detailed circuit wiring diagrams but lack an integrated synaptic activity readout which is essential for functional interpretation of the connectome. Here we resolve this limitation by combining functional synaptic labelling in vivo with focused ion-beam scanning electron microscopy (FIBSEM) and machine learning-based segmentation. Our approach generates high-resolution near-isotropic three-dimensional readouts of activated vesicle pools across large populations of individual synapses in a volume of tissue, opening the way for detailed functional connectomics studies. We apply this method to measure presynaptic activity in an ultrastructural context in synapses activated by sensory input in primary visual cortex in awake head-fixed mice, showing that the numbers of recycling and non-recycling vesicles approximate to a lognormal distribution across a large number of synapses. We also demonstrate that neighbouring boutons of the same axon, which share the same spiking activity, can differ greatly in their presynaptic release probability.

neuroscience↗

bioRxiv2021

An integrative skeletal and paleogenomic analysis of prehistoric stature variation suggests relatively reduced health for early European farmers

Human culture, biology, and health were shaped dramatically by the onset of agriculture ~12,000 years before present (BP). Subsistence shifts from hunting and gathering to agriculture are hypothesized to have resulted in increased individual fitness and population growth as evidenced by archaeological and population genomic data alongside a simultaneous decline in physiological health as inferred from paleopathological analyses and stature reconstructions of skeletal remains. A key component of the health decline inference is that relatively shorter statures observed for early farmers may (at least partly) reflect higher childhood disease burdens and poorer nutrition. However, while such stresses can indeed result in growth stunting, height is also highly heritable, and substantial inter-individual variation in the height genetic component within a population is typical. Moreover, extensive migration and gene flow were characteristics of multiple agricultural transitions worldwide. Here, we consider both osteological and ancient DNA data from the same prehistoric individuals to comprehensively study the trajectory of human stature variation as a proxy for health across a transition to agriculture. Specifically, we compared predicted genetic contributions to height from paleogenomic data and achieved adult osteological height estimated from long bone measurements on a per-individual basis for n=160 ancient Europeans from sites spanning the Upper Paleolithic to the Iron Age (~38,000-2,400 BP). We found that individuals from the Neolithic were shorter than expected (given their individual polygenic height scores) by an average of -4.47 cm relative to individuals from the Upper Paleolithic and Mesolithic (P=0.016). The average osteological vs. expected stature then increased relative to the Neolithic over the Copper (+2.67 cm, P=0.052), Bronze (+3.33 cm, P=0.032), and Iron Ages (+3.95 cm, P=0.094). These results were partly attenuated when we accounted for genome-wide genetic ancestry variation in our sample (which we note is partly duplicative with the individual polygenic score information). For example, in this secondary analysis Neolithic individuals were -3.48 cm shorter than expected on average relative to individuals from the Upper Paleolithic and Mesolithic (P=0.056). We also incorporated observations of paleopathological indicators of non-specific stress that can persist from childhood to adulthood in skeletal remains (linear enamel hypoplasia, cribra orbitalia, and porotic hyperostosis) into our model. Overall, our work highlights the potential of integrating disparate datasets to explore proxies of health in prehistory.

genomics↗

bioRxiv2021

Local Shape Descriptors for Neuron Segmentation

We present a simple, yet effective, auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of Local Shape Descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors are designed to capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a large study comparing several existing methods across various specimen, imaging techniques, and resolutions, we find that auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (Flood-Filling Networks, FFN), while being two orders of magnitudes more efficient--a critical requirement for the processing of future petabyte-sized datasets. Implementations of the new auxiliary learning task, network architectures, training, prediction, and evaluation code, as well as the datasets used in this study are publicly available as a benchmark for future method contributions.

neuroscience↗

bioRxiv2021

Immunogenicity of an AAV-based, room-temperature stable, single dose COVID-19 vaccine in mice and non-human primates

The SARS-CoV-2 pandemic has affected more than 70 million people worldwide and resulted in over 1.5 million deaths. A broad deployment of effective immunization campaigns to achieve population immunity at global scale will depend on the biological and logistical attributes of the vaccine. Here, two adeno-associated viral (AAV)-based vaccine candidates demonstrate potent immunogenicity in mouse and nonhuman primates following a single injection. Peak neutralizing antibody titers remain sustained at 5 months and are complemented by functional memory T-cells responses. The AAVrh32.33 capsid of the AAVCOVID vaccine is an engineered AAV to which no relevant pre-existing immunity exists in humans. Moreover, the vaccine is stable at room temperature for at least one month and is produced at high yields using established commercial manufacturing processes in the gene therapy industry. Thus, this methodology holds as a very promising single dose, thermostable vaccine platform well-suited to address emerging pathogens on a global scale.

immunology↗

bioRxiv2019

Automatic Detection of Synaptic Partners in a Whole-Brain Drosophila EM Dataset

The study of neural circuits requires the reconstruction of neurons and the identification of synaptic connections between them. To scale the reconstruction to the size of whole-brain datasets, semi-automatic methods are needed to solve those tasks. Here, we present an automatic method for synaptic partner identification in insect brains, which uses convolutional neural networks to identify post-synaptic sites and their pre-synaptic partners. The networks can be trained from human generated point annotations alone and require only simple post-processing to obtain final predictions. We used our method to extract 244 million putative synaptic partners in the fifty-teravoxel full adult fly brain (FAFB) electron microscopy (EM) dataset and evaluated its accuracy on 146,643 synapses from 702 neurons with a total cable length of 312 mm in four different brain regions. The predicted synaptic connections can be used together with a neuron segmentation to infer a connectivity graph with high accuracy: between 92% and 96% of edges linking connected neurons are correctly classified as weakly connected (less than five synapses) and strongly connected (at least five synapses). Our synaptic partner predictions for the FAFB dataset are publicly available, together with a query library allowing automatic retrieval of up- and downstream neurons.

neuroscience↗

bioRxiv2019

The neuroanatomical ultrastructure and function of a biological ring attractor

Neural representations of head direction have been discovered in many species. A large body of theoretical work has proposed that the dynamics associated with these representations is generated, maintained, and updated by recurrent network structures called ring attractors. We performed electron microscopy-based circuit reconstruction and RNA profiling of identified cell types in the heading direction system of Drosophila melanogaster to directly determine the underlying neural network. We identified network motifs that have been hypothesized to maintain the heading representation in darkness, update it when the animal turns, and tether it to visual cues. Functional studies provided additional support for the proposed roles of individual circuit elements. We also discovered recurrent connections between neuronal arbors with mixed pre- and post-synaptic specializations. Overall, our results confirm that the Drosophila heading direction network contains the core components of a ring attractor while also revealing unpredicted structural features that might enhance the networks computational power.

neuroscience↗

bioRxiv2018

Population Replacement in Early Neolithic Britain

The roles of migration, admixture and acculturation in the European transition to farming have been debated for over 100 years. Genome-wide ancient DNA studies indicate predominantly Anatolian ancestry for continental Neolithic farmers, but also variable admixture with local Mesolithic hunter-gatherers1-9. Neolithic cultures first appear in Britain c. 6000 years ago (kBP), a millennium after they appear in adjacent areas of northwestern continental Europe. However, the pattern and process of the British Neolithic transition remains unclear10-15. We assembled genome-wide data from six Mesolithic and 67 Neolithic individuals found in Britain, dating from 10.5-4.5 kBP, a dataset that includes 22 newly reported individuals and the first genomic data from British Mesolithic hunter-gatherers. Our analyses reveals persistent genetic affinities between Mesolithic British and Western European hunter-gatherers over a period spanning Britains separation from continental Europe. We find overwhelming support for agriculture being introduced by incoming continental farmers, with small and geographically structured levels of additional hunter-gatherer introgression. We find genetic affinity between British and Iberian Neolithic populations indicating that British Neolithic people derived much of their ancestry from Anatolian farmers who originally followed the Mediterranean route of dispersal and likely entered Britain from northwestern mainland Europe.

evolutionary biology↗