Biology subjects

Cunningham, J.

Publications and source records attributed to Cunningham, J..

17 recordsLinked to original sources

bioRxiv2026

Involvement of Mitophagy in Endothelin-1 Mediated Neurodegeneration in Rodent Models of Glaucoma

The ultimate cause of blindness in glaucoma is the death of retinal ganglion cells, and understanding the mechanism behind retinal ganglion cell loss during glaucoma could lead to the development of novel treatments for glaucoma. Endothelin-1 has been shown to mediate retinal ganglion cell death during glaucoma through impairment of mitochondrial function. Retinal ganglion cells are highly metabolically active, and susceptible to oxidative damage and decreased respiratory capacity. Mitophagy is the process whereby damaged mitochondria are degraded to prevent further propagation of oxidative damage. The current study evaluates the effect of endothelin-1 on mitophagy in retinal ganglion cells. Electron microscopy revealed endothelin-1 administration lead to a decrease in healthy mitochondria in the optic nerve. The MitoQC mouse was used to evalute mitophagy in response to endothelin-1, along with immunohistochemical analysis of mitophagy proteins. Mitophagy follows different trends in the optic nerve and retinal ganglion cell bodies following endothelin-1 administration, mitophagy was increased in the optic nerve but decreased in the retina following endothelin administration. With elevation of intraocular pressure, mitophagy was increased in the retina but decreased in the optic nerve. In retinal ganglion cells, parkin expression and activation was unchanged 24 hours after endothelin-1 administration, but was decreased 72 hours following endothelin-1 administration. Taken together, these results suggest that endothelin-1 impacts mitophagy through parkin-independent mechanisms in retinal ganglion cell bodies, and the ganglion cell bodies and optic nerve appear to have different responses to endothelin-1.

neuroscience↗

bioRxiv2026

Human Lymph Node Cellular Senescence Atlas Reveals Age-Dependent Alteration in Germinal Center B Cell Function and Niches

Immunosenescence, the age-associated decline in immune function, is a key feature of human aging. In human lymphoid organs, however, the specific immune cell populations that acquire senescence-associated phenotypes during aging and how they influence the surrounding tissue microenvironment remain poorly understood. A spatially resolved map of these senescence-associated immune states in human lymphoid tissues could help clarify their relationship with aging and their potential contributions to the progressive decline of immune function. Here, we integrated single-cell and spatial multi-omics to systematically characterize age-related senescence in human lymph nodes (LNs). Single-cell transcriptomics of lymphoid tissues from donors aged 18 to 100 years old identified 34 immune and stromal cell types and revealed age-associated upregulation of senescence signatures in specific populations. Spatial proteomic profiling of 99 LN sections from 51 donors (18-86 years) using high-plex immunofluorescence ([~]20 million cells) mapped senescence markers (p16, p21, HMGB1, -H2AX) at single-cell resolution, revealing diverse senescent-like cell types ("senotypes") and a stepwise shift from extrafollicular to germinal center (GC) localization with age. Notably, we observed focal clonal-like senescence in GC B cells in older donor LNs. Spatial transcriptomics, epigenomics, and metabolic imaging of selected samples further elucidate the multi-omics signatures and underlying mechanisms of functional impairment, metabolic remodeling, and distinct regulatory programs in senescent-like GC B cells. This study presents a comprehensive spatial atlas of senescence-associated immune states in human lymph nodes, revealing cell-type-specific and spatial heterogeneity that may contribute to immunosenescence and the decline of immune function during aging.

immunology↗

bioRxiv2025

S1PR3 mediates glial stimulated tumor invasion in response to interstitial fluid flow

Cellular invasion is a primary challenge to complete resection and treatment of glioblastoma, the most aggressive and deadly primary brain tumor. The brain tumor microenvironment actively stimulates glioma invasion through a multitude of cellular, chemical, and biophysical cues. We and others have shown elevated interstitial fluid flow at the tumor border is one such biophysical cue that directly stimulates invasion through tumor-intrinsic signaling and, in other tumor types, priming of cancer-associated stromal cells. It is currently unclear if interstitial flow similarly primes neuroglial cells to promote glioma cell dissemination and can be targeted for therapeutic purposes. Here, we show elevated interstitial flow upregulates expression of sphingosine-1-phosphate receptor 3 (S1PR3) in glial astrocytes and microglia, which drives glioma cell invasion via chemotaxis. Flow-induced expression of glial S1PR3 is tumor-independent and displays a biphasic relationship to fluid shear stress magnitude in vitro and flow rate in vivo. Inhibition of glial S1PR3 in a tissue engineered culture model and orthotopic mouse model abrogates flow-stimulated invasion, demonstrating a tumor-extrinsic approach to limiting glioblastoma progression. Given prior evidence of a pro-inflammatory role for glial S1PR3, identification of S1PR3 as a disease-agnostic marker of flow-stimulated glia may also have therapeutic implications across myriad neuropathologies.

cancer biology↗

bioRxiv2025

Constraints and adaptations in crocodyliform skull evolution

Crocodyliformes display a diverse range of skull morphologies though their evolutionary history. Extant crocodilians possess platyrostral (broad and flat) snouts, thought to be sub-optimal for resisting feeding loads due to the conflicting demands of feeding and hydrodynamic constraints. In contrast, numerous Mesozoic crocodyliformes possessed oreinirostral (dome-shaped) skulls, were terrestrial and hence free from hydrodynamic constraint. This study aims to examine the role of function in determining skull shape in crocodyliformes, and assesses the difference in stress resistance between oreinirostral and platyrostral taxa. We hypothesise that in the absence of hydrodynamic constraints, oreinirostral taxa have skulls that are better suited for resisting feeding induced loads. Using finite element analysis (FEA), we evaluated biomechanical performance in oreinirostral notosuchian taxa Baurusuchus salgadoensis, Montealtosuchus arrudacamposi and Caipirasuchus paulistanus; compared to the extant platyrostral Alligator mississippiensis, Crocodylus niloticus, and Paleosuchus palpebrosus. Results show that oreinirostral morphologies are comparatively better suited for resisting forces generated during feeding, with increased muscular efficiency, supporting the hypothesis that hydrodynamic constraints influence crocodyliform skull evolution.

paleontology↗

bioRxiv2025

The paradoxical impact of drought on West Nile virus risk: insights from long-term ecological data

Mosquito-borne diseases are deeply embedded within ecological communities, with environmental changes - particularly climate change - shaping their dynamics. Increasingly intesense droughts across the globe have profound implications for the transmission of these diseases, as drought conditions can alter mosquito breeding habitats, host-seeking behaviors, and mosquito-host contact rates. To quantify the effect of drought on disease transmission, we use West Nile virus (WNV) as a model system and leverage a robust mosquito and virus dataset consisting of over 500,000 trap nights collected from 2010-2023, spanning a historic drought period followed by atmospheric rivers. We pair this surveillance dataset with a novel modeling approach that incorporates monthly changes in bird host community competence, along with drought conditions, to estimate the effect of drought severity on WNV risk using panel regression models. Our results show that while drought decreases mosquito abundances, it paradoxically increases WNV infection rates. This counterintuitive pattern likely stems from reduced water availability, which concentrates mosquitos and pathogen-amplifying bird hosts around limited water sources, thereby increasing disease transmission risk. However, the magnitude of the effect depends critically on mosquito species, suggesting species-specific behavioral traits are key to understanding the effect of drought on mosquito-borne disease risk across real landscapes.

ecology↗

bioRxiv2024

Engineered human lymph node stroma model for examining interstitial fluid flow and T cell egress

The lymph node (LN) performs essential roles in immunosurveillance throughout the body. Developing in vitro models of this key tissue is of great importance to enhancing physiological relevance in immunoengineering. The LN consists of stromal populations and immune cells, which are highly organized and bathed in constant interstitial flow. The stroma, notably the fibroblastic reticular cells (FRCs) and the lymphatic endothelial cells (LECs), play crucial roles in guiding T cell migration and are known to be sensitive to fluid flow. During inflammation, interstitial fluid flow rates drastically increase in the LN. It is unknown how these altered flow rates impact crosstalk and cell behavior in the LN, and most existing in vitro models focus on the interactions between T cells, B cells, and dendritic cells rather than with the stroma. To address this gap, we developed a human engineered model of the LN stroma consisting of FRC-laden hydrogel above a monolayer of LECs in a tissue culture insert with gravity-driven interstitial flow. We found that FRCs had enhanced coverage and proliferation in response to high flow rates, while LECs experienced decreased barrier integrity. We added CD4+ and CD8+ T cells and found that their egress was significantly decreased in the presence of interstitial flow, regardless of magnitude. Interestingly, 3.0 {micro}m/s flow, but not 0.8 {micro}m/s flow, correlated with enhanced inflammatory cytokine secretion in the LN stroma. Overall, we demonstrate that interstitial flow is an essential consideration in the lymph node for modulating LN stroma morphology, T cell migration, and inflammation.

bioengineering↗

bioRxiv2024

SORBET: Automated cell-neighborhood analysis of spatial transcriptomics or proteomics for interpretable sample classification via GNN

Spatial cellular profiling technologies have revolutionized our understanding of complex biological processes, from development and disease progression to immunity and aging. Despite their promise, integrating spatial information with multiplexed molecular data to accurately predict phenotypes poses significant challenges, especially in clinical settings. Here, we present SORBET, a geometric deep learning framework that directly analyzes complete spatial profiling data, eliminating the need to compress complete cell profiles into a limited set of annotations, such as cell types. SORBET models tissues as graphs of adjacent cells and applies graph convolutional networks to infer emergent phenotypes, such as responses to immunotherapy. The model leverages a novel data augmentation technique to ensure robust predictions, complemented by tailored interpretability analyses to identify the molecular and spatial patterns underlying the models phenotype inferences. We apply our method to a CosMx spatial transcriptomics dataset of pre-treatment metastatic melanoma samples annotated with response to immunotherapy; we show that spatial information significantly improves clinical endpoint, or phenotype, prediction and identifies important biological patterns. To our knowledge, SORBET is the first example of phenotype prediction on spatial transcriptomics data. We further validated our method using two spatial proteomics datasets, Imaging Mass Cytometry (IMC) and Co-detection by indexing (CODEX), obtained from Non-Small Cell Lung Cancer and Colorectal Cancer samples, respectively. SORBET demonstrates superior accuracy in phenotype prediction over leading spatial and non-spatial methods across various datasets of different observed phenotypes and technologies. SORBET sets a new benchmark for predictive analysis in spatial omics, promising to advance personalized medicine through refined patient treatment stratification, grounded in molecular and spatial tissue profiling.

bioinformatics↗

bioRxiv2023

Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source tools

Contemporary pose estimation methods enable precise measurements of behavior via supervised deep learning with hand-labeled video frames. Although effective in many cases, the supervised approach requires extensive labeling and often produces outputs that are unreliable for downstream analyses. Here, we introduce "Lightning Pose," an efficient pose estimation package with three algorithmic contributions. First, in addition to training on a few labeled video frames, we use many unlabeled videos and penalize the network whenever its predictions violate motion continuity, multiple-view geometry, and posture plausibility (semi-supervised learning). Second, we introduce a network architecture that resolves occlusions by predicting pose on any given frame using surrounding unlabeled frames. Third, we refine the pose predictions post-hoc by combining ensembling and Kalman smoothing. Together, these components render pose trajectories more accurate and scientifically usable. We release a cloud application that allows users to label data, train networks, and predict new videos directly from the browser.

neuroscience↗

bioRxiv2021

Motor cortex activity across movement speeds is predicted by network-level strategies for generating muscle activity

Learned movements can be skillfully performed at different paces. What neural strategies produce this flexibility? Can they be predicted and understood by network modeling? We trained monkeys to perform a cycling task at different speeds, and trained artificial recurrent networks to generate the empirical muscle-activity patterns. Network solutions reflected the principle that smooth well-behaved dynamics require low trajectory tangling. Network solutions had a consistent form, which yielded quantitative and qualitative predictions. To evaluate predictions, we analyzed motor cortex activity recorded during the same task. Responses supported the hypothesis that the dominant neural signals reflect not muscle activity, but network-level strategies for generating muscle activity. Single-neuron responses were better accounted for by network activity than by muscle activity. Similarly, neural population trajectories shared their organization not with muscle trajectories, but with network solutions. Thus, cortical activity could be understood based on the need to generate muscle activity via dynamics that allow smooth, robust control over movement speed.

neuroscience↗

bioRxiv2020

A Novel Multiplex qPCR Assay for Detection of Plasmodium falciparum with Histidine-rich Protein 2 and 3 (pfhrp2 and pfhrp3) Deletions in Polyclonal Infections

BackgroundRapid diagnostic tests (RDTs) that detect the malaria antigen histidine-rich protein 2 (HRP2) are widely used in endemic areas globally to confirm Plasmodium falciparum infection in febrile patients. The emergence of parasites lacking the gene encoding HRP2 and escaping RDT detection threatens progress in malaria control and elimination. Many health facilities in malaria endemic countries are dependent on RDTs for diagnosis and some National Health Service hospitals without expert microscopists rely on them for diagnosis out of hours. It is vital to study the emergence and the extent of such parasites globally to guide diagnostic policy. Currently, verification of the presence of such parasites in a blood sample requires a series of PCR assays to confirm the presence of P. falciparum and in the absence of amplicons from pfhrp2 and/or pfhrp3, which encodes a cross-reactive protein isoform. These tests have different limits of detection and many laboratories have reported difficulty in confirming the absence of pfhrp2 and pfhrp3 with certainty. MethodsWe developed and validated a novel and rapid multiplex real time quantitative (qPCR) assay to detect pfhrp2, pfhrp3, confirmatory parasite and human reference genes simultaneously. We also applied the assay to detect pfhrp2 and pfhrp3 deletion in 462 field samples from different endemic countries and UK travellers. ResultsThe qPCR assay showed limit of detection and quantification of 0.76-1.5 parasites per l. The amplification efficiency, coefficient of determination (R2) and slope for the genes were 96-1.07%, 0.96-0.98 and -3.375 2 to -3.416 respectively. The assay demonstrated diagnostic sensitivity of 100% (n=19, 95% CI= (82.3%; 100%)) and diagnostic specificity of 100% (n=31; 95% CI= (88.8%; 100%)) in detecting pfhrp2 and pfhrp3 in. In addition, the qPCR assay estimates P. falciparum parasite density and can detect pfhrp2 and pfhrp3 deletions masked in polyclonal infections. We report pfhrp2 and pfhrp3 deletions in parasite isolates from Kenya, Tanzania and in UK travellers. ConclusionThe new qPCR assay is simple to use and offers significant advantages in speed and ease of interpretation. It is easily scalable to routine surveillance studies in countries where P. falciparum parasites lacking pfhrp2 and pfhrp3 are a threat to malaria control.

genetics↗

bioRxiv2019

Adopting tipping-point theory to transcriptome profiles unravels disease regulatory trajectory

Differentiation involves bifurcations between discrete cell states, each defined by a distinct gene expression profile. Single-cell RNA profiling allows the detection of bifurcations. However, while current methods capture these events, they do not identify characteristic gene signals. Here we show that BioTIP - a tipping-point theory-based analysis - can accurately, robustly, and reliably identify critical transition signals (CTSs). A CTS is a small group of genes with high covariance in expression that mark the cells approaching a bifurcation. We validated its accuracy in the cardiogenesis with known a tipping point and demonstrated the identified CTSs contain verified differentiation-driving transcription factors. We then demonstrated the application on a published mouse gastrulation dataset, validated the predicted CTSs using independent in-vivo samples, and inferred the key developing mesoderm regulator Etv2. Taken together, BioTIP is broadly applicable for the characterization of the plasticity, heterogeneity, and rapid switches in developmental processes, particularly in single-cell data analysis. HighlightsO_LIIdentifying significant critical transition signals (CTSs) from expression noise C_LIO_LIA significant CTS contains or is targeted by key transcription factors C_LIO_LIBioTIP identifies CTSs accurately and independent of trajectory topologies C_LIO_LISignificant CTSs reproducibly indicate bifurcations across datasets C_LI

systems biology↗

bioRxiv2019

Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data

Widefield calcium imaging enables recording of large-scale neural activity across the mouse dorsal cortex. In order to examine the relationship of these neural signals to the resulting behavior, it is critical to demix the recordings into meaningful spatial and temporal components that can be mapped onto well-defined brain regions. However, no current tools satisfactorily extract the activity of the different brain regions in individual mice in a data-driven manner, while taking into account mouse-specific and preparation-specific differences. Here, we introduce Localized semi-Nonnegative Matrix Factorization (LocaNMF), a method that efficiently decomposes widefield video data and allows us to directly compare activity across multiple mice by outputting mouse-specific localized functional regions that are significantly more interpretable than more traditional decomposition techniques. Moreover, it provides a natural subspace to directly compare correlation maps and neural dynamics across different behaviors, mice, and experimental conditions, and enables identification of task- and movement-related brain regions.

neuroscience↗

bioRxiv2018

Inhibitory and excitatory populations in parietal cortex are equally selective for decision outcome in both novices and experts

Inhibitory neurons, which play a critical role in decision-making models, are often simplified as a single pool of non-selective neurons lacking connection specificity. This assumption is supported by observations in primary visual cortex: inhibitory neurons are broadly tuned in vivo, and show non-specific connectivity in slice. Selectivity of excitatory and inhibitory neurons within decision circuits, and hence the validity of decision-making models, is unknown. We simultaneously measured excitatory and inhibitory neurons in posterior parietal cortex of mice judging multisensory stimuli. Surprisingly, excitatory and inhibitory neurons were equally selective for the animals choice, both at the single cell and population level. Further, both cell types exhibited similar changes in selectivity and temporal dynamics during learning, paralleling behavioral improvements. These observations, combined with modeling, argue against circuit architectures assuming non-selective inhibitory neurons. Instead, they argue for selective subnetworks of inhibitory and excitatory neurons that are shaped by experience to support expert decision-making.

neuroscience↗

bioRxiv2017

Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience

Modern large - scale multineuronal recording methodologies, including multielectrode arrays, calcium imaging, and optogenetic techniques, produce single - neuron resolution data of a magnitude and precision that were the realm of science fiction twenty years ago. The major bottlenecks in systems and circuit neuroscience no longer lie in simply collecting data from large neural populations, but also in understanding this data: developing novel scientific questions, with corresponding analysis techniques and experimental designs to fully harness these new capabilities and meaningfully interrogate these questions. Advances in methods for signal processing, network analysis, dimensionality reduction, and optimal control - developed in lockstep with advances in experimental neurotechnology - - promise major breakthroughs in multiple fundamental neuroscience problems. These trends are clear in a broad array of subfields of modern neuroscience; this review focuses on recent advances in methods for analyzing neural time - series data with single - neuronal precision.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=46 SRC=\"FIGDIR/small/196949_fig1.gif\" ALT=\"Figure 1\">\nView larger version (13K):\norg.highwire.dtl.DTLVardef@16fff01org.highwire.dtl.DTLVardef@1ceacd4org.highwire.dtl.DTLVardef@1b49960org.highwire.dtl.DTLVardef@1c1ad13_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO The central role of data science in modern large - scale neuroscience.Topics reviewed herein are indicated in black.\n\nC_FIG

neuroscience↗

bioRxiv2017

Conservation of preparatory neural events regardless of how movement is initiated

Voluntary movement is believed to be preceded by a preparatory stage. Evidence arises from experiments where a delay separates instruction and execution cues. While this sequence emulates some real-world situations (e.g., swatting a fly upon landing) movements are commonly made at a moment of ones choosing (reaching for a coffee cup) or are made reactively (intercepting a falling cup). To ascertain whether neural events are conserved across such contexts, we examined motor cortex population-level responses in monkeys when reaches were initiated either after an imposed delay, at a self-chosen time, or reactively with very low latency. We found that the same preparatory and movement-related events were conserved. However, preparation was temporally flexible and could be remarkably brief. Our findings support the existing hypothesis that preparation is an obligatory stage that achieves a consistent state prior to movement. Yet our results reveal that preparation can unfold more rapidly than previously supposed.

neuroscience↗

bioRxiv2017

A Statistical Model of Shared Variability in the Songbird Auditory System

Vocal communication evokes robust responses in primary auditory cortex (A1) of songbirds, and single neurons from superficial and deep regions of A1 have been shown to respond selectively to songs over complex, synthetic sounds. However, little is known about how this song selectivity arises and manifests itself on the level of networks of neurons in songbird A1. Here, we examined the network-level coding of song and synthetic sounds in A1 by simultaneously recording the responses of multiple neurons in unanesthetized zebra finches. We developed a latent factor model of the joint simultaneous activity of these neural populations, and found that the shared variability in the activity has a surprisingly simple structure; it is dominated by an unobserved latent source with one degree-of-freedom. This simple model captures the structure of the correlated activity in these populations in both spontaneous and stimulus-driven conditions, and given both song and synthetic stimuli. The inferred latent variability is strongly suppressed under stimulation, consistent with similar observations in a range of mammalian cortical regions.

neuroscience↗

bioRxiv2016

Electrical Stimulus Artifact Cancellation and Neural Spike Detection on Large Multi-Electrode Arrays

Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is greatly complicated by electrical stimulation artifacts across the array, which can exhibit complex and nonlinear waveforms, and overlap temporarily with evoked spikes. Here we develop a scalable algorithm based on a structured Gaussian Process model to estimate the artifact and identify evoked spikes. The effectiveness of our methods is demonstrated in both real and simulated 512-electrode recordings in the peripheral primate retina with single-electrode and several types of multi-electrode stimulation. We establish small error rates in the identification of evoked spikes, with a computational complexity that is compatible with real-time data analysis. This technology may be helpful in the design of future high-resolution sensory prostheses based on tailored stimulation (e.g., retinal prostheses), and for closed-loop neural stimulation at a much larger scale than currently possible.\n\nAuthor SummarySimultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these recordings is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is largely stymied by electrical stimulation artifacts across the array, which are typically larger than the signals of interest. We develop a novel computational framework to estimate and subtract away this contaminating artifact, enabling the large-scale analysis of responses of possibly hundreds of cells to tailored stimulation. Importantly, we suggest that this technology may also be helpful for the development of future high-resolution neural prosthetic devices (e.g., retinal prostheses).

neuroscience↗