Biology subjects

Miller, P.

Publications and source records attributed to Miller, P..

17 recordsLinked to original sources

bioRxiv2026

Single-Cell Proteomics of Human Peripheral Blood Mononuclear Cells Exceeding 600 Cells per Day

Single-cell proteomic (scProteomic) measurements of peripheral blood mononuclear cells (PBMCs) are of considerable value in human health, given their involvement in the maintenance of healthy and diseased states. However, the high heterogeneity and relatively small size of immune cell types demand maximal throughput and sensitivity in proteomic measurements that have yet to be fully realized. Here, we describe an approach that addresses sensitivity and throughput through the implementation of Real-Time spectral Library Searching (RTLS), TMTpro 32-plex labelling, an updated nested-nanodroplet processing in One pot for Trace Samples (N2), and a dual-column liquid chromatography system. By prioritizing tandem mass spectrometry (MS2) features with high similarity to library spectra, RTLS enables greater identification depth and feature reproducibility than a standard shotgun MS2 approach in low-input and single-cell samples. The platform permitted 660 single PBMCs to be measured per day, with an average of 750 protein identifications per cell and 1,648 proteins in total, achieving the necessary throughput and depth to characterize immune cell populations. Application of this scProteomic method and a new cell typing informatics approach to 2,130 PBMCs enabled the identification of both major and low-frequency cell types ([~]1-2%), as well as associated proteomic markers.

immunology↗

bioRxiv2025

Competition between memories for reactivation as a mechanism for long-delay credit assignment

Animals can associate events with their outcomes, even if there is a long delay between the two. For example, in conditioned taste aversion, animals gain an aversion to a taste (the conditioned stimulus, CS) if sickness (an unconditioned stimulus, US) is induced up to 12 hours later. Established correlational plasticity mechanisms, operating on timescales of milliseconds to seconds, do not wholly explain how networks of neurons achieve such long-delay credit assignment. Moreover, if the animal experiences an intervening taste (an interfering stimulus, IS), the IS gains some "credit" for the causality of the outcome, reducing aversion to the CS. We hypothesize that reactivation of prior events at the time of outcome causes specific associative learning between those events and the outcome. We explore the inherent competition underlying credit assignment using a spiking neural network model storing memories through time-decaying synaptic strengthenings in two groups of neurons producing inherently competing attractor states. We show how the later memory can be reactivated more often and reduce the reactivation of a prior memory. Also, we provide a mechanism for the experimental finding of a rebound in association with, and therefore aversion to, the CS if the time between the following IS and US is increased. Such a result can appear paradoxical as associations typically diminish with time, but arises when the IS initially produces a strong decrease in reactivation of the CS, but reactivations of the CS thereafter increase, in spite of weakening synaptic strengths, because competing reactivations of the IS decrease more. By reactivating the memories probabilistically, neural circuits can assign the credit in a biologically plausible way.

neuroscience↗

bioRxiv2025

Inhibitory-stabilization is sufficient for history-dependent computation in a randomly connected attractor network

For effective information processing, the response to a sensory stimulus should depend on both the incoming stimulus and the history of prior stimuli. Existing models of neural circuits based on multiple attractor states produced with strong self-excitation can exhibit these properties, but they do not stabilize at biologically realistic firing rates. We demonstrate how a randomly connected inhibition-stabilized attractor network can preserve the computational abilities of recurrent excitatory networks, while stabilizing at arbitrarily low firing rates. Not only does excitatory-inhibitory balance stabilize network activity, inhibitory-stabilization also plays a functional role in history-dependent computation: transient oscillations made possible by inhibitory feedback are sufficient for state-dependent responses to stimulation. Such networks may underlie many cognitive tasks, suggesting a functional role for inhibition-stabilized dynamics in cortical computation. Author summaryGeneral cognitive behavior requires the interpretation of incoming information within its recent context. For example, in sports, a single sensory stimulus: the referees whistle, can convey diverse messages: "begin," "foul," or "goal" dependent on the events immediately preceding the whistle. We will refer to such situations as "history-dependent," indicating that the appropriate behavioral response to a given stimulus depends on the prior history of stimulation. History-dependent behaviors include counting, oral communication, and sequence discrimination. In each of these instances, behaviorally relevant information depends less on the characteristics of a single stimulus, but rather on the entire set of stimuli, often including their order. Thus, to perform a wide range of cognitive tasks, the brain must possess a mechanism for short-term memory in which neural responses to a given stimulus depend both on the characteristics of that stimulus and on the recent history of stimulation. Here we study how the dynamics of networks of neurons could support history-dependent behaviors.

neuroscience↗

bioRxiv2025

REM sleep prefrontal ripple chains mediate distinct cortical-hippocampal reactivation patterns compared to NREM ripples

REM (rapid eye movement) and non-REM (NREM) sleep stages contribute to systems memory consolidation in hippocampal-cortical circuits. However, the physiological mechanisms underlying REM memory processes remain relatively unclear compared to NREM memory reactivation. Here we report, in rodents, the existence of prefrontal cortical (PFC) high-frequency oscillation (HFO) chains in REM sleep during consolidation of recently acquired spatial memory. High-density tetrode recordings in hippocampal area CA1 and PFC reveal that REM cortical HFOs occur in characteristic chains that are phase modulated by theta oscillations, corresponding to increased CA1-PFC theta coherence and delineating periods of enhanced hippocampal-cortical communication. REM HFO chains sequentially organize sparse PFC ensemble reactivation of behavioral activity during periods of local suppression, distinct from widespread reactivation bursts during NREM ripple oscillations. REM HFO chains also preferentially engage CA1 neuronal populations that demonstrate a shift in their preferred theta-phase from behavior to REM sleep. CA1 neuronal activation during REM HFO chains was correlated with CA1 activity suppression during NREM PFC ripples, and linked to differential changes in CA1 firing rates in sleep, suggesting REM-driven regulation of hippocampal excitability. A cortical network model incorporating the effects of acetylcholine can reproduce the distinct REM and NREM activity patterns, providing a mechanistic basis for widespread coactivity during NREM cortical ripples, compared to sparse, temporally extended reactivation on a background of local suppression during REM HFO chains. Overall, these findings establish a role for PFC high-frequency oscillations in regulating distinct dual sleep stage reactivation patterns.

neuroscience↗

bioRxiv2023

Intrinsic dynamics of randomly clustered networks generate place fields and preplay of novel environments

During both sleep and awake immobility, hippocampal place cells reactivate time-compressed versions of sequences representing recently experienced trajectories in a phenomenon known as replay. Intriguingly, spontaneous sequences can also correspond to forthcoming trajectories in novel environments experienced later, in a phenomenon known as preplay. Here, we present a model showing that sequences of spikes correlated with the place fields underlying spatial trajectories in both previously experienced and future novel environments can arise spontaneously in neural circuits with random, clustered connectivity rather than pre-configured spatial maps. Moreover, the realistic place fields themselves arise in the circuit from minimal, landmark-based inputs. We find that preplay quality depends on the networks balance of cluster isolation and overlap, with optimal preplay occurring in small-world regimes of high clustering yet short path lengths. We validate the results of our model by applying the same place field and preplay analyses to previously published rat hippocampal place cell data. Our results show that clustered recurrent connectivity can generate spontaneous preplay and immediate replay of novel environments. These findings support a framework whereby novel sensory experiences become associated with preexisting "pluripotent" internal neural activity patterns.

neuroscience↗

bioRxiv2023

Identification of potent and orally efficacious phosphodiesterase inhibitors in Cryptosporidium parvum-infected immunocompromised mice

Cryptosporidium species, mostly C. parvum and C. hominis in humans, are intestinal apicomplexan parasites that cause life-threatening diarrhea in young children and people with cell-mediated immune defects, such as due to AIDS. There is only one approved treatment for cryptosporidiosis, but it is ineffective for immunocompromised people and only modestly effective for children. In this study, screening 278 compounds from the Merck KGaA, Darmstadt, Germany collection and accelerated follow-up work enabled by prior investigation of the compounds resulted in identification of a series of pyrazolopyrimidine human phosphodiesterase (PDE)-V inhibitors with potent anticryptosporidial activity and efficacy following oral administration in C. parvum-infected immunocompromised mice. The novel PDE inhibitor leads (compounds PDEi2 and PDEi5) affect parasite egress from infected host cells. They have comparable activity against C. parvum and C. hominis, rapidly eliminate C. parvum in tissue culture, and have minimal off-target effects in a panel of safety screening assays. In comparison, the potent human PDE-V inhibitors sildenafil and the 4-aminoquinoline compound 7a have no useful activity against C. parvum. Based on homology modeling and in silico compound docking, PDEi5 interacts directly with an active-site metal ion and docks well to two C. parvum PDEs. In contrast, larger amino acid side groups (Val900/Tyr11128 and His884/Asn1112) in both C. parvum PDEs replace alanine in human PDE-V and block sildenafil binding, explaining its lack of efficacy. These results identify a promising new drug target and lead series for anticryptosporidial drug development and validates a route to target-based optimization.

microbiology↗

bioRxiv2023

In vivo expansion of gene-targeted hepatocytes through transient inhibition of an essential gene

Homology Directed Repair (HDR)-based genome editing is an approach that could permanently correct a broad range of genetic diseases. However, its utility is limited by inefficient and imprecise DNA repair mechanisms in terminally differentiated tissues. Here, we tested "Repair Drive", a novel method for improving targeted gene insertion in the liver by selectively expanding correctly repaired hepatocytes in vivo. Our system consists of transient conditioning of the liver by knocking down an essential gene, and delivery of an untargetable version of the essential gene in cis with a therapeutic transgene. We show that Repair Drive dramatically increases the percentage of correctly targeted hepatocytes, up to 25%. This resulted in a five-fold increased expression of a therapeutic transgene. Repair Drive was well-tolerated and did not induce toxicity or tumorigenesis in long term follow up. This approach will broaden the range of liver diseases that can be treated with somatic genome editing.

genetics↗

bioRxiv2023

Multistability in neural systems with random cross-connections

Neural circuits with multiple discrete attractor states could support a variety of cognitive tasks according to both empirical data and model simulations. We assess the conditions for such multistability in neural systems, using a firing-rate model framework, in which clusters of neurons with net self-excitation are represented as units, which interact with each other through random connections. We focus on conditions in which individual units lack sufficient self-excitation to become bistable on their own. Rather, multistability can arise via recurrent input from other units as a network effect for subsets of units, whose net input to each other when active is sufficiently positive to maintain such activity. In terms of the strength of within-unit self-excitation and standard-deviation of random cross-connections, the region of multistability depends on the firing-rate curve of units. Indeed, bistability can arise with zero self-excitation, purely through zero-mean random cross-connections, if the firing-rate curve rises supralinearly at low inputs from a value near zero at zero input. We simulate and analyze finite systems, showing that the probability of multistability can peak at intermediate system size, and connect with other literature analyzing similar systems in the infinite-size limit. We find regions of multistability with a bimodal distribution for the number of active units in a stable state. Finally, we find evidence for a log-normal distribution of sizes of attractor basins, which can appear as Zipfs Law when sampled as the proportion of trials within which random initial conditions lead to a particular stable state of the system.

neuroscience↗

bioRxiv2023

Monitoring plasma nucleosome concentrations to measure of disease response and progression in dogs with hematopoietic malignancies.

BackgroundHematopoietic malignancies are extremely common in pet dogs and represent nearly 30% of the malignancies diagnosed in this population each year. Clinicians commonly use existing tools such as physical exam findings, radiographs, ultrasound and baseline blood work to monitor these patients for treatment response and remission. Circulating biomarkers, such as prostate specific antigen or carcinoembryonic antigen, can be useful tools for monitoring treatment response and remission status in human cancer patients. To date, there has a been a lack of useful circulating biomarkers available to veterinary oncology patients. MethodsCirculating plasma nucleosome concentrations were evaluated at diagnosis, throughout treatment and during remission monitoring for 40 dogs with lymphoma, acute myelogenous leukemia and multiple myeloma. Additionally, C-reactive protein and thymidine kinase-1 levels were recorded. ResultsPlasma nucleosome concentrations were significantly higher at diagnosis and progressive disease than they were when dogs were in remission. All but two dogs had plasma nucleosome concentrations that returned to the low range during treatment. These two dogs had the shortest progression free and overall survival times. Dogs with the highest plasma nucleosome concentrations had a significantly shorter first progression free survival than dogs with lower plasma nucleosome concentrations at diagnosis. Plasma nucleosome concentrations correlated better with disease response and progression than either thymidine kinase or C reactive protein. ConclusionsPlasma nucleosome concentrations can be a useful tool for treatment monitoring and disease progression in dogs with hematopoietic malignancies.

cancer biology↗

bioRxiv2022

Sfrp2 is a multifunctional regulator of rodent color patterns

Animal pigment patterns are excellent models to elucidate mechanisms of biological organization. Although theoretical simulations, such as Turing reaction-diffusion systems, recapitulate many animal patterns, they are insufficient to account for those showing a high degree of spatial organization and reproducibility. Here, we compare the coats of the African striped mouse (Rhabdomys pumilio) and the laboratory mouse (Mus musculus) to study the molecular mechanisms controlling stripe pattern formation. By combining transcriptomics, mathematical modeling, and mouse transgenics, we show that Sfrp2 regulates the distribution of hair follicles and establishes an embryonic prepattern that foreshadows pigment stripes. Moreover, by developing and employing in vivo gene editing experiments in striped mice, we find that Sfrp2 knockout is sufficient to alter the stripe pattern. Strikingly, mutants also exhibit changes in coat color, revealing an additional function of Sfrp2 in regulating hair color. Thus, a single factor controls coat pattern formation by acting both as an orienting signaling mechanism and a modulator of pigmentation. By uncovering a multifunctional regulator of stripe formation, our work provides insights into the mechanisms by which spatial patterns are established in developing embryos and the molecular basis of phenotypic novelty.

developmental biology↗

bioRxiv2022

Strategies and Techniques for Quality Control and Semantic Enrichment with Multimodal Data: A Case Study in Colorectal Cancer with eHDPrep

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBO_SCPLOWACKGROUNDC_SCPLOWC_ST_ABSIntegration of data from multiple domains can greatly enhance the quality and applicability of knowledge generated in analysis workflows. However, working with health data is challenging, requiring careful preparation in order to support meaningful interpretation and robust results. Ontologies encapsulate relationships between variables that can enrich the semantic content of health datasets to enhance interpretability and inform downstream analyses. FO_SCPLOWINDINGSC_SCPLOWWe developed an R package for electronic Health Data preparation eHDPrep, demonstrated upon a multi-modal colorectal cancer dataset (n=661 patients, n=155 variables; Colo-661). eHDPrep offers user-friendly methods for quality control, including internal consistency checking and redundancy removal with information-theoretic variable merging. Semantic enrichment functionality is provided, enabling generation of new informative meta-variables according to ontological common ancestry between variables, demonstrated with SNOMED CT and the Gene Ontology in the current study. eHDPrep also facilitates numerical encoding, variable extraction from free-text, completeness analysis and user review of modifications to the dataset. CO_SCPLOWONCLUSIONC_SCPLOWeHDPrep provides effective tools to assess and enhance data quality, laying the foundation for robust performance and interpretability in downstream analyses. Application to a multi-modal colorectal cancer dataset resulted in improved data quality, structuring, and robust encoding, as well as enhanced semantic information. We make eHDPrep available as an R package from CRAN [[URL will go here]].

bioinformatics↗

bioRxiv2021

Impact of hedonic value of stimuli on sampling dynamics during a preference test

Food or taste preference tests are analogous to naturalistic decisions in which the animal selects which stimuli to sample and for how long to sample them. The data acquired in such tests, the relative amounts of the alternative stimuli that are sampled and consumed, indicate the preference for each. While such preferences are typically recorded as a single quantity, an analysis of the ongoing sampling dynamics producing the preference can reveal otherwise hidden aspects of the decision-making process that depend on its underlying neural circuit mechanisms. Here, we perform a dynamic analysis of two factors that give rise to preferences in a two-alternative task, namely the distribution of durations of sampling bouts of each stimulus and the likelihood of returning to the same stimulus or switching to the alternative--i. e., the transition probability--following each bout. The results of our analysis support a specific computational model of decision-making whereby an exponential distribution of bout durations has a mean that is positively correlated with the palatability of that stimulus, but also negatively correlated with the palatability of the alternative. This impact of the alternative stimulus on the distribution of bout durations decays over a timescale of tens of seconds, even though the memory of the alternative stimulus lasts far longer--long enough to impact the transition probabilities upon ending bouts. Together, our findings support a state-transition model for bout durations and suggest a separate memory mechanism for stimulus-selection.

animal behavior and cognition↗

bioRxiv2021

A model of naturalistic decision making in preference tests

Decisions as to whether to continue with an ongoing activity or to switch to an alternative are a constant in an animals natural world, and in particular underlie foraging behavior and performance in food preference tests. Stimuli experienced by the animal both impact the choice and are themselves impacted by the choice, in a dynamic back and forth. Here, we present model neural circuits, based on spiking neurons, in which the choice to switch away from ongoing behavior instantiates this back and forth, arising as a state transition in neural activity. We analyze two classes of circuit, which differ in whether state transitions result from a loss of hedonic input from the stimulus (an "entice to stay" model) or from aversive stimulus input (a "repel to leave" model). In both classes of model, we find that the mean time spent sampling a stimulus decreases with increasing value of the alternative stimulus, a fact that we linked to the inclusion of depressing synapses in our model. The competitive interaction is much greater in "entice to stay" model networks, which has qualitative features of the marginal value theorem, and thereby provides a framework for optimal foraging behavior. We offer suggestions as to how our models could be discriminatively tested through the analysis of electrophysiological and behavioral data. Author summaryMany decisions are of the ilk of whether to continue sampling a stimulus or to switch to an alternative, a key feature of foraging behavior. We produce two classes of model for such stay-switch decisions, which differ in how decisions to switch stimuli can arise. In an "entice-to-stay" model, a reduction in the necessary positive stimulus input causes switching decisions. In a "repel-to-leave" model, a rise in aversive stimulus input produces a switch decision. We find that in tasks where the sampling of one stimulus follows another, adaptive biological processes arising from a highly hedonic stimulus can reduce the time spent at the following stimulus, by up to ten-fold in the "entice-to-stay" models. Along with potentially observable behavioral differences that could distinguish the classes of networks, we also found signatures in neural activity, such as oscillation of neural firing rates and a rapid change in rates preceding the time of choice to leave a stimulus. In summary, our model findings lead to testable predictions and suggest a neural circuit-based framework for explaining foraging choices.

neuroscience↗

bioRxiv2019

Attractor-state itinerancy in neural circuits with synaptic depression

Neural populations with strong excitatory recurrent connections can support bistable states in their mean firing rates. Multiple fixed points in a network of such bistable units can be used to model memory retrieval and pattern separation. The stability of fixed points may change on a slower timescale than that of the dynamics due to short-term synaptic depression, leading to transitions between quasi-stable point attractor states in a sequence that depends on the history of stimuli. To better understand these behaviors, we study a minimal model, which characterizes multiple fixed points and transitions between them in response to stimuli with diverse time- and amplitude-dependences. The interplay between the fast dynamics of firing rate and synaptic responses and the slower timescale of synaptic depression makes the neural activity sensitive to the amplitude and duration of square-pulse stimuli in a non-trivial, history-dependent manner. Weak cross-couplings further deform the basins of attraction for different fixed points into intricate shapes. Our analysis provides a natural explanation for the systems rich responses to stimuli of different durations and amplitudes while demonstrating the encoding capability of bistable neural populations for dynamical features of incoming stimuli.

neuroscience↗

bioRxiv2018

Stable memory and computation in randomly rewiring neural networks

Our brains must maintain a representation of the world over a period of time much longer than the typical lifetime of the biological components producing that representation. For example, recent research suggests that dendritic spines in the adult mouse hippocampus are transient with an average lifetime of approximately 10 days. If this is true, and if turnover is equally likely for all spines, approximately 95-percent of excitatory synapses onto a particular neuron will turn over within 30 days; however, a neurons receptive field can be relatively stable over this period. Here, we use computational modeling to ask how memories can persist in neural circuits such as the hippocampus and visual cortex in the face of synapse turnover. We demonstrate that Hebbian learning during replay of pre-synaptic activity patterns can integrate newly formed synapses into pre-existing memories. Further, we find that Hebbian learning during replay is sufficient to stabilize the receptive fields of hippocampal place cells in a model of the grid-cell-to-place-cell transformation in CA1 and of orientation-selective cells in a model of the center-surround-to-simple-cell transformation in V1. We also ask how synapse turnover affects memory in Hopfield networks with CA3-like, auto-associative properties. We find that attractors of Hopfield networks are remarkably stable if learning occurs during network reactivations. Together, these data suggest that a simple learning rule, correlative Hebbian plasticity of synaptic strengths, is sufficient to preserve neural representations in the face of synapse turnover, even in the absence of Hebbian structural plasticity.

neuroscience↗

bioRxiv2018

Heteromeric GABAA receptor structures in positively-modulated active states

Type-A {gamma}-aminobutyric acid (GABAA) receptors are pentameric ligand-gated ion channels (pLGICs), typically consisting of /{beta}/{gamma} subunit combinations. They are the principal mediators of inhibitory neurotransmission throughout the central nervous system and targets of major clinical drugs, such as benzodiazepines (BZDs) used to treat epilepsy, insomnia, anxiety, panic disorder and muscle spasm. However, the structures of heteromeric receptors and the molecular basis of BZD operation remain unknown. Here we report the cryo-EM structure of a human 1{beta}3{gamma}2 GABAAR in complex with GABA and a nanobody that acts as a novel positive allosteric modulator (PAM). The receptor subunits assume a unified quaternary activated conformation around an open pore. We also present crystal structures of engineered 5 and 5{gamma}2 GABAAR constructs, revealing the interfacial site for allosteric modulation by BZDs, including the binding modes and the conformational impact of the potent anxiolytic and partial PAM, bretazenil, and the BZD antagonist, flumazenil. These findings provide the foundation for understanding the mechanistic basis of GABAAR activation.

biophysics↗

bioRxiv2018

Spatiotemporal discrimination in attractor networks with short-term synaptic plasticity

We demonstrate the ability of a randomly connected attractor network with dynamic synapses to discriminate between similar sequences containing multiple stimuli and suggest such networks provide a general basis for neural computations in the brain. The network is based on units representing assemblies of pools of neurons, with preferentially strong recurrent excitatory connections within each unit. Such excitatory feedback to a unit can generate bistability, though in many networks only under conditions of net excitatory input from other units. Weak interactions between units leads to a multiplicity of attractor states, within which information can persist beyond stimulus offset. When a new stimulus arrives, the prior state of the network impacts the encoding of the incoming information, with short-term synaptic depression ensuring an itinerancy between sets of active units. We assess the ability of such a network to encode the identity of sequences of stimuli, so as to provide a template for sequence recall, or decisions based on accumulation of evidence. Across a range of parameters, such networks produce the primacy (better final encoding of the earliest stimuli) and recency (better final encoding of the latest stimuli) observed in human recall data and can retain the information needed to make a binary choice based on total number of presentations of a specific stimulus. Similarities and differences in the final states of the network produced by different sequences lead to predictions of specific errors that could arise when an animal or human subject generalizes from training data, when the training data comprises a subset of the entire stimulus repertoire. We suggest that such networks can provide the robust general purpose computational engines needed for us to solve many cognitive tasks.

neuroscience↗