Biology subjects

Landry, M. P.

Publications and source records attributed to Landry, M. P..

At least 19 recordsLinked to original sources

bioRxiv2026

Acute partner loss enhances social motivation and nucleus accumbens dopamine release in prairie voles

The loss of close social relationships leads to profound mental and physical health consequences across social species. We utilized the socially selective prairie vole (Microtus ochrogaster) to investigate how 5-day separation from a bonded mate or peer partner shapes social motivation and dopamine release kinetics in males and females. Prior to partner loss, baseline social motivation to access a partner or a novel object differed by sex and relationship type (mate vs. peer). Across all groups, however, separation induced heightened partner-seeking. Partner loss also enhanced dopamine signaling in the nucleus accumbens, increasing evoked dopamine release, the number of putative dopamine release sites, and tyrosine hydroxylase abundance, regardless of sex or relationship type. These findings suggest that mesolimbic reward circuitry adapts rapidly to the loss of a bonded partner, potentially signaling a critical social deficit across relationship types. Further, by leveraging high-resolution synthetic nanosensors, this study provides subcellular insights into presynaptic dopaminergic plasticity following partner loss, linking relationship loss to heightened social motivation and shifts in reward signaling.

neuroscience↗

bioRxiv2026

DNA-Programmed Condensate-Membrane Wetting and Cellular Internalization

Deformable condensates offer dynamic interfaces for biomolecule delivery, yet membrane adhesion does not necessarily lead to cellular internalization. The physical transition that determines whether a membrane-bound soft material remains surface-anchored or proceeds through wetting towards productive uptake remains poorly understood, particularly at active living-cell membranes. Here, we engineer sequence-defined DNA condensates through liquid-liquid phase separation (LLPS) and program their interfacial behavior by tuning sticky-end valency and cholesterol organization. These molecular designs precisely regulate condensate fluidity, fusion dynamics and internal organization, generating distinct states of weak contact, persistent anchoring and rapid wetting. Increasing cholesterol-mediated affinity does not enhance uptake. Instead, productive internalization emerges from a balance between membrane adhesion and condensate fluidity and deformability. Native membrane composition further modulates condensate interfacial fate across mammalian cells and plant protoplasts. DNA condensates enrich and deliver CpG ODNs, mRNA ([~]2000 nt) and proteins, while cargo loading experiments reveal that preserving condensate architecture is essential for functional delivery. Our findings identify wetting competence as a design parameter for controlling soft material engagement and cellular entry.

biophysics↗

bioRxiv2026

Adapting a two-photon scanning microscope for simultaneous single-photon imaging of an infrared dopamine sensor

We describe a novel method for adapting a two-photon scanning microscope to enable simultaneous detection of two-photon generated visible fluorescence and single-photon generated near-infrared (nIR) fluorescence. In this configuration, nIR fluorescence is routed through a single-mode optical fiber before detection by a photomultiplier tube. This fiber coupling offers two advantages: first, the optical fiber functions as a pinhole aperture, allowing for improved optical sectioning of the nIR signal; second, it minimizes nIR background fluorescence. To validate the effectiveness of this design, we conducted two sets of experiments. First, we compare two fluorescence indicators of the neurotransmitter dopamine: the genetically encoded indicator GRABDA and single walled carbon nanotube based optical nanosensors (nIRCats). Although nIRCats exhibit lower affinity for dopamine than GRABDA, this property allows for identification of high concentration release sites in the striatum. Second, we simultaneously imaged depolarization-induced calcium changes and dopamine release in the retina. Together, these results demonstrate the utility of integrating confocal nIR detection into a two-photon platform for simultaneous functional imaging across complementary spectral channels.

bioengineering↗

bioRxiv2025

Quantifying Misuse of Color in Biological Research

Colors are widely used to report biological research data. Effective interpretation of colors relies on accurate, intuitive, and accessible colormaps that are properly labeled. Misuse of colormaps, such as using rainbow, mismatched, and other inaccessible colormaps, hinders reader interpretation. Despite their ubiquitous use to represent biological data, the prevalence of colormap misuse has not been quantified. Using established guidelines, we analyzed over 6,000 articles in 10 high-impact biological journals spanning 2020-2024 to quantify the prevalence of colormap use and misuse. Over 67% of articles contain at least one colormap, of which 81% contain some form of misuse. Among the over 11,000 acquired colormap-associated figures, 60% contain some form of misuse. Heatmaps, scatter plots, images, and image overlays are the most commonly used figures with colormaps. These statistics vary by journal but remain constant over time, demonstrating the urgent need to increase awareness and education about color-based data reporting guidelines.

scientific communication and education↗

bioRxiv2025

Absence of testes at puberty impacts functional development of nigrostriatal but not mesoaccumbal dopamine terminals in a wild-derived mouse

The nigrostriatal and mesoaccumbal dopamine systems are thought to contribute to changes in behavior and learning during adolescence, yet it is unclear how the rise in gonadal hormones at puberty impacts the function of these systems. We studied the impact of prepubertal gonadectomy on evoked dopamine release in male Mus spicilegus, a mouse whose adolescent life history has been carefully characterized in the wild and laboratory. To examine how puberty impacts the dopamine systems in M. spicilegus males, we removed the gonads prepubertally at P25 and then examined evoked dopamine release in the dorsomedial, dorsolateral, and nucleus accumbens core regions of striatal slices at P60-70. To measure dopamine release, we used near-infrared catecholamine nanosensors (nIRCats) to enable study of spatial distribution of dopamine release sites in each striatal region. We found that prepubertal gonadectomy led to a significantly reduced density of dopamine release sites and reduced dopamine release at each site in the dorsolateral nigrostriatal system compared to sham controls. By contrast, mesoaccumbal dopamine release was comparable between sham and gonadectomized groups. Our data suggest that during adolescence the development of the nigrostriatal dopamine system is significantly affected by the rise in gonadal hormones in males, while the mesoaccumbal system shows no detectable sensitivity at this time point. These data are consistent with molecular studies in rodents that suggest nigrostriatal neurons are sensitive to androgens at puberty, and extend our understanding of how gonadal hormones could impact the spatial distribution and release potential of dopamine terminals in the striatum. Significance StatementHere we use a wild-derived species, Mus spicilegus, to study adolescent development. This wild-derived species has value over standard lab mice because it is more likely to exhibit evolved developmental programs relevant to dispersal and other natural behaviors. By using this wild-derived species and metrics of evoked dopamine release with spatial resolution, we can test if the rise in gonadal hormones at puberty plays a role in maturation of dopamine terminal function in the striatum. These findings may help us better understand developmental programs in humans that orchestrate changes in behavior at adolescent milestones in contexts of both health and disease.

neuroscience↗

bioRxiv2025

Oxytocin receptor absence reduces selectivity in peer relationships and alters neurochemical release dynamics in prairie voles

Abstract/SummaryFriendships, or selective peer relationships, are a vital component of healthy social functioning in humans, while deficits in these relationships are associated with negative physical and mental health consequences. Like humans, prairie voles are among the few mammalian species that form selective social bonds with both peers and mates, making them an excellent model for the mechanistic investigation of selective social attachment. Here, we explored the role of oxytocin receptors in selective peer attachment using prairie voles lacking a functional oxytocin receptor gene (Oxtr1-/-). We found that Oxtr1-/- animals exhibited significant delays in peer relationship formation compared to wildtype animals. Oxytocin receptor function also contributed to the maintenance of peer bonds, as Oxtr1-/- voles displayed reduced relationship stability and lost selective attachments rapidly in a multi-chamber, group-living habitat. Oxtr1-/- voles also showed deficits in both general social reward as well as selective social reward for a peer partner over an unfamiliar conspecific. Evoked oxytocin release in the nucleus accumbens was reduced in Oxtr1-/- animals compared to their wildtype counterparts, indicating that these voles do not have a compensatory increase in oxytocinergic signaling. Together, these data indicate that oxytocin receptors influence the formation, persistence, and reward value of peer relationships.

animal behavior and cognition↗

bioRxiv2025

Neurochemical imaging reveals changes in dopamine dynamics with photoperiod in a seasonally social vole species

Studying dopamine signaling in non-model organisms is crucial for understanding the broad range of behaviors not represented in traditional model systems. However, exploring new species is often hindered by a scarcity of tools suitable for non-genetic models. In this work, we introduce near-infrared catecholamine nanosensors (nIRCats) to investigate dopamine dynamics in meadow voles, a rodent species that exhibits distinct changes in social behavior and neurobiology across photoperiods. We observe increased dopamine release and release site density in social voles under short photoperiods, suggesting adaptations linked to environmental changes. Moreover, pharmacological and extracellular manipulations demonstrate that social voles exhibit heightened responsiveness to dopamine-increasing interventions and resilience against suppressive conditions. These findings highlight a significant association between dopamine signaling and photoperiod-driven changes in social behavior and establish nIRCats as an effective tool for expanding our understanding of dopamine dynamics across non-model organisms.

neuroscience↗

bioRxiv2025

The third alpha helix of plant homeoproteins are generally cell-penetrating to plant cells

Homeoproteins are transcription factors involved in developmental regulation. Animal homeoproteins are generally cell-penetrating to animal cells, capable of crossing membranes through a conserved 3rd alpha helix "penetration" domain. While plants also possess homeoproteins, the cell-penetrating ability of plant homeoproteins has not been investigated. We catalog the 3rd alpha helixes of plant homeoproteins across 851 species and screen a subset for the ability to penetrate walled-plant cells. We identify plant 3rd alpha helixes which are cell-penetrating to plants, the efficiency of which is amino acid sequence dependent, and use these peptides for cytosolic delivery of recombinant protein cargoes. We discover plant homeoproteins are generally cell-penetrating to plants, mirroring the behavior of animal homeoproteins, with applications in plant biotechnology and implications for fundamental plant biology.

plant biology↗

bioRxiv2025

Anionic polymers stabilize Cas9 ribonucleoprotein nanoparticles to improve direct protein delivery and genome editing efficiencies in plant protoplasts

The application of CRISPR-based genome editing tools in plants is often challenged by low editing efficiencies, requiring most plant editing workflows to proceed through the delivery of pre-formed ribonucleoprotein (RNP) complexes to protoplasts. Here, we report increases in protoplast-based RNP delivery and genome editing efficiencies through the addition of anionic polymers to standard protoplast transfection protocols. We test addition of various polymers and peptides for their ability to increase genome editing efficiencies in both Nicotiana benthamiana and Arabidopsis thaliana protoplasts, by adding these components to standard PEG-based protoplast transfection protocols: i) non-covalent addition of charged polymers, ii) non-covalent addition of amphiphilic peptide A5K, and iii) tyrosinase-mediated covalent conjugation of various relevant peptide motifs directly to the RNP. Incorporation of the amphiphilic peptide A5K or covalent attachment of peptides to the RNP had no positive effect on editing efficiencies. However, we found that addition of anionic polymer polyglutamic acid to standard PEG transfection protocols significantly improved editing efficiencies in both Nicotiana benthamiana and Arabidopsis thaliana protoplasts relative to RNPs alone without negatively impacting protoplast viability. Our results suggest anionic polymers stabilize the RNP and increase the colloidal stability of the protoplast transfection workflow. This simple and straightforward method of stabilizing Cas9 RNPs can be easily adopted by others working on direct protein delivery to plant protoplasts to increase genome editing efficiencies. Key messageThe addition of anionic polymer polyglutamic acid to standard protoplast PEG transfection workflows enhance CRISPR-Cas9-mediated gene editing in plant protoplasts. Our results suggest the mechanism of increased transfection efficiency is due to colloidal stabilization of RNPs. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/635105v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@1101f0org.highwire.dtl.DTLVardef@964402org.highwire.dtl.DTLVardef@14afe61org.highwire.dtl.DTLVardef@82a982_HPS_FORMAT_FIGEXP M_FIG C_FIG

plant biology↗

bioRxiv2024

Deep profiling of plant stress biomarkers following bacterial pathogen infection with protein corona based nano-omics

Detection and remediation of stress in crops is vital to ensure agricultural productivity. Conventional forms of assessing stress in plants are limited by feasibility, delayed phenotypic responses, inadequate specificity, and lack of sensitivity during initial phases of stress. While mass spectrometry is remarkably precise and achieves high-resolution, complex samples, such as plant tissues, require time-consuming and biased depletion strategies to effectively identify low-abundant stress biomarkers. Here, we bypassed these reduction methods via a nano-omics approach, where gold nanoparticles were used to enrich time- and temperature-dependent stress-related proteins through biomolecular corona formation that were subsequently analyzed by ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS). This nano-omic approach was more effective than a conventional proteomic analysis using UHPLC- MS/MS for resolving biotic-stress induced responses at early stages of pathogen infection in Arabidopsis thaliana, well before the development of visible phenotypic symptoms, as well as in distal tissues of pathogen infected plants at early timepoints. The enhanced sensitivity of this nano-omic approach enables the identification of stress-related proteins at early critical timepoints, providing a more nuanced understanding of plant-pathogen interactions that can be leveraged for the development of early intervention strategies for sustainable agriculture.

plant biology↗

bioRxiv2024

Quantifying Data Distortion in Bar Graphs in Biological Research

Over 88% of biological research articles use bar graphs, of which 29% have undocumented data distortion mistakes that over- or under-state findings. We developed a framework to quantify data distortion and analyzed bar graphs published across 3387 articles in 15 journals, finding consistent data distortions across journals and common biological data types. To reduce bar graph-induced data distortion, we propose recommendations to improve data visualization literacy and guidelines for effective data visualization.

scientific communication and education↗

bioRxiv2024

AI-based Prediction of Protein Corona Composition on DNA Nanostructures

DNA nanotechnology has emerged as a powerful approach to engineering biophysical tools, therapeutics, and diagnostics because it enables the construction of designer nanoscale structures with high programmability. Based on DNA base pairing rules, nanostructure size, shape, surface functionality, and structural reconfiguration can be programmed with a degree of spatial, temporal, and energetic precision that is difficult to achieve with other methods. However, the properties and structure of DNA constructs are greatly altered in vivo due to spontaneous protein adsorption from biofluids. These adsorbed proteins, referred to as the protein corona, remain challenging to control or predict, and subsequently, their functionality and fate in vivo are difficult to engineer. To address these challenges, we prepared a library of diverse DNA nanostructures and investigated the relationship between their design features and the composition of their protein corona. We identified protein characteristics important for their adsorption to DNA nanostructures and developed a machine-learning model that predicts which proteins will be enriched on a DNA nanostructure based on the DNA structures design features and protein properties. Our work will help to understand and program the function of DNA nanostructures in vivo for biophysical and biomedical applications.

bioengineering↗

bioRxiv2024

Optical Fibers Functionalized with Single-Walled Carbon Nanotubes for Flexible Fluorescent Catecholamine Detection

Despite the blockbuster popularity of drugs that act on catecholamine receptors, catecholamine dynamics in human health and disease remain an incomplete picture. Recent advances in fluorescent sensors have enabled unprecedented access to catecholamine dynamics in preclinical animal models, but the requirements of these technologies limit translational value for clinical diagnostics. Here, we present a flexible and convenient tool for fluorescent catecholamine detection by functionalizing optical fibers with single-walled carbon nanotube (SWNT)-based near-infrared catecholamine sensors (nIRCats), a form factor that has potential for more convenient and less invasive clinical translation. We show that these near-infrared functionalized (nIRF) fibers respond to dopamine in a biologically-relevant concentration range (10nM through 1 M) with a mean {Delta}F/F0 of 0.022 through 0.411, with no statistically significant effect on signal magnitude after 16-hour exposure to human blood plasma. We further demonstrate the utility of these fibers in as little as 10 L volumes of clinically relevant biofluids up to 24 weeks after preparation, with a {Delta}F/F0 of up to 0.059 through 1.127 for 10 nM through 1 M dopamine. We also introduce a compact, mobile dual-near-infrared fiber photometry rig and demonstrate its success detecting dopamine with 0.005 {Delta}F/F0 in acute brain slices with nIRF fibers. Together, this fiber-based tool and photometry rig expand the toolbox of catecholamine detection technologies to a broader range of applications.

biochemistry↗

bioRxiv2024

Deep Plasma Proteome Profiling by Modulating Single Nanoparticle Protein Corona with Small Molecules

The protein corona, a dynamic biomolecular layer that forms on nanoparticle (NP) surfaces upon exposure to biological fluids is emerging as a valuable diagnostic tool for improving plasma proteome coverage analyzed by liquid chromatography-mass spectrometry (LC-MS/MS). Here, we show that spiking small molecules, including metabolites, lipids, vitamins, and nutrients (namely, glucose, triglyceride, diglycerol, phosphatidylcholine, phosphatidylethanolamine, L--phosphatidylinositol, inosine 5'-monophosphate, and B complex), into plasma can induce diverse protein corona patterns on otherwise identical NPs, significantly enhancing the depth of plasma proteome profiling. The protein coronas on polystyrene NPs when exposed to plasma treated with an array of small molecules (n=10) allowed for detection of 1793 proteins marking an 8.25-fold increase in the number of quantified proteins compared to plasma alone (218 proteins) and a 2.63-fold increase relative to the untreated protein corona (681 proteins). Furthermore, we discovered that adding 1000 {micro}g/ml phosphatidylcholine could singularly enable the detection of 897 proteins. At this specific concentration, phosphatidylcholine selectively depleted the four most abundant plasma proteins, including albumin, thus reducing the dynamic range of plasma proteome and enabling the detection of proteins with lower abundance. By employing an optimized data-independent acquisition (DIA) approach, the inclusion of phosphatidylcholine led to the detection of 1436 proteins in a single plasma sample. Our molecular dynamic results revealed that phosphatidylcholine interacts with albumin via hydrophobic interactions, h-bonds, and water-bridges. Addition of phosphatidylcholine also enabled the detection of 337 additional proteoforms compared to untreated protein corona using a top-down proteomics approach. These significant achievements are made utilizing only a single NP type and one small molecule to analyze a single plasma sample, setting a new standard in plasma proteome profiling. Given the critical role of plasma proteomics in biomarker discovery and disease monitoring, we anticipate widespread adoption of this methodology for identification and clinical translation of proteomic biomarkers into FDA approved diagnostics.

bioengineering↗

bioRxiv2024

Predicting Serotonin Detection with DNA-Carbon Nanotube Sensors Across Multiple Spectral Wavelengths

Owing to the value of DNA-wrapped single-walled carbon nanotube (SWNT) based sensors for chemically-specific imaging in biology, we explore machine learning (ML) predictions DNA-SWNT serotonin sensor responsivity as a function of DNA sequence based on the whole SWNT fluorescence spectra. Our analysis reveals the crucial role of DNA sequence in the binding modes of DNA-SWNTs to serotonin, with a smaller influence of SWNT chirality. Regression ML models trained on existing datasets predict the change in the fluorescence emission in response to serotonin, {Delta}F/F, at over a hundred wavelengths for new DNA-SWNT conjugates, successfully identifying some high- and low-response DNA sequences. Despite successful predictions, we also show that the finite size of the training dataset leads to limitations on prediction accuracy. Nevertheless, incorporating entire spectra into ML models enhances prediction robustness and facilitates the discovery of novel DNA-SWNT sensors. Our approaches show promise for identifying new chemical systems with specific sensing response characteristics, marking a valuable advancement in DNA-based system discovery.

bioengineering↗

bioRxiv2023

Covalent Attachment of Horseradish Peroxidase to Single-Walled Carbon Nanotubes for Hydrogen Peroxide Detection

Single-walled carbon nanotubes (SWCNTs) are desirable nanoparticles for sensing biological analytes due to their photostability and intrinsic near-infrared fluorescence. Previous strategies for generating SWCNT nanosensors have leveraged nonspecific adsorption of sensing modalities to the hydrophobic SWCNT surface that often require engineering new molecular recognition elements. An attractive alternate strategy is to leverage pre-existing molecular recognition of proteins for analyte specificity, yet attaching proteins to SWCNT for nanosensor generation remains challenging. Towards this end, we introduce a generalizable platform to generate protein-SWCNT-based optical sensors and use this strategy to synthesize a hydrogen peroxide (H2O2) nanosensor by covalently attaching horseradish peroxidase (HRP) to the SWCNT surface. We demonstrate a concentration-dependent response to H2O2, confirm the nanosensor can image H2O2 in real-time, and assess the nanosensors selectivity for H2O2 against a panel of biologically relevant analytes. Taken together, these results demonstrate successful covalent attachment of enzymes to SWCNTs while preserving both intrinsic SWCNT fluorescence and enzyme function. We anticipate this platform can be adapted to covalently attach other proteins of interest including other enzymes for sensing or antibodies for targeted imaging and cargo delivery.

bioengineering↗

bioRxiv2023

Mapping the Morphology of DNA on Carbon Nanotube-Based Sensors in Solution using X-ray Scattering Interferometry

Single-walled carbon nanotubes (SWCNTs) with adsorbed single-stranded DNA (ssDNA) are applied as sensors to investigate biological systems, with applications ranging from clinical diagnostics to agricultural biotechnology. Unique ssDNA sequences render SWCNTs selectively responsive to target analytes. However, it remains unclear how the ssDNA conformation on the SWCNT surface contributes to their ultimate functionality, as observations have been constrained to computational models or experiments under dehydrated states that differ substantially from the aqueous biological environments in which the nanosensors are applied. Herein, we demonstrate a direct mode of measuring in-solution ssDNA geometries on SWCNTs via X-ray scattering interferometry (XSI), which leverages the interference pattern produced by AuNP tags conjugated to ssDNA on the SWCNT surface. We employ XSI to quantify distinct surface-adsorbed morphologies for two ssDNA oligomer lengths, conformational changes as a function of ionic strength, and the mechanism of dopamine sensing for a previously established ssDNA-SWCNT nanosensor, with corresponding ab initio modeling for visualization. We show that the shorter oligomer, (GT)6, adopts a highly ordered structure of stacked rings along the SWCNT axis, compared to the longer, less periodic (GT)15 wrapping. The presence of dopamine elicits a simultaneous axial elongation and radial constriction of the ssDNA closer to the SWCNT surface. Application of XSI to probe solution-phase morphologies of nanoparticle-based tools will yield insights into sensing mechanisms and inform future design strategies for polymer-functionalized SWCNT technologies.

biophysics↗

bioRxiv2023

Identifying Neural Signatures of Dopamine Signaling with Machine Learning

The emergence of new tools to image neurotransmitters, neuromodulators, and neuropeptides has transformed our understanding of the role of neurochemistry in brain development and cognition, yet analysis of this new dimension of neurobiological information remains challenging. Here, we image dopamine modulation in striatal brain tissue slices with near infrared catecholamine nanosensors (nIRCat) and implement machine learning to determine which features of dopamine modulation are unique to changes in stimulation strength, and to different neuroanatomical regions. We trained a support vector machine and a random forest classifier to determine whether recordings were made from the dorsolateral striatum (DLS) versus the dorsomedial striatum (DMS) and find that machine learning is able to accurately distinguish dopamine release that occurs in DLS from that occurring in DMS in a manner unachievable with canonical statistical analysis. Furthermore, our analysis determines that dopamine modulatory signals including the number of unique dopamine release sites and peak dopamine released per stimulation event are most predictive of neuroanatomy yet note that integrated neuromodulator amount is the conventional metric currently used to monitor neuromodulation in animal studies. Lastly, our study finds that machine learning discrimination of different stimulation strengths or neuroanatomical regions is only possible in adult animals, suggesting a high degree of variability in dopamine modulatory kinetics during animal development. Our study highlights that machine learning could become a broadly-utilized tool to differentiate between neuroanatomical regions, or between neurotypical and disease states, with features not detectable by conventional statistical analysis.

neuroscience↗