Biology subjects

Lee, R. T.

Publications and source records attributed to Lee, R. T..

8 recordsLinked to original sources

bioRxiv2026

Recombinant GDF11 Improves Neurological Recovery in Models of Hemorrhagic Stroke and Traumatic Brain Injury

BackgroundIntracerebral hemorrhage (ICH) and traumatic brain injury (TBI) are leading causes of long-term neurological disability and mortality worldwide, with no approved therapies that promote functional recovery. Growth differentiation factor 11 (GDF11), a circulating TGF{beta}-family protein, has shown regenerative and neurorestorative potential in models of ischemic stroke. MethodsWe evaluated recombinant GDF11 (rGDF11) in mouse models of ICH and TBI. ICH was induced by intrastriatal collagenase injection, and neurological recovery was assessed using Neuroseverity Score (NSS), Rotarod (RR), and CatWalk (CW) analyses up to 28 days post-injury. Histological assessments of vascularization, neuronal density, and microglial/macrophage density were performed 28 days after ICH. For TBI, a closed-head injury model using a pneumatic impactor was employed, and NSS and RR assessments were conducted through 28 days post-injury. ResultsrGDF11 treatment significantly improved neurobehavioral performance following ICH, including NSS, RR, and CW parameters (forelimb base of support and average speed). Histological analyses revealed enhanced vascular area and neuronal density, with reduced microglial/macrophage density in rGDF11-treated mice. Following TBI, rGDF11 accelerated functional recovery, improving RR latency by day 6 and NSS by day 28 post-injury. ConclusionrGDF11 promotes structural and functional recovery after both hemorrhagic and traumatic brain injury in mice. These findings, together with prior evidence in ischemic stroke, support rGDF11 as a promising neurorestorative biologic with broad therapeutic potential for diverse forms of brain injury. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=178 SRC="FIGDIR/small/730114v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@8c624forg.highwire.dtl.DTLVardef@8a6107org.highwire.dtl.DTLVardef@e83ce6org.highwire.dtl.DTLVardef@f65f14_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

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

Multimodal gene and targeted drug therapy for chronic myelogenous leukemia: Computational target analysis and therapeutic validation

Developing an efficient and safe therapy necessitates a mechanistic understanding of the complex underlying pathology and manipulation of the multiple pathways at the molecular and genetic level. Network-based simulation of chronic myeloid leukemia (CML), a relatively well-understood cancer model, revealed the dynamics of simultaneously expressing pro-apoptotic BIM and silencing pro-survival MCL-1 in combination with the BCR-ABL-targeted tyrosine kinase inhibitor dasatinib. Viral/nonviral chimeric nanoparticles (ChNPs) composed of a BIM-expressing adeno-associated virus (AAV) core and a degradable polymeric shell that encapsulates MCL-1 siRNA (BIM/MCL-1 ChNPs) synergistically and selectively killed BCR-ABL+ CML cells in combination with dasatinib. In a mouse CML model, the BIM/MCL-1 ChNPs and dasatinib combination therapy suppressed proliferation of BCR-ABL+ hematopoietic cells and prevented leukemic infiltration of organs. The synergistic anti-leukemic effect was further pronounced in an acute phase model of the disease. This study investigated a strategy of developing a versatile and tunable multimodal therapy assisted by a computational toolset that analyzes the molecular foundation of a disease and predicts therapeutic response. The interdisciplinary approach developed and validated in this study can be used in discovering new therapies for cancer and other diseases. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=182 SRC="FIGDIR/small/656072v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@1e3632borg.highwire.dtl.DTLVardef@1839f12org.highwire.dtl.DTLVardef@4060c0org.highwire.dtl.DTLVardef@8ff0de_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO summarizing the study design and approach. A schematic representation of the integrated in silico and in vivo pipeline utilized in the study. The workflow begins with in silico simulations, including Boolean network modeling and protein-protein interaction (PPI) network analyses, leading to target discovery, optimized nanoparticle design, and validation in pathological contexts. This was followed by therapeutic efficacy assessments of BIM/MCL-1 ChNPs and their combination with dasatinib in BCR-ABL+ leukemia models in vitro and in vivo. C_FIG

cancer biology↗

bioRxiv2025

In Situ Graphene-Seq: Spatial Transcriptomics and Chronic Electrophysiological Characterization of Tissue Microenvironments

Biological systems are composed of diverse, interconnected cell types, yet capturing both their functional dynamics and molecular identities at high spatiotemporal resolution remains challenging. While electrophysiological measurements provide real-time insights into cellular activities, they cannot fully describe the molecular architecture and states of the measured cells. Conversely, transcriptomics reveals cell gene expression patterns but does not capture functional states. Bridging these modalities is essential for a holistic understanding of the molecular mechanisms driving functional changes. In this study, we introduce in situ graphene-sequencing (graphene-seq), a unique platform that seamlessly integrates chronic electrophysiology with imaging-based, spatially resolved 3D transcriptomics, overcoming longstanding limitations of current multimodal approaches. This system leverages stretchable mesh nanoelectronics for long-term, single-cell-level interfacing and incorporates transparent graphene/PEDOT:PSS electrodes, enabling seamless integration of electrical recordings and optical imaging. By combining electrophysiology with high-throughput, imaging-based in situ sequencing, this platform allows comprehensive multimodal, spatially resolved analysis of cell microenvironment within spatially heterogeneous tissues. We validate in situ graphene-seq by charting multimodal profiles of human-induced pluripotent stem cell-derived cardiomyocyte and endothelial cell co-cultures, examining how spatial heterogeneity in cell composition influences both electrophysiological activity and gene expression. This scalable, integrated approach offers a powerful tool for studying the complex interplay between cellular function and molecular identity. It also provides insights into how tissue microenvironments shape cell behavior and molecular states, advancing applications in regenerative medicine, stem cell therapy, and disease modeling.

bioengineering↗

bioRxiv2024

A machine learning approach reveals that spore levels in organic bulk tank milk are dependent on farm characteristics and meteorological factors

Bacterial spores in raw milk can lead to quality issues in milk and milk derived products. Since these spores originate from farm environments, it is important to understand contributions of farm-level factors to spore levels in raw milk. Identifying highly influential factors will guide interventions to control the transmission of spores from farm environments into bulk tank raw milk and therefore minimize spoilage in the finished products. The objective of this study was to investigate the impact of farm management practices and meteorological factors on levels of different spore types in organic raw milk by leveraging machine learning models. In this study, raw milk from certified organic dairy farms (n = 102) located across 11 states was collected 6 times over a year and tested for standard plate count, psychrotolerant spore count, mesophilic spore count, thermophilic spore count, and butyric acid bacteria. At each sampling date, a survey was collected from each farm to obtain structured data about farm management practices. Meteorological factors related to temperature, precipitation, solar radiation, and wind were obtained on the date of sampling as well as 1, 2, and 3 days prior to the date of sampling from an open-source website. The dataset was stratified separately based on the use of a parlor for milking, number of years since organic certification, and whether the lactating herd was exposed to pasture time into sub-datasets to address the potential confounders. Using the entire datasets and 6 sub-datasets respectively, we constructed random forest regression models to predict log10 mesophilic spore count, log10 thermophilic spore count, and log10 butyric acid bacteria most probable number as well as a random forest classification model to classify the presence of psychrotolerant spores in each raw milk sample. The summary statistics showed that spore levels vary considerably between certified organic farms but were only slightly higher than spore levels previously reported from conventional dairy farms. The variable importance plots from the random forest models suggest that herd size, certification year, employee-related variables (e.g., number of people milking cows per week), clipping and flaming udders, stocking density, and principal component representing air temperatures are among the top variables influencing the spore levels in organic raw milk, despite the limitation in the model performance (the highest performance for regression and classification is R2 of 0.36 for predicting TSC for farms with a parlor and accuracy of 0.73 for classifying positive PSC for farms without a parlor, respectively). The relatively small effects of top variables as demonstrated by the partial dependence plots suggest that an individualized approach that synergistically considers multiple farm and environmental factors is needed to enable a risk-based approach for managing spore levels. While at the current stage, these models were insufficiently accurate to be used as predictive tools, incorporating novel data streams such as video surveillance and daily farm observations with computer vision and natural language processing, respectively, has the potential to enhance the performance of the model as a real-time monitoring tool for spores as an indicator of milk microbiological quality.

microbiology↗

bioRxiv2022

Tissue- and sex-specific DNA damage tracks aging in rodents and humans

DNA damage causes genomic instability underlying many human diseases. Traditional approaches to DNA damage analysis provide minimal insights into the spectrum of disease-driving DNA lesions and the mechanisms causing imbalances in damage formation and repair. Here we used untargeted mass spectrometry-based adductomics1 to discover 114 putative DNA lesions and modifications consistently detected in humans and two independent analyses in rats, showing species-, tissue-, age-, and sex-biases. As evidence of methodologic rigor, 10 selected adductomic signals were structurally validated as epigenetic marks: 5-MdC, 5-HMdC, 5-FdC; DNA damage products: N2-CMdG, 1,N6 {varepsilon}-dA, 3,N4-{varepsilon}dC, M1dG, O6/N2-MdG, and 8-Oxo-dG; and established analytical artifacts: cyclobutane dimers of 2-deoxycytosine. With steady-state levels of putative DNA adducts integrating multiple cell types in each tissue, there was strong age-dependent variation for many putative adducts, including N2-CMdG, 5-HMdC, and 8-Oxo-dG in rats and 1,N6 {varepsilon}-dA in human heart, as well as sex biases for 67 putative adducts in rat tissues. These results demonstrate the potential of untargeted adductomic analysis for defining DNA adducts as disease determinants, assigning substrates to DNA repair pathways, discovering new metabolically-driven DNA lesions, and quantifying inter-individual variation in DNA damage and repair across populations.

biochemistry↗

bioRxiv2019

In vivo glucose imaging in multiple model organisms with an engineered single-wavelength sensor

Glucose is arguably the most important molecule in metabolism, and its mismanagement underlies diseases of vast societal import, most notably diabetes. Although glucose-related metabolism has been the subject of intense study for over a century, tools to track glucose in living organisms with high spatio-temporal resolution are lacking. We describe the engineering of a family of genetically encoded glucose sensors with high signal-to-noise ratio, fast kinetics and affinities varying over four orders of magnitude (1 {micro}M to 10 mM). The sensors allow rigorous mechanistic characterization of glucose transporters expressed in cultured cells with high spatial and temporal resolution. Imaging of neuron/glia co-cultures revealed [~]3-fold higher glucose changes in astrocytes versus neurons. In larval Drosophila central nervous system explants, imaging of intracellular neuronal glucose suggested a novel rostro-caudal transport pathway in the ventral nerve cord neuropil, with paradoxically slower uptake into the peripheral cell bodies and brain lobes. In living zebrafish, expected glucose-related physiological sequelae of insulin and epinephrine treatments were directly visualized in real time. Additionally, spontaneous muscle twitches induced glucose uptake in muscle, and sensory- and pharmacological perturbations gave rise to large but enigmatic changes in the brain. These sensors will enable myriad experiments, most notably rapid, high-resolution imaging of glucose influx, efflux, and metabolism in behaving animals.

bioengineering↗

bioRxiv2017

Molecular characterization of latent GDF8 reveals mechanisms of activation

Growth/differentiation factor 8 (GDF8) or myostatin negatively regulates muscle mass. GDF8 is held in a latent state through interactions with its N-terminal prodomain, much like TGF-{beta} Using a combination of small angle X-ray scattering and mutagenesis, we characterized the interactions of GDF8 with its prodomain. Our results show that the prodomain:GDF8 complex can exist in a fully latent state and an activated or triggered state where the prodomain remains in complex with the mature domain. However, these states are not reversible, indicating the latent GDF8 is spring-loaded. Structural analysis shows that the prodomain:GDF8 complex adopts an open configuration, distinct from the latency state of TGF- and more similar to the open state of Activin A and BMP9 (non-latent complexes). We determined that GDF{beta}8 maintains similar features for latency, including the alpha-1 helix and fastener elements, and identified a series of mutations in the prodomain of GDF8 that alleviate latency, including I56E, which does not require activation by the protease Tolloid. In vivo, active GDF8 variants were potent negative regulators of muscle mass, compared to wild-type GDF8. Collectively, these results help characterize the latency and activation mechanisms of GDF8.

biochemistry↗