Biology subjects

Paul, I.

Publications and source records attributed to Paul, I..

6 recordsLinked to original sources

bioRxiv2026

Dynamic Nanoparticle Assembly-Based Biomedical Microrobots

Precise drug delivery within anatomically complex tissues demands systems capable of both active navigation and deep tissue access, properties that have remained difficult to reconcile in existing nanocarriers and microrobots. Here we introduce Dynabots, a dynamic microrobotic assembly constructed from multifunctional nanoparticles covalently linked by thermally cleavable molecular connectors. This nanoparticle-rich architecture enables the integration of magnetic, imaging, and therapeutic components while preserving a high content of functional material. Collective assembly imparts enhanced magnetic responsiveness and maneuverability, enabling controlled navigation through tortuous biological environments. Upon exposure to mild thermal stimuli, the assemblies undergo programmed disassembly, releasing individual nanoparticles that can diffuse through tissue for localized therapeutic action. We establish the programmable transitions, biocompatibility, and therapeutic efficacy of this process across in vitro and in vivo models, including real-time fluoroscopic guidance within anatomically realistic phantoms and live rodent and porcine systems. By integrating magnetic control, reconfigurable architecture, and stimulus-triggered disassembly, Dynabots unite navigational precision with tissue permeability, providing a versatile platform for adaptive and deep-tissue drug delivery.

bioengineering↗

bioRxiv2023

A role for LGI1 in regulating pain sensitivity.

Chronic pain represents a major unmet clinical need. Neuropathic pain, that is pain arising due to damage or disease of the somatosensory nervous system, represents a sizeable proportion of chronic pain cases, affecting around 8% of the general population. Neuronal hyperexcitability is a key driver of neuropathic pain. Leucine glioma inactivated 1 (LGI1), is a secreted protein known to regulate excitability within the nervous system and is the target of autoantibodies from neuropathic pain patients. Therapies that block or reduce antibody levels are highly effective at relieving pain in these patients, suggesting that LGI1 has an important role in clinical pain. Here we have studied the role of LGI1 in regulating pain using mouse models to specifically ablate LGI1 in neuron populations. LGI1 has been well studied at the level of the brain. Here we show that LGI1 is highly expressed in dorsal root ganglion (DRG) neurons and in dorsal horn neurons of the spinal cord. Using transgenic mice, we ablated LGI1, either specifically in nociceptors (LGI1fl/Nav1.8(+/-)), or in all DRG and spinal neurons (LGI1fl/Hoxb8(+/-)). On acute pain assays, mild phenotypes were observed when compared to littermate controls with limited changes in DRG neuron excitability. No differences were seen in the first phase of the formalin test, however LGI1fl/Hoxb8(+/-) mice displayed a significant increase in nocifensive behaviours in the second phase compared to littermate controls. Using the spared nerve injury model, we assessed the impact of LGI1 ablation on neuropathic pain-like behaviours. LGI1fl/Nav1.8(+/-) mice showed no differences in nerve injury induced mechanical hypersensitivity, brush-evoked allodynia or spontaneous pain behaviour compared to controls. However, LGI1fl/Hoxb8(+/-) mice showed a significant exacerbation of mechanical hypersensitivity and allodynia. These data show that LGI1 has a role in regulating pain sensitivity particularly in the context of nerve injury. We suggest this effect is likely mediated at the spinal level since no differences were observed following specific ablation of LGI1 in nociceptors. Neurons in dorsal horn of the spinal cord are important in gating nerve injury induced mechanical pain and our findings suggest that LGI1 plays an important role in this process.

neuroscience↗

bioRxiv2023

DESP: Demixing Cell State Profiles from Dynamic Bulk Molecular Measurements

There is wide interest to determine the dynamic expression of proteins and other molecules that drive phenotypic remodeling in development and pathobiology, but due to technical limitations these systems remain largely unexplored at the foundational resolution of the underlying cell states. Here, we present DESP, a novel algorithm that leverages independent estimates of cell state proportions, such as from single-cell RNA-sequencing or cell sorting, to resolve the relative contributions of cell states to bulk molecular measurements, most notably quantitative proteomics, recorded in parallel. We applied DESP to an in-vitro model of the epithelial-to-mesenchymal transition and demonstrated its ability to accurately reconstruct cell state signatures from bulk-level measurements of both the proteome and transcriptome providing insights into transient regulatory mechanisms. DESP provides a generalizable computational framework for modeling the relationship between bulk and single-cell molecular measurements, enabling the study of proteomes and other molecular profiles at the cell state-level using established bulk-level workflows.

bioinformatics↗

bioRxiv2021

A multi-tiered map of EMT defines major transition points and identifies vulnerabilities

Epithelial to mesenchymal transition (EMT) is a complex cellular program proceeding through a hybrid E/M state linked to cancer-associated stemness, migration and chemoresistance. Deeper molecular understanding of this dynamic physiological landscape is needed to define events which regulate the transition and entry into and exit from the E/M state. Here, we quantified >60,000 molecules across ten time points and twelve omic layers in human mammary epithelial cells undergoing TGF{beta}-induced EMT. Deep proteomic profiles of whole cells, nuclei, extracellular vesicles, secretome, membrane and phosphoproteome defined state-specific signatures and major transition points. Parallel metabolomics showed metabolic reprogramming preceded changes in other layers, while single-cell RNA sequencing identified transcription factors controlling entry into E/M. Covariance analysis exposed unexpected discordance between the molecular layers. Integrative causal modeling revealed co-dependencies governing entry into E/M that were verified experimentally using combinatorial inhibition. Overall, this dataset provides an unprecedented resource on TGF{beta} signaling, EMT and cancer.

systems biology↗

bioRxiv2019

Pancreatic tumor organoids for modeling in vivo drug response and discovering clinically-actionable biomarkers

Patient-derived models are transforming translational cancer research. It is not clear if the emergence of patient-derived organoid (PDO) models can extend the utility of the widely used patient-derived xenograft (PDX). In addition, the utility of PDO models for serum biomarker discovery is not known. Here, we demonstrate that PDO models recapitulate the genomics, cell biology, glycomics and drug responses observed in PDX models. Furthermore, we demonstrate the applicability of PDO models for identification of N-glycans that are enriched in the glycome of pancreatic ductal adenocarcinoma (PDAC). Surprisingly, among all the glycans observed in PDX and PDOs, a core set of 57 N-glycans represent 50-94% of the relative abundance of all N-glycans detected, suggesting that only a subset of glycans dominate the cell surface landscape in PDAC. In addition, we outline a tumor organoid-based pipeline to identify surface proteins in extracellular vesicles (EV) from media supernatant of PDO cultures. When combined with the affinity-based validation platform, the EV surface proteins discovered in PDOs are effective in differentiating patients with PDAC from those with benign pancreatitis in the clinic, identifying PDO as powerful discovery platform for serum biomarkers. Thus, PDOs extend the utility of the archival collections of PDX models for translational research and function as a powerful platform for identification of clinically-actionable biomarkers in patients blood. Significance statementTumor organoids extend the utility of PDX models as platforms for investigating drug response, glycosylation changes and function as new platforms for discovering blood-based biomarkers

cancer biology↗

bioRxiv2017

Infant Weight Gain Trajectories Linked To Oral Microbiome Composition

Gut and oral microbiome perturbations have been observed in obese adults and adolescents. Less is known about how weight gain in early childhood is influenced by gut, and particularly oral, microbiomes. Here we analyze the relationships among weight gain and gut and oral microbiomes in 226 two-year-olds who were followed during the first two years of life, as part of a larger study, with weight and length measured at seven time points. We used these data to identify children with rapid weight gain (a strong risk factor for childhood obesity), and to derive growth curves with novel Functional Data Analysis (FDA) techniques. The childrens oral and gut microbiomes were sampled at the end of the two-year period, and surveyed with 16S sequencing. First, we show that growth curves are associated negatively with diversity and positively with Firmicutes-to-Bacteroidetes ratio of the oral microbiome - a relationship that is also observed in children with rapid (vs. non-rapid) weight gain. We also demonstrate an association between the gut microbiome and child growth, but only when considering the effect of diet on the microbiome. Lastly, we identify several bacterial genera that are associated with child growth patterns. These results suggest that by the age of two, the oral microbiome may have already begun to establish patterns often seen in older obese individuals. They also suggest that the gut microbiome, while strongly influenced by diet, at age two does not harbor obesity signatures many researchers identified in later life stages.

genomics↗