Biology subjects

Neu, J.

Publications and source records attributed to Neu, J..

8 recordsLinked to original sources

bioRxiv2026

Photothermal Recycling Biosensing for Continuous, Sensitive Molecular Quantification

Continuous biochemical sensing provides valuable insights into an individuals physiological state and the mechanisms underlying pathophysiological changes. However, most existing bioanalytical methods are not compatible with continuous biochemical sensing. A major technical challenge lies in achieving rapid measurement readouts while maintaining high specificity and sensitivity in complex biological fluids. Sensitive molecular detection typically requires slow analyte-binder dissociation and long incubation to reach equilibrium, whereas rapid and frequent measurements demand fast association-dissociation kinetics that are difficult to reconcile for low-abundance analytes. To address this challenge, we introduce a sensing mechanism termed photothermal recycling (PTR), which mimics the thermal cycling process in polymerase chain reaction. Using plasmonic photothermal effects, PTR rapidly recycles binders to enable frequent measurements. We demonstrate a digital PTR assay capable of multi-hour biochemical monitoring with subpicomolar(pM) sensitivity in buffer, diluted serum, and saliva. This approach leverages localized thermal energy to dynamically modulate biomolecular recognition, offering a new bioanalytical paradigm for continuous biochemical sensing across diverse application settings.

bioengineering↗

bioRxiv2025

Direct Optical Quantification of Chain Collapse, Reduced Dielectric, and Water Release Driving Protein Phase Separation

Biomolecular condensates represent unique microenvironments that organize intracellular biology and promote biochemical reactions. However, the biomolecular interactions driving condensate phase separation are often weak, transient, and heterogeneous. Investigating the structural biology and chemical properties of condensate interiors has therefore proven experimentally challenging, often requiring the use of perturbative probes. To overcome this challenge, we combine label-free optical scattering and vibrational spectroscopy approaches spanning ultraviolet, visible, mid-infrared, and terahertz wavelengths with deep-learning-based ensemble prediction of intrinsically disordered protein conformations. Our experimental and computational results reveal that the intrinsically disordered N-terminal domain of the RNA Deadbox helicase 4 (DDX4) deviates from random coil behavior and undergoes chain collapse that correlates with phase separation, which leads to lower dielectric and reduced water content inside condensates. Our data support a model of DDX4 phase separation whereby chain collapse, reduced dielectric, and water release enhance the strength of multivalent protein-protein interactions within condensates, driving condensate growth and phase separation through positive feedback. Our study addresses the critical driving forces of biomolecular phase separation across a range of length scales, providing quantitative insights into protein-protein/protein-solvent interactions and the chemical properties of condensate interiors.

biophysics↗

bioRxiv2025

Kinking and buckling instability in growing bacterial chains

Many gram-positive bacteria like Bacillus subtilis and Clostridium species, exhibit a growing chain-mediated sliding motility that is driven entirely by the force of cell growth. Particularly, the bacteria maintain cell-cell linkage after cell division and form long chains of many cells. The cells in a chain are continuously pushed outward by the mechanical force of cell growth. As the cell number in a chain grows, the cells toward the tip of the chain accelerate, and can in principle reach very high speeds. Although this seems to suggest a highly efficient motility mechanism, recent modeling work predicted mechanical stress builds up in the growing chain and the resulting chain breakage beyond a critical chain length, which ultimately sets a mechanical limitation in the maximum speed of the chain-mediated sliding. In this work we developed models to show that under different conditions the chain can either form sharp kinks or smooth buckles under the increasing stress. This can explain differential behaviors observed in different bacterial species. Our model further predicted how kinking and buckling affect the susceptibility of the chain to breakage. Our model provides a theoretical framework for predicting the dynamics and efficiency of growing chain-mediated bacterial sliding, and suggest cell properties that can optimize sliding efficiency.

biophysics↗

bioRxiv2020

Repulsive expansion dynamics in colony growth and patterning

Spatial expansion of a population of cells can arise from growth of microorganisms, plant cells, and mammalian cells. It underlies normal or dysfunctional tissue development and it can be exploited as the foundation for programming spatial patterns. This expansion is often driven by continuous growth and division of cells within a colony, which in turn pushes the peripheral cells outward. This process generates a repulsion velocity field at each location within the colony. Here we show that this process can be approximated as coarse-grained repulsive-expansion kinetics. This framework enables accurate and efficient simulation of growth and gene expression dynamics in radially symmetric colonies with homogenous z-directional distribution. It is robust even if cells are not spherical and vary in size. The simplicity of the resulting mathematical framework also greatly facilitates generation of mechanistic insights.

systems biology↗

bioRxiv2020

Antibiotics effects on the fecal metabolome in preterm infants

Within a randomized prospective pilot study of preterm infants born less than 33 weeks gestation, fecal samples were collected weekly and metabolomic analysis was performed. The objective is to evaluate for differences in fecal metabolites in infants exposed to antibiotics vs not exposed to antibiotics in the first 48hours after birth. Significant differences were seen in the antibiotics vs no antibiotics group, including pathways related to vitamin biosynthesis, bile acids, amino acid metabolism and neurotransmitters. Early antibiotic exposure in preterm infants may alter metabolites in the intestinal tract of preterm infants. Broader multi-omic studies that address mechanisms will guide more prudent antibiotic use in this population.

microbiology↗

bioRxiv2020

Antibiotics may influence gut microbiome signaling to the brain in preterm neonates

Antibiotic use in neonates can have detrimental effects on the developing gut microbiome, increasing the risk of morbidity. A majority of preterm neonates receive antibiotics after birth without clear evidence to guide this practice. Here microbiome, metabolomic, and immune marker results from the Routine Early Antibiotic use in SymptOmatic preterm Neonates (REASON) study are presented. The REASON study is the first trial to randomize symptomatic preterm neonates to receive or not receive antibiotics in the first 48 hours after birth. Using 16S rRNA sequencing of stool samples collected longitudinally for 91 neonates, the effect of such antibiotic use on microbiome diversity is assessed. The results illustrate that type of nutrition shapes the early infant gut microbiome. By integrating data for the gut microbiome, stool metabolites, stool immune markers, and inferred metabolic pathways, an association was discovered between Veillonella and the neurotransmitter gamma-aminobutyric acid (GABA). These results suggest early antibiotic use may impact the gut-brain axis with the potential for consequences in early life development, a finding that needs to be validated in a larger cohort.

microbiology↗

bioRxiv2019

Overcoming high nanopore basecaller error rates for DNA storage via basecaller-decoder integration and convolutional codes

As magnetization and semiconductor based storage technologies approach their limits, bio-molecules, such as DNA, have been identified as promising media for future storage systems, due to their high storage density (petabytes/gram) and long-term durability (thousands of years). Furthermore, nanopore DNA sequencing enables high-throughput sequencing using devices as small as a USB thumb drive and thus is ideally suited for DNA storage applications. Due to the high insertion/deletion error rates associated with basecalled nanopore reads, current approaches rely heavily on consensus among multiple reads and thus incur very high reading costs. We propose a novel approach which overcomes the high error rates in basecalled sequences by integrating a Viterbi error correction decoder with the basecaller, enabling the decoder to exploit the soft information available in the deep learning based basecaller pipeline. Using convolutional codes for error correction, we experimentally observed 3x lower reading costs than the state-of-the-art techniques at comparable writing costs. The code, data and Supplementary Material is available at https://github.com/shubhamchandak94/nanopore_dna_storage.

bioengineering↗

bioRxiv2019

Improved read/write cost tradeoff in DNA-based data storage using LDPC codes

With the amount of data being stored increasing rapidly, there is significant interest in exploring alternative storage technologies. In this context, DNA-based storage systems can offer significantly higher storage densities (petabytes/gram) and durability (thousands of years) than current technologies. Specifically, DNA has been found to be stable over extended periods of time which has been demonstrated in the analysis of organisms long since extinct. Recent advances in DNA sequencing and synthesis pipelines have made DNA-based storage a promising candidate for the storage technology of the future.\n\nRecently, there have been multiple efforts in this direction, focusing on aspects such as error correction for synthesis/sequencing errors and erasure correction for handling missing sequences. The typical approach is to use separate codes for handling errors and erasures, but there is limited understanding of the efficiency of this framework. Furthermore, the existing techniques use short block-length codes and heavily rely on read consensus, both of which are known to be suboptimal in coding theory.\n\nIn this work, we study the tradeoff between the writing and reading costs involved in DNA-based storage and propose a practical scheme to achieve an improved tradeoff between these quantities. Our scheme breaks with the traditional separation framework and instead uses a single large block-length LDPC code for both erasure and error correction. We also introduce novel techniques to handle insertion and deletion errors introduced by the synthesis process. For a range of writing costs, the proposed scheme achieves 30-40% lower reading costs than state-of-the-art techniques on experimental data obtained using array synthesis and Illumina sequencing.\n\nThe code, data, and Supplementary Material is available at https://github.com/shubhamchandak94/LDPC_DNA_storage.

bioengineering↗