Search arXivSearch

arXiv · 2305.07199

More than magnetic isolation: Dynabeads as strong Raman reporters towards simultaneous capture and identification of targets

Abstract

Dynabeads are superparamagnetic particles used for immunomagnetic purification of cells and biomolecules. Post-capture, however, target identification relies on tedious culturing, fluorescence staining and/or target amplification. Raman spectroscopy presents a rapid detection alternative, but current implementations target cells themselves with weak Raman signals. We present antibody-coated Dynabeads as strong Raman reporter labels whose effect can be considered a Raman parallel of immunofluorescent probes. Recent developments in techniques for separating target-bound Dynabeads from unbound Dynabeads makes such an implementation feasible. We deploy Dynabeads anti-Salmonella to bind and identify Salmonella enterica, a major foodborne pathogen. Dynabeads present signature peaks at 1000 and 1600 1/cm from aliphatic and aromatic C-C stretching of polystyrene, and 1350 1/cm and 1600 1/cm from amide, alpha-helix and beta-sheet of antibody coatings of the Fe2O3 core, confirmed with electron dispersive X-ray (EDX) imaging. Their Raman signature can be measured in dry and liquid samples even at single shot ~30 x 30-micrometer area imaging using 0.5 s, 7 mW laser acquisition with single and clustered beads providing a 44- and 68-fold larger Raman intensity compared to signature from cells. Higher polystyrene and antibody content in clusters yields to the larger signal intensity and conjugation to bacteria strengthens clustering as a bacterium can bind to more than one bead as observed via transmission electron microscopy (TEM). Our findings shed light on the intrinsic Raman reporter nature of Dynabeads, demonstrating their dual function for target isolation and detection without additional sample preparation, staining, or unique plasmonic substrate engineering, advancing their applications in heterogeneous samples like food, water, and blood.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jongwan Lee, Marissa McDonald, Nikiwe Mhlanga, Jeon Woong Kang, Rohit Karnik, Loza F. Tadesse. 2023-07-22. More than magnetic isolation: Dynabeads as strong Raman reporters towards simultaneous capture and identification of targets. https://arxiv.org/abs/2305.07199

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Spatially Resolved Nucleated Polymerization: A Free-Boundary Model of Protein Aggregation in Concentrated Solutions

Kinetic models of protein aggregation describe populations by size, not by spatial organization or morphology. We extend Lumry-Eyring nucleated polymerization to a model in which the monomer is a density field and each aggregate is a region bounded by a level set. Growth is a flux condition on the available sites of a surface. Condensation is a reaction between the bonding sites of two surfaces in contact, at a rate set by the bond rate and the contact geometry. The availability of those sites is a field on the interface, and its equilibrium value follows from Wertheim's perturbation theory. The collision efficiency and the Fuchs stability ratio are therefore computed, not fitted. In a well-mixed limit the model's spatial averages satisfy the rate equations term by term; the monomer fraction agrees to eight parts in ten thousand, a difference that arises from equating aggregate size with volume. The condensation kernel's exponent is $0.5806\pm0.0013$ against the $0.600\pm0.010$ fitted to a monoclonal antibody. The computed stability ratio reproduces thirteen of fourteen published conditions at twelve $k_BT$, but only with the bond rate at the top of its range. In a many-body box, aggregates merge at $2.2$ to $3.8$ times the two-body rate.

physics.bio-ph

Deep Generative Markov State Models with Experimental Restraints

A central challenge in molecular modeling is reconciling simulations with experimental observables, as force-field inaccuracies can distort equilibrium populations and long-timescale kinetics. While time-dependent restraints can improve simulated kinetics, time-dependent structural observables remain limited. Thus, most experimental data are time-averaged, informing equilibrium ensembles but not directly dynamics. Although ensemble refinement can improve thermodynamic accuracy, incorporating time-averaged observables into kinetic models remains difficult. Here, we introduce BICePs-reweighted Reversible DeepMSMs, combining Bayesian Inference of Conformational Populations (BICePs), variational learning of Markov processes, and maximum entropy (MaxEnt)/maximum caliber (MaxCal) principles to infer consistent thermodynamics and minimally perturbed kinetics. At its core is a reversible DeepMSM prior, obtained by applying an orthogonal transformation post-hoc to a pre-trained dynamics model (e.g., VAMPnet). This preserves the eigenspectrum exactly while yielding a valid transition matrix that is nonnegative, row-stochastic, and reversible. The reweighted model refines stationary populations, transition dynamics, and state-conditioned configurational landing densities. We validate the approach on a quadruple-well toy system and alanine dipeptide using backbone dihedral angles and J-coupling constants. In both systems, perturbing the observables induces predictable changes in the equilibrium ensemble and corresponding inferred kinetics. The resulting relaxation timescales and dynamical modes closely agree with analytical and MaxCal reference models. Furthermore, a diffusion-based generative model trained on MaxEnt landing densities produces physically realistic alanine dipeptide trajectories that reproduce thermodynamics and kinetics while satisfying the imposed experimental restraints.

physics.bio-ph

Tracking and distinguishing slime mold solutions to traveling salesperson problems through synchronized amplification in the non-equilibrium steady state

The plasmodium of the true slime mold Physarum polycephalum-an ancient, unicellular, aneural organism-serves as a platform for studying the information-processing capacities of active matter. Previous experiments used Physarum's intricate morphological dynamics and photoavoidance in stellate chips to solve $N$-city traveling salesperson problems (TSPs) of up to eight cities, scaling linearly in time with TSP size. Optical feedback controlled by a modified Hopfield network illuminated specific lanes at regular intervals, prompting Physarum to elongate or retract selected branches. When the illumination pattern stabilized in a non-equilibrium steady state, branches bifurcated reproducibly into solution and non-solution groups, with the former exhibiting lower-frequency, higher-amplitude, and more synchronized oscillations than the latter across 41 trials with valid TSP solutions. Physarum's synchronization dynamics efficiently predict 100% of selected solutions by the midpoint of the optical-feedback interval, achieving statistically significant (paired t-test, $p<0.005$) discrimination from alternate tours well before the non-equilibrium steady state. Observed frequency downconversions and synchronized power amplifications scale linearly and quadratically, respectively, for small-to-moderate TSP size, as captured by a toy model of energy redistribution with saturating optical absorption. Tuning these features in native biomolecular chromophore networks may thus improve both the quality and efficiency of TSP solutions from Physarum-based biocomputers, which exploit the effects of organismal-scale coherence.

physics.bio-ph