Biology subjects

Kilic, Z.

Publications and source records attributed to Kilic, Z..

9 recordsLinked to original sources

bioRxiv2026

Biophysical properties of IgD determine thresholds for self-tolerance and selection into germinal centers

Immunoglobulin D (IgD) is among the most conserved antibody isotypes, found in virtually all jawed vertebrates1. Unlike other isotypes, IgD contains an unusually long hinge region of up to 160 amino acids that connects the constant and variable regions. Its expression pattern is also conserved; IgD is co-expressed with IgM on transitional and mature naive B cells. However, the function of IgD has remained enigmatic since its discovery in 19652,3. Here we present and test a biophysical model positing that IgD increases the entropic cost of bivalent antigen binding. Single-molecule measurements revealed that the antigen-binding arms of IgD are substantially more dynamic than those of IgM, suggesting that cell surface IgD would be energetically penalized in bivalent antigen binding. Consistent with the model and biophysical data, we find that the long hinge compromises antigen capture by IgD B cell receptors (BCRs) compared to IgM BCRs. To determine how the difference in antigen binding impacts immunity, we produced mice that express only IgM and IgD, exclusively IgM or IgD, or IgD with a truncated hinge region. The data indicate that the increased entropic cost of antigen binding imposed by the IgD hinge attenuates negative selection by self-antigen while increasing the affinity-based threshold for positive selection into the germinal center (GC). Together the results indicate that IgD functions physiologically to desensitize B cells to antigen, thereby expanding the B cell repertoire while optimizing affinity-based selection into the GC.

immunology↗

bioRxiv2025

Metabolic Flexibility of Microglia: Energy Substrate Utilization and Impact on Neuronal Metabolism

Microglia, the main resident immune cells of the brain, play critical roles in maintaining neuronal function and homeostasis. Microglia metabolic flexibility enables rapid adaptation to environmental changes, yet the full extent of their metabolic capabilities and influence on neuronal metabolism remains unclear. While microglia predominantly rely on glucose oxidative metabolism under homeostatic conditions, they shift toward glycolysis upon proinflammatory activation. In this study, we investigated microglial metabolism and its impact on neuronal metabolic homeostasis using isotope tracing with stable carbon 13C-enriched substrates and gas chromatography-mass spectrometry (GC-MS) analysis. Primary microglia were incubated with 13C-labeled glucose, glutamine, or GABA in the presence or absence of lipopolysaccharide (LPS) to assess metabolic adaptations upon an inflammatory challenge. Additionally, neurons co-cultured with quiescent or activated microglia (either with LPS or amyloid-{beta}) were incubated with 13C-enriched glucose to examine microglia-neuron metabolic interactions. Our findings confirm that microglia readily metabolize glucose and glutamine, with LPS stimulation slightly changing the glycolytic activity, as indicated by subtle changes in extracellular lactate. Importantly, we demonstrate for the first time that microglia take up and metabolize the inhibitory neurotransmitter GABA, suggesting a novel metabolic function. Furthermore, microglial presence directly influences neuronal metabolism and neurotransmitter homeostasis, highlighting a previously unrecognized aspect of neuron-microglia metabolic crosstalk. Collectively, these findings provide new insights into microglial metabolism and its role in neuronal function, with implications for neuroinflammatory and neurodegenerative diseases in which microglial metabolism is dysregulated.

neuroscience↗

bioRxiv2025

Simultaneous particle tracking, phase retrieval and point spread function reconstruction

3D tracking and localization of particles, typically fluorescently labeled biomolecules, provides a direct means of monitoring cellular transport and communication. However, sample-induced wavefront distortions of emitted fluorescent light as it passes through the sample and onto the detector often yield point spread function (PSF) aberrations, presenting an important challenge to 3D particle tracking using pre-calibrated PSFs. PSF calibration is typically performed outside cellular samples, ignoring sample-induced aberrations, which can result in localization errors on the order of tens to hundreds of nanometers, ultimately compromising sub-diffraction limited tracking. In practice, correcting sample-induced aberrations currently requires sample-specific hardware adjustments, such as adaptive optics. Yet, information on sample-induced aberrations and PSF shape can be directly decoded from data collected using a 3D imaging setup (e.g., bi-focal). To this end, we propose a framework for simultaneous particle tracking, phase retrieval, and PSF reconstruction (SPT-PR) directly from the input data themselves. We apply it to sub-diffraction tracking of lytic granules released at the immunological synapse of T cells revealing slower motions in proximity of the plasma cell membrane, consistent with assembly of the fusion machinery and, ultimately, degranulation and release of toxic payloads. To accomplish this, we operate within a Bayesian paradigm, placing continuous priors on all possible pupil phase and amplitudes warranted by the data without limiting ourselves to a finite Zernike set-thereby allowing capture of intricate pupil phase details. We benchmark our framework using a wide range of synthetic and experimental data from static to diffusing particles, and generalize to multiple diffusing particles with overlapping PSFs. Further, as a result of simultaneous particle tracking, phase retrieval, and PSF reconstruction, we retrieve the pupil phase with errors smaller than 10% under a range of realistic scenarios while demonstrating that for tracking lytic granules under an idealized Gaussian PSF assumption, we recover discrepancies as large as hundreds of nanometers.

biophysics↗

bioRxiv2023

Single-Molecule Reaction-Diffusion

We propose to capture reaction-diffusion on a molecule-by-molecule basis from the fastest acquirable timescale, namely individual photon arrivals. We illustrate our method on intrinsically disordered human proteins, the linker histone H1.0 as well as its chaperone prothymosin , as these diffuse through an illuminated confocal spot and interact forming larger ternary complexes on millisecond timescales. Most importantly, single-molecule reaction-diffusion, smRD, reveals single molecule properties without trapping or otherwise confining molecules to surfaces. We achieve smRD within a Bayesian paradigm and term our method Bayes-smRD. Bayes-smRD is further free of the average, bulk, results inherent to the analysis of long photon arrival traces by fluorescence correlation spectroscopy. In learning from thousands of photon arrivals continuous spatial positions and discrete conformational and photophysical state changes, Bayes-smRD estimates kinetic parameters on a molecule-by-molecule basis with two to three orders of magnitude less data than tools such as fluorescence correlation spectroscopy thereby also dramatically reducing sample photodamage.

biophysics↗

bioRxiv2023

BNP-Track: A framework for multi-particle superresolved tracking

When tracking fluorescently labeled molecules (termed "emitters") under widefield microscopes, point spread function overlap of neighboring molecules is inevitable in both dilute and especially crowded environments. In such cases, superresolution methods leveraging rare photophysical events to distinguish static targets nearby in space introduce temporal delays that compromise tracking. As we have shown in a companion manuscript, for dynamic targets, information on neighboring fluorescent molecules is encoded as spatial intensity correlations across pixels and temporal correlations in intensity patterns across time frames. We then demonstrated how we used all spatiotemporal correlations encoded in the data to achieve superresolved tracking. That is, we showed the results of full posterior inference over both the number of emitters and their associated tracks simultaneously and self-consistently through Bayesian nonparametrics. In this companion manuscript we focus on testing the robustness of our tracking tool, BNP-Track, across sets of parameter regimes and compare BNP-Track to competing tracking methods in the spirit of a prior Nature Methods tracking competition. We explore additional features of BNP-Track including how a stochastic treatment of background yields greater accuracy in emitter number determination and how BNP-Track corrects for point spread function blur (or "aliasing") introduced by intraframe motion in addition to propagating error originating from myriad sources (such as criss-crossing tracks, out-of-focus particles, pixelation, shot and camera artefact, stochastic background) in posterior inference over emitter numbers and their associated tracks. While head-to-head comparison with other tracking methods is not possible (as competitors cannot simultaneously learn molecule numbers and associated tracks), we can give competing methods some advantages in order to perform approximate head-to-head comparison. We show that even under such optimistic scenarios, BNP-Track is capable of tracking multiple diffraction-limited point emitters conventional tracking methods cannot resolve thereby extending the superresolution paradigm to dynamical targets.

biophysics↗

bioRxiv2022

Inferring gene expression models from snapshot RNA data

1Gene networks, key toward understanding a cells regulatory response, underlie experimental observations of single cell transcriptional dynamics. While information on the gene network is encoded in RNA expression data, existing computational frameworks cannot currently infer gene networks from such data. Rather, gene networks--composed of gene states, their connectivities, and associated parameters--are currently deduced by pre-specifying gene state numbers and connectivity prior to learning associated rate parameters. As such, the correctness of gene networks cannot be independently assessed which can lead to strong biases. By contrast, here we propose a method to learn full distributions over gene states, state connectivities, and associated rate parameters, simultaneously and self-consistently from single molecule level RNA counts. Notably, our method propagates noise originating from fluctuating RNA counts over networks warranted by the data by treating networks themselves as random variables. We achieve this by operating within a Bayesian nonparametric paradigm. We demonstrate our method on the lacZ pathway in Escherichia coli cells, the STL1 pathway in Saccharomyces cerevisiae yeast cells, and verify its robustness on synthetic data.

biophysics↗

bioRxiv2020

A Continuous Time Representation of smFRET for the Extraction of Rapid Kinetics

Our goal is to learn kinetic rates from single molecule FRET (smFRET) data even if these exceed the data acquisition rate. To achieve this, we develop a variant of our recently proposed hidden Markov jump process (HMJP) with which we learn transition kinetics from parallel measurements in donor and acceptor channels. Our HMJP generalizes the hidden Markov model (HMM) paradigm in two critical ways: (1) it deals with physical smFRET systems as they switch between conformational states in continuous time; (2) it estimates the transition rates between conformational states directly without having recourse to transition probabilities or assuming slow dynamics (as is necessary of the HMM). Our continuous time treatment learns transition kinetics and photon emission rates for dynamical regimes inaccessible to the HMM which treats system kinetics in discrete time. We validate the robustness of our framework on simulated data and demonstrate its performance on experimental data from FRET labeled Holliday junctions.

biophysics↗

bioRxiv2020

Residence Time Analysis of RNA Polymerase Transcription Dynamics: A Bayesian Sticky HMM Approach

The time spent by a single RNA polymerase (RNAP) at specific locations along the DNA, termed "residence time", reports on the initiation, elongation and termination stages of transcription. At the single molecule level, this information can be obtained from dual ultra-stable optical trapping experiments, revealing a transcriptional elongation of RNAP interspersed with residence times of variable duration. Successfully discriminating between long and short residence times was used by previous approaches to learn about RNAPs transcription elongation dynamics. Here, we propose an approach based on the Bayesian sticky hidden Markov models that treats all residence times, for an E. Coli RNAP, on an equal footing without a priori discriminating between long and short residence times. In addition, our method has two additional advantages, we provide: full distributions around key point statistics; and directly treat the sequence-dependence of RNAPs elongation rate. By applying our approach to experimental data, we find: no emergent separation between long and short residence times warranted by the data; force dependent average residence time transcription elongation dynamics; limited effects of GreB on average backtracking durations and counts; and a slight drop in the average residence time as a function of applied force in RNaseAs presence. STATEMENT OF SIGNIFICANCEMuch of what we know about RNA Polymerase, and its associated transcription factors, relies on successfully discriminating between what are believed to be short and long residence times in the data. This is achieved by applying pause-detection algorithms to trace analysis. Here we propose a new method relying on Bayesian sticky hidden Markov models to interpret time traces provided by dual optical trapping experiments associated with transcription elongation of RNAP. Our method does not discriminate between short and long residence times from the offset in the analysis. It allows for DNA site-dependent transition probabilities of RNAP to neighboring sites (thereby accounting for chemical variability in site to site transitions) and does not demand any time trace pre-processing (such as denoising).

biophysics↗

bioRxiv2020

Generalizing HMMs to Continuous Time for Fast Kinetics: Hidden Markov Jump Processes

The hidden Markov model (HMM) is a framework for time series analysis widely applied to single molecule experiments. It has traditionally been used to interpret signals generated by systems, such as single molecules, evolving in a discrete state space observed at discrete time levels dictated by the data acquisition rate. Within the HMM framework, originally developed for applications outside the Natural Sciences, such as speech recognition, transitions between states, such as molecular conformational states, are modeled as occurring at the end of each data acquisition period and are described using transition probabilities. Yet, while measurements are often performed at discrete time levels in the Natural Sciences, physical systems evolve in continuous time according to transition rates. It then follows that the modeling assumptions underlying the HMM are justified if the transition rates of a physical process from state to state are small as compared to the data acquisition rate. In other words, HMMs apply to slow kinetics. The problem is, as the transition rates are unknown in principle, it is unclear, a priori, whether the HMM applies to a particular system. For this reason, we must generalize HMMs for physical systems, such as single molecules, as these switch between discrete states in continuous time. We do so by exploiting recent mathematical tools developed in the context of inferring Markov jump processes and propose the hidden Markov jump process (HMJP). We explicitly show in what limit the HMJP reduces to the HMM. Resolving the discrete time discrepancy of the HMM has clear implications: we no longer need to assume that processes, such as molecular events, must occur on timescales slower than data acquisition and can learn transition rates even if these are on the same timescale or otherwise exceed data acquisition rates.

biophysics↗