Biology subjects

Heo, W.

Publications and source records attributed to Heo, W..

4 recordsLinked to original sources

bioRxiv2026

A reproducibility-audit framework for generalizable versus dataset-specific molecular transition boundaries in Alzheimer's disease

Molecular staging of Alzheimer's disease (AD) increasingly defines transition boundaries along single-cell pseudo-progression trajectories, yet whether such boundaries reproduce across brain regions, cohorts and molecular modalities is rarely tested. We present a permutation-controlled audit that combines nine boundary-detection algorithms with a fixed marker panel and four orthogonal reproducibility axes-algorithmic consensus, region, cohort and modality. On synthetic data with planted ground-truth boundaries the audit reaches 100% sensitivity and 94% specificity, rejecting four distinct artefact classes each by a different axis. Applied to the Seattle Alzheimer's Disease Brain Cell Atlas middle temporal gyrus, it localizes a transition that is robust across algorithms and recovered in most cell types but does not generalize: its leading marker is attenuated or absent in prefrontal cortex, entorhinal cortex and cerebrospinal fluid, and an apparent cross-region conservation of glial metabolic genes proves to be a global-expression offset rather than a shared program. The same audit nonetheless certifies an externally validated marker (astrocytic PTGDS) as reproducible across regions and modalities, showing that it separates generalizable anchors from dataset-specific ones rather than rejecting all signals. We provide this four-axis audit as a transferable, code-available standard to apply before a trajectory boundary is read as a biological stage, in AD and other progressive proteinopathies.

neuroscience↗

bioRxiv2024

PIEZO acts in an intestinal valve to regulate swallowing in C. elegans

Sensations of the internal state of the body play crucial roles in regulating the physiological processes and maintaining homeostasis of an organism. However, our understanding of how internal signals are sensed, processed, and integrated to generate appropriate biological responses remains limited. Here, we show that the C. elegans PIEZO channel, encoded by pezo-1, regulates food movement in the intestine by detecting food accumulation in the anterior part of the intestinal lumen, thereby triggering rhythmical movement of the pharynx, referred to as the pharyngeal plunge. pezo-1 deletion mutants exhibit defects in the pharyngeal plunge, which is rescued by PEZO-1 or mouse PIEZO1 expression, but not by PIEZO2, in a single isolated non-neuronal tissue of the digestive tract, the pharyngeal-intestinal valve. Genetic ablation or optogenetic activation of this valve inhibits or induces the pharyngeal plunge, respectively. Moreover, pressure built in the anterior lumen of the intestine results in a pezo-1-dependent pharyngeal plunge, which is driven by head muscle contraction. These findings illustrate how interoceptive processes in a digestive organ regulate swallowing through the PIEZO channel, providing insights into how interoception coordinates ingestive processes in higher animals, including humans.

neuroscience↗

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

Direct Photon-by-photon Analysis of Time-Resolved Pulsed Excitation Data using Bayesian Nonparametrics

Lifetimes of chemical species are typically estimated, across each illuminated spot of a sample, by either fitting time correlated single photon counting (TCSPC) decay histograms or, more recently, through phasor analysis from time-resolved photon arrivals. While both methods yield lifetimes in a computationally efficient manner, the performance of both methods is limited by the choices made when fitting a TCSPC histogram. In addition, phasor analysis also requires setting the number of chemical species by hand before lifetimes can be determined. Yet the number of species itself is encoded in the photon arrival times collected for each illuminated spot and need not be set by hand a priori. Here we propose a direct photo-by-photon analysis of data drawn from pulsed excitation experiments to infer, simultaneously and self-consistently, the number of species and their associated lifetimes from as little as a few thousand photons for two species. We do so by leveraging new mathematical tools within the Bayesian nonparametric (BNP) paradigm that we have previously exploited in the analysis of single photon arrivals from single spot confocal microscopy. We benchmark our method on simulated as well as experimental data for one, two, three, and four species with data sets from both immobilized and freely diffusing molecules at the level of one illuminated spot. SUMMARYPhoton arrivals obtained from fluorescence experiments encode not only the lifetimes of chemical species but also the number of chemical species involved in the experiment. Traditional methods of analysis, such as phasor methods and methods relying on maximum likelihood or (parametric) Bayesian analysis of photon arrivals or photon arrival histograms of TCSPC data, must first ascertain the number of chemical species separately and, once specified, determine their associated lifetimes. Here we develop a method to learn the number of fluorescence species and their associated lifetimes simultaneously. We achieve this by exploiting Bayesian nonparametrics. We benchmark our approach on both simulated and experimental data for one species and mixtures of two to four species.

biophysics↗