Biology subjects

Nestor, J.

Publications and source records attributed to Nestor, J..

5 recordsLinked to original sources

bioRxiv2026

A Community-Driven Single-Cell PBMC Reference Integrating Landmark Datasets Spanning Health and Disease

Blood provides an accessible window into human health, yet the absence of a peripheral blood mononuclear cells (PBMCs) reference with standardized immune cell annotations has constrained comparisons between single-cell RNA sequencing (scRNAseq) studies. Here, we present HARP (Human Cell Atlas Reference for PBMCs), an integrated atlas of ~9 million PBMCs from >2,600 donors across 15 studies spanning four continents, encompassing diverse populations, neonates to 97 years, across health and diverse immune-related diseases. We developed optimized integration workflows, including novel label-free metrics to assess integration quality, and generated community-driven consensus annotations for 192 immune cell subsets, identifying rare populations representing as few as 0.004% of PBMCs. HARP reveals coordinated cellular modules associated with age, sex, and disease, identifies female-biased interferon and inflammatory gene programs and their perturbation by COVID-19, and uncovers a novel sexual dimorphism in prostaglandin signaling. Finally, we introduce scTiger, a hierarchical label-transfer framework that accurately projects these 192 cell annotations onto ~18 million additional PBMCs, providing a robust and transferable reference for harmonizing future PBMC studies. HARP provides a high-resolution community-driven reference atlas for PBMC annotation and analysis, as a basis for emerging clinical applications of single-cell genomics.

immunology↗

bioRxiv2026

Time-resolved decoding of uncertainty about stimulus features from human EEG

Internal and external noise produce uncertainty in the neural representations of sensory stimuli. Uncertainty about basic stimulus features can be decoded from responses in sensory brain regions by estimating full probability distributions over stimulus features, rather than point estimates. Such decoded uncertainty correlates with subjective uncertainty reports, providing insight into the neural basis of metacognitive judgments. However, in humans, probabilistic decoding has only been applied to functional magnetic resonance imaging (fMRI) data, which has low temporal resolution and thus can give only limited insight into the dynamics of uncertainty in the brain. Here, we assessed whether probabilistic decoding of uncertainty about stimulus features could be extended to electroencephalography (EEG) data, which has higher temporal resolution but lower spatial resolution and different noise properties compared to fMRI. Participants performed a spatial location estimation task and provided subjective uncertainty reports. We found that time-resolved probabilistic decoding in EEG was feasible, as decoders produced accurate predictions of stimulus location following stimulus onset, and decoding error correlated trial-by-trial with decoded uncertainty. The choice of noise covariance structure critically impacted these metrics. Further, decoded uncertainty was a more reliable trial-by-trial indicator of stimulus information than decoding error, illustrating the advantages of probabilistic decoding over standard decoding approaches. However, uncertainty decoded from EEG had no significant trial-by-trial correlation with subjective uncertainty at any time point. Based on these results, uncertainty decoded from EEG provides a time-resolved estimate of stimulus information available from the brain signal on a single trial but may not relate to metacognitive reports.

neuroscience↗

bioRxiv2023

Interpretable Inflammation Landscape of Circulating Immune cells

Inflammation is a biological phenomenon involved in a wide variety of physiological and pathological processes. Although a controlled inflammatory response is beneficial for restoring homeostasis, it can become unfavorable if dysregulated. In recent years, major progress has been made in characterizing acute and chronic inflammation in specific diseases. However, a global, holistic understanding of inflammation is still elusive. This is particularly intriguing, considering the crucial function of inflammation for human health and its potential for modern medicine if fully deciphered. Here, we leverage advances in the field of single-cell genomics to delineate the full spectrum of circulating immune cell activation underlying inflammatory processes during infection, immune-mediated inflammatory diseases and cancer. Our single-cell atlas of >6.5 million peripheral blood mononuclear cells from 1047 patients and 19 diseases allowed us to learn a comprehensive model of inflammation in circulating immune cells. The atlas expanded our current knowledge of the biology of inflammation of immune-mediated diseases, acute and chronic inflammatory diseases, infection and solid tumors, and laid the foundation to develop a precision medicine framework using unsupervised as well as explainable machine learning. Beyond a disease-centered analysis, we charted altered activity of inflammatory molecules in peripheral blood cells, depicting discriminative inflammation-related genes to further understand mechanisms of inflammation. Finally, we have laid the groundwork for developing precision medicine diagnostic tools for patients experiencing pathologic inflammation by learning a classifier for inflammatory diseases, presenting cells in circulation as a powerful resource for patient diagnosis.

immunology↗

bioRxiv2019

Lupus auto-antibodies act as positive allosteric modulators at NMDA receptors and induce spatial memory deficits

Patients with Systemic lupus erythematosus (SLE) experience various peripheral and central nervous system manifestations including spatial memory impairment. A subset of auto-antibodies (DNRAbs) cross-react with the GluN2A and GluN2B subunits of the NMDA receptor (NMDAR). We find that these DNRAbs act as positive allosteric modulators on NMDARs with GluN2A-containing NMDARs, even those containing a single GluN2A subunit, exhibiting a much greater sensitivity to DNRAbs than those with exclusively GluN2B. Accordingly, GluN2A-specific antagonists provide greater protection from DNRAb-mediated neuronal cell death than GluN2B antagonists. Using transgenic mice to perturb expression of either GluN2A or GluN2B in vivo, we find that DNRAb-mediated disruption of spatial memory characterized by early neuronal cell death and subsequent microglia-dependent pathologies requires GluN2A-containing NMDARs. Our results indicate that GluN2A-specific antagonists or negative allosteric modulators are strong candidates to treat SLE patients with nervous system dysfunction.

neuroscience↗

bioRxiv2019

High-content interrogation of human induced pluripotent stem cell-derived cortical organoid platforms.

The need for scalable and high-throughput approaches to screening using 3D human stem cell models remains a central challenge in using stem cell disease models for drug discovery. It is imperative to develop standardized systems for phenotypic screening, yet most researchers screen cells across different platforms using a multitude of assays. In this study, we have developed a workflow centered on a small array of assays that can be employed to screen 3D stem cell cultures across a set of platforms. This workflow can be used as a starting point for a standardized approach to phenotypic screening. In this manuscript we hope to provide a roadmap for groups looking to start high-content screening using 3D organoid systems. To do this, we employ serum-free embryoid bodies (SFEBs) created from human induced pluripotent stem cells (hiPSCs). SFEBs are used in this study because they do not display the same level of heterogeneity observed in other neural organoid systems and they are amenable to high content imaging without cryosectioning. They contain populations of excitatory and inhibitory neurons that form synaptically active networks1 and medium- to high-throughput electrophysiology can be performed using SFEBs via the multielectrode array (MEA). The assays outlined in this study allow SFEBs to be scanned for neurite outgrowth, cell number and electrophysiological activity. SFEBs derived from control and disease hiPSCs can be used in combination with high-throughput screening assays to generate sufficient statistical power to compensate for the biological and experimental variability common in 3D cultures, while significantly decreasing processing speed, thus making this an efficient starting point for phenotypic drug screening.

neuroscience↗