Biology subjects

de la Iglesia, H. O.

Publications and source records attributed to de la Iglesia, H. O..

6 recordsLinked to original sources

bioRxiv2026

Lunar gravity predicts sleep timing

Biological rhythms are fundamental to survival, synchronizing physiological and behavioural processes like the sleep-wake cycle with predictable environmental cycles like the solar day1. However, the extent to which human sleep responds to the lunar cycle remains a subject of controversy, particularly given the ubiquity of artificial light and conflicting reports regarding the influence of moonlight2-5. While lunar illuminance has been the primary candidate for such modulation, it fails to account for semilunar rhythms2,6 or effects observed during the dark new moon phase2,7. Here, we show that human and non-human primate sleep timing is synchronized with the gravimetric cycles exerted by the Moon and Sun. By analysing longitudinal actigraphic recordings from urban cohorts in Seattle, indigenous Toba/Qom communities in Argentina, and captive titi monkeys with attenuated access to natural light, we found that sleep onset is consistently delayed around periods of maximal gravitational variations. Crucially, this synchronization aligns with gravimetric peaks regardless of whether they occur at the full or new moon. Furthermore, while human sleep timing is heavily influenced by social constraints and self-selected light exposure, this synchronization persists in titi monkeys, in which these confounds are absent. These findings identify lunar gravity as a distinct environmental cue that may regulate sleep-wake behaviour. Although this regulation may be mediated by other geophysical factors oscillating with lunisolar gravitational tides, the results suggest that the mechanism is an evolutionarily conserved trait that shapes biological timing beyond the influence of light.

animal behavior and cognition↗

bioRxiv2025

Brainwide genetic capture for conscious state transitions

Neural circuits underlying unconsciousness remain poorly defined. We test the hypothesis that unconsciousness arises from specific, distributed circuits using general anesthesia in mice as a reproducible model. We identify a cortical-to-subcortical shift in neural activity during isoflurane anesthesia that is organized into nine discrete functional communities mapped at single-cell resolution. The lateral parabrachial nucleus (LPB) emerges as a central hub, exhibiting high interconnectivity, spontaneous firing under anesthesia, and preferential recruitment during reactivation of the brain-wide ensemble confirmed by single unit recordings. Chemogenetic reactivation of the captured brain-wide ensemble induces sedation, slow wave oscillations, hypothermia, and analgesia, which are components of anesthesia-induced unconsciousness. Reactivation of the LPB ensemble alone recapitulates a subset of these effects. Together, we define a global neural substrate for unconsciousness and recapitulate its dissociable autonomic, neurophysiologic, and behavioral effects using brain-wide ensemble manipulations. These results establish a neural circuit framework for anesthesia-induced unconsciousness in the mammalian brain.

neuroscience↗

bioRxiv2025

Sleep in People Experiencing Homelessness Under Different Conditions and Seasons

Poor sleep represents a central health disparity for people experiencing homelessness, and any intervention to alleviate the impacts of homelessness should aim to improve sleep. We measured actimetry-based sleep in homeless adults spending their nights in four types of shelters in Seattle, WA, during the summer and winter. Homeless participants experienced more sleepless nights than housed participants in both seasons. During the summer sleeping nights, homeless participants experienced sleep patterns similar to housed subjects, but during the winter, their sleep duration was up to 1.5 hours shorter. Similarly, sleep quality, determined through sleep variability index, activity during the night, and intraindividual variability of sleep parameters, was poorer during the winter in homeless than in housed participants. Our study demonstrates the feasibility of using objectively measured sleep as a proxy for assessing the value of specific interventions to improve living conditions in people experiencing homelessness. Given the bidirectional relationship between sleep and both physical and mental health, our study reveals the health inequities of chronic sleep disparity for those living outdoors. Significance statementSleep disparities are central to the poor physical and mental health experienced by houseless people. Here we show that measured sleep through wrist actigraphy captures different aspects of this sleep disparity and represents a useful, objective metric of the impact of interventions to improve living conditions among people experiencing homelessness.

physiology↗

bioRxiv2024

Single Objective Light Sheet Microscopy allows high-resolution in vivo brain imaging of Drosophila.

In vivo imaging of dynamic sub-cellular brain structures in Drosophila melanogaster is key to understanding several phenomena in neuroscience. However, its implementation has been hindered by a trade-off between spatial resolution, speed, photobleaching, phototoxicity, and setup complexity required to access the specific target regions of the small brain of Drosophila. Here, we present a single objective light-sheet microscope, customized for in vivo imaging of adult flies and optimized for maximum resolution. With it, we imaged the axonal projections of small lateral ventral neurons (known as s-LNvs) in intact adult flies. We imaged the plasma membrane, mitochondria, and dense-core vesicles with high spatial resolution up to 370 nm, ten times lower photobleaching than confocal microscopy, lower invasiveness and complexity in sample mounting than alternative light-sheet technologies, and without relying on phototoxic pulsed infrared lasers. This unique set of features paves the way for new long-term, dynamic studies in the brains of living flies.

neuroscience↗

bioRxiv2023

The circadian molecular clock in the suprachiasmatic nucleus is necessary but not sufficient for fear entrainment in the mouse

Nocturnal aversive stimuli presented to mice during eating and drinking outside of their safe nest can entrain circadian behaviors, leading to a shift toward daytime activity. We show that the canonical molecular circadian clock is necessary for fear entrainment and that an intact molecular clockwork in the suprachiasmatic nucleus (SCN), the site of the central circadian pacemaker, is necessary but not sufficient to sustain fear entrainment of circadian rhythms. Our results demonstrate that entrainment of a circadian clock by cyclic fearful stimuli can lead to severely mistimed circadian behavior that persists even after the aversive stimulus is removed. Together, our results support the interpretation that circadian and sleep symptoms associated with fear and anxiety disorders may represent the output of a fear-entrained clock. One-Sentence SummaryCyclic fearful stimuli can entrain circadian rhythms in mice, and the molecular clock within the central circadian pacemaker is necessary but not sufficient for fear-entrainment.

neuroscience↗

bioRxiv2020

Sleep Identification Enabled by Supervised Training Algorithms (SIESTA): An open-source platform for automatic sleep staging of rodent polysomnographic data

The temporal distribution of sleep stages is critical for the study of sleep function, regulation, and disorders in higher vertebrates. This temporal distribution is typically determined polysomnographically. In laboratory rodents, scoring of electrocorticography (ECoG) and electromyography (EMG) recordings is usually performed manually, where 5-10 second epochs are categorized as one of three specific stages: wakefulness, rapid-eye-movement (REM) sleep and non-REM (NREM) sleep. This process is laborious, time-consuming, and particularly impractical for large experimental cohorts with recordings lasting longer than 24 hours. To circumvent this problem, we developed an open-source Python toolkit, Sleep Identification Enabled by Supervised Training Algorithms (SIESTA), that automates the detection of these three main behavioral stages in mice. Our supervised machine learning algorithm extracts features from the ECoG and EMG signals, then automatically scores recordings with a hierarchical classifier based on Bagging Random Forest approaches. We evaluated this approach on data collected from wild-type mice housed under both normal and different lighting conditions, as well as from a mutant mouse line with abnormal sleep phenotypes. To validate its performance on test data, we compared SIESTA with manually scored data and obtained F1 scores of 0.92 for wakefulness, 0.81 for REM, and 0.93 for NREM. SIESTA has a user-friendly interface that can be used without coding expertise. To our knowledge, this is the first time that such a strategy has been developed using all open-source and freely available resources, and our aim is that SIESTA becomes a useful tool that facilitates further research of sleep in rodent models.

neuroscience↗