Utilizing Wearable Device Data for Syndromic Surveillance: A Fever Detection Approach

Commercially available wearable devices (wearables) show promise for continuous physiological monitoring. Previous works have demonstrated that wearables can be used to detect the onset of acute infectious diseases, particularly those characterized by fever. We aimed to evaluate whether these devices could be used for the more general task of syndromic surveillance. We obtained wearable device data (Oura Ring) from 63,153 participants. We constructed a dataset using participants' wearable device data and participants' responses to daily online questionnaires. We included days from the participants if they (1) completed the questionnaire, (2) reported not experiencing fever and reported a self-collected body temperature below 38 °C (negative class), or reported experiencing fever and reported a self-collected body temperature at or above 38 °C (positive class), and (3) wore the wearable device the nights before and after that day. We used wearable device data (i.e., skin temperature, heart rate, and sleep) from the nights before and after participants' fever day to train a tree-based classifier to detect self-reported fevers. We evaluated the performance of our model using a five-fold cross-validation scheme. Sixteen thousand, seven hundred, and ninety-four participants provided at least one valid ground truth day; there were a total of 724 fever days (positive class examples) from 463 participants and 342,430 non-fever days (negative class examples) from 16,687 participants. Our model exhibited an area under the receiver operating characteristic curve (AUROC) of 0.85 and an average precision (AP) of 0.25. At a sensitivity of 0.50, our calibrated model had a false positive rate of 0.8%. Our results suggest that it might be possible to leverage data from these devices at a public health level for live fever surveillance. Implementing these models could increase our ability to detect disease prevalence and spread in real-time during infectious disease outbreaks.

Elevated body temperature is associated with depressive symptoms: results from the TemPredict Study

Correlations between altered body temperature and depression have been reported in small samples; greater confidence in these associations would provide a rationale for further examining potential mechanisms of depression related to body temperature regulation. We sought to test the hypotheses that greater depression symptom severity is associated with (1) higher body temperature, (2) smaller differences between body temperature when awake versus asleep, and (3) lower diurnal body temperature amplitude. Data collected included both self-reported body temperature (using standard thermometers), wearable sensor-assessed distal body temperature (using an off-the-shelf wearable sensor that collected minute-level physiological data), and self-reported depressive symptoms from > 20,000 participants over the course of ~ 7 months as part of the TemPredict Study. Higher self-reported and wearable sensor-assessed body temperatures when awake were associated with greater depression symptom severity. Lower diurnal body temperature amplitude, computed using wearable sensor-assessed distal body temperature data, tended to be associated with greater depression symptom severity, though this association did not achieve statistical significance. These findings, drawn from a large sample, replicate and expand upon prior data pointing to body temperature alterations as potentially relevant factors in depression etiology and may hold implications for development of novel approaches to the treatment of major depressive disorder.

Variability of temperature measurements recorded by a wearable device by biological sex

BACKGROUND

Females have been historically excluded from biomedical research due in part to the documented presumption that results with male subjects will generalize effectively to females. This has been justified in part by the assumption that ovarian rhythms will increase the overall variance of pooled random samples. But not all variance in samples is random. Human biometrics are continuously changing in response to stimuli and biological rhythms; single measurements taken sporadically do not easily support exploration of variance across time scales. Recently we reported that in mice, core body temperature measured longitudinally shows higher variance in males than cycling females, both within and across individuals at multiple time scales.

METHODS

Here, we explore longitudinal human distal body temperature, measured by a wearable sensor device (Oura Ring), for 6 months in females and males ranging in age from 20 to 79 years. In this study, we did not limit the comparisons to female versus male, but instead we developed a method for categorizing individuals as cyclic or acyclic depending on the presence of a roughly monthly pattern to their nightly temperature. We then compared structure and variance across time scales using multiple standard instruments.

RESULTS

Sex differences exist as expected, but across multiple statistical comparisons and timescales, there was no one group that consistently exceeded the others in variance. When variability was assessed across time, females, whether or not their temperature contained monthly cycles, did not significantly differ from males both on daily and monthly time scales.

CONCLUSIONS

These findings contradict the viewpoint that human females are too variable across menstrual cycles to include in biomedical research. Longitudinal temperature of females does not accumulate greater measurement error over time than do males and the majority of unexplained variance is within sex category, not between them.

Assessing Adherence to Multi-Modal Oura Ring Wearables From COVID-19 Detection Among Healthcare Workers

Background Identifying early signs of a SARS-CoV-2 infection in healthcare workers could be a critical tool in reducing disease transmission. To provide this information, both daily symptom surveys and wearable device monitoring could have utility, assuming there is a sufficiently high level of participant adherence. Purpose The aim of this study is to evaluate adherence to a daily symptom survey and a wearable device (Oura Ring) among healthcare professionals (attending physicians and other clinical staff) and trainees (residents and medical students) in a hospital setting during the early stages of the COVID-19 pandemic. Methods In this mixed-methods observational study, the data were a subset (N=91) of those collected as part of the larger TemPredict Study. Demographic data analyses were conducted with descriptive statistics. Participant adherence to the wearable device protocol was reported as the percentage of days that sleep was recorded, and adherence to the daily survey was reported as the percentage of days with submitted surveys. Comparisons for the primary (wearable and survey adherence of groups) and secondary (adherence patterns among subgroups) outcomes were conducted using descriptive statistics, two-tailed independent t-tests, and Welch's ANOVA with post hoc analysis using Games-Howell. Results Wearable device adherence was significantly higher than the daily symptom survey adherence for most participants. Overall, participants were highly adherent to the wearable device, wearing the device an average of 87.8 ± 11.6% of study nights compared to survey submission, showing an average of 63.8 ± 27.4% of study days. In subgroup analysis, we found that healthcare professionals (HCPs) and medical students had the highest adherence to wearing the wearable device, while medical residents had lower adherence in both wearable adherence and daily symptom survey adherence. Conclusions These results indicated high participant adherence to wearable devices to monitor for impending infection in the course of a research study conducted as part of clinical practice. Subgroup analysis indicated HCPs and medical students maintained high adherence, but residents' adherence was lower, which is likely multifactorial, with differences in work demands and stress contributing to the findings. These results can guide the development of adherence strategies for a wearable device to increase the quality of data collection and assist in disease detection in this and future pandemics.

Modified Wavelet Analyses Permit Quantification of Dynamic Interactions Between Ultradian and Circadian Rhythms

Circadian rhythms provide daily temporal structure to cellular and organismal biological processes, ranging from gene expression to cognition. Higher-frequency (intradaily) ultradian rhythms are similarly ubiquitous but have garnered far less empirical study, in part because of the properties that define them-multimodal periods, non-stationarity, circadian harmonics, and diurnal modulation-pose challenges to their accurate and precise quantification. Wavelet analyses are ideally suited to address these challenges, but wavelet-based measurement of ultradian rhythms has remained largely idiographic. Here, we describe novel analytical approaches, based on discrete and continuous wavelet transforms, which permit quantification of rhythmic power distribution across a broad ultradian spectrum, as well as precise identification of period within empirically determined ultradian bands. Moreover, the aggregation of normalized wavelet matrices allows group-level analyses of experimental treatments, thereby circumventing limitations of idiographic approaches. The accuracy and precision of these wavelet analyses were validated using in silico and in vivo models with known ultradian features. Experiments in male and female mice yielded robust and repeatable measures of ultradian period and power in home cage locomotor activity, confirming and extending reports of ultradian rhythm modulation by sex, gonadal hormones, and circadian entrainment. Seasonal changes in day length modulated ultradian period and power, and exerted opposite effects in the light and dark phases of the 24 h day, underscoring the importance of evaluating ultradian rhythms with attention to circadian phase. Sex differences in ultradian rhythms were more prominent at night and depended on gonadal hormones in male mice. Thus, relatively straightforward modifications to the wavelet procedure allowed quantification of ultradian rhythms with appropriate time-frequency resolution, generating accurate, and repeatable measures of period and power which are suitable for group-level analyses. These analytical tools may afford deeper understanding of how ultradian rhythms are generated and respond to interoceptive and exteroceptive cues.

Methods for detecting probable COVID-19 cases from large-scale survey data also reveal probable sex differences in symptom profiles

BACKGROUND

Daily symptom reporting collected via web-based symptom survey tools holds the potential to improve disease monitoring. Such screening tools might be able to not only discriminate between states of acute illness and non-illness, but also make use of additional demographic information so as to identify how illnesses may differ across groups, such as biological sex. These capabilities may play an important role in the context of future disease outbreaks.

OBJECTIVE

Use data collected via a daily web-based symptom survey tool to develop a Bayesian model that could differentiate between COVID-19 and other illnesses and refine this model to identify illness profiles that differ by biological sex.

METHODS

We used daily symptom profiles to plot symptom progressions for COVID-19, influenza (flu), and the common cold. We then built a Bayesian network to discriminate between these three illnesses based on daily symptom reports. We further separated out the COVID-19 cohort into self-reported female and male subgroups to observe any differences in symptoms relating to sex. We identified key symptoms that contributed to a COVID-19 prediction in both males and females using a logistic regression model.

RESULTS

Although the Bayesian model performed only moderately well in identifying a COVID-19 diagnosis (71.6% true positive rate), the model showed promise in being able to differentiate between COVID-19, flu, and the common cold, as well as periods of acute illness vs. non-illness. Additionally, COVID-19 symptoms differed between the biological sexes; specifically, fever was a more important symptom in identifying subsequent COVID-19 infection among males than among females.

CONCLUSION

Web-based symptom survey tools hold promise as tools to identify illness and may help with coordinated disease outbreak responses. Incorporating demographic factors such as biological sex into predictive models may elucidate important differences in symptom profiles that hold implications for disease detection.

Analysis of wearable time series data in endocrine and metabolic research

Many hormones in the body oscillate with different frequencies and amplitudes, creating a dynamic environment that is essential to maintain health. In humans, disruptions to these rhythms are strongly associated with increased morbidity and mortality. While mathematical models can help us understand rhythm misalignment, translating this insight into personalised healthcare technologies requires solving additional challenges. Here, we discuss how combining minimally invasive, high-frequency biosampling technologies with wearable devices can assist the development of hormonal surrogates. We review bespoke algorithms that can help analyse multidimensional, noisy, time series data and identify wearable signals that could constitute clinical proxies of endocrine rhythms. These techniques can support the development of computational biomarkers to support the diagnosis and management of endocrine and metabolic conditions.

Metrics from Wearable Devices as Candidate Predictors of Antibody Response following Vaccination against COVID-19: Data from the Second TemPredict Study

There is significant variability in neutralizing antibody responses (which correlate with immune protection) after COVID-19 vaccination, but only limited information is available about predictors of these responses. We investigated whether device-generated summaries of physiological metrics collected by a wearable device correlated with post-vaccination levels of antibodies to the SARS-CoV-2 receptor-binding domain (RBD), the target of neutralizing antibodies generated by existing COVID-19 vaccines. One thousand, one hundred and seventy-nine participants wore an off-the-shelf wearable device (Oura Ring), reported dates of COVID-19 vaccinations, and completed testing for antibodies to the SARS-CoV-2 RBD during the U.S. COVID-19 vaccination rollout. We found that on the night immediately following the second mRNA injection (Moderna-NIAID and Pfizer-BioNTech) increases in dermal temperature deviation and resting heart rate, and decreases in heart rate variability (a measure of sympathetic nervous system activation) and deep sleep were each statistically significantly correlated with greater RBD antibody responses. These associations were stronger in models using metrics adjusted for the pre-vaccination baseline period. Greater temperature deviation emerged as the strongest independent predictor of greater RBD antibody responses in multivariable models. In contrast to data on certain other vaccines, we did not find clear associations between increased sleep surrounding vaccination and antibody responses.

Lower variability in female students than male students at multiple timescales supports the use of sex as a biological variable in human studies

BACKGROUND

Men have been, and still are, included in more studies than women, in large part because of the lingering belief that ovulatory cycles result in women showing too much variability to be economically viable subjects. This belief has scientific and social consequences, and yet, it remains largely untested. Recent work in rodents has shown either that there is no appreciable difference in overall variability across a wealth of traits, or that in fact males may show more variability than females.

METHODS

We analyzed learning management system logins associated to gender records spanning 2 years from 13,777 students at Northeastern Illinois University. These data were used to assess variability in daily rhythms in a heterogeneous human population.

RESULTS

At the population level, men are more likely than women to show extreme chronotypes (very early or very late phases of activity). Men were also found to be more variable than women across and within individuals. Variance correlated negatively with academic performance, which also showed a gender difference. Whereas a complaint against using female subjects is that their variance is the driver of statistical sex differences, only 6% of the gender performance difference is potentially accounted for by variance, suggesting that variability is not the driver of sex differences here.

CONCLUSIONS

Our findings do not support the idea that women are more behaviorally variable than men and may support the opposite. Our findings support including sex as a biological variable and do not support variance-based arguments for the exclusion of women as research subjects.

Maternal stress during pregnancy alters fetal cortico-cerebellar connectivity in utero and increases child sleep problems after birth

Child sleep disorders are increasingly prevalent and understanding early predictors of sleep problems, starting in utero, may meaningfully guide future prevention efforts. Here, we investigated whether prenatal exposure to maternal psychological stress is associated with increased sleep problems in toddlers. We also examined whether fetal brain connectivity has direct or indirect influence on this putative association. Pregnant women underwent fetal resting-state functional connectivity MRI and completed questionnaires on stress, worry, and negative affect. At 3-year follow-up, 64 mothers reported on child sleep problems, and in the subset that have reached 5-year follow-up, actigraphy data (N = 25) has also been obtained. We observe that higher maternal prenatal stress is associated with increased toddler sleep concerns, with actigraphy sleep metrics, and with decreased fetal cerebellar-insular connectivity. Specific mediating effects were not identified for the fetal brain regions examined. The search for underlying mechanisms of the link between maternal prenatal stress and child sleep problems should be continued and extended to other brain areas.

Feasibility of continuous fever monitoring using wearable devices

Elevated core temperature constitutes an important biomarker for COVID-19 infection; however, no standards currently exist to monitor fever using wearable peripheral temperature sensors. Evidence that sensors could be used to develop fever monitoring capabilities would enable large-scale health-monitoring research and provide high-temporal resolution data on fever responses across heterogeneous populations. We launched the TemPredict study in March of 2020 to capture continuous physiological data, including peripheral temperature, from a commercially available wearable device during the novel coronavirus pandemic. We coupled these data with symptom reports and COVID-19 diagnosis data. Here we report findings from the first 50 subjects who reported COVID-19 infections. These cases provide the first evidence that illness-associated elevations in peripheral temperature are observable using wearable devices and correlate with self-reported fever. Our analyses support the hypothesis that wearable sensors can detect illnesses in the absence of symptom recognition. Finally, these data support the hypothesis that prediction of illness onset is possible using continuously generated physiological data collected by wearable sensors. Our findings should encourage further research into the role of wearable sensors in public health efforts aimed at illness detection, and underscore the importance of integrating temperature sensors into commercially available wearables.

Evidence for a Coupled Oscillator Model of Endocrine Ultradian Rhythms

Whereas long-period temporal structures in endocrine dynamics have been well studied, endocrine rhythms on the scale of hours are relatively unexplored. The study of these ultradian rhythms (URs) has remained nascent, in part, because a theoretical framework unifying ultradian patterns across systems has not been established. The present overview proposes a conceptual coupled oscillator network model of URs in which oscillating hormonal outputs, or nodes, are connected by edges representing the strength of node-node coupling. We propose that variable-strength coupling exists both within and across classic hormonal axes. Because coupled oscillators synchronize, such a model implies that changes across hormonal systems could be inferred by surveying accessible nodes in the network. This implication would at once simplify the study of URs and open new avenues of exploration into conditions affecting coupling. In support of this proposed framework, we review mammalian evidence for (1) URs of the gut-brain axis and the hypothalamo-pituitary-thyroid, -adrenal, and -gonadal axes, (2) UR coupling within and across these axes; and (3) the relation of these URs to body temperature. URs across these systems exhibit behavior broadly consistent with a coupled oscillator network, maintaining both consistent URs and coupling within and across axes. This model may aid the exploration of mammalian physiology at high temporal resolution and improve the understanding of endocrine system dynamics within individuals.

3.4 million real-world learning management system logins reveal the majority of students experience social jet lag correlated with decreased performance

Misalignments between endogenous circadian rhythms and the built environment (i.e., social jet lag, SJL) result in learning and attention deficits. Currently, there is no way to assess the impact of SJL on learning outcomes of large populations as a response to schedule choices, let alone to assess which individuals are most negatively impacted by these choices. We analyzed two years of learning management system login events for 14,894 Northeastern Illinois University (NEIU) students to investigate the capacity of such systems as tools for mapping the impact of SJL over large populations while maintaining the ability to generate insights about individuals. Personal daily activity profiles were validated against known biological timing effects, and revealed a majority of students experience more than 30 minutes of SJL on average, with greater amplitude correlating strongly with a significant decrease in academic performance, especially in people with later apparent chronotypes. Our findings demonstrate that online records can be used to map individual- and population-level SJL, allow deep mining for patterns across demographics, and could guide schedule choices in an effort to minimize SJL’s negative impact on learning outcomes.

Sex differences in variability across timescales in BALB/c mice

BACKGROUND

Females are markedly underinvestigated in the biological and behavioral sciences due to the presumption that cyclic hormonal changes across the ovulatory cycle introduce excess variability to measures of interest in comparison to males. However, recent analyses indicate that male and female mice and rats exhibit comparable variability across numerous physiological and behavioral measures, even when the stage of the estrous cycle is not considered. Hormonal changes across the ovulatory cycle likely contribute cyclic, intra-individual variability in females, but the source(s) of male variability has, to our knowledge, not been investigated. It is unclear whether male variability, like that of females, is temporally structured and, therefore, quantifiable and predictable. Finally, whether males and females exhibit variability on similar time scales has not been explored.

METHODS

These questions were addressed by collecting chronic, high temporal resolution locomotor activity (LA) and core body temperature (CBT) data from male and female BALB/c mice.

RESULTS

Contrary to expectation, males are more variable than females over the course of the day (diel variability) and exhibit higher intra-individual daily range than females in both LA and CBT. Between mice of a given sex, variability is comparable for LA but the inter-individual daily range in CBT is greater for males. To identify potential rhythmic processes contributing to these sex differences, we employed wavelet transformations across a range of periodicities (1-39 h).

CONCLUSIONS

Although variability in circadian power is comparable between the sexes for both LA and CBT, infradian variability is greater in females and ultradian variability is greater in males. Thus, exclusion of female mice from studies because of estrous cycle variability may increase variance in investigations where only male measures are collected over a span of several hours and limit generalization of findings from males to females.

Detection of Successful and Unsuccessful Pregnancies in Mice within Hours of Pairing through Frequency Analysis of High Temporal Resolution Core Body Temperature Data

Many controllable factors negatively impact fetal development, underscoring the importance of early pregnancy detection and identification of events that reliably predict potential complications. Clinically, core body temperature (CBT) is used to aid family planning and pregnancy detection. However, such temperature data typically are gathered in single, daily measurements. In animal studies, interventions or cell/tissue harvesting at defined stages of fetal development are arduous, requiring timed mating by trained observers. The value of continuous temperature measurements remains largely unexplored, but the advent of small, inexpensive, and increasingly ubiquitous, accurate sensor devices makes continuous measures feasible. Here, using a mouse model, we show that continuous, 1-min resolution CBT measurements reliably allow for the earliest and most accurate detection of pregnancy (100%, within 14 h of initial pairing), without requiring interaction with the animal for data collection. This method also reveals a subset of females that exhibit a pregnancy-like response following pairing that persists for a variable number of days. Application of wavelet analysis that permits frequency analysis while preserving temporal resolution, uncovers significant differences in ultradian frequencies of CBT; these rhythms are significantly increased in the 12 h after the day of pairing for pregnancies carried to term compared to apparent pregnancies that failed. High temporal resolution CBT and wavelet analysis permit strikingly early detection and separation of successful pregnancies and pregnancy-like events.

A time to remember: the role of circadian clocks in learning and memory

The circadian system has pronounced influence on learning and memory, manifesting as marked changes in memory acquisition and recall across the day. From a mechanistic perspective, the majority of studies have investigated mammalian hippocampal-dependent learning and memory, as this system is highly tractable. The hippocampus plays a major role in learning and memory, and has the potential to integrate circadian information in many ways, including information from local, independent oscillators, and through circadian modulation of neurogenesis, synaptic remodeling, intracellular cascades, and epigenetic regulation of gene expression. These local processes are combined with input from other oscillatory systems to synergistically augment hippocampal rhythmic function. This overview presents an account of the current state of knowledge on circadian interactions with learning and memory circuitry and provides a framework for those interested in further exploring these interactions.

Oestrogen-independent circadian clock gene expression in the anteroventral periventricular nucleus in female rats: possible role as an integrator for circadian and ovarian signals timing the luteinising hormone surge

Periodic ovulation in rats, mice and hamsters is the result of a surge in luteinising hormone (LH) that depends on circadian gating signals emerging from the master circadian clock within the suprachiasmatic nucleus (SCN) and rising ovarian oestrogen levels. These two signals converge into the anteroventral periventricular nucleus (AVPV) and lead to the release of kisspeptin, which is responsible for surges of gonadotrophin-releasing hormone and, in turn, of LH release. How the AVPV integrates circadian and reproductive signals remains unclear. In the present study, we show that the female rat AVPV itself shows circadian oscillations in the expression of the clock genes PER1 and BMAL1, which lie at the core circadian clockwork of mammals. In ovariectomised females treated with oestradiol (E₂), these oscillations are in synchrony with the AVPV rhythmic expression of the KISS1 gene and the gene that codes for the arginine-vasopressin (AVP) receptor AVPr1a. Although clock gene oscillations are independent of oestrogen levels, circadian expression of Kiss1 and Avpr1a (also referred to as V1a) mRNA is blunted and absent, respectively, in ovariectomised animals without E₂ replacement. Because AVP is considered to be a critical SCN transmitter to gate the LH surge, our data suggest that there is a circadian oscillator located in the AVPV, and that such a putative oscillator could, in an oestrogen-dependent manner, time the sensitivity to circadian signals emerging from the SCN and the release of kisspeptin.

Forced desynchrony reveals independent contributions of suprachiasmatic oscillators to the daily plasma corticosterone rhythm in male rats

The suprachiasmatic nucleus (SCN) is required for the daily rhythm of plasma glucocorticoids; however, the independent contributions from oscillators within the different subregions of the SCN to the glucocorticoid rhythm remain unclear. Here, we use genetically and neurologically intact, forced desynchronized rats to test the hypothesis that the daily rhythm of the glucocorticoid, corticosterone, is regulated by both light responsive and light-dissociated circadian oscillators in the ventrolateral (vl-) and dorsomedial (dm-) SCN, respectively. We show that when the vlSCN and dmSCN are in maximum phase misalignment, the peak of the plasma corticosterone rhythm is shifted and the amplitude reduced; whereas, the peak of the plasma adrenocorticotropic hormone (ACTH) rhythm is also reduced, the phase is dissociated from that of the corticosterone rhythm. These data support previous studies suggesting an ACTH-independent pathway contributes to the corticosterone rhythm. To determine if either SCN subregion independently regulates corticosterone through the sympathetic nervous system, we compared unilateral adrenalectomized, desynchronized rats that had undergone either transection of the thoracic splanchnic nerve or sham transection to the remaining adrenal. Splanchnicectomy reduced and phase advanced the peak of both the corticosterone and ACTH rhythms. These data suggest that both the vlSCN and dmSCN contribute to the corticosterone rhythm by both reducing plasma ACTH and differentially regulating plasma corticosterone through an ACTH- and sympathetic nervous system-independent pathway.

Re-examining "temporal niche"

The circadian system temporally organizes physiology and behavior throughout the 24-h day. At the core of this organization lies a network of multiple circadian oscillators located within the central nervous system as well as in virtually every peripheral organ. These oscillators define a 24-h temporal landscape of mutually interacting circadian rhythms that is known as the temporal niche of a species. This temporal niche is constituted by the collective phases of all biological rhythms emerging from this multi-oscillatory system. We review evidence showing that under different environmental conditions, this system can adopt different harmonic configurations. Thus, the classic chronobiological approach of searching for "the" circadian phase of an animal-typically by studying circadian rhythms of locomotor activity-represents a narrow look into the circadian system of an animal. We propose that the study of hormonal rhythms may lead to a more insightful assessment of a species' temporal niche.

The dorsomedial suprachiasmatic nucleus times circadian expression of Kiss1 and the luteinizing hormone surge

Ovulation in mammals is gated by a master circadian clock in the suprachiasmatic nucleus (SCN). GnRH neurons represent the converging pathway through which the brain triggers ovulation, but precisely how the SCN times GnRH neurons is unknown. We tested the hypothesis that neurons expressing kisspeptin, a neuropeptide coded by the Kiss1 gene and necessary for the activation of GnRH cells during ovulation, represent a relay station for circadian information that times ovulation. We first show that the circadian increase of Kiss1 expression, as well as the activation of GnRH cells, relies on intact ipsilateral neural input from the SCN. Second, by desynchronizing the dorsomedial (dm) and ventrolateral (vl) subregions of the SCN, we show that a clock residing in the dmSCN acts independently of the light-dark cycle, and the vlSCN, to time Kiss1 expression in the anteroventral periventricular nucleus of the hypothalamus and that this rhythm is always in phase with the LH surge. In addition, we show that although the timing of the LH surge is governed by the dmSCN, its amplitude likely depends on the phase coherence between the vlSCN and dmSCN. Our results suggest that whereas dmSCN neuronal oscillators are sufficient to time the LH surge through input to kisspeptin cells in the anteroventral periventricular nucleus of the hypothalamus, the phase coherence among dmSCN, vlSCN, and extra-SCN oscillators is critical for shaping it. They also suggest that female reproductive disorders associated with nocturnal shift work could emerge from the desynchronization between subregional oscillators within the master circadian clock.

Light and electron microscopic localization of multiple proteins using quantum dots

Our understanding of basic cell structure and function has been greatly aided by the identification of proteins at the ultrastructural level. However, the current methods for high-resolution labeling of proteins in situ, and for directly correlating observations made by light microscopy (LM) and electron microscopy (EM) although invaluable, have a number of substantial limitations. These range from poor label penetration, difficulty to perform simultaneous multiprotein labeling, or the need to take the samples all the way to the electron microscope to evaluate labeling efficacy. Here we demonstrate an approach using quantum dots for pre-embedding immunolabeling of multiple diverse proteins for both LM and EM that overcomes many of these problems.

Redefining the concept of reactive astrocytes as cells that remain within their unique domains upon reaction to injury

Reactive astrocytes in neurotrauma, stroke, or neurodegeneration are thought to undergo cellular hypertrophy, based on their morphological appearance revealed by immunohistochemical detection of glial fibrillary acidic protein, vimentin, or nestin, all of them forming intermediate filaments, a part of the cytoskeleton. Here, we used a recently established dye-filling method to reveal the full three-dimensional shape of astrocytes assessing the morphology of reactive astrocytes in two neurotrauma models. Both in the denervated hippocampal region and the lesioned cerebral cortex, reactive astrocytes increased the thickness of their main cellular processes but did not extend to occupy a greater volume of tissue than nonreactive astrocytes. Despite this hypertrophy of glial fibrillary acidic protein-containing cellular processes, interdigitation between adjacent hippocampal astrocytes remained minimal. This work helps to redefine the century-old concept of hypertrophy of reactive astrocytes.

Correlated light and electron microscopic imaging of multiple endogenous proteins using Quantum dots

The importance of locating proteins in their context within cells has been heightened recently by the accomplishments in molecular structure and systems biology. Although light microscopy (LM) has been extensively used for mapping protein localization, many studies require the additional resolution of the electron microscope. Here we report the application of small nanocrystals (Quantum dots; QDs) to specifically and efficiently label multiple distinct endogenous proteins. QDs are both fluorescent and electron dense, facilitating their use for correlated microscopic analysis. Furthermore, QDs can be discriminated optically by their emission wavelength and physically by size, making them invaluable for multilabeling analysis. We developed pre-embedding labeling criteria using QDs that allows optimization at the light level, before continuing with electron microscopy (EM). We provide examples of double and triple immunolabeling using light, electron and correlated microscopy in rat cells and mouse tissue. We conclude that QDs aid precise high-throughput determination of protein distribution.