Accurate circadian time prediction from a single bulk and single-cell transcriptomic data by tauFisher
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PROVIDER: PRJNA924846 | ENA |
REPOSITORIES: ENA
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Project description:As the circadian clock regulates fundamental biological processes, disrupted clocks are often observed in patients and diseased tissues. Determining the circadian time of the patient or the tissue of focus is essential in circadian medicine and research. Here we present tauFisher, a computational pipeline that accurately predicts circadian time from a single transcriptomic sample. We demonstrate tauFisher’s outstanding performance in both bulk and single cell transcriptomic data collected from multiple tissue types and experimental settings. Application of tauFisher at a cell-type level in a single cell RNA-seq dataset collected from mouse dermal skin implies that greater circadian phase heterogeneity may explain the dampened rhythm of collective core clock gene expression in dermal immune cells compared to dermal fibroblasts. Given its robustness and generalizability across assay platforms, experimental setups, and tissue types, as well as its potential application in single cell RNA-seq data analysis, tauFisher is a promising tool that facilitates circadian medicine and research.
2023-03-30 | GSE223109 | GEO
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Project description:Profiles of circadian gene expression in whole blood from 11 healthy adult subjects. Abstract: Circadian clocks play a key role in regulating a vast array of biological processes, with significant implications for human health. Accurate assessment of physiological time using transcriptional biomarkers found in human blood can significantly improve diagnosis of circadian disorders and optimize the delivery time of therapeutic treatments. To be useful, such a test must be accurate, minimally burden some to the patient, and readily generalizable to new data. A major obstacle in development of gene expression biomarker tests is the diversity of measurement platforms and the inherent variability of the data, often resulting in predictors that perform well in the original datasets but cannot be universally applied to new samples collected in other settings. Here we introduce TimeSignature, a new algorithm that robustly infers circadian time from gene expression. We demonstrate its application in data from three independent studies using Pdistinct microarrays, and further validate it against a new set of samples profiled by RNA-Seq. Our results show that TimeSignature is more accurate and efficient than competing methods, estimating circadian time to within 2h for the majority of samples. Importantly, we demonstrate that once trained on data from a single study, the resulting predictor can be universally applied to yield highly accurate results in new data from other studies independent of differences in study population, patient protocol, or assay platform without renormalizing the data or retraining. This feature is unique amongst expression–based predictors, and addresses a major challenge in the development of generalizable, clinically–useful tests.
2018-10-08 | GSE113883 | GEO
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Project description:Meal timing is essential in synchronization of circadian rhythms in different organ systems through clock-dependent and -independent mechanisms. The liver is a critical metabolic organ whose circadian clock and transcriptome can be readily reset by meal timing. However, it remains largely unexplored how circadian rhythms in the liver are organized in time-restricted feeding that intervenes meal timing. Here, we applied data-independent acquisition proteomics to characterize circadian features associated with day/sleep- (DRF) and night/wake (NRF)-time restricted feeding in nocturnal female mice. The transcriptomics and metabolomics datasets are public (see www.circametdb.org.cn).
2023-08-22 | PXD038276 | Pride
Project description:Purpose Aromatase inhibitors (AIs) have an established role in breast cancer treatment. Response rates are only 50-70% in the neoadjuvant setting and lower in advanced disease. Accurate biomarkers are urgently needed to predict response in these settings and to determine which individuals will benefit from adjuvant AI therapy. Participants and Methods Pre- and on-treatment (after 2 weeks and 3 months) biopsies were obtained from 89 post-menopausal women with ER+ breast cancer receiving neoadjuvant letrozole for transcript profiling. Dynamic clinical response was assessed by three-dimensional ultrasound measurements. Results The molecular response to letrozole was characterised and a four gene classifier of clinical response was established (accuracy of 96%) based upon the level of two genes prior to treatment (one associated with immune signalling, IL6ST and the other with apoptosis, NGFRAP1) and two proliferation genes (ASPM, MCM4) at 2 weeks of therapy. The four gene signature was found to be 91% accurate in a blinded, completely independent validation dataset of patients treated with anastrozole. Matched 2 week on-treatment biopsies improved predictive power over pre-treatment biopsies alone. This signature also significantly predicted recurrence free survival (p=0.029) and breast cancer specific survival (p=0.009). We demonstrate that the test can also be performed using quantitative PCR or immunohistochemistry. Conclusion A four gene predictive model of clinical response to AIs by two weeks has been generated and validated. Deregulated immune and apoptotic responses before treatment and a failure to reduce proliferation by 2 weeks are functional characteristics of breast tumours that do not respond to AIs. 25 Pre treatment, 25 two week and 25 three month on-treatment primary breast tumour samples from the same patients. Data was analysed with two previously generated datasets, GSE55374 which was also processed on Illumina HT-12v4 BeadChips (GPL10558) and GSE20181 on Affymetrix U133A GeneChips (GPL96)
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Project description:Meal timing is essential in synchronization of circadian rhythms in different organ systems through clock-dependent and -independent mechanisms. The liver is a critical metabolic organ whose circadian clock and transcriptome can be readily reset by meal timing. However, it remains largely unexplored how circadian rhythms in the liver are organized in time-restricted feeding that intervenes meal timing. Here, we applied affinity-purification based shotgun proteomics for succinylation to characterize circadian features associated with day/sleep- (DRF) and night/wake (NRF)-time restricted feeding in nocturnal female mice. The transcriptomics and metabolomics datasets are public (see www.circametdb.org.cn).
2023-08-22 | PXD037604 | Pride
Project description:Meal timing is essential in synchronization of circadian rhythms in different organ systems through clock-dependent and -independent mechanisms. The liver is a critical metabolic organ whose circadian clock and transcriptome can be readily reset by meal timing. However, it remains largely unexplored how circadian rhythms in the liver are organized in time-restricted feeding that intervenes meal timing. Here, we applied affinity-purification based shotgun proteomics for ubiquitylation to characterize circadian features associated with day/sleep- (DRF) and night/wake (NRF)-time restricted feeding in nocturnal female mice. The transcriptomics and metabolomics datasets are public (see www.circametdb.org.cn).
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