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A time-series transcriptomic dataset of the mouse olfactory bulb across pregnancy and lactation
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  • Published: 12 February 2026

A time-series transcriptomic dataset of the mouse olfactory bulb across pregnancy and lactation

  • Xiaolei Song1 na1,
  • Gengwei Zhang2 na1,
  • Fengzhu Zhang3,
  • Tongye Fu4,
  • Jingzhe Yu4,
  • Danyu Han4,
  • Wenhui Li1 &
  • …
  • Rongliang Guo2,5 

Scientific Data , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Abstract

Olfaction is the primary sensory modality governing maternal behavior in rodents. To meet the demands of maternal care, the brain undergoes extensive and temporally dynamic plasticity during the perinatal period, particularly within the olfactory bulb (OB). However, longitudinal data describing the molecular landscape of the OB across the entire reproductive cycle are currently unavailable. We generated a high-resolution transcriptomic dataset of the mouse OB to map molecular reprogramming events during reproduction. Samples were collected at five strategic time points: non-pregnancy, gestation day 10, parturition, postpartum day 7, and weaning. Using bulk RNA-seq, we constructed a dynamic transcriptomic atlas of the maternal OB. This dataset captures stage-specific gene expression changes associated with neurogenesis, synaptic plasticity, and neuromodulation. This work provides a critical molecular resource to facilitate future research into the adaptive remodeling of the maternal neural circuit.

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Data availability

The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (GSA: CRA034278) that are publicly accessible at https:/ngdc.cncb.ac.cn/gsa/browse/CRA034278. Processed gene expression matrices have been deposited in Figshare at https://doi.org/10.6084/m9.figshare.31156873. The study group assignment and data accession details of each sample are also provided in Table 1.

Code availability

Publicly available bioinformatics tools were used to analyze the raw sequencing data. All analyses were performed with standard software packages, and no custom scripts were generated. All custom R scripts used for data processing and analysis have been deposited in Figshare and are accessible at https://doi.org/10.6084/m9.figshare.3115687334.

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Acknowledgements

We thank Prof. Zhengang Yang for his generous help with the experiments. We are grateful to Dr. Zhejun Xu for his constructive suggestions and help with experimental design, data analysis, and the preparation of this manuscript. This work was supported by the National Natural Science Foundation of China (82501435), Natural Science Foundation of Hebei Province (H2025402018), Health Commission of Hebei Province (20250165).

Author information

Author notes
  1. These authors contributed equally: Xiaolei Song, Gengwei Zhang.

Authors and Affiliations

  1. Collaborative Innovation Center for Biomedicine, Shanghai University of Medicine and Health Sciences, Shanghai, China

    Xiaolei Song & Wenhui Li

  2. School of Clinical Medicine, Hebei University of Engineering, Handan, Hebei, China

    Gengwei Zhang & Rongliang Guo

  3. Shanghai Yangpu District Mental Health Center, Shanghai University of Medicine and Health Sciences, Shanghai, China

    Fengzhu Zhang

  4. State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China

    Tongye Fu, Jingzhe Yu & Danyu Han

  5. Department of Central Laboratory, Affiliated Hospital of Hebei Engineering University, Handan, Hebei, China

    Rongliang Guo

Authors
  1. Xiaolei Song
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Contributions

X.S. contributed to all aspects of the project. G.Z., F.Z. and T.F. analyzed the data. J.Y. and D.H. performed most of the experiments. G.Z. and W.L. quantified the data. R.G. supervised the project. X.S. and R.G. produced all figures and wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Xiaolei Song or Rongliang Guo.

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Song, X., Zhang, G., Zhang, F. et al. A time-series transcriptomic dataset of the mouse olfactory bulb across pregnancy and lactation. Sci Data (2026). https://doi.org/10.1038/s41597-026-06833-z

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  • Received: 01 December 2025

  • Accepted: 05 February 2026

  • Published: 12 February 2026

  • DOI: https://doi.org/10.1038/s41597-026-06833-z

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