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Abstract
Biomedical research on mammals has traditionally neglected females, raising the concern that some scientific findings may generalize poorly to half the population. Although this lack of sex inclusion has been broadly documented, its extent within circadian genomics remains undescribed. To address this gap, we examined sex inclusion practices in a comprehensive collection of publicly available transcriptome studies on daily rhythms. Among 148 studies having samples from mammals in vivo, we found strong underrepresentation of females across organisms and tissues. Overall, only 23 of 123 studies in mice, 0 of 10 studies in rats, and 9 of 15 studies in humans included samples from females. In addition, studies having samples from both sexes tended to have more samples from males than from females. These trends appear to have changed little over time, including since 2016, when the US National Institutes of Health began requiring investigators to consider sex as a biological variable. Our findings highlight an opportunity to dramatically improve representation of females in circadian research and to explore sex differences in daily rhythms at the genome level.
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Affiliation(s)
- Dora Obodo
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee,Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Elliot H. Outland
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jacob J. Hughey
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee,Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, Tennessee,Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee,Jacob J. Hughey, Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End Ave., Suite 1475, Nashville, TN 37232, USA; e-mail:
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2
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Li A, Zhang X, Singla J, White K, Loconte V, Hu C, Zhang C, Li S, Li W, Francis JP, Wang C, Sali A, Sun L, He X, Stevens RC. Auto-segmentation and time-dependent systematic analysis of mesoscale cellular structure in β-cells during insulin secretion. PLoS One 2022; 17:e0265567. [PMID: 35324950 PMCID: PMC8947144 DOI: 10.1371/journal.pone.0265567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 03/03/2022] [Indexed: 02/07/2023] Open
Abstract
The mesoscale description of the subcellular organization informs about cellular mechanisms in disease state. However, applications of soft X-ray tomography (SXT), an important approach for characterizing organelle organization, are limited by labor-intensive manual segmentation. Here we report a pipeline for automated segmentation and systematic analysis of SXT tomograms. Our approach combines semantic and first-applied instance segmentation to produce separate organelle masks with high Dice and Recall indexes, followed by analysis of organelle localization based on the radial distribution function. We demonstrated this technique by investigating the organization of INS-1E pancreatic β-cell organization under different treatments at multiple time points. Consistent with a previous analysis of a similar dataset, our results revealed the impact of glucose stimulation on the localization and molecular density of insulin vesicles and mitochondria. This pipeline can be extended to SXT tomograms of any cell type to shed light on the subcellular rearrangements under different drug treatments.
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Affiliation(s)
- Angdi Li
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xiangyi Zhang
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Jitin Singla
- Department of Biological Sciences, Bridge Institute, University of Southern California, Los Angeles, CA, United States of America
| | - Kate White
- Department of Biological Sciences, Bridge Institute, University of Southern California, Los Angeles, CA, United States of America
| | - Valentina Loconte
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Chuanyang Hu
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Chuyu Zhang
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Shuailin Li
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Weimin Li
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - John Paul Francis
- Department of Computer Science, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA, United States of America
| | - Chenxi Wang
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Andrej Sali
- California Institute for Quantitative Biosciences, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, United States of America
| | - Liping Sun
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Xuming He
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
- Shanghai Engineering Research Center of Intelligent Vision and Imaging, Shanghai, China
| | - Raymond C. Stevens
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- Department of Biological Sciences, Bridge Institute, University of Southern California, Los Angeles, CA, United States of America
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3
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Zhang X, Carter SD, Singla J, White KL, Butler PC, Stevens RC, Jensen GJ. Visualizing insulin vesicle neighborhoods in β cells by cryo-electron tomography. SCIENCE ADVANCES 2020; 6:eabc8258. [PMID: 33298442 PMCID: PMC7725471 DOI: 10.1126/sciadv.abc8258] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Accepted: 10/22/2020] [Indexed: 05/04/2023]
Abstract
Subcellular neighborhoods, comprising specific ratios of organelles and proteins, serve a multitude of biological functions and are of particular importance in secretory cells. However, the role of subcellular neighborhoods in insulin vesicle maturation is poorly understood. Here, we present single-cell multiple distinct tomogram acquisitions of β cells for in situ visualization of distinct subcellular neighborhoods that are involved in the insulin vesicle secretory pathway. We propose that these neighborhoods play an essential role in the specific function of cellular material. In the regions where we observed insulin vesicles, a measurable increase in both the fraction of cellular volume occupied by vesicles and the average size (diameter) of the vesicles was apparent as sampling moved from the area near the nucleus toward the plasma membrane. These findings describe the important role of the nanometer-scale organization of subcellular neighborhoods on insulin vesicle maturation.
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Affiliation(s)
- Xianjun Zhang
- Department of Biological Sciences, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Stephen D Carter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Jitin Singla
- Department of Biological Sciences, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Kate L White
- Department of Biological Sciences, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Peter C Butler
- Larry Hillblom Islet Research Center, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Raymond C Stevens
- Department of Biological Sciences, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA.
- Department of Chemistry, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Grant J Jensen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
- Howard Hughes Medical Institute (HHMI), California Institute of Technology, Pasadena, CA 91125, USA
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