Subcellular Feature-Based Classification of α and β Cells Using Soft X-ray Tomography

Deshmukh, Aneesh; Chang, Kevin; Cuala, Janielle; Vanslembrouck, Bieke; Georgia, Senta; Loconte, Valentina; White, Kate L.

doi:10.3390/cells13100869

Title: Subcellular Feature-Based Classification of α and β Cells Using Soft X-ray Tomography

Journal Article · 18 May 2024 · Cells

DOI: https://doi.org/10.3390/cells13100869 · OSTI ID:2471074

^[1];

^[1]; Vanslembrouck, Bieke ^[2];

^[1];

^[2]; White, Kate L. ^[3]

Univ. of Southern California, Los Angeles, CA (United States)
Univ. of California, San Francisco, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Univ. of Southern California, Los Angeles, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

The dysfunction of α and β cells in pancreatic islets can lead to diabetes. Many questions remain on the subcellular organization of islet cells during the progression of disease. Existing three-dimensional cellular mapping approaches face challenges such as time-intensive sample sectioning and subjective cellular identification. To address these challenges, we have developed a subcellular feature-based classification approach, which allows us to identify α and β cells and quantify their subcellular structural characteristics using soft X-ray tomography (SXT). We observed significant differences in whole-cell morphological and organelle statistics between the two cell types. Additionally, we characterize subtle biophysical differences between individual insulin and glucagon vesicles by analyzing vesicle size and molecular density distributions, which were not previously possible using other methods. These sub-vesicular parameters enable us to predict cell types systematically using supervised machine learning. We also visualize distinct vesicle and cell subtypes using Uniform Manifold Approximation and Projection (UMAP) embeddings, which provides us with an innovative approach to explore structural heterogeneity in islet cells. This methodology presents an innovative approach for tracking biologically meaningful heterogeneity in cells that can be applied to any cellular system.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Advanced Light Source (ALS)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2471074

Journal Information:: Cells, Journal Name: Cells Journal Issue: 10 Vol. 13; ISSN 2073-4409

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

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59 BASIC BIOLOGICAL SCIENCES
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Title: Subcellular Feature-Based Classification of α and β Cells Using Soft X-ray Tomography

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