Cell spatial analysis in Crohn's disease: unveiling local cell arrangement pattern with graph-based signatures Article Swipe
Shunxing Bao
,
Si-Chen Zhu
,
Vasantha L. Kolachala
,
Lucas W. Remedios
,
Yeonjoo Hwang
,
Yutong Sun
,
Ruining Deng
,
Can Cui
,
Rendong Zhang
,
Yike Li
,
Jia Li
,
Joseph T. Roland
,
Qi Liu
,
Ken S. Lau
,
Subra Kugathasan
,
Peng Qiu
,
Keith T. Wilson
,
Lori A. Coburn
,
Bennett A. Landman
,
Yuankai Huo
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1117/12.3006675
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1117/12.3006675
Crohn's disease (CD) is a chronic and relapsing inflammatory condition that affects segments of the gastrointestinal tract. CD activity is determined by histological findings, particularly the density of neutrophils observed on Hematoxylin and Eosin stains (H&E) imaging. However, understanding the broader morphometry and local cell arrangement beyond cell counting and tissue morphology remains challenging. To address this, we characterize six distinct cell types from H&E images and develop a novel approach for the local spatial signature of each cell. Specifically, we create a 10-cell neighborhood matrix, representing neighboring cell arrangements for each individual cell. Utilizing t-SNE for non-linear spatial projection in scatter-plot and Kernel Density Estimation contour-plot formats, our study examines patterns of differences in the cellular environment associated with the odds ratio of spatial patterns between active CD and control groups. This analysis is based on data collected at the two research institutes. The findings reveal heterogeneous nearest-neighbor patterns, signifying distinct tendencies of cell clustering, with a particular focus on the rectum region. These variations underscore the impact of data heterogeneity on cell spatial arrangements in CD patients. Moreover, the spatial distribution disparities between the two research sites highlight the significance of collaborative efforts among healthcare organizations. All research analysis pipeline tools are available at https://github.com/MASILab/cellNN.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1117/12.3006675
- OA Status
- green
- Cited By
- 2
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393404071
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4393404071Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1117/12.3006675Digital Object Identifier
- Title
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Cell spatial analysis in Crohn's disease: unveiling local cell arrangement pattern with graph-based signaturesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-04-02Full publication date if available
- Authors
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Shunxing Bao, Si-Chen Zhu, Vasantha L. Kolachala, Lucas W. Remedios, Yeonjoo Hwang, Yutong Sun, Ruining Deng, Can Cui, Rendong Zhang, Yike Li, Jia Li, Joseph T. Roland, Qi Liu, Ken S. Lau, Subra Kugathasan, Peng Qiu, Keith T. Wilson, Lori A. Coburn, Bennett A. Landman, Yuankai HuoList of authors in order
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https://doi.org/10.1117/12.3006675Publisher landing page
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
- OA URL
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https://www.ncbi.nlm.nih.gov/pmc/articles/11415268Direct OA link when available
- Concepts
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Spatial analysis, Cluster analysis, Computer science, Pattern recognition (psychology), Artificial intelligence, Computational biology, Cartography, Pathology, Biology, Medicine, Geography, Remote sensingTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.based | 135 |
| abstract_inverted_index.cell. | 78, 93 |
| abstract_inverted_index.focus | 159 |
| abstract_inverted_index.local | 43, 73 |
| abstract_inverted_index.novel | 69 |
| abstract_inverted_index.ratio | 122 |
| abstract_inverted_index.sites | 188 |
| abstract_inverted_index.study | 109 |
| abstract_inverted_index.t-SNE | 95 |
| abstract_inverted_index.this, | 56 |
| abstract_inverted_index.tools | 202 |
| abstract_inverted_index.types | 62 |
| abstract_inverted_index.Kernel | 103 |
| abstract_inverted_index.active | 127 |
| abstract_inverted_index.beyond | 46 |
| abstract_inverted_index.create | 81 |
| abstract_inverted_index.images | 65 |
| abstract_inverted_index.impact | 168 |
| abstract_inverted_index.rectum | 162 |
| abstract_inverted_index.reveal | 146 |
| abstract_inverted_index.stains | 34 |
| abstract_inverted_index.tissue | 50 |
| abstract_inverted_index.tract. | 16 |
| abstract_inverted_index.10-cell | 83 |
| abstract_inverted_index.Crohn's | 0 |
| abstract_inverted_index.Density | 104 |
| abstract_inverted_index.address | 55 |
| abstract_inverted_index.affects | 11 |
| abstract_inverted_index.between | 126, 184 |
| abstract_inverted_index.broader | 40 |
| abstract_inverted_index.chronic | 5 |
| abstract_inverted_index.control | 130 |
| abstract_inverted_index.density | 26 |
| abstract_inverted_index.develop | 67 |
| abstract_inverted_index.disease | 1 |
| abstract_inverted_index.efforts | 194 |
| abstract_inverted_index.groups. | 131 |
| abstract_inverted_index.matrix, | 85 |
| abstract_inverted_index.region. | 163 |
| abstract_inverted_index.remains | 52 |
| abstract_inverted_index.spatial | 74, 98, 124, 174, 181 |
| abstract_inverted_index.However, | 37 |
| abstract_inverted_index.activity | 18 |
| abstract_inverted_index.analysis | 133, 200 |
| abstract_inverted_index.approach | 70 |
| abstract_inverted_index.cellular | 116 |
| abstract_inverted_index.counting | 48 |
| abstract_inverted_index.distinct | 60, 151 |
| abstract_inverted_index.examines | 110 |
| abstract_inverted_index.findings | 145 |
| abstract_inverted_index.formats, | 107 |
| abstract_inverted_index.imaging. | 36 |
| abstract_inverted_index.observed | 29 |
| abstract_inverted_index.patterns | 111, 125 |
| abstract_inverted_index.pipeline | 201 |
| abstract_inverted_index.research | 142, 187, 199 |
| abstract_inverted_index.segments | 12 |
| abstract_inverted_index.Moreover, | 179 |
| abstract_inverted_index.Utilizing | 94 |
| abstract_inverted_index.available | 204 |
| abstract_inverted_index.collected | 138 |
| abstract_inverted_index.condition | 9 |
| abstract_inverted_index.findings, | 23 |
| abstract_inverted_index.highlight | 189 |
| abstract_inverted_index.patients. | 178 |
| abstract_inverted_index.patterns, | 149 |
| abstract_inverted_index.relapsing | 7 |
| abstract_inverted_index.signature | 75 |
| abstract_inverted_index.Estimation | 105 |
| abstract_inverted_index.associated | 118 |
| abstract_inverted_index.determined | 20 |
| abstract_inverted_index.healthcare | 196 |
| abstract_inverted_index.individual | 92 |
| abstract_inverted_index.morphology | 51 |
| abstract_inverted_index.non-linear | 97 |
| abstract_inverted_index.particular | 158 |
| abstract_inverted_index.projection | 99 |
| abstract_inverted_index.signifying | 150 |
| abstract_inverted_index.tendencies | 152 |
| abstract_inverted_index.underscore | 166 |
| abstract_inverted_index.variations | 165 |
| abstract_inverted_index.Hematoxylin | 31 |
| abstract_inverted_index.arrangement | 45 |
| abstract_inverted_index.clustering, | 155 |
| abstract_inverted_index.differences | 113 |
| abstract_inverted_index.disparities | 183 |
| abstract_inverted_index.environment | 117 |
| abstract_inverted_index.institutes. | 143 |
| abstract_inverted_index.morphometry | 41 |
| abstract_inverted_index.neighboring | 87 |
| abstract_inverted_index.neutrophils | 28 |
| abstract_inverted_index.arrangements | 89, 175 |
| abstract_inverted_index.challenging. | 53 |
| abstract_inverted_index.characterize | 58 |
| abstract_inverted_index.contour-plot | 106 |
| abstract_inverted_index.distribution | 182 |
| abstract_inverted_index.histological | 22 |
| abstract_inverted_index.inflammatory | 8 |
| abstract_inverted_index.neighborhood | 84 |
| abstract_inverted_index.particularly | 24 |
| abstract_inverted_index.representing | 86 |
| abstract_inverted_index.scatter-plot | 101 |
| abstract_inverted_index.significance | 191 |
| abstract_inverted_index.Specifically, | 79 |
| abstract_inverted_index.collaborative | 193 |
| abstract_inverted_index.heterogeneity | 171 |
| abstract_inverted_index.heterogeneous | 147 |
| abstract_inverted_index.understanding | 38 |
| abstract_inverted_index.organizations. | 197 |
| abstract_inverted_index.gastrointestinal | 15 |
| abstract_inverted_index.nearest-neighbor | 148 |
| abstract_inverted_index.https://github.com/MASILab/cellNN. | 206 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 20 |
| citation_normalized_percentile.value | 0.81412418 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |