Spatial cell graph analysis reveals skin tissue organization characteristic for cutaneous T cell lymphoma.

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Suryadipto Sarkar, Anna Möller, Anne Hartebrodt, Michael Erdmann, Christian Ostalecki, Andreas Baur, David B Blumenthal
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引用次数: 0

Abstract

Cutaneous T-cell lymphomas (CTCLs) are non-Hodgkin lymphomas caused by malignant T cells which migrate to the skin and lead to rash-like lesions which can be difficult to distinguish from inflammatory skin conditions like atopic dermatitis (AD) and psoriasis (PSO). To characterize CTCL in comparison to these differential diagnoses, we carried out multi-antigen imaging on 69 skin tissue samples (21 CTCL, 23 AD, 25 PSO). The resulting protein abundance maps were then analyzed via scoring functions to quantify the heterogeneity of the individual cells' neighborhoods within spatial graphs inferred from the cells' positions in the tissue samples. Our analyses reveal characteristic patterns of skin tissue organization in CTCL as compared to AD and PSO, including a combination of increased local entropy and egophily in T-cell neighborhoods. These results could not only pave the way for high-precision diagnosis of CTCL, but may also facilitate further insights into cellular disease mechanisms.

空间细胞图分析揭示皮肤T细胞淋巴瘤的皮肤组织特征。
皮肤T细胞淋巴瘤(CTCLs)是由恶性T细胞迁移到皮肤引起的非霍奇金淋巴瘤,并导致皮疹样病变,很难与炎症性皮肤疾病如特应性皮炎(AD)和牛皮癣(PSO)区分开来。为了与这些鉴别诊断比较CTCL的特征,我们对69个皮肤组织样本(21个CTCL, 23个AD, 25个PSO)进行了多抗原成像。然后通过评分函数分析所得的蛋白质丰度图,以量化从细胞在组织样本中的位置推断的空间图中单个细胞邻近区域的异质性。我们的分析揭示了与AD和PSO相比,CTCL的皮肤组织特征模式,包括t细胞社区局部熵和表皮性增加的组合。这些结果不仅可以为CTCL的高精度诊断铺平道路,而且可能有助于进一步了解细胞疾病机制。
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来源期刊
NPJ Systems Biology and Applications
NPJ Systems Biology and Applications Mathematics-Applied Mathematics
CiteScore
5.80
自引率
0.00%
发文量
46
审稿时长
8 weeks
期刊介绍: npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.
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