Characterization of the microenvironment of diabetic foot ulcers and potential drug identification based on scRNA-seq.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36686438", "kind": "science-record", "revision": 3, "content_hash": "sha256:cc204a9e400ff7a2f3202d79d793243daf2b12983929668292cfd29d249b14c7", "created_at": "2026-11-01T19:01:14Z", "updated_at": "2026-11-01T22:51:06Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Characterization of the microenvironment of diabetic foot ulcers and potential drug identification based on scRNA-seq.", "doi": "10.3389/fendo.2022.997880", "pmid": "36686438", "authors": ["Yao Li", "Shuai Ju", "Xiaoyan Li", "Wenqiang Li", "Siyuan Zhou", "Guili Wang", "Yunmin Cai", "Zhihui Dong"], "journal": "Frontiers in endocrinology", "year": 2022, "abstract": "Background: Diabetes foot ulcers (DFUs) are a type of foot infection, ulcer, and/or deep tissue destruction caused by neuropathy and vascular disease in the distal extremities of diabetic patients. Its pathogenesis and its microenvironment are not entirely understood.\nMethods: Initially, the GSE165816 data set from the GEO database was utilized for single cell analysis to reveal the microenvironment and functional status of DFUs. The GSE199939 RNA-seq data set was utilized for external validation. On the basis of the logistic regression machine learning algorithm (OCLR), pseudo time series analysis, dryness index analysis, and drug target gene analysis were then performed. By constructing drug-gene and gene-gene networks, we can locate the most recent DFUs treatments. Finally, immunofluorescence technology was used to detect the cell-related markers of the DFUs microenvironment, and qPCR was used to detect the expression of drug targets in DFUs.\nResults: Firstly, we used the Cell Maker database to obtain information about human cells and related gene markers, and manually reviewed a total of 45 kinds of cells and maker information that may appear in the DFUs microenvironment, which were divided into 17 cell clusters after annotation. Subsequently, we counted the proportions of DM and DFUs in different types of cells, and the results showed that the proportions of macrophages, white blood cells, and monocytes were higher in patients with DFUs, while the proportions of pluripotent stem cells and stromal cells were higher in patients with DM. The Pseudo-time series analysis of cells in DFUs showed that the differentiation pathways of immune cells, mesenchymal cells and stem cells were similar in the three states, while the other cells were distributed in different stages. At the level of a single cell, the scores of both multipotential stem cells and hematopoietic stem cells were significantly lower in DFU healing and non-healing than in DM. Additionally, the highly expressed genes in DFU were chosen as drug targets. We identified seven potential target genes and discovered twenty drugs with high significance. Finally, the colocalization relationship between CD19, ITGAM, and HLA-DR expression in monocytes and macrophages of DFU skin tissue and healthy subjects was analyzed by laser confocal microscopy with the immunofluorescence triple labeling method. The results showed that the expressions of CD19, ITGAM, and HLA-DR in the skin of DFUs were significantly higher than those in the skin of healthy subjects, and the co-localization relationship was significant in DFUs.\nConclusion: This study can serve as a resource for the treatment of DFUs.", "claims": [{"id": "claim-method", "statement": "The OCLR algorithm is a logistic regression machine learning algorithm (OCLR, One Class Linear Regression) published in Cell in 2018 that measures the level of moisture in tumor samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36686438", "locator": "sections[9] \"Dry analysis of individual cells and bulk samples\" paragraph 2", "quote": "The OCLR algorithm is a logistic regression machine learning algorithm (OCLR, One Class Linear Regression) published in Cell in 2018 that measures the level of moisture in tumor samples."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material .", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36686438", "locator": "sections[24] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. 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Finally, immunofluorescence technology was used to detect the cell-relat", "alyzed by laser confocal microscopy with the immunofluorescence triple labeling method. The results showed t", "people were chosen and fixed with formalin. Immunofluorescence detection was then used to find the disease.", "detection was then used to find the disease. Immunofluorescence method The paraffin sections were put into w"], "order_basis": "paper_text_order:862", "id": "assay:2"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["ed markers of the DFUs microenvironment, and qPCR was used to detect the expression of drug ta"], "order_basis": "paper_text_order:970", "id": "assay:3"}, {"role": "data_generation", "definition_id": "cell_imaging", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["e and healthy subjects was analyzed by laser confocal microscopy with the immunofluorescence triple labeling", "lthy subjects was analyzed by laser confocal microscopy with the immunofluorescence triple labeling"], "order_basis": "paper_text_order:2394", "id": "assay:4"}, {"role": "analysis", "definition_id": "regression_modelling", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["ols_fit"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The OCLR algorithm is a logistic regression machine learning algorithm (OCLR, One Class Linear Regression) published in Cell in 2018 that measures the level of moisture in tumor samples."], "order_basis": "consumes_earlier_output", "id": "assay:5"}]}