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. 2023 Jun 28;21:419. doi: 10.1186/s12967-023-04294-1

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 5 — The pseudotime analysis of monocytes/macrophages. a The pseudotime trajectory of Mono1, Mono2 and Mono4. All cells were clustered into 3 states. The No.1 point represent as the differentiation point in pseudotime differentiation. b The distribution of monocytes/macrophages clusters in the pseudotime trajectory. Mono2 was at the beginning of the trajectory path, and the majority cells of clusters Mono1 and Mono4 were at the terminals of the bifurcation, respectively c Changes in lipid metabolism scores along the direction of pseudotime trajectory. d The expression of key genes along the pseudotime trajectory. Genes related to lipid metabolism, including APOC1, APOE, FABP5 and TREM2 were increased, while genes related to inflammation, including LYZ, S100A12, S100A8 and S100A9 were decreased